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Multi-Energy Complementary Collaborative Peak-Load Shifting Strategy Based on Electro-Thermal Hybrid Energy Storage System |
Zhang Chao1, Feng Zhongnan2, Deng Shaoping1, Jia Changjie1, Lu Sheng1 |
1. Power China Hubei Electric Engineering Co. Ltd Wuhan 430040 China; 2. State Key Laboratory of Advanced Electromagnetic Engineering and Technology Huazhong University of Science and Technology Wuhan 430074 China |
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Abstract In view of the increasingly load fluctuation in power grid, the electro-thermal hybrid energy storage system was firstly proposed for peak-load shifting in integrated energy community, considering the deep coupling of multi-energy on the load side. Based on the hybrid operation model of battery and phase change material energy storage system, an electro-thermal combined peak-load shifting strategy was put forward with multi-attribute decision making, taking load variance and operation cost into consideration. The stochastic volatility model was used to forecast the load of the community, and the anti-risk ability of the proposed strategy to deal with load fluctuation was analyzed. According to the case study of a business community in Shenzhen, the electro-thermal hybrid energy storage system can making full use of the flexible bidirectional charging characteristic of battery energy storage and the long-term scale adjustment ability of phase change material energy storage. And the proposed strategy is capable of effectively promoting the ability of peak-load shifting and improving the operation economic benefits of the community on the premise of meeting the thermal demand of users.
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Received: 11 July 2020
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[1] Yang Xiaojiao, Sun Liangliang, Yuan Yanping, et al.Experimental investigation on performance com- parison of PV/T-PCM system and PV/T system[J]. Renewable Energy, 2018, 119: 152-159. [2] Wang Yibo, Shao Xinyang, Liu Chuang, et al.Analy- sis of wind farm output characteristics based on descriptive statistical analysis and envelope domain[J]. Energy, 2019, 170: 580-591. [3] 陶顺, 陈鹏伟, 肖湘宁, 等. 智能配电网不确定性建模与供电特征优化技术综述[J]. 电工技术学报, 2017, 32(10): 77-91. Tao Shun, Chen Pengwei, Xiao Xiangning, et al.Review on uncertainty modeling and power supply characteristics optimization technology in smart distribution network[J]. Transactions of China Elec- trotechnical Society, 2017, 32(10): 77-91. [4] 孙丙香, 李旸熙, 龚敏明, 等. 参与AGC辅助服务的锂离子电池储能系统经济性研究[J]. 电工技术学报, 2020, 35(19): 4048-4061. Sun Bingxiang, Li Yangxi, Gong Minming, et al.Study on the economy of energy storage system with lithium-ion battery participating in AGC auxiliary service[J]. Transactions of China Electrotechnical Society, 2020, 35(19): 4048-4061. [5] Alramlawi M, Li Pu.Design optimization of a residential PV-battery microgrid with a detailed battery lifetime estimation model[J]. IEEE Transa- ctions on Industry Applications, 2020, 56(2): 20-30. [6] Uddin M, Romlie M F, Abdullah M F, et al. A novel peak shaving algorithm for islanded microgrid using battery energy storage system[J]. Energy, 2020, 196(Apr.1): 117084.1-117084.13. [7] 赵乙潼, 王慧芳, 何奔腾, 等. 面向用户侧的电池储能配置与运行优化策略[J]. 电力系统自动化, 2020, 44(6): 121-130. Zhao Yitong, Wang Huifang, He Benteng, et al.Optimization strategy of configuration and operation for user-side battery energy storage[J]. Automation of Electric Power Systems, 2020, 44(6): 121-130. [8] 卫志农, 张思德, 孙国强, 等. 计及电转气的电-气互联综合能源系统削峰填谷研究[J]. 中国电机工程学报, 2017, 37(16): 4601-4609, 4885. Wei Zhinong, Zhang Side, Sun Guoqiang, et al.Power-to-gas considered peak load shifting research for integrated electricity and natural-gas energy systems[J]. Proceedings of the CSEE, 2017, 37(16): 4601-4609, 4885. [9] 秦强强, 郭婷婷, 林飞, 等. 基于能量转移的城轨交通电池储能系统能量管理和容量配置优化[J]. 电工技术学报, 2019, 34(增刊1): 414-423. Qin Qiangqiang, Guo Tingting, Lin Fei, et al.Optimal research for energy management and con- figuration of battery ESS in urban rail transit based on energy transfer[J]. Transactions of China Elec- trotechnical Society, 2019, 34(S1): 414-423. [10] Prasatsap U, Kiravittaya S, Polprasert J.Deter- mination of optimal energy storage system for peak shaving to reduce electricity cost in a university[J]. Energy Procedia, 2017, 138: 967-972. [11] 郑顺, 周翔, 张静思, 等. 上海地区住宅夏季空调能耗调查分析[J]. 暖通空调, 2016, 46(3): 38-41. Zheng Shun, Zhou Xiang, Zhang Jingsi, et al.Survey and analysis on energy consumption of air con- ditioning for residential buildings in Shanghai in summer[J]. Heating Ventilating & Air Conditioning, 2016, 46(3): 38-41. [12] 魏繁荣, 林湘宁, 陈乐, 等. 基于建筑相变材料储能的微网综合能源消纳系统[J]. 中国电机工程学报, 2018, 38(3): 792-804. Wei Fanrong, Lin Xiangning, Chen Le, et al.Microgrid comprehensive energy consumption system based on phase change building materials[J]. Proceedings of the CSEE, 2018, 38(3): 792-804. [13] 刁涵彬, 李培强, 王继飞, 等. 考虑电/热储能互补协调的综合能源系统优化调度[J]. 电工技术学报, 2020, 35(21): 4532-4543. Diao Hanbin, Li Peiqiang, Wang Jifei, et al.Optimal dispatch of integrated energy system considering complementary coordination of electric/thermal energy storage[J]. Transactions of China Electrotechnical Society, 2020, 35(21): 4532-4543. [14] 杜进桥, 李艳, 随权, 等. 基于相变储能系统主动响应能力挖掘的配电网经济调度[J]. 电力自动化设备, 2020, 40(4): 210-217. Du Jinqiao, Li Yan, Sui Quan, et al.Economic dispatch of distribution network based on active response capability mining of phase change material energy storage system[J]. Electric Power Automation Equipment, 2020, 40(4): 210-217. [15] 张媛琳, 谭宽. 相变储能系统在数据中心中的应用[J]. 信息与电脑, 2019, 31(22): 6-7. Zhang Yuanlin, Tan Kuan.Application on phase change energy storage system in IDC[J]. China Computer & Communication, 2019, 31(22): 6-7. [16] 任永峰, 薛宇, 云平平, 等. 马尔可夫预测的多目标优化储能系统平抑风电场功率波动[J]. 电力系统自动化, 2020, 44(6): 67-76. Ren Yongfeng, Xue Yu, Yun Pingping, et al.Multi-objective optimization of energy storage system with Markov prediction for power fluctuation suppression of wind farm[J]. Automation of Electric Power Systems, 2020, 44(6): 67-76. [17] 张健, 王凯悦. 考虑电压稳定性的含分布式电源配电网多目标无功优化[J]. 电气技术, 2020, 21(3): 64-69. Zhang Jian, Wang Kaiyue.Multi-objective reactive power optimization in distribution network with distributed generation considering voltage stability[J]. Electrical Engineering, 2020, 21(3): 64-69. [18] 江岳文, 陈晓榕. 基于D-U空间混合多属性决策的风电场装机容量优化[J]. 电网技术, 2019, 43(12): 4451-4461. Jiang Yuewen, Chen Xiaorong.Optimization of installed capacity of wind farm with mixed multiple attribute decisions based on D-U space[J]. Power System Technology, 2019, 43(12): 4451-4461. [19] 赵冬梅, 殷加玞. 考虑源荷双侧不确定性的模糊随机机会约束优先目标规划调度模型[J]. 电工技术学报, 2018, 33(5): 1076-1085. Zhao Dongmei, Yin Jiafu.Fuzzy random chance constrained preemptive goal programming scheduling model considering source-side and load-side uncer- tainty[J]. Transactions of China Electrotechnical Society, 2018, 33(5): 1076-1085. [20] 陈昊, 王玉荣. 基于随机波动模型的短期负荷预测[J]. 电力自动化设备, 2010, 30(11): 86-89. Chen Hao, Wang Yurong.Short-term load forecasting based on SV model[J]. Electric Power Automation Equipment, 2010, 30(11): 86-89. [21] 周双酉, 钱夕元. 基于经验似然贝叶斯计算方法在随机波动模型中的应用[J]. 数学的实践与认识, 2020, 50(6): 8-15. Zhou Shuangyou, Qian Xiyuan.SV model estimation based on Bayesian computation with empirical like- lihood[J]. Mathematics in Practice and Theory, 2020, 50(6): 8-15. |
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