Two-Level Optimal Dispatching Strategy for Regional Integrated Energy System Considering Demand Response

doi:10.11930/j.issn.1004-9649.202012014

Abstract

Abstract: Demand response aggregator aggregates users’ transferable loads and reducible loads through demand response to improve the flexibility and economy of regional integrated energy system. Considering the interactive game relationship between the integrated energy system operators and the demand response aggregators, a two-level optimal dispatching model of the regional integrated energy system is established with consideration of the demand response. By taking the maximum economic benefits of the regional integrated energy system operators and the demand response aggregators as objective respectively for the upper and lower layers, the KKT conditions and linearization method are used to convert the two-layer model into a single-layer mixed integer linear optimization model for solution. The results show that using the time-of-use electricity price and the demand response compensation price to guide users to adjust their energy use plans can increase the profits of integrated energy system operators and demand response aggregators while achieving peak load shaving and reducing the impact on the safe and stable operation of the power grid.

Key words: integrated energy system, two-level optimization, demand response, dispatch operation, mixed integer linear programming

ZHANG Haijing, YANG Yongqi, ZHAO Xin, XU Nan, LI Chenhui, XUE Wanlei. Two-Level Optimal Dispatching Strategy for Regional Integrated Energy System Considering Demand Response[J]. Electric Power, 2021, 54(4): 141-150.

Add to citation manager EndNote|Ris|BibTeX

URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202012014

https://www.electricpower.com.cn/EN/Y2021/V54/I4/141

References

[1] 袁智勇, 赵懿祺, 郭祚刚, 等. 面向能源互联网的综合能源系统规划研究综述[J]. 南方电网技术, 2019, 13(7): 1-9
YUAN Zhiyong, ZHAO Yiqi, GUO Zuogang, et al. Research summary of integrated energy systems planning for energy Internet[J]. Southern Power System Technology, 2019, 13(7): 1-9
[2] 曾鸣. 构建综合能源系统[N]. 人民日报, 2018-04-09.
[3] 魏震波, 任小林, 黄宇涵. 考虑综合需求侧响应的区域综合能源系统多目标优化调度[J]. 电力建设, 2020, 41(7): 92-99
WEI Zhenbo, REN Xiaolin, HUANG Yuhan. Multi-objective optimal dispatch for integrated energy system considering integrated demand response[J]. Electric Power Construction, 2020, 41(7): 92-99
[4] 曾博, 胡强, 张玉莹. 含高比例风电的电-气互联系统概率能流计算分析[J]. 太阳能学报, 2020, 41(4): 205-214
ZENG Bo, HU Qiang, ZHANG Yuying. Probabilistic energy flow calculation and analysis of integrated electricity-gas systems with high penetration wind power[J]. Acta Energiae Solaris Sinica, 2020, 41(4): 205-214
[5] 吕振华, 李强, 韩华春, 等. 区域综合能源系统双层多目标模糊优化模型预测控制方法[J]. 电力建设, 2020, 41(12): 121-130
LÜ Zhenhua, LI Qiang, HAN Huachun, et al. Model predictive control method of bi-level multi-objective fuzzy optimization for regional integrated energy system[J]. Electric Power Construction, 2020, 41(12): 121-130
[6] 吴福保, 史如新, 桑丙玉, 等. 考虑能量成本和污染排放的综合能源系统优化配置[J]. 热力发电, 2021, 50(2): 10-17
WU Fubao, SHI Ruxin, SANG Bingyu, et al. Optimization of integrated energy system considering energy cost and pollution emission[J]. Thermal Power Generation, 2021, 50(2): 10-17
[7] 于壮状, 曾鸣, 刘英新, 等. 基于分层次DBSCAN-VBSO算法的区域综合能源系统两阶段调度优化[J]. 电力自动化设备, 2019, 39(12): 63-72
YU Zhuangzhuang, ZENG Ming, LIU Yingxin, et al. Two-stage dispatching optimization of regional integrated energy system based on hierarchical DBSCAN-VBSO algorithm[J]. Electric Power Automation Equipment, 2019, 39(12): 63-72
[8] 郭尊, 李庚银, 周明, 等. 考虑网络约束和源荷不确定性的区域综合能源系统两阶段鲁棒优化调度[J]. 电网技术, 2019, 43(9): 3090-3100
GUO Zun, LI Gengyin, ZHOU Ming, et al. Two-stage robust optimal scheduling of regional integrated energy system considering network constraints and uncertainties in source and load[J]. Power System Technology, 2019, 43(9): 3090-3100
[9] 单福州, 李晓露, 宋燕敏, 等. 基于改进两阶段鲁棒优化的区域综合能源系统经济调度[J]. 电测与仪表, 2018, 55(23): 103-108
SHAN Fuzhou, LI Xiaolu, SONG Yanmin, et al. Economic dispatching of regional integrated energy system based on improvement two-stage robust optimization[J]. Electrical Measurement & Instrumentation, 2018, 55(23): 103-108
[10] 王成山, 吕超贤, 李鹏, 等. 园区型综合能源系统多时间尺度模型预测优化调度[J]. 中国电机工程学报, 2019, 39(23): 6791-6803, 7093
WANG Chengshan, LÜ Chaoxian, LI Peng, et al. Multiple time-scale optimal scheduling of community integrated energy system based on model predictive control[J]. Proceedings of the CSEE, 2019, 39(23): 6791-6803, 7093
[11] LV C, YU H, LI P, et al. Model predictive control based robust scheduling of community integrated energy system with operational flexibility[J]. Applied Energy, 2019, 243: 250-265.
[12] 陈中豪, 林晓明, 陈丽萍, 等. 社区级综合能源系统多目标模糊日前优化调度模型[J]. 广东电力, 2019, 32(1): 93-99
CHEN Zhonghao, LIN Xiaoming, CHEN Liping, et al. Multi-objective fuzzy optimal day-ahead dispatching model for integrated community energy system[J]. Guangdong Electric Power, 2019, 32(1): 93-99
[13] 彭春华, 郑聪, 陈婧, 等. 基于置信间隙决策的综合能源系统鲁棒优化调度[J/OL]. 中国电机工程学报: 1-10 [2020-11-21]. https://doi.org/10.13334/j.0258-8013.pcsee.201222.
PENG Chunhua, ZHENG Cong, CHEN Jing, et al. Robust optimal dispatching of integrated energy system based on confidence gap decision[J/OL]. Proceedings of the CSEE: 1-10 [2020-11-21]. https://doi.org/10.13334/j.0258-8013.pcsee.201222.
[14] SOLANKI B V, RAGHURAJAN A, BHATTACHARYA K, et al. Including smart loads for optimal demand response in integrated energy management systems for isolated microgrids[J]. IEEE Transactions on Smart Grid, 2017, 8(4): 1739-1748.
[15] 陈泽雄, 高军伟, 林亚培, 等. 园区综合能源微网动态优化调度算法[J]. 广东电力, 2020, 33(10): 64-74
CHEN Zexiong, GAO Junwei, LIN Yapei, et al. Mixed integer linear programming algorithm for dynamic optimal scheduling of integrated energy campus microgrid[J]. Guangdong Electric Power, 2020, 33(10): 64-74
[16] 都成, 魏震波. 基于模糊激励型需求响应的微电网两阶段优化调度模型[J]. 电力建设, 2020, 41(5): 37-44
DU Cheng, WEI Zhenbo. Two-stage optimal dispatching model of microgrid based on fuzzy incentive demand response[J]. Electric Power Construction, 2020, 41(5): 37-44
[17] MANSOURI S A, AHMARINEJAD A, JAVADI M S, et al. Two-stage stochastic framework for energy hubs planning considering demand response programs[J]. Energy, 2020, 206: 118124.
[18] 李姚旺, 苗世洪, 刘君瑶, 等. 考虑需求响应不确定性的光伏微电网储能系统优化配置[J]. 电力系统保护与控制, 2018, 46(20): 69-77
LI Yaowang, MIAO Shihong, LIU Junyao, et al. Optimal allocation of energy storage system in PV micro grid considering uncertainty of demand response[J]. Power System Protection and Control, 2018, 46(20): 69-77
[19] 王海洋, 李珂, 张承慧, 等. 基于主从博弈的社区综合能源系统分布式协同优化运行策略[J]. 中国电机工程学报, 2020, 40(17): 5435-5445
WANG Haiyang, LI Ke, ZHANG Chenghui, et al. Distributed coordinative optimal operation of community integrated energy system based on stackelberg game[J]. Proceedings of the CSEE, 2020, 40(17): 5435-5445
[20] AHMADI A, CHARWAND M, SIANO P, et al. A novel two-stage stochastic programming model for uncertainty characterization in short-term optimal strategy for a distribution company[J]. Energy, 2016, 117: 1-9.
[21] 曾博, 胡强, 刘裕, 等. 考虑需求响应复杂不确定性的电-气互联系统动态概率能流计算[J]. 中国电机工程学报, 2020, 40(4): 1161-1171, 1408
ZENG Bo, HU Qiang, LIU Yu, et al. Dynamic probabilistic energy flow calculation for interconnected electricity-gas system considering complex uncertainties of demand response[J]. Proceedings of the CSEE, 2020, 40(4): 1161-1171, 1408
[22] 袁晓冬, 费骏韬, 胡波, 等. 资源聚合商模式下的分布式电源、储能与柔性负荷联合调度模型[J]. 电力系统保护与控制, 2019, 47(22): 17-26
YUAN Xiaodong, FEI Juntao, HU Bo, et al. Joint scheduling model of distributed generation, energy storage and flexible load under resource aggregator mode[J]. Power System Protection and Control, 2019, 47(22): 17-26
[23] BAHRAMARA S, PARSA MOGHADDAM M, HAGHIFAM M R. A bi-level optimization model for operation of distribution networks with micro-grids[J]. International Journal of Electrical Power & Energy Systems, 2016, 82: 169-178.
[24] HAGHIFAM S, ZARE K, DADASHI M. Bi-level operational planning of microgrids with considering demand response technology and contingency analysis[J]. IET Generation, Transmission & Distribution, 2019, 13(13): 2721-2730.
[25] DADASHI M, HAGHIFAM S, ZARE K, et al. Short-term scheduling of electricity retailers in the presence of demand response aggregators: a two-stage stochastic bi-level programming approach[J]. Energy, 2020, 205: 117926.