Load shifting in a new perspective - TU Delft

Policy Analysis |Multi Actor Systems

Faculty of Technology, Policy and Management

Jaffalaan 5, 2628 BX Delft

P.O. Box 5015, 2600GA Delft

The Netherlands

http://tbm.tudelft.nl/

Load-shifting in a new perspective

Smart scheduling of smart household appliances using an

Agent-Based Modelling Approach

M.Sc. Thesis

Maurits de Blécourt

Graduation committee:

Prof.dr.ir. W.A.H. Thissen Policy Analysis (TU Delft)

dr. P.W.G. Bots Policy Analysis (TU Delft)

dr.ir. I. Bouwmans Energy & Industry (TU Delft)

ir. M.O.W. Grond Asset Management | Innovation (Enexis B.V.)

Programme SEPAM – Systems Engineering, Policy Analyses and Management

Student no. 1232649

Date January 2012

Enexis B.V.

Innovatie | Asset Management

Burg. Burgerslaan 40, 5245 NH Rosmalen

P.O. Box 856, 5201 AW Den Bosch

The Netherlands

www.enexis.nl

http://tbm.tudelft.nl/

http://www.enexis.nl/

III

Preface

The thesis before you is the result of six month of hard work. I would like to thank several people who

helped me during my graduation project. First of all, I would like to thank my first supervisor Pieter

Bots for his useful feedback and advice. We spend many hour discussing my project and hope I was

able to process the feedback and advice in my thesis. Furthermore, I would like to thank my

supervisor at Enexis, Marinus Grond, for his feedback and introducing me to Enexis. Last but not least,

I would like to thank Margot, my family and my friends for their support.

Maurits de Blécourt

V

Summary

The electricity demand of households in the Netherlands has been growing rapidly for the last decades

and will continue to grow in the near future. This is specifically the case during peak periods. High

peak loads could exceed the available capacity, resulting in overloaded network components (assets)

which lead to an excessive reduction in life expectancy of these assets. The present aging distribution

network will not have the capacity to cope with these future peak loads. The increase of electricity

demand by the end-users therefore seriously reduces the reliability and safety of the electricity

distribution. This poses an important problem for the Distribution Network Operators, who are

responsible for the transport of electricity, maintenance and management of the regional electricity

distribution networks.

The traditional method to cope with capacity availability during peak periods is to invest heavily in

placing more electricity cables. However, Demand-Side Management programs using load-shifting

techniques also show good potential for reducing the peak loads in the network demand pattern.

Load-shifting focuses on scheduling smart household appliances from peak load periods to off-peak

periods. The aim of Demand-Side Management is to increase the efficiency of the system by bringing

both demand and supply to the best possible low value. To measure the effectiveness of Demand-

Side Management, the Key Performance Indicators (KPIs) “Levelling Effect” (LE) and “Height of Peak

loads” (HP) are used. LE measures for a day the deviation of loads from the average load of the

network. HP measures the highest load (in W) that occurs during a day in the network.

Both the traditional way of improving the network, and a Demand-Side Management approach will

require high investments. To ensure that such investments are economically viable, DNOs should now

the extent to which Demand-side Management of households will affect these KPIs.

Assessing load-shifting potential by scheduling smart appliances

Because load-shifting takes place through the individual scheduling of household appliances, the focus

lies on a the household’s appliances level. On this level, the irregularities of the demand pattern are

important, which are caused by the simultaneous usage of household appliance. We therefore

constructed a simulation model using an Agent-Based Modelling approach, which takes into account

these aspects. This simulation model represents a low-voltage network with one hundred households

connected to it. Each household owns appliances, which build-up the electricity demand of the

household. Smart appliances are modelled as individual agents to allow the scheduling of these

appliances. Non-smart appliances are combined and generate the “other-loads”. The scheduler uses a

“lowest-point” principle for the scheduling of smart appliances. Furthermore, all appliances are always

scheduled and they cannot be rescheduled.

VI

The model simulates the demand pattern on the network during one working. External influences

(e.g. weather) are ignored. From the literature and the available data, we made a trade-off between

the required accuracy and computation, and opted for a time step of 15 minutes.

Simulation results

As expected, the introduction of a smart system to the network was found to level the demand

pattern and lower the peaks by a maximum of 13%. In this simulation, 16% of the total demand

could be shifted. Non-cooling appliances (dishwashers, washing machines and tumble dryers)

represent about 8% of the total load, and cooling appliance (refrigerators and freezers) the other 8%.

The rescheduling of appliances did however increase the number of excessive peak loads, which in

real life could form a serious risk for overloading the network. The scheduler does not take into

account the profile of the smart appliances when scheduling. Appliances with a low start demand

could therefore be scheduled to a low load timeslot while the load on subsequent timeslots could

increase to peak loads when the appliances reach their full demand. More advanced scheduling

algorithms that also take into account the appliance demand profile should resolve this.

Apart from the above mentioned aspect, a closer examination of the smartness variable also showed

some additional interesting developments. As expected, better forecasting and longer operational

horizons will give better results. Unexpectedly however, was the lack of effect of the scheduling

schemes. This is most likely because appliances are always scheduled, which results in a lack of

advantage of being first in the schedulers queue. More advanced scheduling algorithms that allow

appliances not to be scheduled may resolve this.

The sequencing of the cooling appliances created a layer of smart cooling load that absorbs all the

small irregularities in the demand pattern. Because of their short operational time, cooling appliances

may therefore successfully be used for smoothing of the network demand pattern.

Table 1: KPIs of scenario's

KPI LE LE2 HP HP.D

Base 22.7 7.95 1.63 0.83

Perfect conditions 13.9 2.92 1.42 0.76

Maximum gap filling 8.12 1.05 1.29 0.76

Only smart non-cooling appliances

14.7 3.38 1.49 0.81

Only smart cooling appliances

22.6 7.91 1.56 0.79

VII

Scheduling appliances using a “lowest-point” principle proved to work very well, but only for non-

cooling smart appliances. The multiple usages of the smart cooling appliances caused them to turn on

less in low-peak periods but more on the slopes towards peak loads. This scheduling artefact is

caused by the time-step of 15 minutes and a too simplistic scheduling algorithm. Too few timeslots

were available for effective scheduling of these appliances. Using a smaller time step in combination

with a more advanced scheduling algorithm should improve the scheduling of smart cooling

appliances. However, a smaller time-step would not necessarily increase the quality of the result, this

also applies to a better representation of the non-smart “other-loads” profiles. A smaller time-step

would increase the variation in irregularities on the demand pattern, but the overall network demand

pattern would stay the same. Although the scheduler does take into account these small variations on

the demand pattern, the general network demand pattern determines the areas were the smart

appliances are scheduled to.

Conclusion of research

Our study has shown that load shifting by scheduling smart appliances is likely to produce more

levelled demand pattern. Peaks in the network demand pattern can be reduced by 13% and the gaps

are filled resulting in a more levelled demand pattern.

A time step of 15 minutes works well for non-cooling appliances, but it limits the effective scheduling

of (the present) smart cooling appliances. But a shorter time step would not necessarily have

produced better results. The overall network demand patterns, and thus the overall scheduling places

of the smart appliances, will most likely stay the same.

What potentially could make a difference is a more advanced scheduler. Allowing rescheduling and

the possibility for smart appliance not to be scheduled could result in a higher effectiveness of the

scheduling schemes and more optimal scheduling of the smart appliances.

IX

Table of Contents

Part I: Introduction................................................................................................................... 1

1. Introduction ...................................................................................................................... 3

1.1. Background .................................................................................................................... 3

1.2. The problem ................................................................................................................... 3

1.3. Research Questions ......................................................................................................... 6

1.4. Structure of thesis ........................................................................................................... 6

2. System Description ............................................................................................................ 8

2.1. Dutch Electricity System .................................................................................................. 8

2.2. Social system.................................................................................................................. 8

2.3. Electricity Demand Pattern in the low voltage distribution network ...................................... 11

2.4. A closer look at Demand-side Management (DSM) ............................................................ 13

2.5. Demand-Side Management simulations ........................................................................... 16

3. Conceptualisation of DSM elements in the network .............................................................. 18

3.1. Households .................................................................................................................. 18

3.2. Low-voltage network ..................................................................................................... 22

3.3. Control and Management of smart appliances .................................................................. 23

Part II: Development of the Smart Network model ..................................................................... 25

4. Agent-Based Modelling as modelling approach .................................................................... 27

4.1. Netlogo: An Agent-Based-Modelling platform.................................................................... 27

5. Implementation of Agent-based Model ............................................................................... 28

5.1. Implementation of agents .............................................................................................. 28

5.2. Model flow - Pseudo code .............................................................................................. 34

6. Simulation Setup ............................................................................................................. 36

6.1. Time step ..................................................................................................................... 36

6.2. Run length ................................................................................................................... 36

6.3. Warm-up time .............................................................................................................. 36

6.4. Total simulation run length............................................................................................. 37

X

6.5. Replications .................................................................................................................. 37

6.6. Variables ...................................................................................................................... 38

6.7. Output variable ............................................................................................................. 38

7. Verification & Validation ................................................................................................... 42

7.1. Verification ................................................................................................................... 42

7.2. Validation ..................................................................................................................... 43

8. Experimental Design ........................................................................................................ 45

8.1. Scenario 0: non-smart network ....................................................................................... 45

8.2. Internal variables .......................................................................................................... 45

8.3. External variables .......................................................................................................... 45

8.4. Perfect Condition with 100% Smart Penetration................................................................ 46

8.5. Random Operational Horizon .......................................................................................... 46

8.6. Cooling Scenario – Only Refrigerators and Freezers........................................................... 46

Part III: Simulation Results ...................................................................................................... 47

9. Simulation Results ........................................................................................................... 49

Base line scenario vs. Perfect Conditions ................................................................................ 49

Base line scenario vs. number of smart appliances .................................................................. 55

Base line vs. Cooling appliances ............................................................................................ 55

10. Reflection, Conclusions & Recommendations ................................................................... 59

10.1. Reflection on modelling choices .................................................................................... 59

10.2. Conclusions on smart effect .......................................................................................... 61

10.3. Recommendations ....................................................................................................... 63

11. Personal reflection ....................................................................................................... 65

Part IV: Bibliography & Appendices .......................................................................................... 67

Bibliography ........................................................................................................................... 69

Appendix A ............................................................................................................................. 73

Appendix B ............................................................................................................................. 76

Appendix C ............................................................................................................................. 77

XI

Appendix D ............................................................................................................................. 79

Appendix E ............................................................................................................................. 80

Appendix F ............................................................................................................................. 82

Appendix G ............................................................................................................................. 83

Appendix H ............................................................................................................................. 89

Appendix I ................................................................................................................................ 1

XII

Tables, Graph’s and Figures

Tables

Table 1: KPIs of scenario's ....................................................................................................... VI

Table 2: Scheduling level and smartness level ............................................................................ 23

Table 3: Example of combination of other-load. .......................................................................... 30

Table 4: Household type, yearly usage and network proportion. ................................................... 31

Table 5: Scheduling level - agents acting as schedulers ............................................................... 31

Table 6: Variables that are varied .............................................................................................. 38

Table 7: KPI's of Base vs. Maximum Scenario of a network of 100 households ............................... 49

Table 8: Minimum KPI value ..................................................................................................... 51

Table 9: Influence of Schedulers Memory on the KPI's ................................................................. 53

Table 10: List of scheduling scheme’s (Source) ........................................................................... 76

Table 11: Appliance attributes .................................................................................................. 77

Table 12: Penetration degree, turn-on probability and empirical distribution of average daily time to

turn on (Stamminger, 2009) ..................................................................................................... 78

Table 13: Other-load Combination-profile numbers ..................................................................... 79

Table 14: Table of most important of simulation results. .............................................................. 83

Table 15: Box-plots of most important simulation results ............................................................. 84

Graphs

Graph 1: Daily average demand pattern on a network of a year ..................................................... 3

Graph 2: Shifting electricity load using Demand-Side Management .................................................. 5

Graph 3: 24 hour demand pattern of two household of a day generated with QWatts (Bots et al.,

2011) ..................................................................................................................................... 11

Graph 4: Coincidence factor as a function of the number of households (Strbac, 2008) ................... 12

Graph 5: Average consumption for a Day for a Typical Household (Grinden & Feilberg, 2010) .......... 18

Graph 6: Standard Deviation per Replication per Household ......................................................... 38

Graph 7: Household & network demand pattern ......................................................................... 43

Graph 8: Maximal possible filling of gaps with smart load ............................................................. 52

XIII

Graph 9: Network demand patter of a network with only smart cooling appliances ........................ 56

Graph 10: Network load and number of usages of cooling appliances* .......................................... 58

Figures

Figure 1: Physical system (diagram adapted from L. de Vries (2008) ............................................... 8

Figure 2: Conceptual overview of low-voltage electricity distribution network. ................................ 22

Figure 3: Household including all smart appliances and other-load represented in Netlogo. .............. 31

Figure 4: Dispatcher flow chart ................................................................................................. 33

Figure 5: Network with households owning smart appliances represented in Netlogo ...................... 34

Figure 6: maximum load shifting possibilities .............................................................................. 50

Figure 7: SE against network size. ............................................................................................. 55

Figure 8: Appliances flow chart ................................................................................................. 73

Figure 9: Scheduling mechanism flow chart ................................................................................ 74

Figure 10: Dispatcher flow chart ............................................................................................... 75

Figure 11: Average demand profiles per replication per network size ............................................. 82

Figure 12: Appliances DSM penetration degrees against LE. ......................................................... 87

Figure 13: Household DSM penetration degrees against LE. ......................................................... 88

Figure 14: Demand Response techniques taken from N. Gudi (2010) .............................................. 1

1

Part I:

Introduction

Peak loads in low voltage network, what’s the problem?

3

1. Introduction

1.1. Background

Electricity plays a fundamental role in our lives. In 2009, households in the Netherlands used 87·1015J

of electricity (CBS et al., 2010a). Distribution Network Operators (DNOs) distribute electricity at a

regional level. They are responsible for the maintenance and management of the electricity

distribution networks and the transport of electricity on the low-voltage networks (NMa, 2011). Enexis

is one of the largest DNOs in the Netherlands and operates in the North, East, and South of the

Netherlands (Enexis, 2011c). Their goal is to provide affordable, reliable, and sustainable energy

distribution (Enexis, 2011b). The current network of Enexis is capable of successfully fulfilling their

goals. However, the electricity system is changing. A growing electricity demand and an electricity

system in transition affect the operations of Enexis, as well as all the other DNOs. To ensure that the

DNOs can secure their goals in the future, they have to deal with these developments.

1.2. The problem

The electricity demand of households in the Netherlands has been growing rapidly for the last decades

(CBS, et al., 2010a). This is caused by both the growing number of end-users (CBS, 2010) as well as

the increase in electricity demand per end-user (CBS et al., 2010b). Both factors will continue to

increase in the coming years. This means that the distribution network will have to distribute more

electricity in the future than today. If the shape of the daily demand pattern of the electricity load on

the network stays the same (see Graph 1), the increasing demand will also increase the peak loads on

the network. During peak periods, the electricity demand is significantly higher than the average

electricity demand during off-peak periods. The electricity load on parts of the distribution network

could then exceed the available capacity, leading to overloaded network components (assets). The

consequence of this will be an excessive reduction in life expectancy of these assets, which will

eventually break down. The current aging distribution network will not have the capacity to cope with

these future peak loads (Veldman et al., 2010). The increase of electricity demand by the end-users

therefore seriously reduces the reliability and safety of the electricity distribution, which poses an

important problem for the DNOs.

Graph 1: Daily average demand pattern on a network of a year

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Power

Hours

4

To cope with the increasing electricity demand, especially during peak periods, the network has to

improve. The traditional method is to invest heavily in placing more electricity cables to increase the

capacity of the network (Veldman, et al., 2010). However, besides the increasing demand for

electricity, the present trend to a more sustainable society shows several developments that can

seriously affect the electricity system. These upcoming developments are changing our electricity

system, which may make the traditional method of improving the network not the most effective way

of coping with the problem.

Distributed Generation (DG): More electricity will be generated locally by smaller power

plants, e.g. solar, wind and micro combined heat and power (µCHP) generation. The

intermittent character of these sources will require more flexible balancing of supply and

demand. This requires the electricity network to not only allow down-stream flows but also

upstream flows (Ackermann et al., 2001; Akorede et al., 2010; Bouffard & Kirschen, 2008;

Pepermans et al., 2005; WADE, 2010).

Electrical vehicles (EV): The transition of the transportation system from fossil-fuelled vehicles

to electrical vehicles (EV) to reduce CO2 emissions and reduce the dependence on oil, will

result in a significant increase in the electricity demand and hence burden on the electricity

network. This will augment the need for more capacity and more efficient use of the network

(Andersson et al., 2010; Enexis, 2011a; Galus & Andersson, 2008).

Energy storage (ES): ES is an integral part of renewable and distributed energy resources.

Excess energy can be stored locally and used by the consumer at a later moment. Stored

electricity can also be fed back into the distribution network when needed, which will require a

more flexible use of electricity flows (Hadjipaschalis et al., 2008; Mohd et al., 2008).

Demand Side Management (DSM): DSM programs try to control, and influence the demand

for electricity in order to reduce electricity demand and increase the efficiency of the system

and thus reduce new investment needs in the electricity system. Furthermore, DSM can

significantly support the introduction of DG, EV and ES. (IEA, 2011; McKinsey, 2010; Strbac,

2008).

These developments do not progress on their own, they often support each other’s development. For

example, storage can help balancing the variable energy supply of DG by temporarily storing

electricity (Mohd, et al., 2008). There are even plans to use EVs “as providers of regulating power in

the form of primary, secondary and tertiary frequency control” (Andersson, et al., 2010:1).

Furthermore, DSM can support DG, ES, and EV by allowing more flexibility in network balancing to

better align demand and supply (IEA, 2008). The current network is not equipped to accommodate all

the developments mentioned above, the electricity network has to enhance to a more advance

system. A popular system that supports the above changes is a Smart Grid. A Smart Grid system

allows for dynamic control of the production and demand of electricity and has a two-way digital

information system to support this. The system has intelligent monitoring systems to keep track of all

5

the electricity flows in the system, both down-stream and up-stream. A smart grid can dynamically

respond to changes in the electricity grid (Energy Valley, 2011; European Commission, 2006;

McKinsey, 2010; U.S. Department of Energy, 2008). The Smart Grid system is an all-in-one package

that supports many different developments. At this moment however, DSM is the most important

development that shows good potential for reducing these peak loads (IEA, 2011).

Demand-Side Management of households in a low-voltage network

The goal of DSM is to reduce electricity demand, and increase the efficiency of the system (IEA, 2011)

by “bringing both demand and supply to the best possible low value” (Sinha, 2009:9). Important is

that DSM tries to manage the demand on the customer side of the meter and not the supply side.

DNOs operate on the low-voltage distribution network. Since most of the low-voltage networks are

residential areas, the energy demand of the low-voltage network originates mostly from the electricity

demand of households. This research will therefore focus on the Demand-Side Management of

households in the low-voltage network only. Electricity demand from other sources, such as

streetlights or industrial and commercial activities, will not be included.

DSM of household focuses on shifting loads of household appliances away from peak periods to off-

peak periods (McKinsey, 2010). These loads are then used to fill up gaps in the demand pattern. DSM

will therefore change the demand pattern of the network (see Graph 2). To measure how effective

this shift is, two performance indicators can be identified: Levelling Effect and Height of Peaks.

Graph 2: Shifting electricity load using Demand-Side Management

The shift of loads from peak periods to off-peak periods will have a levelling effect on the demand

pattern. A key measure of performance is therefore the Levelling Effect (LE), measures the deviation

to the average load of the network of a day. From a perspective of the Distribution Network

Operators, the reduction of the peak loads in the network is important. Lower peak loads mean lower

electricity losses as well as lower thermal strains on the power lines, which will increase the efficiency

of transporting electricity (AHAM, 2009). Another key performance indicator (KPI) is therefore the

1 17 33 49 65 81 97

Power

Time Average Load New Average Load

6

Height of the Peak loads (HP) in the network, which is defined as the highest load (in W) that occurs

during a day.

1.3. Research Questions

Demand-Side Management may be an effective alternative to the traditional method to cope with

peak loads in the network. However, both the traditional way of improving the network and the DSM

approach will require high investments. To ensure that investments in a DSM programme are cost

effective and economical viable, more research is necessary on the effectiveness of Demand-Side

Management for households on the peak loads in a low-voltage network. The effectiveness can be

tested by looking at the effect of DSM on the KPI’s Levelling Effect and Height of the Peak loads. The

main research question is therefore:

“To what extent will Demand-side Management of households affect the Levelling Effect and the

Height of the Peak loads of the demand pattern in the low-voltage distribution networks?”

The following sub-questions will help in answering the main research question:

To what extent will different "smartness" levels of smart appliances and smart meters

influence the effectiveness of Demand-Side Management?

To what extent will different aggregation levels for controlling and managing smart appliances


To what extent will the penetration of smart meters and smart appliances in households


1.4. Structure of thesis

This part, Part I, is about problem exploration and setting up the research framework:

The next chapter, Chapter 2, will give a description of the system. First, a short description

of the Dutch electricity system is given, followed by the social system were important actor

are described and their position to DSM. After this, an introduction on the electricity demand

pattern in the low-voltage network is given, after which we will take a closer look at Demand-

Side Management and load shifting. This chapter ends with a section about simulating an

energy system with Demand-Side Management.

Chapter 3 discusses the conceptualisation of the system. First, the households and their

electricity device (appliances and e-meters) are introduced; here the distinction between

smart and non-smart devices is made. Second, the low-voltage network is described at a high

aggregation level, after which the scheduler of smart appliances is described. Here the

7

scheduling trigger, different scheduling schemes, forecasting methods and different

aggregation levels for scheduling are introduced.

Part II, describes the development of the model:

In Chapter 4, the Agent-based Modelling approach is explained and the simulation program

Netlogo is introduced.

Chapter 5 presents the implementation of the agent-based model. Modelling choices per

agent in the simulation are briefly discussed. Furthermore, a short pseudo code of the main

flow of the model is shown.

The simulation setup in Chapter 6, will discusses important simulation settings, e.g. the time

step used and the number of replication used. Furthermore, the Key Performance Indicators

are further operationalized.

The verification and validation of the model are discussed in Chapter 7.

The scenarios used are described in Chapter 8: the Experimental Design.

Part III presents and discusses the results:

The simulation results are presented in Chapter 9.

Chapter 10 the reflection, conclusion and recommendations are presented. First a reflection

on important modelling choices and using Agent-Based Modelling as a modelling approach are

discussed. This is followed by the conclusion of this study. This chapter ends with several

recommendations for future research.

In Chapter 11 a personal reflection is given on the six month of working on my thesis.

The Bibliography and Appendices can be found in Part IV.

8

2. System Description

2.1. Dutch Electricity System

A low-voltage distribution network is part of a much larger electrical power system. Large power

plants generate massive amounts of electricity, which are transported to the end-users using a

hierarchical structure of high-voltage (HV) transmission networks, medium-voltage (MV) distribution

network, and low-voltage (LV) distribution networks. In the mid-80s, provincial and municipal

authorities owned many energy companies. Liberalization of the energy market in the 90’s led to the

unbundling of monopolistic and commercial activities. The liberalisation of the energy market gives

consumers the freedom to choose their energy supplier, which should enable more competition

between energy companies in the European Union. This in turn should lead to better services and

lower energy prices for the consumer (Veldman, et al., 2010). In the current situation, the ownership

of the generation and supplying activities of electricity is separated from the transportation activities

(See Figure 1).

Legend

Transmission

Operator

Transmission

network

Distribution

NetworkLoadGeneration

System

Operator

Distribution

Network

Operator

Import /

Export

Suppliers Consumers

Process

Actor

Electricity

Control

Figure 1: Physical system (diagram adapted from L. de Vries (2008)

2.2. Social system

The electricity system is a multi-actor system. Key actors are briefly discussed to highlight their

position to DSM.

Generation - Energy Companies (EC)

Energy companies (E-companies) often (but not always) fulfil two tasks: generation and supply of

electricity. In the Netherlands, e-companies generate electricity from different energy sources. These

9

are coal -, gas- and nuclear power plants, and renewable energy plants that use wind, solar, and

biogas. Energy companies that generate electricity sell their electricity to energy suppliers.

The efficiency of power plants and thus their operational costs differs much. Base load power plants

produce continuous energy at low cost. Intermediate power plants produce electricity to meet the

fluctuations in the energy demand during the day and operate about 30 to 60 percent of the time.

They have higher operational costs then the base load power plants. Peak power plants only operate

10 to 15 percent of the time and provide power only during peak periods. They can react quickly to

changes in the electricity demand, but they are expensive to operate (Cordaro, 2008).

DSM can lower the demand peak and smoothen the demand for energy to a more stable demand

pattern. This would mean that the expensive peak power plants could be used less often, and lower

operational cost (intermediate) power plants could be used more. DSM can therefore reduce the

operational costs for the e-companies (Strbac, 2008).

Transmission – TenneT (TSO)

TenneT is the Transmission System Operator (TSO) and is responsible for balancing the electricity

demand and supply on the national electricity grid (system operator), the maintenance and

management of the grid, and the transport of electricity on the grid (transmission operator) (TenneT,

2010). It transports the generated electricity to the regional distribution network.

DSM can lower the peak demand and so the peak loads in the transmission network. Lower peak

loads will reduce the need for more capacity and so the need for new investments. In addition, peak

loads in the transmission network result in higher energy losses, as well as higher thermal strains on

the lines, which further increase the inefficiency (AHAM, 2009). In short, reduced energy losses result

in lower costs for transporting electricity. DSM can therefore reduce the operational cost of

transporting electricity through the national electricity grid.

Distribution – Distribution Network Operations (DNO)

Distribution Network Operators (DNOs) are responsible for the distribution of electricity on a regional

level. They are responsible for the maintenance and management of the electricity distribution

networks, and the transport of electricity on the networks (NMa, 2011). They transport the electricity

received from the national transmission network to the end-user, in this case: the households.

DSM can lower the peak demand and so the peak loads in the distribution network. Lower peak loads

will reduce the need for new capacity and so the need for new investments. In addition, as with the

transmission network, high peak loads in the distribution network will lead to higher energy losses, as

well as higher thermal strains on the lines, which further increase the inefficiency (AHAM, 2009).

Lower peak loads will reduce the energy losses and so the operational costs for the distribution of

electricity.

10

Suppliers – Energy Companies (EC)

Suppliers buy electricity from energy companies that generate electricity, and sell the electricity to the

end-user. They pass all costs for transportation and distribution to the end-users.

DSM creates opportunities for e-companies for dynamic tariff pricing and other smart system services.

Furthermore, a more stable demand pattern could make electricity prediction easier and better and so

improve their forecasting schedules for electricity. Every day, companies have to summit to the

network operator a forecast of tomorrow’s energy demand of their customers. The network operator

uses these to balance the demand and supply of electricity on the networks. The electricity demand

and supply have to be balanced to prevent overloading of the network. If an imbalance occurs, the

network operator corrects this by shutting down or turning on withheld capacity. Costs for correcting

the imbalance are passed on the responsible e-company. Better forecasting will reduce the probability

of imbalance and thus the probability of additional imbalance costs for e-companies (Tromp, 2011).

End-users – Households

End-users are all actors that eventually use the electricity, like industrial, commercial or residential

consumers. In this case, the appliances of the residential end-users (Households) eventually use the

electricity. End-users are free to choose from which electricity company they want to buy their

electricity. They cannot choose which DNO delivers them the electricity, since there is only one in

there region. The consumer eventually pays for the electricity generation, transportation and

distribution costs.

DSM can shift appliances load to off-peak, lower priced periods. This shift could reduce consumer’s

electricity bills. Furthermore, if many households shift their load away from peak periods to off-peak

periods, this would reduce the electricity peaks in the whole system. Lower peak loads in the demand

pattern results in lower peak loads in the transmission network and distribution network. This will

lower energy losses, which in turn lower costs for transportation and distribution. Furthermore, lower

peak loads also means that high operational cost peak power plants need to be operated less, further

reducing the cost. If these cost reductions are incorporated in the electricity prices, they can result in

lower prices for consumers.

Supervisor – NMa (Netherlands Competition Authority)

Because it is economically inefficient to have multiple systems next to each other, there is only one

national transmission system and one distribution system per region. This means that the TNO and

DNOs have a monopolistic position in their market activity. To ensure that the TSO and DNOs do not

abuse their monopolistic position, the NMa supervises them. The NMa regulates the TSO and DNOs

and caps transport prices to protect consumers against monopolistic activities (NMa, 2011).

11

DSM creates a variety of new opportunities for the electricity market. The NMa has to ensure that

these new opportunities do not affect the consumers in a negative way, e.g. (artificial) rising prices,

reduced quality and reliability of electricity supply.

Commercial actors

These actors are all parties the that through commercial activities are connected to the electricity

system. From construction sources for generation, transportation and distribution to user appliances

that eventually use the electricity.

With DSM for households, the households require smart meters, and smart appliance. Commercial

companies will need to construct these devices for the household to use them. Furthermore, the e-

companies, and DNOs need to facilitate the use of these devices by consumers. Close cooperation on

the technical standards will be required to ensure that the smart system can operate with the smart

appliances.

2.3. Electricity Demand Pattern in the low voltage distribution network

The electricity demand pattern in the low-voltage distribution network originates from the electricity

demand of households. Households own appliances that use electricity when turned on or set to

standby. The sum of the electricity used by the appliances during the day forms the demand pattern

of a household for that day. The collective electricity demand over time of all appliances owned by

every household in the network forms the demand pattern of the network. Graph 2 (shown earlier), is

a daily yearly average demand pattern of a network. This is a smooth line with no spikes, because it is

an average of a year. The day-to-day demand pattern of a network shows many peaks and gaps due

to the irregular and chaotic nature of the creators of the demand patterns (the household appliances).

In Graph 3, an example of the daily demand patterns for two different households are shown.

Graph 3: 24 hour demand pattern of two household of a day generated with QWatts (Bots et al., 2011)

0

500

1000

1500

2000

2500

3000

3500

4000

4500

2 4 6 8 10 12 14 16 18 20 22 24

Watt

Total load: childeren, one parent working Total load: single adult

12

Clearly shown is the irregular and chaotic nature of the demand patterns. The peaks in the demand

pattern may be caused by a single use of a high-demand appliance (e.g. dishwasher), or the

simultaneous use of several low-demand appliances. The height and duration of a peak depends on

the electricity demand, duration of use, and demand profile of the appliances. The demand pattern

varies throughout the day and is mainly occupant based A household with a single (outdoor) working

adult typically only uses electricity in the morning (get out of bed, to go to work) and evening (home

after work). During the day, the electricity is at bare minimum, only standby appliances and

appliances that are used semi-continuously (like refrigerators and freezer) are active. The household

demand patterns of a household where the occupants are at home shows many peaks throughout the

day. In addition, because there are more users, more simultaneous uses tend to occur, so the height

of the demand pattern is higher, just as the peak load.

Coincidence factor

The distribution network is designed to cope with the daily and seasonally demand pattern, and meet

peak demand. The peak demand is not the maximum demand possible in the network. The

theoretically maximum peak would be the load of all appliances of all connected households in the

network. The probability of this simultaneous usage of every appliance is very low. The network is

therefore dimensioned to the most probable maximum demand.

A household rarely uses all its appliances at the same time. To calculate the probability of

simultaneous use of appliances in the network a coincidence factor is used. “The coincidence factor is

the ratio between maximum coincident total demand of a group of households and the sum of

maximum demands (peak loads) of individual consumers comprising the group. In other words, the

coincidence factor represents the ratio of the capacity of a system required to supply a certain number

of households” (Strbac, 2008:2) (see Graph 4).

The coincidence factor is calculated by: ( )

,

where and x represent the consumers’ stochastic behaviour at the time of the yearly peak loading

(Gwisdorf et al., 2010).

Example: With a network of a 100 households, the

coincidence factor is about 0.2. This means that the

maximum load on the network will only be 20% of

the maximum possible load. With an increase of the

number of households in the network the coincidence

factor decreases, i.e. the probability of 500

appliances to turn on simultaneously is lower than

the probability of 5 appliances to turn on

simultaneously.

Graph 4: Coincidence factor as a function of the number of households (Strbac, 2008)

13

Building a network with a capacity that can accommodate the simultaneous maximum demand of

every household in the network would be highly expensive and inefficient (overdimensioned). The

network is therefore dimensioned to accommodate the normal load of the combined demand of

households. The network load on the low-voltage network is however, not a straight-levelled load (see

Graph 1). During off-peak periods the load is much lower than during peak load periods. This low

utilisation of the network is a very inefficient. With DSM load-shifting programs, this utilisation can be

improved. Reducing the peak loads in the network by shifting loads from peak periods to off-peak

periods will reduce the need for new investments in higher capacity.

DSM techniques however, can disturb the natural diversity of the network loads (Strbac, 2008). When

smart appliances become more common in households, the controlled load shifting will change the

coincidence factor. The simultaneous usage of appliances will not be caused by probability but will be

regulated. When smart appliances become more common in households, (local) control may be

needed to prevent new peak loads from forming, e.g. the simultaneous shift of many smart appliances

to a certain time of day when the electricity price is low. When looking at the effect of DSM, it is

therefore important that this simultaneous usage of appliances is taking into account.

2.4. A closer look at Demand-side Management (DSM)

Demand-Side Management programs are policies and measures that try to control, influence and

reduce the quantity or pattern of electricity used by the demand side of the electricity system (Strbac,

2008). The goal of DSM is to reduce peak loads and to increase the efficiency of the system (IEA,

2011) by “bringing both demand and supply to the best possible low value“ (Sinha, 2009:9).

Important angle is that DSM tries to manage the demand on the customer side of the meter and not

the supply side.

Reducing consumption and shifting loads during periods when the system is constrained will result in

less system losses (both production and transportation losses), lower system balancing costs, reduced

network reinforcement investments and so increase system efficiency. Furthermore, DSM programs

can reduce electricity prices as a shift of demand during peak periods could reduce the need for

higher marginal cost generation power plants to operate. In addition, DSM can play a valuable role in

achieving ambitious environmental policy objectives (Pina et al., 2011). The intermittent character of

renewable energy sources can create stability, and reliability problems on the grid. These problems

can be minimized by large-scale storage, but these are still not available or too expensive to install.

DSM can bring flexibility to the system to better balance supply from renewable energy source with

demand, and so contribute significantly to the introduction of renewable energy sources. (ENERNOC,

2009; Torriti et al., 2010).

This research will however not go into further detail into these subjects; it will focus on DSM of

households in a low-voltage network only.

14

DSM can be classified in (1) Energy Efficiency (EE) programs and (2) Demand Response (DR)

programs.

1. Energy efficiency (EE) and saving programs

These programs try to reduce the electricity demand throughout the year by focusing on energy

reduction and overall demand for energy (Gudi, 2010; McKinsey, 2010). EE programs try to increase

efficiency of the appliances used, e.g. by replacing old inefficient appliances with more efficient

models. Energy savings programs try to inspire people to use less energy, e.g. turn the thermostat

lower and put on a sweater. They also try to educate consumers by giving them more insight in their

electricity usage. For example, End Use Monitoring and Feedback (EUMF) programs give consumers a

breakdown of their electricity usage, cost, and environmental impact. With these insights, the program

tries to motivate consumers to take energy saving actions (IEA, 2007, 2008).

2. Demand Response (DR)

Demand Response programs focus on shifting the electricity load of households from high demand

periods to low demand periods. Key features are dynamic tariffs or pricing, and indirect or direct load

control of flexible household appliance (IEA, 2007, 2008; McKinsey, 2010). Common techniques used

to reduce consumption or shifting loads are: peak clipping, valley filling, load shifting, strategic

conservation, strategic load growth, and flexible load shape (Gudi, 2010) (see Appendix I, section 1

for more information).

For the DNOs the reduction of the peak loads is important, for it will reduce the need for new

investments in new capacity. However, DNOs cannot reduce the demand for electricity per household.

They can only try to influence households to shift their demand away from peak periods to off-peak

periods Graph 2. Therefore, this research focusses on load-shifting.

Load shifting programs (LS)

Load shifting programs try to shift flexible household appliances from peak periods to off-peak

periods. These operations can take place on the daily electricity pattern cycle level but also on smaller

times scales, like the hourly or quarterly periods.

Encouraging consumers to shift their load is done by (1) Indirect Load Control or (2) Direct Load

Control.

1. Indirect Load Control (ILC)

Indirect control programs require the consumers to change their consumption behaviour by

themselves (Gudi, 2010; IEA, 2008). The consumer is responsible for the control of the household

loads, and not with external parties like the System Operator , Network Operator, or e-company. For

example, an electronic messaging service which alerts consumer of high loads and prices, or real-time

15

electricity price displays, to persuade consumers to reduce their consumption by turning “off” non-

essential appliances (Gudi, 2010). But also giving consumers more insight in their usage by more

monitoring and feedback, in the line of EUMF (IEA, 2007, 2008).

2. Direct Load Control (DLC)

Direct Load Control or Automatic load shifting, requires consumers to own smart hardware and a

communication infrastructure (IEA, 2008) that allows for the use of a smart systems, e.g. smart

meters, and smart appliances that communicate through various mediums like Broadband over Power

Line (BPL), Internet, WiFi, Bluetooth, etc. (Gudi, 2010).

When households allow DLC, consumer loads can be: (i) scheduled, (ii) interrupted, and (iii)

sequenced.

(i) Scheduling: Appliances can be scheduled for off-peak periods. Not only on a day to day

basis, but also on an hourly basis.

(ii) Interruption: During high load periods, appliances can be interrupted to reduce

consumption. Appliances will resume when the load returns to off-peak periods, e.g.

dishwashers, etc.

(iii) Sequencing: Appliances with multiple interval usage, e.g. cooling or heating appliances,

can be sequenced to reduce peak building and create a more continuous demand load.

A direct load control smart system does not necessarily have to be under external control. The direct

load control system can be aggregated at a households or appliance level. Appliances are not directly

controlled by external parties but by the internal household smart system. External parties (TSO,

DNO, and EC) can only provide the smart system with information. By providing information they can

indirectly influence the way these appliances are managed, e.g. provide a multi-tariff pricing system

(Time of Use pricing) to allow the flexible load to shift away from peak periods (high tariffs) to off-

peak periods (low tariffs) or general real-time load patterns for interrupting sequencing appliances.

When these types of smart systems aggregate to (local) network level, control does fall onto external

parties.

Load shifting of smart appliances requires a Direct Load Control approach. This research therefore

does not look at the Indirect Load Control approach but only at the Direct Load Control.

Incentive trigger for load shifting

Traditional DNOs implemented DSM using price incentives. By lowering the tariffs during off-peak

periods, consumers are encouraged to shift their flexible loads from the higher tariff periods towards

the lower tariff periods. Assuming that the smart system allows for automatic metering at scheduled

intervals, utility companies can then provide households with time- and demand-based pricing. This

creates the opportunity for electricity companies to use more complex incentive models like dynamic

16

price, see Appendix I, section 2, for some examples (A. Faruqui et al., 2009; IEA, 2007, 2008; M.

Goldberg, 2010; Mohalkar et al., 2004; Saffre & Gedge, 2010a).

Pricing does not necessarily have to be the trigger for load shifting. To enable load shifting, a trigger

is required that indicates the difference between high and low loads. A perfect non-financial trigger is

the load itself. This indicator directly changes according to a shift in network load by smart appliances.

Using the price as a trigger would add a level of complexity that is not required to determine the

effectiveness of DSM in reducing peak loads. Using the actual load on the network as trigger is

sufficient for the load shifting of smart household appliances.

In the wide variety of DSM options, this research takes a closer look at load-shifting programs.

Specifically, it will look at how automatic load shifting of household appliances can reduce the peak

load in the demand pattern to a more optimal pattern. The main indicator trigger for load shifting, will

be the network load itself.

2.5. Demand-Side Management simulations

When the load of smart appliances are shifted form peak periods to off-peak periods, the coincidence

factor changes, e.g. the natural probability of appliances to turn on simultaneously. To ensure that no

new peak loads are formed due to load-shifting, it is important that the simultaneous usage of smart

appliances is taken into account. Using a simulation will incorporate these simultaneous usages.

Therefore, a simulation is be used that is capable of simulating households with individual appliances

that are schedulable.

Many DSM simulation models have already been made. A clear distinction can be made between two

simulation techniques: Top-down, and Bottom-Up.

Top-down

The Top-down approach considers the energy system as a whole. It uses high level aggregated

historic data on energy consumption combined with high level variables such as macroeconomic

indicators (e.g. gross domestic product, unemployment), energy price and general climate to generate

the energy consumption of the household sector. Top-down is often used to calculate the energy

consumption on the long-term. Strengths of Top-Down modelling are its simplicity, and reliance on

aggregated historical data, which is widely available (Swan & Ugursal, 2009). Top-Down is the

conventional approach often used to forecast demand at the energy supply level and it is therefore

also often used for the research of long-term policy issues (Guo et al., 2010).The lack of detail

regarding individual energy consumption does however reduce the capability to identify key

improvements on energy consumption. This lack of detail means that no individual daily patterns of

households can be generated that capture the arbitrary or seemingly chaotic behaviour of the pattern

that is created by the household appliances.

17

Bottom-up

The bottom-up approach is the opposite of top-down and is used increasingly more often in the

modelling of energy systems (McArthur & Davidson, 2005). It covers all models that use input data

from a hierarchical level less than that of the system as a whole (as is with top-down). The bottom-up

approach allows the use of individual end-users to generate the consumption on a higher aggregation

level. The combined energy usage of individual households or appliances generates the demand of the

system. Strengths are the high level of detail that make individual modelling of system components

possible, such as consumers, generators, appliance, or technologies etc. (Swan & Ugursal, 2009). This

approach is able to generate daily load profiles of households that behave arbitrarily or seemingly

chaotically by simulating individual appliances.

A wide variety of simulation models exist that research the effect of DSM programs, using top-down

and bottom-up approaches. The focus ranges from price-focused based simulation (Gottwalt et al.,

2011; Saffre & Gedge, 2010a) to frequency dips, from balancing of renewable energy and storage

(Gudi et al., 2011) to sequencing of cooling devices (e.g. freezer, air-conditioners)(Bigler et al., 2011;

Stadler et al., 2009) and more . These all use the load shifting principle but have different triggers and

focus. For this research, the focus will be on the load shifting, using the load pattern of the network

itself as main indicator.

18

3. Conceptualisation of DSM elements in the network

In this chapter, first the DSM parts (appliances and electricity meters) for households are discussed,

followed by the DSM aspects for the low-voltage network. This chapter end with the a section about

the control and management of smart appliances.

3.1. Households

For households two concepts are important when talking about Load-shifting: smart meters and smart

appliances.

Household appliances

Household appliances are the source of electricity demand of the household. A household has

different types of appliances, each with different power demand, runtime, and number of usages.

Graph 5 shows the demand pattern of an average household constructed from the different load

proportions of the different appliance categories. Some of these appliances can be scheduled to

periods with a low network load. However, not every appliance is well suited to be managed or

scheduled. Appliances can therefore be divided into non-smart appliances and potential smart

appliances.

Graph 5: Average consumption for a Day for a Typical Household (Grinden & Feilberg, 2010)

19

Non-smart appliances: other-loads

These appliances cannot be scheduled or managed. These appliances require to operate directly when

the user turns them on, their function is of direct use to the user, e.g. when you want to turn on the

lights, they must work right away and not be scheduled for later. Since these appliances cannot be

made smart, their individual operation and demand are not of further interest. They are not required

to be simulated individually and will therefore be seen as one unit: the other-loads. The demand

pattern of the other-loads is the combined demand of all the non-smart appliances.

Potential smart appliances

The occupants of the households generally do not care, or lack the time or means to actively manage

their electricity usage. Smart appliances help households to manage their electricity usage for them,

according to their own household preferences. The smart system can monitor information provided by

the energy suppliers or DNOs, and adjust the operation of smart appliances to the needs of its owner

automatically, which will help the households to reduce their electricity bills. For example, by

monitoring the price of electricity, the smart system can schedule smart appliances to turn on when

the price of electricity is low, and off, when the price is high (AHAM, 2009; Energywatch, 2005;

Timpe, 2009).

Potentially smart appliances are appliances for which the function is of no direct use for the consumer,

only the end result is, e.g. a dishwasher or refrigerator. These appliances can be split into two groups:

appliances that are used a few time or less a day, and appliances that have multiple interval usages

per day.

Appliances with a few or less usages a day are appliances that users turn on and forget about. Only

the output at the end of the operation run is relevant to the users. It does not matter when the

appliance finishes its operation, as long as it is finished before a certain time. For example, when a

dishwasher is planned to run during the night, it does not matter when it runs, as long as it is finished

in the morning before breakfast.

The multi interval usages per day appliances are not operated by the end-users. These appliance are

automated and have an on/off energy profile, e.g. refrigerator, air conditioner, or electric heater. The

flexibility in scheduling their load to other periods is limited due to their function. For example, a

freezer only turns on for a short period when the temperature inside gets too high. Because the

temperature rises slowly inside the freezer the freezer has to turn on multiple time a day to keep the

inside cool. It is not desirable to deep freeze the inside in one usage during off-peak periods and let

the inside heat up slowly during the peak-periods. This is the same for air conditioner and electric

heaters. Imagine living in a household that get very hot and slowly cools down again, and then gets

very hot again.

Although the schedulability of these appliances is limited, their usages can be sequenced. Sequencing

the usages of the appliances of many households can avoid the simultaneous usages of these

20

appliances and so, reduce the peak load created by these appliances and thus reduce the load in the

network. Furthermore, because these appliances have a short operation time, they can be used for

smoothening the demand pattern of the network by filling up smaller gaps in the pattern, which the

few or less usage per day appliance cannot fill because of their longer operational time. Because the

appliances are already automated they can be easily sequenced.

Smartness of appliance

When a house has a smart e-meter installed, that household can also have smart appliances. Smart

appliances can be pre-programmed by the households members to turn on or off according to their

wishes and needs. The smart appliances are managed by the scheduler, see below for more on the

scheduler. Depending on the “smartness” of the meter and the appliances, there are multiple ways to

programme the appliances. Four common types are standard, smart, smarter, and smartest.

Standard

Appliances can only be turned on or off by a household member.

Smart

A scheduler manages the appliance. The appliance can send its configuration information to the

scheduler, which uses this information to schedule the appliance to the best turn-on time for that

appliance. The appliance can receive turn on/off signals from the scheduler.

Smarter

The appliance is its own scheduler. It can determine for itself when to turn on. It receives information

from the smart meter, which it processes and analyses on its own. A household member can

programme the appliance according to his or her wishes.

Smartest

This appliance has the same capabilities as appliances that are “smart”. In addition, this appliance has

auctioning capabilities. When a local electricity auctioning system is present, the appliances can join

these auctions and bid on energy. Because this research focuses on load shifting and not price

incentive load shifting, this smartness option is not further looked at.

Household electricity-meters (e-meters)

The e-meter is the connection between the (low-voltage) network and the household appliance.

Standard electricity meters only record the total amount of kWh used. Their functionality can be

extended to more advanced options by increasing the smartness of the devices. Smart meters enable

two-way communication between the meter and the household’s electricity supplier and DNO. This

enables the electricity supplier and DNO to provide the household with specific information, e.g. the

households electricity usage, and current/past electricity price. Furthermore, when the household has

21

more insight in its electricity usage, the energy supplier has the opportunity to introduce different

electricity prices dependent on the demand and time of day. This will create incentives for the

household to reduce its electricity usage and/or spread its usage more away from peak periods

(Energywatch, 2005; Enexis, 2011d).

Smartness

Depending on the smartness of the e-meter, the smart system of the household has different

capabilities. Four common types of meters are standard, smart, smarter, and smartest.

Standard

The standard meter is the basic unit that most houses currently have installed. This unit only records

the total amount of kWh used.

Smart

A smart meter can receive, send, and analyse information it receives form both internal sources

(appliances) and external sources (DNOs and energy companies). It only acts as an information

analyser and controller. It provides the schedulers with information and provides users with

information about their current and past usage, and electricity prices.

Smarter

This meter can receive, record, analyse, and display information it receives from both internal sources

(appliances) and external sources (energy company or DNO). In addition, the meter can send signals

to the appliances (turn “on” or “off”) and send information back to the energy company (e.g.

electricity consumption). The e-meter manages the appliances, it acts as a scheduler. (See scheduler

for more information on this). So this smarter meter no only measures but also controls.

Smartest

This meter can receive, send, record, analyse, and display information. It can also receive, and send

signals to internal and external source. In addition, the meter has auction capabilities. When a local

electricity auctioning system is present, the meter can join, bid, and buy electricity from this auction.

The e-meter can manage the appliances to lower the total electricity load of the household. Because

this research focuses on load shifting and not price incentive load shifting (related to energy prices),

this smartness option is not further looked at.

Households

Human behaviour is hard to influence. Here the focus lies on appliances that are easily influenced or

controlled without human actions, except for the activation of the appliance. No human action is

therefore required for the appliance to actually operate. Humans do not need to be individually

included in the model. Humans are therefore represented as a household, which includes every

22

person living in that house. How often and at what time an appliance turns on, depends on the type

of household.

3.2. Low-voltage network

The low-voltage (LV) distribution network distributes the electricity regionally to the households. A

MD/LV transformer is the transfer point between the low-voltage and medium-voltage (MV) network.

An average low-voltage network has several main power cables and every cable has multiple

households connected to it (See Figure 2).

Appliances

Household

E-meter

Low-voltage distribution network

MV/LVtransformer

Demand

Figure 2: Conceptual overview of low-voltage electricity distribution network.

The focus is on the effectiveness of shifting loads of smart household appliances in current low-

voltage network that are not (yet) constrained by the capacity available. Therefore, the capacity is not

a variable or constrain of the network in this simulation case. Furthermore, the network will be linear,

no electrical properties are included, e.g. energy network losses, or voltage drops. The demand

pattern will only be measured at the transformer and the individual households. There will be no

research into the demand pattern on other positions half way or at power line intersections.

Therefore, the topology (layout) of the network is not included. This also means that the scheduler

will not take the position of the household in the network into account. E.g. otherwise, when demand

is high, the scheduler could choose to allow an appliance to turn on at the beginning of the network,

for that would only burden a small part of the network, the short distance from the household to the

transformer. An appliance at the end of a network power cable burdens the entire power cable with

its load, and therefore a less attractive one to turn on.

23

3.3. Control and Management of smart appliances

To shift the load of appliances, these appliances have to be scheduled. Scheduling does not

necessarily have to be organised centrally. Depending on the type of smart appliances and household

preferences, scheduling can be organised at different levels in the system. Furthermore, there are

several ways how the scheduling can be performed.

Scheduling Level

The scheduling of appliances could be programmed at different level of the system: Appliances level,

Household level, and Network level (see Table 2).

Appliances level

The smart appliance is its own scheduler and determines for itself when to turn on or off. This is a

smarter or smartest type of appliance.

Household level

The e-meter acts as the scheduler. All the smart appliances of the household are manage by one

central scheduler. Only smart appliances types can be scheduled which means that the household can

only own smart appliances. No smarter or smartest type of appliances can be owned by the

household. It would not make any sense to have these types of appliances when the e-meter can

schedule everything. In real live these situations can occur but these will not be taken into account in

this research.

Network level

Here, a central scheduling unit manages every smart appliance (type: smart) of every household in

the network. In this setup it is not unlikely that many household have other types of appliances then

the smart appliance needed to be scheduled by the smart street scheduler. Unlike at household level,

these different types are allowed when scheduling at network level

Table 2: Scheduling level and smartness level

Scheduling level E-meter types Appliance types

Appliance Smart Smarter, Smartest

Household Smarter, Smartest Smart

Network Smart, Smarter, Smartest Smart, Smarter, Smartest

24

Scheduling scheme’s

The scheduler can use different scheduling scheme to schedule the appliances. For example: First

Come First Serve (FCFS) or Earliest Deadline First (EDF). For a more extended list of possible

scheduling schemes, see Appendix B.

Different scheduling schemes may affect the way the appliances are scheduled. For example, FCFS is

the fairest scheme: the household who activates its appliance first, will get scheduled first. However,

appliances that have a short operational time (shorter time left until deadline), may not be scheduled

optimal because these positions are already filled by other appliance that were first in queue. These

differences may affect the effectiveness of the smart system.

Scheduling trigger

AS already stated before, a scheduler can use different trigger for managing the appliance.

Traditionally DSM usage a price incentive as a trigger. For this research, the scheduler uses the

network load as the trigger for scheduling. It looks for the lowest possible value in the time periods in

which the appliance must turn on, and schedules the appliance in that timeslot.

25

Part II:

Development of the Smart Network model

Smart appliances in households in a low-voltage network.

27

4. Agent-Based Modelling as modelling approach

A simulation is required to test the effectiveness DSM for households in a low voltage network. The

simulation model must be able to:

Simulate an electricity demand pattern in an electricity grid over time.

Simulate individual households, appliances & meters.

Allow interactions between the households and the electricity grid

Allow interaction between the households and his appliances & meters.

Easily define different attributes of the individual component like households and appliances

to ensure heterogeneity of components

Allow different smart element implementations (rule-based behaviour for the DSM scheduling

programs)

Display simple visuals for easy comprehension of model.

Use discrete time, e.g. appliances turn on/off.

An Agent-Based Modelling (ABM) approach is well suited for this research. ABM is a Bottom-Up

approach that models a system as “a collection of autonomous decision–making entities called agents”

(Bonabeau, 2002:7280). In ABM, the global system behaviour is not defined. The behaviour is defined

at the individual level and the systems behaviour emerges from the simultaneous interaction of

individual agents with each other and their environment (Macal & North, 2006). ABM models can

capture heterogeneity across individuals and in the network of interactions among them (Rahmandad

& Sterman, 2008).

4.1. Netlogo: An Agent-Based-Modelling platform

There are many ABM simulation platforms available (Rob Allan, 2009). For this research, Netlogo

(Wilensky, 1999) is used because it:

Is a powerful simulation program, which is well suited for modelling complex systems that

develop over time.

Is easy to use and to learn.

Has simple but clear visuals

Comes with extensive documentation, tutorial and example models.

Is possible to extent to other programs, e.g. “R”, “MATLAB”.

Is free to use.

28

5. Implementation of Agent-based Model

Five different types of agents can be identified that have to be modelled; appliances, e-meters,

households, transformer and a scheduler/dispatcher. Below, each will be discussed.

5.1. Implementation of agents

Appliances

There are two electricity load-generating agents: smart appliances and other-loads.

Smart appliances

There are 5 types of smart appliances: washing machine (WM), tumble dryer (TD),

dishwasher (DW), refrigerator (RF), and freezer (FR). There are more household appliances

that can be potentially smart, e.g. air conditioners, heat pumps, electric heaters. These are

however not included in this research because they have a very low household penetration

degree in the Netherlands.

Each type of smart appliance has its own demand profile. Appliances of the same type all use

the same demand profile, see Appendix C for an overview of demand profiles.

A household can own a maximum of one appliance per appliance-type. There are five

appliance types, so households can own a maximum of five smart appliances.

The number of appliances owned by a household is statistically determined. During this

process, it is also determined whether an appliance is smart or not (depending on the

appliance smartness penetration degree). If an appliance is smart, it is modelled individually.

When an appliance is not smart, its load is incorporated with the other-load agent.

Power of appliances of the same type is equal.

Smart appliances have 3 phases: Off, Standby, On

When a smart appliance is turned to standby, it cannot return to status off. It will always be

scheduled and turned on, after which it will return to status off.

Once a smart appliance is turned on, it cannot be turned off or set to standby. It continuous

to operate until its runtime has completed, and then it turns off.

Every smart appliance has its own information list that the scheduler needs in order to

schedule the appliance. When an appliance is set to standby mode, it will update its own

information list and send this information to the scheduler.

TOIL (Turn-On-Information-List) = [Agent ID, Begin-Time, End-Time, Duration, Power].

See Appendix A for a flow chart of smart appliances

29

Non-smart other-loads

The other-loads of a household represent all the other loads in the household, including the

potentially smart appliances that are now not smart.

Depending on the combination of other-load with non-smart appliances, the other-loads have

a specific combination profile (See Appendix D).

There are 100 different demand-profiles for each combination.

These demand-profiles have a length of one day (24 hours; i.e. 96 quarters).

Every new day, this profile is refreshed to a new one chosen randomly from the list of profiles

that correspond to the other-loads combination. This ensures that every day a households has

a different demand pattern.

Other-loads are always on, they cannot turn off or turn to standby.

Load profiles for the Other-loads

Because the non-smart appliance are not scheduled, they are not required to be simulated

individually. However, the simultaneous (coincidence factor) usage of these appliance should be taken

into account when creating the other-loads profile. These load profiles are therefore generated with

the program QWatts (Bots, et al., 2011). QWatts enables the quick and easy generation of daily load

profiles of individual households. These profiles are generated stochastically using the probability

distribution of the appliances attributes. These profiles can be generated for different household types

that own different individual appliances.

The other-load represents all the other non-smart household appliances, e.g. TV’s, lights, vacuum

cleaner, coffee maker etc. When a potential smart appliance is not appointed as smart, its load is

incorporate into the other-loads. This means that for every possible combination of other-load and

potentially smart appliances, a different profile is created. Therefore, also profiles of the potentially

smart appliances are created to combine them with the other-load. Per type of households there are

25 = 32 combinations possible between the other-load and smart appliances. For every profile 100

replications a generated. With 100 replications per profile there are in total 3200 profiles available per

household type. With three household types, this results in a total of 9600 available profiles for the

simulation.

The generation of the profile followed the following steps:

Generation of other-load (without smart appliances) load profiles

Generation of smart appliances load profiles

Combining other-loads profiles with every possible combination of smart appliances profiles.

30

Important note is that these profile are also generated using a 15 minute time-step. For as this is the

time-step of the main model (see Simulation Setup (chapter 6.1), part one simulation Time step.

The profiles used by the other-loads depends on the combination of other-loads including non-smart

appliances. Table 3 shows several examples of these combination profiles that combine the other-load

with the smart appliances.

Table 3: Example of combination of other-load.

Smart appliances

owned by household Other-load profile

Household

Type

Combination

household

Other-load

profile

combination

Dish washer (3)

Other-load + Washing

machine + tumble dryer

+ refrigerator + freezer.

1 t1comb03 t1comb3

Washing machine (1),

refrigerator (4),

Other-load + Dish

washer + tumble dryer

+ freezer.

2 t2comb014 t2comb8

Washing machine (1),

tumble dryer (2),

refrigerator (4), freezer

(5).

Other-load + Dish

washer 3 t3comb01245 t3comb28

Appliances have a number to generate a profile: Other-loads (0), WM (1), TD (2), DW (3), RF (4), FR

(5). See Appendix D for full list.

E-meters

A smart system requires certain information from DNOs and energy companies to function, like energy

price and energy usage. Although there are many device that can receive, and send information from

DNO and energy companies, for simplicity the e-meter is set as the central unit which can receive and

send information to DNOs and energy companies. A household must therefore have a smart e-meter

to be able to use smart appliances.

Every household has a meter to which all the appliance (inc. other-load) of that household are

connected.

Smart meter is required in a household for smart appliances

Smartness: smart and smarter.

When the scheduling level is set to household, the meter acts as the scheduler. (See below

for more info on schedulers

31

Houses

Households own the meter, appliances and other-load (see Figure 3). Depending on the household

type, the probability of a household having a smart appliance is determined and the probability of an

appliance to turn on this day is determined (see Appendix C, Table 12).

There are 3 types of household, which have a different yearly demand, and so different daily usage.

Each type is also represented by a proportion in the network (see Table 4).

Table 4: Household type, yearly usage and network

proportion.

Household

type

Yearly kWh

(Nibud,

2011)

Proportion

(CBS, 2011)

Type 1:

1 person 2500 0.37

Type 2:

2/3 persons 3500 0.44

Type 3:

4+ persons 5000 0.19

Figure 3: Household including all smart appliances and

other-load represented in Netlogo.

Scheduler

The scheduler determines when a smart appliance must turn on. Scheduling can happen at three

different levels: Appliance level, Household level, and Network level (Table 5). The scheduler is

however not an individual agent. Depending on the scheduling level, the corresponding agent acts as

the scheduler.

Table 5: Scheduling level - agents acting as schedulers

Scheduling-level Agent acting as scheduler

“Appliance” Appliances

“Household” E-Meter

“District” Transformer

At every time-step all schedulers are called upon to check if there are any appliances in their

Scheduling-queue list waiting to be scheduled. If so, these appliances are scheduled and removed

from the queue. Their turn-on time is send to the dispatcher.

32

Every time-step, the queuing list is cleared by scheduling all appliances that are in the

queuing list. At the end of the scheduling sequence, the scheduling-queue list is empty. It is

therefore not possible that appliance are not scheduled and do not operate.

The scheduler uses a prediction of today’s demand pattern to schedule the appliance.

There are 3 types of demand-pattern predictions: today’s demand (perfect), yesterday’s

demand, and an average demand of 5 days (see Scheduling-method below for more

information).

All schedulers use the same demand-pattern information according to their scheduling method

(scheduling-scheme) and memory (demand pattern forecast)

Appliances can be scheduled ahead for a maximum of 80 quarters (20 hours)

Once an appliance is scheduled, it cannot be rescheduled.

A scheduled appliance cannot be interrupted during operation and scheduled later.

Scheduling-method

The scheduler uses a prediction of today’s demand pattern to schedule the appliance. There are three

types used: today’s forecast, yesterday’s forecast, and average forecast.

Today’s forecast is the sum of the demand pattern of all the other-load profiles and the already

scheduled smart appliances in the network of today. This gives a “perfect” forecast of the expected

demand pattern on the network. This is of course not realistic and not possible in real life.

Nevertheless, it is useful to see how the system works under perfect forecast conditions.

Yesterday’s forecast uses the actual demand pattern of yesterday combined with the already

scheduled appliance profiles. In real life, the demand profile of yesterday is the most similar

comparison of today’s demand profile. In this simulation the main contributor to the demand profiles

are the other-loads. These profiles are renewed at midnight and are randomly chosen. Todays used

profiles do not effect this random selection of new profiles. Therefore, yesterday’s profile is not a

predictor for today’s profile in this simulation. Yesterday’s prediction is merely a prediction of the

demand pattern of a different day.

Average forecast is the average of the last 5 days. Peaks and gaps will be smoothed and only show in

places where they are most often.

Scheduling mechanism

The scheduler uses the TOIL (Turn-On-Information-List) of the to-be scheduled appliances to

schedule. An operational window subset is created from the forecasted network demand pattern

33

corresponding to the Begin-Time and End-Time of the to-be scheduled appliance. When an appliance

operational window goes past midnight, the timeline wraps around to the beginning of the predicted

demand pattern. The predicted demand pattern values of the beginning of the day are then used. In

this operational window, the lowest point of the demand pattern is selected as the starting time of the

appliance. If the appliance is scheduled for today, it is set in “todays-scheduled” list, otherwise in

“tomorrows-scheduled” list. When a new day start, “tomorrows-scheduled” list is set to “todays-

scheduled” list and “tomorrows-scheduled” list is cleared. See Appendix A flow chart of the scheduling

mechanism.

Using just the lowest point is expectable because the scheduling of one appliance on that spot does

not contribute proportionately much in a big network. After being scheduled, another appliance will be

scheduled just before this one (because that will be the lowest point now) and so the lowest point

shifts to the left, and thus the dip is filled-up. The overlapping load of appliances scheduled right

before each other, could form a high peak. Expected is that this will be proportionately minor to the

load of the network.

Dispatcher

The dispatcher is a passive agent which only job is to turn-on appliances that are scheduled. After an

appliance is scheduled, the scheduler puts the appliance identification number in the corresponding

timeslot of the dispatcher Scheduled-List. Every time-step, the dispatcher checks if there are any

appliances listed in his Scheduled-List by looking at the timeslot of this particular moment. If any

appliances are listed, it turns them on and removes them from the list. Every time-step, all listed

appliances are turned-on and removed from the list. The order in which this happens does not matter;

this is already handled in the scheduling-scheme used by the scheduler. The dispatcher just starts at

the beginning of the list (see flow chart Figure 4).

Get TimeSlot =

Now

Turn-On all listed

agents

(appliances)

Start Clear TimeSlot End

ID’s appliances

[ ]ID’s appliances

[ 2,3,15,200,35,124, … ]

List of

scheduled

appliance per

timeslot.

Figure 4: Dispatcher flow chart

34

Network

The combined electricity load of all the appliances in the network flows on the network. The network

holds all the agents together in a visual comprehensible way. Furthermore, it is used for validation

purposes.

The network consists of a transformer from which the power lines flow. These power lines are only

used for visualisation purposes. To the power line, the households are connected. Every household

has a e-meter (not visible) to which the other-load agent and the smart appliance agents are

connected to (see Figure 5 below).

Figure 5: Network with households owning smart appliances represented in Netlogo

External influences

The seasons or other weather patterns are not included. Although they have a profound influence on

the demand pattern, this falls outside the scope of this research. Furthermore, only working days will

be simulated. No weekend days will be simulated or special occasions like national holidays (Queens

day), or national soccer matches.

5.2. Model flow - Pseudo code

To give an indication of the flow of the simulation model, below the simplified pseudo code is shown.

Setup

Create network

..

Go

..

If time = 00:00

35

[ pick new other-load profile per household]

Ask appliance with [app-kind] = other-loads [ Select new demand according to time_now ]

Ask Schedulers [ Schedule appliances in queue ]

Ask Dispatcher [ Turn on appliances in scheduled-list timeslot = time_now ]

Ask households [

If time = 00:00 [

Determine which appliance turn on today

Determine when these appliances turn on

]

Determine current demand of appliance owned

]

Calculate grid load

Record data

Set clock + 1

Do Plots

End

36

6. Simulation Setup

6.1. Time step

This simulation uses a time step of 15-minutes. From the literature and the available data a

consideration between the required accuracy and simulation computational time available was made.

With a 15 minute time step enough difference between appliances can be simulated, and enough

potential timeslot for the scheduling of appliance will be present. A smaller time step will increase the

variation and so accuracy of the network demand pattern. However, a higher level of accuracy will not

be necessary to research the effectiveness of load-shifting.

6.2. Run length

The system does not have a clear beginning and end, it does not return to its initial state. The system

is therefore not an ending system but a continuous system. Households can turn on appliances

throughout the day, and the appliances can be scheduled up to 20 hours later from that moment

(depending on their operational window). Appliances can therefore be scheduled to turn-on the next

day. Furthermore, because appliances can turn-on throughout the day, they can also operate past

midnight: e.g. turn-on at 11:00 and end at 1:00.

Although the model does not have a clear start and end, it does follow a day-to-day cycle. The smart

effect will also follow this day-to-day cycle. The smartness does not get better overtime because there

is no learning effect. To evaluate the effects of the smart system, a run-length of one day is therefore

enough, starting at 00:00 and ending at 24:00.

6.3. Warm-up time

Appliances can be scheduled for the next day and operate past midnight. Therefore, at least 1 day

start-up time is necessary to ensure that there are yesterday’s scheduled appliances or already

turned-on appliances for this coming day.

Furthermore, with the scheduler prediction method set to average, the scheduler takes the average of

the network demand profile of the last 5 days. The start-up time must be at least this number of days

to ensure the memory of the network demand profile is full. However, because a start-up time of five

day would take up 5/6 of the total runtime. With many replications, the total simulation time needed

would be huge. This is very inefficient use of time and computing power. Therefore, the runs for the

average variable are done separate from the runs with Today’s and Yesterday’s memory, with a start-

up time of 5 days. The other variables are run with a start-up time of 1 day.

37

6.4. Total simulation run length

When using Today’s or Yesterday’s demand the total timespan of a simulation run is two days: One

day start-up time and one day of measuring.

For average runs with the average scheduling method a total timespan of six days is used, five days

start-up time and one day run.

6.5. Replications

Setup

To determine the effect of the smart system on the demand pattern, additional replications are

required to cover the full range of variability in the model. Every new day, all households randomly

pick a new profile for their “other-loads”. With multiple replications, the variability of the used profiles

will be ensured.

The replications are generated independently and no sub-runs (days as replications) of one long

simulation are used. Households are configured at the setup of the model; their configuration stays

the same during the simulation (e.g., the number and types of appliances owned). This means that

replications (sub-runs) picked from one long simulation run cannot cover the full range of variability.

When the system resets, households are configured randomly again. Therefore, the model has to

reset for a new replication simulation run to start.

Seed of random generator

The model uses many random numbers to selecting a profile, to determine percentages, or to

determine the empirical density function, etc. The random numbers in Netlogo are generated by a

random generator. The seed of the random generator is not reset when a new replication is set-up.

This ensures the randomness because the random generator continuous to draw numbers from the

same series. When the seed number is reset to the same number when a replication is reset, the

same sequence of random number would be generated by the random generator.

Number of Replications

The number of replications is determined by looking at the standard deviation of the main output per

number of replications. The number of replications needed, is derived frim the moment the standard

deviation no longer shows erratic or chaotic behaviour but settles to a more stable state. As can be

seen in Graph 6, the bigger the network the smaller the number of replications needed.

38

Graph 6: Standard Deviation per Replication per Household

In a bigger network, there are more households present that use more different profiles per

replication than in a smaller network. The variability is therefore higher in a bigger network, which

therefore required less replication runs. Due to time and computer computational power restrictions,

different number of replications are used for different network sizes, e.g. 50 replications for a network

of 200 households, to 200 replication of a network of 5 households.

6.6. Variables

Table 6: Variables that are varied

Variable Settings

Scheduling level Appliances/Household/Network

Memory configuration Today/Yesterday/Average

Scheduling schemes EDF/FCFS/SIRO

Operational appliance window 2/6/12/20 (Hours)

Penetration DSM households 25/50/75/100 (%)

Penetration DSM appliances 33/66/100 (%)

#Households 5/25/50/100/200

6.7. Output variable

The following variables are the most important output variables:

• Electricity demand pattern in the network.

• Ideal line

• Deviation from ideal line (KPI)

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

0 100 200 300 400 500 600 700 800 900

De

viat

ion

Households 5

Households 10

Households 25

Households 50

Households 100

Households 200

39

Electricity demand pattern in the network.

The electricity demand pattern is important because it should change when smart appliance are

present in the network. Peaks in the demand pattern should reduce and gaps should rise because

appliances are scheduled there.

Key Performance Indicator

Introducing smart appliances will change the demand pattern. To see if a smart system actually

works, an indicator is needed that can capture the changes in demand pattern. The sum of the

deviation to the avenge (ideal line) of the network is a good indicator. When peak loads decrease and

gaps are filled, the demand patterns deviation to the ideal line reduces. To calculate this indicator,

first the ideal line, or average, needs to be calculated.

Ideal line - µ

Using the demand pattern, the ideal line of the network pattern is calculated. The ideal line is unique

to every network created. When multiple replications of the network are done, the network

configuration changes also with these replications. Different network settings generate different

network patterns. Therefore, the ideal line has to be calculated for every replication separately.

∑

with t in quarters and µ representing the average demand of the network.

Levelling Effect - LE

Now that the ideal-line can be calculated, the deviation of the demand pattern to the ideal line can be

calculated. The total deviation is the sum of the absolute difference between the ideal-line and the

demand pattern.

∑


To be able to compare different network sizes with each other, the total deviation is divided by the

ideal-line. This value is the Key Performance Indicator (KPI) of the system, indicated by LE, which

stands for Levelling Effect.

∑


40

When peaks are reduced and gaps are filled, the deviation to the ideal line decreases, and so the KPI.

The level of effectiveness of the smart network is therefore indicated by a lower value of the KPI of

the smart network compared to the KPI value of a non-smart network.

Second KPI - LE2

The deviation to the ideal line makes no distinction between which gaps are filled or which peaks are

reduced. The filling of lower gaps does not weigh more than the filling of less lower gaps, and the

reduction of higher peaks does not weigh more than the reduction of lower peaks. The scheduler does

make a distinction, because it always chooses the lowest point (lowest gap). However, the lowest

point in the operation window of the appliance is not necessarily the lowest point in the network. To

enable a measure of which gaps are filled, or which peaks are reduced, a squared deviation value is

used. This way, lower gaps or higher peaks give higher deviation values. To be able to compare this

between different networks sizes, these values are then divided by the squared average.

∑ ( )


For the SE2 also means that a lower value compared to the SE2 value of a non-smart network indicates

a greater effectiveness of the smart system.

Third KPI - Height of Peaks in the network demand pattern

Load-shifting will reduce the peak loads in the system. To measure this reduction the maximum peak

of the network demand is divided by the average.

( )


To see if the shape of the peak loads changes, the duration (d) of the demand pattern above a certain

percentage of the maximum network load is measured. In this simulation, a percentage of 80% is

used.

∑

with t in quarters and α in percentages.

41

To give an indication of the number of gaps that are still available for filling (D.HP) with smart loads, d

is then divided by the number of time steps and subtracting from 1.

with t in quarters.

42

7. Verification & Validation

7.1. Verification

During the iterative process of model building, many of the models parts already underwent thorough

verification. Below, several important parts that were individually tested are highlighted.

Appliances

The network load originates from household appliances. It is therefore important to check if these

appliances are modelled correctly. The following tests and checks have been performed, and came

back positive.

• During the simulation setup, are appliances assigned different types (e.g. Washing

machine , freezer), and type related attributed.

• Do the other-loads get the right combination-profile number assigned, (depending on the

household smart appliances makeup), and do they generate a load corresponding to their

combination-profile.

• Do the other-loads receive a new random profile from the combination-profile list that

corresponds with their combination-profile number.

• When an appliance turns “on”, does it generate a load according to its demand profile,

and does it turn “off” again, when its runtime is done.

Scheduler

The scheduler is the most important part of the smart system. The effectiveness of the scheduler

determines the effect of the smart system. It is therefore important that it is modelled correctly. The

following tests and checks have been performed, and came back positive.

• Does every scheduler run every time-step, and does it scheduler every appliance in its

queue (clear its list)?

• Does the scheduler put together the operational-window of an appliance correctly? This

contains the predicted network-demand-pattern and must be generated using the begin-

and end-time values of the appliance, even when the operational-window is after

midnight.

• Does the assigned best turn-on time of an appliance correspond to the lowest point of the

predicted network-demand-pattern in the appliance’s operation-window?

• Do schedulers plan scheduled appliances for today in today’s scheduled-list and

tomorrows scheduled appliances in tomorrow’s scheduled-list. Are the scheduled

appliance’s ID’s assigned to the corresponding timeslots of the dispatcher’s list correctly?

43

7.2. Validation

The model simulates a real-live household demand pattern, which in turn should generate a real-live

network demand pattern. To check if these generated patterns behaviour like real-live patterns they

need to be validated.

Profile generation

The other-loads of the households are the main contributor to the network demand pattern. In a non-

smart network, the other-loads represent the entire demand of the household. The load profiles of a

households other-load should then show the irregular behaviour of a households demand pattern. To

validate these profiles, the average load of the other-load should correspond to the average load of a

household (when no smart appliances are present in that household). Furthermore, the load

generated by all the households, should generate a realistic demand pattern on the network. To

validate the network load, the average demand pattern of the network is compared to a real life

average demand pattern of a network of the same size. These real life average demand patterns were

derived from the the normalized aggregated residential load profiles created for the Dutch branch

organisation EnergieNed. Load measurements of 400 households used with a 15 min interval, to

create these profile.

Household demand pattern Network demand pattern

Graph 7: Household & network demand pattern

Oversampling

When a network is created, the number of smart appliances in a household determine which

combination-profile number the other-loads get. The other-loads pick a combination-profile from the

corresponding combination-profile group. Every profile group contains 100 profiles. When there are

only a few smart appliances in the network, most of the other-loads will have the same combination-

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1 17 33 49 65 81

kW

Quarters household load

average household load

average real household load

0

10

20

30

40

50

60

70

80

1 17 33 49 65 81

kW

Quarters actual network load

average network load

average real network load

44

profile number and so, pick their profile from the same profile group. In bigger network, with low

smart appliances penetration degree, the number of other-loads that pick their profile from the same

profile group can lead to oversampling.

As a result, the network profiles will then look more alike. Although the variability of the network

pattern will still be large (see Appendix F), the average network load will look alike.

Percentage of smart load compared to literature

The non-cooling appliances contribute about 16% to the total load, and cooling about 13% to the

total load (SenterNovem, 2008). In this simulation, the total smart load can represent about 16% of

the total load; the smart non-cooling appliances represent about 8% of the total smart load, and the

smart cooling appliances the other 8%. The smart appliances load therefore only contributes about

half of the contribution found in the literature. This means that the effect of the smart load will be less

than what is could be. However, the average demand of a household does match the demand found

in the literature. This means that the other-loads generate too big of a demand.

Other validation tests

Other-load profiles. If the other-load profiles are flawed, the network pattern will also be flowed.

Although the scheduler will still do what it is supposed to do, no clear conclusion on the effects of the

smart system can be made.

Does the average of the simulated household demand, equal the average household demand

per day (according its household type) of a real house? E.g. does a household of type 2 (3500

kWh a year), generate on average, a daily demand load that equals 9.6 kWh (3500/365).

Does the network average equal the network average of a real-live network? E.g. does a

network of 100 household generate, on average, a network load that corresponds to the

average of a real-live network?

Does the demand pattern show the peak irregularities caused by simultaneous usage of

appliances?

Does an actual load shift occur?

45

8. Experimental Design

The seven variables can each be configured three to five ways, so in total 8640 different scenarios can

be constructed. A full factorial design is very in efficient for many configuration do not need to be

tested, e.g. in a non-smart network the variables associated with a smart network do not need to be

varied. The experimental design is therefore split up into several different experimental runs that will

inspect the effect of the variables on the network. The six most important experimental runs are

discussed below.

Experimental runs:

Scenario 0: Non-smart network

Internal variables

External variables

Perfect conditions

Random Operational Horizon

Refrigerator & Freezer

When during an experimental runs a variable is inspected, the other variables are either also

inspected or hold to their most logical or average value. For a more detail on the simulation setting

see Appendix E.

8.1. Scenario 0: non-smart network

This scenario is used as a base line to compare the effects of a smart network. Since this is a non-

smart network, the only influence on the demand pattern is the number of households.

8.2. Internal variables

These variables affect the smartness of the smart system. Varying these may increase or reduce the

effectiveness of the smart system

Memory

Scheduling Scheme

Operational Window

8.3. External variables

These variables affect the scope of the DSM. With higher values, the DSM effect will be greater.

Scheduling level

Penetration DSM Households

Penetration DSM Appliance

Number of Households

46

8.4. Perfect Condition with 100% Smart Penetration

This specific run is used to see how the smart system performs under perfect conditions with a 100%

penetration degree.

8.5. Random Operational Horizon

This specific runs is used to see how random operational horizon can influence the scheduling

schemes used and the memory of the scheduler used.

8.6. Cooling Scenario – Only Refrigerators and Freezers

Refrigerators and freezers turn on about 24 times a day for short periods of time. Sequencing these

appliances can decrease the peaks and gap that are created by the simultaneous uses of these

appliances. Furthermore, since they turn on so often, and have a short operational time, they are

excellent for filling small gaps in the network. These are difficult to fill by the other smart appliances

because of their longer operational time.

47

Part III:

Simulation Results

49

9. Simulation Results

The effectiveness of the smart system is determined by deviation of the network load to the average

network load. There are two groups of Key Performance Indicators: Levelling Effect indicators (LEs)

and Height of Peaks indicators (HPs). The LEs measure the deviation to the ideal line. Lower LEs

indicates a higher effectiveness of the scheduling of smart appliances. HPs measures the effectiveness

on peak reduction.

Base line scenario vs. Perfect Conditions

In the maximum smartness scenario, the penetration degree of smart appliances is set to 100%.

To be expected was that a smart network would lower the LEs and HPs values of the network. As can

be seen in Table 7, the expected effect of a smart network has indeed resulted in lower LEs and HPs

for the maximum scenario, compared to the Base scenario. This clearly indicates that the smart

appliances loads are shifted from peaks to gaps and that the peak load is reduced.

Table 7: KPI's of Base vs. Maximum Scenario of a network of 100 households

KPI Base

Line

Perfect

Conditions

LE 22,7 13.9

LE2 7.95 2.92

HP 1.63 1.42

HP.D 0.83 0.76

Closer examinations shows that although the overall peaks are reduced, there are now more outliers

(very high peaks) compared to a non-smart network (see Appendix G, Table 14 and Table 15). They

are short (one timeslot) peaks, that can even be higher than the maximum of the general network

demand pattern. These are generated by the simultaneous usage of some appliances that have a

peak during their operational run, like the dishwasher and the washing machine. The scheduler only

takes the lowest point on the network demand pattern into consideration, and not if the whole

demand profile of the appliances fit in the subsequent timeslots. When an appliance has been

scheduled, its demand profile is taken into account for the scheduling of other appliances. If a smart

appliance starts with a low power output (like the dishwasher and the washing machine), there is

room for other appliances to be scheduled to that timeslot because it has a low load (at the

beginning). While the first timeslot will rise slowly, the subsequent slot can rise harder due to the

peaks in the demand profile. Smart appliances can therefore be scheduled to turn on at a time period

that has a low network load, while only a few steps later the demand pattern can form a peak by the

50

demand profile of the scheduled appliances themselves and the other-loads demand pattern. Although

this happens rarely, it is a serious risk that in real live could lead to overloading the network. The

smart system is supposed to prevent peaks like these. This is however, something that could be

solved by using more advanced scheduling algorithms, that take into account the effect of the smart

appliances load on the demand pattern during their entire operation.

With the reduction of the peaks and a more levelled demand pattern, the 80% boundary for the HP.D

KPI also decreases. Because the smart load fills up most of the gap (see Figure 6), the network

demand pattern already comes closer to this boundary. The probability of the network load to surpass

this boundary therefore increases. The levelling of the demand pattern therefore decreases the the

HP.D values.

Although, the difference between the KPIs of the Base and Maximum scenarios are is great, the smart

system did not reduce the KPIs to their minimum values; LEs and HP.D to zero and R. HPs to one.

Apparently, large peaks and gaps there are still present in the network demand pattern.

Maximal achievable value’s for the SE’s

The Key Performance Indicators measure how much the demand pattern differs from the networks

ideal-line (Figure 6 at a). The closer the demand pattern lies to the ideal line, the lower the value of

the KPIs. In this simulation, the other-loads make up around 83% of the total load. Furthermore, the

general demand pattern of the other-loads does not change. The ideal line will therefore always be

under the maximum load of the second peak of the other-loads (Figure 6 at b).

a b C

Figure 6: maximum load shifting possibilities

When the gaps are ideally filled, the KPIs would not reach there minimum valeus because of the

deviation of the other loads to the ideal line by the second peak. This also means that as long as the

ideal line is lower than the maximum value of the other-loads, the KPIs will not reach their minimum

values.

Watt

Time

Watt

Time

Watt

Time

51

The minimum KPI values could be reached when the ideal line would be on the maximum of the

second peak of the other-loads. The LEs and R.HP would be zero and the HP.D would be one. This

would mean that the load of the smart appliances should at least equal the green shaded area (Figure

6 at c,space below the ideal line but above the other-loads). This would mean that there would be no

peaks and gaps in the demand pattern anymore and the demand pattern would look like a perfect

straight line.

In this simulation, to achieve the minimal values of the KPIs the smart appliances load should

represent around 35% of the total load. The smart appliances load can however, only represent a

maximum of about 16% of the total load, which is not enough to reach KPIs of zero.

When this 16% is all scheduled perfectly to the gaps, it can reach KPI values lower than that of the

maximum scenario (see Table 8). This 16% also includes the multi-interval cooling appliances, which

represent about 50% of the total smart load (so 8% of the total load). Scheduling all the smart

cooling loads to the gaps would mean that they would be turned off for about 7 hours from 17:00 hrs

until midnight (see Graph 8). Because they cannot (yet) be turned off for these long periods without

warming up too much and because their (present) interval usage requires them to turn on about once

an hour, can their load not (yet) totally be scheduled to the gap.

Table 8: Minimum KPI value

KPI Base Max gap

filling

Max smart

penetration

LE 22.7 8.12 13.9

LE2 7.95 1.05 2.92

HP 1.63 1.29 1.42

HP.D 0.83 0.76 0.76

As can be seen in Graph 8 with a 100% penetration degree of smart appliances, the smart load

already almost fills up most of the gaps of the other-loads pattern (blue and green area) and already

comes close to the situation were the gaps are filled to their maximum (red area). Furthermore, Graph

8 clearly shows the expected smoothing effect of the cooling appliances. When all the cooling

appliances are scheduled to the gaps (red area), this smoothing effect will be lost and a spiky demand

pattern will be left on the far right peak on the demand pattern. A smoother demand pattern is

preferable above a spike profile. Load shifting of all the smart appliances may therefore not be the

most optimal solution.

52

Graph 8: Maximal possible filling of gaps with smart load

Knowing that about 16% of the total load is schedulable, the influence of the smartness variables of

the scheduler on the KPIs is discussed in the next section.

Smartness scheduler

It was expected that the smartness of the scheduler had a big effect on the effectiveness of the smart

system. The scheduler’s smartness consists of three aspects: Memory forecaster, scheduling scheme,

and operation horizon.

Memory forecaster

The scheduler’s memory determines which type of forecaster is used to schedule smart appliances.

For this simulation, three types have been used: Today’s, Yesterday’s and an Average forecaster.

Expected was that Today’s forecaster would give the best result, because it gives a perfect forecast of

today’s demand pattern. Second and third would be Average and Yesterdays forecaster because their

forecast will differ more from the actual demand pattern.

Today’s forecast does indeed give the best result (see Table 9). Today’s forecast results in a perfect

scheduled demand pattern. Smart appliances fill-up the small gaps of the other-load, resulting in a

clear smooth demand pattern.

As expected, this is not the case with the Average and Yesterday’s memory setting. The Average

forecasterdoes show a smoother demand pattern than Yesterday’s demand pattern, because the

spikes (peaks or gaps) on the predicted demand pattern are at the average places and therefore less

high and deep. Therefore, the smart-load is spread-out more over the other-load pattern. Yesterday’s

pattern clearly however, shows more spikes than the Average pattern and is therefore proves to be

the worst of the three Memory forecaster types. The deviation will be the greatest because

Yesterday’s forecast is a specific pattern of a day, but not this day. Peaks and gaps are higher and

deeper compared to the demand pattern of the other memory settings. The smart load spread

1 17 33 49 65 81

Watt

Time demand.other.loads cooling.load

non-cooling.load average actual load

Max vally filling smart level

53

focusses focus more on filling-up these gaps. When the smart-load is scheduled to a timeslot which in

today’s network load is not a gap, the smart load will create a new peak. This peak is higher than

when a smart-load is not on the right spot when using the Average memory. This effect can clearly be

seen by the difference in the KPIs (see Table 9).

Table 9: Influence of Schedulers Memory on the KPI's

KPI Base line Today Yesterday Average

LE 22.7 14.6 15.4 14.9

LE2 7.95 3.26 3.61 3.40

HP 1.63 1.40 1.48 1.45

HP.D 0.83 0.74 0.80 0.77

As a perfect forecast is not possible in real live, the Average forecaster is second best. In this

simulation, an average forecast of 5 days is used. A longer memory of the average will result in more

averaged forecasting patterns and so probably lower KPIs. What should be take into account however,

is that this simulation only looked at working days and does not make a distinction between working,

weekend or special event days.

Scheduling schemes

For this simulation, three scheduling schemes have been used: First Come First Serve (FCFS), Earliest

Deadline First (EDF) and Service in Random Order (SIRO).

Expected was that different scheduling schemes would influence the effectiveness of the smart

system. This however, proved not to be the case. There is little to no significant difference between

the different scheduling schemes (Appendix G, Table 14 and Table 15). It was suspected that the

scheduling level of the appliances and the smoothing effect of the cooling appliances would cause

this. However, most likely this is caused by the way the scheduler has been modelled. To explain this,

at first the scheduling level influence will be discussed, followed by the influence on the scheduling

scheme by the cooling appliances. This section concludes with the probable reason for the small effect

of the schemes.

Scheduling level

As anticipated, the effect between the scheduling schemes did not differ on lower scheduling levels.

This is because the number of appliances in the queue list of the scheduler, are then very low. On an

appliances level there can only be a maximum of 1 appliance, the appliance itself. On a household

level, there can be a maximum of 5 appliances. This however, rarely happened because there are 96

timeslot on with an appliance can turn on. It will most often be a cooling device with every now and

then a non-cooling smart appliance. On a network level, however, there should be enough smart

54

appliances in the network, to sufficiently fill the schedulers queue with appliances to let the scheduling

schemes have different effects. However, there was still no difference in the scheduling effect

between the scheduling schemes (Appendix G, Table 14 and Table 15 – Scenario: All appliances). The

KPIs still did not differ much from each other per scheduling scheme. Different scheduling level

appeared not have any significant influence on the effect of the different scheduling schemes.

Cooling appliances

Initially it was then suspected that the smoothing effect of the cooling appliances would have a huge

influence on the scheduling schemes (see Base line vs. Cooling appliances for more on this smoothing

effect). The smoothing effect would annul the result of different scheduling schemes by smoothing

out all the differences on the demand pattern. However, the smoothing effect was not the (main)

cause. When simulating a network without smart cooling appliances, the scheduling schemes still did

not have any significant effect (Appendix G, Table 14 and Table 15 – Scenario: Only non-cooling).

Apparently, the scheduling schemes just did not have any significant effect.

On closer examination, this is probably caused by the scheduling mechanism itself. The main idea of

different scheduling schemes is that being first in queue, has a clear effect on the effectiveness of

scheduling. However, the scheduler always schedules every appliance in its queue. Being first in

queue therefore does not have a clear advantage, since there is no risk for appliances not to be

scheduled in. This is prabably the reason why different scheduling schemes do not have any effect.

Operational horizon

The operational horizon comes in four sizes: 4, 24, 48, and 80 quarters.

Predicted was, that a longer operational time would increase the effectiveness of the scheduler and so

lower the KPIs. Furthermore, it was expected that different operational horizons for the non-cooling

smart appliances would better show the effect of the EDF scheduling scheme.

As expected, the operation horizon has a big influence on the effectiveness of the scheduler. Longer

operational times result in more time available for the scheduler to schedule an appliance, and so a

higher probability of a deep gap during these periods to be filled. Thus, longer operation windows

result in lower KPIs.

Different operational horizons however, did not better show the effect of the EDF. With a random

operational horizon, there is still no significant difference between the scheduling schemes. This does

support the expected reason mentioned in the previous section: the lack of an advantage for being

first in queue of the scheduler. With random operation window times, there will be a clear difference

between deadlines for appliances. Thus, a scheduling priority for an appliance with an earlier deadline

then other appliances should have a clear effect on the effectiveness of the scheduler.

55

Overall an understandable increase in the smart system effectiveness arises when the operational

window and the accuracy of the forecasted demand pattern increase. The scheduling scheme does

not have any influence, most probably due to the lack of a clear advantage of being first in queue.

Base line scenario vs. number of smart appliances

As expected, a higher penetration degree of smart appliances significantly influences the KPIs. Higher

penetration degrees increase the effect of the smart system and so result in lower KPIs.

The effect of the penetration degrees is almost linear. With twice as many smart appliances, the

deviation is almost twice as small. With more smart appliances in the network, peaks are lower and

gaps are filled more. The deviation to the ideal-line would decrease and so the KPIs become lower

(Appendix , Figure 12 and Figure 13.)

The influence of the number of households is however, not linear but reduces logarithmic. With higher

percentages, the effect reduces and the KPIs decreases less and less (see Figure 7). This is because,

the network pattern variations will reduce as the network size increases. With many households, large

peaks and values of households patterns are averaged out and a smoother pattern emerges,

compared with a smaller network. With a smoothened demand pattern that looks alike more with an

increasing network size, the deviation will also be more the same and thus converge on an average

value (Figure 7).

Figure 7: SE against network size.

Base line vs. Cooling appliances

Expected was that when the percentage of cooling appliances would increase, the effectiveness of the

smart network would increase and so the KPIs would decrease.

This however, does not happen. At first, the KPIS decrease as expected. However, when the

percentage increases, the LPKIs start to rise too, which means that the deviation to the ideal-line is

LE

56

increasing again. To explain this, first the effect of smart cooling appliances is discussed that should

have led to lower KPIs. Then, the reason why this does not happen is discussed.

The smoothing effect of smart cooling appliances

Cooling appliances have a short runtime and turn on about once an hour. With multiple smart cooling

appliances, these will form a smart load layer that lies on top of the demand pattern. This layer

absorbs the many small peaks and gaps on the demand pattern, creating a general smooth line that

will follow the demand pattern. This effect can clearly be seen in Graph 9 (Notice in particular, that

the smart load of the cooling appliances almost has the inverse pattern of the other-loads, which gives

it its absorbing effect).

Graph 9: Network demand patter of a network with only smart cooling appliances

In this simulation, smart cooling appliances are processed later than the other smart non-cooling

appliances. Cooling appliances are therefore always queued last in the schedulers queue. With their

short runtimes, the cooling appliances can easily fill-up all the small gaps on the demand pattern

created by scheduling of other smart appliances and the other-loads. This creates the smoothing

effect that absorbs the many spikes on the demand pattern.

It was expected that this smoothing effect would change with different scheduling schemes, this

however, proved not to be the case.

With a FCFS scheduling scheme, the cooling appliances will stay last in queue and be scheduled last,

and so smoothen the demand pattern. With EDF, the cooling devices are first to be scheduled, after

which the longer runtime appliances are scheduled. Initially it was expected that this would lead to

some chaotic effects on the demand pattern because the smoothing effect of the smart cooling

appliances had already happened after which the other smart non-cooling appliances would be

1 9 17 25 33 41 49 57 65 73 81 89

Wat

t

Time

actual.network.load

other-loads

ideal.subset

Cooling appliances

57

scheduled in. After a closer examination, this obviously is not the case. Cooling devices are scheduled

only a maximum of 1,5 hour forward and the other smart appliances can be scheduled up to 20 hours

forward. This means that when the simulation time reaches a gap in the demand pattern, most non-

cooling appliances have already scheduled been to that period. The cooling appliances will then come

along and fill-up all the left gaps, resulting in the same effect when using a FCFS scheduling scheme.

It is possible that some non-cooling appliances are to-be-scheduled around that period also. They

would then be scheduled to that period to but after the cooling appliances. These non-cooling

appliances are then scheduled on a smoothened demand pattern. The spiky profile of a few

appliances on top will however, proportionately not make much of a difference.

Reason for higher LEs

The smoothing effect of the smart cooling appliances clearly absorbs the many peaks and gaps.

However, when these gaps are filled at parts of the demand pattern that already surpass the ideal-

line, it increases the distance from the demand pattern to the ideal line, and so the deviation. This

means that the smart layer adds to the deviation (and so the LEs) when the network demand pattern

is above its ideal-line, and only reduces the deviation (and so the LEs) when the network demand

pattern is below its ideal line.

What would be expected is that this effect would (mostly) be cancelled out, by the reduction of the

other-loads pattern that now does not include the cooling appliances, meaning less deviation above

the ideal-line but more deviation below the ideal-line. This however does not happen, the LEs increase

when smart cooling applinaces are introduced. This means that the amount of smart load from the

cooling device is bigger above the ideal-line, then below the ideal line. When the network demand

pattern is increasing from its lowest gap, the number of times the smart cooling appliances turn on

also increases. In addition, when the network demand pattern declines to a gap, the smart cooling

appliances turn on less often.

Closer examination shows that this behaviour is caused by the scheduling mechanism itself, in

combination with the smart cooling appliances attributes. The scheduler always plans in an appliance

on the lowest point. For appliances with a small operational window (like cooling appliances) this does

not work very well. When the demand pattern in the small operational window increases, the lowest

point is right after the moment the cooling device turns off. When the demand pattern is decreasing,

the lowest point is at the end of the operational window, which is only a few timeslots away. This

means that the cooling devices turn on more often when the demand pattern is increasing, and less

when the demand pattern is decreasing.

In this case, the cooling device turns on four only times in the large gap and about 17 times on the

slopes to the peaks above the ideal line (Graph 10). This behaviour proved to be the same with all the

smart cooling appliances. Apparently, cooling appliances turn on less often in gap then they do during

rising demand patterns.

58

Graph 10: Network load and number of usages of cooling appliances*

*Graph 10 shows a network demand pattern and the demand pattern of a single smart cooling appliance. The load of the

cooling appliance is added to the network load to illustrate more clearly its behaviour.

This would not matter much if the appliance only turns on a few times a day. However, the cooling

appliance turn on many times a day to ensure the temperature inside stays low enough. In this

simulation, the cooling appliances therefore turn on twice as many times during rising demand

patterns then during declining demand pattern.

This effect could be reduce by stating that the smart appliance must stay off for at least one hour,

and then turn on in a small operational range of about an half hour. However, because the simulation

time step is 15 minutes, this would mean a two choice scheduling space. This is too small and would

annul the total smart effect. Clearly, the time step of 15 minutes constrains the effective smart

scheduling of cooling appliances.

Although this effect of smart cooling appliances is big, because this effect is generated at all the other

runs it is still possible to compare the other runs with each other. Possibly, the KPIS of these runs

could thus be lower.

1 17 33 49 65 81

Wat

t

Time

Cooling appliances

network-load perhousehoold

ideal per HH

59

10. Reflection, Conclusions & Recommendations

In the reflection on the modelling some important modelling choices are discussed, followed by a

reflection on Agent-Based Modelling as a modelling approach. After these the conclusions of the

research project follow, and the recommendations for future research.

10.1. Reflection on modelling choices

A 15 minute time step

An important choice was the a 15 minute time step, which was based on the level of accuracy

required and simulation speed needed.

Cooling appliances proved to have difficulties with a time steps of 15 minutes. A 15 minute time step

allows for four timeslot per hour. Since the cooling appliances are require to turn on about once an

hour, the available timeslots for them were very slim. For non-cooling appliance, this did not matter

much because they could have a minimum of 8 timeslots and a maximum of a whole day for

timeslots. The simplistic “lowest point” scheduling mechanism just did not work very well for the

cooling appliance, because of their short runtimes and many usages per day. For appliances with

longer runtimes and less usages per day, (non-cooling appliances) this did not matter much. In

addition, in real live the cooling appliances will often run less than 15 minute, making them too

powerful in this simulation.

Another choice, which was influenced by the used time step, was to have all appliances of the same

type have the same attributes: e.g. same power output, same demand profile. This is of course the

case in real live. With a time step of 15 minutes however, most appliances have only six to eight time

steps to make a difference. Different attribute setting between appliances of the same type would

therefore not have mattered much. With a smaller time step, the difference of attributes of appliances

would show more.

Also, the profiles created for the other-loads in QWatts used a time step of 15 minutes. The other-

loads is the combined load of all the other non-smart appliances in the household. Many of these non-

smart appliances have a runtime of less than 15 minutes, or a demand profile that changes many

times within 15 minute. To achieve a better representation of all the other loads in the household a

smaller time step should have been used.

Important question is whether it would have mattered if a smaller time step then 15 minutes was

used. This would probably have resulted in more variation in the household demand profiles.

However, for the network demand pattern this may not have had much effect. The other-loads make

up most of the total demand pattern. If the overall demand pattern of the other-loads does not

change, so will the main flow of the network demand pattern. There would be some more variations

60

on the demand pattern of the network, but the average demand pattern would still follow the same

pattern. Although the scheduler does take into account these small variations in peaks and gaps on

the demand pattern, it is the overall demand pattern that determines the area of scheduling.

A smaller time step will therefore increase the variability of the demand pattern and so the accuracy

of the pattern compared to a real-live pattern. However, for this research a smaller time-step would

probably not have added much to the overall results.

Profile generation in QWatts

Although still under construction, QWatts was able to easily create household profiles by combining

the loads of individual appliances. Some minor issues, due to still being under construction, did cause

some delays. However, these were quickly overcome with the help of its creator, P. Bots.

Generating a real-life household demand pattern however, turned out to be more difficult than initially

expected. Initially, real individual appliances as building blocks were used to create a realistic demand

pattern. Unfortunately, these profiles did not even came close to a real household demand pattern.

My own knowledge of human behaviour using household appliances proved to be too limited. In

addition, information and data to fill this gap of knowledge was difficult to find. To create a set of

appliances that could generate a household load by taking into account the household type, number

of occupants, number of different appliances owned, human behavioural usage of the appliances etc.,

turned out to be too complex to construct in the time available.

To resolve this problem, a two-step approach of creating other-load profiles was used and proved

successful. At first, a top down approach was used to identify the expected pattern of a household’s

other-load. Secondly, a bottom-up approach was used to “build” this expected other- load profile

using individual blocks. By conceptually seeing the blocks as household appliances, but by not naming

them and linking them to real household appliance, the possibility to freely build-up a household load

was possible. By using individual blocks, the variability and irregular behaviour of an individual

household pattern could still be maintained. Unfortunately, this entire process did cause at least 2

weeks of delay.

Important question is if it would have mattered if a more real live demand pattern was created for the

other-loads using real-live appliance blocks. This would have added to the variation of the household

profile, and the validation. But as with a smaller time step mentioned before, the overall network

demand pattern would have stayed the same. Therefore, it would probably not have made a big

difference for the final results.

Number of smart appliance

In this simulation only five different smart appliance were used. These were the most logical for their

penetration degrees are very high in the Netherlands. The use of other more uncommon appliances

may however increase in the near future, like the air-conditioned or heat pumps. Furthermore, with

61

the introduction of the electric vehicles (EVs) the burden on the network will significantly increase.

Smart recharging of their batteries may help to reduce the added burden on the network. The

increase of possible other smart appliances should therefore also be considered.

Reflection on Agent-Base Modelling as approach

An Agent-based Modelling approach has proven to be very successful for this research. By simulating

individual appliances, a combined demand pattern on a network is successfully generated. The

individual household demand patter showed many of the irregularities (peaks and gaps) as a real

household demand pattern. These irregular demand patterns of the households successfully produced

a network demand pattern to emerge, that on average, comes close to an average network demand

profile.

A drawback of ABM that clearly presented itself is the level of detail ABM requires. The information

required for simulating individual appliance was difficult to find (see below Profile generation for more

on this). Furthermore, the simulation of individual appliance resulted in an explosion in computational

times. A full factorial experimental design was not possible any more. Keeping the time needed for the

experimental design within limits, the experimental design had to be split-up in different part to

enable more efficient use of the time available.

10.2. Conclusions on smart effect

This research has looked at the extent to which Demand-side Management of households will affect

the Levelling Effect and Height of the Peak loads of the demand pattern in the low-voltage distribution

networks.

It is clear that the scheduling of smart appliances has a significant effect on the demand pattern.

Smart appliances can be successfully scheduled to off-peak periods, using a simple “lowest point”

scheduling mechanism, to reduce peaks and fill-up gaps in the demand pattern. In this simulation, it

was possible to almost entirely fill the off-peak period (night time).

Although the peaks could be successfully reduced, due to the simplistic scheduling method, very high

new peak load could arise. These were caused by the scheduling of the dishwasher and washing

machine s appliances, due to the form of their demand profile. In real live these would present a

serious risk concerning network overloads. A more advanced scheduling algorithm that also takes into

account the demand profile of the appliances it schedulers could resolve this.

A big surprise is the lack of effect of different scheduling schemes. Expected was, that the lack of

advantage of being first in the schedulers queue is the reason for this. Using a more advantaged

scheduling mechanism that would allow appliances not to be scheduled, could change this.

62

The lack of effect of the scheduling schemes is also the reason why different aggregation levels of the

scheduler did not show any effect. A higher aggregation levels will increase the number of smart

appliances in the schedulers queue, which should have increased the effect of the different scheduling

schemes and so the effectiveness of different aggregation levels.

Smart cooling appliances are ideal for smoothing the demand pattern by filling-up the smaller

irregularities on the demand pattern created by smaller peaks and gaps. This effect is best seen using

today’s perfect forecast. Here the demand patter becomes a smooth line due to a layer of smart-loads

(mostly smart cooling appliances) that absorbs all the irregularities that are on the demand pattern of

the network. Smart cooling appliance or other milt usage appliance with short runtimes (e.g. airco,

heat pumps etc.) are therefore perfect for filling up the smaller spikes in the demand patern.

Today’s perfect forecast understandably gives the best results, this perfect forecast will however in

real life not be possible. Using an average of the demand pattern of the last five days has proven to

be a good alternative. Using the demand profile of the day before (Yesterdays memory profile), or any

other daily profile that is not the current day, has a less desirable effect on the demand pattern and

reduces the effectiveness of the smart system. It is however still better than a non-smart system.

The number of smart appliances in the network shows an understandable great effect. The more

smart appliances in the network due to a larger network or higher penetration degrees of smart

appliances, increases the effectiveness of the smart system.

Using a simple “lowest point” scheduling method has proven to work very well for smart appliances

that only run a few times or less per day. The effectiveness increase with longer operation horizon

times of the smart appliances. However, appliances with a short operational horizon (less than 2

hours) and multi-interval usages per day , i.e. the cooling appliance, require a more advance

scheduling method then the simple ”lowest point” scheduling method. Due to the short operational

windows and the logic of the scheduling method, these appliances turn-on more often when the

demand pattern increases to a peak but less often in the gaps.

The problems with the cooling appliances has mainly been caused by the time step that this

simulation uses. The 15 minute time step provided with too little possible timeslots for the smart

cooling appliances to be effectively scheduled. Using a smaller time-step may solve this problem.

Furthermore, a smaller time step will increase the accuracy of the demand pattern, and so lead to

more realistic scheduling. However, although a higher variation on the demand pattern can be

simulated with a smaller time step, the general network demand pattern will stay unchanged. Even

though the scheduler takes into account the smaller variation on the demand pattern, it is the general

demand pattern that determines the areas were the smart loads are scheduled to. Using a smaller

time step will therefore not necessarily give better results.

63

10.3. Recommendations

Benefits of a smart system

Effectiveness smart system

It has clearly been showed that smart appliance can be effectively scheduled to the gaps in the

demand patter. However, DSM will require high investment. A cost-benefit analyses should be done to

analyse how much smart-load needs to be schedulable, before it becomes economical viable,

compared to the traditional method of investing in more electricity cables

Possible other smart appliances

Only five smart appliances were used in this research. These were the most logic appliance with high

penetrations degrees in Dutch households that could be schedulable. However, with an increase in air-

conditioners, heat pumps and many other devices, the potential number of smart appliances

increases. With more potential smart appliances, the potential load that could be shifted also

increases, and thus the effectiveness of the smart system. Furthermore, new developments in smart

and non-smart appliances themselves (e.g. fitting small batteries in appliances that load during off-

peak periods and can be used during peak load periods) could contribute to a more optimal

scheduling and thus a more optimal use of electricity. In the above recommended cost-benefits

analyses, this should also be taken into account.

More extensive model

Other smart system components

A smart system consist of more than only DSM of households, other sectors also use DSM. Additional

research of DSM in the commercial and industrial sector should be done.

Other energy system components.

DSM can also significantly help the introduction of renewable energy source, the storage of energy,

and EVs. These components can be added to the model.

More advance scheduling algorithms

Enable re-scheduling of smart appliances

Enabling re-scheduling of smart appliances may increase the effectiveness of the scheduling. Smart

appliances are now always scheduled. This reduces the advantage of being first in queue and so the

effect of the scheduling scheme used. Furthermore, re-scheduling will allow continuous optimisation of

the smart appliances scheduled position.

Better scheduling algorithms for multi-interval smart appliances.

64

Multi-interval smart appliances have the potential to be used as a load smoother. This will required a

more advanced scheduling algorithm than used in this research, but foremost a smaller time step then

15 minutes.

Scheduling trigger

Network load as primary scheduling trigger

In the literature, the trigger for scheduling is primarily the price of electricity, which is often

constructed using complex algorithms. The network load has proved to be very capable as the main

trigger for the scheduler. More research into price-based scheduling compared to network load-based

scheduling is needed.

(Smart) appliances

(Smart) appliances behaviour

Appliances are the source of the network demand pattern. It is however very difficult to generate a

realistic load of a household which comprises of the sum of all the individually simulated appliances of

that household. Not many detailed sources are available on what happen on the consumer side of the

electricity meter. For a more realistic generation of other-load profiles more research in the

ownership, usage and many other attributed of household appliances is needed.

Smart appliances Time of Use (TOU)

Smart appliances are now turned-on using the Time of Use (TOU) information of non-smart appliance.

The TOU may change when households start using smart appliances. This can greatly affect the

possible times that smart appliance can be scheduled. More research on how this will change is

required.

65

11. Personal reflection

This was the first time I did such a big project. In the beginning, this can be a bit overwhelming. The

potential possibilities are huge, making priority setting and focussing very important. With such a big

project, there is always something around the corner, which could potentially make my research even

more interesting. Of course, it was not possible to do everything I wanted. Making choices was

sometimes hard, but also a relief in knowing what to do and what not.

Focusing my research took a lot of time. I often used the “Christmas tree” approach. Start from a

focus point and spread-out identifying many possible directions, focus a on a possible direction and

spread-out again, etc. This approach provides a good guideline in focusing. It prevented me many

times from focusing on unimportant (but personally interesting) aspects that would take a lot of time

to research. When I eventually started modelling, my project finally took off. This was a very iterative

process. Many times, I had to re-think possible modelling concepts. Combined with a few setbacks

these caused some delays, (see profile generation above), but overall I think I kept a good working

flow.

Netlogo as simulation program

Netlogo is a very easy to use simulation program. The programming language is easy to learn and

use, and there are many code examples available. It strengths lie mostly on the simplicity of use, and

still be able to make and handle complex systems. Netlogo is however more suitable for smaller

models. After every code change, the whole code is checked on syntax flaws. In writing code, often

little tweaks are necessary and switching quickly back and forth between simulation and code writing

often happens. At the end of programming, the amount of code became so large that this checking

required more than 10 seconds. Because this automatic code checking sequence happened every time

something was changed, it resulted in many short frustration moments.

Using the internal behaviour-testing tool is very helpful in quickly running multiple scenarios.

However, with this large model, the data set it needed to record became very large quickly, resulting

in an ever-slower simulation time. Much time was lost by this and a few data sets also. Sometimes,

Netlogo could not handle the size of the dataset, resulting in a program freeze and a shut down.

Temporarily storing data and combining them when the simulation runs are finissed could optimise

this more.

Initially, I wanted to conceptualise the model in very high detail before I started writing code. This

however was not always possible. To learn more about the capabilities of Netlogo and my own

programming skills, I often learned by doing. I used many small sub-models to test a concept before

adding it to the main model. This worked very well. I quickly learned was possible in Netlogo and

what an effective way of modelling was.

66

Analysing the results was also something I had not done on such a large scale before. The dataset

generated was huge, compared to my previous project. Luckily, I was able to relatively quickly, get a

good feel on how to use the programme “R”, which I used for data analysing. Here also, focusing was

very important. There are always some extra interesting analyses to do or something interesting to

find out. This sometimes even let to seeing thing in the data that were not there. Luckily, re-double-

checking data is a good defence against such dangers.

Writing a project report is not one of my strongest points. Writing a clear story line that says what I

think it says has always been difficult for me. In previous projects, finding the motivation to start

writing was often difficult. In writing my thesis however, this was largely not the case. I generally had

a high motivation because I enjoyed doing this project very much. I feel lucky that I was able to find

such an interesting project. Being able to choose your own graduation project really made a

difference I think. My motivation would have been much lower if I had to choose from a limited

number of project presented to me by the faculty.

Not only have I learned a lot concerning projects, but also my appreciation for the complexity of the

energy system has increased a lot. As a SEPAM with Energy and Industry as domain, I knew the

system was very complex but I did not really grasp the size and scope of the complexity. My focus on

smart networks already gave me so much more insight in the scope and size of only this subject.

During my project, I often had discussion sessions with my supervisors. Both with my TU supervisor

as with my external supervisor form Enexis. These discussions were a great way of reflection on what

I was doing. These sessions really assisted me in finding my focus and finding solutions to problem. I

am very thankful for the time my supervisor took for these sessions.

Doing my thesis for Enexis was also very interesting. After spending already so many years at the

university, it was nice to go somewhere else to work. Going to the office also gave a sense of

importance and so motivation. Enexis also provide a much broader information sources specific for

this subject. Talking with people working on smart systems for a living, really got me focused in the

possibilities, and provided me with many new sources of information, some that would otherwise have

been difficult to find.

As concluding words I can say, that this was the best project I worked on during my studying years.

For now, my next step after my graduation will definitely be in the energy sector, and preferably

something concerning smart grids and simulations.

67

Part IV:

Bibliography & Appendices

69

Bibliography

A. Faruqui, R. Hledik, & J. Tsoukalis. (2009). The Power of Dynamic Pricing. The Electricity Journal,, 22(3), 42-56.

Ackermann, T., Andersson, G., & Söder, L. (2001). Distributed generation: a definition. Electric Power Systems Research, 57(3), 195-204.

AHAM (2009), Smart Grid White Paper. Washington: Association of Home Appliances Manufacturers.

Akorede, M. F., Hizam, H., & Pouresmaeil, E. (2010). Distributed energy resources and benefits to the environment. Renewable and Sustainable Energy Reviews, 14(2), 724-734.

Andersson, S. L., Elofsson, A. K., Galus, M. D., Göransson, L., Karlsson, S., Johnsson, F., et al. (2010). Plug-in hybrid electric vehicles as regulating power providers: Case studies of Sweden and Germany. Energy Policy, 38(6), 2751-2762.

Bigler, T., Gaderer, G., Loschmidt, P., & Sauter, T. (2011). SmartFridge: Demand Side Management for the Device Level. Emerging Technologies & Factory Automation (ETFA), 2011 IEEE 16th Conference on 5(9), 1-8.

Bonabeau, E. (2002). Agent-based modeling: Methods and techniques for simulating human systems. Proceedings of the National Academy of Sciences of the United States of America, 99(Suppl 3), 7280-7287.

Bots, P., Du, W., & Slootweg, J. G. (2011). Monte Carlo Simulation of Load Profiles for Low-Voltage Electricity Distribution Grid Asset Planning. CIRED, 21st International Conference on Electricity Distribution, 6(9).

Bouffard, F., & Kirschen, D. S. (2008). Centralised and distributed electricity systems. Energy Policy, 36(12), 4504-4508.

CBS. (2010). Bevolking. Retrieved 16th of August, 2011, from Centraal Bureau voor de Statistiek: http://statline.cbs.nl/StatWeb/publication/?DM=SLNL&PA=37296ned&D1=0&D2=a&HDR=T&STB=G1&CHARTTYPE=3&VW=G

CBS (2011), Particuliere huishoudens, http://statline.cbs.nl/StatWeb/publication/?DM=SLNL&PA=37312&D1=a&D2=a,!1-4,!6-7&HDR=G1&STB=T&VW=T. Accessed on 20th September 2011

CBS, PBL, & UR, W. (2010a), Energieverbruik door huishoudens, 1990-2009, http://www.compendiumvoordeleefomgeving.nl/indicatoren/nl0035-Energieverbruik-door-huishoudens.html?i=6-40. Accessed on 16th of August 2011.CBS, Den Haag; Planbureau voor de Leefomgeving, Den Haag/Bilthoven en Wageningen UR, Wageningen.

CBS, PBL, & UR, W. (2010b), Huishoudelijk energieverbruik per inwoner (Domestic energy consumption per capita), 1950-2009, http://www.compendiumvoordeleefomgeving.nl/indicatoren/nl0036-Huishoudelijk-energieverbruik-per-inwoner.html?i=6-40. Accessed on 16th of August 2011.CBS, Den Haag; Planbureau voor de Leefomgeving, Den Haag/Bilthoven en Wageningen UR, Wageningen.

Cordaro, M. (2008), Understanding Base Load Power, What it is and Why it Matters: New York

Affordable Reliable Electricity Alliance (New York AREA).

de Vries, L. (2008). Introduction electricity sector, Lecture for Policy, Economics and Law (spm3530:Beleid, Economie en Recht): University of Technology Delft.

Energy Valley (2011), Smart Grids in 10 vragen, http://www.energyvalley.nl/nl/projecten/werkthemas/21943-smart-grids-in-10-vragen. Accessed on 18th of March 2011

Energywatch (2005), Get Smart, Bringing meters into the 21th Century: Gas and Electricity Consumer Council. http://www.founter.com/uploads/pdfs/Get%20Smart%20(UK).pdf.

ENERNOC. (2009). Demand response: A multi-purpose resource for utilities and grid operators.

http://statline.cbs.nl/StatWeb/publication/?DM=SLNL&PA=37296ned&D1=0&D2=a&HDR=T&STB=G1&CHARTTYPE=3&VW=G

http://statline.cbs.nl/StatWeb/publication/?DM=SLNL&PA=37296ned&D1=0&D2=a&HDR=T&STB=G1&CHARTTYPE=3&VW=G

http://statline.cbs.nl/StatWeb/publication/?DM=SLNL&PA=37312&D1=a&D2=a,!1-4,!6-7&HDR=G1&STB=T&VW=T

http://statline.cbs.nl/StatWeb/publication/?DM=SLNL&PA=37312&D1=a&D2=a,!1-4,!6-7&HDR=G1&STB=T&VW=T

http://www.compendiumvoordeleefomgeving.nl/indicatoren/nl0035-Energieverbruik-door-huishoudens.html?i=6-40

http://www.compendiumvoordeleefomgeving.nl/indicatoren/nl0035-Energieverbruik-door-huishoudens.html?i=6-40

http://www.compendiumvoordeleefomgeving.nl/indicatoren/nl0036-Huishoudelijk-energieverbruik-per-inwoner.html?i=6-40

http://www.compendiumvoordeleefomgeving.nl/indicatoren/nl0036-Huishoudelijk-energieverbruik-per-inwoner.html?i=6-40

http://www.energyvalley.nl/nl/projecten/werkthemas/21943-smart-grids-in-10-vragen

http://www.founter.com/uploads/pdfs/Get%20Smart%20(UK).pdf

70

Enexis (2011a), Electrisch rijden, http://www.enexis.nl/site/slimnet/elektrischrijden/. Accessed on 21st of March 2011

Enexis (2011b), Enexis en publieke belangen (Enexis and public values, http://www.enexis.nl/site/over_enexis/publieke_belangen/. Accessed on 16th of August 2011

Enexis (2011c), Over Enexis (About Enexis), http://www.enexis.nl/site/over_enexis/. Accessed on 16th of August 2011

Enexis (2011d), Slimme Meters (Smart Meters), http://www.enexis.nl/site/slimnet/slimmemeter/. Accessed on 21st of March 2011

European Commission (2006), European Smart Grids Technology Platfom; Vision and Strategy for Europe’s Electricity Networks of the Future.

Galus, M. D., & Andersson, G. (2008, 17-18 Nov. 2008). Demand Management of Grid Connected Plug-In Hybrid Electric Vehicles (PHEV). Paper presented at the Energy 2030 Conference, 2008. ENERGY 2008. IEEE.

Gottwalt, S., Ketter, W., Block, C., Collins, J., & Weinhardt, C. (2011). Demand side management—A simulation of household behavior under variable prices. Energy Policy, 39, 8163-8174.

Grinden, B., & Feilberg, N. (2010), Residential Monitoring to Decrease Energy Use and Carbon Emissions in Europe

Intelligent Energy Europe.

Gudi, N. (2010). A Simulation Platform to Demonstrate Active Demand-Side Management by Incorporating Heuristic Optimization for Home Energy Management. University of Toledo.

Gudi, N., Wang, L., Devabhaktuni, V., & Depuru, S. S. S. R. (2011). A Demand-Side Management Simulation Platform Incorporating Optimal Management of Distributed Renewable Resources.

Guo, Y., Zeman, A., & Li, R. (2010). Utility Simulation Tool For Automated Energy Demand Side Management.

Gwisdorf, B., Stepanescu, S., & Rehtanz, C. (2010, 11-13 Oct. 2010). Effects of Demand Side Management on the planning and operation of distribution grids. Paper presented at the Innovative Smart Grid Technologies Conference Europe (ISGT Europe), 2010 IEEE PES.

Hadjipaschalis, L., Poullikkas, A., & Efthimiou, V. (2008). Overview of current and future energy storage technologies fo electric power applications. Renewable and Sustainable Energy Reviews, 13, 1513-1522.

IEA (2007), Time of Use Pricing and Energy Use for Demand Management Delivery: International Energy Agency - DSM.

IEA (2008), Integration of Demand Side Management, Distributed Generation, Renewable Energy Sources and Energy Storages: International Energy Agency - DSM.

IEA (2011), Implementing Agreement on Demand-side Management Technologies and Programmes (2010 Annual Report): International Energy Agency - DSM.

M. Goldberg. (2010). Measure Twice, Cut Once. IEEE Power and Energy Magazine, , 8(3), 46-54.

Macal, C. M., & North, M. J. (2006, 3-6 Dec. 2006). Tutorial on Agent-Based Modeling and Simulation PART 2: How to Model with Agents. Paper presented at the Proceedings of the Winter Simulation Conference, 2006. WSC 06.

McArthur, S. D. J., & Davidson, E. M. (2005). Concepts and Approaches in Multi-Agent Systems for Power Applications.

McKinsey (2010), McKinsey on Smart Grid: McKinsey & Company's Electric Power and Natural Gas Practice and Buseness technology Office. http://www.mckinsey.com/Client_Service/Electric_Power_and_Natural_Gas/Latest_thinking/McKinsey_on_Smart_Grid.

http://www.enexis.nl/site/slimnet/elektrischrijden/

http://www.enexis.nl/site/over_enexis/publieke_belangen/

http://www.enexis.nl/site/over_enexis/

http://www.enexis.nl/site/slimnet/slimmemeter/

http://www.mckinsey.com/Client_Service/Electric_Power_and_Natural_Gas/Latest_thinking/McKinsey_on_Smart_Grid

http://www.mckinsey.com/Client_Service/Electric_Power_and_Natural_Gas/Latest_thinking/McKinsey_on_Smart_Grid

71

Mohalkar, A., Klinkhachorn, P., & Feliachi, A. (2004). Effects of dynamic pricing on residential electricity bill. IEEE Power System. Conf. Expo., 2(1030-1035).

Mohd, A., Ortjohann, E., Schmelter, A., Hamsic, N., & Morton, D. (2008). Challenges in integrating distributed energy storage systems into future smart grid. Paper presented at the International Symposium on Industrial Electronics, 2008. ISIE 2008. IEEE

Nibud (2011), Stroomverbruik naar grootte huishouden, http://www.gaslicht.com/energiebesparing/energieverbruik.aspx. Accessed on 20th September 2011

NMa (2011), Nederlandse Mededingingsautoriteit (Netherlands Competition Authority), http://www.nma.nl. Accessed on 21st of October 2011

Pepermans, G., Driesen, J., Heaseldonckx, D., Belmans, R., & D'heaseleer, W. (2005). Distributed generation: definition, benefits and issues. Energy Policy, 33, 787-798.

Pina, A., Silva, C., & Ferrão, P. (2011). The impact of demand side management strategies in the penetration of renewable electricity. Energy, In Press, Corrected Proof(http://www.sciencedirect.com/science/article/pii/S0360544211003902).

Rahmandad, H., & Sterman, J. D. (2008). Heterogeneity and Network Structure in the Dynamics of Diffusion: Comparing Agent-Based and Differential Equation Models. Management Science, 54(5), 998-1014.

Saffre, F., & Gedge, R. (2010a). Demand-Side Management for the Smart Grid.

Saffre, F., & Gedge, R. (2010b, 19-23 April 2010). Demand-Side Management for the Smart Grid. Paper presented at the Network Operations and Management Symposium Workshops (NOMS Wksps), 2010 IEEE/IFIP.

SenterNovem (2008), Elektrische apparatuur in Nederlandse huishouden. Delft. http://www.vhk.nl/downloads/Definitief_Hoofdrapport_8dec2008.pdf.

Sinha, R. (2009). Smart control of Residential buildings. University of Strathclyde, Department of Mechanical Engineering.

Stadler, M., Krause, W., Sonnenschein, M., & Vogel, U. (2009). Modelling and evaluation of control schemes for enhancing load shift of electricity demand for cooling devices. Environmental Modelling & Software, 24, 285-295.

Stamminger, R. (2009), Synergy Potential of Smart Appliances: Smart-A project. http://smart-a.org/WP2_D_2_3_Synergy_Potential_of_Smart_Appliances.pdf. University of Bonn.

Strbac, G. (2008). Demand side management: Benefits and challenges. Energy Policy, 36(12), 4419-4426.

Swan, L. G., & Ugursal, V. I. (2009). Modeling of end-use energy consumption in the residential sector: A review, of modeling techniques. Renewable and Sustainable Energy Reviews, 13(8), 1819–1835.

TenneT (2010), TenneT, http://www.tennet.org/. Accessed on 21st of October 2011

Timpe, C. (2009), Smart Domestic Appliances Supporting the System Integration of Renewable Energy: Öko-Institut e.V. www.smart-a.org.

Torriti, J., Hassan, M. G., & Leach, M. (2010). Demand response experience in Europe: Policies, programmes and implementation. Energy, 35(4), 1575-1583.

Tromp, B. T. (2011). Verminderen van onbalanskosten op elecktriciteitsverbruik. Vrije Universiteit Amsterdam, Amsterdam.

U.S. Department of Energy (2008), The Smart Grid, an introduction, http://www.oe.energy.gov/SmartGridIntroduction.htm. Accessed on 21st of March 2011

Veldman, E., Geldtmeijer, D. A. M., & Slootweg, J. G. (2010). Smart Grid Put into Practice: Technical and Regulatory Aspects. Competition and Regulation in Network Industries, 11(3), 287-306.

http://www.gaslicht.com/energiebesparing/energieverbruik.aspx

http://www.nma.nl/

http://www.sciencedirect.com/science/article/pii/S0360544211003902)

http://www.vhk.nl/downloads/Definitief_Hoofdrapport_8dec2008.pdf

http://smart-a.org/WP2_D_2_3_Synergy_Potential_of_Smart_Appliances.pdf

http://smart-a.org/WP2_D_2_3_Synergy_Potential_of_Smart_Appliances.pdf

http://www.tennet.org/

http://www.smart-a.org/

http://www.oe.energy.gov/SmartGridIntroduction.htm

72

WADE (2010), World Alliance for Decentralized Energy, http://www.localpower.org/. Accessed on 25th of March 2011

Wilensky, U. (1999). Netlogo,. Northwestern University, Northwestern University, IL: Center for Connected Learning (CCL) and Computer-Based Modeling. http://ccl.northwestern.edu/netlogo/

http://www.localpower.org/

http://ccl.northwestern.edu/netlogo/

73

Appendix A

Flow chart of:

• Appliances

• Scheduler

• Dispatcher

Appliances

YesRunTimeLeft = 0? Turn Off

Yes

No

Yes

No

RunTimeLeft - 1

Update TOIList and send

to scheduler' TBSList

StandbyTime + 1

No

Yes

No

Turn to Standby

mode

Status = “on”

Status =

“Standby”

TurnOn-

Time = Now

TBSList

Scheduler

[TOIList]

Start

End

Figure 8: Appliances flow chart

1) When the appliance status equals “on”, its RunTimeLeft is checked. If the appliance

RunTimeLeft is 0 the appliance is turned off. Otherwise, the RunTimeLeft is set to

RunTimeLeft – 1.

2) If an appliance status equals standby, the StandbyTime is set to StandbyTime + 1

3) If the appliance is not turned on or set to Standby mode, its status is “off”. When its

TurnOnTime equals Time-Now, the appliance is set to Standby mode. The appliance updates

its TurnOnInformation-List (TOIList) and add the TOIList to its schedulers ToBeScheduled-List

(TBSList).

4) If the appliance TurnOn-Time does not equal Time-Now.now, the appliance status stays “off”.

Flow chart Legend

Stored

List

Variable

setting

Process

Choice

Next step

Data send

Data retrieved

74

Scheduler

Re-organise TBSList according to

scheduling scheme

Pick first item in

TBSList

TBSList > 0

Yes

No

Create operational

window: BeginTime to

EndTime

Find lowest point in

operational window

Predicted

demand pattern

profile

Today’s

scheduled

appliances

profiles

Tomorrow’s

scheduled

appliances

profiles

Scheduling

scheme:

EDF, FCFS,

SIRO

Schedule appliance

Update Today’s / Tomorrow’s

scheduled profile lists

Scheduled-List

Dispatcher

Put appliance ID in

SList Dispatcher

Start

End

TBSList[TOIList]

Remove first

item from

TBSList

Figure 9: Scheduling mechanism flow chart

1) The TBSList (To-Be-Scheduled-List) is re-organised according to the scheduling-scheme of the

scheduler. The items in the TBSList are the TOIList’s (Turn-On-Information-List’s) of to-be

scheduled appliances.

2) The first item in the TBSList is picked. If there are no items in the TBSList present, the

scheduling process is finished.

3) From the first item (a TOIList of an appliance), the Begin-Time and End-Time are used to

create an operational window subset of the planned network load. The planned network load

75

is the combined profile of the Predicted demand profile, Today’s planned appliances profile list

and Tomorrow’s planned appliances profile list.

4) In this subset, the lowest network load point is located. This is the lowest gap in this subset.

5) The scheduler schedules the appliance in this timeslot by adding the appliances ID (retrieved

from TOIList) to corresponding timeslot of the Scheduled-List (SList) of the Dispatcher.

Furthermore, depending on the question whether the appliance turns on today or tomorrow,

the Today’s scheduled appliances profile list or Tomorrow’s scheduled appliances profile list is

updated. The demand-profile of the appliance is added to the timeslot of these profiles,

ensuring that the pattern of the appliances load is incorporated in the lists.

6) The first item (the just scheduled appliance TOIList) is removed from the TBSList. The

scheduler starts at step 2 again.

Dispatcher

Get TimeSlot =

Now

Turn-On all listed

agents

(appliances)

Start Clear TimeSlot End

ID’s appliances

[ ]ID’s appliances

[ 2,3,15,200,35,124, … ]

List of

scheduled

appliances per

timeslot.

Figure 10: Dispatcher flow chart

76

Appendix B

Scheduling Schemes

Table 10: List of scheduling scheme’s (Source)

Scheduler Scheme Abbreviation.

First Come First Serve FCFS

Last Come First Serve (not modelled, not fair) LCFS

Random Order (Service) RO

Priority Serve (how to determine? Price categories?, app categories? PS

Earliest Deadline First (Earliest due date (EDD)) EDF

Least Slack Time LST

Longest Processing Time LPT

Shortest Processing Time SPT

Longest Operation Window LOW

Shortest Operation Window SOW

77

Appendix C

Table 11: Appliance attributes

Table 12: Penetration degree, turn-on probability and empirical distribution of average daily

time to turn on (Stamminger, 2009)

Table 11: Appliance attributes

Appliance Profile Duration Power

Washing machine

(WM)

[0.08,1,0.4,0.08,0.08,0.13,0.03,0]

2 hours or 8

quarters 1200 W

Tumble Dryer (TD)

[1,1,1,0.8,0.65,0.48,0,0]

2 hours or 8

quarters 2000 W

Dish washer (DW)

[0.03,1,0.03,0.03,0.03,1,0.15,0.1,]

2 hours or 8

quarters 1800 W

Refrigerator (RF)

[1]

1 hour or 4

quarters 150 W

Freezer (FR)

[1]

1 hour or 4

quarters 100 W

Other-load Depends on combination type (see list below) 24 hours or

96 quarters

Depends on

combination

type (see list

0

0.5

1

1 2 3 4 5 6 7 8

0

0.5

1

1 2 3 4 5 6 7 8 9 10 11

0

0.5

1

1 2 3 4 5 6 7 8 9 10 11

0

1

0

1

78

below)

Table 12: Penetration degree, turn-on probability and empirical distribution of average daily time to turn on

(Stamminger, 2009)

Penetration

degree per

household type

(%)

Daily turn-on

probability

household type

(%)

Daily average turn-on period

Appliance Type

1

Type

2

Type

3

Type

1

Type

2

Type

3 The same for all types of households

Washing

machine (WM) 95% 97% 99% 33% 50% 75%

Tumble Dryer

(TD) 20% 60% 70% 16% 25% 37%

Dish washer

(DW) 25% 55% 95% 33% 50% 75%

Refrigerator

(RF) 100% 100% 100% The refrigerator and freezer directly turn to standby mode when turning off

and are continuously active

Freezer (FR) 25% 52% 65%

Other-load

100%

Are required in a

household.

The other-loads are continuously active

0

0.02

0.04

0.06

0.08

1 3 5 7 9 11 13 15 17 19 21

Time

0

0.02

0.04

0.06

0.08

1 3 5 7 9 11 13 15 17 19 21 23

Time

0

0.02

0.04

0.06

0.08

1 3 5 7 9 11131517192123

Time

79

Appendix D

Combination profiles

The numbers represent the following appliances: Other-loads (0), WM (1), TD (2), DW (3), RF (4), FR

(5). Depending on the combination profile number and the type of the household, the other-loads

receive a combination. Used as: type 1 household and combination 01 = t1comb1, type 2 household

and combination 012 = t2comb6, etc.

Table 13: Other-load Combination-profile numbers

Combination profile number Combination

(comb)

0 0

01 1

02 2

03 3

04 4

05 5

012 6

013 7

014 8

015 9

023 10

024 11

025 12

034 13

035 14

045 15

0123 16

0124 17

0125 18

0134 19

0135 20

0145 21

0234 22

0235 23

0245 24

0345 25

01234 26

01235 27

01245 28

01345 29

02345 30

012345 31

80

Appendix E

Scenario variables that are changed

Scenario 0: non-smart network

Changed variable:

Number of households 5 10 25 50 75 100 125 150 175 200

Internal variables

These variables affect the smartness of the smart system.

Memory [Today, Yesterday, Average]

Scheduling Scheme [FCFS, EDF, SIRO]

Operational Window [8,24,48,80] (in quarters)

While varying these variables, the external variables are set to one constant value that is most likely

or an average.

Scheduling level: “Household”

Penetration DSM households: “50”

Penetration DSM appliances: “50”

Number of Households ”100”

External variables

These variables affect the scope of the DSM. With higher values, the DSM effect will be greater.

Scheduling level

Penetration DSM Households

Penetration DSM Appliance

Number of Households

While varying these variables, the other internal variables are set to one constant value that is most

likely.

Memory: “Today” (perfect)

Scheduling Scheme: “FCFS”

Operational Horizon Window: “48 quarters” (12 hours )

Perfect Condition of 100% Smart Penetration

How well does the smart system perform under perfect conditions?

Scheduling level: “Appliance”, “Household”, “Network”

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Memory: “Same-day” (perfect), “Yesterday”, “Average”

Scheduling Scheme: "FCFS", "EDF", "SIRO"

Operational Horizon Window: “8, 24, 48, 80” (quarters)

Penetration DSM households: “100”



Random Operational Horizon

What influence does a random operational horizon have on the scheduling schemes used and the

memory used.

Memory: "Same-day", "Yesterday", "Average"]

Scheduling scheme: "FCFS", "EDF", "SIRO"

Operational Horizon Window: "Random"

Scheduling-level: "District"

Penetration DSM households: “0, 25, 50, 75, 100”



Refrigerator and Freezer Scenario

Variables changed

Scheduling Level: Off, Appliances, Household, Network

Scheduler Memory: Today, Yesterday, Average

Scheduling Schemes: FCFS

Operational Appliance Window: 24 quarters

Penetration DSM Network: 25 50 75 100

Penetration DSM Households : 100

Number of Households: 100

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Appendix F

Average network demand profiles

Network demand profiles per network size. Because this is a non-smart network these demand

patterns are the combined profiles of all the other-loads in the network.

Figure 11: Average demand profiles per replication per network size

The above figure shows the network demand of every replication of the simulation model per network

size of a day, including the average of the network. Although the variations in the demand pattern are

great, the average demand pattern looks more and more the same as the network size increase. This

is partly caused by the oversampling of the profiles used.

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Appendix G

Simulation Results

Table 14: Table of most important of simulation results.

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All appliances same-day Off FCFS 22.66 7.95 1.62 0.83 0.0 0.0 0.0 100.0

All appliances same-day Off EDF 22.70 7.96 1.64 0.85 0.0 0.0 0.0 100.0

All appliances same-day Off SIRO 22.68 7.93 1.62 0.82 0.0 0.0 0.0 100.0

All appliances same-day Household FCFS 13.70 2.86 1.44 0.77 16.2 7.9 8.3 83.8

All appliances same-day Household EDF 13.74 2.89 1.45 0.77 16.2 7.9 8.3 83.8

All appliances same-day Household SIRO 13.63 2.85 1.44 0.78 16.4 7.9 8.5 83.6

All appliances same-day District FCFS 13.85 2.92 1.42 0.76 16.1 7.9 8.2 83.9

All appliances same-day District EDF 13.58 2.84 1.43 0.77 16.1 7.9 8.2 83.9

All appliances same-day District SIRO 13.73 2.87 1.42 0.76 16.1 7.9 8.2 83.9

Only non-cooling same-day Off FCFS 22.66 7.93 1.62 0.83 0.0 0.0 0.0 100.0

Only non-cooling same-day Off EDF 22.57 7.87 1.61 0.83 0.0 0.0 0.0 100.0

Only non-cooling same-day Off SIRO 22.62 7.91 1.62 0.84 0.0 0.0 0.0 100.0

Only non-cooling same-day Household FCFS 14.84 3.42 1.49 0.81 7.7 0.0 7.7 92.3

Only non-cooling same-day Household EDF 14.55 3.29 1.49 0.81 8.1 0.0 8.1 91.9

Only non-cooling same-day Household SIRO 14.84 3.38 1.48 0.79 7.9 0.0 7.9 92.1

Only non-cooling same-day District FCFS 14.70 3.35 1.50 0.81 8.0 0.0 8.0 92.0

Only non-cooling same-day District EDF 14.65 3.31 1.47 0.80 7.9 0.0 7.9 92.1

Only non-cooling same-day District SIRO 14.95 3.48 1.51 0.82 7.8 0.0 7.8 92.2

Only cooling same-day Off FCFS 22.74 7.96 1.62 0.83 0.0 0.0 0.0 100.0

Only cooling same-day Off EDF 22.60 7.88 1.62 0.83 0.0 0.0 0.0 100.0

Only cooling same-day Off SIRO 22.68 7.89 1.62 0.83 0.0 0.0 0.0 100.0

Only cooling same-day Household FCFS 22.48 7.92 1.55 0.79 8.4 8.4 0.0 91.6

Only cooling same-day Household EDF 22.56 7.94 1.56 0.79 8.4 8.4 0.0 91.6

Only cooling same-day Household SIRO 22.72 8.05 1.57 0.80 8.4 8.4 0.0 91.6

Only cooling same-day District FCFS 22.60 8.01 1.57 0.79 8.4 8.4 0.0 91.6

Only cooling same-day District EDF 22.55 7.90 1.56 0.79 8.4 8.4 0.0 91.6

Only cooling same-day District SIRO 22.64 7.93 1.56 0.79 8.3 8.3 0.0 91.7

All appliance incl. average

average Off FCFS 22.59 7.94 1.63 0.84 0.0 0.0 0.0 100.0

All appliance

incl. average average District FCFS 14.94 3.40 1.45 0.77 14.4 7.9 6.5 85.6


same-day Off FCFS 22.72 7.93 1.62 0.83 0.0 0.0 0.0 100.0


same-day District FCFS 14.56 3.26 1.40 0.74 14.6 8.1 6.5 85.4

All appliance

incl. average yesterday Off FCFS 22.73 7.98 1.63 0.84 0.0 0.0 0.0 100.0


yesterday District FCFS 15.45 3.61 1.48 0.80 14.6 8.1 6.5 85.4

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Table 15: Box-plots of most important simulation results

All appliances scenario with scheduling method vs. scheduling level

From the above boxplot, clearly can be seen that a smart system lowers the LEs values and the HP values. However, there are some new outliers in the HP values of the smart system. These are short peaks that are caused by applinaces that have a peak during their operational time, like the dishwasher and washing machine. Furthermore, althought the peaks are generally lower, the duration of these peak has increased. This can be expected because the load is now spread out over the demand pattern, and so the probability of passing over the 80% boudry increases, resulting in longer proportion HP times

Only Cooling scenario with scheduling method vs. scheduling level

HP.D

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In a network with only smart cooling applinace the LEs do not differ. The HPs differ only a little. As can be exspected, the peaks are reduce just a little, and the duration here also slightly increases. This is because the smart cooling appliances load cannot all be schedulled to the gaps because they have to turn on about once an hour.

Only Non-cooling scenario with scheduling method vs. scheduling level

Here a clear difference can be seen between the LEs of a smart and non-smart network. Also the HPs are different. The peaks are reduced and the duration again has increased.

HP.D

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All appliance scenario with memory scheduler vs. scheduling level

Here a clear difference can be seen between the LEs of a smart and non-smart network. Also the HPs are different. The peaks are reduce but the duration again has increased. Furthermore, the difference between the forecasters are shows. Cleary, todays forcaster gives the best results, followed by the average and yesterdays forecast.

HP.D

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External – Appliances DSM penetration against network size

Figure 12: Appliances DSM penetration degrees against LE.

LE

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External – Household DSM penetration against network size

Figure 13: Household DSM penetration degrees against LE.

LE

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Appendix H

Netlogo (Wilensky, 1999), a small network of 10 households with smart appliance (grey – “off” or blue – “standby”) and the “other-loads” ( green – “on).

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Appendix I

Additional information on:

1. DSM load manipulating techniques

2. Dynamic Pricing models

1) DSM load manipulating techniques

Figure 14: Demand Response techniques taken from N. Gudi (2010)

Explanation of Demand Response Techniques (Gudi, 2010)

Peak clipping: reducing the load on the grid (mainly) during peak demand periods.

Valley filling: increasing load during off-peak period by improving system load factors

Load shifting: reducing the load on the grid during peak demand periods and simultaneously

increasing the load during off-peak periods, often by shifting peak loads to off-peak periods.

Conservation: reducing the load throughout the day by utilizing more energy efficient

appliances and/or by reducing overall consumption.

Load building: increasing the load by increasing the overall consumption.

Flexible load shape: specific contracts and tariffs with the possibility of flexibly controlling

consumers‘equipment.

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2) Dynamic Pricing models

Several dynamic Pricing models (A. Faruqui, et al., 2009; IEA, 2007, 2008; M. Goldberg, 2010;

Mohalkar, et al., 2004; Saffre & Gedge, 2010b) .

Time of Use (TOU) pricing presents consumers with different tariffs at different times. Prices

can vary significantly according to the time and location of the electricity consumed, e.g. 2-

fold tariff system: peak-tariff and off-peak tariff.

Critical Peak Pricing (CPP) can identify and locate spikes in peak demand periods, and

communicated to consumers to reduce their consumption locally.

Real Time Pricing (RTP) can change the price according to generation cost fluctuations in real-

time. Like CPP, this requires a (short) notice communication infrastructure to alert consumers

(and consumer appliances) in short notice of price changes.

Demand Side Bidding (DSB) methods give consumers the capability to participate in the

electricity trading, e.g. tariff packages, peak load usage contract or, timeslot purchasing.

Peak-Time Rebate (PTR) pricing rebated consumers for reducing their energy consumption

during peak periods.

Load shifting in a new perspective - TU Delft

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