| 引用本文: | 殷加玞,赵冬梅.基于全概率风险度量的电力系统备用风险评估方法[J].电力自动化设备,2020,40(1): |
| YIN Jiafu,ZHAO Dongmei.Reserve risk assessment method of power system based on total probability risk measure[J].Electric Power Automation Equipment,2020,40(1): |
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| 摘要: |
| 为了量化分析源荷双侧预测误差引起的电力系统备用缺额风险，提出了一种基于全概率公式和条件风险价值的风险度量，作为表征随机误差潜在风险的评估指标。为了明确不同的风险等级，以风险价值为临界点，将常规机组的备用缺额风险划分为缓冲区和极端区，并计算不同风险区域的条件期望损失。针对风电、负荷预测和可中断负荷的预测误差，引入机会约束目标规划构建常规机组的可调备用约束，依据不同的风险等级将备用缺额风险转化为不同类型的风险成本，引入模型目标函数中以确保电网运行的安全性。为了提高模型的求解效率，采用分段线性化方法将非线性模型转化为混合整数线性规划。 |
| 关键词: 电力系统 不确定性 风险评估 全概率 条件风险价值 机会约束目标规划 |
| DOI：10.16081/j.epae.201912024 |
| 分类号:TM732 |
| 基金项目:国家重点研发计划项目(2017YFB0902600)；国家电网公司科技项目(SGJS0000DKJS1700840) |
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| Reserve risk assessment method of power system based on total probability risk measure |
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YIN Jiafu, ZHAO Dongmei
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School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China
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| Abstract: |
| To quantitatively evaluate the reserve deficiency risk caused by forecasting error at source and load sides for power system, the risk measure based on the combination of total probability formula and conditional value-at-risk is proposed to evaluate the potential risk caused by stochastic errors. In order to identify the operation risk with different levels, the reserve deficiency risk of normal units is divided into buffer risk zone and extreme risk zone with the value-at-risk serving as the critical point between two risk zones, and the conditional expectation loss of different risk zones can be separately calculated. To accommodate the forecasting error of wind power generations, electricity demands and interruptible loads, the chance constrained goal programming is introduced to provide adjustable reserve constraints. In accordance with different risk levels, the reserve deficiency risk is transformed into the risk cost, which is further included in the objective function of dispatch model. To improve the computational efficiency, the piecewise linearization method is employed to transform the nonlinear programming model into a mixed integer linear programming problem. |
| Key words: electric power systems uncertainty risk assessment total probability conditional value-at-risk chance constrained goal programming |
