多重不确定性下水风光多能互补长期优化调度方法

Long-term optimization scheduling method for hydro-wind-PV multi energycomplementary systems considering multi uncertainty

如何应对水风光多重不确定性及其导致的高维优化求解难题是流域水风光多能互补长期调度面临的关键挑战。为此,提出基于马尔科夫链和Copula函数的水风光联合场景生成方法,并通过同步回代缩减法进行场景削减,量化表征水风光多重不确定性;以此为输入,构建流域水风光多能互补长期两阶段随机优化调度模型,并通过Benders分解算法和凸化线性化建模技术实现高维非线性优化问题的高效求解。最后以金沙江下游清洁能源基地为研究对象进行了仿真验证。通过对比分析,证明了所提方法能够有效提升长期调度方案对水风光不确定环境的适应性,提高了多能互补综合效益。在样本外检验中,所提方法比传统方法的发电量增加了0.552亿kWh,弃水量减少了1.694亿m~3,表现得更具可靠性。

Multi uncertainty of hydro-wind-PV systems and its optimal solution with high dimension is a key challenge in the long-term scheduling of hydro-wind-PV multi energy complementary systems.By employing a hydro-wind-PV scene generation method based on Markov chain and Copula function,and utilizing a reduction technique to reduce the number of scenes,the uncertainties of the hydro-wind-PV system can be quantified.Taking the reduced scenes as input,we developed a long-term two-stage stochastic optimal scheduling model that incorporates Benders decomposition algorithm and convex linearization to realize high efficient solution for high dimension problems.The model was used to simulate the scheduling process of a clean energy base in downstream of Jinsha River,which demonstrated the method's effectiveness in enhancing adaptability to the uncertain hydro-wind-PV systems and in improving overall benefits.In out-of-sample testing,the proposed method increased 55.2 million kWh power generation and decreased 169.4 million m 3 abandoned water compared to traditional methods,demonstrating a greater performance.