计及源-荷不确定性的高比例可再生能源系统协同优化运行

在含高比例可再生能源的电力系统中，由于风光出力的波动性和随机性，增加了电力系统协同优化运行的困难。首先通过拉丁超立方抽样进行风光、负荷实时出力的场景生成，再依据欧氏距离进行样本削减，将不确定性问题转化为场景分析问题。在此基础上分析不同用户类型在不同时间尺度的调度补偿策略，引入了由电价型和激励型需求响应组成的互补机制。以系统运行的经济性最高为目标，对比分析了不同调度策略下的可再生能源消纳率。算例表明计及源-荷双重不确定性后，所提策略能更好地平抑负荷曲线，提高风光消纳率，实现高比例可再生能源电力系统的协同优化运行。

Collaborative Optimization Operation of Power Systems with High Proportion of Renewable Energy Considering Uncertainty of Generation and Load

Under the background of high proportion of renewable energy, wind and photovoltaic power generation have become the key means to cope with climate change and promote energy conservation and emission reduction. However, due to their volatility and randomness, the difficulty of collaborative optimization operation of power system is increased. In this paper, the scene generation and load real-time output is generated by the Latin hypercube sampling, and the sample reduction is carried out according to Euclidean distance, and the uncertainty problem is transformed into the scene analysis problem. On this basis, the scheduling compensation strategy of different user types at different time scales is analyzed, and the complementary mechanism composed of electricity price type and incentive demand response is introduced. Taking the highest economic efficiency of the system operation, the renewable energy consumption rate under different scheduling strategies is compared and analyzed. The example shows that after considering the source-load uncertainty, the proposed strategy can better stabilize the load curve, improve the scenery consumption rate, and realize the collaborative optimization operation of high-proportion renewable energy power system.