Table of Contents

Acknowledgements vii
1 Introduction 1
1.1 The need for sustainable development . . . . . . . . . . . . . . . . . . . . 1
1.2 Towards a more active demand side . . . . . . . . . . . . . . . . . . . . . 2
1.2.1 Distributed generation technology . . . . . . . . . . . . . . . . . . 2
1.2.2 Distributed generation in the electricity infrastructure . . . . . . . . 2
1.2.3 Households as electricity producers . . . . . . . . . . . . . . . . . 4
1.3 Micro cogeneration as novel heating technology . . . . . . . . . . . . . . . 5
1.3.1 The concept of micro cogeneration (micro-CHP) . . . . . . . . . . 5
1.3.2 Possible prime mover technologies . . . . . . . . . . . . . . . . . . 7
1.3.3 Expected markets for micro-CHP . . . . . . . . . . . . . . . . . . 7
1.3.4 Potential network impacts . . . . . . . . . . . . . . . . . . . . . . 9
1.3.5 System investment costs . . . . . . . . . . . . . . . . . . . . . . . 9
1.3.6 Cost reductions through intelligence . . . . . . . . . . . . . . . . . 10
2 Research framework 11
2.1 Problem description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.1.1 Economic feasibility of micro-CHP . . . . . . . . . . . . . . . . . 11
2.1.2 Intervention options . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.1.3 Smart homes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.2 Research objective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.2.1 Research questions . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.2.2 Demarcation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.2.3 Actors to which this research is of interest . . . . . . . . . . . . . . 17
2.3 Thesis outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
3 ‘Fit & forget’: standard application of micro cogeneration systems 19
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
3.2 Literature on standard decentralized control . . . . . . . . . . . . . . . . . 20
3.2.1 Control design and cost performance . . . . . . . . . . . . . . . . 20
3.2.2 Savings in energy use and CO2 emission savings . . . . . . . . . . 21
3.2.3 Novelties of this chapter . . . . . . . . . . . . . . . . . . . . . . . 21
3.3 Modeling micro-CHP under standard decentralized control . . . . . . . . . 21
3.3.1 Modeling domestic space heating . . . . . . . . . . . . . . . . . . 21
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3.3.2 Chosen hot water storage configuration . . . . . . . . . . . . . . . 22
3.3.3 Household system description . . . . . . . . . . . . . . . . . . . . 24
3.3.4 Modeling assumptions and model parameters . . . . . . . . . . . . 26
3.3.5 Heat-led control of Stirling engines and PEM fuel cells . . . . . . . 29
3.3.6 Electricity-led control of Stirling engines and PEM fuel cells . . . . 34
3.4 Model input . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
3.4.1 Domestic electricity and heat demand profiles . . . . . . . . . . . . 37
3.4.2 Domestic electricity and gas tariffs . . . . . . . . . . . . . . . . . . 39
3.5 Simulation results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
3.5.1 Variable energy costs for households . . . . . . . . . . . . . . . . . 45
3.5.2 Return on micro-CHP investment . . . . . . . . . . . . . . . . . . 49
3.5.3 The influence of technical parameter values . . . . . . . . . . . . . 53
3.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
4 ‘Rate & react’: local demand response with micro cogeneration systems 57
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
4.2 Demand response . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
4.3 Literature on least-cost control of CHP . . . . . . . . . . . . . . . . . . . . 59
4.3.1 Overall system optimization . . . . . . . . . . . . . . . . . . . . . 59
4.3.2 Larger CHP systems . . . . . . . . . . . . . . . . . . . . . . . . . 59
4.3.3 Least-cost control of micro-CHP . . . . . . . . . . . . . . . . . . . 60
4.3.4 Novelties of this chapter . . . . . . . . . . . . . . . . . . . . . . . 60
4.4 Model formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
4.4.1 Specific modeling assumptions . . . . . . . . . . . . . . . . . . . . 61
4.4.2 Mathematical model of a household with Stirling micro-CHP . . . 61
4.4.3 Mathematical model of a household with PEMFC micro-CHP . . . 63
4.5 Control strategy: model predictive control (MPC) . . . . . . . . . . . . . . 64
4.5.1 Model predictive control . . . . . . . . . . . . . . . . . . . . . . . 64
4.5.2 MPC problem formulation and control objective . . . . . . . . . . 67
4.6 Model input . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
4.7 Simulation results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
4.7.1 Variable energy costs depending on pricing regime . . . . . . . . . 71
4.7.2 The impact of energy demand prediction accuracy . . . . . . . . . 73
4.7.3 Variable energy costs of different household types . . . . . . . . . 74
4.7.4 The influence of system capacity and heat storage volume . . . . . 75
4.8 Cost savings due to economies of flexibility . . . . . . . . . . . . . . . . . 76
4.9 Model predictive control with batteries . . . . . . . . . . . . . . . . . . . . 78
4.9.1 Distributed energy resources . . . . . . . . . . . . . . . . . . . . . 78
4.9.2 System description . . . . . . . . . . . . . . . . . . . . . . . . . . 79
4.9.3 Model formulation . . . . . . . . . . . . . . . . . . . . . . . . . . 79
4.9.4 Simulation results . . . . . . . . . . . . . . . . . . . . . . . . . . 80
4.10 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
Contents xi
5 ‘Cluster & control’: aggregate intelligence in virtual power plants 85
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
5.2 Virtual power plants (VPPs) . . . . . . . . . . . . . . . . . . . . . . . . . 86
5.3 Aggregator definition and possible aggregators . . . . . . . . . . . . . . . 87
5.4 Literature on VPPs with micro-CHP . . . . . . . . . . . . . . . . . . . . . 88
5.5 Aggregate demand response . . . . . . . . . . . . . . . . . . . . . . . . . 88
5.6 More control objectives for VPPs . . . . . . . . . . . . . . . . . . . . . . . 90
5.7 Modeling application: balancing wind power . . . . . . . . . . . . . . . . 90
5.7.1 Stochastic generation and the need for flexible balancing power . . 91
5.7.2 The Dutch balancing market . . . . . . . . . . . . . . . . . . . . . 92
5.7.3 System description . . . . . . . . . . . . . . . . . . . . . . . . . . 93
5.7.4 VPP control scheme design . . . . . . . . . . . . . . . . . . . . . 95
5.7.5 Modeling assumptions . . . . . . . . . . . . . . . . . . . . . . . . 97
5.8 Model input . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98
5.9 Simulation results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100
5.10 Economic incentives in VPPs with micro-CHP . . . . . . . . . . . . . . . 107
5.10.1 Incentives to invest in micro-CHP . . . . . . . . . . . . . . . . . . 107
5.10.2 Incentives to initiate VPPs . . . . . . . . . . . . . . . . . . . . . . 108
5.10.3 Including households in VPPs . . . . . . . . . . . . . . . . . . . . 108
5.11 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109
6 Conclusions and recommendations 111
6.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
6.1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
6.1.2 Reference case: ‘fit and forget’ application of micro cogeneration . 112
6.1.3 ‘Rate & react’ in local demand response . . . . . . . . . . . . . . . 113
6.1.4 ‘Cluster & control’ in virtual power plants . . . . . . . . . . . . . . 115
6.2 Recommendations for future research . . . . . . . . . . . . . . . . . . . . 117
6.3 Policy recommendations . . . . . . . . . . . . . . . . . . . . . . . . . . . 119
Appendices 125
A Energy flows and CO2 emissions with ‘fit & forget’ application of micro-CHP 125
A.1 Heat storage use . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125
A.2 Simulation results on energy flows and CO2 emissions . . . . . . . . . . . 125
B Impact of large-scale micro-CHP application on aggregate load 133
B.1 Monte-Carlo simulation setup . . . . . . . . . . . . . . . . . . . . . . . . 133
B.2 Simulation results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133
C Details on the design of the VPP control scheme 137
C.1 Detailed control scheme . . . . . . . . . . . . . . . . . . . . . . . . . . . 137
C.2 Prediction model design . . . . . . . . . . . . . . . . . . . . . . . . . . . 137
C.3 Translating variables into the cluster model and the VPP . . . . . . . . . . 139
C.4 Resolving the imbalance . . . . . . . . . . . . . . . . . . . . . . . . . . . 140
Bibliography 141
xii Contents
Nomenclature 157
Summary 163
Samenvatting 167
Curriculum vitae 171
List of publications 173
NGInfra PhD Thesis Series on Infrastructures 177

Abstract

Distributed generation (DG) contributes to a more sustainable electricity supply. Large-scale adoption of DG will bring radical changes to the traditional model of generation and supply as well as to the business model of the power industry. Furthermore, with innovations in information and communication technology (e.g. smart metering), energy and information infrastructures become more and more entwined and smart grids are enabled. Specific potential for applying DG at the domestic level lies in the efficient use of heat and electricity from micro combined heat and power systems (micro-CHP). The main problem with micro-CHP is the high up-front investment costs in comparison with conventional gas-fired boilers. With current investment costs, micro-CHP is economically infeasible. This thesis explores the potential cost savings with intelligent control of micro-CHP. Such control effectively utilizes micro-CHP’s inherent flexibility and can thereby increase the economic feasibility of the technology. Intelligent control schemes are designed and model simulations assess their economic performance. As a first control objective, demand response is looked into, in which retail companies provide real-time electricity tariffs and controllers in households react to those tariffs when dispatching micro-CHP units. In a second case, intelligence is lifted to a higher system level and virtual power plants (VPPs) of micro-CHPs units provide balancing services to wind farm operators. The main conclusion drawn from both cases is that variable cost savings with intelligent control of micro-CHP do not substantially improve the technology’s economic feasibility. When investment costs drop in the future and investments in micro-CHP (operated under standard control) are deemed economically feasible, the additional savings with intelligent control provide an aggregate incentive to initiate VPPs, because the total savings from groups of households are considerable. The value of micro-CHP’s inherent flexibility therefore lies in the clustered, intelligent application of this technology.

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