Optimal Power Flow Based on Optimization of Wind-Photovoltaic-Storage Hybrid System
-
摘要: 针对分布式能源发电的间歇性和不确定性,提出了风、光互补发电和储能实时在线优化结合的方法。以风、光日前预测发电与实时发电误差最小、储能出力最少为目标,建立风-光-储联合优化模型;使用改进PSO算法对风、光出力进行实时优化,优化后的分布式发电可以有效降低分布式电源带来的日前调度偏差。储能优化后的风、光出力在进行潮流计算时可直接处理为负荷模型,因此可以有效降低分布式能源建模的复杂度。最后,以IEEE30节点系统为例,以发电机发电费用最低为目标函数,将风-光-储联合出力等效为单个节点,利用遗传算法对最优潮流模型进行求解,验证了本文方法的正确性和有效性。Abstract: With the vigorous development and application of distributed energy, the planning and operation of traditional power grids are facing more and more challenges. The intermittent influence of distributed energy needs to be solved urgently, e.g., the wind power and photovoltaic power generation on energy dispatch and the impact of uncertainty on the power grid. In view of the intermittency and uncertainty of distributed power generation, this paper established the wind-solar energy-storage joint optimization model to achieve the minimization of wind-solar forecast power generation error and energy storage output. Besides, this paper proposes a real-time online optimization method of wind-solar complementary power generation and energy storage. The improved PSO (particle swarm optimization) algorithm is used to optimize the model in real time. Based on the established wind-solar-storage complementary model, the co-generation of wind-solar storage is regarded as an equivalent node to construct an optimal power flow model with the objective of optimizing the diesel generators outputs. Moreover, GA (genetic algorithm) is used to solve the optimal power flow model. Finally, the experiments via IEEE30-node system are made to verify the effectiveness of the proposed joint optimization strategy.
-
表 1 两种优化结果对比
Table 1. Result comparsion between two methods
Method Matching degree with planned/% Energy storage charge/% Power difference/MW This paper 99.48 8.57 18.2255 Literature[19] 93.26 19.32 2.2041 表 2 发电机损耗参数
Table 2. Generation parameters setting
Node a b c Minimum
generation/MWMaximum
generation/MW1 0.0375 3.00 0 10 80 2 0.0475 1.15 0 10 80 5 0.0325 1.20 0 10 80 8 0.0430 1.25 0 10 80 11 0.0450 2.00 0 10 80 13 0.0350 2.50 0 10 80 -
[1] 常硕, 牛玉刚, 陈凯炎. 多目标约束的电动汽车实时调度策略[J]. 华东理工大学学报(自然科学版), 2021, 47(4): 465-474. doi: 10.14135/j.cnki.1006-3080.2020081700 [2] 陈磊, 牛玉刚, 贾廷纲. 基于主从博弈的多微网能量调度策略[J]. 电力系统保护与控制, 2020, 48(19): 35-42. [3] 曾丹, 姚建国, 杨胜春, 等. 应对风电消纳中基于安全约束的价格型需求响应优化调度建模[J]. 中国电机工程学报, 2014, 34(31): 5571-5578. [4] 牛瑞杰, 郭俊文, 李晓博, 等. 风光储联合发电系统储能控制策略[J]. 热力发电, 2020, 49(8): 150-155. [5] 戚永志, 刘玉田. 风光储联合系统输出功率滚动优化与实时控制[J]. 电工技术学报, 2014, 29(8): 265-273. [6] 何宇斌, 文云峰, 戴赛, 等. 基于故障风险指标排序的安全约束最优潮流[J]. 电力系统保护与控制, 2015, 43(13): 52-59. [7] YORINO N, ABDILLAH M, SASAKI Y, et al. Robust power system security assessment under uncertainties using bi-level optimization[J]. IEEE Transactions on Power Systems, 2018, 33(1): 352-362. doi: 10.1109/TPWRS.2017.2689808 [8] TEEPARTHI K, KUMAR D M. Security-constrained optimal power flow with wind and thermal power generators using fuzzy adaptive artificial physics optimization algorithm[J]. Neural Computing and Applications, 2018, 29(3): 855-871. doi: 10.1007/s00521-016-2476-4 [9] 廖迎晨, 甘德强, 陈星莺, 等. 考虑分布式电源出力不确定性的城市电网模糊最优潮流分析[J]. 电力自动化设备, 2012, 32(9): 35-39. [10] 曹佳, 曹建国, 胡家喜, 等. 考虑需求响应的多目标概率最优潮流问题研究[J]. 控制与信息技术, 2019(2): 32-39. [11] 陈兰萍, 牛玉刚. 基于多代理的微电网分区分布式最优潮流分析[J]. 华东理工大学学报(自然科学版), 2020, 46(4): 549-555. [12] 王思明, 牛玉刚, 祖其武. 并网模式下基于多代理技术的微电网多目标优化[J]. 华东理工大学学报(自然科学版), 2017, 43(6): 829-836, 889. [13] 崔杨, 张家瑞, 王铮, 等. 计及价格型需求响应的风–光–光热联合发电系统日前调度策略[J]. 中国电机工程学报, 2020, 40(10): 3103-3114. [14] CARPENTIER J. Contribution al'etude du dispatching economique[J]. Bulletin de la Societe Francaise des Electriciens, 1962, 12(1): 444-447. [15] MAZIDI M, ZAKARIAZADEH A, JADID S, et al. Integrated scheduling of renewable generation and demand response programs in a microgrid[J]. Energy Conversion and Management, 2014, 86: 1118-1127. doi: 10.1016/j.enconman.2014.06.078 [16] AKDAG S A, DINLER A. A new method to estimate Weibull parameters for wind energy applications[J]. Energy Conversion and Management, 2009, 50: 1761-1766. doi: 10.1016/j.enconman.2009.03.020 [17] BOUKTIR T, SLIMANI L, BELKACEMI M. A genetic algorithm for solving the optimal power flow problem[J]. Leonardo Journal of Sciences, 2004, 3(4): 44-58. [18] FRANK S, STEPONAVICE I, REBENNACK S. Optimal power flow: A bibliographic survey II[J]. Energy System, 2012, 3: 259-289. doi: 10.1007/s12667-012-0057-x [19] 赵书强, 刘大正, 谢宇琪, 等. 基于相关机会目标规划的风光储联合发电系统储能调度策略[J]. 电力系统自动化, 2015, 14(39): 30-36, 53. [20] ROY P, CHAKRABARTI A. Modified shuffled frog leaping algorithm with genetic algorithm crossover for solving economic load dispatch problem with valve-point effect[J]. Applied Soft Computing, 2013, 13(11): 4244-4252. doi: 10.1016/j.asoc.2013.07.006 [21] 曹佳, 严正, 李建华, 等. 含风电场交直流混联系统的概率潮流计算[J]. 电力自动化设备, 2016, 36(11): 94-100. [22] SUN D I, ASHLEY B, BREWER B, et al. Optimal power flow by newton approach[J]. IEEE Transactions on Power Apparatus and Systems, 1984, 103(10): 2864-2880. [23] BURCHETT R C, HAPP H H, VIERATH D R. Quadratically convergent optimal power flow[J]. IEEE Transactions on Power Apparatus and Systems, 1984, 4(11): 3267-3275. [24] YANG X D, ZHANG Y B, ZHAO B, et al. Optimal energy flow control strategy for a residential energy local network combined with demand-side management and real-time pricing[J]. Energy and Buildings, 2017, 150(1): 177-188. [25] 祖其武, 牛玉刚, 邹媛媛, 等. 基于弹性负荷分时调度和多电源联合供电的微网经济运行[J]. 电力系统保护与控制, 2018, 46(4): 20-27. -