Abstract The fluctuating nature of renewable energy is a key limit for its large-scale integration. Considering the fast response characteristics of incentive-based demand response (IBDR) with and the uncertainty of renewable energy and IBDR, a method of utilizing the multi-type IBDR to balance the fluctuation of renewable energy is proposed. Firstly, the robust optimization theory is introduced, and the uncertainty of the renewable energy and IBDR is quantitatively described by multiple time scales in the form of robust intervals. Then, the multi-objective IBDR robust optimization configuration model is established with the objective function of the operation cost and the renewable energy utilization rate, which considers the renewable energy integration constraints, IBDR capacity constraints, and power balance constraints. Finally, the uncertainty problem is transformed into a deterministic problem by using counterpart transformation, and a non-dominated set genetic algorithm (NSGA-II) is used to solve the problem. Taking an actual distribution network as an example, the proposed model and algorithm are verified. The calculation results show that the robust optimal configuration using Multi-type IBDR can effectively balance the fluctuation of renewable energy.
Yi Wenfei,Zhang Yiwei,Zeng Bo等. Robust Optimization Allocation for Multi-Type Incentive-Based Demand Response Collaboration to Balance Renewable Energy Fluctuations[J]. Transactions of China Electrotechnical Society, 2018, 33(23): 5541-5554.
[1] 王晓晶, 陈星莺, 陈楷, 等. 智能配电网清洁性评估指标研究[J]. 中国电机工程学报, 2013, 33(31): 43-50. Wang Xiaojing, Chen Xingying, Chen Kai, et al.Research on cleaning evaluation indices of smart distribution grid[J]. Proceedings of the CSEE, 2013, 33(31): 43-50. [2] Ai Qian, Wang Xiaohong, He Xing.The impact of large-scale distributed generation on power grid and microgrids[J]. Renewable Energy, 2014, 62(3): 417-423. [3] 林俐, 田欣雨. 基于火电机组分级深度调峰的电力系统经济调度及效益分析[J]. 电网技术, 2017, 41(7): 2255-2262. Lin Li, Tian Xinyu, Analysis of deep peak regulation and its benefit of thermal units in power system with large scale wind power integrated[J]. Power System Technology, 2017, 41(7): 2255-2262. [4] 李建林, 马会萌, 惠东. 储能技术融合分布式可再生能源的现状及发展趋势[J]. 电工技术学报, 2016, 31(14): 1-10. Li Jianlin, Ma Huimeng, Hui Dong.Present development condition and trends of energy storage technology in the integration of distributed renewable energy[J]. Transactions of China Electrotechnical Society, 2016, 31(14): 1-10. [5] Kwag H G, Kim J O.Optimal combined scheduling of generation and demand response with demand resource constraints[J]. Applied Energy, 2012, 96(1): 161-170. [6] Aryandoust A, Lilliestam J.The potential and usefulness of demand response to provide electricity system services[J]. Applied Energy, 2017, 204: 749-766. [7] Muhssin M T, Cipcigan L M, Sami S S, et al.Potential of demand side response aggregation for the stabilization of the grids frequency[J]. Applied Energy, 2018, 220: 643-656. [8] 姚建国, 杨胜春, 王珂, 等. 平衡风功率波动的需求响应调度框架与策略设计[J]. 电力系统自动化, 2014, 38(9): 85-92. Yao Jianguo, Yang Shengchun, Wang Ke, et al.Framework and strategy design of demand response scheduling for balancing wind power fluctuation[J]. Automation of Electric Power Systems, 2014, 38(9): 85-92. [9] 杨胜春, 刘建涛, 姚建国, 等. 多时间尺度协调的柔性负荷互动响应调度模型与策略[J]. 中国电机工程学报, 2014, 34(22): 3664-3673. Yang Shengchun, Liu Jiantao, Yao Jianguo, et al.Model and strategy for multi-time scale coordinated flexible load interactive scheduling[J]. Proceedings of the CSEE, 2014, 34(22): 3664-3673. [10] 彭政, 崔雪, 王恒, 等. 考虑储能和需求侧响应的微网光伏消纳能力研究[J]. 电力系统保护与控制, 2017, 45(22): 63-69. Peng Zheng, Cui Xue, Wang Heng, et al.Research on the accommodation of photovoltaic power considering storage system and demand response in microgrid[J]. Power System Protection and Control, 2017, 45(22): 63-69. [11] 王蓓蓓, 刘小聪, 李扬. 面向大容量风电接入考虑用户侧互动的系统日前调度和运行模拟研究[J]. 中国电机工程学报, 2013, 36(22): 35-44. Wang Beibei, Liu Xiaocong, Li Yang.Day-ahead generation scheduling and operation simulation considering demand response in large-capacity wind power integrated systems[J]. Proceedings of the CSEE, 2013, 36(22): 35-44. [12] 刘小聪, 王蓓蓓, 李扬, 等. 计及需求侧资源的大规模风电消纳随机机组组合模型[J]. 中国电机工程学报, 2015, 35(14): 3714-3723. Liu Xiaocong, Wang Beibei, Li Yang, et al.Stochastic unit commitment model for high wind power integration considering demand side resources[J]. Proceedings of the CSEE, 2015, 35(14): 3714-3723. [13] Galvan E, Alcaraz G G, Cabrera N G.Two-phase short-term scheduling approach with intermittent renewable energy resources and demand response[J]. IEEE Latin America Transactions, 2015, 13(1): 181-187. [14] Soares J, Vale Z, Borges N, et al.Multi-objective robust optimization to solve energy scheduling in buildings under uncertainty[C]//International Conference on Intelligent System Application to Power Systems, San Antonio, TX, USA, 2017: 1-6. [15] 曾鸣, 杨雍琦, 向红伟, 等. 计及需求侧响应的电力系统鲁棒优化规划模型[J]. 电力系统自动化, 2016, 40(17): 137-145. Zeng Ming, Yang Yongqi, Xiang Hongwei, et al.Robust optimization planning model of power system considering demand response[J]. Automation of Electric Power Systems, 2016, 40(17): 137-145. [16] Bertsimas D, Litvinov E, Sun X A, et al.Adaptive robust optimization for the security constrained unit commitment problem[J]. IEEE Transactions on Power Systems, 2013, 28(1): 52-63. [17] Ferreira R S, Barroso L A, Carvalho M M.Demand response models with correlated price data: a robust optimization approach[J]. Applied Energy, 2012, 96(3): 133-149. [18] 朱光远, 林济铿, 罗治强, 等. 鲁棒优化在电力系统发电计划中的应用综述[J]. 中国电机工程学报, 2017, 37(20): 5881-5892. Zhu Guangyuan, Lin Jikeng, Luo Zhiqiang, et al.Review of robust optimization for generation scheduling in power systems[J]. Proceedings of the CSEE, 2017, 37(20): 5881-5892. [19] 田世明, 王蓓蓓, 张晶. 智能电网条件下的需求响应关键技术[J]. 中国电机工程学报, 2014, 34(22): 3576-3589. Tian Shiming, Wang Beibei, Zhang Jing.Key technologies for demand response in smart grid[J]. Proceedings of the CSEE, 2014, 34(22): 3576-3589. [20] Wang Yang, Xia Qing, Kang Chongqing.Unit commitment with volatile node injections by using interval optimization[J]. IEEE Transactions on Power Systems, 2011, 26(3): 1705-1713. [21] Wu Lei, Shahidehpour M, Li Zuyi.Comparison of scenario-based and interval optimization approaches to stochastic SCUC[J]. IEEE Transactions on Power Systems, 2012, 27(2): 913-921. [22] Bienstock D, Chertkov M, Harnett S.Chance constrained optimal power flow: risk-aware network control under uncertainty[J]. Siam Review, 2012, 56(3): 67. [23] 赵冬梅, 殷家玞. 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, 2017, 32(6): 50-59. Zhao Dongmei, Yin Jiafu.Fuzzy random chance constrained preemptive goal programming scheduling model considering source-side and load-side uncertainty[J]. Transactions of China Electrotechnical Society, 2017, 32(6): 50-59. [24] Ben-Tal A, Nemirovski A.Robust optimization-methodology and applications[J]. Mathematical Programming, 2002, 92(3): 453-480. [25] 王境彪, 晁勤, 王一波, 等. 基于主次双尺度交集切割效应的混合储能平衡风功率波动控制[J]. 电网技术, 2015, 39(12): 3369-3377. Wang Jingbiao, Chao Qin, Wang Yibo, et al.A control of hybrid energy storage system for suppressing fluctuation of wind power based on primary-secondary scale intersection cutting effect[J]. Power System Technology, 2015, 39(12): 3369-3377. [26] GB/T 19963—2011. 风电场接入电力系统技术规定B/T 19963—2011. 风电场接入电力系统技术规定[S]. 2011. [27] 韩晓娟, 陈跃燕, 张浩, 等. 基于小波包分解的混合储能技术在平衡风电场功率波动中的应用[J]. 中国电机工程学报, 2013, 33(19): 8-13. Han Xiaojuan, Chen Yueyan, Zhang Hao, et al.Application of hybrid energy storage technology based on wavelet packet decomposition in smoothing the fluctuations of wind power[J]. Proceedings of the CSEE, 2013, 33(19): 8-13. [28] 刘吉臻. 新能源电力系统建模与控制[M]. 北京: 科学出版社, 2015. [29] 曾博, 杨雍琦, 段金辉, 等. 新能源电力系统中需求侧响应关键问题及未来研究展望[J]. 电力系统自动化, 2015, 39(17): 10-18. Zeng Bo, Yang Yongqi, Duan Jinhui, et al.Key issues and research prospects for demand-side response in alternate electrical power systems with renewable energy sources[J]. Automation of Electric Power System, 2015, 39(17): 10-18. [30] 陈征, 肖湘宁, 路欣怡, 等. 含光伏发电系统的电动汽车充电站多目标容量优化配置方法[J]. 电工技术学报, 2013, 28(6): 238-248. Chen Zheng, Xiao Xiangning, Lu Xinyi, et al.Multi-objective optimization for capacity configuration of PV-based electric vehicle charging stations[J]. Transactions of China Electrotechnical Society, 2013, 28(6): 238-248. [31] 罗毅, 邵周策, 张磊, 等. 考虑风电不确定性和气网运行约束的鲁棒经济调度和备用配置[J]. 电工技术学报, 2018, 33(11): 2456-2467. Luo Yi, Shao Zhouce, Zhang Lei, et al.Robust economic dispatch and reserve configuration considering wind uncertainty and gas network constraints[J]. Transactions of China Electrotechnical Society, 2018, 33(11): 2456-2467. [32] Li Zukui, Ding R, Floudas C A.A comparative theoretical and computational study on robust counterpart optimization: I. robust linear optimization and robust mixed integer linear optimization[J]. Industrial & Engineering Chemistry Research, 2011, 50(18): 10567-10603. [33] Deb K, Pratap A, Agarwal S, et al.A fast and elitist multiobjective genetic algorithm: NSGA-II[J]. IEEE Transactions on Evolutionary Computation, 2002, 6(2): 182-197. [34] 刘东奇, 王耀南, 袁小芳. 电动汽车充放电与风力/火力发电系统的协同优化运行[J]. 电工技术学报, 2017, 32(3): 18-26. Liu Dongqi, Wang Yaonan, Yuan Xiaofang.Cooperative dispatch of large-scale electric vehicles with wind-thermal power generating system[J]. Transactions of China Electrotechnical Society, 2017, 32(3): 18-26. [35] 王茜, 张粒子. 采用NSGA-II混合智能算法的风电场多目标电网规划[J]. 中国电机工程学报, 2011, 31(19): 17-24. Wang Qian, Zhang Lizi.Multi-objective transmission planning associated with wind farms applying nsga-ii hybrid intelligent algorithm[J]. Proceedings of the CSEE, 2011, 31(19): 17-24. [36] 孙宇军, 王岩, 王蓓蓓, 等. 考虑需求响应不确定性的多时间尺度源荷互动决策方法[J]. 电力系统自动化, 2018, 42(2): 106-113. Sun Yujun, Wang Yan, Wang Beibei, et al.Multi-time scale decision method for source-load interaction considering demand response uncertainty[J]. Automation of Electric Power System, 2018, 42(2): 106-113. [37] 张坤, 毛承雄, 谢俊文, 等. 风电场复合储能系统容量配置的优化设计[J]. 中国电机工程学报, 2012, 32(25): 79-87. Zhang Kun, Mao Chengxiong, Xie Junwen, et al.Optimal design of hybrid energy storage system capacity for wind farms[J]. Proceedings of the CSEE, 2012, 32(25): 79-87. [38] 邱威. 考虑间歇性能源接入和运行安全的多目标有功优化调度[D]. 北京: 华北电力大学, 2012.
2018, Vol. 33 
