风力与火力发电并网优化调度仿真研究

Simulation on Wind Power and Coal Power Grid Integration Optimizing Schedule

在风力和火力发电并网的优化调度节能减排的研究中,风电出力的随机性使得风电并网时达到最优调度成为难题。为解决传统方法进行优化调度仿真时,在煤耗成本上易陷入局部最优,并兼顾优化效率,提出了两层策略对风电并网的优化调度问题进行求解。外层采用加入部分贪心变异策略的量子离散粒子群算法确定机组启停,内层使用二次规划法求解负荷经济分配问题。以含风电场的10机组系统为算例,求解了机组分钟级爬坡速率约束下和不同置信度水平情况下的调度方案。计算结果表明,优化结果明显优于传统方法的求解精度和效率,为风电并网优化调度提供了新思路。

Optimization scheduling of wind power and coal power grid integration is of great significance for energy consumption and pollution reduction, but randomness of wind brings a big problem for a wind farm to provide an optimal schedule. Traditional approaches often fall into premature and do not consider efficiency during optimization and simulation, so the optimization scheduling problem of unit commitment with wind farms is solved in two layers. The outer layer, determination of the unit commitment, is solved via a quantum-inspired binary particle swarm optimization method with greedy mutation strategy; as for the inner layer, optimal load dispatching is solved via quadratic programming. Parts of the individuals are mutated through quantum bits during the iterative process. The algorithms are tested in a system composed of 10 thermal units with a wind farm under unit ramp rate constraints by minute and under different confidence probability. The computation results verify that the proposed method is more superior than traditional methods in precision and efficiency, and it supplies a new thought for optimization scheduling of wind power and coal power grid integration.