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1. 丹麦科技大学电气工程系电力与能源中心,灵比 2800,丹麦
2. 清华大学深圳国际研究生院清华-伯克利深圳学院,广东省 深圳市 518055
3. 电网智能化调度与控制教育部重点实验室(山东大学),山东省 济南市 250061
曹永吉(1992—),男,博士后,主要研究方向:电力系统频率稳定分析与控制、可再生能源并网及储能技术应用。E-mail:caoyong@dtu.dk
吴秋伟(1977—),男,通信作者,博士,副教授,博士生导师,主要研究方向:高比例可再生能源并网下新型电力能源系统优化运行控制。E-mail:qiuwu@sz.tsinghua.edu.cn
张恒旭(1975—),男,博士,教授,博士生导师,主要研究方向:电力系统稳定分析与控制、电力系统监测和电力系统数值仿真。E-mail:zhanghx@sdu.edu.cn
收稿:2022-03-30,
修回:2022-07-14,
网络出版:2022-11-07,
纸质出版:2023-01-25
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曹永吉,吴秋伟,张恒旭等.考虑分时电价和最大暂态频率偏移的储能机会约束优化配置[J].电力系统自动化,2023,47(2):61-68.
CAO Yongji,WU Qiuwei,ZHANG Hengxu,et al.Chance-constrained Optimal Configuration for Energy Storage System Considering Time-of-use Price and Maximum Transient Frequency Deviation[J].Automation of Electric Power Systems,2023,47(2):61-68.
曹永吉,吴秋伟,张恒旭等.考虑分时电价和最大暂态频率偏移的储能机会约束优化配置[J].电力系统自动化,2023,47(2):61-68. DOI: 10.7500/AEPS20220330005.
CAO Yongji,WU Qiuwei,ZHANG Hengxu,et al.Chance-constrained Optimal Configuration for Energy Storage System Considering Time-of-use Price and Maximum Transient Frequency Deviation[J].Automation of Electric Power Systems,2023,47(2):61-68. DOI: 10.7500/AEPS20220330005.
针对可再生能源规模化接入电网引起的频率稳定控制资源减少的问题,提出一种考虑分时电价和最大暂态频率偏移(MTFD)的储能系统(ESS)机会约束优化配置方案。首先,建立含ESS的扩展系统频率响应模型,估计预想事故下的频率动态响应特征,采用基于最大仿射函数的分段线性化方法构造MTFD约束;其次,考虑预想事故发生的不确定性,构建以投资成本最小和峰谷套利收益最大为目标的ESS容量机会约束优化模型;然后,利用线性加权方法和基于蒙特卡洛模拟的非线性递减惯性权重粒子群优化算法求解,以确定最优的ESS容量;最后,通过算例分析验证了所提方法的有效性。
In order to compensate the decreasing frequency stability control resource caused by the large-scale integration of the renewable energy in the power system
a chance-constrained optimal configuration scheme considering the time-of-use price and the maximum transient frequency deviation (MTFD) is proposed for the energy storage system (ESS). Firstly
an extended system frequency response model is established to estimate the frequency dynamic response characteristics under the predefined contingency
from which the constraints of MTFD are extracted by the max-affine function based piecewise linearization method. Secondly
taking into account the uncertainty of contingencies
a chance-constrained optimization model is built to optimize the capacity of the ESS
with the objective of minimizing the investment cost and maximizing the peak-valley arbitrage income. Thirdly
the linear weighted method and the Monte Carlo- and nonlinear decreasing inertia weight-based particle swarm optimization algorithm are used to attain the optimal capacity of the ESS. Finally
a case study is carried out to validate the effectiveness of the proposed method.
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