基于网格搜索的建筑集群电力调峰调度策略研究

Research on the Scheduling Strategy for Power Peak Shaving inBuilding Clusters Based on Grid Search

针对需求响应技术中存在的不确定性,尤其建筑集群用户的负荷调节潜力及各用户用电如何分摊,匹配电网调峰需求缺少量化进行研究。基于20栋建筑集群的实际用能数据,分析不同年份空调、照明用电情况,得出建筑集群全年用电特性曲线。按照响应电网侧5%的峰值负荷为调控目标,建立距离模型计算照明、空调的调节参数,利用网格搜索寻优计算耦合调节系数。结果表明,建筑集群各用户为实现全年5%调峰指标,照明与空调用电应耦合调节,各建筑全年照明负荷应调节为各家峰值负荷的80%~99%,在冬季空调用电几乎不用调节,夏季空调应调节为各家峰值负荷的70%~83%,仅通过调高设定温度难以实现调峰,需采用其他节能手段或用可再生能源调峰。最后量化需求响应时间内各家在不同时刻应减少的负荷指标,指导建筑集群各用户进行负荷管理,响应需求侧调峰信号。

Aiming at the lack of quantitative research on the uncertainty in demand response,especially to confirm the regulatory potential of building cluster users and how to regulate every users’electrical power usage to match the peak shaving demand of the power grid.This article is based on the actual power energy consumption data of 20 buildings,analyzing the electricity consumption of air conditioning and lighting in different years,and calculate the annual electricity consumption characteristic curve of the building cluster.And based on the regulation target of responding to the annual peak load of 5%on the power grid side,establish the Eula’s distance model to calculate the regulation parameters of lighting and air conditioning,using grid search to optimize and calculate coupling adjustment coefficient.The results indicate that if each user of the building cluster achieves the annual 5%peak shaving target,the electricity consumption coefficient of lighting and air conditioning should be coupled and adjusted.The annual lighting load coefficient should be adjusted between 80%-99%of the peak load of each household,and the electricity consumption for air conditioning in winter hardly needs to be adjusted.The summer air conditioning coefficient should be adjusted to between 70%-83%of the peak load of each household,but it’s unable to achieve the target solely by raising the set temperature.It is necessary to use energy-saving methods or use renewable energy for peak shaving.Finally,specific numerical suggestions are provided for the load that each user should reduce on different times during the peak shaving period,guiding users of the building cluster to manage their load and respond to demand side peak shaving signals.