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Smart Grid 2021
基于柔性行动器–评判器的电–热综合能源系统协调优化
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Abstract:
| [1] | Wikipedia (2013) Quantum Entanglement. https://en.wikipedia.org/wiki/Quantum_entanglement |
| [2] | 余晓丹, 徐宪东, 陈硕翼, 吴建中, 贾宏杰. 综合能源系统与能源互联网简述[J]. 电工技术学报, 2016, 31(1): 1-13. |
| [3] | 吴晨雨. 电热综合能源系统的建模及优化运行[D]: [博士学位论文]. 南京: 东南大学, 2019. |
| [4] | Lu, S., Gu, W., Zhou, J.H., Zhang, X.S. and Wu, C.Y. (2018) Coordinated Dispatch of Multi-Energy System with District Heating Network: Modeling and Solution Strategy. Energy, 152, 358-370.
https://doi.org/10.1016/j.energy.2018.03.088 |
| [5] | Li, Z., et al. (2017) Combined Heat and Power Dispatch Considering Pipeline Energy Storage of District Heating Network. IEEE Transactions on Sustainable Energy, 7, 12-22. https://doi.org/10.3390/en10070893 |
| [6] | Zeng, Y.J. and Sun, Y.G. (2014) An Improved Particle Swarm Optimization for the Combined Heat and Power Dynamic Economic Dispatch Problem. Electric Power Components and Systems, 42, 1700-1716.
https://doi.org/10.1080/15325008.2014.949913 |
| [7] | Yang, J., Zhang, N., Botterud, A. and Kang, C. (2020) On An Equivalent Representation of the Dynamics in District Heating Networks for Combined Electricity-Heat Operation. IEEE Transactions on Power Systems, 35, 560-570.
https://doi.org/10.1109/TPWRS.2019.2935748 |
| [8] | Zhang, B., Huang, Q., Chen, Z. and Blaabjerg, F. (2019) Deep Reinforcement Learning-Based Approach for Optimizing Energy Conversion in Integrated Electrical and Heating System with Renewable Energy. Energy Conversion and Management, 202, Article ID: 112199. https://doi.org/10.1016/j.enconman.2019.112199 |
| [9] | Zhang, B., Hu, W.H., Li, J.H., Cao, D., Huang, R., Huang, Q., Chen, Z. and Blaabjerg, F. (2020) Dynamic Energy Conversion and Management Strategy for an Integrated Electricity and Natural Gas System with Renewable Energy: Deep Reinforcement Learning Approach. Energy Conversion and Management, 220, Article ID: 113063.
https://doi.org/10.1016/j.enconman.2020.113063 |
| [10] | 李宏仲, 王磊, 林冬, 张雪莹. 多主体参与可再生能源消纳的Nash博弈模型及其迁移强化学习求解[J]. 中国电机工程学报, 2019, 39(14): 4135-4150. |
| [11] | Ji, Y., Wang, J.H., Xu, J.C., Fang, X.K. and Zhang, H.G. (2019) Real-Time Energy Management of a Microgrid Using Deep Reinforcement Learning. Energies, 12, 2291. https://doi.org/10.3390/en12122291 |
| [12] | Hua, H.C., Chao, Y., Hao, C.T. and Cao, J.W. (2019) Optimal Energy Management Strategies for Energy Internet via Deep Reinforcement Learning Approach. Applied Energy, 239, 598-609.
https://doi.org/10.1016/j.apenergy.2019.01.145 |
| [13] | Liu, X.Z., Wu, J.Z., Jenkins, N. and Bagdanavicius, A. (2016) Combined Analysis of Electricity and Heat Networks. Applied Energy, 162, 1238-1250. https://doi.org/10.1016/j.apenergy.2015.01.102 |
| [14] | Haarnoja, T., Zhou, A., Abbeel, P. and Levine, S. (2018) Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor. 2018 International Conference on Machine Learning (ICML), Stockholmsm？ssan, 10-15 July 2018, 1861-1870. |
| [15] | Huang, J., Li, Z. and Wu, Q.H. (2017) Coordinated Dispatch of Electric Power and District Heating Networks: A Decentralized Solution Using Optimality Condition Decomposition. Applied Energy, 206, 1508-1522.
https://doi.org/10.1016/j.apenergy.2017.09.112 |
