基于极端场景分析的中长期交易校核方法

为解决新能源、负荷等边界数据波动影响电力市场交易校核结果的问题,提出一种基于极端场景分析的中长期交易校核方法。首先,根据边界数据预测偏差对校核结果的影响,将波动性边界数据划分为正向与负向数据两大类;其次,对照传统确定性校核方法,定义中长期交易校核中的极端场景;然后,基于多时序机组组合模型,构建面向极端场景的中长期交易校核方法,量化不同发电企业交易电量的预期执行情况;最后,对我国某省电网实际数据构造的算例进行分析,结果表明极端场景分析能有效辨识基态场景下难以发现的运行问题。所提方法能更准确地辨识极端场景下中长期交易电量的执行偏差,提升交易校核结果的可行性,适用于高比例新能源接入的省级电网。

Long and medium-term power market security check based on extreme scenario analysis

In order to solve the problem of boundary data fluctuation influence on the security check result caused by new energy and load forecast and improve the adaptability of the security check result to the forecast deviation, a security check method based on ex-treme scenario analysis is proposed for the long and medium-term power market. The volatility boundary data is divided into two categories, namely positive data and negative data, according to their forecast deviation influence on the security check result. On this basis, the extreme scenario of long and medium-term power market is defined, compared to the traditional deterministic secu-rity check method. Based on the multi-period unit commitment model, a long and medium-term security check method for extreme scenario is constructed. Based on extreme scenario boundary data, this model can quantify the expected execution of the transac-tion quantity of different power generation enterprises. Finally, a case study based on the actual data of a provincial power grid in China shows that the deviation of the transaction result reaches 174 million kWh in extreme scenario, while it could be passed under the normal scenario. This result indicates that the influence of forecast error must be considered in the power grid with a large in-stalled new energy, to improve the accuracy of transaction check results.