考虑电价不确定性的负荷价-量曲线聚合方法

随着电力市场的发展,需求侧逐渐参与电力市场报价。报价曲线需要反映需求侧的真实价格响应特征。然而,需求侧的负荷资源具有数量多、单个容量小的性质。大量用户聚合上报的价-量曲线尽量准确地反映需求侧特征,有利于售电侧准确掌握聚合用电特性,维护电力市场经济信号准确性。针对此,提出一种考虑电价不确定性的需求侧用户价-量曲线聚合方法,该方法可有效保留各负荷的价格响应特性,并为大量需求侧用户参与市场出清提供技术支撑。首先,该文考虑电价预测的不确定性,基于后验误差与核密度估计,建立适应多种电价预测模型的电价概率分布生成方法;在此基础上,提出需求侧用户价-量曲线的最优聚合模型,能在预测电价附近展现良好的聚合精度,且适用于不同出清模式的电力市场。针对所提模型具有的分段非线性特性,该文采用启发式算法-粒子群算法求解该模型。最后,基于真实电价数据验证了所提方法的有效性。

Bidding Curve Aggregation Considering Uncertainty of Electricity Price

With the development of electricity market, demand side will bid in market gradually. However, the demand side resources usually have small capacity and large quantity, integrating a group of users then submit the bidding curve to reflect the demand side characteristics is conducive to grasping the integrated characteristics of users and maintaining economy signal. Therefore, this paper proposed an idea to aggregate bidding curve considering uncertainty of electricity price, which could effectively maintain the price responsiveness of each load, and provide possibility for a large number of price-responsive loads to participate market clearing. In addition, this paper considered the uncertainty of electricity price prediction, established the probability distribution generation method for electricity price based on posterior error and kernel density estimation, which is applicable to various electricity price prediction models. On this basis, the optimal aggregation model of loads’ bidding curve was proposed, which shows better accuracy around the predicted price and is suitable for different clearing modes. The proposed model has piecewise nonlinearity. Therefore, the heuristic algorithm, particle swarm algorithm, is used to find solution. The effectiveness of proposed method is verified based on real electricity price data.