Interval Optimal Dispatching of Community Integrated Energy System Considering Multiple Uncertainties

doi:10.11930/j.issn.1004-9649.202107253

Abstract

Abstract: Under electricity market environment, the fluctuation of electricity prices needs to be considered in formulating next day's dispatching plan for community integrated energy system (CIES). At the same time, the uncertain output of distributed generators such as wind turbine and photovoltaic power generation brings significant challenges to the economic operation of CIES. Therefore, an interval optimal scheduling method of CIES considering multiple uncertainties is proposed. Firstly, the interval theory is used to model the uncertainties of electricity price and output of distributed generators. Then, taking the lowest daily operation and maintenance cost as objective function, and considering the operation constraints of the CIES, a day-ahead optimal dispatching model of CIES considering uncertainties is established. Finally, the interval linear programming method is used to solve the model. The results have verified the effectiveness of the proposed model and method.

Key words: community integrated energy system, optimal dispatching, interval optimization, uncertainty

GUO Zuogang, XU Min, YU Hao, LEI Jinyong, JI Haoran, LI Peng. Interval Optimal Dispatching of Community Integrated Energy System Considering Multiple Uncertainties[J]. Electric Power, 2022, 55(11): 121-128,141.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202107253

https://www.electricpower.com.cn/EN/Y2022/V55/I11/121

References

[1] GU W, WANG J, LU S, et al. Optimal operation for integrated energy system considering thermal inertia of district heating network and buildings[J]. Applied Energy, 2017, 199: 234–246.
[2] NAZARI M, AKBARI FOROUD A. Optimal strategy planning for a retailer considering medium and short-term decisions[J]. International Journal of Electrical Power & Energy Systems, 2013, 45(1): 107–116.
[3] 卢炳文, 魏震波, 魏平桉, 等. 考虑多重区间不确定性的用户侧综合能源系统双层优化配置[J]. 中国电力, 2022, 55(3): 193–202
LU Bingwen, WEI Zhenbo, WEI Pingan, et al. Two-level optimal configuration of user-side integrated energy system considering interval uncertainties[J]. Electric Power, 2022, 55(3): 193–202
[4] 黄瀚燕, 周明, 李庚银. 考虑多重不确定性和备用互济的含风电互联电力系统分散协调调度方法[J]. 电网技术, 2019, 43(2): 381–389
HUANG Hanyan, ZHOU Ming, LI Gengyin. Coordinated decentralized dispatch of wind-power-integrated multi-area interconnected power systems considering multiple uncertainties and mutual reserve support[J]. Power System Technology, 2019, 43(2): 381–389
[5] RAHMANI-ANDEBILI M. Stochastic, adaptive, and dynamic control of energy storage systems integrated with renewable energy sources for power loss minimization[J]. Renewable Energy, 2017, 113: 1462–1471.
[6] 李鹏, 杨莘博, 魏澄宙, 等. 计及能源政策的园区综合能源系统鲁棒随机优化模型[J/OL]. (2022-02-28) [2022-09-14]. http://kns.cnki.net/kcms/detail/11.3265.TM.20220228.1412.004.html.
[7] 吴雄, 王秀丽, 李骏, 等. 风电储能混合系统的联合调度模型及求解[J]. 中国电机工程学报, 2013, 33(13): 10–17
WU Xiong, WANG Xiuli, LI Jun, et al. A joint operation model and solution for hybrid wind energy storage systems[J]. Proceedings of the CSEE, 2013, 33(13): 10–17
[8] 王志成, 王秀丽, 王帅. 考虑实时市场平衡费用的含风电日前市场电能-备用联合出清模型[J]. 中国电力, 2020, 53(9): 19–27
WANG Zhicheng, WANG Xiuli, WANG Shuai. Day-ahead market energy-reserve joint clearing model considering real-time market balancing cost under participation of wind power generation[J]. Electric Power, 2020, 53(9): 19–27
[9] SHAMS M H, SHAHABI M, KHODAYAR M E. Stochastic day-ahead scheduling of multiple energy carrier microgrids with demand response[J]. Energy, 2018, 155: 326–338.
[10] JU L W, TAN Z F, YUAN J Y, et al. A bi-level stochastic scheduling optimization model for a virtual power plant connected to a wind-photovoltaic-energy storage system considering the uncertainty and demand response[J]. Applied Energy, 2016, 171: 184–199.
[11] VAHID-PAKDEL M J, NOJAVAN S, MOHAMMADI-IVATLOO B, et al. Stochastic optimization of energy hub operation with consideration of thermal energy market and demand response[J]. Energy Conversion and Management, 2017, 145: 117–128.
[12] NAJAFI A, FALAGHI H, CONTRERAS J, et al. A stochastic bilevel model for the energy hub manager problem[J]. IEEE Transactions on Smart Grid, 2017, 8(5): 2394–2404.
[13] 张彦, 张涛, 刘亚杰, 等. 基于随机模型预测控制的能源局域网优化调度研究[J]. 中国电机工程学报, 2016, 36(13): 3451–3462,3364
ZHANG Yan, ZHANG Tao, LIU Yajie, et al. Stochastic model predictive control for energy management optimization of an energy local network[J]. Proceedings of the CSEE, 2016, 36(13): 3451–3462,3364
[14] 姜潮. 基于区间的不确定性优化理论与算法[D]. 长沙: 湖南大学, 2008.
JIANG Chao. Theories and algorithms of uncertain optimization based on interval[D]. Changsha: Hunan University, 2008.
[15] 鲍海波, 韦化, 郭小璇. 考虑新能源的交直流系统区间最优潮流[J]. 中国电机工程学报, 2015, 35(16): 4006–4014
BAO Haibo, WEI Hua, GUO Xiaoxuan. Interval optimal power flow calculation for AC/DC power system considering renewable energy source[J]. Proceedings of the CSEE, 2015, 35(16): 4006–4014
[16] 王守相, 王栋, 韩亮. 考虑不确定性的微网日前经济优化调度区间线性规划方法[J]. 电力系统自动化, 2014, 38(24): 5–11,47
WANG Shouxiang, WANG Dong, HAN Liang. Interval linear programming method for day-ahead optimal economic dispatching of microgrid considering uncertainty[J]. Automation of Electric Power Systems, 2014, 38(24): 5–11,47
[17] 白牧可, 王越, 唐巍, 等. 基于区间线性规划的区域综合能源系统日前优化调度[J]. 电网技术, 2017, 41(12): 3963–3970
BAI Muke, WANG Yue, TANG Wei, et al. Day-ahead optimal dispatching of regional integrated energy system based on interval linear programming[J]. Power System Technology, 2017, 41(12): 3963–3970
[18] MAJIDI M, ZARE K. Integration of smart energy hubs in distribution networks under uncertainties and demand response concept[J]. IEEE Transactions on Power Systems, 2019, 34(1): 566–574.
[19] 苏永新, 聂伟棋, 谭貌. 考虑风电接入和气电转换的综合能源系统日前区间优化[J]. 电力系统自动化, 2019, 43(17): 63–71
SU Yongxin, NIE Weiqi, TAN Mao. Day-ahead interval optimization of integrated energy system considering wind power integration and gas-electricity transformation[J]. Automation of Electric Power Systems, 2019, 43(17): 63–71
[20] BAI L Q, LI F X, CUI H T, et al. Interval optimization based operating strategy for gas-electricity integrated energy systems considering demand response and wind uncertainty[J]. Applied Energy, 2016, 167: 270–279.
[21] BAHRAMI S, SAFE F. A financial approach to evaluate an optimized combined cooling, heat and power system[J]. Energy and Power Engineering, 2013, 5(5): 352–362.
[22] 苏剑, 周莉梅, 李蕊. 分布式光伏发电并网的成本/效益分析[J]. 中国电机工程学报, 2013, 33(34): 50–56,11
SU Jian, ZHOU Limei, LI Rui. Cost-benefit analysis of distributed grid-connected photovoltaic power generation[J]. Proceedings of the CSEE, 2013, 33(34): 50–56,11
[23] LI G Q, ZHANG R F, JIANG T, et al. Security-constrained bi-level economic dispatch model for integrated natural gas and electricity systems considering wind power and power-to-gas process[J]. Applied Energy, 2017, 194: 696–704.
[24] LIU G D, XU Y, TOMSOVIC K. Bidding strategy for microgrid in day-ahead market based on hybrid stochastic/robust optimization[J]. IEEE Transactions on Smart Grid, 2016, 7(1): 227–237.
[25] WAN C, XU Z, PINSON P, et al. Probabilistic forecasting of wind power generation using extreme learning machine[J]. IEEE Transactions on Power Systems, 2014, 29(3): 1033–1044.
[26] 李阳, 曹华珍, 吴亚雄, 等. 考虑能源互联网耦合特性的智慧工业园区协同规划研究[J]. 智慧电力, 2018, 46(12): 15–22
LI Yang, CAO Huazhen, WU Yaxiong, et al. Collaborative planning of smart industrial park considering coupling characteristics of energy Internet[J]. Smart Power, 2018, 46(12): 15–22
[27] 黄伟, 高怡芳, 张勇军, 等. 计及不确定性的分布式光伏接入配电网极限容量评估[J]. 电力系统保护与控制, 2018, 46(14): 9–16
HUANG Wei, GAO Yifang, ZHANG Yongjun, et al. Evaluation of ultimate capacity for distributed photovoltaic access distribution network considering uncertainty[J]. Power System Protection and Control, 2018, 46(14): 9–16
[28] 王珺, 顾伟, 张成龙, 等. 智能社区综合能源优化管理研究[J]. 电力系统保护与控制, 2017, 45(1): 89–97
WANG Jun, GU Wei, ZHANG Chenglong, et al. Research on integrated energy management for smart community[J]. Power System Protection and Control, 2017, 45(1): 89–97