Optimal Operation of Integrated Multi-Energy Systems Under Uncertainty discusses core concepts, advanced modeling and key operation strategies for integrated multi-energy systems geared for use in optimal operation. The book particularly focuses on reviewing novel operating strategies supported by relevant code in MATLAB and GAMS. It covers foundational concepts, key challenges and opportunities in operational implementation, followed by discussions of conventional approaches to modeling electricity, heat and gas networks. This modeling is the base for more detailed operation strategies for optimal operation of integrated multi-energy systems under uncertainty covered in the latter part of the work.
- Reviews advanced modeling approaches relevant to the integration of electricity, heat and gas systems in operation studies
- Covers stochastic and robust optimal operation of integrated multi-energy systems
- Evaluates MPC based, real-time dispatch of integrated multi-energy systems
- Considers uncertainty modeling for stochastic and robust optimization
- Assesses optimal operation and real-time dispatch for multi-energy building complexes
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ix | |
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1 Introduction of integrated energy systems |
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1 | (16) |
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1 | (4) |
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1.2 Integrated energy system |
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5 | (4) |
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1.3 Current status of integrated energy systems in China and Denmark |
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9 | (2) |
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1.4 Recommendations for further development of integrated energy systems |
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11 | (1) |
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12 | (5) |
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13 | (4) |
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2 Mathematical model of multi-energy systems |
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17 | (38) |
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17 | (2) |
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2.2 Modeling of coupling devices |
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19 | (4) |
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2.3 Mathematical model of the district heating network |
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23 | (7) |
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2.4 Mathematical model of the electric power network |
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30 | (1) |
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2.5 Modeling of the natural gas system |
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31 | (24) |
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50 | (5) |
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55 | (24) |
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55 | (2) |
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3.2 Scenario generation with spatial-temporal correlations in SO |
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57 | (6) |
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3.3 Partition-combine uncertainty set modeling in RO |
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63 | (5) |
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68 | (8) |
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76 | (3) |
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76 | (3) |
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4 Optimal operation of the multi-energy building complex |
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79 | (32) |
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79 | (2) |
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4.2 Configuration of a BC |
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81 | (2) |
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4.3 PMIB with the HVAC system |
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83 | (8) |
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4.4 Formulation of the hiewrchical method |
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91 | (5) |
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4.5 Results and discussions |
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96 | (11) |
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107 | (4) |
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108 | (3) |
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5 MPC-based real-time dispatch of multi-energy building complex |
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111 | (34) |
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111 | (3) |
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5.2 Configuration and modeling of the BC |
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114 | (5) |
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5.3 The multi-time scale and MPC-based scheduling method |
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119 | (8) |
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5.4 Results and discussions |
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127 | (13) |
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140 | (1) |
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141 | (4) |
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141 | (4) |
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6 Adaptive robust energy and reserve co-optimization of an integrated electricity and heating system considering wind uncertainty |
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145 | (26) |
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145 | (2) |
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6.2 Mathematical formulation of adaptive robust energy and reserve co-optimization for the IEHS |
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147 | (10) |
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157 | (3) |
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160 | (7) |
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6.5 Summary and conclusion |
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167 | (4) |
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167 | (4) |
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7 Decentralized robust energy and reserve co-optimization for multiple integrated electricity and heating systems |
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171 | (24) |
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171 | (2) |
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7.2 Structure and decentralized operation framework of multiple lEHSs |
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173 | (3) |
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7.3 Mathematical formulation of decentralized robust energy and reserve co-optimization for multiple lEHSs |
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176 | (5) |
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181 | (4) |
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185 | (7) |
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192 | (3) |
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193 | (2) |
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8 Chance-constrained energy and multi-type reserves scheduling exploiting flexibility from combined power and heat units and heat pumps |
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195 | (26) |
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195 | (2) |
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8.2 Framework of chance-constrained two-stage energy and multi-type reserves scheduling |
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197 | (1) |
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8.3 Primary FRR and following reserve provision from CHP units and HPs |
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198 | (6) |
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8.4 Mathematical formulation of decentralized robust energy and reserve co-optimization for multiple lEHSs |
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204 | (7) |
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8.5 Reformulation as a mixed-integer linear program |
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211 | (2) |
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213 | (6) |
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219 | (2) |
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219 | (2) |
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9 Day-ahead stochastic optimal operation of the integrated electricity and heating system considering reserve of flexible devices |
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221 | (28) |
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221 | (2) |
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9.2 Two-stage stochastic optimal dispatching scheme of the IEHS |
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223 | (2) |
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9.3 Reserve provision and heat regulation from condensing CHP units |
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225 | (3) |
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9.4 Mathematical formulation of stochastic optimal operation of the IEHS |
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228 | (10) |
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238 | (8) |
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246 | (3) |
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246 | (3) |
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10 Two-stage stochastic optimal operation of integrated energy systems |
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249 | (46) |
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249 | (1) |
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10.2 Background and DA scheduling |
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249 | (5) |
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10.3 Mathematical model of the IES for two-stage DA scheduling |
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254 | (16) |
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10.4 Scenario generation and reduction method |
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270 | (6) |
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10.5 Example of a case study |
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276 | (14) |
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290 | (5) |
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291 | (4) |
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11 MPC-based real-time operation of integrated energy systems |
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295 | (42) |
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295 | (1) |
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11.2 Background and RT scheduling |
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296 | (6) |
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11.3 MPC-based RT scheduling |
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302 | (6) |
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11.4 Mathematical models of the IES for MPC-based RT scheduling |
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308 | (11) |
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11.5 Solution process and case study |
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319 | (3) |
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322 | (11) |
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333 | (4) |
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334 | (3) |
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Appendix A Basics of stochastic optimization |
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337 | (12) |
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A.1 Stochastic optimization fundamentals |
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338 | (4) |
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A.2 Scenario generation and reduction |
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342 | (3) |
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A.3 General formulation of two stage optimization |
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345 | (4) |
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346 | (3) |
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Appendix B Introduction to adaptive robust optimization |
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349 | (6) |
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B.1 Formulation of ARO with resource |
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350 | (1) |
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350 | (5) |
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353 | (2) |
| Index |
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355 | |
Qiuwei Wu received the PhD degree in Electrical Engineering from Nanyang Technological University, Singapore, in 2009. He is a professor with the School of Electronics, Electrical Engineering, and Computer Science (EEECS), Queens University Belfast, the UK. His research interests are distributed optimal operation and control of low carbon power and energy systems, including distributed optimal control of wind power, optimal operation of active distribution networks, and optimal operation and planning of integrated energy systems.
Jin Tan received her Ph.D. degree in Electrical Engineering from the Technical University of Denmark, Denmark, in 2022, following a MSc at the Department of Electrical Engineering, Wuhan University, China (2018). Her research interests include the optimal operation of integrated electricity and heating system and renewable energy integration. Menglin Zhang received the B.S. degree in electrical engineering from Southwest Jiaotong University (SWJTU), Chengdu, China, in 2011, and the Ph.D. degree in electrical engineering from Wuhan University (WHU), Wuhan, China, in 2017. She was with the Department of Electrical Engineering, Huazhong University of Science and Technology (HUST), Wuhan, China from 2017 to 2019. Currently, she is a Post-Doctoral Researcher with the Center for Electric Power and Energy, Technical University of Denmark (DTU). Her current research interests include the modeling of temporal-spatial correlation of renewables in stochastic programming and advanced uncertainty set to reduce conservativeness in robust optimization, the modeling of optimal operation of integrated electricity and heat system considering flexibility, and the accelerated solving algorithm for the bulk system. Xiaolong Jin obtained the Ph.D. degree from the School of Electrical and Information Engineering, Tianjin University, Tianjin, China, in 2019. He is now a Postdoc researcher with Technical University of Denmark (DTU). His research interests include energy management of multi-energy systems and multi-energy buildings. Specifically, his research focuses on improving energy efficiency and reducing operating cost of multi-energy systems and multi-energy buildings with designed energy management frameworks, which uses the flexibilities from three aspects: 1) Use the demand-side flexibility by dispatching the flexible multi-energy loads in smart buildings; 2) Use the network-side flexibility by coordinating the multi-vector energy networks; 3) Use the supply-side flexibility by scheduling the various generations in the energy stations and the distributed energy resources connected with multi-energy systems and multi-energy buildings. Ana Turk received the B.S. degree from the Faculty of Electrical Engineering and Computer Science at University of Maribor in Slovenia and MSc degree in Energy Engineering from Faculty of Engineering and Science at Aalborg University in Denmark in 2018. She is currently pursuing a Ph.D. at the Center of Electric Power and Energy (CEE) at the Department of Electrical Engineering at the Technical University of Denmark (DTU). Her research interest include integration and modeling of multi-energy systems (district heating, natural gas and electric power system), stochastic programming and optimal operation and scheduling of multi-energy systems. In particular, special focus is on optimal operation and real time control of integrated energy systems by using model predictive control.
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