基于场景缩减优化引导置信度选值的园区微能源网改进CVaR日前调度模型

Improved CVaR Day-ahead Dispatch Model of Micro-energy-grid Based on Scenario-reduction-optimization Guiding Confidence Value Selection

园区微能源网在波动的电力现货价格下常面临调度成本的不确定性,易造成额外的损失成本。然而,常用的随机优化手段——典型场景规划、条件风险价值(condition value at risk,CVaR)存在忽略场景合并损失及置信度主观选值的问题。为此,提出兼顾场景相似度与合并损失下的改进场景缩减优化方法,提取典型市场场景集,将场景缩减后的损失度作为置信度的选值依据,形成改进CVaR日前经济调度模型。算例分析表明,基于场景缩减优化引导置信度选值的方法有效促使CVaR值反映实际调度的损失成本,且较主观选值而言更接近理论最低的尾部风险损失,即表明园区微能源网的日前经济调度成本与实际环境更为接近,并进一步讨论了考虑风电不确定性及其他场景缩减方案下的模型推广性。

Under the fluctuating power spot price,micro-energy-grid often faces the uncertainty of dispatch costs and additional costs.However,the commonly-used stochastic optimization methods,namely,typical scenario optimization and conditional value at risk(CVaR),have the problem of ignoring the loss of scenario reduction and the subjective selection of confidence value.To this end,an improved comprehensive scenario-reduction-optimization method in which both the scenario similarity and the combined loss are taken into account is proposed and the loss degree after scenaio reduction is used as the basis for the selection of confidence value,forming an improved CVaR day-ahead economic dispatch model.The cases study shows that the method based on scenario-reduction-optimization guiding confidence value selection can effectively reflect the actual dispatch loss cost by CVaR,and is closer to the theoretical minimum tail risk loss than the subjective value,thus the day-ahead dispatch cost reaches a more optimal level in the actual market environment.Furthermore,the applicability of improved CVa R model is discussed when further considering the uncertainty of wind power and different schemes of scenario reduction.