The Influence of Participation in Ancillary Service Markets on Optimal Energy Hub Operation

101 Pages·2017·3.88 MB·English

eeh
power systems
laboratory
Foteini Rafaela Tsaousi
The Influence of Participation in Ancillary
Service Markets on Optimal Energy Hub
Operation
Master Thesis
PSL0815
EEH – Power Systems Laboratory
Swiss Federal Institute of Technology (ETH) Zurich
Urban Energy Systems Laboratory
Swiss Federal Laboratories for Materials Science and Technology (EMPA)
Examiner: Prof. Dr. Gabriela Hug
Supervisors: Dr. L. Andrew Bollinger, Conor O’Malley
Zurich, August 29, 2017

Abstract
Ancillary services (AS) play an important role in ensuring security of supply and high
quality service in the electricity grid. Participation in AS markets offers a good revenue
possibility to energy producers as well as consumers. Distributed multi-energy systems
(DMES) in Switzerland have the opportunity to participate in AS markets by producing
orconsumingelectricitytostabilisethegrid. Theenergyhub(EH)approachisamethod
to model DMES as conceptual nodes with various in- and outflow energy streams. The
main goal of this thesis is the development of a model that investigates the effect of
AS market participation on the optimal operation of an EH in Switzerland. The AS of
tertiary control is considered. Firstly, different modelling approaches are investigated in
order to determine the most suitable one. Secondly, a two stage stochastic mixed-integer
linear program (MILP) is developed. The objective is to minimize the EH operational
cost by optimizing the bidding strategy and the EH dispatch schedule. The stochastic
modellingcapturestheuncertaintyregardingpossibleenergyprovisionofreservedcapac-
ity. Constraints representing the bidding strategy in the capacity market are expressed
as first stage whereas constraints regarding the energy market and provision of energy
are expressed as second stage. Simulation results of a case with specific design and price
parameters show the effect of AS provision on the EH operation and cost. The results
arecomparedinordertoevaluatethegeneralperformanceofthemodelandtoassessthe
uncertainty modelling. It is concluded by the comparison that the two stage stochastic
model captures the uncertainty of the tertiary control energy market well. Finally, the
model is used in a parameter sensitivity analysis to investigate the importance of various
input parameters and determine under which conditions AS participation can be proven
profitable for an EH.
ii

Acknowledgements
This master thesis has been a very rewarding experience during which I had the oppor-
tunity to expand my knowledge on a topic I am mostly interested in, build on an idea
andrealiseitandintheprocesscooperatewithmanynicepeopletowhomIamsincerely
grateful.
First and foremost, I offer my sincerest gratitude to my supervisors Conor O’ Malley
and Dr. Andrew Bollinger, for their valuable support and continuous guidance through-
out this thesis. Their feedback and advice always helped me to move forward with my
research. Theywerealwayswillingtooffermetheirexpertiseandtheyalwaysmotivated
me to do my best.
I would also like to thank Prof. Dr. Gabriela Hug for giving me the opportunity to
conductthisthesisinthePowerSystemsLaboratoryandforbeingsoopenwhenIneeded
her guidance. Furthermore, my grateful thanks go to the Urban Systems Laboratory of
EMPA for giving me the possibility to wade into this really interesting topic and to
contribute to their research.
MygenuineappreciationandthanksgotoTom`asTinocodeRubira,GeorgiosMavro-
matidisandJos´eEspejo-Uribeforgivingmetheirprofessionaladviceandhelponmatters
of stochastic modelling. Many thanks go also to Stavros Karagiannopoulos for his help
with all questions I had during my stay at PSL. I would also like to thank my fellows at
both PSL and the Urban Energy Systems Laboratory for their warm welcome and for
making my stay there so much fun.
My warmest appreciation goes to my friends who have always been there for me. I
would like to thank separately Kristof K¨olker for his valuable help, constant encourage-
ment and belief in me. Finally and most of all, I owe the greatest appreciation to my
family without which I would not be where I am today.
iv

Contents
List of Figures ix
List of Tables x
Nomenclature xii
1 Introduction 1
2 The Energy Hub Approach 5
2.1 EHub Modelling Tool . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
3 Electricity Ancillary Services in Switzerland 9
3.1 Primary Control Reserve . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
3.2 Secondary Control Reserve . . . . . . . . . . . . . . . . . . . . . . . . . . 12
3.3 Tertiary Control Reserve . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
3.4 Summary Tables of AS in Switzerland . . . . . . . . . . . . . . . . . . . . 14
3.5 Control Pooling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
3.6 Regulation for Load Participation . . . . . . . . . . . . . . . . . . . . . . . 17
4 Market Bidding 19
5 Problem Definition and Modelling Methods 21
5.1 Stochastic Modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
5.1.1 First Stage Optimization Problem - Objective Function . . . . . . 24
5.1.2 First Stage Constraints . . . . . . . . . . . . . . . . . . . . . . . . 24
5.1.3 Second Stage Optimization Problem - Objective Function . . . . . 26
5.1.4 Second Stage Constraints . . . . . . . . . . . . . . . . . . . . . . . 26
5.1.5 Approximation of the Expected Cost . . . . . . . . . . . . . . . . . 31
5.1.6 Scenario Generation . . . . . . . . . . . . . . . . . . . . . . . . . . 31
6 Numerical Experiments 35
6.1 Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
6.2 Base Case . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
6.2.1 Design parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
6.2.2 Price Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
vi
CONTENTS vii
6.2.3 Base Case Simulation . . . . . . . . . . . . . . . . . . . . . . . . . 40
6.3 Model Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
6.4 Parameter Sensitivity Analysis . . . . . . . . . . . . . . . . . . . . . . . . 57
7 Discussion and Conclusion 67
8 Outlook 71
Appendices 73
A Cost Deviation Table 74
B Total Income Tables 75
C Electricity consumed from the Grid 76
D Total Activation Income 78
E Table of Model Parameters and Variables 80
Bibliography 83
List of Figures
2.1 EH configuration with inflows of different types of input energy, outflows
of different energy carriers and six conversion technologies . . . . . . . . . 6
2.2 EHub Modelling Tool - Work Flow . . . . . . . . . . . . . . . . . . . . . . 7
3.1 Energy provision and payment scheme between TSO and AS market par-
ticipant . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
3.2 Basic scheme of TRL and TRE markets; products and mechanisms . . . . 13
5.1 Optimization horizon of the two stage stochastic MILP model . . . . . . . 22
5.2 Scenario tree of the two stage stochastic MILP model . . . . . . . . . . . 23
5.3 NRMSD value of each predicted scenario for TRE+ and TRE− prices . . 33
6.1 Framework of the two stage stochastic MILP model . . . . . . . . . . . . 36
6.2 Energy demand of (a) a typical summer day, (b) a typical winter day and
(c) a day with positive bidding for the Base Case simulation . . . . . . . . 41
6.3 Electricity and heat produced during a typical summer day for the Base
Case simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
6.4 Bidding capacity and income for activation of a typical summer day, in
positive and negative TRL for the Base Case simulation . . . . . . . . . . 43
6.5 Electricity and heat produced during a typical winter day for the Base
Case simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
6.6 Bidding capacity and income for activation of a typical winter day, in
positive and negative TRL for the Base Case simulation . . . . . . . . . . 45
6.7 Electricity and heat produced during a day with positive bidding for the
Base Case simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
6.8 Bidding capacity and income for activation of a day with positive activa-
tion, in positive and negative TRL for the Base Case simulation. . . . . . 47
6.9 Model Performance - Evaluation Process . . . . . . . . . . . . . . . . . . . 48
6.10 Totalcostpermonth. Comparisonbetween’PerfectForecast’,’Stochastic’
and ’No-Bidding’ versions . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
6.11 Availability and activation income per month. Comparison between ’Per-
fect Forecast’ and ’Stochastic’ versions . . . . . . . . . . . . . . . . . . . . 50
6.12 Day-aheadNegativeBid-Averagebiddingcapacityandactivationincome
permonth. Comparisonbetween’PerfectForecast’and’Stochastic’versions 52
viii
LIST OF FIGURES ix
6.13 Day-ahead Positive Bid - Average bidding capacity and activation income
permonth. Comparisonbetween’PerfectForecast’and’Stochastic’versions 53
6.14 HeatproducedbytheCHPduring(a)adaywithnobidding,(b)asummer
day, (c) a day with positive bidding and (d) a winter day. Comparison
between ’Perfect Forecast’, ’Stochastic’ and ’No-Bidding’ versions . . . . . 54
6.15 Heat produced by the heat pump during (a) a day with no bidding, (b) a
summer day, (c) a day with positive bidding and (d) a winter day. Com-
parison between ’Perfect Forecast’, ’Stochastic’ and ’No-Bidding’ versions 56
6.16 Total cost of the year for each case of the parameter sensitivity analysis
in comparison with the Base Case and the ’No-Bidding’ case . . . . . . . 58
6.17 Availability income per month for each case of the parameter sensitivity
analysis in comparison with the Base Case . . . . . . . . . . . . . . . . . . 60
6.18 CHP Cases - Activation income per month for positive and negative TRL 61
6.19 Heat Storage Cases - Activation income per month for positive and neg-
ative TRL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
6.20 HeatPumpCases-Activationincomepermonthforpositiveandnegative
TRL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
6.21 TRE− Bid Price Cases - Activation income per month for positive and
negative TRL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
6.22 Cost deviation from the ’No-Bidding’ Case. . . . . . . . . . . . . . . . . . 65
C.1 Electricity provided by the grid during (a) a day with no bidding, (b) a
summer day, (c) a day with positive bidding and (d) a winter day. Com-
parison between ’Perfect Forecast’, ’Stochastic’ and ’No-Bidding’ versions 77
D.1 Activation income per month for each case of the parameter sensitivity
analysis in comparison with the Base Case . . . . . . . . . . . . . . . . . . 79