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Summary
Summary
The smart-grid concept can mean many things, however there is a consensus that its objective involves seamlessly adopting new technologies to existing infrastructures and maximising the use of resources. Modelling Distributed Energy Resources in Energy Service Networks focuses on modelling two key infrastructures in urban energy systems with embedded technologies. These infrastructures are natural gas and electricity networks and the embedded technologies include cogeneration and electric vehicle devices. The subject is addressed using a holistic modelling framework which serves as a means to an end; this end being to optimise in a coordinated manner the operation of natural gas and electrical infrastructures under the presence of distributed energy resources, thus paving the way in which smart-grids should be managed. The modelling approach developed and presented in this book, under the name 'time coordinated optimal power flow' (TCOPF), functions as a decision maker entity that aggregates and coordinates the available DERs according to multiple criteria such as energy prices and utility conditions. The examples prove the TCOPF acts effectively as an unbiased intermediary entity that manages cost-effective interactions between the connected technologies and the distribution network operators, therefore showcasing an integral approach on how to manage new technologies for the benefit of all stakeholders.
regates and coordinates the available DERs according to multiple criteria such as energy prices and utility conditions. The examples prove the TCOPF acts effectively as an unbiased intermediary entity that manages cost-effective interactions between the connected technologies and the distribution network operators, therefore showcasing an integral approach on how to manage new technologies for the benefit of all stakeholders.regates and coordinates the available DERs according to multiple criteria such as energy prices and utility conditions. The examples prove the TCOPF acts effectively as an unbiased intermediary entity that manages cost-effective interactions between the connected technologies and the distribution network operators, therefore showcasing an integral approach on how to manage new technologies for the benefit of all stakeholders.regates and coordinates the available DERs according to multiple criteria such as energy prices and utility conditions. The examples prove the TCOPF acts effectively as an unbiased intermediary entity that manages cost-effective interactions between the connected technologies and the distribution network operators, therefore showcasing an integral approach on how to manage new technologies for the benefit of all stakeholders.Author Notes
Salvador Acha is a Research Fellow and Team Leader of the Imperial College - Sainsbury's Partnership. The partnership has two goals: promoting energy efficiency use by implementing smart controls in stores and sustainably reducing Sainsbury's carbon footprint through holistic energy investment decisions. Energy efficiency strategies, energy modelling and forecasting, and decarbonisation roadmaps are key strong points of the research team. Dr Acha's other research interests include smart-grid frameworks, roll out of plug-in hybrid electric vehicles, optimal management of distributed sources of energy, energy forecasting, and environmental reporting.
Table of Contents
Foreword | p. xi |
Preface | p. xiii |
Abbreviations | p. xvii |
Symbols | p. xix |
1 Challenges in effectively managing energy resources, infrastructures and conversion technologies | p. 1 |
1.1 Global urbanisation and efficiency of energy systems | p. 1 |
1.2 Evolution of urban energy systems | p. 5 |
1.3 Integrated management of energy systems | p. 8 |
2 Integrated modelling review | p. 13 |
2.1 Modelling issues concerning DERs | p. 13 |
2.1.1 Meeting the challenges of decentralised power generation | p. 13 |
2.1.2 Impacts of cogeneration technology on electric networks | p. 15 |
2.1.3 Impacts of PHEV technology on electric networks | p. 19 |
2.2 Approaches on modelling multiple energy networks | p. 24 |
2.2.1 Multi-generation analysis | p. 24 |
2.2.2 Integrated energy transportation systems | p. 25 |
2.2.3 Modelling of energy hubs | p. 26 |
2.2.4 Integrated natural gas and electricity studies | p. 27 |
3 Modelling of energy service networks | p. 29 |
3.1 Modelling electrical networks | p. 29 |
3.1.1 Fundamentals of electrical systems | p. 29 |
3.1.2 Defining the electrical load flow problem | p. 31 |
3.1.3 Nodal formulation and the admittance matrix | p. 32 |
3.2 Modelling natural gas networks | p. 35 |
3.2.1 Fundamentals of natural gas systems | p. 35 |
3.2.2 Defining the natural gas load flow problem | p. 37 |
3.2.3 Nodal formulation and the incidence matrix | p. 38 |
3.3 Analogies in energy service networks | p. 42 |
3.3.1 Modelling components and variables | p. 42 |
3.3.2 The Newton-Raphson algorithm | p. 43 |
3.3.2.1 The electrical system Jacobian matrix | p. 44 |
3.3.2.2 The natural gas system Jacobian matrix | p. 46 |
3.3.2.3 Load flow conclusions | p. 48 |
4 Modelling embedded technologies in energy service networks | p. 51 |
4.1 Modelling on-load tap-changer transformers | p. 51 |
4.1.1 Fundamentals of OLTC transformers | p. 51 |
4.1.2 OLTC modelling equations | p. 53 |
4.2 Modelling compressor stations | p. 56 |
4.2.1 Fundamentals of compressor stations | p. 56 |
4.2.2 Compressor modelling equations | p. 58 |
4.3 Modelling CHP technologies | p. 59 |
4.3.1 Fundamentals of combined heat and power units | p. 59 |
4.3.2 Nodal formulation of natural gas networks with CHPs | p. 65 |
4.3.3 Thermal energy storage management equations | p. 68 |
4.4 Modelling PHEV technologies | p. 71 |
4.4.1 Fundamentals of plug-in hybrid electric vehicles | p. 71 |
4.4.2 Nodal formulation of electrical networks with PHEVs | p. 81 |
4.4.3 Electrochemical energy storage management equations | p. 84 |
5 Time-coordinated optimal power flow for energy service networks | p. 89 |
5.1 TCOPF problem outline | p. 89 |
5.1.1 Problem description | p. 89 |
5.1.2 Optimisation solver | p. 93 |
5.1.3 Input data and assumptions of the TCOPF tool | p. 94 |
5.2 TCOPF objective functions | p. 96 |
5.2.1 Plug and forget | p. 96 |
5.2.2 Fuel cost | p. 96 |
5.2.3 Energy loss | p. 97 |
5.2.4 Energy cost | p. 97 |
5.2.5 Composite objectives | p. 97 |
5.3 Mathematical TCOPF formulation | p. 98 |
5.3.1 Objective function formulations | p. 98 |
5.3.1.1 For plug-and-forget scenario | p. 98 |
5.3.1.2 For fuel cost minimisation | p. 99 |
5.3.1.3 For energy loss minimisation | p. 99 |
5.3.1.4 For energy cost minimisation | p. 100 |
5.3.1.5 For composite objective minimization (e.g. cost of spot prices vs. cost of emissions) | p. 100 |
5.3.2 Constraints | p. 101 |
5.3.2.1 Concerning electrical networks | p. 102 |
5.3.2.2 Concerning natural gas networks | p. 102 |
5.3.2.3 Concerning PHEV devices embedded in electrical networks | p. 103 |
5.3.2.4 Concerning CHP devices embedded in natural gas networks | p. 103 |
5.3.3 TCOPF problem and solution characteristics | p. 104 |
6 Optimising DERs in energy service networks: a case study | p. 107 |
6.1 TCOPF energy service network case studies | p. 107 |
6.1.1 Input data and assumptions | p. 107 |
6.1.2 Description of case studies and energy system parameters | p. 110 |
6.2 Techno-economical results | p. 116 |
6.2.1 Overview | p. 116 |
6.2.2 Integrated versus non-integrated systems | p. 117 |
6.2.3 Natural gas network | p. 120 |
6.2.4 CHP technology | p. 123 |
6.2.5 Electrical network | p. 130 |
6.2.6 PHEV technology | p. 135 |
6.3 Summary of results | p. 142 |
7 Modelling electric vehicle mobility in energy service networks | p. 145 |
7.1 Modelling PHEV mobility | p. 146 |
7.1.1 Modelling methods | p. 146 |
7.2 Combining agent-based and load flow models | p. 147 |
7.2.1 Agent-based model for vehicles | p. 148 |
7.2.2 PHEV optimal power flow formulation | p. 149 |
7.2.2.1 For PHEV charging cost minimisation scenario | p. 150 |
7.3 ABM-TCOPF case study for charging of PHEVs | p. 151 |
7.3.1 Input data and assumptions | p. 151 |
7.3.1.1 Driver profiles | p. 151 |
7.3.1.2 PHEV features | p. 152 |
7.3.1.3 City layout | p. 152 |
7.3.1.4 Electricity load profiles and network characteristics | p. 153 |
7.3.2 Case studies and energy system parameters | p. 153 |
7.4 Techno-economical results | p. 154 |
7.4.1 Agent-based model results | p. 154 |
7.4.2 Optimal power flow model results | p. 159 |
8 Concluding remarks | p. 163 |
8.1 Summary and contributions | p. 163 |
8.2 Research beneficiaries | p. 166 |
8.3 Future work suggestions | p. 166 |
Appendix A Urban agglomeration data | p. 169 |
Appendix B UK energy flow analysis | p. 171 |
Appendix C Electrical load flow code | p. 173 |
Appendix D Natural gas load flow code | p. 177 |
Appendix E OLTC derivatives | p. 181 |
Appendix F Per unit system values | p. 183 |
Appendix G KKT optimally conditions | p. 185 |
Appendix H Newton's method | p. 187 |
References | p. 189 |
Index | p. 205 |