智慧社区多能流随机响应面模型预测控制方法

MA Rui

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智慧社区多能流随机响应面模型预测控制方法

更新时间：2025-08-13

- 智慧社区多能流随机响应面模型预测控制方法
- Automation of Electric Power Systems Vol. 42, Issue 4, Pages: 121-127(2018)
- 作者机构：
  
  长沙理工大学电气与信息工程学院,湖南省,长沙市,410114
- 作者简介：
- 基金信息：
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- Published：2018
- 稿件说明：
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MA Rui. [J]. Automation of electric power systems, 2018, 42(4): 121-127.
DOI：

MA Rui. [J]. Automation of electric power systems, 2018, 42(4): 121-127. DOI：

摘要

提出一种智慧社区多能流随机响应面模型预测控制方法。采用随机响应面法获取分布式风电和光伏出力、负荷需求以及实时电价等预测误差分布特性，得到预测误差概率密度分布曲线并将其离散化，利用轮盘赌算法生成初始场景集并采用最近邻聚类法进行场景削减。考虑冷热电联供、电动汽车、储能设备等的技术经济特性，构建了兼顾经济性和环保性的多能流多目标优化调度模型，并利用随机模型预测控制方法对多能流调度模型进行在线滚动优化，从而实现日前优化和实时滚动优化的有效统一。算例结果验证了文中所提出方法的有效性和优越性。

Abstract

A model predictive control method for multi-energy flow of smart community combined with stochastic response surface method is proposed. In this method

the stochastic response surface method is adopted to obtain the distribution characteristics of forecasting errors of the distributed wind power outputs and photovoltaic power outputs

the load demand as well as the spot price

and the distribution curve of probability density is obtained and discretized. Then the roulette algorithm is used to get the initial scene set and the nearest neighbor clustering method is adopted to reduce the number of scenes. A multi-objective optimal scheduling model for multi-energy flow considering the economic and environmental effect is built by taking into account the technologic and economic characteristics of combined cooling heating and power

electric vehicles and energy storage equipment. The stochastic model predictive control method is adopted to implement the online receding horizon optimization of the multi-energy flow scheduling model. The effective unification of the day-ahead optimization and the real-time receding horizon optimization is realized. The simulation results verify the effectiveness and superiority of the proposed method.

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