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电制氢协同的含高比例光伏配电网两阶段电压随机优化控制OA北大核心CSTPCD

Two-Stage Stochastic Optimal Voltage Control of High-Proportional Photovoltaic Distribution Networks Considering Auxiliary Power to Hydrogen

中文摘要英文摘要

针对传统配电网电压控制方法存在的调节资源有限、调控成本较高、响应速度较慢等问题,提出一种利用电制氢(power to hydrogen,P2H)辅助的两阶段电压随机优化控制策略.首先,在建立P2H装置在内的调压设备运行约束以及配电网线路约束的基础上,建立了考虑电解制气收益的日前-日内两阶段电压优化控制模型.其次,针对分布式能源出力和负荷需求日内短时扰动引发的电压波动甚至越限问题,基于拉丁超立方抽样与Kantorovich距离削减技术构建了配电网典型运行场景,并以各场景下日内阶段目标函数期望最小为目标求解电压控制策略.算例结果表明,所提方法相比于不考虑P2H辅助的常规电压控制方案,有效避免了电压越限问题,并且总控制成本降低了26.43%.

To address the problems such as limited regulation resources,high regulation costs,and slow response speed in traditional distribution network voltage control methods,a two-stage stochastic optimal control strategy with participations of power-to-hydrogen(P2H)devices is investigated.Firstly,voltage regulation device operation constraints and distribution network line constraints are modeled.Then a two-stage day-ahead and intra-day voltage optimal control model is developed considering electrolytic gas production revenue.Secondly,to deal with the voltage fluctuations or even over-limitation problem caused by short-term disturbances of renewables and load demands,typical operation scenarios of distribution networks are constructed by adopting Latin hypercube sampling and Kantorovich distance reduction techniques.Then the voltage control strategy is solved by minimizing the expectation of intraday-stage objective functions under all scenarios.Finally,case studies have shown that compared with the traditional voltage methods without considering P2H participations,the voltage over-limitation can be effectively avoided and the total regulation costs can be reduced by over 26.43%by using the proposed method.

张亚健;陈茨;薛飞;马丽;郑敏

上海大学机电工程与自动化学院,上海 200444国网宁夏电力有限公司电力科学研究院,宁夏银川 750000国网天津市电力公司东丽供电分公司,天津 300000

电制氢配电网协调电压控制随机优化不确定性

power to hydrogendistribution networkcoordinated voltage controlstochastic optimizationuncertainties

《中国电力》 2024 (008)

23-35 / 13

国家自然科学基金青年项目(62103254);国网江苏省电力有限公司科技项目(J2021203).This work is supported by National Natural Science Foundation of China(No.62103254),State Grid Jiangsu Electric Power Co.,Ltd.Technology Project(No.J2021203).

10.11930/j.issn.1004-9649.202307084

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