Cover image for Modelling distributed energy resources in energy service networks
Title:
Modelling distributed energy resources in energy service networks
Personal Author:
Series:
IET renewable energy series ; 16
Publication Information:
London : Institution of Engineering and Technology, 2013
ISBN:
9781849195591

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35000000001555 HD9502.A2 A24 2013 Open Access Book Book
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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

Forewordp. xi
Prefacep. xiii
Abbreviationsp. xvii
Symbolsp. xix
1 Challenges in effectively managing energy resources, infrastructures and conversion technologiesp. 1
1.1 Global urbanisation and efficiency of energy systemsp. 1
1.2 Evolution of urban energy systemsp. 5
1.3 Integrated management of energy systemsp. 8
2 Integrated modelling reviewp. 13
2.1 Modelling issues concerning DERsp. 13
2.1.1 Meeting the challenges of decentralised power generationp. 13
2.1.2 Impacts of cogeneration technology on electric networksp. 15
2.1.3 Impacts of PHEV technology on electric networksp. 19
2.2 Approaches on modelling multiple energy networksp. 24
2.2.1 Multi-generation analysisp. 24
2.2.2 Integrated energy transportation systemsp. 25
2.2.3 Modelling of energy hubsp. 26
2.2.4 Integrated natural gas and electricity studiesp. 27
3 Modelling of energy service networksp. 29
3.1 Modelling electrical networksp. 29
3.1.1 Fundamentals of electrical systemsp. 29
3.1.2 Defining the electrical load flow problemp. 31
3.1.3 Nodal formulation and the admittance matrixp. 32
3.2 Modelling natural gas networksp. 35
3.2.1 Fundamentals of natural gas systemsp. 35
3.2.2 Defining the natural gas load flow problemp. 37
3.2.3 Nodal formulation and the incidence matrixp. 38
3.3 Analogies in energy service networksp. 42
3.3.1 Modelling components and variablesp. 42
3.3.2 The Newton-Raphson algorithmp. 43
3.3.2.1 The electrical system Jacobian matrixp. 44
3.3.2.2 The natural gas system Jacobian matrixp. 46
3.3.2.3 Load flow conclusionsp. 48
4 Modelling embedded technologies in energy service networksp. 51
4.1 Modelling on-load tap-changer transformersp. 51
4.1.1 Fundamentals of OLTC transformersp. 51
4.1.2 OLTC modelling equationsp. 53
4.2 Modelling compressor stationsp. 56
4.2.1 Fundamentals of compressor stationsp. 56
4.2.2 Compressor modelling equationsp. 58
4.3 Modelling CHP technologiesp. 59
4.3.1 Fundamentals of combined heat and power unitsp. 59
4.3.2 Nodal formulation of natural gas networks with CHPsp. 65
4.3.3 Thermal energy storage management equationsp. 68
4.4 Modelling PHEV technologiesp. 71
4.4.1 Fundamentals of plug-in hybrid electric vehiclesp. 71
4.4.2 Nodal formulation of electrical networks with PHEVsp. 81
4.4.3 Electrochemical energy storage management equationsp. 84
5 Time-coordinated optimal power flow for energy service networksp. 89
5.1 TCOPF problem outlinep. 89
5.1.1 Problem descriptionp. 89
5.1.2 Optimisation solverp. 93
5.1.3 Input data and assumptions of the TCOPF toolp. 94
5.2 TCOPF objective functionsp. 96
5.2.1 Plug and forgetp. 96
5.2.2 Fuel costp. 96
5.2.3 Energy lossp. 97
5.2.4 Energy costp. 97
5.2.5 Composite objectivesp. 97
5.3 Mathematical TCOPF formulationp. 98
5.3.1 Objective function formulationsp. 98
5.3.1.1 For plug-and-forget scenariop. 98
5.3.1.2 For fuel cost minimisationp. 99
5.3.1.3 For energy loss minimisationp. 99
5.3.1.4 For energy cost minimisationp. 100
5.3.1.5 For composite objective minimization (e.g. cost of spot prices vs. cost of emissions)p. 100
5.3.2 Constraintsp. 101
5.3.2.1 Concerning electrical networksp. 102
5.3.2.2 Concerning natural gas networksp. 102
5.3.2.3 Concerning PHEV devices embedded in electrical networksp. 103
5.3.2.4 Concerning CHP devices embedded in natural gas networksp. 103
5.3.3 TCOPF problem and solution characteristicsp. 104
6 Optimising DERs in energy service networks: a case studyp. 107
6.1 TCOPF energy service network case studiesp. 107
6.1.1 Input data and assumptionsp. 107
6.1.2 Description of case studies and energy system parametersp. 110
6.2 Techno-economical resultsp. 116
6.2.1 Overviewp. 116
6.2.2 Integrated versus non-integrated systemsp. 117
6.2.3 Natural gas networkp. 120
6.2.4 CHP technologyp. 123
6.2.5 Electrical networkp. 130
6.2.6 PHEV technologyp. 135
6.3 Summary of resultsp. 142
7 Modelling electric vehicle mobility in energy service networksp. 145
7.1 Modelling PHEV mobilityp. 146
7.1.1 Modelling methodsp. 146
7.2 Combining agent-based and load flow modelsp. 147
7.2.1 Agent-based model for vehiclesp. 148
7.2.2 PHEV optimal power flow formulationp. 149
7.2.2.1 For PHEV charging cost minimisation scenariop. 150
7.3 ABM-TCOPF case study for charging of PHEVsp. 151
7.3.1 Input data and assumptionsp. 151
7.3.1.1 Driver profilesp. 151
7.3.1.2 PHEV featuresp. 152
7.3.1.3 City layoutp. 152
7.3.1.4 Electricity load profiles and network characteristicsp. 153
7.3.2 Case studies and energy system parametersp. 153
7.4 Techno-economical resultsp. 154
7.4.1 Agent-based model resultsp. 154
7.4.2 Optimal power flow model resultsp. 159
8 Concluding remarksp. 163
8.1 Summary and contributionsp. 163
8.2 Research beneficiariesp. 166
8.3 Future work suggestionsp. 166
Appendix A Urban agglomeration datap. 169
Appendix B UK energy flow analysisp. 171
Appendix C Electrical load flow codep. 173
Appendix D Natural gas load flow codep. 177
Appendix E OLTC derivativesp. 181
Appendix F Per unit system valuesp. 183
Appendix G KKT optimally conditionsp. 185
Appendix H Newton's methodp. 187
Referencesp. 189
Indexp. 205