径流式小水电群随机环境经济调度方法

摘要

针对径流式小水电群的电力系统，建立了以污染气体排放和购电费用最小为目标的随机动态环境经济调度模型。借助场景法将该模型转化为大规模两目标确定性动态调度模型。采用法线边界交叉法将这个两目标优化问题转换为一系列单目标优化问题，并采用非线性原对偶内点法求解。在迭代过程中，按照场景顺序将简化修正方程的系数矩阵排列为对角加边形式，方便对其实施解耦，并运用异步块迭代法求解，从而将一组高维修正方程组的求解转化为若干个分别与预测场景和误差场景相对应的低维修正方程组的求解。采用某省级电网的真实数据进行计算，在高性能集群上建立了并行计算框架以缓解计算占用内存并提高计算速度。通过这个计算架构，可以获得一组日前调度计划，且使得更加全面的折中优化结果可以应用于电力系统的调度。%This paper investigates a stochastic economic emission dispatch (SEED) problem considering variable wind power integration and transforms this problem into an equivalent large-scale bi-objective deterministic optimization model based on the scenario method. It simultaneously minimizes power purchase costs and polluting gas emissions. The normal boundary intersection (NBI) method is introduced to convert the bi-objective optimization (MOO) model into a series of single-objective optimization (SOO) problems, which are solved using the interior-point method (IPM). In the process of solving each SOO problem, this paper rearranges the coefficient matrix of the correction equation in the block bordered diagonal form (BBDF) according to the sequence of the forecast scenario and sampling scenarios. Thus, it is able to decompose this correction equation further into a number of low-dimensional equations corresponding to the forecast scenario and sampling scenarios, respectively, and solve them using the asynchronous block iteration method. Furthermore, the proposed algorithm is implemented on a real provincial power system, and a parallel computational framework is built on high-performance clusters to demonstrate the enhancements in computational speed and the reduced memory requirements obtained by parallelization. Through this framework, scheduling of the outputs of generators on a day-ahead basis can be obtained. In addition, itindicates that the comprehensive compromised optimal solution can be used as an optimal dispatching scheme of power system operation.