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Energy storage optimal control strategy for renewable energy based industrial park under electricity market |
Ji Bin, Tan Jiancheng, Zeng Xuetong |
Institute of Power System Optimization (Guangxi University), Nanning 530004 |
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Abstract An optimal control strategy of energy storage systems is proposed in the paper for applications in an renewable energy based industrial park under electricity market. Driven by the short term forecast of hourly electricity generation of wind and solar plants, a load daily operational mode is chosen that best fit the application. The controlled energy storage system interacts with the wholesale electricity market, selling and purchase electricity to compensate the imbalance hourly power in the industrial park, as well contribute to the valley filling and peak shifting under the hourly price incentives. A nonlinear optimization programming problem of the energy storage system is formulated in the paper and solved using a Matlab solver. Analyzing into the three scenario examples have proven that the proposed energy storage control strategy is feasible. The paper discusses the uncertainty associated with wind and solar power projections, where measures in real-time market to deal with the uncertainty are analyzed. By optimal control of the energy storage system, seamless connection and trading to the wholesale electricity market, dispatching between the renewable energy based industrial park and intermittent renewable energy accommodation are realized.
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Received: 26 March 2018
Published: 31 August 2018
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Cite this article: |
Ji Bin,Tan Jiancheng,Zeng Xuetong. Energy storage optimal control strategy for renewable energy based industrial park under electricity market[J]. Electrical Engineering, 2018, 19(8): 22-29.
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URL: |
http://dqjs.cesmedia.cn/EN/Y2018/V19/I8/22
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[1] 杜欣. 我国弃风弃光现象的成因及对策分析[J]. 产业与科技论坛, 2017, 16(19): 85-87. [2] Wang Qianfan, Guan Yongpei, Wang Jianhui.A Chance-Constrained Two-Stage stochastic program for unit commitment with uncertain wind power output[J]. Power Systems, IEEE Transactions on, 2012, 27(1): 206-215. [3] 张坤亚, 马平, 赵世文, 等. 含风电场的电力系统环保经济调度[J]. 电气技术, 2017, 18(3): 25-29, 46. [4] 熊虎, 向铁元, 陈红坤, 等. 含大规模间歇式电源的模糊机会约束机组组合研究[J]. 中国电机工程学报, 2013, 33(13): 36-44, 前插5. [5] Teng F, Trovato V, Strbac G.Stochastic scheduling with inertia-dependent fast frequency response requirements[J]. IEEE Transactions on Power Systems, 2016, 31(2): 1557-1566. [6] 高亚静, 李瑞环, 梁海峰, 等. 考虑间歇性电源与负荷不确定性情况下基于多场景技术的主动配电系统两步优化调度[J]. 中国电机工程学报, 2015, 35(7): 1657-1665. [7] 国家电网公司“电网新技术前景研究”项目咨询组, 王松岑, 来小康, 等. 大规模储能技术在电力系统中的应用前景分析[J]. 电力系统自动化, 2013, 37(1): 3-8, 30. [8] 陈聪伟, 江修波, 刘丽军. 考虑时序与储能配合的分布式电源优化配置研究[J]. 电气技术, 2017, 18(6): 41-47. [9] 张晴, 李欣然, 杨明, 等. 净效益最大的平抑风电功率波动的混合储能容量配置方法[J]. 电工技术学报, 2016, 31(14): 40-48. [10] 甘伟, 艾小猛, 方家琨, 等. 风-火-水-储-气联合优化调度策略[J]. 电工技术学报, 2017, 32(S1): 11-20. [11] 赵书强, 王扬, 徐岩, 等. 基于机会约束目标规划的高风电接入比例下大规模储能与火电协调调度[J]. 中国电机工程学报, 2016, 36(4): 969-977. [12] 颜宁, 厉伟, 邢作霞, 等. 复合储能在主动配电网中的容量配置[J]. 电工技术学报, 2017, 32(19): 180-186. [13] 崔强, 王秀丽, 刘祖永. 市场环境下计及储能电站运行的联动电价研究及其效益分析[J]. 中国电机工程学报, 2013, 33(13): 62-68, 前插8. [14] 杨德友, 温佳鑫, 陈家荣, 等. 用于提高风电场可调度性的储能系统预测控制策略[J]. 高电压技术, 2017, 43(3): 1043-1048. [15] Wind Power Forecasting, www.aeso.ca [16] 邓恢平. 储能进入商业化“临界点”[N]. 中国电力报, 2017-10-19(007) [17] 陈深, 毛晓明, 房敏. 风力和光伏发电短期功率预测研究进展与展望[J]. 广东电力, 2014, 27(1): 18-23. |
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