Faculty of Bioscience Engineering Academic year …...water sanitation, in urban areas this can even...

Faculty of Bioscience Engineering

Academic year 2015 – 2016

The impact of fluctuating energy prices on WWTP cost optimisation

Vincent Van De Maele

Promoter: Prof. dr. ir. Ingmar Nopens Tutors: Ir. Chaïm De Mulder & Ir. Giacomo Bellandi

Masterproef voorgedragen tot het behalen van de graad van Master na Master in de Milieusanering en het Milieubeheer

Table of ContentsTable of ContentsTable of ContentsTable of Contents Acknowledgments

Abstract (Eng.)

Abstract (Nl.)

List with abbreviations

1. Introduction ..................................................................................................................................... 1

2. Literature Study ............................................................................................................................... 2

2.1. Wastewater Treatment Plants ................................................................................................ 2

2.1.1. Introduction ..................................................................................................................... 2

2.1.2. Important parameters ..................................................................................................... 2

2.1.3. Activated Sludge Process ................................................................................................. 3

2.1.4. Energy usage and costs in wastewater treatment plants ............................................... 4

2.2. Modelling Wastewater Treatment Plants ............................................................................... 5

2.3. Energy costs ............................................................................................................................. 7

2.3.1. Wastewater Treatment Plants ........................................................................................ 7

2.3.2. Fluctuating energy prices ................................................................................................ 9

2.4. Goal and relevance of this study ........................................................................................... 10

3. Software and models ..................................................................................................................... 11

3.1. WEST ...................................................................................................................................... 11

3.2. Excel ....................................................................................................................................... 11

3.3. Benchmark Simulation Model ............................................................................................... 11

3.4. Eindhoven Model .................................................................................................................. 12

4. Data ............................................................................................................................................... 14

4.1. Used datasets ........................................................................................................................ 14

4.1.1. BELPEX-data ................................................................................................................... 14

4.1.2. Denmark-data ................................................................................................................ 15

5. Implementation ............................................................................................................................. 16

5.1. Implementation of non-linear cost-functions: step-function ............................................... 16

5.2. Extension of the BSM for the scenario analysis .................................................................... 19

5.3. Implementation of a ‘three limit controller’ ......................................................................... 20

6. Scenario analysis ........................................................................................................................... 23

6.1.1. Scenario 1: fixed energy price ....................................................................................... 24

6.1.2. Scenario 2 and scenario 3: fluctuating energy price via input file ................................ 24

6.1.3. Scenario 4: usage of a buffer tank ................................................................................. 26

6.1.4. Scenario 5: varying the ammonia set-point .................................................................. 29

6.1.5. Scenario 6: combination of buffer tank and ammonia set-point .................................. 29

7. Results ........................................................................................................................................... 30

7.1. BSM........................................................................................................................................ 30

7.1.1. Scenarios without model adaptations........................................................................... 30

7.1.2. Scenarios with model adaptations ................................................................................ 32

7.2. Eindhoven .............................................................................................................................. 34

7.2.1. Scenarios without model adaptations........................................................................... 34

7.2.2. Scenarios with model adaptations ................................................................................ 36

7.3. Global summary .................................................................................................................... 38

8. Discussion ...................................................................................................................................... 40

8.1. BSM........................................................................................................................................ 40

8.2. Eindhoven model ................................................................................................................... 41

8.3. Economic evaluation ............................................................................................................. 42

8.4. Possible future research ........................................................................................................ 43

9. Conclusion ..................................................................................................................................... 44

References ............................................................................................................................................. 46

Appendix A ............................................................................................................................................ 50

Appendix B ............................................................................................................................................ 51

AcknowledgementsAcknowledgementsAcknowledgementsAcknowledgements Of course, this thesis would not be possible if it was not for the help of a lot of persons. In the first

place I would like to thank my promoter for giving me the opportunity to work on this project. I liked

it a lot and cannot imagine having chosen another subject. Also, the meetings with you brought also a

lot of fresh insights, which was needed to get the work were it stands now. Finally, I would also like to

thank you for critically evaluating the final text, as I know time is very limited for you, I very much

appreciated it.

Of course, I cannot forget my tutors Chaïm and Giacomo who helped me with literally every problem

that occurred. As I was completely new to modelling and had only limited experience in programming,

I can imagine that the beginning of my research started rather slow for them. Nevertheless, they

always stayed relaxed and positive. Also, they checked every bit of my results and without complaining

till the very end. The insights they provided me with are the main base of this thesis. Heel hard

bedankt! Grazie mille!

As this is, hopefully, the final step of my studies after all these years, I would also like to thank everyone

who had a part (big or small) in my studies the last 6, or even 12, years. I’m very happy with the study

choices I made, but that’s of course only the merit of the people who tutored me. They all had an

influence on the person that I am today, something I am very thankful for. Also my classmates during

all these classes had of course their contribution on my studies, either with some notes they gave me

when needed or either with joking around during boring lectures. Both very important.

Last but not least, I would like to thank my family and friends which kept me relaxed during the whole

process. Everyone had of course its own influence on this whole process. My parents who always stood

behind me and helped me making the right choices. My sisters in letting me annoy them when needed

and of course supporting whenever I needed it. My friends in relativizing the importance of studies, as

good friends and healthy babies might be our most important priorities right now. The last word is of

course for Silke, without whom this never would be possible. I hope we still have a lot of great years

in front of us.

AbstractAbstractAbstractAbstract (Eng.)(Eng.)(Eng.)(Eng.) As wastewater treatment plants use a big part of the electrical energy in urban areas, lowering the

costs associated with that energy would help cities and WWTP managers a lot in achieving climate-or

cost related goals. Although lots of models make use of a fixed energy price, reality showed that a

fluctuating energy price is more realistic. In the scope of this thesis, the influence of the varying energy

price was investigated in comparison with the fixed price. It was also investigated if adopting these

varying prices in a payment scheme could, together with some model adaptations, help reducing the

operational costs. The results showed that the varying price indeed had its influence on the costs,

especially peak prices had a big influence on the costs. Using a dataset that fits the situation of the

used WWTP will provide more realistic costs, and make anticipating easier when trying to implement

control strategies. The used control strategies to anticipate on the fluctuating prices in this thesis were

the usage of a buffer tank, to send influent to when prices peak, and the usage of an ammonia control

where the set-point was dependent on the energy price. This was done by defining a three limit

controller: a controller that generated a different output dependent on how the energy prices relate

to the mean of the dataset. Most savings made with these adaptations were limited (up to 2 %),

however high savings were noticed (up to 30 %) when prices peaked. The fact that the savings

decreased to a constant savings value, was due to the chosen control parameters and still could be

optimized. As this research was done in two different models, BSM and a model based on the

Eindhoven WWTP, there was found that these control strategies could not be implemented in each

model without changing the control parameters. This optimization should take several things into

account, including the plant size, the aerobic volume of the ASUs and the ratio between the different

costs.

Key words: modelling, WWTP, Benchmark Simulation Model, EHV, fluctuating energy price, cost

function

Abstract (Abstract (Abstract (Abstract (NLNLNLNL.).).).) Afvalwaterzuiveringsinstallaties (AWZI) verbruiken een groot deel van de elektrische energie in steden,

daarom kan het verlagen van de kosten, veroorzaakt door dit energieverbruik, helpen om klimaat- of

kost-gerelateerde doelstellingen te halen van steden en AWZI’s. Vele AWZI-modellen maken gebruik

van een vaste energieprijs om kosten te berekenen. Onderzoek uit de praktijk leerde echter dat het

gebruik van een variabele energieprijs realistischer was. In het kader van deze thesis, werd dan ook de

invloed van deze variabele energieprijs bepaald, in vergelijking met de vaste prijs. Ook werd

onderzocht of deze variabele prijs, samen met aanpassingen aan de modellen, kon leiden tot

kostenbesparingen. De resultaten toonden aan dat de variabele prijs weldegelijk een invloed had op

de kosten, zeker wanneer de variabele prijzen op sommige tijdstippen pieken bevatten. Wanneer dus

een dataset gebruikt wordt die realistische prijzen bevat voor de beschouwde AWZI, zullen de kosten

realistischer worden. Bovendien wordt het ook eenvoudiger om de juiste controle mechanismen toe

te passen. De controle mechanismen die in deze thesis beschouwd werden, waren het gebruik van een

buffertank, waar een deel van het influent naartoe werd gezonden op momenten wanneer de prijzen

hoog waren, en het gebruik van een ammonium controle. Bij deze ammonium control werd de

richtwaarde afhankelijk gemaakt van de prijs. Dit werd in het model geïmplementeerd door gebruik te

maken van een ‘three limit controller’, dit was een controller die een verschillende output gaf,

afhankelijk van waar de energieprijs zich bevond tegenover het gemiddelde. De meeste

kostenbesparingen waren, met deze aanpassingen, echter beperkt (maximaal 2 %). Toch werden hoge

besparingen vastgesteld op piekprijsmomenten (tot 30 %). De verklaring dat deze besparingen na deze

piekmomenten echter daalden tot een constante waarde, kon verklaard worden door de gekozen

controle parameters. Deze hebben dus nog ruimte voor optimalisatie. Dit onderzoek werd uitgevoerd

met twee verschillende modellen: BSM en een model gebaseerd op de AWZI van Eindhoven. Bij de

vergelijking tussen deze modellen werd vastgesteld dat de gebruikte controle strategieën niet zomaar

tussen de twee modellen vervangen kon worden, zonder de controle parameter aan te passen. Deze

aanpassing moet rekening houden met de grootte van de AWZI, het beluchtingsvolume van de

gebruikte tanks en de verhouding tussen de verschillende kosten.

Kernwoorden: modellen, AWZI, Benchmark Simulation Model, EHV, variabele energieprijzen,

kostenbepaling

List with abbreviationsList with abbreviationsList with abbreviationsList with abbreviations

A/O Anoxic-Oxic

APX Amsterdam Power Exchange

ARIMA Autoregressive Integrated Moving Average

AS Activated Sludge

ASM2d Activated Sludge Model No. 2d

ASU Activated Sludge Unit

BEP Best Efficiency Point

BOD Biochemical Oxygen Demand

BSM Benchmark Simulation Model

CAPEX Capital Expenditures

CHP Combined Heat and Power

COD Chemical Oxygen Demand

COST European Co-Operation in the Field of Scientific and Technical Research

CSTR Continuous Stirred-Tank Reactor

DO Dissolved Oxygen

DTU Technical University of Denmark

EHV Eindhoven Model

EQI Environmental Quality Index

F/M Substrate to Biomass

IEA International Energy Agency

IWA International Water Association

MSL Model Specification Language

OCI Operational Cost Index

OLAND Oxygen-Limited Autotrophic Nitrification-Denitrification

OPEC Organization of the Petroleum Exporting Countries

OPEX Operating Expenditures

PE Population-equivalent

ROI Return-on-Investment

SIMBA Simulation System for Sewer, Wastewater Treatment Plants, Sludge Treatment and

River Water Quality

TkN Total Kjeldahl Nitrogen

TOU Time-of-Use Rate

TP Total Phosphorus

TSS Total Suspended Solids

UCT University of Cape Town

VFD Variable Frequency Drive

WEST Wastewater Treatment Plant Engine for Simulation and Training

WWTP Wastewater Treatment Plant

1

1.1.1.1. Introduction Introduction Introduction Introduction Water scarcity is still a worldwide problem. According to a report made by the United Nations in 2015,

748 million people do not have a sustainable drinking water source. Moreover, global water demand

is projected to increase with 55 % by 2050. In many urbanized regions, however, the easiest available

sources of usable water will have been depleted by then. This means that one has to dig deeper into

the ground to get their water supply or they have to invest more in reusing water (UN-Water, 2015).

This method of sanitizing used water will also help to get a sustainable drinking water source for those

that have not got any yet.

One of the main problems with reusing and cleaning water, are the costs that go with it. The worldwide

capital expenditures (CAPEX) of wastewater treatment plants were estimated to be between 77 and

84 billion euros in 2016 (Caffoor, 2008). Apart from these CAPEX, also the operating expenditures

(OPEX) are a big expense of a wastewater treatment plant (WWTP). A big part of those OPEX are the

energy costs: approximately 2 to 3 % of the worldwide electrical energy is used for water supply and

water sanitation, in urban areas this can even increase to 18 %. This corresponds to approximately 45

000 €/month to 280 000 €/month per wastewater treatment plant, dependant on the population-

equivalent (PE) of the wastewater treatment plant (Aymerich, et al., 2015).

Even though a lot of attempts have made to decrease the energy usage of WWTPs, these costs remain

huge. A topic that has not had as many attention as reducing energy usage, however, is the reduction

of the costs by optimizing the price payed for energy. Most industrial companies, including wastewater

treatment plants, pay their energy based on fixed (daily, monthly or yearly) price. However, energy

prices fluctuate on short- and on long-term. Dependant on the local conditions, it can however be a

possibility of picking a fluctuating energy price. By optimizing the plant lay-out and process, it may be

possible to save costs when a fluctuating energy price is used. The scope of this thesis is to investigate

if this indeed is true. This investigation is carried out by testing different scenarios (with fixed and

fluctuating energy prices) on two important models (Benchmark Simulation Model and the Eindhoven

model, see Section 3.3 and Section 3.4).

2

2.2.2.2. Literature StudyLiterature StudyLiterature StudyLiterature Study

2.1. Wastewater Treatment Plants

2.1.1. Introduction

Wastewater is water-based waste, and can be discharged from industry, residences and agriculture. It

can contain various contaminants and pathogens (originating from the human intestine), phosphorus

and nitrogen-compounds (possibly causing eutrophication) and various toxins that can harm human

and aquatic life. A large fraction of urban wastewater can consist of organic compounds

(Tchobanoglous, Burton, & Stensel, 2003). When discharged into a river, a lot of the dissolved oxygen

in the water is used principally by microorganisms to oxidize the organic carbon. This causes a lack of

oxygen for aquatic life, and thus the destruction of this aquatic life (Grady, Daigger, & Lim, 1999).

The goal of wastewater treatment is mainly to remove organic compounds and nutrients harmful for

the environment, but also to sensibly reduce toxins and potential pathogens. This can be achieved in

many ways, but generally the treatment consists of a number of fixed steps. The first step is often a

preliminary coarse cleaning, in which large objects such as sticks are sieved out. After this, follows a

primary treatment where coarse suspended solids (e.g. sand) and a fraction of the organic matter are

removed (e.g. by gravity in a sedimentation tank). The secondary treatment is normally the core part

of the treatment process where the major fraction of the contaminants is removed. This largely

happens biologically, with the use of an active biomass such as in the activated sludge (AS) process, as

will be explained in section 2.1.3. Finally, a solids separation step takes place (e.g. gravity settling) and

an advanced treatment (e.g. chlorine disinfection) ensures a safe water discharge. Beside the water

treatment line, the excess sludge due to the biomass growth also needs to be treated (Tchobanoglous,

Burton, & Stensel, 2003). An example of a schematic overview of a wastewater treatment plant, with

an activated sludge process, is given in Figure 1.

Figure 1: Schematic overview of a possible WWTP, taken from (Spellman, 2009).

2.1.2. Important parameters

When controlling a WWTP, a lot of parameters can be used to define the quality of the wastewater.

Some of the most important parameters are the chemical oxygen demand (COD) and the biochemical

oxygen demand (BOD). The COD is the oxygen needed to theoretically oxidize all the organic carbon in

the wastewater to CO2, H2O and ammonia, whereas the BOD is the biodegradable part of this COD.

3

The BOD gets mostly expressed as BOD5, which is the amount of oxygen needed to biochemically

degrade wastewater-carbon within 5 days (Bitton, 2005).

Another parameter is the total suspended solids (TSS), which not only includes the organic (or other)

waste in the wastewater, but also the newly formed biomass (’sludge’, see Section 2.1.3). The organic

part of those suspended solids are the volatile suspended solids. Also the amounts of phosphorus (P)

and nitrogen (N) in the wastewater are, naturally, good indicators of the wastewater quality.

Phosphorus is mostly found in wastewater as orthophosphate, polyphosphate or organic phosphate.

The forms in which nitrogen appears are much more complex, due to the multiple oxidation states

(going from –III till +V) nitrogen can have. The total nitrogen fraction is comprised of nitrite, nitrate,

ammonia, and organic nitrogen (e.g. amino acids). Part of this fraction is called the Kjeldahl nitrogen,

named after the method to determine this fraction. Total Kjeldahl nitrogen (TKN) consists of the total

organic fraction and ammonia (Tchobanoglous, Burton, & Stensel, 2003).

To express all these parameters into a single variable, the environmental quality index was developed

(EQI). The EQI is hence the weighted sum of the TSS, COD, BOD5, TKN, nitrates and the total phosphorus

(TP). A lot of methods are developed to determine the correct used weights per parameter, one of the

most common ones, from (Vanrolleghem, et al., 1996), is based on the Flanders’ effluent quality

formula for calculating fees, according to (Benedetti, Bixio, & Vanrolleghem, 2006).

2.1.3. Activated Sludge Process

The activated sludge (AS) process, first applied by Lockett and Ardern in 1914 (Tchobanoglous, Burton,

& Stensel, 2003), is one of the most common processes to treat wastewater. It is a biologically,

secondary treatment method in which microorganisms, forming a flocculating sludge, degrade the

organic dissolved fraction of the wastewater. In general, the intercurrence of aerobic and anoxic (and

sometimes even anaerobic) conditions can favour specific bacterial communities and target the

removal of inorganic nitrogen rather than organic substances. In the aeration tank, the heterotrophic

bacteria are favoured providing dissolved oxygen (DO) for their growth and for the degradation of

organic matter. Normally, DO is supplied by means of submerged or surface aerators blowing air into

the mixed liquor (Bitton, 2005).

Organic compounds get mineralized into simple compounds (CO2, H2O and NH4) and metabolized into

new cell biomass. To make sure that the cells have a longer residence time than the wastewater in the

tanks, ensuring an effective degradation of the waste, a large part of the AS gets recycled from the

secondary sedimentation into the AS tank. Not all the sludge is recycled to ensure sufficient substrate

to biomass ratio (F/M ratio). However, the F/M ratio cannot be too high for the degradation to happen

in a reasonable hydraulic residence time. When the microorganisms are starved, the degradation of

organic compounds will be more efficient. An important fraction of solids that escaped the primary

sedimentation, parasite organisms and pathogens are, at this point of the treatment, attached to the

sludge flocs. The sedimentation is therefore an important part of the secondary treatment (Bitton,

2005).

Nitrogen (mainly NH4) removal consists of both aerobic and anoxic phases. In aerobic conditions the

available N gets oxidized into nitrite (NO2) and finally to nitrate (NO3) by the heterotrophic biomass.

The anoxic step ensures the optimal conditions for autotrophic bacteria to accomplish denitrification,

thus reducing NO3- to N2 gas. A lot of variations on this process exist, the most well-known variations

are the Sharon-Anammox process (Van Dongen, et al., 2001) and the OLAND process (Kuai &

Verstraete, 1998). Phosphorus (P) also gets removed with a combination of aerobic, anoxic and

anaerobic phases favouring specific microbial species that tend to accumulate P inside their cells. The

4

most common P removal processes are the A/O process (Deakyne, Patel, & Krichten, 1984) and the

UCT process (Meganck & Faup, 1988).

As the UCT process is an important part the Eindhoven WWTP (see Section 3.4), it gets looked upon a

bit more in detail. In the UCT process (short for University of Cape Town, where this process was

developed), the wastewater goes from an anaerobic tank, into an anoxic tank, where after the

wastewater gets into an aerobic tank. To guarantee the strictly anaerobic conditions in the first tank,

the wastewater only gets recycled to the second, anoxic tank. In this way, nitrate does not get into the

anaerobic zone, which increases the phosphorus removal capacity. This process is definitely more

efficient for weaker wastewaters and produces good settling sludge, but the operation gets more

complex as there is an additional recycle system needed (Bitton, 2005; Water Environment Federation,

2009).

2.1.4. Energy usage and costs in wastewater treatment plants

A typical overview of the

energy usage in a WWTP is

presented in Figure 2.

Important to notice is, that

this figure is only an

indication. The energy

usages in a WWTP are

dependent on a lot of

variables, such as the loading

rate, the required treatment

efficiency and of course the

energy price. The energy

price can be constant or

fluctuating, so this has a

direct effect on the costs. This is why this thesis aims to investigate how large the effects are of those

energy prices. Also, the treatment processes have a great influence on the costs. Activated sludge (AS)

processes are generally known for their bigger energy usage than for example rotating biological

contactor processes. However, the choice between these different processes is not only dependent on

the energy usage, but also on the environmental conditions and the treatment requirements. This is

mainly caused by the higher aeration needed for the AS processes. Costs can mainly be split into

primary costs (directly associated with the actual process e.g. the costs for aeration) and secondary

costs (associated with transport and chemical dosing) (Water Environment Federation, 2009).

The biggest part of the energy usage in WWTPs is caused by aeration, needed for the AS process.

Oxygen is needed for the growth of aerobic microorganisms and has to be provided and distributed in

the tanks by aerators. Aerators consist of different types, each with advantages and disadvantages. As

seen in Section 2.1.3, these aerators consist mostly out of two important classes: surface aerators and

submerged aerators. Other important features when choosing an aeration strategy are the oxygen

transfer rate (how well the oxygen is dissolved in the mixed liquor) and the oxygen demand of the

wastewater (dependent on the COD and BOD). When these factors are not well determined on

beforehand, the major threats are that the treatment processes might not reach the required removal

efficiency or there might be an over-aeration, leading to excess energy costs (Water Environment

Federation, 2009).

Figure 2: Typical overview of the energy usage in a WWTP, taken from (Escapa & San-

Martín, 2014) (Water Environment Federation, 2009).

5

Next to aeration, also the pumping energy is an important part of the energy usage. Pumps are

continuously used to transport the wastewater and the sludge to different locations in the plant. Saving

energy with pumps might appear harder than saving energy with aeration, because the pumps have

design constraints regarding their flow and head requirements. Also the margin of energy consumption

is normally much higher for aeration. The usage of intermittent pumping can be used as an alternative

way of reducing pumping costs, however this is not always possible, especially when recirculating the

sludge which might settle and cause pipe clogging (Water Environment Federation, 2009). When using

pumps, it is important to work as close as possible around the Best Efficiency Point (BEP). This is the

flow for which the pump has the highest efficiency, keeping in mind the dynamic and static head.

However, pumps are not always working at this BEP, because the flow is usually not constant. The

usage of valves or variable frequency drive (VFD) pumps can however limit the effects of these

fluctuations around the BEP. Observations learn that this is not very common in practice and that a lot

of pumps are being over-designed, leading to a lot of energy losses. The usage of newly developed

models that predict pumping energy consumption could lead to a reduction of energy consumption

due to pumps (Amerlinck, 2015).

Other important energy usages in the wastewater treatment process are the anaerobic digestion, used

to break down organic compounds. This anaerobic digestion uses energy in the form of heating and

mixing energy. In this process, the microorganisms break down those compounds in absence of oxygen

and form a biofuel, a mixture of methane and CO2. This methane can be used to yield energy and so

reduce the total energy consumption of the plant. As this anaerobic digestion also yields less sludge

and does not need oxygen, the energy balance is still positive in most cases (Water Environment

Federation, 2009; Tchobanoglous, Burton, & Stensel, 2003). In Figure 2, however, only the energy

consumption is shown and this does not take this energy recovery into account.

Important to mention are also the preliminary treatment and the primary cleaning and the sludge

treatment and disposal. These parts of the process do not use a lot of the total energy, but cannot be

neglected. A proper operation of the whole process, and thus a low energy usage, is merely guaranteed

if the preliminary treatment and primary cleaning work correctly. They get heavy and/or big solids

removed out of the wastewater (with grits, sedimentation tanks, etc.) before it reaches the secondary

treatment, ensuring thus that those solids do not affect the energy usage by e.g. damaging pumps

(Water Environment Federation, 2009).

Apart from saving energy in these different parts of the WWTP, energy can also be saved (or

recuperated) in many other ways. Examples are heat recovery, combined heat and power (CHP)

systems and biogas (methane) production (due to the anaerobic breakdown of organic matter) (Gude,

2015). Another, more experimental, possibility is the usage of algae in the activated sludge as biofuel

(Pittman, Dean, & Olumayowa, 2011).

This work will mainly focus on the two primary energy consumers in a WWTP: aeration and pumping

energy. The other parts of the energy usage will be ignored, as the energy price does not have as big

as an impact compared to aeration and pumping energy.

2.2. Modelling Wastewater Treatment Plants

A model can be defined as “a physical, mathematical, or logical representation of a system entity,

phenomenon, or process” (Department of Defense - Systems Management College, 2001). To

investigate how a model will behave under different circumstances, a simulation, the imitation of that

entity, phenomenon or process over time, can be used (Banks, et. al, 2001). Models and their

simulations can help to understand and analyse real-world systems and concepts (Department of

Defense - Systems Management College, 2001).

6

Models can be used in a lot of domains, e.g. mechanical and electrical. Hence, models can also be used

to predict WWTPs performances. However, the main difficulty of biological processes modelling is the

lack of well-described system dynamics such as biological reactions or loading variability. For example,

the non-linear dynamics of the biological processes in wastewater treatment are not yet very well

understood and cannot be described in a single system (Vanhooren, et al., 2002). Also, calibration of

models is a time-consuming task, although generally accepted approaches facilitate proper

implementations (Petersen, et al., 2002). Another problem is the unbalanced attention in WWTP sub-

models: there is a high focus given to biokinetic processes, while other processes, which are equally or

even more important (e.g. aeration, influent characterization, sedimentation, and hydrodynamics)

remain underexposed (Amerlinck, 2015). Modelling WWTP not only can simplify a complex system,

and thus make understanding easier, it also reduces the time to investigate different designing

solutions. Based on Amerlinck (2015) and Vanhooren, et al. (2002), some additional reasons can be

given why modelling WWTPs is relevant in the scope of this thesis:

• Models can be a tool to test and evaluate new cost functions and choose the right

optimizations (e.g. use of a buffer tank) before they get operated at full-scale.

• Models can be used to make the right evaluations and evaluate the implementations on

multiple criteria (e.g. the EQI, aeration and pumping cost can all be used to evaluate different

cost functions).

• Models can help to predict the effects from different cost functions (e.g. the difference

between peak prices and none peak prices).

To use WWTP models and run simulations, the use of specific software is indispensable. Moreover,

this software can help to link WWTP models to models of the influent stream and models of the river

where the water is discharged providing a more refined representation of the whole process and its

environmental effects (Meirlaen, et al., 2001). Examples of such software are WEST (Mike by DHI) and

SIMBA (ifak e.V. Magdeburg) among others. Because WEST is used in the further development of this

thesis, it is described in a bit more detail in Section 3.1.

A big question when applying simulations of models is however how to evaluate or compare these

simulations. This is because all wastewater treatment processes are different in one or more ways.

Regional differences, differences in influent quality or cost level differences are some examples. Thus,

there is a need of a reference situation with standard evaluation criteria (Jeppsson, et al., 2006). Since

1993, such benchmarks were developed, e.g. the Kodak Tenessee Eastman Process (Downs & Vogel,

1993) and the COST/IWA benchmark (Jeppsson & Pons, 2004; Copps, 2002). This COST/IWA

benchmark, developed by the IWA Task Group on Respirometry and the framework of COST Actions

682 and 624, was a simple platform-independent simulation environment. In this environment a

simple plant layout with different influent loads and evaluation criteria was defined. The need for such

a benchmark, was illustrated by the fact that it was used in more than 100 publications worldwide

(Jeppsson, et al., 2006). This is why this COST/IWA was further developed and extended, into the

Benchmark Simulation Model No. 1 (BSM1).

Like the COST/IWA benchmark, BSM1 is a simulation environment where a plant layout is defined, just

like the influent data (proposed by (Vanhooren & Nguyen, 1996)), test procedures and evaluation

criteria. Important to mention however is that this model can be used on every simulation platform,

which is important for the universality of the benchmark (Alex, et al., 2008).

However, BSM1 is just a simplification of the processes in wastewater treatment plant and does not

include all processes (it has a great focus on the activated sludge process). This is why an expansion of

7

BSM1 was developed: Benchmark Simulation Model No. 2 (BSM2). This BSM2 stays however out of

the scope of this thesis.

2.3. Energy costs

In the previous sections, the different parts of energy usages and costs of a WWTP were discussed, just

as the modelling of a WWTP. These 2 parts are brought together, as this section investigates how

energy costs of a WWTP are considered in modelling environments and how these costs are charged

in reality. The main focus of this section will be on the issue of the fluctuating energy prices and their

relevance for the primary costs of a WWTP. Secondary costs will also not be considered in the scope

of this thesis.

2.3.1. Wastewater Treatment Plants

2.3.1.1. Modelling cost factors

The operational cost factors in BSM1 are well described in the model’s protocol and can be split up in

different parts (Alex, et al., 2008; Jeppsson, et al., 2007):

• The disposal of the overproduced sludge (SP) (kg.d-1): the sum of the total sludge accumulating

in the system and the sludge being discharged with the wastage

• The total disposal of the overproduced sludge (SPtotal) (kg.d-1): in contrast to the previous

factor, it also takes the sludge lost at the weir into account

• The aeration energy (AE) (kWh.d-1): calculated from the oxygen transfer coefficient (kLa),

which is dependent on the type of diffuser, the bubble size and the layout of the aeration

tanks.

• The pumping energy (PE) (kWh.d-1): the total energy consumed by the internal and external

flow recycle pumps, dependent on the plant layout and the distance and placement between

the different tanks

• The addition of an external carbon source (EC) (kg COD.d-1): sometimes an external carbon

source is added to the wastewater when the COD-load of the influent is too low to complete

denitrification (cfr. C/N). The cost of this carbon source is of course dependent on which source

is used (glucose, methanol, ethanol, acetic acid… (United States Environmental Protection

Agency, 2013)).

• Mixing energy (ME) (kWh.d-1): the energy used to mix the sludge in the tanks to avoid settling,

dependent on the volume of the tanks

An example on how the total operational cost index (OCI) can be calculated is given with Formula 1

(WEST, 2014). However these weights are location dependent and can vary between locations.

�� = �� + �� + 5. �� + 3. �� +

Formula 1: Calculation of the total operational cost of the WWTP in WEST.

As seen above, these costs are only expressed in energy or mass usages per day (kWh.d-1 and kg.d-1).

This means that actual costs (in euro.d-1) cannot be calculated with the protocols from the BSM. In

WEST however, it is possible to calculate those costs, either with an input file (which makes it possible

to use variable energy price) or with the use of the default (fixed) average energy price for all the

different compartments, presented in Table 1.

8

Table 1: Energy costs for the different processes in a WWTP, according to default values in the WEST model library (WEST,

2014).

Different energy costs Value

Aeration cost factor 0.07 euro.kW-1

Pumping cost factor 0.07 euro.kW-1

Mixing cost factor 0.07 euro.kW-1

Sludge cost factor 0.58 euro.kg-1

External carbon source cost factor 0.50 euro.m-3

As written above, the main focus of this research will be on the primary costs. This means that the

sludge disposal costs and the external carbon source costs will not be included, as they are not

dependent on the energy price.

However, recent research from Aymerich et al., (2015) showed that these fixed average costs could

give a wrong, non-realistic idea. Energy prices in real life are being calculated according to different

cost structures (see Section 2.3.1.2), from which the fixed costs are only one possibility. The difference

between a more realistic cost model (based on these structures in Spain) and the model making use of

a fixed mean cost is between 7 and 30 % in monthly costs, with implementing a basic aeration control.

Choosing a cost evaluation that is not accurate also may lead to the wrong control strategies, e.g. for

aeration (the biggest energy consumer in a WWTP). It is however difficult to generate a specific cost

model that is ideal for all WWTPs, because the costs can be very site specific (Aymerich, et al., 2015).

2.3.1.2. Cases in real life

In reality, water utilities have different payment modalities for their energy costs. Frequent examples

can be a fixed price contract or the use of fluctuating prices. Although it may be difficult to give a

general rule of thumb, three main energy price structures have been identified (Aymerich et al., 2015):

• Fixed cost rate (flat rate structure):

The charged price is the same for each unit of energy used, independently from the quantity

of energy used or the peak power demanded and the time of day. Although this is the simplest

structure, this may not be the most cost-effective structure and is rarely applied in large

WWTPs.

• Time-of-use rate (TOU):

The price of the energy is dependent on the time of the day. Normally, the day gets split up in

two or three periods, each with their own fixed price: normally the lowest prices are charged

during night (when the demand is low) and the highest prices during day (when the demand is

high). These prices can however vary monthly or seasonally. Normally the highest activity in

WWTPs is during the day, when the prices are highest, however this structure is still widely

applied among WWTPs.

• Step rate (tiered rate):

In this structure, the price of the energy is dependent on the amount of energy used. Different

energy prices are agreed for different energy usage intervals. The more energy used, the

higher the charged price. WWTPs are frequently using this structure.

In order to have a main overview of the different cost structures of several WWTPs around the world,

a small survey was performed. The results of this survey are summarized in Table 2.

9

Table 2: Overview of different cost structures in different countries and WWTPs.

Country Energy cost structure Source

Belgium Contracts based on fixed monthly prices (ENECO, pers.

Communic., 2016)

The

Netherlands

Eindhoven: TOU for two different periods per day (07:00-

21:00: peak price, 21:00-07:00: off-peak price). Prices for these

periods are fixed during 2 years.

Breda: Step rate (price depends on used volume), specifically

in which the price gets determined by buying 90% of the

predicted volumes of energy once every month (on the Endex-

market) and 10 % of those volumes on the strong fluctuating

spot-market when needed.

(WWTP Eindhoven,

Waterboard De

Dommel, pers.

Communic. , 2016)

(WWTP Nieuwveer,

Waterboard

Brabantse Delta,

pers. Communic.,

2016)

Sweden Käppala: 50 % of the price is fixed (depending on the maximum

power consumption), 50 % of the price is TOU.

Gyraab AB: Energy volumes get bought at fixed price per year,

some parts of the energy volume even get a fixed price for the

next 3 years.

Linköping: Step rate: mostly a fixed price, based on the

energy voltage.

VA SYD: Fixed electricity price, in which there is a contract with

the electricity company that runs for four years. The heating

energy is mostly provided by the firm itself, additional heating

energy can be bought with a price that is dependent on

different parameters (like the season in which the energy is

bought).

(WWTP Rya,

Gyraab AB, pers.

Communic., 2016)

(WWTP Käppala,

Käppala

Association, pers.

Communic. 2016)

(WWTP Linköping,

Linköping Nät AB,

pers. Communic.,

2016)

(WWTP Malmö,

Waterboard VA

SYD, pers.

Communic., 2016)

2.3.2. Fluctuating energy prices

Energy prices are not stable in time and they can fluctuate both on long and short-term (daily or even

hourly). This is called the volatility of the energy prices. The causes of this volatility are numerous, but

they all come down to the general rule of supply and demand. When this ratio between supply and

demand is high, the prices will be low, when this ratio is low, the prices will be high.

On the long-term, these fluctuations have mainly an origin in events on a world-wide scale. Some

examples, provided by Lieberman & Doherty (2008):

• The growing interest of China in establishing coal power plants made the demand for coal rise

worldwide, which resulted in a higher coal price.

• The oil crisis in 1973, in which the OPEC countries claimed an oil embargo, made the price of

oil rise exponentially.

• Long-term climate change (e.g. global warming) can lead to shifts in energy demand, and thus

also shifts in energy prices.

These events are rather unpredictable, which makes it difficult to make long-term predictions.

However, some models tried to describe the effects of the energy-prices on long-term. These take into

account previous prices, temperature effects, economic growth and calendar effects. The model of

Hyndman & Fan (2010) was able to predict the correct prices for the next year, within the quantifiable

uncertainty of the model.

10

Fluctuations on short-term (hourly) are more affected by demand and a random component, resulting

in high volatility of the prices and a non-constant mean and variances. However, some calendar effects

(differences between weekdays and weekends) and effects of the weather can be observed (Nogales,

et al., 2002). These weather effects are definitely of interest with renewable energy. For example,

when there is less wind, wind turbines can generate less energy, which leads, with the same demand,

to a higher energy price (Tavner, et al., 2013). Stochastic models have also been developed to predict

short-term energy price variation. Examples of such models are models based on ARIMA

(autoregressive integrated moving average) models, artificial neural networks, dynamic regression or

transfer function (Contreras, et al., 2003; Nogales, et al., 2002; Szkuta, Sanabria, & Dillon, 1999).

Another factor that has, nowadays more and more, an influence on the fluctuating prices is the storage

of energy. Dutch research from Bolado, et al. (2014) showed that the evolution towards higher

amounts of storage systems, the prices will be more flattened and look more like a fixed price: the on-

peak prices will be lower and the off-peak prices will be higher. This will reduce the random component

of the energy price.

A global prediction of the energy prices is done by the International Energy Agency (IEA). The IEA is an

autonomous organization, which has 29 members, including Belgium, China and the United States.

They make a report (the World Energy Outlook) about their predictions every year. For 2015, the main

focus was on the low energy prices and the influence of renewable energy on these prices

(International Energy Agency, 2015).

The effects of a fluctuating (hourly) price on WWTPs have already been briefly investigated by Møller

et al. (2014). This showed that the fixed prices, used in the models, were not always the cheapest

solution. Especially with dry and rain conditions, the fluctuating price resulted in a lower total cost. In

storm conditions however, the fixed price leads to the cheapest overall cost. This research showed

that there is definitely a good reason to implement the fluctuating costs in the existing BSM, as it can

lead to a lower operating cost. However, further, deeper research is still required.

2.4. Goal and relevance of this study

As wastewater treatment plants use 1-18 % of the electrical energy in urban areas (Olsson, 2012),

lowering the costs associated with that energy would help cities and WWTP managers a lot in achieving

climate-or cost related goals. Previous sections showed that costs in WWTP-models were mainly based

on fixed energy prices, while in reality this is not the main energy structure used in WWTPs. Changing

the cost functions in existing models to fluctuating energy prices, might lead to more accurate cost

saving strategies or even to more efficient plant operating strategies. In this framework, the goal is to

implement a fluctuating cost function in existing models: the Benchmark Simulation Model and a case-

study model based on the WWTP of Eindhoven, operated by Waterboard De Dommel. This fluctuating

cost-function will be based on real-life data, originating from Belgium and Denmark. By applying

different scenarios on this model, a more detailed insight in the operating costs could be achieved.

This might lead to a more efficient operating strategy and providing an insight into the optimal cost

structure that could be used by the WWTP.

11

3.3.3.3. Software and modelsSoftware and modelsSoftware and modelsSoftware and models

3.1. WEST

WEST (Wastewater Treatment Plant Engine for Simulation and Training) is a tool used for modelling

and simulating WWTPs. Models in WEST can be built from scratch or used from the extensive model

library. These existing models can be completely customised, as one of the big advantages of WEST is

its open structure. The modelling and the actual simulation of the models are performed in two

different user environments (i.e. the WEST environment itself and the model editor) (Vanhooren, et

al., 2002).

The models in WEST are written in an object-oriented model specification language (MSL-USER). This

language aims to ease model development, re-use and classification, promoting customization and

structured work. An additional important feature of WEST is the multi-abstraction language, which

favours the implementation of physics and biological principles by means of different methods (e.g.

differential and algebraic abstractions, in C++ or python code) (Vangheluwe, Claeys, & Vansteenkiste).

In this code, the different cost functions will be implemented.

To make the use of the simple simulation, the different parts of the WWTP are displayed graphically,

with the use of different ‘blocks’. Another example of the flexibility of WEST is the fact that both text

and Excel files can be read in WEST as input files from e.g. the influent stream data (WEST, 2014). In

this thesis the version of 2014 of WEST is used (WEST2014).

3.2. Excel

Analysis and plotting of the data and the model results was done in Excel (Microsoft, USA). Modelled

data was exported from WEST and later imported and plotted in Excel to make graph customization

easier.

3.3. Benchmark Simulation Model

As mentioned before, in Section 2.2, a benchmark model was used to evaluate and compare different

simulations. Also in the scope of this research, Benchmark Simulation Model No. 1 is used to compare

the different cost functions and proposed strategies with each other.

The layout of BSM1 consists of 5 tanks in which the AS process takes place: the first two tanks are the

anoxic tanks (used for pre-denitrification), the last three tanks are the aerobic tanks (used for

nitrification). These five tanks are followed by a secondary settler, consisting of 10 modelled layers.

The sixth of these layers is the feeding layer. This plant layout is presented in Figure 3. The AS process

is described mathematically by 8 basic biological processes in BSM1. The mass balances, pumping

energy, cost function and other characteristics of these reactors and the sedimentation tank are

described mathematically. Also sensors and actuators can be implemented in the benchmark, to

evaluate or test specific concepts (Alex, et al., 2008). BSM1 is available for simulations in WEST, the

layout of this model is also presented, in Figure 4.

12

Figure 3: Plant layout of Benchmark Simulation Model No. 1. taken from (Meneses, Concepción, & Vilanova, 2016).

Figure 4: Plant layout of Benchmark Simulation Model No. 1. as it is presented in WEST (WEST, 2014).

3.4. Eindhoven Model

Another model that is being used to test the different cost functions, is the model of the Eindhoven

WWTP. The Eindhoven WWTP, operated by Waterboard De Dommel, treats the wastewater of ten

municipalities (750 000 PE) and is the third largest WWTP in The Netherlands. The effluent is

discharged in the Dommel River, a lowland river flowing through Eindhoven. In summer, this effluent

can contribute to approximately 50 % of the river flow (Langeveld, et al., 2013). The WWTP consists of

three parallel lines, each containing a biological tank and four secondary clarifiers, following the UCT

configuration, explained in section 2.1. The schematic plant layout is presented in Figure 5.

The biological tanks are a special system, consisting of three rings working with plug-flow. The

wastewater is coming into the reactor via the inner ring. This ring is the anaerobic part of the tank and

consists of four parts, which ensures the plug-flow. After this anaerobic treatment, the water gets

transported to the middle (anoxic) ring and the outer ring. The outer ring consists of alternating aerobic

and anoxic zones. Air to these aerobic zones is provided with plate aerators. The amount of air supplied

to the system is controlled with an ammonia-DO feedback cascade: if the amount of ammonia is below

a certain level, the air supply is reduced. At the end of the cycle, a fraction of the sludge gets

recirculated, helping the phosphorus removal (Amerlinck, 2015; Langeveld, et al., 2013).

The plant is modelled in WEST, with the help of the ASM2d biokinetic model for implementing the

biological processes (Henze, et al., 2000). There were already a lot of developments to the Eindhoven

model, and in this thesis, the most recent version of this model was used: EHV10. This version is

compatible with WEST2014. The plant layout of the WWTP, as presented in WEST is shown in Figure

6. In this model, the 3 rings of the biological tank are translated into 3 zones with multiple CSTR-tanks

in series. In this way, it was also possible to simulate the plug-flow of the tanks. In fact, there are two

versions of this model. The first version of this model makes use of an input file, based on online

13

measurement data, which helps imitating the most important control actions. This model is so used

for model calibration, where those input files avoid that those control actions are modelled with a

certain error. In the second model, those control actions are nevertheless being integrated as models

with controllers. This means that the second model is much more complex, however to make use of

this model, those complexities get a bit simplified (Amerlinck, 2015). More specific information about

this Eindhoven model can be found in (Amerlinck, 2015).

Figure 5: Schematic plant layout of WWTP De Dommel in Eindhoven, taken from (Amerlinck, 2015), in which PST is short for

primary sedimentation tank, AST is short for activated sludge tank and SST is short for secondary sedimentation tank.

Figure 6: Plant layout of WWTP De Dommel in Eindhoven as it is presented in WEST (WEST, 2014), taken from (Bellandi, 2014).

To keep the overview clear, the control mechanisms and input blocks used for temperature and pressure simulations have

been left out of this figure.

14

4.4.4.4. Data Data Data Data

4.1. Used datasets

For this research, it was important to get some (realistic) values of fluctuating energy prices, which

could be implemented in the models. Two such datasets were found and further used in this thesis.

The first dataset was provided by BELPEX, the second one by the Technical University of Denmark.

4.1.1. BELPEX-data

BELPEX is an electricity trading company, based in Belgium, selling its energy on a day-ahead hourly

basis. Its prices are coupled with other companies over the world (such as APX in the Netherlands and

Nord Pool Spot in the Nordic regions). Its prices (in EUR/MWh) are freely available and can be

downloaded from their website (www.belpex.be) by all members, just like the traded volumes. To

become a member, all you have to do is register with your own e-mail address. In the scope of this

thesis, the data from the first of January until the 21st of March 2016 is used. This data is graphically

shown in Figure 7. As can be seen on this figure, the energy prices fluctuate between 0.02 and 0.04

€/kWh. Except for the period around day 20, when the prices are higher and there is even a peak price

of almost 0.13 €/kWh. Also, a weekly pattern can be noticed: the prices in the 5 weekdays seem higher,

on a visual basis than the 2 weekend days right after. This pattern can be noticed during the whole

period.

In the scenario analysis, this dataset is broken up into two datasets of 21 days. The first dataset of 21

days, shown in green, (without peak prices) goes from February 22nd, 2016 till March 13th, 2016. The

second dataset, shown in red, (with peak prices) goes from January 10th, 2016 till January 31st, 2016.

This dataset thus consists of the region around day 20 where the highest prices were noticed. This is

also shown in Figure 7.

Figure 7: BELPEX-dataset for the fluctuating energy prices from January 1st, 2016 till March 21st, 2016.

0

20

40

60

80

100

120

140

0 20 40 60 80

En

erg

y p

rice

s [E

ur/

MW

h]

Time [d]

Dataset without peaks

Dataset with peaks

15

4.1.2. Denmark-data

The Denmark-data was provided by the Technical University of Denmark (DTU), and originates from

the Nord Pool Spot (www.nordpoolspot.com). It consists of energy prices per hour (in EUR/MWh) for

whole 2013. This data was already used in a research from the DTU in which the different evaluation

criteria of BSM1 were evaluated (Møller Jensen, 2014), as earlier discussed in Section 2.3.2. This data

is shown in Figure 8. As this is the data of a whole year, a lot more peaks can be observed, in

comparison with Figure 7. The biggest peak is observed around day 157 (approximately 1900 €/MWh).

No clear explanation for this high peak could be found. Some reasons might be related to oil prices or

the traded energy volumes, but as these prices are from Denmark, it was impossible to find more

information. Also, the weekly pattern is retrievable in this data, but due to larger timescale and the

many peaks, this pattern is harder to perceive in Figure 8.

Also this dataset is broken up into two datasets of 21 days, in preparation of the scenario analysis. The

first dataset of 21 days, shown in green, (without peak prices) goes April 28th, 2013 from till May 19th,

2013. The second dataset, shown in red, (with peak prices) goes from June 3rd, 2013 till June 24th 2013.

This is also shown in Figure 8. This dataset contains naturally the peak price of approximately 1900

€/MWh.

Figure 8: DTU-dataset for the fluctuating energy prices from January 1st, 2013 till December 31st, 2013. To give a clear image

of the data and its fluctuations, the peak price around day 157 corresponding to 1901.32 €/MWh was cut off from the figure.

0

50

100

150

200

250

300

350

400

0 50 100 150 200 250 300 350

En

erg

y p

rice

[E

UR

/MW

h]

Time [d]

Dataset without peaks

Dataset with peaks

16

5.5.5.5. ImplementationImplementationImplementationImplementation

5.1. Implementation of non-linear cost-functions: step-function

Before the BELPEX- and DTU-datasets were implemented in the two models, it was important to

understand and to interpret the existing cost-function, in order to make the right adaptations. This was

done by implementing some mathematical functions, instead of the non-fluctuating price. This was

done to test fluctuating cost models and its influence on different parameters of the WWTP. Out of

these functions, a step-function based on the BELPEX-dataset was considered the most important one

and will be described in the next Sections.

The cost-function in WEST is described in the cost model, which can be found in the

“wwtp.base.evaluator.msl”-file in the standard WEST model library. As described earlier in Section

2.3.1.1, the energy prices that are programmed by default in the cost model are fixed prices. The exact

values for the different prices can be found in Table 1, also in Section 2.3.1.1.

To implement a step-function, it was of course important to create such function based on the BELPEX-

data. To do this, the mean values of every hour per day for the whole dataset were calculated.

Important to notice, is that the mean values of the weekdays were separated from those of the

weekend days. This was done, because the energy prices in the weekend are, as described earlier,

lower than those of weekdays. This was based both on a visual basis, based on Figure 7, as on a

common knowledge basis. In Figure 9, these plotted mean values for the weekend and weekdays are

shown. A simple step-function could then be constructed from this data by calculating the mean of

values that were located close to each other. This step-function is also shown in Figure 9.

This step-function was then translated into code and could be implemented in the cost model via the

Model Editor. For increased flexibility another feature was added: each week, the prices rose by 0.001

€/kWh. This was done to simulate some kind of price increase on semi-long term. The most important

parts of that code are shown in Code snippet 1.

17

state <-

{…

// 6) COSTS

OBJ EnergyPriceWeekend "Energy price in the weekend": Euro := {: group <- "Cost" :};

OBJ EnergyPriceWeek "Energy price on weekdays": Euro := {: group <- "Cost" :};

OBJ EnergyPrice "Energy price": Euro := {: group <- "Cost" :};

OBJ FractieDag "Fractional part of the time": Real ;

OBJ ExtraDag "Help variable to determine if it is weekend": Time ;

OBJ Weekend (* hidden = "0" *) "determine if it is weekend or not": Boolean ;

OBJ WeekCounter (* hidden = "0" *) "the amount of weeks that have passed": Real ;

…};

equations <-

{…

// 6) COSTS

"Get the fractional part of the time"

state.FractieDag = independent.t - floor(independent.t);

"Help variable to determine the weekend"

state.ExtraDag = independent.t + 1.0;

"Calculate how many weeks have passed"

state.WeekCounter = IF (fmod(independent.t),7)==0)

THEN (state.WeekCounter + 1)

ELSE state.WeekCounter;

"Stepmodel for the weekdays"

state.EnergyPriceWeek = IF (state.FractieDag <0.26)

THEN 0.021+0.001*state.WeekCounter

ELSE IF (state.FractieDag <0.71)




ELSE 0.03+0.001*state.WeekCounter;

"Stepmodel for the weekenddays"

state.EnergyPriceWeekend = IF (state.FractieDag <0.29)








ELSE 0.04+0.001*state.WeekCounter;

"Determine if it is weekend or not (weekend = day 6 and 7 of every week)"

state.Weekend = IF (fmod(ceil(independent.t),7) == 0)

THEN 1

ELSE IF (fmod(ceil(state.ExtraDag),7) == 0)

THEN 1

ELSE 0;

"Determine whether the price of the weekend or the weekdays should be used"

state.EnergyPrice = IF(state.Weekend)

THEN state.EnergyPriceWeekend

ELSE state.EnergyPriceWeek;

state.AerationCost = previous (state.AerationCost) + (state.EnergyPrice *

(state.TotalAerationEnergy - previous (state.TotalAerationEnergy)));

state.PumpingCost = previous (state.PumpingCost) + (state.EnergyPrice *

(state.TotalPumpingEnergy - previous (state.TotalPumpingEnergy)));

…};

Code snippet 1: Alterations made to “wwtp.base.evaluator.msl” for the implementation of step-function based on the

BELPEX-dataset.

18

Figure 9: Mean values per hour of the BELPEX-dataset for weekdays and weekenddays (black, full lines) an approximative

step-function based on this data (grey, dashed lines).

Hereafter, this model library and the adapted cost model were used and tested with the BSM, where

the aeration and pumping energy of the different parts of the model were linked with the cost model.

Testing against the expectations showed that implementation was done correctly. This shows that it is

possible to implement a fluctuating cost-function in WEST, which might be helpful for possible future

studies related to cost modeling.

19

5.2. Extension of the BSM for the scenario analysis

For the scenario analysis it was important to control the aeration of the BSM. As this was not yet

implemented in the model, it was important to make such control. Normally, this aeration control was

only necessary from Scenario 4, but to guarantee a good comparison between the different scenarios,

this aeration control was implemented for all the scenarios. The aeration control was based on the

aeration control of the Eindhoven model and consists of three main components:

A. An ammonia controller (PI controller)

B. An aeration controller (PI controller)

C. An aerator/actuator

The set-point of the ammonia controller was coupled to the ammonia-measurement of the last aerobic

activated sludge unit, the set-point of the aeration controller was coupled to the dissolved oxygen-

amount of the same aerobic activated sludge unit and the kLa-values of all three aerobic activated

sludge units were linked to the aerator. The amount of air needed was calculated by the aeration

controller (u) and linked with the aerator. This structure is shown in Figure 10.

Figure 10: Updated BSM plant layout with the aeration control, with A: the ammonia controller, B: the aeration controller and

C: the aerator.

Not only the structure of the aeration control was based on the Eindhoven model, also the most

important values for the control were copied from the Eindhoven model. The values used for the

ammonia control and the aeration control are given in Table 3.

Table 3: Manipulated variables of the NH4 controller and the aeration controller in the aeration control for the BSM.

Manipulated variables of the

controllers

NH4 controller Aeration controller

Factor of proportionality K_P -2.5 7.0323

Derivative time T_D 0 d 0 d

Integral time T_I 425 s 0.3476 s

No error action u0 1 149813.84

Maximum control action u_max 5 876343

Minimum control action u_min 0.5 13680

Set-point value y_S Dependent on

the scenario

u from the NH4 controller

For the set-point value of ammonia, in all scenarios (except for scenario 4 and 6), 2 mg/l is chosen in

the Eindhoven model and 1 mg/l in the BSM. These values are target values, based on Dutch and

Belgian norm values (resp. for the Eindhoven model and BSM model). The norm values are 1.3 mg/l

(1.0 mg N/l) for Belgium (in the worst case) (Vlarem II - Bijlage 5.3.2. Sectorale lozingsvoorwaarden

voor bedrijfsafvalwater). For the Netherlands, norm values are not any more in effect, because they

differ from case to case. This is why the target value is based on the previous norm value (3 mg/l)

(Lozingenbesluit WVO stedelijk afvalwater). The set-point values are chosen a bit lower than the norm

values, to make sure the norm values are not exceeded because of fluctuations around the set-point.

20

5.3. Implementation of a ‘three limit controller’

For Scenario 4 and 5 (Section 6.1.3 and Section 6.1.4), different parts of the BSM model and Eindhoven

model (flow to buffer tank and aeration) had to be controlled, based on the costs. To make this easier

and more flexible, a new controller was developed that was able to generate different output signals

(flow and ammonia set-point) based on an input signal (in this case: the fluctuating energy prices). This

controller was called a ‘three limit controller’ because of the fact that the generated output was

dependent on three predefined thresholds. In specific, this meant that these three thresholds

delimited four zones. Each zone could correspond with another output value, as illustrated in Table 4.

Table 4: Definition of the different zones declared in the three limit controller and the output generated at each zone.

Zone Situation of input Output generated

1 Above the upper limit (y_Upper) u1

2 Between the upper (y_Upper) and

the middle limit (y_Middle)

u2

3 Between the middle (y_Middle) and

the lower limit (y_Lower)

u3

4 Under the lower limit (y_Lower) u4

This controller was not yet defined in WEST and had to be fully implemented in the framework of this

thesis. First, the code for this model was written and saved in a new file that was named

“wwtp.base.controllers.threelimit.msl” (see Code snippet 2). This file was then saved in the

“Models/controller”-folder of the standard WEST library. In order to include this with the rest of the

controllers, a small addition was also made in the “wwtp.base.controllers.msl”-file (see Code snippet

3). For visualization the same block was chosen as the one used for other controllers (e.g. On Off

controller, P controller…) and was renamed as “three_limit_controller”. This block was then included

in the used palette library (“WEST.WWTP.CN.PaletteLib.xml”). After this, the controller was useable

in WEST, as illustrated in Figure 11.

Figure 11: Implementation of the three limit controller is WEST, with A: the block library, B: implementing the controller in

BSM, where the cost determines the outflow of the buffer tank (see further) and C: an overview of the adjustable parameters

of the three limit controller.

21

#ifndef WWTP_BASE_CONTROLLERS_THREELIMIT

#define WWTP_BASE_CONTROLLERS_THREELIMIT

CLASS THREELIMIT

(* icon = "three_limit_controller"; is_default = "true" *)

"A controller in which three limit values can be chosen, generating 4 possible outputs"

SPECIALISES

PhysicalDAEModelType :=

{:

comments <- "A model for a three-limit controller";

interface <-

{

OBJ y_M (* terminal = "in_1"; is_favorite = "1" *) "Sensor measured output" :

Real := {: causality <- "CIN" ; group <- "Measurement data" :};

OBJ u (* terminal = "out_1"; is_favorite = "1" *) "Controlled variable" :

Real := {: causality <- "COUT" ; group <- "Control action" :};

OBJ y_Middle (* terminal = "in_2"; manip = "1"; is_favorite = "1" *) "Middle limit" :

Real := {: causality <- "CIN" ; value <- 2 ; group <- "Operational":};

OBJ y_Lower (* terminal = "in_2"; manip = "1"; is_favorite = "1" *) "Lower limit" :


OBJ y_Upper (* terminal = "in_2"; manip = "1"; is_favorite = "1" *) "Upper limit" :


OBJ u1 (* terminal = "in_2"; manip = "1"; is_favorite = "1" *) "Output when measured

value is above the upper limit":

Real := {: causality <- "CIN" ; value <- 10 ; group <- "Operational" :};


value is between the upper limit and the middle limit" :



value is between the middle limit and the lower limit" :



value is below the lower limit" :


};

parameters <-

{

};

independent <-

{

OBJ t "Time" : Time := {: group <- "Time" :};

};

state <-

{

};

equations <-

{

state.e = interface.y_Middle - interface.y_M;

state.help_u = interface.u;

state.help_t = independent.t;

interface.u = IF(interface.y_M > interface.y_Upper)

THEN interface.u1

ELSE IF(interface.y_M > interface.y_Middle)

THEN interface.u2

ELSE IF(interface.y_M > interface.y_Lower)

THEN interface.u3

ELSE interface.u4;

};

:};

#endif // WWTP_BASE_CONTROLLERS_THREELIMIT

Code snippet 2: Written code for the 'three limit controller' in the “wwtp.base.controllers.threelimit.msl”-file.

22

#ifndef WWTP_BASE_CONTROLLERS

#define WWTP_BASE_CONTROLLERS

…

#include "controller/wwtp.base.controllers.threelimit.msl"

#endif // WWTP_BASE_CONTROLLERS

Code snippet 3: Alterations made to the "wwtp.base.controllers.msl"-file to implement the three limit controller.

This three limit controller was used in the following scenarios to split the varying energy price in certain

zones. The determination of the zones was based on the mean and standard deviation of the datasets

without peaks. What this practically meant for the BELPEX and Denmark-dataset is shown in Table 5.

Table 5: Different price zones for the two datasets.

Price zones Real values for

BELPEX-dataset

Real values for

Denmark-dataset

Zone 1 Above mean + 1 standard

deviation

> 0.03444 Eur/kWh > 0.04504 Eur/kWh

Zone 2 Between mean and mean

+ 1 standard deviation

0.03444 Eur/kWh -

0.02727 Eur/kWh

0.04504 Eur/kWh -

0.03834 Eur/kWh

Zone 3 Between mean and mean

– 1 standard deviation

0.02727 Eur/kWh -

0.02010 Eur/kWh

0.03834 Eur/kWh -

0.03164Eur/kWh

Zone 4 Under mean – 1 standard

deviation

< 0.02010 Eur/kWh < 0.03164 Eur/kWh

To give a better image on what this exactly means for the two datasets, those limits are also shown for

the two datasets in Figure 12.

Figure 12: BELPEX and Denmark-datasets with the limits of the zones on top of it. Legend: upper dashed line: one standard

deviation above the mean of the dataset without peak, middle full line: mean of the dataset without peak, lower dashed line:

one standard deviaton under the mean of the dataset without peak. Remark: the y-axis of the Denmark-dataset without peak

was cut-off to give a clearer view. The peak at day 3.5 corresponds with a price of 1900 eur/MWh, the peak at day 16.375

corresponds with a price of 220 eur/MWh.

23

6.6.6.6. Scenario analysisScenario analysisScenario analysisScenario analysis To give a correct and complete idea of the influence of fluctuating energy prices on the costs of a

WWTP, different scenarios had to be developed and compared with each other. Eventually, six such

scenarios were defined:

• Scenario 1: usage of a fixed energy price

• Scenario 2: usage of a variable energy price

• Scenario 3: usage of a variable energy price with peaks

• Scenario 4: usage of a buffer tank based on the price with the variable energy price (peaks and

non-peaks)

• Scenario 5: usage of an aeration control, based on a fluctuating ammonia set-point based on

the price with the variable energy price (peaks and non-peaks)

• Scenario 6: combination of scenario 4 and 5 with the variable energy price (peaks and non-

peaks)

For these scenarios, different parameters of the model will be monitored and compared between the

different scenarios. The parameters that are being monitored are:

• The energy price

• The aeration energy of the whole plant (total and momentary)

• The pumping energy of the whole plant (total and momentary)

• The aeration cost

• The pumping cost

• The Environmental Quality Index (EQI) of the effluent (see Section 2.1.2)

• The fractions of the influent sent in and out of the buffer tank dependent on the fluctuating

energy cost (only scenario 4 and 6)

• The ammonia set-point of the aeration tanks dependent on the fluctuating energy cost (only

scenario 5 and 6)

The formula that is being used to calculate the EQI of the effluent stream is given in Formula 2, taken

from the BSM2 protocol (Jeppsson, et al., 2007):

�� = �2 ∗ �� + �� + 2 ∗�� + 30 ∗ �� + 10 ∗ �� ∗ �

Formula 2: Calculation of the EQI, according to (Jeppsson, et al., 2007).

In which TSS, COD, BOD5, NH4 and NO3 are all parameters of the effluent stream and expressed in g/m³,

while Q is the flow of the effluent stream and expressed in m³/d. This means the EQI is expressed is

g/d.

All these scenarios were performed with the two models (BSM and Eindhoven) and the two datasets

(BELPEX and Denmark). First the scenarios were simulated in a steady state until the steady state was

reached. After this, the scenarios were simulated during 21 days in the dynamic state. In these models,

the aeration and pumping energy of the different parts of the model were linked with the cost model.

In Section 6.1.1 till Section 6.1.5, the different scenarios will be explained more in detail, as well as

their implementation in the models and the possible alteration of those models.

24

6.1.1. Scenario 1: fixed energy price

In the first scenario, ‘a state of the art scenario’, the dynamics of the energy prices are not altered to

fluctuating prices. Only the values of the fixed energy prices are used. For these values, the mean of

the dataset without peaks is chosen, both for the BELPEX-data and the Denmark-data. The exact values

are shown in Table 6.

Table 6: Values of the fixed energy prices, used for the BELPEX- the Denmark-dataset in the first scenario.

Dataset Fixed energy price

[Eur/kWh]

BELPEX 0.027273

Denmark 0.038339

These values are added to the “wwtp.base.evaluator.msl”-file in the standard WEST model library.

The most important parts of that code are given in Code snippet 4.

state <-

{

…

// 6) COSTS


…

};

equations <-

{

….

// 6) COSTS

state.EnergyPrice = 0.027273;





….

};

Code snippet 4: Alterations made to “wwtp.base.evaluator.msl” for Scenario 1 for the BELPEX-dataset.

To the models themselves, nothing is altered. Only, as mentioned earlier, the aeration and pumping

energy of the different parts of the model are linked with the cost model.

6.1.2. Scenario 2 and scenario 3: fluctuating energy price via input file

In scenario 2 and scenario 3, the fluctuating prices are linked to the models. To do this, the BELPEX-

and Denmark-datasets are saved in a text file, so they are able to be read as an input-file into WEST.

Because the default Cost-block is only able to work with a fixed energy price, the code had to be

adapted in the “wwtp.base.evaluator.msl”-file in the standard WEST model library, as shown in Code

snippet 5.

25

interface <-

{

…

// 6) COST INPUT

OBJ INP_COST (* terminal = "in_1" *) "energy prices via input file": Ratio := {:

causality <- "CIN" ; group <- "Cost" :};

};

state <-

{

…

// 6) COSTS


…

};

equations <-

{

….

// 6) COSTS

state.EnergyPrice = interface.INP_COST;





….

};

Code snippet 5: Alterations made to “wwtp.base.evaluator.msl” for Scenario 2.

In the models themselves, an input block is used to read in the datasets. This input-block is then linked

with the Cost-block (‘costs’ was linked with INP_COST). This is shown in Figure 13. The rest of the model

is left unchanged.

Figure 13: Linkage of an input file with the cost block, with A: the linkage as seen in the layout, B: the interface of the input

block, C: the definition of the top-level interface variable 'costs' that gets linked with INP_COST from the cost block.

For scenario 2, the datasets without peak-prices are chosen. For scenario 3, the datasets with peak-

prices were chosen.

26

6.1.3. Scenario 4: usage of a buffer tank

In scenario 4, a part of the influent was sent to a buffer tank. The amount of the influent that was sent

to this tank was based on the price. The higher the price, the more was sent to the buffer tank, which

meant that relatively more wastewater was treated when the prices for aeration and pumping were

low. Because the volume of the buffer tank was limited, the flow had to be redirected to the beginning

of the WWTP. Also this flow was being controlled by the fluctuating price. When the price was low, the

buffer tank was emptied, when the price was high, most of the water stayed in the tank.

The outflow of the buffer tank already could be linked with the three limit controller with the standard

WEST model library: u from the controller was linked with Q_pump of the buffer tank. For the inflow

of the buffer tank, a relative flow splitter was used. In this way: u from the controller was linked with

f_Out2 of the flow splitter (the fraction of the flow that gets send to the buffer tank). Important to

notice is the fact that two different three limit controllers to determine the inflow and the outflow of

the buffer tank, as obviously these flow could not be the same at the same moment. The alterations

made in scenario 2 and 3 in the “wwtp.base.evaluator.msl”-file stayed valid. This scenario gets

simulated with the non-peak price dataset (scenario 4a) and the peak price dataset (scenario 4b), both

for the BELPEX- as the Denmark-dataset.

Of course, the models themselves also had to be adapted: in the BSM a buffer tank was placed, with a

volume that was equal to the percentage buffer volume in comparison with the volume of the

activated sludge units (ASUs) in the Eindhoven model. The volume of the buffer tank in the BSM was

fixed at 1164 m³, the volume of the buffer tank in the Eindhoven model was 17532 m³. This was more

than the standard buffer volume of the buffer tank used in Eindhoven to handle rain conditions (7967

m³), but as an expansion of this buffer tank is planned, this was already implemented (by adding the

planned extra volume to the buffer tank).In the Eindhoven model an extra flow splitter, which was

linked with the dataset through an input-block, was placed after the first flow splitter to the buffer

tank. The streams of these two flow splitters were than combined (with a combiner) and sent to the

buffer tank. Figure 14 shows what this looked like. This scenario was simulated with the non-peak price

dataset from scenario 2 (scenario 4a) and the peak price dataset from scenario 3 (scenario 4b), both

for the BELPEX- as the Denmark-dataset.

Figure 14: Adapted plant layout for scenario 4, with the addition of a buffertank in BSM and the addition of a flow splitter in

the Eindhoven model.

27

The flow percentages from and to the buffer tank were not the same in the two models. They were

however determined in the same manner. How and what these flow exactly are is explained in Section

6.1.3.1 and Section 6.1.3.2.

6.1.3.1. Parameters for the BSM model

To determine how big the flow to the buffer tank could be, the peak times are determined. Peaks are

hereby determined as zone 1 in Table 5, so every value bigger than the mean plus 1 standard deviation.

Analysis of the data found that the mean peak time for the BELPEX-datasets with peaks was 9.0 hours

and the mean peak time for the Denmark-dataset with peaks was 6.4 hours. With a buffer tank of 1164

m³, this meant that that max influent flow rate to exactly completely fill the tank in that period was

129 m³/h for the BELPEX-dataset and 182 m³/h for the Denmark-data.

In the dynamic simulation, the influent has a mean flow of 770 m³/h. This meant that in theory 16.7 %

of the influent for the BELPEX-dataset and 23.6 % of the influent for the Denmark-dataset could flow

to the tank in peak periods. Because of the fact that these peaks sometimes follow each other quickly

and that there is also a flow to the buffer tank when the prices are lower than the peaks (zone 2 and

zone 3 in Table 5), these percentages are probably too high. Therefore, after a trial and error session,

it was chosen to halve these values for zone 1 and choose an even lower fraction for zone 2 and 3, as

illustrated in Table 7.

Table 7: Fraction of the influent flow that gets sent to the buffer tank for each price zone in the BSM-model for the two

datasets.

Fraction of the influent

flow that gets sent to the

buffer tank for the

BELPEX-dataset [%]

Fraction of the

influent flow that

gets sent to the

buffer tank for the

Denmark-dataset [%]

Zone 1 8.5 12

Zone 2 5 6

Zone 3 2.5 3

Zone 4 0 0

However, even with these lower percentages, the buffer tank was filled most of the time when a fixed

outflow of the buffer tank was used (100 m³/d) (see Figure 15), what obviously meant that the buffer

tank only had a minor influence. To make this influence greater, the outflow values were also made

variable, dependent on the price. To do this, two cases were defined. In the first case, each zone had

its own flow, where the flow gradually got higher per zone. In the second case, however, the tank was

emptied fast when the price was low (zone 3 and zone 4) and the water was almost completely

retained when the price was higher (zone 1 and zone 2). In this second case, attention had to be made

that emptying the buffer tank was not going to happen too quick as this could have a big influence on

the influent flow and look like an artificial rain event. However, with the chosen values, the maximum

extra influent due to emptying the buffer tank was less than 10 % of the total influent flow, so this was

not considered as a problem. The chosen values for the two cases are given in Table 8. The results on

the buffer volume are summarized in Figure 15.

28

Table 8: Outflow of the buffer tank for each price zone in the BSM-model, for two different cases.

Outflow of the buffer

tank: Case 1 [m³/d]


tank: Case 2 [m³/d]

Zone 1 100 10

Zone 2 250 100

Zone 3 400 750

Zone 4 550 1500

Figure 15: The fluctuating volume of the buffer tank when there was a fixed outflow out the buffer tank (full line), when the

outflow was regulated as in Case 1 (dashed line) and when the outflow was regulated as in Case 2 (dotted line).

Figure 15 shows that the buffer tank was almost all the time completely full when there was a fixed

outflow and when the outflows of Case 1 were used. In Case 2, however, the influent peaks can be

better handled as the buffer tank empties quicker. Even higher outflows could probably handle those

peaks even better and empty even quicker, but in that case the risk of a too high extra influent flow

exists. This is why the flows of Case 2 (and not even higher flows) were used in the scenario analysis.

6.1.3.2. Parameters for the Eindhoven model

For the Eindhoven model, a similar analysis was performed. An extra addition in comparison with the

BSM is that a bypass was present in the Eindhoven model: a flow splitter after the buffer tank sends

part of the buffer outflow directly to the effluent. Results showed that this had a bad influence on the

EQI, this is why this bypass was disabled in this and the other scenarios. This was done by making the

outflow to the effluent fixed at 0 m³/d. The values for the inflow and the outflow that were determined

for the Eindhoven model are given in Table 9.

29

Table 9: Fraction of the influent to and outflow of the buffer tank for each price zone in the Eindhoven-model, for the two

datasets.

Fraction of the influent flow

being sent to the buffer

tank for the BELPEX-dataset

[%]

Fraction of the influent

flow being sent to the

buffer tank for the

Denmark-dataset [%]


tank [m³/d]

Zone 1 12 17 10

Zone 2 6 8 500

Zone 3 3 4 5000

Zone 4 0 0 10000

6.1.4. Scenario 5: varying the ammonia set-point

In scenario 5, the aeration of the aerobic ASUs was based on the price. This was done by including a

controller and making the ammonia set-point dependent on the price. Just as in scenario 4, this was

done by making use of a three limit controller, in which the price zones were the same as described in

Table 5. As mentioned in Section 5.2, up until now, a fixed ammonia set-point was used, based on the

norm values for Belgium and the Netherlands. In this scenario, this ammonia set-point fluctuates

dependent on the price and has in this way an influence on the aeration. The higher the energy price,

the higher the ammonia set-point so less air is required to reach this set-point. The opposite happens

when the price is low. Of course, these set-points fluctuations cannot be too high, because in the end

the norm values still have too be reached and the EQI cannot be too high. After testing some values to

ensure that the ammonia limit were not exceeded when this control was used, satisfying set-point

values were found. These are shown in Table 10.

Table 10: Different ammonia set-point for each price zone for the BSM and the Eindhoven model.

Ammonia set-point for the

BSM [g/m³]

Ammonia set-point for the

Eindhoven model [g/m³]

Zone 1 2.50 4.00

Zone 2 1.75 3.00

Zone 3 1.00 2.00

Zone 4 0.75 1.50

The alterations made in scenario 2 and 3 in the “wwtp.base.evaluator.msl”-file stayed valid. After

these adaptations, it was possible to link an input-file to the three way controller that was linked with

the ammonia set-point in the last ASU in the BSM (see block A on Figure 10 in Section 5.2). In the

Eindhoven model, the same was then done with the already implemented ammonia controller. This

scenario gets, just like scenario 4, simulated with the non-peak price dataset (scenario 5a) and the

peak price dataset (scenario 5b), both for the BELPEX- as the Denmark-dataset.

6.1.5. Scenario 6: combination of buffer tank and ammonia set-point

Scenario 6 is a combination of scenario 4 and scenario 5: specifically, this means that the fluctuating

cost price influences both the amount of influent that goes in and out the buffer tank and the ammonia

set-point of the ASUs. This is in fact a straightforward scenario and is done by linking the input-block

with the price datasets to flow splitter, buffer tank and the ammonia controller, instead of linking it to

only the in- and outflow of the buffer tank or the ammonia controller. No additional changes in the

code or in the model layout are needed. This scenario is, as scenario 4 and 5, simulated with the non-

peak price dataset (scenario 6a) and the peak price dataset (scenario 6b), both for the BELPEX- as the

Denmark-dataset.

30

7.7.7.7. ResultsResultsResultsResults

7.1. BSM

After the execution of the simulations in the Benchmark Simulation Model, the parameters mentioned

in Section 6 could be extracted from the simulation outputs and studied. This was done by splitting up

the scenarios, where the most relevant ones were studied together.

7.1.1. Scenarios without model adaptations

In the first place, only the influence of the varying energy prices (Scenario 2 and 3, see Section 6.1.2)

in comparison with the fixed price (Scenario 1, see Section 6.1.1) on the aeration cost was studied.

Figure 16 shows the total aeration costs (cumulative) for the three first scenarios.

Figure 16: Total aeration cost for the simulations of the different scenarios with the BSM.

Figure 16 shows that the total aeration costs are the highest when the datasets with peaks is being

used, both for the BELPEX-dataset (32.8 % higher than Scenario 1) and the Denmark-dataset (61.2 %

higher than Scenario 1). In the dynamics of these costs, the real difference is made when the peaks in

the datasets occur: day 7 till 14 for the BELPEX-dataset and day 3.528 and 16.464 for the Denmark-

dataset (see also Figure 7 and Figure 8). With the costs from Scenario 1 and 2, the observed differences

were much less, both for the BELPEX-dataset (1.03 % higher than Scenario 1) and the Denmark-dataset

(1.01 % higher than Scenario 1). Of course, the total aeration energy used is the same in all three

scenarios (see Appendix A).

31

Secondly, the total pumping costs (cumulative) for the first three scenarios are plotted in Figure 17.

Figure 17: Total pumping cost for the simulations of the different scenarios with the BSM.

The results are very similar to those of the total aeration cost. As in Figure 16, Figure 17 show that the

costs are highest when the dataset with peaks are used: both for the BELPEX-dataset (27.8 % higher

than Scenario 1) and the Denmark-dataset (55.4 % higher than Scenario 1). Between the dataset

without peaks and the fixed price, almost no difference can be observed. Just as with the aeration

energy, the pumping energy is the same in all three scenarios (see Appendix A).

To determine where the most profit can be made, the pumping cost and aeration costs are being

compared with each other in Figure 18. This shows that the aeration costs contribute to 59.8 % of the

total considered costs, while the pumping

costs only contribute for 40.2 %. This means

that most of the profit can be made by

reducing aeration costs. However, the

pumping costs cannot be ignored as they still

represent a big fraction of the costs. This does

not mean however that other costs (see

Figure 2) do not have an influence in this

model, they just are not considered in the

scope of this research.

Figure 18: Ratio between total aeration and pumping cost for the

BSM, based on the mean price for Scenario 1 till 3 for both datasets.

32

Another important parameter was the EQI. This is plotted in Figure 19 and is the same for all three

scenarios and the two datasets. This is logical as there were no model differences between the first

three scenarios or the two datasets, only the price differed. The momentary EQI fluctuates around its

mean value of 5.175.106 g/d.

Figure 19: EQI for the first three scenarios and the two datasets in the BSM.

7.1.2. Scenarios with model adaptations

In Scenario 4, 5 and 6, adaptations to the BSM layout were necessary in order to add the necessary

submodels (i.e. buffer tank, ammonia control. Therefore, it must be pointed out that the relative

savings in comparison to the non-adapted models which use the same dataset are more important

than the absolute costs. To have a clearer view, the datasets with and without peaks are split up. The

relative aeration savings for the scenarios which use the dataset without peaks are shown in

comparison with Scenario 2, the relative aeration savings for the scenarios which use the dataset with

peaks are shown in comparison with Scenario 3. This is plotted in Figure 20.

Figure 20: Relative aeration savings for the BSM, with the dashed lines: scenario 4, dotted lines: scenario 5 and the full lines:

scenario 6.

33

Figure 20 shows that the saved aeration costs are in all cases less than 2 % in the end, so rather limited,

however at some points in time savings above 12 % (which agrees with a momentary saving of € 1.97

at that point in time) are reached. In the beginning, the saved costs go to a maximum peak, after which

they decrease to a seemingly constant value. Most costs are saved in Scenario 6, the combination of

Scenario 4 and 5. With the variation of the ammonia set-point (Scenario 5), the least aeration costs are

saved. In Figure 21 the same was done for the pumping costs.

Figure 21: Relative pumping savings for the BSM, with the dashed lines: scenario 4 (not visible because they overlap with the

full lines), dotted lines: scenario 5 and the full lines: scenario 6.

Figure 21 shows that the saved pumping costs are negative: which means that the costs for pumping

in these scenarios are higher in comparison with the base cases (Scenario 2 and Scenario 3). However,

with the variation of the ammonia set-point (Scenario 5), no extra pumping costs are generated. This

is why the dashed lines of Scenario 4 are not visible on Figure 21: they overlap with those of Scenario

6.

Also the EQI was compared with the earlier scenarios. In Figure 22, only the EQI of Scenario 1 and

Scenario 6a for the BELPEX-dataset is shown. This was done because the EQIs from the different

scenarios were very similar as they followed a very similar pattern.

34

Figure 22: EQI for Scenario 1 (dashed lines) and Scenario 6 (BELPEX-dataset without peaks in energy prices) (full lines) in the

BSM.

The main difference visible in Figure 22 concerns the peaks in EQI (both positive and negative) which

were less pronounced in Scenario 6 as the result of the absence of peaks in energy prices. This means

that the adaptations in the models are better in handling high fluctuations. This means also that the

mean values, however, did not change much, as Table 11 shows. A slight amelioration (lower EQI) was

observed in most cases.

Table 11: Mean EQI in comparison with Scenario 1 for Scenario 4-6 and the two datasets in the BSM, with green numbers:

lower mean EQI than Scenario 1 and red numbers: higher mean EQI than Scenario 1.

Mean EQI in

comparison with

Scenario 1

BELPEX-dataset

without peaks

BELPEX-dataset

with peaks

Denmark-dataset

without peaks

Denmark-dataset

with peaks

Scenario 4 -1.83 % -0.78 % -1.39 % -0.93 %

Scenario 5 0.06 % -1.00 % 0.08 % 0.00 %

Scenario 6 -1.76 % -1.56 % -1.31 % -0.95 %

7.2. Eindhoven

Similar to the case of the BSM, the different scenarios were applied to the case of the Eindhoven WWTP

model and the parameters mentioned in Section 6 could be extracted from the simulations and the

most relevant ones get studied. A distinction in the results was made between the scenarios without

model adaptation (Scenario 1, 2 and 3) and the scenarios with model adaptations (Scenario 4, 5 and

6).

7.2.1. Scenarios without model adaptations

First, the total aeration costs are compared. These costs are plotted in Figure 23.

Figure 23: Total aeration cost for the simulations of the different scenarios with the Eindhoven model.

35

The results in Figure 23 showed that the total aeration costs are the highest when the datasets with

peaks is being used, both for the BELPEX-dataset (28.8 % higher than Scenario 1) and the Denmark-

dataset (59.4 % higher than Scenario 1). The real difference is made when the peaks in the datasets

occur: day 7 till 14 for the BELPEX-dataset and day 3.528 and 16.464 for the Denmark-dataset; see also

Figure 7 and Figure 8. With the costs from Scenario 1 and 2, the observed differences were less than

1 % and so almost not observable. It is worth pointing out that the value of the total aeration energy

is in all three scenarios the same, but different than the value of the total aeration energy of the BSM.

This dynamic of the aeration (and pumping) energy was shown for the Eindhoven model in Appendix

B. After the aeration costs, the pumping costs are plotted in Figure 24.

Figure 24: Total pumping cost for the simulations of the different scenarios with the Eindhoven model.

Figure 24 is also very similar with the dynamics of the aeration costs (Figure 23). This means that the

costs are highest when the dataset with peaks are used: both for the BELPEX-dataset (27.8 % higher

than Scenario 1) and the Denmark-dataset (55.8 % higher than Scenario 1). Between the dataset

without peaks and the fixed price, the difference is again less than 1 %. Just as with the aeration energy,

the pumping energy is the same in all three scenarios (see Appendix B).

36

In the Eindhoven model the biggest part of the costs

is by far due to the pumping (94.9 %) in comparison

with the aeration costs (5.1 %) as shown in Figure

25. This means that here potential savings in

pumping costs are more relevant than aeration

savings.

No differences are noticeable among the first three

scenarios in terms of the EQI (Figure 26). The mean

EQI is 1.397.107 g/d.

Figure 26: EQI for the first three scenarios and the two datasets in the Eindhoven model.

7.2.2. Scenarios with model adaptations

For scenarios 4, 5 and 6, it is more correct to consider relative costs in comparison with the first

scenarios than the absolute costs. Figure 27 shows this for the aeration costs of both the scenarios

which use the dataset without peaks are shown in comparison with Scenario 2, and the scenarios which

use the dataset with peaks are shown in comparison with Scenario 3.

Figure 25: Ratio between total aeration and pumping cost for the

Eindhoven model, based on the mean price for Scenario 1 till 3 for

both datasets.

37

Figure 27: Relative aeration savings for the Eindhoven model, with the dashed lines: scenario 4, dotted lines: scenario 5 and

the full lines: scenario 6.

Figure 27 shows that the saved aeration costs are consistently higher in the datasets with peaks

(savings up till 30 %) than in the datasets without peaks (all savings less than 5 %). With the Denmark-

dataset with peaks, a high savings peak of more than 30 % (which agrees with a momentary saving of

€ 804.07 at that point in time) can be observed at the same time point of the energy price peak in the

dataset (day 3.528, see Figure 8). The savings seem to evaluate to a constant saving percentage in time.

However, this steady value seems not (yet?) reached in the simulations with the datasets with peaks.

For the BELPEX-dataset with peaks the savings reached (mean value of the three scenarios); for the

Denmark-dataset with peaks the savings reached (mean value of the three scenarios). In Figure 28 the

relative pumping costs are presented.

Figure 28: Relative pumping savings for the Eindhoven model, with the dashed lines: scenario 4, dotted lines: scenario 5 and

the full lines: scenario 6.

38

Figure 28 shows that the saved pumping costs are minimal. In the datasets with peaks, 1 % of the

pumping costs gets saved, in the datasets without peaks, this saving is less (around 0 %). Also the EQI

was compared with the earlier scenarios. However, in Figure 29, only the EQI of Scenario 1 and

Scenario 6a for the BELPEX-dataset was shown. This was done because the other EQIs were very

similar.

Figure 29: EQI for Scenario 1 (dashed lines) and Scenario 6 (BELPEX-dataset without peaks) (full lines) in the Eindhoven

model.

In the Eindhoven model results the EQI peaks (both positive and negative) are more pronounced in

Scenario 6. This meant that the adaptations in the models were worse in handling high fluctuations.

Also, the mean values are mostly higher than the mean value of the EQI in Scenario 1, as Table 12

shows. A raise of the mean to almost 8 % can even be observed in some cases.

Table 12: Mean EQI in comparison with Scenario 1 for Scenario 4-6 and the two datasets in the Eindhoven model, with green

numbers: lower mean EQI than Scenario 1 and red numbers: higher mean EQI than Scenario 1.

Mean EQI in

comparison with

Scenario 1

BELPEX-dataset

without peaks

BELPEX-dataset

with peaks

Denmark-dataset

without peaks

Denmark-dataset

with peaks

Scenario 4 0.05 % -0.08 % 0.39 % 0.11 %

Scenario 5 1.99 % 7.95 % 3.26 % 4.01 %

Scenario 6 2.02 % 7.82 % 2.81 % 4.20 %

7.3. Global summary

To get an overall idea of the total potential savings, a global overview is plotted in Figure 30. In this

overview, the total cost savings in the 21 simulated days are plotted: this is the sum of the pumping

savings and the aeration savings, keeping in mind their specific weight in each model. This was done

for the two datasets, in which the datasets with peaks are compared with Scenario 3 and the datasets

without peaks are compared with Scenario 2. This is done for the BELPEX- and Denmark-dataset, and

for the BSM and Eindhoven model.

39

Figure 30: Model results for total (aeration + pumping) relative savings for Scenario 4, 5, 6 as compared to the base cases of

Scenario 2 and 3. Results from both the BSM and Eindhoven models using the BELPEX-dataset and the Denmark-dataset (with

and without peaks).

Figure 30 shows that the total savings or losses are very limited: in most cases no more than 0.5 % of

the costs is saved or lost (in comparison with Scenario 2 and 3). In the Eindhoven model and the

Denmark-dataset with peaks (up till 2 % in Scenario 6), the biggest savings are made. Also, it can be

noticed, that the total savings in Scenario 6 are not necessarily the sum of the savings made in Scenario

4 and Scenario 5.

40

8.8.8.8. DiscussionDiscussionDiscussionDiscussion

8.1. BSM

When looking at the dynamics of the aeration and pumping costs in the simulations without model

adaptations (Scenario 1 till 3; Figure 16 and Figure 17), it was noticed that the dynamics of the pumping

and aeration costs are very similar. As the pumping and aeration energy (see Appendix A) have a linear

increase, with no influence of model adaptations, and do not fluctuate, the relative influence on the

costs due the varying energy price is much more visible. It is no surprise that the highest costs are

reached with the dataset with peaks. When looked at the ratio of the costs, the aeration costs

contribute 59.8 % to the total considered costs, while the pumping costs only contribute to 40.2 % (see

Figure 18). This can be explained by the fact that the BSM is a rather small installation (so little

pumping), with relatively much aerobic volume (66.7 % of the ASU-volume is aerobic). The most

potential in savings is therefore the aeration. In the dynamics of the EQI (see Figure 19) a weekly

pattern can be noticed: a similar daily pattern for the first 5 days and then a similar, lower EQI for the

following 2 (weekend) days. This is caused by the input file for the influent.

For the simulations with model adaptations (Scenario 4 till 6), the pumping and aeration savings were

monitored (see Figure 20 and Figure 21) when a buffer tank and/or a fluctuating ammonia set-point

was used. For the aeration, the savings peaked in the first part of the simulations, after which they

decreased rapidly and evolved to a constant saving over time. It seems so that the models need an

adaption time in the beginning of the simulations. This might be due to a not fully reached steady state

with the steady state simulations. However, the steady state simulations were run for a sufficiently

long time (21 days), according to the observations made after these simulations. Another explanation

might be that the buffer tank is completely empty in the beginning of the simulations, while this is

almost never the case anymore when the buffer tank was taken into use when the simulations started.

Even with adaptations made to the outflow (see Section 6.1.3), it was difficult to reach this initial effect

of larger savings for a long time. For the pumping, these peaks were much less pronounced in the

beginning. However, when a buffer tank was used in the models, the constant relative pumping savings

were negative (so extra costs were generated). This could be explained by the fact that adding two

extra flows, out and in the buffer tank, generates extra pumping energy and costs. Considering that

only very limited pumping was present in the initial model layout, the addition of these two extra

pumps has a large overall impact.

The fact that after a while no extra profits or losses were noticed (and the savings stayed constant),

showed that additional costs and profits cancelled each other out. This might be due to the fact that

the additional implementations made in the models to save the costs, also caused some extra costs

themselves. For example, the costs that are saved by sending a percentage of the influent to the buffer

tank, provide extra costs when this water gets sent back and causes an extra load. The same holds for

the aeration: the costs saved by lifting the ammonia set-point when the price is high, go together with

extra costs by lowering the set-point when the prices are low. These fluctuations cancel each other out

after a while, creating a constant saving.

However, with the Denmark-dataset, a high savings peak (more than 4 % savings) was noticed with the

aeration savings (Figure 20) when a peak occurred in the energy prices at day 3.528. This shows that

the adaptations in the models are capable in handling high price peaks. However, when the price does

not peak, the savings mostly seem constant or sometimes even decrease. The question also arises as

to this can be improved. The answer might be to not focus anymore on creating better effluent quality

or handling more influent than in other periods. This creates more energy usage and so higher

(relative) costs in comparison with the first scenarios when the energy price is low. This can eventually

41

be solved by not lowering the ammonia set-point at low energy prices. With regard to the buffer tank,

a possibility is to pump a constant flow out of the buffer tank. However, attention has to be made that

this flow is high enough, to avoid a full buffer tank most of the time. A disadvantage of these

suggestions might be that they, despite the possible extra savings, might lead to a higher EQI. Local

governments then have to be consulted to check if the EQI limits might be exceeded sometimes.

These findings indicate that it is indeed important to take the varying energy price into account, as the

aeration and pumping costs definitely changed when datasets with varying energy prices were used.

Also, as these results show, savings can be generated in the plant by adapting operational strategies

according to these prices. The operational strategies that were proposed in this research (e.g. the use

of a buffer tank and changing the ammonia control) have the benefits that they are relatively easy to

test and, certainly in the case of the ammonia control, are relatively easy and cheap to implement in a

real-life WWTP. Moreover, these strategies are very realistic. Care however should be taken that these

models are optimized, so choosing the right ammonia set-point and flows in and out the buffer tank is

crucial. Also, the most important function of a WWTP is still to clean water, so the effluent quality

cannot be ignored in order to save costs.

8.2. Eindhoven model

After the BSM, the results of the Eindhoven model were studied. These models were handled in the

same way, but the Eindhoven model is a real case, so these results have to be taken with more

attention than the BSM, as more influencing factors have an effect in Eindhoven. However, the

conclusions made in this section can, as always, differ from other real cases.

When looking at the dynamics of the aeration and pumping costs in the simulations without model

adaptations (Scenario 1 till 3; Figure 23 and Figure 24), there was noticed that, just as with the BSM,

the dynamics between the pumping and aeration costs are very similar. This could again be explained

by the fact that the relative influence on the costs due to the variating energy price is much more

visible than the influence of the energy due to the linear dynamics of the pumping and aeration energy

(see Appendix B). The costs are in the Eindhoven model also highest when the dataset with peaks was

used.

Apart from these similarities between the Eindhoven model and the BSM, some differences exist. In

the first place, the ratio of pumping costs over aeration costs is much higher in the Eindhoven model

(94.9/5.1) than in the BSM (40.2/59.8). This difference could be explained by the fact that the relative

aerobic volume is much higher in the BSM (66.7 % of the ASU-volume, as earlier mentioned) than in

the Eindhoven model (17.8 % of the ASU-volume). Nevertheless, this ratio still seems high for the

Eindhoven model. As no real explanation could be found for this high pumping cost, but definitely is

too high to be correct, this could possibly be explained by some (undiscovered) model or simulation

inaccuracies. As the EQI showed a daily pattern in the Eindhoven model, this is also a difference with

the BSM, where the EQI showed a weekly pattern. This is due to the fact that other influent input files

are used for the two models and so variations in the composition at different times exist. Also, as

earlier mentioned, the Eindhoven model is a real case, which means the effluent regulations should be

stricter than with the BSM. This can also be an explanation why the variation of the EQI is indeed less

with the Eindhoven model.

For the simulations with model adaptations (Scenario 4 till 6), the pumping and aeration savings were

monitored (see Figure 27 and Figure 28) when a buffer tank and/or a fluctuating ammonia set-point

was used. Just as in the BSM, the savings peaked in the beginning. Also very high savings were noticed

when prices peaked (up to 30 % for the aeration costs with the Denmark-dataset with peaks). However,

these high peaks were not maintained and just, as in the BSM, evolved to a lower, constant savings

42

value. This constant value was mostly different from that of the BSM: while the pumping savings were

mostly negative in the BSM, they were positive in the Eindhoven model. This can be explained by the

fact that in the Eindhoven model, the pumping energy is determined by more objects (e.g. flow

splitters, tanks…) than with the BSM because it is a larger plant. This means that when two extra flows

are added, which generate extra pumping energy, this has relatively more influence on the BSM than

on the Eindhoven model.

The absolute values of the relative aerations savings were mostly higher in Eindhoven model than in

the BSM and also the dynamics of the EQI were different (see Figure 22 and Figure 29). In the BSM, the

adaptations were better in handling peaks in the EQI in comparison with Scenario 1, resulting in the

same mean EQI. However, with the Eindhoven model, the EQI peaks were bigger than the peaks in the

first Scenario, resulting in a higher mean EQI. This means that these adaptations are not

interchangeable between each model, without making the right optimizations for the WWTP. These

optimizations must take into account the plant size, the aerobic volume of the ASUs and local limits

for the effluent quality (to choose a correct ammonia set-point).

The fact that cost modelling is not uniform and must take into account the specific site factors and

other parameters, was already concluded in some other studies. As earlier mentioned in Section

2.3.1.1, Aymerich (2015) found that energy cost modelling is very site-specific. Ideally, when not aiming

for cost control, the used energy tariff should be implemented in the model, as this can have its

influence on the chosen control strategies. Møller Jensen (2014) found that also the weather was an

important factor in choosing the right energy price model.

The global summary (Figure 30) shows that most final total relative savings were made in the

Eindhoven model with the Denmark-dataset with peaks (up till 2 %). However, this resulted in increase

of the EQI till 4.20 %. This might be a problem with certain legislations, depending on what exactly is

causing this higher EQI. As Formula 2 states, the EQI is dependent on a lot of parameters, so it is needed

to investigate these parameters to determine what is causing the higher EQI. Overall, the savings were

limited. In some scenarios with the BSM, there were even extra costs noticed in comparison with the

first scenarios. This was due to the extra pumping energy, derived from the flow in and out the buffer

tank, as these costs were only noticed in Scenario 4 and Scenario 6. It was also found that the savings

made in Scenario 6 were not necessarily the sum of the savings made in Scenario 4 and 5. Reason for

this can be that the model adaptations in fact had an influence on each other. Due to the flow in and

out of the buffer tank, the treated water flow fluctuates, which has an influence on the amount of air

needed to treat the water. This means that when the prices are low, more water gets pumped into the

ASUs, so more air is needed to treat the water in comparison to when the buffer tank was absent.

8.3. Economic evaluation

A small economic evaluation can be made, to evaluate if placing a buffer tank might actually save costs.

As the Eindhoven model already has a buffer tank, used for handling rain water, this evaluation is not

considered for the Eindhoven model. According to the Belgian Energy- and Environmental Informatics

System, the CAPEX of a concrete buffer tank is 100 €/m³ (EMIS-VITO, 2016). For the BSM, this leads to

a total CAPEX of € 116 400. With the BSM, the savings were too small to consider a buffer tank when

no varying ammonia set-point was used (maximum 0.06 %, see Figure 30). When additionally a varying

ammonia set-point was used, the maximum savings of the considered OPEX is 0.5 % (BELPEX-dataset

with peaks, see Figure 30). After 21 days, the total aeration and pumping cost of the BSM-model is €

5474.80. Assume that the savings stay constant during the whole period at 0.5 %, € 27.37 is saved

during that 21 days. Assuming similar circumstances during the rest of time, it will take 11.5 years to

make the cost of the buffer tank profitable. Of course, this period is too long to effectively implement

43

this buffer tank in real WWTPs. As this would be profitable of the return on investment (ROI) was 2 till

3 years, this would only be possible if the energy prices rise with 810 %. This seems most unlikely.

8.4. Possible future research

As Figure 30 shows, the total savings are limited. This might be disappointing, but does not bring down

the relevance of this study. This research might not give specific examples in how to save the costs of

a WWTP, but does hand, however, new tools and possibilities on how to save those costs. These energy

cost structures and scenarios can now be used in other, existing models. Future research can be split

up in 4 possibilities:

• Optimizing the existing scenarios: as earlier mentioned, the most obvious way to optimize the

existing scenarios is by using a constant outflow out of the buffer tank or by not lowering the

ammonia set-point when the price is low. However, as mentioned earlier, these optimizations

are very site-specific, e.g. some plants do not have the possibility to install a buffer tank or

have very strict regulations with regard to the ammonia concentration in the effluent which

make it difficult to alter the ammonia set-point.

• Developing new scenarios: the first possibility for another scenario could be making use of a

proportional controller to handle peak prices. Instead of making use of the developed three

limit controller, where the prices gets divided in zones, a proportional controller could help to

generate a certain signal when the price exceeds a certain limit. In this way the model

adaptations would only be used when the prices peaks. When the price is lower however,

these adaptions would be disabled. Another possibility might be to focus on energy availability,

which goes together with the varying price. When using a source of renewable energy

(eventually even on the site of a WWTP), the energy price varies with availability: when there

is more wind, more wind energy gets generated, resulting in a lower energy price (Ketterer,

2012). When adjusting the control parameters based on energy availability, instead of directly

on the price, possible new insights can be gained. This might definitely be interesting as in

Belgium a growing fraction of energy is provided by renewable energy: in 2015 a raise of more

than 13 % in production capacity of wind energy was observed (Wind Energy Market

Intelligence, 2016). To avoid problems regarding to the EQI when model adaptations are used,

possibly a control system that switches between economical (higher ammonia set-points) and

ecological (lower ammonia set-points), dependent on the circumstances (e.g. weather

conditions).

• Usage of additional datasets: other datasets might also provide extra information about the

scenarios applied in this research. For example, datasets where peaks lay closer to each other

might give other insights. With the used datasets and model adaptation in this research, it

looked like these model adaptations were capable in handling peaks as the costs savings were

highest. However, two peaks closer to one another might mess up the control strategy, or give

the same savings. This is something worth investigating in the future.

• Usage of (short term) price predictions: short-term predictive models, based on ARIMA or

artificial neural networks (see Section 2.3.2) can be used to predict the future energy prices

based on the current data. In this way, it will be much easier to predict future events in the

WWTP and take anticipative actions.

Finally, another possibility is looking at the other, not considered, secondary costs of the WWTP (e.g.

sludge handling and the external carbon source). As this does not really fit in the scope of this research,

this is not discussed further, but altogether might be a consideration for other researches.

44

9.9.9.9. ConclusionConclusionConclusionConclusion In this research, the influence of varying energy prices on the costs of a WWTP was investigated. This

was done by changing the cost functions in two existing models with datasets of real-life fluctuating

energy prices, provided by BELPEX and DTU. The models used for this study were the Benchmark

Simulation Model and a case-study model based on the WWTP of Eindhoven, operated by Waterboard

De Dommel. To give a correct and complete idea of the influence of varying energy prices on the costs

of a WWTP, different scenarios were developed and compared with each other, in some cases leading

to an adapted model set-up. Eventually, six scenarios were developed, where the use of a fixed price,

the usage of the datasets (with and without peak prices) and the implementation of a buffer tank

and/or ammonia control were the variables.

Both the flow in and out the buffer tank and ammonia control parameters were controlled by the

fluctuating prices: when the price was high, water got collected in the buffer tank and a higher

ammonia set-point was tolerated. The opposite was true when the price was low. To make this work

in the models, a ‘three limit controller’ was developed. This was linked with the datasets with the

energy price. The position of the momentary energy price between three predefined limits,

determined which output value (e.g. the ammonia set-point or the in- and outflow of the buffer tank)

was generated. These scenarios were run dynamically for 21 days, after which the most important

parameters (including aeration and pumping cost and the EQI) were monitored and studied.

Out of these results, it could be concluded that including energy prices indeed is important. As

monitoring the aeration and pumping costs showed, different costs were reached when different price

datasets were used. Of course, the highest costs were reached when the datasets with peak prices

were used, in both models. Using a dataset that fits the situation of the used WWTP will provide more

realistic costs, and make anticipating easier when trying to implement control strategies.

The results with the control strategies (buffer tank and/or ammonia controller) showed that it was

possible to generate savings. Most savings, however, were limited, but a total savings of 2 % was

reached with the Eindhoven model and the Denmark-dataset with peaks when both the buffer tank

and the ammonia control was used. Despite the fact that the total savings were limited, the control

strategies showed that they were very good in handling peaks: savings up till 30 % were noticed when

a price peak occurred. This implies that the savings decreased to a constant value that is much lower

(between 0 and 2 %) when no peaks occurred. This was explained by the fact that the requirements

imposed on the WWTP when prices were low, were possibly too high. A higher loading rate and lower

ammonia set-point when the price is low, as tested here, generate extra costs, levelling out the extra

savings made when the prices are high.

Comparing models with each other and implementing the same control strategies in other models,

cannot be done without taking some things into consideration. The results showed that the plant size,

the aerobic volume of the ASUs and of course the used control strategies all had its influence on the

results. The differences between Eindhoven and the BSM existed mostly in the aerobic volume (17.8

% of the ASU-volume in Eindhoven, 66.7 % in the BSM) and the plant size. This subsequently had its

influence on the ratio of the different costs (in this research only pumping and aeration costs were

monitored). Pumping costs provided 95.1 % of the total considered costs in Eindhoven, while this was

only 40.2 % in the BSM. However, this ratio was considered to be very high in Eindhoven and might

also be the consequence of model inaccuracies. Still, this had its influence on the total costs savings,

where in the Eindhoven model the total savings were much more dependent on these pumping costs.

Also the implementation of a buffer tank had much more influence on the pumping costs in the BSM

than in the Eindhoven model, as the Eindhoven plant is bigger and more complex, so the extra flows

45

out and in the buffer tank only have a minor influence. In the BSM, however, these flows already make

up a considerable part of the smaller plant. The fact that cost modelling is not uniform and must take

into account the specific site factors and other parameters, was already concluded in some other

studies (Aymerich, et al., 2015) (Møller Jensen, 2014).

Possible future research involves using other scenarios (e.g. using a proportional controller or make

the control strategies dependent on energy availability), optimizing current scenarios (e.g. optimizing

ammonia set-points and buffer flows), the usage of other datasets (e.g. datasets where the peaks lay

closer to each other) and the usage of short term price prediction (e.g. predictions based on ARIMA

and artificial neural networks).

46

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Appendix AAppendix AAppendix AAppendix A

Figure 31: Aeration and pumping energy in the BSM for the three scenarios without model adaptations for the two datasets.

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Appendix BAppendix BAppendix BAppendix B

Figure 32: Aeration and pumping energy in the Eindhoven model for the three scenarios without model adaptations for the

two datasets.

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