electrical design and operation of sustainable business...

Simon De Clercq

Electrical design and operation of sustainable business parks

Academic year 2016-2017Faculty of Engineering and ArchitectureChair: Prof. dr. ir. Luc DuprDepartment of Electrical Energy, Metals, Mechanical Constructions & Systems

Master of Science in Electromechanical EngineeringMaster's dissertation submitted in order to obtain the academic degree of

Counsellor: Dr. Brecht ZwaenepoelSupervisor: Prof. dr. ir. Lieven Vandevelde

The author gives permission to make this master dissertation available for consultation and

to copy parts of this master dissertation for personal use. In the case of any other use, the

copyright terms have to be respected, in particular with regard to the obligation to state

expressly the source when quoting results from this master dissertation.

Ghent, June 2017

The promotor The advisor The author

Prof. dr. ir. L. Vandevelde Dr. B. Zwaenepoel Simon De Clercq

Preface

It is a pleasure to be able to finish my engineering studies by writing this dissertation thesis.

This work, and the learning process that preceded it, have allowed me to explore the mul-

tidisciplinary world of industrial symbiosis and the integration of renewable power sources.

It has taught me to enjoy the freedom of independent research and gave me the opportunity

to broaden my vision on renewable energy.

I would like to thank my promotors professor Vandevelde and Brecht Zwaenepoel for their

support and guidance over the course of this work. I also particularly thank Hendrik Ver-

meersch for his input throughout the year; our conversations on the Belgian energy sector

were a pleasure. I would like to gratefully acknowledge Christof Deckmyn for his explanation

on genetic algorithms.

I want to thank Mieke Gevaerts at SOGent for introducing me to Eiland Zwijnaarde and

showing me a glimpse of what urban development is.

To everyone who took their time to review my writing, Brecht, Hendrik and Samie: thank

you very much.

Finally, I would like to thank everyone who was there for me, my family, friends, fellow

students, house mates, . . . Thank you for making this year so enjoyable.

Summary

Electrical design and operation of sustainablebusiness parks

Simon De Clercq

Supervisor: Prof. dr. ir. Lieven Vandevelde

Counsellor: Dr. Brecht Zwaenepoel

Faculty of Engineering and Architecture

Ghent University

Academic Year 2016-2017

Abstract - In this study a techno-economic optimisation is carried out on the design and

operation of energy systems on newly-developed industrial parks. An optimal sizing model is

constructed that uses a genetic algorithm to find the optimal system configuration of an on-

grid power system. The system is evaluated according to its life cycle cost and green house gas

emissions. While optimal sizing algorithms have been developed for different types of hybrid

power systems, these often do not consider the socio-organisational drivers and limitations for

inter-firm energy supply facilities. This work presents a list of recommendations for industrial

park developers to determine and implement the parks optimal power system. The current

development of the project Eiland Zwijnaarde in Ghent provides the basis for a concrete

case study in which the opportunities for an inter-firm power system are identified. The

main results suggest that a significant share of renewable generation is part of the optimal

configuration under varying mean electricity and gas prices. A medium sized cogeneration unit

can compensate the renewables intermittent behaviour and lower the thermal energy cost.

Large scale electrical storage is found not to be profitable under the used control structure

and tariff scheme. A gradual ingress of firms in the park and the subsequent sloped annual

energy demand has a negative effect on the fraction of shared facilities in the power systems

optimal configuration.

Keywords - On-grid hybrid power system, Optimal sizing, Eco-industrial Park, Industrial

microgrid, Genetic Algorithm, Fuzzy logic

Electrical design and operation of sustainablebusiness parks

Simon De Clercq

Supervisor(s): Prof. dr. ir. Lieven Vandevelde, Dr. Brecht Zwaenepoel

Ghent University, Faculty of Engineering and Architecture,Department of Electrical Energy, Metals, Mechanical constructions & Systems

Academic year 2016-2017

Abstract In this study a techno-economic optimisation is carried outon the design and operation of energy systems on newly-developed indus-trial parks. An optimal sizing model is constructed that uses a geneticalgorithm to find the optimal system configuration of an on-grid powersystem. The system is evaluated according to its life cycle cost and greenhouse gas emissions. While optimal sizing algorithms have been developedfor different types of hybrid power systems, these often do not considerthe socio-organisational drivers and limitations for inter-firm energy sup-ply facilities. This work presents a list of recommendations for industrialpark developers to determine and implement the parks optimal power sys-tem. The current development of the project Eiland Zwijnaarde in Ghentprovides the basis for a concrete case study in which the opportunities foran inter-firm power system are identified. The main results suggest thata significant share of renewable generation is part of the optimal config-uration under varying mean electricity and gas prices. A medium sizedcogeneration unit can compensate the renewables intermittent behaviourand lower the thermal energy cost. Large scale electrical storage is foundnot to be profitable under the used control structure and tariff scheme. Agradual ingress of firms in the park and the subsequent sloped annual en-ergy demand have a negative effect on the fraction of shared facilities in thepower systems optimal configuration.

KeywordsOn-grid hybrid power system, Optimal sizing, Eco-industrialPark, Industrial microgrid, Genetic Algorithm, Fuzzy logic

I. INTRODUCTION

Limitless economic growth, ecological collapse and resourcescarcity are forcing industry as a whole to rethink its funda-mental principles and resort to more sustainable practices [1].As defined in the Brundtland Report [2], sustainable develop-ment is development that meets the needs of the present withoutcompromising the ability of future generations to meet their ownneeds. In this discourse, industrial symbiosis has emerged asan approach in which traditionally separate industries collabor-ate in order to find synergies that offer competitive advantages.This can involve physical exchange of materials, energy, wa-ter, etc. [3]. Geographic proximity is an important facilitatingfactor, which is why eco-industrial parks (EIP), with their col-lective infrastructure and spatial density of firms, have becomean important topic of study in the field of sustainability [1].

The transition towards a carbon neutral industrial sector isdriven by the global phenomenon of climate change. The IPCCwarns that the continued emission of greenhouse gases willcause further warming of global climate patterns and result inlong-lasting changes in all components of the climate system.In Europe in 2014, 26% of CO2 equivalent emissions were dueto the consumption of electricity and gas [4]. Of all electricityconsumption in Europe, 36% is consumed by industry [5]. Op-timizing industrial electricity consumption can therefore signi-

ficantly reduce CO2 emissions and mitigate the effects of globalwarming.

An industrial parks power system is characterised by loc-alised electric and thermal loads, high energetic consumptionand spatial opportunities for the integration of renewable en-ergy sources. For these reasons, the prevailing paradigm forelectric distribution on an EIP is that of a microgrid. Accord-ing to [6], the microgrid concept assumes a cluster of loadsand microsources operating as a single controllable system thatprovides both power and heat to the local area. The compon-ents in this energy cluster function independently in possibleinteraction with the surrounding macrogrid [7]. This results inenhanced local control, which can lower the cost of energy dis-tribution, aid the integration of renewable sources and therebyreduce green house gas emissions [8]. Furthermore, the pos-sibility of islanding improves the system security of supply bydetaching the microgrids reliability from that of the macrogrid.

Several methods for the optimal sizing of hybrid power sys-tems have been developed [9]. In many cases the power sys-tem is a stand-alone microgrid and consists of wind generation,solar PV, a micro source and electrical storage [10] [11] [12][13] [14]. An industrial parks high electrical demand, both interms of quantity and quality, makes permanent islanding a com-plicated design choice [15]. Furthermore, dense electrificationin Flanders means that an industrial park can be connected tothe macrogrid at a relatively low cost. An on-grid power systemis therefore considered in this study.

While cogeneration offers high efficiency and flexibility [16],only very few papers dealing with the optimal sizing of hybridpower system take this feature into account [11]. A CHP modelis developed and included in the system model.

A genetic algorithm is selected to carry out the optimisa-tion process because of its computational efficiency in multi-objective optimisation problems with a high number of optim-ised variables [10] [11]. It has been used extensively in the op-timisation of hybrid power systems [9] [11].

II. HYBRID POWER SYSTEM MODEL

The system model simulates the power flows in the microgridon an hourly basis during its lifetime according to given loadprofiles, meteorological data and the system parameters. Thesystem parameters consist of the installed solar power Pinst.solar,the installed wind power Pinst.wind, the installed CHP powerPinst.CHP, the battery storage capacity Einst.bat, and the maximum

battery power Pinst.bat. The grid topology is shown in Figure 1.

Fig. 1: Microgrid topology

A. Wind power

Wind power can be modelled using wind speed data and aturbines power curve [10], [17] [18]. However, wind speed datafor the region of Flanders is hard to come by or simply does notexist. An alternative to wind speed data is historical data of windpower production [13] which in Belgium is easily accessible.Data for on-shore wind generation in Belgium in 2015 is usedto calculate the wind power production at every time step. Thenormalised hourly time series of capacity factors is multipliedby the installed wind power Pinst.wind to obtain the amount ofhourly wind production.

P iwind = capacity factoriwind Pinst.wind (1)

The annual average capacity factor is equal to 0.244.The total generated electricity during one year is equal to the

sum of wind generation during every time step.

Ewind =

8760

i=1

P iwind (2)

B. Solar power

Historical solar production data for the region of East-Flanders is used to calculate the hourly solar generation. Theannual average power factor is 0.119.

P isolar = capacity factorisolar Pinst.solar (3)

C. Cogeneration

The cogeneration unit follows the electric load according tothe instantaneous difference between the renewable production

and the parks electrical demand. If the difference is higher thanhalf of the installed power Pinst.CHP, the unit is turned on andfulfils the demand as far as its operating region allows. Thisregion is linearised and electric production is limited between50% and 100% of the installed capacity [19] [20]. The nom-inal power-to-heat ratio is 1:1 [21] and there is always maximalheat production. The units thermal and electrical efficienciesare variable and depend on the electrical set point, as shown inTable I. The heat demand that is not produced by the CHP in-stallation is generated using an auxiliary gas boiler with a fixedefficiency of 0.80.

Table I: CHP prime mover efficienciesElectric load level el th tot0.5 0.20 0.57 0.770.75 0.23 0.545 0.7751 0.26 0.52 0.78

D. Battery storage

Electrical power can be stored or withdrawn from an elec-trical storage unit. The storage is controlled using a fuzzy logicbased control structure that was adapted from [22]. The mainaim of the control structure is to charge the battery when theelectricity price is low and discharge when the electricty priceis high. This means that the battery can charge even though thepower flow in the grid is negative, or discharge when the powerflow is positive. However, under no circumstances should theinstalled battery require fortification of the grid connection (andthus a greater grid dependency).

The input parameters are the previous state of charge SOCi1

and the instantaneous power flow at time i, PF i. These are nor-malised and fuzzified according to 5 categories ranging betweenvery low and very high. The linguistic control rules are shown inTable II. The resulting value Pfuzz is withdrawn from the batteryas far as the physical constraints allow.

Table II: Fuzzy control rules for battery storagepricei PF i P ifuzz

L / VPM / ZH / VN

VH / VN(L) VH N(VH) VL P(H) VL P

VL: very low, L: low, M: medium, H: high, VH: very highVN: very neg., N: neg, Z: zero, P: pos. VP: very pos.

Four constraints are considered.The power constraint dictates that power drawn from or fed

to the battery during one time step can not exceed the maximuminstalled power Pinst.bat [23].

The efficiency constraint takes into account the power lossthat occurs when energy is converted in the storage system. Intheory the charging efficiency charge and the discharging effi-ciency discharge will have different values. However, separatevalues for these parameters are hard to measure [12] and battery

manufacturers will therefore define a round trip efficiency forone entire charge and discharge cycle. In the model used herethe charge and discharge efficiency are both assumed to be equalto the square root of the round trip efficiency. bat = charge =discharge =

round = 0.90.

Storage systems exhibit self-discharge behaviour, whichmeans that over time a certain fraction of the stored power willbe dissipated and lost [24] [17] [15]. The rate of self-dischargeper time step is denoted by the symbol .

By introducing a maximum depth of discharge of the elec-trical storage unit, the longevity of the system can be prolonged[24] [25]. Concretely this means that when the battery is atits minimum state of charge, it no longer allows power to bewithdrawn. SOCmax is in practice almost always equal to 1 andSOCmin is here equal to 0.15.

The four constraints can mathematically be expressed asshown below.

0 < |P exibat| < Pbat (4)SOCi = (1 ) SOCi1 (5)

SOCi =

{SOCi1 + bat P ex

ibat

EbatP exibat < 0

SOCi1 + 1bat P exibatEbat

P exibat > 0(6)

SOCmin < SOCi < SOCmax (7)

E. Demand Response

In this study a heuristic approach is used to model demandresponse. It looks at the daily interaction with the utility gridand shifts a certain percentage of the total daily energy profilefrom times of high to times of low consumption. An examplefor the original and resulting power flow for one week is shownin Figure 2. Using this method, a shift of 2.5% of the dailyenergy interaction is found to correspond to an annual powerpeak reduction by 10.7%.

hour0 20 40 60 80 100 120 140 160

Grid

pow

er in

kW

-2500

-2000

-1500

-1000

-500

0

500

without DRwith DR

Fig. 2: Demand response model

III. SYSTEM EVALUATION AND OPTIMISATION

A. Economic evaluation

The system is evaluated according to its life cycle cost (LCC).This consists of the initial investment cost Cinitial and the dis-counted sum of the annual cost Cjannual for each year [10] [26][15].

LCC = Cinitial +LT

j=1

Cjannual(1 + r)j

(8)

The initial investment cost depends on the size of the installedpower and is equal to the sum of the investment cost for the sep-arate components according to the parameters in Table III. Thebatteries have a double investment cost because of their smallerlife time.

Cinitial = CAPEXsolar Pinst.solar + CAPEXwind Pinst.wind+2

(CAPEXc,bat Einst.bat + CAPEXp,bat Pinst.bat

)

+CAPEXCHP Pinst.CHP(9)

The annual cost is equal to the sum of the operational costs ac-cording to Table III.

Cjannual = OPEXsolar Esolar + OPEXwind Ewind+OPEXf,CHP + OPEXv,CHP ECHP

+grid cost + fuel cost

(10)

Electricity is bought (P igrid < 0) at the instantaneous Belpexprice plus an additional fee for the network costs. During timesteps with surplus electricity (P igrid > 0) it is sold at the Belpexprice. The annual grid cost is calculated according to Equa-tion 11.

8760

i=1

(P igrid < 0) (Belpexi + fee) + (P igrid > 0) Belpexi (11)

The fuel consumption at every time step is calculated using theCHP set point, the CHP efficiency, and the auxiliary boilersconsumption. The total annual fuel cost is the sum of the con-sumption multiplied by the gas price.

8760

i=1

fuel consumptioni gas price (12)

Table III: Economic parameters [27] [28]Battery

Solar Wind CHP Capacity PowerCAPEX (e/kW) 1570 1911 3940 175 175

OPEXv (e/kWh) 0.021 0.017 0.0108 - -OPEXf (e/year) - - 9345 - -

Lifetime (year) 25 25 25 12.5

B. Emissions evaluation

The microgrids annual CO2 emissions are calculated ac-cording to the total annual production per source and theirtechnology-specific reference values (Table IV).

COtot2 = COrel.solar2 Esolar + COrel.wind2 Ewind+COrel.CHP2 ECHP + COrel.grid2 Egrid

(13)

In the case of a netto export of electricity to the grid, the emis-sions due to the additional electricity production are subtractedfrom the microgrids emissions. The relative share of each ofthe microgrids generation units allows a relative value for theCO2 emissions per kWh of produced power to be calculated.

COmix2 =COrel.solar2 Esolar + COrel.wind2 Ewind + COrel.CHP2 ECHP

Esolar + Ewind + ECHP(14)

For netto export, this value replace the value of COrel.grid2 inEquation 13.

Table IV: Relative CO2 emissions [29]Solar Wind CHP Grid

COrel2 (gCO2/kWh) 41 11 300 210

IV. EILAND ZWIJNAARDE

Eiland Zwijnaarde is an industrial park in the region of Ghentthat is currently under development. The developers are assess-ing the possibilities of collective energy infrastructure and it istherefore an appropriate case study for the developed model.The sites annual electric consumption is estimated around 60GWh. and the annual thermal demand at 40 GWh. The electricload profile is based on the consumption profile of a technologypark in the region of Ghent (Figure 3). The thermal load pro-file is a generic load profile from the Flemish Energy Regulator(Figure 4). The peak electrical demand is 12 MW and the peakthermal demand is 14 MW.

hour0 1000 2000 3000 4000 5000 6000 7000 8000 9000

Ele

ctric

load

pro

file

(kW

)

4000

5000

6000

7000

8000

9000

10000

11000

12000

Fig. 3: Electric load profilehour

0 1000 2000 3000 4000 5000 6000 7000 8000 9000

The

rmal

load

pro

file

(kW

)

0

5000

10000

15000

Fig. 4: Thermal load profile

The upper boundary for the optimal size of the installed tech-nologies are derived from the physical boundaries of the site. Intotal there is 350000 hectares available for companies. At a con-servative estimate of 10m2/kWinst. for solar power, this meansthat the maximum amount of solar peak power that can be in-stalled is 35MW. For wind power it is estimated that there isspace to install 3 large turbines. At 3.3 MW/turbine, the res-ulting upper boundary for installed peak power is 10 MW. Theupper boundary for CHP is the electric peak demand, 10 MW.There is no upper boundary for the installed battery power or itsenergetic capacity.

V. CASE STUDY AND SIMULATION RESULTS

The optimal sizing results for an average electricity buyingprice of 115e/MWh and a gas price of 63e/MWh are tabulatedbelow. Initially, demand response is not considered.Psolar : 24.70 MW Esolar : 25700 MWh/yearPwind : 9.91 MW Ewind : 21150 MWh/yearPCHP : 3.56 MW ECHP : 17070 MWh/yearPgrid : 17.40 MW Egrid : -3925 MWh/yearEbat : 18 kWh

There is a high share of both solar and wind power in the totalannual electricity generation. The amount of wind power is justbelow the sites upper boundary and there is a netto export ofelectricity to the utility grid. The amount of installed batterypower is negligible on this scale.

Figure 5 shows the microgrids hourly production profiles forone week in the summer. There are strong daily generationpeaks due to the solar power production. These are almost dir-ectly translated into energy export peaks towards the grid. TheCHP mainly works at night to compensate the absence of solargeneration. There is a very clear netto positive power exchangewith the grid.

The value for the LCC in this scenario is 188 millione.100.24 gCO2 is emitted per consumed kWh. The LCC for areference case with the same electricity and gas buying pricebut without on-site production is 243 millione. The referenceemissions are equal to the grids emissions, 210 gCO2/kWh. Theproposed power system thus offers an improvement accordingto both evaluation methods.

Time (hours)20 40 60 80 100 120 140 160

Ave

rage

pow

er (

kW)

104

-1.5

-1

-0.5

0

0.5

1

1.5

Psolar

Pwind

PCHP

Pgrid

Load profile

Fig. 5: Hourly production profile over one week

A. DR

Demand response is included in the model as describedabove. On a daily basis 2.5% of the total electrical demand isshifted from times of high demand to times of low demand.

The resulting optimal installed power for each technology isshown in Figure 6. There is a general decrease in installed powercompared to the optimisation results without demand response.The total size reduction is about 15%. The reduction is mostpronounced for solar power and the grid connection; both de-crease by more than 3MW. In terms of energy, shown in Figure7, there is less solar power production and a subsequently smal-ler energy export to the utility grid. Overall, there is a smallerinteraction with the utility grid while maintaining a high shareof renewable power.

Solar Wind CHP Grid0

10

20

30

Inst

alle

dpo

wer

(MW

) DR No DR

Fig. 6: Installed power with demand response

B. Gradual ingress

In the previous sections the hybrid power system was sizedaccording to an equal energetic demand for every year of its life-

Solar Wind CHP Grid-1

0

1

2

3

104G

ener

ated

pow

er(M

Wh/

year

)

DR No DR

Fig. 7: Generated power with demand reponse

time. However, a gradual ingress of companies on the site willresult in a much smaller load profile during the first years of thepark. In the model, the first years electric and thermal demandare set to 10% of the nominal value. During the following tenyears the demands increase by 10% every year. After 10 yearsthe electric and thermal demand are at their nominal value. Thepower system is sized once and the installed capacities are fixedduring the entire lifetime of the installation.

The simulation results are shown in Figures 8 and 9. There isa clear absence of cogeneration in the optimised system. In-stead, all of the thermal energy is provided by the auxiliaryboiler. The CHP unit is controlled according to the electricaldemand, which means that during the first years it will prac-tically never be turned on. Instead the electrical power mainlycomes from the grid and the wind turbines. While in the previ-ous optimisations there was always a netto export of electricityto the grid, in this case there is a large netto import. This griddependency does not result in a larger grid connection; the gridsinstalled power decreases from around 18 MW to 13 MW. Thisis due to the lower installed solar fraction. Solar power giveslarge production peaks during summer which requires a stronggrid connection to inject the energy surplus on the grid. De-creasing the solar fraction thus means that the grid connectioncan be made smaller.

The relative CO2 emissions are 95.7 gCO2/kWh. This isslightly lower than the reference scenario without gradual in-gress.

Solar Wind CHP Grid0

10

20

30

Inst

alle

dpo

wer

(MW

) Ingress No ingress

Fig. 8: Installed power with gradual ingress

C. Multi-dimensional optimisation

A multi-objective optimisation is carried out that finds thenondominated solution set for the LCC and the CO2 emissionsper kWh. The pareto curve is shown in Figure 10.

The configuration with the lowest LCC and the highest CO2lies around 18.8 millione. This corresponds to the result of thesingle-objective optimisation. The CO2 emissions initially drop

Solar Wind CHP Grid-1

0

1

2

3

104

Gen

erat

edpo

wer

(MW

h/ye

ar)

Ingress No ingress

Fig. 9: Generated power with gradual ingress

rapidly for an increasing value of LCC. On average, a reduc-tion of 1 gCO2/kWh comes at an additional cost of 117000eover the lifetime of the installation. This corresponds to 4680eon an annual basis. For an LCC greater than 19 millione thecurve is practically linear and the total cost of saving an ad-ditional gC02/kWh is equal to 340000e, or an annual cost of13600e/year .

108

1.88 1.9 1.92 1.94 1.96 1.98 2 2.02 2.04

GH

G e

mis

sio

ns in

gC

O2

/kW

h

50

55

60

65

70

75

80

85

90

95

100Pareto front

PauseStop

Fig. 10: Pareto curve

Figure 11 shows each technologys annually generated elec-tricity for every point on the pareto front. For low LCC, thereduction in CO2 is due to a decrease in cogeneration and anincrease in solar power. This corresponds to the steepest partof the pareto curve, which means that this reduction comes atthe lowest cost per gCO2 saved. The wind power fraction andgrid export stay relatively constant. For increasing LCC, thegenerated solar power reaches its maximum. For a further CO2reduction the installed power of the CHP keeps decreasing andthe energy deficit is compensated by less electricity exports tothe grid. This way the emissions can be reduced down to about54 gCO2/kWh. In comparison, the average in electricity in Bel-gium are between 180 and 210 gCO2/kWh [30] and in Europethis is around 320 gCO2/kWh [31]. For very low values of CO2the netto power exchange with the grid becomes positive.

VI. CONCLUSION

The optimal system is assessed for a range of scenarios. Theresults suggest a large scale roll-out of renewable generation inpractically all cases. For maximal installed renewable produc-tion the renewable power sources are theoretically able to supplyall the parks annual energy demand. However, the intermittent

95.6gCO2/kWh 71.74gCO2/kWh 53.94gCO2/kWh

Gen

erat

ed p

ower

(kW

h)

107

-1

-0.5

0

0.5

1

1.5

2

2.5

3

3.5

Esolar

Ewind

ECHP

Egrid

Fig. 11: Generated power on pareto front

behaviour of these sources and the current price structure meansthat a solid grid connection is in generally favourable.

A small amount of demand response can have a large impacton the size of the installation. A power system with DR is moreself-sufficient without increasing the fraction of non-renewableproduction.

A gradual ingress of firms in the park and the subsequentsloped annual energy demand has a very significant effect onthe power systems optimal configuration. Dynamically sizingthe sites power system according to a changing annual energydemand could be a future topic of research to address this issue.

The implemented storage control structure is not able to offerany economic incentives for the large scale deployment of bat-tery storage. This suggests that new remuneration methods areneeded in order to successfully introduce this technology.

A multi-objective optimisation identifies the specific cost ofdecreasing CO2 emissions. Increasing the solar share and redu-cing the size of the CHP installation results in reduced emissionsbut comes at an additional cost.

While in all scenarios there is a high share of renewable gen-eration, there is limit to the physical amount of energy that canbe generated on-site. Solar and wind power clearly play an im-portant role in the optimal power system, but their share is lim-ited by the sites physical boundaries. Demand reduction shouldtherefore be considered in order to make the park independentof exhaustive energy sources.

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[19] H. Wang, W. Yin, E. Abdollahi, R. Lahdelma, and W. Jiao, Modelling andoptimization of CHP based district heating system with renewable energyproduction and energy storage, Applied Energy, vol. 159, pp. 401421,2015.

[20] M. Nazari-Heris, S. Abapour, and B. Mohammadi-Ivatloo, Optimal eco-nomic dispatch of FC-CHP based heat and power micro-grids, AppliedThermal Engineering, vol. 114, pp. 756769, 2017.

[21] P. Ghadimi, S. Kara, and B. Kornfeld, The optimal selection of on-siteCHP systems through integrated sizing and operational strategy, AppliedEnergy, vol. 126, pp. 3846, 2014.

[22] J. P. Fossati, A. Galarza, A. Martin-Villate, J. M. Echeverria, andL. Fontan, Optimal scheduling of a microgrid with a fuzzy logic con-trolled storage system, International Journal of Electrical Power and En-ergy Systems, vol. 68, pp. 6170, 2015.

[23] J. P. Fossati, A. Galarza, A. Martin-Villate, and L. Fontan, A method foroptimal sizing energy storage systems for microgrids, Renewable Energy,vol. 77, pp. 539549, 2015.

[24] H. Yang, W. Zhou, L. Lu, and Z. Fang, Optimal sizing method for stand-alone hybrid solar-wind system with LPSP technology by using geneticalgorithm, Solar Energy, vol. 82, no. 4, pp. 354367, 2008.

[25] A. Chaouachi, R. M. Kamel, R. Andoulsi, and K. Nagasaka, Multiobject-ive Intelligent Energy Management for a Microgrid, IEEE Transactionson Industrial Electronics, vol. 60, no. 4, pp. 16881699, 2013.

[26] A. Gonzalez, J. R. Riba, A. Rius, and R. Puig, Optimal sizing of a hy-brid grid-connected photovoltaic and wind power system, Applied En-ergy, vol. 154, pp. 752762, 2015.

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Contents

1 Introduction 1

1.1 Eco-industrial parks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.1.1 Definitions and concepts . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.1.2 Drivers and limitations of eco-industrial parks . . . . . . . . . . . . . . 3

1.1.3 Inter-firm energy supply . . . . . . . . . . . . . . . . . . . . . . . . . . 5

1.1.4 Industrial microgrids . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

1.2 Eiland Zwijnaarde . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

1.2.1 Eiland Zwijnaarde as an eco-industrial park . . . . . . . . . . . . . . . 10

1.2.2 Vision of project . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

1.2.3 Current state of affairs . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

2 Microgrids in the Belgian electricity sector 15

2.1 The liberalised electricity market . . . . . . . . . . . . . . . . . . . . . . . . . 15

2.2 Price of electricity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

2.2.1 Commodity price . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

2.2.2 Transmission tarrifs . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

2.2.3 Distribution tarrifs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

2.2.4 Industrial energy contract . . . . . . . . . . . . . . . . . . . . . . . . . 21

2.3 Energy policy in Flanders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

2.4 CO2 market . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

2.5 Grid structure and microgrid opportunities . . . . . . . . . . . . . . . . . . . 25

2.5.1 Functionality and economic incentives . . . . . . . . . . . . . . . . . . 25

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2.5.2 Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

2.5.3 Ownership . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

3 Hybrid Power systems 30

3.1 Wind power . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

3.1.1 Wind turbine power curve . . . . . . . . . . . . . . . . . . . . . . . . . 32

3.1.2 Wind speed data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

3.1.3 Dynamic economic analysis . . . . . . . . . . . . . . . . . . . . . . . . 35

3.2 Solar power . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

3.2.1 PV-curve & Maximum power point (MPP) . . . . . . . . . . . . . . . 38

3.2.2 Dynamic economic analysis . . . . . . . . . . . . . . . . . . . . . . . . 40

3.3 Cogeneration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

3.3.1 Cogeneration technologies . . . . . . . . . . . . . . . . . . . . . . . . . 43

3.3.2 Modelling and sizing . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

3.3.3 Modes of operation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

3.3.4 Dynamic techno-economic analysis . . . . . . . . . . . . . . . . . . . . 51

3.3.5 System evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

3.3.6 Reference case . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

3.3.7 Electric load following: . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

3.3.8 Thermal load following: . . . . . . . . . . . . . . . . . . . . . . . . . . 56

3.3.9 Conclusion on CHP model . . . . . . . . . . . . . . . . . . . . . . . . . 58

3.4 Electrical storage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

3.4.1 Modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

3.4.2 Battery control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

3.5 Demand-side management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68

3.5.1 Demand response modeling . . . . . . . . . . . . . . . . . . . . . . . . 69

4 Optimal design and sizing 71

4.1 Evaluation of electricity production system . . . . . . . . . . . . . . . . . . . 71

4.1.1 Economic evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71

4.1.2 Technical evaluation and constraints . . . . . . . . . . . . . . . . . . . 75

4.2 Optimised variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77

4.3 Multi-objective optimisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78

4.3.1 Pareto front . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78

4.4 Genetic Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79

5 Simulation results and discussion 82

5.1 Energy key figures and model parameters . . . . . . . . . . . . . . . . . . . . 82

5.2 Sensitivity analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83

5.2.1 System evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84

5.2.2 Installed power per technology . . . . . . . . . . . . . . . . . . . . . . 85

5.2.3 Average weekly power flow . . . . . . . . . . . . . . . . . . . . . . . . 87

5.3 Future prices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90

5.4 Impact of ETS market . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93

5.5 Impact of demand response . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94

5.6 Impact of gradual ingress . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96

5.7 Multi-dimensional analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100

6 Conclusion 103

List of Abbreviations and Symbols

ARP Access responsible party

ACS Annualized cost of system

BREAAM Building Research Establishment Environmental Assessment Method

Cinitial Initial investment cost

Cannual Annual system cost

CAPEXsolar Capital expenditure for solar power

CAPEXwind Capital expenditure for wind power

CAPEXCHP Capital expenditure for CHP unit

CAPEXc,bat Capital expenditure for battery capacity

CAPEXp,bat Capital expenditure for battery power

CHP Combined heat and power

CO2,mix Average CO2 emissions per kWh of electricity produced locally in the microgrid

COE Cost of electricity

DR Demand response

DSM Demand side management

DSO Distribution system operator

ECHP Annually produced electrical CHP power

Egrid Annual power exchange with macrogrid

Esolar Annually produced solar power

Ewind Annually produced wind power

Einst.bat Maximum amount of energy that can be stored in the storage system

EE Embodied energy

EES Electrical energy storage

EIP Eco-industrial park

ESCO Energy service company

ETS Emission trading system

GHG Green house gas

LCC Life cycle cost

LP iE Instantaneous electrical demand

LP iT Instantaneous thermal demand

LPSP Loss of power supply probability

LT Lifetime

MPP PV maximum power point

MOC Mean operation cost

OPEXf,CHP Fixed operational expenditure CHP

OPEXsolar Operational expenditure solar power

OPEXv,CHP Variable operational expenditure CHP

OPEXwind Operational expenditure wind power

OTC Over the counter

PB Power balance

P exibat Instantaneous power exchange with storage system

PF i Instantaneous difference between the electric production and the electric consumption

PF inorm Normalised instantaneous power flow

Pgrid1 Grid interaction because of the batterys power limitation

Pgrid2 Grid interaction because of the batterys state of charge limitation

PI/O Reference value for CHP on-off decision

P iCHP Instantaneous CHP electrical power production

P ifuzz Batterys instantaneous economic set point

P igrid Instantaneous interaction with macrogrid

P isolar Instantaneous solar power production

P iwind Instantaneous wind power production

Pinst.bat Maximum amount of power that can be drawn or stored in the battery during one time step

Pinst.CHP Installed electrical CHP power

Pinst.grid Installed grid power

Pinst.solar Installed solar power

Pinst.wind Installed wind power

PCC Point of common coupling

P-Q ratio Power to heat ratio

priceinorm Normalised instantaneous electricity price

PV Photovoltaic

QiCHP Instantaneous CHP heat production

Qiboiler Instantaneous boiler heat production

r Interest rate

REP Renewable energy penetration

ROI Return on investment

SOCi State of charge

SOCmin EES minimum state of charge

SOCmax EES maximum state of charge

SPV Special purpose vehicle

TSO Transmission system operator

UPS Uninterruptible power supply

vi Instantaneous wind speed

vci Cut-in wind speed

vr Rated wind speed

vco Cut-out wind speed

EES hourly self-discharge coefficient

bat EES charging and discharging efficiency

el Electrical CHP efficiency

th Thermal CHP efficiency

tot Total CHP efficiency

List of Figures

1.1 Classification of industrial parks . . . . . . . . . . . . . . . . . . . . . . . . . 3

1.2 Microgrid topology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

1.3 Map of the site Eiland Zwijnaarde . . . . . . . . . . . . . . . . . . . . . . . . 9

1.4 Development scenarios for Eiland Zwijnaarde . . . . . . . . . . . . . . . . . . 11

1.5 Trias Energetica . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

2.1 Lay-out of liberalised electricity market . . . . . . . . . . . . . . . . . . . . . 16

2.2 Breakdown of the price of electricity . . . . . . . . . . . . . . . . . . . . . . . 19

2.3 Hourly Belpex price in 2015 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

2.4 Projection of ETS CO2 price . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

3.1 Wind turbine power curve . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

3.2 Wind speed profiles based on the log and power law . . . . . . . . . . . . . . 34

3.3 Historical wind production data . . . . . . . . . . . . . . . . . . . . . . . . . . 35

3.4 Hourly load profile . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

3.5 Hourly wind power production . . . . . . . . . . . . . . . . . . . . . . . . . . 37

3.6 Hourly power exchange with the grid . . . . . . . . . . . . . . . . . . . . . . . 37

3.7 Power flows over one week . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

3.8 3D Simulation results for wind power . . . . . . . . . . . . . . . . . . . . . . . 38

3.9 Simulation results for specific grid price . . . . . . . . . . . . . . . . . . . . . 39

3.10 PV power-voltage characteristic . . . . . . . . . . . . . . . . . . . . . . . . . . 40

3.11 Historical solar production data . . . . . . . . . . . . . . . . . . . . . . . . . . 40

3.12 Hourly solar power production . . . . . . . . . . . . . . . . . . . . . . . . . . 41

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3.13 Power flows over one week . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

3.14 3D Simulation results solar power . . . . . . . . . . . . . . . . . . . . . . . . . 42

3.15 Simulation results for specific grid price . . . . . . . . . . . . . . . . . . . . . 42

3.16 Lay-out of a CHP system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

3.17 CHP operating region . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

3.18 Operational cost of CHP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

3.19 Linearised CHP operating region . . . . . . . . . . . . . . . . . . . . . . . . . 50

3.20 Hourly electric load profile . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

3.21 Hourly thermal load profile . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

3.22 Hourly Belpex price . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

3.23 Electric load following control algorithm . . . . . . . . . . . . . . . . . . . . . 55

3.24 Simulation results with electric load following . . . . . . . . . . . . . . . . . . 56

3.25 Thermal load following control algorithm . . . . . . . . . . . . . . . . . . . . 57

3.26 Simulation results with thermal load following . . . . . . . . . . . . . . . . . . 57

3.27 Battery control algorithm - maximal usage . . . . . . . . . . . . . . . . . . . . 64

3.28 Battery control algorithm - fuzzy logic . . . . . . . . . . . . . . . . . . . . . . 65

3.29 Membership functions for power flow . . . . . . . . . . . . . . . . . . . . . . . 67

3.30 Membership functions for electricity price . . . . . . . . . . . . . . . . . . . . 67

3.31 Membership functions for Pfuzz . . . . . . . . . . . . . . . . . . . . . . . . . . 68

3.32 Demand response over one day . . . . . . . . . . . . . . . . . . . . . . . . . . 70

3.33 Demand response over one week . . . . . . . . . . . . . . . . . . . . . . . . . 70

3.34 Pgrid without DR (entire year) . . . . . . . . . . . . . . . . . . . . . . . . . . 70

3.35 Pgrid with DR (entire year) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70

4.1 Graphical representation of a pareto front . . . . . . . . . . . . . . . . . . . . 79

4.2 Flowchart of a genetic algorithm . . . . . . . . . . . . . . . . . . . . . . . . . 80

5.1 Hourly electric load profile . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83

5.2 Hourly thermal load profile . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83

5.3 Life cycle cost . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85

5.4 Cost of electricity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85

5.5 CO2 emissions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86

5.6 Power exchange with grid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86

5.7 Installed solar power . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87

5.8 Installed wind power . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87

5.9 Installed CHP power . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87

5.10 Installed storage capacity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87

5.11 Weekly power flow during the entire year . . . . . . . . . . . . . . . . . . . . 88

5.12 Hourly simulation during one week in winter . . . . . . . . . . . . . . . . . . 89

5.13 Hourly simulation during one week in summer . . . . . . . . . . . . . . . . . . 90

5.14 Life cycle cost . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91

5.15 Cost of electricity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91

5.16 CO2 emissions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91

5.17 Installed solar power . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92

5.18 Installed wind power . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92

5.19 Installed CHP power . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92

5.20 Installed batter capacity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92

5.21 Installed power with ETS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94

5.22 Generated power with ETS . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94

5.23 Installed power with DR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96

5.24 Generated power with DR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96

5.25 Effect of ingress on energetic demand . . . . . . . . . . . . . . . . . . . . . . . 97

5.26 Installed power with ingress . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98

5.27 Generated power with ingress . . . . . . . . . . . . . . . . . . . . . . . . . . . 98

5.28 Weekly power flow for the fourth and tenth year . . . . . . . . . . . . . . . . 99

5.29 Hourly power curves over one week in summer for different years . . . . . . . 100

5.30 2-dimensional pareto front . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101

5.31 Installed power on pareto front . . . . . . . . . . . . . . . . . . . . . . . . . . 101

5.32 Generated power on pareto front . . . . . . . . . . . . . . . . . . . . . . . . . 101

List of Tables

2.1 Example of an industrial energy contract . . . . . . . . . . . . . . . . . . . . . 22

3.1 Overview of technologies in studied literature . . . . . . . . . . . . . . . . . . 31

3.2 Commercial wind turbines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

3.3 Simulation parameters for wind power . . . . . . . . . . . . . . . . . . . . . . 36

3.4 Overview simulation parameters for solar power . . . . . . . . . . . . . . . . . 41

3.5 Overview of CHP technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

3.6 CHP cost function coefficients . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

3.7 Cogeneration prime mover efficiencies . . . . . . . . . . . . . . . . . . . . . . 50

3.8 Overview simulation parameters for cogeneration case . . . . . . . . . . . . . 53

3.9 Technical parameters for different storage technologies . . . . . . . . . . . . . 59

3.10 Economic parameters for storage technologies . . . . . . . . . . . . . . . . . . 60

3.11 Fuzzy control rules for battery storage . . . . . . . . . . . . . . . . . . . . . . 68

3.12 Demand response simulation results . . . . . . . . . . . . . . . . . . . . . . . 70

4.1 Overview of optimisation methods in studied literature . . . . . . . . . . . . . 72

4.2 List of abbreviations and occurrence . . . . . . . . . . . . . . . . . . . . . . . 73

4.3 CO2 emissions per technology in gCO2eq/kWh . . . . . . . . . . . . . . . . . 76

5.1 Predicted energetic consumption for different scenarios . . . . . . . . . . . . . 82

5.2 Economic simulation parameters . . . . . . . . . . . . . . . . . . . . . . . . . 84

5.3 Future economic simulation parameters . . . . . . . . . . . . . . . . . . . . . 90

5.4 Simulation results with ETS . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93

5.5 Simulation results with DR . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95

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5.6 Simulation results with gradual ingress . . . . . . . . . . . . . . . . . . . . . . 97

1 Sensitivity analysis for current market prices . . . . . . . . . . . . . . . . . . 119

2 Sensitivity analysis for future market prices . . . . . . . . . . . . . . . . . . . 120

Chapter 1

Introduction

Limitless economic growth, ecological collapse and resource scarcity are forcing industry as

a whole to rethink its fundamental principles and resort to more sustainable practices [1].

As defined in the Brundtland Report [2], sustainable development is development that meets

the needs of the present without compromising the ability of future generations to meet their

own needs. In this discourse, industrial symbiosis has emerged as an approach in which

traditionally separate industries collaborate in order to find synergies that offer competitive

advantages. This can involve physical exchange of materials, energy, water, etc. [3]. Geo-

graphic proximity is an important facilitating factor, which is why eco-industrial parks (EIP),

with their collective infrastructure and spatial density of firms, have become an important

topic of study in the field of sustainability [1].

The transition towards a carbon neutral industrial sector is driven by the global phenomenon

of climate change. The IPCC warns that the continued emission of greenhouse gases will cause

further warming of global climate patterns and result in long-lasting changes in all components

of the climate system. According to their 2014 assessment report, these change will increase

the likelihood of severe, pervasive and irreversible impacts for people and ecosystems [4]. In

Europe in 2014, 26% of CO2 equivalent emissions were due to the consumption of electricity

and gas [5]. Of all electricity consumption in Europe, 36% is consumed by industry [6].

Optimizing industrial electricity consumption can therefore significantly reduce CO2 emissions

and mitigate the effects of global warming.

The aim of this study is to carry out a techno-economic analysis on the design and operation

of energy systems on newly-developed industrial parks. A microgrid model is constructed

that optimises the system configuration according to its life cycle cost and green house gas

emissions. The socio-organisational framework is assessed by means of a literature study

which results in a list of recommendations for park developers. The current development of

the project Eiland Zwijnaarde in Ghent provides the basis for a concrete case study in which

the opportunities for an inter-firm power system are identified.

1

Chapter 1. Introduction 2

1.1 Eco-industrial parks

1.1.1 Definitions and concepts

What sets EIPs apart from conventional business parks is that the ecological footprint and

carbon dioxide emissions of the individual businesses as well as the park as a whole are actively

reduced. Energy and construction technologies are employed to optimise energy efficiency and

material cycles. An integrated management system facilitates inter-firm exchange of energy,

materials and information. A regulatory system encourages the companies to meet their

performance goals [7].

Four main definitions in the field of eco-industrial parks can be identified. They are schem-

atically represented in Figure 1.1.

Sustainable industrial parks

A sustainable industrial park is the most far-reaching concept in terms of sustainability

and collectivity in the context of industrial park design. Technological, economic, social

and spatial opportunities offered by the geographical proximity are combined in ways

that go beyond the firms individuality [8]. This results in a collective and integrated

approach regarding the supply and exchange of energy and materials, use of equipment

and facilities, etc. Sustainable industrial parks are well-integrated in the surrounding

area and require extensive measures regarding ownership and stakeholders relationships.

Eco-industrial parks

In EIPs, individual companies exploit inter-firm synergies in term of materials, energy

and water. There is a strong focus on industrial symbiosis between the participants,

although the firms pursue their individual objectives. An eco-industrial park can be seen

as the composition of a number of industrial symbiosis projects between different firms

in the same location [9]. It forms an industrial ecosystem in which the consumption of

energy and materials is optimised, waste generations is minimised and residues, whether

in terms of materials or energy, are optimally reinjected in the value chain [10].

Green industrial parks

A green industrial park is a collection of firms that individually strive towards sustain-

ability. Possibilities to exploit inter-firm synergies are not actively investigated. The

majority of sites in Flanders with an eco-label belong to this group [11].

Low carbon industrial parks

Low carbon industrial parks is the combination of green industrial parks and eco-

industrial parks. Firms focus on renewable energy and clean processes on an individual


level while looking for opportunities of interaction with neighbouring sites.

Sustainable

industrial parks

Eco-industrial

parks

Green

industrial parks

Low carbon industrial parks

Figure 1.1: Classification of industrial parks [11]

1.1.2 Drivers and limitations of eco-industrial parks

Since the concept of industrial symbiosis was first used in academic literature in the 1980s,

there have been many attempts to harness its advantages [12]. A review of literature is

carried out to identify the drivers and limitations of the implementation of industrial ecology

concepts in industrial parks. Many of the cases cover a broad range of technologies, from

energy and resource supply to transport, employee facilities and water treatment. The scope

of this study is confined to the possibilities regarding collective supply of electrical power.

The fact that eco-industrial parks are such multi-disciplinary entities makes them hard to

study. The technical aspects, the business framework around them and the social interaction

with the parks surroundings form an extensive socio-organisational web [7].

A list of drivers and opportunities of EIPs is presented, as adapted from [13], [12], [7] and

[14].

While the ideological basis for the development of an EIP can be environmental, the

most important driver for a successful project is the financial gain. Business performance

and environmental performance should therefore both play an important role in the

design of the park and inter-firm cooperation will only then offer advantages compared

to regular (more wasteful) business park design.

Two ways of thinking exist as to whether EIPs should be artificially engineered or self-

organised. The first perspective focuses on the technical feasibility of the inter-firm

linkages, while the second believes that organic growth of connections will result in


enhanced ownership and a more resilient system. What is most important is that the

system is fully integrated in its surrounding and that it possesses sufficient flexibility to

survive in a dynamic economic environment.

Ideally, the initiative for inter-firm cooperation should come from the firms themselves

and not from any other authority. The role of the park management should be to

facilitate information flow between the firms and to provide a platform for open com-

munication.

Physical proximity and complementary profiles in terms of energy and materials is

important and direct economic competition between firms should be avoided [15]. In

any case, a level of trust between the participants is crucial.

An active participation of the firms in collective projects ensures continuity and sus-

tained ownership. Participation from additional stakeholders such as other companies,

the local community, environmental organisations, as well as technical experts from

different fields can be a strong asset in the success of a project.

During the planning phase of the site, the focus should be primarily on utility sharing.

The subsequent economic and environmental advantages will convince the companies of

the benefits of symbiotic exchanges [12]. In a second instance, energy, water and material

waste exchanges can be considered. Finally more company specific and economically

challenging projects can be developed.

The most famous example of a successful EIP is the Kalundborg Park in Denmark. It is the

first realisation of industrial symbiosis that was published in literature [3]. Nine different firms,

including a coal-fired power station, an oil refinery and a pharmaceutical plant are connected

through a network of eleven physical linkages. Remarkable is that the inter-connections were

not conceived from the design of the park but instead grew gradually through independent

and economically driven actions [16]. The Asnaes Power Station has a very central role in the

system, which shows the fundamental role of energy supply in EIP design. The plant provides

electricity as well as heat and process steam to its industrial and residential neighbours. The

power station is still coal fired but a transition to biomass is planned in the near future [17].

While there are inherent advantages for interactions between firms, technical, economic, in-

formational, organisational and regulatory barriers can be identified [12].

The main limitation of EIP parks is the potential for system fragility. If one of the parks

main firms decides to re-locate, other companies might need to find other sources for

their raw materials thus affecting the functioning of the entire chain. Electrical power

however, as opposed to physical primary resources, is homogeneous independently of


its source, and therefore collective power supply is less affected by this fragility. Dis-

tribution systems though will be sized and reinforced according to the type of installed

generation, which means that a change in the production might result in poor power

quality and reliability. This is worse in the case of heat exchange, because quality and

temperature are very important parameters for process heat. One way of mitigating

this vulnerability is a diversification of sources [13].

Another limitation is that the price of inputs and output, such as electricity and other

energy forms, vary over time. This means that the profitability of inter-firm projects

is subject to uncertainty. Furthermore, low market prices for (non-renewable) energy

sources means that companies often do not have enough incentives to invest in collective

renewable energy projects.

In the energy sector the legal framework is often a hindrance. Administrative barriers,

public opposition and ever-changing or non-existent legislation create an unfavourable

environment for the development of inter-firm energy supply. This limitation is more

broadly assessed in chapter 2 on microgrids in the Belgian market.

1.1.3 Inter-firm energy supply

The high investment cost and significant green house gas emissions in industrial energy supply

mean that sharing facilities between firms is a promising approach [18]. The collective nature

of electrical distribution means that several advantages of an inter-firm power grid can be

identified [18].

Liberalisation of energy markets might open up new opportunities for inter-firm energy

projects. Using an existing installation to participate in new market mechanisms such

as spot markets and ancillary services can lead to financial gains.

Bundling power production and demand can flatten a sites power curve, which means

that the size of the grid connection can be downscaled.

Advancements in terms of online communication and power system control allow an

increase in the implementation of demand response programs.

Economies of scale can mean that joint, inter-firm projects can be carried out more

cost-efficiently. This way a diverse set of technologies can be installed at a lower cost,

which will lead to a more integrated power system.

It is important to note the potential of combined heat and power technology. On-site

linking of the electric and thermal grid allows an increased share of renewables without

jeopardizing stability [19].


Notwithstanding these opportunities, optimising an on-site power system is not an unam-

biguous task because of the multitude of involved stakeholders [9].

1.1.4 Industrial microgrids

An industrial parks power system is characterised by localised electrical and thermal loads,

high energetic consumption and spatial opportunities for the integration of renewable energy

sources. For these reasons, the prevailing paradigm for electric power distribution on an

eco-industrial park is that of a microgrid. According to Lasseter [20], the microgrid concept

assumes a cluster of loads and microsources operating as a single controllable system that

provides both power and heat to the local area. The components in this energy cluster function

independently in possible interaction with the surrounding macrogrid [21]. This results in

enhanced local control, which can lower the cost of energy distribution, aid the integration

of renewable sources and thereby reduce green house gas emissions [22]. Furthermore, the

possibility of islanding improves the system security of supply by detaching the microgrids

reliability from that of the macrogrid.

Broadly following the categorisation of the International Energy Agency [23], five kinds of

flexibility resources can be used to supply and balance the microgrids variable energetic

demand: dispatchable power plants, non-dispatchable power plants, demand side management

and demand response programs, energy storage facilities and interconnection with adjacent

grids. A set of the most interesting technologies for a hybrid power system in Flanders is

selected.

In their white paper A Journey to Green Energy [24], Flemish distribution system operator

(DSO) Eandis confirms that the most important renewable technologies in Belgium are and

will continue to be solar and wind power. In 2015, 15% of electricity in Belgium was produced

by renewable sources, with 4% of netto production by solar power and 6.5% by wind power

[25]. The potential of PV and wind energy depends strongly on the solar radiation and the

wind speed profile at the considered site and production is therefore inherently variable in

time [26].

In a power system, renewable sources with intermittent behaviour can be combined with dis-

patchable units in order to maintain the system balance [24]. Cogeneration, the simultaneous

generation of heat and power, has a high flexibility and can therefore allow the integration of

a large share of fluctuating electricity sources [19]. A combined heat and power (CHP) plant

significantly increases the overall fuel efficiency compared to separate production and can

therefore lead to an important reduction in CO2 emissions [27]. At the moment the Flemish

government has a certificate-based subsidy scheme for CHP installations that shows their


interest in this technology. The share of electricity produced by cogeneration in Flanders

is expected to increase [28] and therefore this technology is chosen as a component in the

microgrid.

Historically, the inability to store electricity laid the foundation for the current electrical

power system; the instantaneous balance between production and consumption is one of its

building blocks. However, recent development in electrical storage technologies means that

storing electricity has become more viable, even without large infrastructure such as a pumped

hydroelectric energy storage [23]. Different aspects of microgrid flexibility (load levelling, peak

shaving and seasonal storage, etc.), can therefore be offered using on-site electrical storage

[29]. For these reasons, this study will assess the possibilities for electrical storage in industrial

microgrid design.

Nowadays, through the introduction of the smart grid concept, the greater need for flexibility

and the active participation of the end-user, demand can be seen as a new kind of resource [30].

Demand side management (DSM) is the set of methods that aims to assure grid reliability

through active participation of the costumer in the electricity market [31]. Instead of changing

the production side, it intents to modify the consumers load profile in response to financial

incentives [31]. Demand management programs in a microgrid context can bring significant

changes regarding economy, reliability and flexibility [32] and will therefore be included in

this study.

The point of common coupling (PCC) is the connection point where the microgrid is con-

nected to the macrogrid. While stand-alone microgrids exist [33] [34], dense electrification in

Flanders means that a grid-connection can be installed at a relatively low cost. Connecting

the microgrid to the utility grid makes bi-directional power exchanges possible, which allows

electricity imports or exports in order to meet an excess or surplus of local production.

Figure 1.2 gives a schematic overview of the grid topology according to the technologies that

were listed above. In chapter 3 each of the technologies is discussed at length and a microgrid

optimisation model is developed.


Figure 1.2: Microgrid topology


1.2 Eiland Zwijnaarde

Eiland Zwijnaarde is an industrial park in the region of Ghent that is currently under develop-

ment. It aims to accommodate a combination of high-tech companies, laboratories, university

buildings and water-based logistics. It is located on an island bordered by the Ghent Canal

ring in the north, the river the Scheldt in the East and the Tijarm in the south and west. An

aerial view of the site is shown in Figure 1.3.

The site is being developed by Waterwegen en Zeekanaal NV and NV Eiland Zwijnaarde. NV

Eiland Zwijnaarde is a collaboration between the Ghent city developer SOGent, provincie

Oost Vlaanderen, and two private partners. Ghent university is expected to be one of present

institutions on the park.

Figure 1.3: Map of the site Eiland Zwijnaarde

Historically the site was a dumping place for toxic waste from a nearby textile factory. At

the end of last century it had become a so-called brownfield and was remediated and leveled

in order to prepare it for new development.


Waterwegen en Zeekanaal NV is the owner of the sites northern part and is planning to

establish a water-based transport and logistics hub. The rest of the lands belong to NV Eiland

Zwijnaarde, a public-private partnership that was created to develop the sites southern part

into a technology campus. The project is a collaboration with Ghent University, under the

joint venture Tech Lane Ghent.

1.2.1 Eiland Zwijnaarde as an eco-industrial park

The City of Ghent, driven by the green-socialist coalition in power since 2012, has high

ambitions regarding sustainability. By 2050 it aims to be climate neutral, which means

that the entire Ghent region should have no negative impact on climate change. This is to

guarantee the citys quality of life. A climate plan (Klimaatplan) was ratified in 2015 to

outline the actions that must be taken to reach this goal.

For business parks in Ghent the climate plan states that all means will be employed in or-

der to assure sustainability, from their design until their decommissioning [35]. Specifically,

reduction in energy consumption, local renewable production and rational use of energy and

materials are stimulated. Furthermore, collective energy projects such as district heating will

be facilitated. These sustainable principles are upheld in the development of Eiland Zwijn-

aarde and the sustainability ambitions were further developed in concrete vision documents.

1.2.2 Vision of project

Two concepts play a main role in the development of the projects vision: centralisation and

sustainability. Centralisation is to what extent the firms in the park are collectively organised.

Sustainability depends on how far-reaching the commitment to sustainability will be.

Two visions were developed in detail as part of projects design process: Centralised - Maximal

and Decentralised - Moderate. These are shown and explained in Figure 1.4.

Energy

In terms of energy, the two scenarios translate into the following objectives.

Central-Maximal

The Central-Maximal approach aims to maximally take advantages of possible synergies

between the inhabitants of the parks. The site is climate neutral and completely self-

sufficient in terms of energy on a regional level. Employees, as well as residents of

surrounding residential areas, participate in projects on sustainable energy supply. The


Intens and ambitious

cooperation between

public and private parties.

An unbinding guidance

of the private partners

by the public party

Centralised Decentralised

Local organisation

Maximal

Moderate

Sustainability

Figure 1.4: Development scenarios for Eiland Zwijnaarde

central park managements list of responsibilities is extensive and many decisions on

sustainability issues are taken collectively on a park level. A code of conduct makes

sure that the parks residents comply with its sustainability principles.

The ambitions regarding energy are an increase of locally produced power from 20% at

the sites startup to 100% by 2050. To this end, there will be large scale wind power and

solar PV, complemented with electrical storage. Later in the project a cogeneration unit

can be installed that is connected to a thermal grid. BREAAM certification (see section

below) is the general guideline for the energy performance of buildings, with a focus on

passive construction and maximal insulation. There is a centralised and integral energy

management system that regularly carries out energy audits. The electric distribution

grid is designed taking into account the renewable production and a high percentage of

electric vehicles.

Decentral-Moderate

For the Decentralised-Moderate scenario, the ambitions are along similar lines. However,

there is less drive to carry out joint projects and the final goals are slightly less ambitious.

Instead of 100% self-sufficiency by 2050, the Decentral-Moderate approach aims for 80%.

Shared facilities such as storage and generation are considered but on an individual

building-level instead of a park-level. A central heating network is absent, but inter-firm

heat sharing between two or more firms can be considered according to the opportunities

that arise.


BREAAM certification

The Building Research Establishment Environmental Assessment Method (BREAAM) is an

evaluation method used to assess the sustainability of buildings and regions. Building and

park owners, as well as city councils, governments and project developers can choose to certify

their building using BREAAM in order to show their dedication to sustainability.

The certification is not limited to energy savings and efficiency, instead it attempts to make

a wholesome assessment according to the following categories:

Responsible management

Synergy with the surroundings

Sourcing of water, materials and energy

Spatial development

Well-being and prosperity

Regional climate and environment

Under each categories there are different topics for which credits can be earned according to

sustainability criteria. These credits are awarded according to evidence-based proof.

Trias Ecologica

An important basis for the BREAAM certificate are the Trias Ecologica and its application

on energy, the Trias Energetica. Both these strategies for sustainable development were

developed by Kees Duijvestein at the TU Delft.

The Trias Ecologica is a way of acting and thinking in the field of sustainable design. The

fundamental idea is to limit the input of energy, materials and water during the construction

process of a building. Once it is built, the Trias aims to limit the buildings outflow of energy,

materials and water. Concretely this means that renewable sources should be used rationally

during construction, and that waste should be avoided or reused during its lifetime [11].

The application of the Trias Ecologica on the energetic design of a system results in the Trias

Energetica, as shown in Figure 1.5. It consists of three strategic measures to sustainably de-

velop and operate energy systems. The first step limits the systems energetic consumption by

eliminating losses and increasing the energetic efficiency. Secondly, consumed energy should

be generated using sustainable and renewable sources. Thirdly, if non-renewable sources are

inevitable, they should be used at the highest efficiency [36].


Reduce energy demand

Employ renewable sources Clean use of fossil fuels

Figure 1.5: Trias Energetica [36]

1.2.3 Current state of affairs

In order to learn about the recent advancements in the development of Eimand Zwijnaarde, an

interview with Mieke Gevaerts was conducted. She is the sites project manager at SOGent.

The fact that both public and private stakeholders are involved in the project makes it harder

to pursue a collective vision. There is political motivation to develop a sustainable industrial

park that is progressive in terms of energy, mobility and ecology. However this is opposed by

the very market-driven industrial park development business. Furthermore, the development

process consists of many stages and in every stage each stakeholder pursues its own interests.

At the moment the project developers are trying to translate the projects vision documents

into an attractive business environment for firms. Agreements with several companies are

under way and there is a large commitment from Ghent University to move to the park. As

identified earlier in this chapter, active participation of the firms is a determining factor in the

success of inter-firm cooperation and it is therefore important that they are involved from an

early stage. However, for Eiland Zwijnaarde, the commercialisation of the project has meant

that some compromises have been made on the original vision documents.

The code of conduct that would bind the companies to follow certain principles regarding

sustainability will not be implemented since it would over-complicate the sales contract. In-

stead, obligatory BREAAM certification for all buildings will be used to assure sustainability.

In a sales contract it is not possible to include future development regarding construction

and technology. BREAAM certification, despite its high price, has the advantage that it is

updated regularly and will therefore never be outdated.

Several studies have been done on the design of the sites energetic infrastructure. Because of

the uncertain demand profiles and a possible slow ingress of companies, the proposed collective

infrastructure projects such as a heating network and on-site cogeneration have been found


economically infeasible. Instead the traditional electrical and gas grid will be installed. The

potential of heat exchange with a nearby incineration facility was assessed but the fact that it

can take until 2030 before the park is fully filled means that the initial thermal demand will be

too low for profitability. In terms of renewable generation there are still plans for large scale

wind and solar PV. At the moment one wind turbine is being certified and building-based

geothermal energy is considered as a potential renewable source.

A special-purpose vehicle SPV Energie was founded to facilitate future projects regarding

energy. Its role is not completely defined and can be adapted according to the sites devel-

opment. It could for example finance collective energy projects or be used as a platform for

inter-firm communication on energy supply. The structure also allows participation of other

stakeholders such as surrounding inhabitants or energy cooperatives. There have been talks

with Energent, a Ghent-based energy cooperative, but at the moment they are not involved

in the development of the site.

The different development scenarios for Eiland Zwijnaarde can be classified according to the

EIP concepts defined in the previous section. In the Maximal-Central scenario, far-reaching

collective measures were proposed that would affect not only the technical but also the social

aspects of the park. This would lead to a very integrated industrial eco-system with strong

links between the firms. The result would most closely correspond to a sustainable industrial

park. The Moderate-Decentral scenario still has a focus on inter-firm cooperation but allows

for more individuality. It would therefore result in an eco-industrial park. In the development

scenario that is actually being carried out, the firms can choose their own role in the parks

sustainability. While the firms have to fulfil certain sustainability criteria, the final degree of

sustainability will strongly depend on their own initiatives. Their motivation will be decisive

as to whether Eiland Zwijnaarde will result in a green or eco-industrial park.

Chapter 2

Microgrids in the Belgian electricity

sector

The Belgian energy market is a complex picture with many different actors. In order to

properly assess the profitability of the microgrids components as outlined in the section

above, a general overview of the Belgian electricity sector is necessary. The aim here is to

identify the roles a microgrid can play within the Belgian power sector and to assess the

opportunities new technologies present in an economic context.

2.1 The liberalised electricity market

The recent transition from a state-owned, vertically integrated energy sector to a liberalised

market has changed energy planning and introduced various new market roles in the elec-

tricity market. While before a single company could operate across the entire value chain

of energy, different EU liberalisation directives have disaggregated the market. Concretely

this has caused a separation of the electric power business and the operation of the network

infrastructure (distinguishing competitive and non-competitive parts of the industry). It al-

lows access to energy infrastructure to third parties and implies a liberated supply side of the

market with unrestricted change of supplier for the consumers [37]. Independent regulators,

in Belgium the VREG, CREG, etc., were created to monitor the sector.

The aim of the transition is to lower energy prices due to increased competition and develop

customised energy services. However, in the energy sector there is a long time between the

initiation and completion of projects, which can be hard to reconcile with the liberalised mar-

kets favour for high returns and quick payback times. Furthermore, for inter-firm electricity

production, the strict unbundling of production and distribution as well as the prohibition of

one-to-one electric connections between firms pose restrictions on the possibilities for electrical

energy clustering [38].

15

Chapter 2. Microgrids in the Belgian electricity sector 16

52 ABB review 4|14

Distributed generation is increasing mainly because of photovoltaics and combined heat and power and will cause a significant share of genera-tion to be covered by a very large number of small units.

Volatile production from wind and solar energy is leading to faster and larger supply-side fluctuations of only limited predictability.

These three changes have technical im-plications in all aspects of the supply and use of electrical energy 10. Two c

electrical design and operation of sustainable business...

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