基于遗传蚁群的光储电站运行效益提升策略研究

doi:10.11930/j.issn.1004-9649.202101001

中国电力 ›› 2022, Vol. 55 ›› Issue (2): 9-18.DOI: 10.11930/j.issn.1004-9649.202101001

• 国家“十三五”智能电网重大专项专栏：（十一）新型储能与能源转化关键技术 • 上一篇下一篇

基于遗传蚁群的光储电站运行效益提升策略研究

曹雅琦¹, 赵波¹, 王丽婕¹, 李相俊², 高彬桓³

1. 北京信息科技大学，北京 100192;
2. 新能源与储能运行控制国家重点实验室（中国电力科学研究院有限公司），北京 100192;
3. 华北电力大学电气与电子工程学院，北京 102206

收稿日期:2021-01-05 修回日期:2021-04-05 出版日期:2022-02-28 发布日期:2022-02-23
作者简介:曹雅琦（1996—），女，硕士，从事储能电池研究，E-mail：1197869028@qq.com;赵波（1977—），男，博士，高级工程师（研究员级），从事电力电子技术在电力系统中的应用研究，E-mail：13910889512@126.com;王丽婕（1983—），女，通信作者，博士，副教授，从事新能源功率预测、机器学习、系统优化等研究，E-mail：wanglijie_0203@126.com;李相俊（1979—），男，博士，高级工程师（教授级），从事大规模储能技术、新能源与分布式发电等研究，E-mail：Li_xiangjun@126.com
基金资助:
国家自然科学基金资助项目（基于数值天气预报信息融合的并网风电场短期发电功率预测研究，51607009）；新能源与储能运行控制国家重点实验室开放基金资助项目（大容量储能电站等值模型及特征参数识别技术研究，DGB51201901183）；北京市属高校高水平教师队伍建设支持计划青年拔尖人才培养计划（CIT&TCD201804053）。

Research on Operational Benefit Improvement Strategy of Optical Storage Power Station Based on Genetic Ant Colony Algorithm

CAO Yaqi¹, ZHAO Bo¹, WANG Lijie¹, LI Xiangjun², GAO Binheng³

1. Beijing Information Science and Technology University, Beijing 100192, China;
2. State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems, China Electric Power Research Institute, Beijing 100192, China;
3. School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China

Received:2021-01-05 Revised:2021-04-05 Online:2022-02-28 Published:2022-02-23
Supported by:
This work is supported by the National Natural Science Foundation of China (Research on Short-Term Generation Power Prediction of Grid-connected Wind Farms Based on Numerical Weather Prediction Information Fusion, No.51607009), Open Fund of Operation and Control of Renewable Energy & Storage Systems(Research on Equivalent Model and Characteristic Parameter Identification Technology of Large Capacity Energy Storage Power Station, No. DGB51201901183) and Young Talent Cultivation Plan of High-Level Teachers Construction of Beijing Municipal Institutions (No.CIT&TCD201804053).

摘要/Abstract

摘要： 以并网光储电站为研究对象，以充分发挥储能系统灵活调节作用并提高系统运行经济性为出发点，在考虑度电成本的基础上，提出一种实时调整储能系统运行状态的控制策略，建立以净收益最大、向大电网取电量最少为目标的优化模型，采用基于精英策略的带有惩罚函数的遗传-蚁群算法对优化模型进行求解，从投资人角度对并网光储系统进行投资收益分析。最后通过对江苏省某地区实际数据仿真分析，给出该地区“光伏+储能”优化控制策略及其经济效益分析结果，验证了该模型及算法的可行性。

关键词: 光伏+储能, 储能实时优化控制策略, 运行经济效益, 净现值, 投资回笼期

Abstract: This article takes the grid-connected optical storage regional power grid as the research object, and starts the research with the full use of the flexible regulation role of the energy storage system and the improvement of the system operation economy. On the basis of considering the cost of electricity, a control strategy for real-time adjustment of the BESS operating state is proposed, and an optimization model is established with the goal of maximizing net income, minimizing total cost, and extracting electricity from the large grid. The genetic-ant colony algorithm of the penalty function solves the optimization model, and analyzes the investment income of the grid-connected optical storage system from the perspective of investors. Finally, through the simulation analysis of actual data in a certain area of Jiangsu, the optimization control strategy of "photovoltaic and energy storage" in this area and the results of economic benefit analysis are given to verify the feasibility of this model and algorithm.

Key words: photovoltaic and energy storage, energy storage real-time optimization control strategy, operational economic benefits, net present value, investment recovery period

曹雅琦, 赵波, 王丽婕, 李相俊, 高彬桓. 基于遗传蚁群的光储电站运行效益提升策略研究[J]. 中国电力, 2022, 55(2): 9-18.

CAO Yaqi, ZHAO Bo, WANG Lijie, LI Xiangjun, GAO Binheng. Research on Operational Benefit Improvement Strategy of Optical Storage Power Station Based on Genetic Ant Colony Algorithm[J]. Electric Power, 2022, 55(2): 9-18.

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基于遗传蚁群的光储电站运行效益提升策略研究

Research on Operational Benefit Improvement Strategy of Optical Storage Power Station Based on Genetic Ant Colony Algorithm

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Research on Operational Benefit Improvement Strategy of Optical Storage Power Station Based on Genetic Ant Colony Algorithm

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