Demand side management algorithms and modeling

in smart grids A customers behavior based study

El Hassan Et-Tolba*, Mohamed Maaroufi

Department of Electrical Engineering,

Ecole Mohammadia dIngnieurs, Mohammed V-Agdal University, Rabat, Morocco

*tolhaha@gmail.com, maaroufi@emi.ac.ma

Mohammed Ouassaid, Member, IEEE

Department of Industrial Engineering,

Ecole Nationale des Sciences Appliques-Safi,

Cadi Ayyad University, Safi, Morocco

ouassaid@emi.ac.ma

AbstractThis paper presents algorithms and architecture models for a home energy management system. Its based on

customers behavior that is modeled by a decision-making chain, and smart appliances use for demand side management. The proposed architecture is scalable and extensible to upper levels of

smart grid as the development approach used is bottom-up. Once

the model is validated for home use, we can go up and apply it for

holdings, factories, and micro-grids contexts. Scalability models

and strategies are also presented and discussed. Ensuring supply

and demand balance at real time is the main problematic of

smart grids. The proposed solution meets this objective, because

it allows large scale renewable energy resources integration.

Hence, it leads to global energy efficiency and demand side

management optimization in smart grids

Keywords-smart grids; demand side management; smart

appliances; customers behavior; renewable energy; distribution network; energy consumption;

I. INTRODUCTION

Demand side management is very important in smart grids to ensure the offer and demand balance in real time. It has been realized that modification to the way we use energy in the demand side could not only save money but also satisfy the level of quality demanded by users of energy services [1]. The term Demand-Side Management (DSM) did not exist prior to 1973. It appeared within an atmosphere of chaos in relation to an impending energy crisis and uncertainties, which prevailed at that time [2]. Hence the agreement of controlling some loads when they are needed was reached and the term DSM officially came into being [1]. Nowadays, as many high coverage blackouts occurred around the world these last recent years, the architecture of traditional electrical networks has been challenged. So they are gradually giving up in favor of smart grids. This new electrical network generation mobilized both utilities and researchers and has led to many scientific proposals and ideas.

Smart grids can be defined from a functional point of view as an electric network able to integrate all the branched customers and producers actions to distribute electric energy efficiently, successfully, at low operating costs and safely [3]. In a business case study of CISCO, smart grids are described as the combined view that uses the information network to

enhance the functioning of the electricity grid [4]. It can also be seen essentially as a control problem including optimization of delivery, demand, asset, reliability and renewable resources integration that will lead to operational and energy efficiency, customer satisfaction (Quality of Service) and CO2 reduction [5].

There are two key technologies enabling demand side load optimization [6]:

Building automation

Smart metering

This paper interests in the first technology above. Many researchers studied and discussed these two technologies from different aspects. About the first technology, Building automation, it has been concluded that the presence of distributed generation (solar, Wind, biomass) and storage facilities (batteries, fuel cell, Plug-in Hybrid Electric Vehicles) will help to create Zero Net Energy Buildings and Districts (ZNEBs) [7]. Then, a general philosophy of demand side load management for adjusting energy demand/offer balance is presented in [8]. Where an explanation of home automation importance for power load control and how smart appliances should be redesigned is also given. Then, the major requirements from a hardware and software perspective for smart appliances design, with analysis of potential communication techniques, each with their specific advantages and disadvantages are presented in [9]. In addition to a universal appliance interface enabling design of a controller with different interfaces types for a wide range of common use appliances that is given in [10]. The demand forecasting process become more efficient with the use of flexible loads that can be managed and controlled to reduce sizes of generation units, buying and selling energy from distribution grid and total cost of smart microgrid [11]. This process can use top down or bottom up approaches, based on statistical or engineering methods. It models energy consumption according to thermal characteristics of houses, consumption profile of appliances and behavior of householders [12]. These last points are mainly discussed in this paper; our proposed system architecture and models are based on smart appliances use and consumers behavior modeling. Otherwise, a new three-layer household energy control system capable both to satisfy the

978-1-4673-6374-7/13/$31.00 2013 IEEE

maximum available electrical energy constraint and to maximize user comfort criteria is presented in [13]. Another distributed load control and demand side management system which is able to even out fluctuations in the daily energy load is studied by [14]. Moreover, in [15], a framework for distributed D/R with user adaptation based on assessed techniques in telecommunications networks decongestion is given. The reliability of these systems depends on their maintenance. It has been proved that by enabling preventive maintenance, some precautions like computing thermo images of electrical devices or determining the load capability and insulation aging factor can reduce by 2.5 times the failure risk [6].

As for the second technology, smart metering, it has also been an interesting subject for several studies. In a business based study, a general overview on smart meters with analyzes of their functionalities and supported services were presented in [16]. And their social benefits have been investigated in [17]. Then, its very important to make devices smarter, but not with any cost; as it has been affirmed: attributing intelligence, which implies value, to these technologies begs the question on how to measure the gains to realize from making such investments. Not surprisingly making devices smarter is not by itself sufficient to produce benefits to exceed their costs [17]. About the social welfare, it can be maximized by dynamic pricing that has been studied in [18]. Its affirmed that when the customer is price taking, he commits to shedding or increasing its consumption according to its bid and the energy price thats dynamically defined by utilities. But, conversely in [19], its shown that in an oligopolistic market where customers are price-anticipating and strategic, the system achieves a unique Nash equilibrium that maximizes another additive, global objective function.". Then, a mathematical approach for distributed optimal power flow computation using smart meters, distributed generation facilities and remote load control was presented in [20]. The study showed how the upper layers in smart grids provide information and control to lower layers as it is explained by Figure 1.

Figure 1. Control layered architecture in smart grids

Another important aspect of smart meters is information communication. The state of the art about smart metering communication standards relevant to the smart grid and smart house concepts is analyzed in [21] with an architectural overview of the existing information and communication technologies (ICT) standards related. Then, an implementation of power line communication (PLC) system with an improved orthogonal frequency multiplex algorithm to limit narrow band

noises interfering with the carrier signal is presented in [22]. Moreover, simulations for home communication network based on PLC technology and investigated security and data consistency issues are made in [23].

As we already mentioned above, the concern of this paper and objective is to give a home automation system architecture that is based on smart appliances integration and customers behavior modeling. This last aspect is generally missed in literature or described as uncontrollable. We provide here a technique that consists in decision making chain modeling that will be explained clearly in section III of this paper. For the interaction between users, smart appliances and the home energy management system (HEM), we used Unified Modeling Language (UML) use cases diagram, to show what functionalities are offered by the HEM to users. Then, to explain the structure of this system, we used UML class diagram that allowed seeing each component description, functions and relation with other components; this diagram allowed also to classify these elements and showed clearly the organization of the proposed system architecture. These diagrams are made with the free ArgoUML software. We choose it because it allows code generation in many programming languages. We, hence, can deduct the operating algorithms of HEM and smart appliances.

The next section of this paper presents the demand side management problem and program, while section III concerns DSM modeling and algorithms. The presented models and results are discussed in section IV and some issues and future work are given before concluding.

II. DEMAND SIDE MANAGEMENT PROBLEM

A. Domestic loads classification

Domestic energy requests have different time scales, which allow classifying loads in three categories based on appliances intrinsic characteristics [24] :

Baseline loads refers to those appliances that must be activated immediately at any time, or maintained at Stand by mode. Their economic value doesnt allow any intelligence integration, and they are not controllable because they depend on consumer behavior and comfort [24]. Lighting, TV, and computing are some examples.

Regular loads are those corresponding to the appliances that are operated for long time periods like fridge and water heater

Burst loads concerns the appliances that must operate for a limited time period within deadlines. This last type can be flexible and so delayed to start operation in another moment, like washing machine, dryer and dishwasher.

The peak loads problem is mainly caused by regular and burst loads combination.

Domestic

Local

Region

Country

Co

ntro

l

Info

rma

tion

B. Demand Side Management program

The Figure 2 shows traditional and advanced forms of load management. Traditional ones are represented by left hand curves {(a), (b), and (c)}, while advanced ones are given by right hand curves {(d), (e), and (f)}

Figure 2. Load shape for DSM program

There are six load shape objectives for load management programs that are stated in the literature [1] which are categorized under basic level (peak clipping, valley filling and load shifting) and advanced level (strategic conservation, strategic load growth and flexible load shape). This paper goes with the advanced level, more specifically flexible loads (Figure 2 (f)).

III. DSM MODELING AND ALGORITHMS

A. Smart appliances design

The development and redesign of mart appliances will make revolutionary change in demand side energy consumption and consumers habits, since they allow operation and control automation. To define a generic design for a smart appliance is extremely difficult due to the many different vendors and device functions [9]. This can also be seen by the features of existing solutions, which cover several aspects but still leave potential for improvement and standardization. Following in Figure 3, we give and describe a smart appliance interface that is appropriate to be used with the proposed system architecture.

Figure 3. Smart appliance interface design

This interface design shows the required inputs and outputs data and signals for smart appliances to operate and communicate automatically with the HEM. The inputs are Synch. Clock for clock synchronization signal, Start and Stop correspond to user manual switch On/Off the appliance while Time is considered as an explicit internal signal [24]. As for the outputs, they are Status that provides the appliance status (Run, Off, Ready,..), Preemption witch indicates whether the appliance can be suspended or not, Required Energy, Heuristic Value that expresses the appliance operation urgency, Power Load and Nominal Power

B. Components Class diagram

The Figure 4 shows the UML (Unified Modeling Language) class diagram of the DSM system components and interaction with smart appliances. This diagram is made with the free ArgoUML software that provides the advantage of multiple programming languages (Cpp, Java, Php, C#, etc.) code generation, as we will see later in this paper. The main classes modeled are the three load categories: Baseline_Load, Regular_Load, and Burst_Load, Smart_Appliance, HEM (Home Energy Manager), and the three HEM sub layers: Admission_Control, Load_Balancer, and Load_Forecaster all with their interaction and communication links. The class Load is a generalization of classes Baseline_load and Smart_Appliance which is also a generalization of classes Regular_Load and Burst_Load. The class Load can send a Request to HEM witch accepts or refuse its corresponding smart appliance operation. The HEM is structured in three sub layers: the bottom layer is Admission_Control that accepts or refuses a request according to the available capacity. The layer above is Load_Balancer that schedules the appliances operation and reschedule the refused request by Admission_Control. The top layer, Load_Forecaster is the interface with smart grid that gives the demand forecasting based on information about available capacity and energy cost.

Outputs:

Status

Preemption

Required Energy

Heuristic Value

Power Load

Nominal Power

Inputs:

Synch. Clock

Start

Stop

Time

User Interface

t(s)

Dem

an

d (

MW

)

(a) Peak Clipping

t(s)

Dem

an

d (

MW

)

(b) Valley Filling

t(s)

Dem

an

d (

MW

)

(c) Load Shifting

t(s)

Dem

an

d (

MW

)

(d) Strategic Conservation

t(s)

Dem

an

d (

MW

)

(e) Strategic Load Growth

t(s)

Dem

an

d (

MW

)

(f) Flexible Load Shape

Figure 4. Components class diagram

C. Home Energy Manager Use cases diagram

The Figure 5 shows the UML use cases diagram made with the same software above. This diagram shows users and smart appliances needs from the HEM. It answers the question: who can do what? without any how? answer. Note that smart appliances support both automatic activation and manual control operation by users. The diagram presents two actors: User and Smart Appliance that can initiate much functionality offered by the system. These functionalities are the use cases represented by ellipses in the diagram. Those offered to the actor User are: Choose profile, in this case,

the profile is defined by User, Delay Flexible Appliance and Accept/Refuse Proposed Profile, in this case the profile is proposed by the HEM. These three use cases are conditioned by the availability of capacity and energy cost information. As for the actor Smart Appliance, the use cases that can be initiated are: Send_Request that consists on sending the interface outputs (Figure 3) to the HEM, and Receive Activation Signal explains the acceptance of smart appliance operation. This last case is conditioned by the availability of capacity.

Figure 5. HEM use cases diagram

D. HEM Operating algorithms

As we already mentioned, following we present some examples of Java language code generation from last diagrams. We opted for this language as its the most used in applications development, then its clearly commented and helps to go back for extracting algorithmic description of the system operation. The given classes code bone examples concern HEM, Smart_Appliance and Admission_Control respectively.

E. Consumer behavior modeling

To model the customer behavior, we start from a point where the customer is in a situation S and can make a decision D based on a given available capacity C and energy cost profile K. The decision made brings him to another making decision situation and so on until hes satisfied or unavailability of capacity and energy cost information. The Figure 6 shows an example of this making decision chain where the customer can choose between four decisions D1, D2, D3, and D4. Each made decision brings him to another situation S1, S2, S3, and S4 with an available capacity C1, C2, C3, and C4; and an energy cost K1, K2, K3, and K4. The chain starts by beginning state and finishes with final one.

Figure 6. Customers decision making chain

F. DSM scalability for Smart grids

Once the bottom level (HEM System) is operational, we can go up and extend the architecture to cover the whole smart grid. So two choices are possible:

Subsystems view (Microgrids)

Layered architecture

For subsystems view, its a vertical decomposition of smart grids where every operational DSM system is considered as an autonomous entity that can manage smart appliances requests with an available capacity and energy cost information or profile. The Figure 7 shows the smart grid construction strategy based on this technique. The clouds represent smart microgrids which are the global autonomous entities; themselves contain other autonomous sub entities and so on until the bottom level that is HEM.

requests

Figure 7. Smart grid subsystems view

As for the layered architecture, a horizontal view, the interconnection between many local DSM systems allows to construct the bottom layer Layer 1 which role can be similar to the Admission_Control role in HEM. Then, by the same way when many Global Load Balancers are joined, they represent the second layer above Layer 2. Finally, the third layer Layer 3 came from the connection between many Micro Grid Managers. The Figure 8 gives the model corresponding to this technique.

import java.util.Vector;

public class HEM {

/**

* @element-type Load

*/

public Vector Request;

public Admission_Control

myAdmission_Control;

/**

* @element-type Layer

*/

public Vector myLayer;

public void SendActivationControl() {

}

}

public class Smart_Appliance extends Load {

public Integer App_Id;

public void SendRequest() {

}

}

public class Admission_Control extends Layer

{

public Integer Capacity;

public HEM myHEM;

public Load_Balancer myLoad_Balancer;

public void Accept() {

}

public void Reject() {

}

}

D2

D1

D4

D3

D2

D1

D4

D3

S1, C=?, K=?

S3, C=?, K=?

S2, C=?, K=?

S4, C=?, K=?

D2

D1

D4

D3

D2

D1

D4

D3

Legend:

D : Decision

S : Situation

C : Capacity

K : Cost

DSM1 DSM2 DSM3

Global Load Balancer

Micro Grid Manager

Smart Grid

DSM1 DSM2 DSM3

Global Load Balancer

Micro Grid Manager

DSM1 DSM2 DSM3

Global Load Balancer

Micro Grid Manager

Figure 8. Smart grid layered architecture

IV. DISCUSSION

The DSM is actually one of the important studied aspects for smart grids. Hence, it has been a subject for many researches and studies. From our point of view we gave some algorithms and models to explain the operation of this new involving technology. We could give a clear idea about customers behavior, thing that can lead to an efficient DSM. The presented models are validated by code generation. Smart grids are reach area of research and propositions; even if their context is different over the world, but a need for standardization of smart appliances and their use is already felt.

V. CONCLUSION

In this paper we presented a demand side management architecture models and algorithms based on customers behavior and smart appliances integration. We gave a smart appliance interface modeling with required data and signal inputs and outputs. Then UML class and use case diagrams presented and explained in section III, allowed us to generate a java bone code for the proposed architecture. After that, we modeled the customer behavior by a decision making chain and finally discussed the scalability of the proposition. The code generation is the main obtained results, as it will allow simulating the architecture in a future work and show its effective gain from the presented system.

REFERENCES

[1] M. Jun Hong B.Eng., "The Development, Implementation, and Application of Demand Side Management and control (DSM+c)

Algorithm for Integrating Microgeneration System within Built

Environment," University of Strathclyde, 2009.

[2] F. P. Sioshansi, "Demand-side management: The third wave," Energy

Policy, vol. 23, no. 2, pp. 111-114, 1995.

[3] F. L. Bellifemine, C. Borean and R. De Bonis, Smart grid: Energy & ict., vol. 3, Notiziario tecnico Telecom, Italy, 2009, pp. 15-32.

[4] V. Pothamsetty and S. Malik, "Smart grid: Leveraging intelligent

communications to transform the power infrastructure," CISCO Systems, 2009.

[5] J. Mcdonald, "The Smart Grid," Energy Summit, Paris, 2010.

[6] P. B. R. Flynn, "Key Smart Grid Applications," Protection & Control Journal, 2009.

[7] J. Kleissi and Y. Agarwal, "Cyber-physical energy systems: Focus on

smart buildings," in Preceedings of Annual ACM/IEEE Design Automation Conference, 2010.

[8] K. P. Wacks, "Utility load management using home automation,"

IEEE Transactions on Consumer electronics, vol. 37, pp. 168-174,

May 1991.

[9] W. Elmenreich and D. Egarter, "Design Guidelines for Smart Appliances," in 10th International Workshop on Intelligent Solutions

in Embedded Systems, 2012, Austria, 2012.

[10] J. Nichols, B. A. Myers, M. Higgins, J. Hughes, T. K. Harris, R. Rosenfeld and M. Pignol, "Generating remote control interfaces for

complex appliances," in the 15th annual ACM symposium on User

interface software and technology, Paris, 2002.

[11] S. M. Hakimi and S. M. Moghaddas-Tafreshi, "Optimization of smart

microgrid considering domestic flexible loads," J. Renewable

Sustainable Energy, vol. 4, no. 042702, 2012.

[12] L. G. Swan and V. I. Ugursal, "Modeling of end-use energy

consumption in the residential sector: A review of modeling techniques," Renewable and Sustainable Energy Reviews, vol. 13, no.

8, pp. 1819-1835, 2009.

[13] D. L. Ha, S. Ploix and E. Zamai, "A HOME AUTOMATION SYSTEM TO IMPROVE HOUSEHOLD ENERGY CONTROL," in

INCOM2006 12th IFAC Symposium on Information Control Problems

in Manufacturing, 2006., Saint-Etienne, 2006.

[14] F. Wernstedt and C. Johansson, "INTELLIGENT DISTRIBUTED

LOAD CONTROL," in The 11th International Symposium on District

Heating and Cooling, August 31 to September 2, 2008, Reykjavik, ICELAND.

[15] Z. Fan, "Distributed demand response and user adaptation in smart

grid," arXiv:1007.5425 [cs.NI], 30 Jul 2010.

[16] S. Karnouskos, O. Terzidis and P. Karnouskos, "An advanced metering infrastructure for future energy networks,," in IFIP/IEEE 1st International Conference on New Technologies, Mobility and Security (NTMS 2007), Paris, 2007.

[17] B. Neenan and R. C. Hemphill, "Societal Benefits of Smart Metering

Investments," The Electricity Journal, vol. 21, no. 8, pp. 32-45, October 2008.

[18] P. D. Clemperer and M. A. Meyer, "Supply function equilibria in

oligopoly under uncertainty," Econometrica, vol. 57, no. 6, pp. 1243-1277, November 1989.

[19] L. Chen, N. Li, S. H. Low and J. C. Doyle, "Two Market Models for

Demand Response in Power Networks," in 1st IEEE International Conference on Smart Grid Communications, Geithersburg, 2010.

[20] S. Bruno, S. Lamonaca, M. L. Scala, G. Rotondo and U. Stecchi,

"Load control through smartmetering on distribution networks," in PowerTech, 2009 IEEE Bucharest, July 2009 , Bucharest, 2009.

[21] K. De Craemer and G. Deconinck, "Analysis of State-of-the-art Smart

Metering Communication Standards," in Young Researchers Symposium (YRS) edition:5 location:Leuven date:29-30 March 2010.

[22] C.-H. Lien, Y.-W. Bai, H.-C. Chen and C.-H. Hung, "Home Appliance

Energy Monit oring and Controlling Based on Power Line Communication," in IMTC 2008 IEEE International Instrumentation and Measurement Technology Conference, Victoria,

Vancouver Island, Canada, May 1215, 2008.

[23] N. Kushiro, S. Suzuki, M. Nakata, H. Takahara and M. Inoue,

"Integrated Residential Gateway Controller for Home Energy

Management System," IEEE Transaction on Consumer Electronics, vol. 49, no. 3, pp. 629-636, August 2003.

[24] G. T. Costanzo, "demand side management in smart grid," Montral

University, Montral, 2011.

DSM1 DSM2 DSM3

Global Load Balancer

Micro Grid Manager

DSM1 DSM2 DSM3

Global Load Balancer

Micro Grid Manager

DSM1 DSM2 DSM3

Global Load Balancer

Micro Grid Manager Layer 3

Layer 2

Layer 1