气候变化条件下基于智能预测模型的虚拟电厂不确定性运行优化研究
摘要：为有效应对气候变化，促进虚拟电厂(virtual power plant，VPP)的健康发展，基于区域气候模型(providing regional climate for impact studies，PRECIS)、BP神经网络预测模型和区间优化算法，提出了适应气候变化的VPP运行优化模型。应用PRECIS模拟2025年不同碳排放情景下气温、风速和辐射量等气象要素的变化规律；基于PRECIS气象要素模拟结果，应用BP神经网络模型预测2025年光伏电站的发电量；将区间优化算法与发电量预测结果相耦合，以此降低光伏发电不确定性对优化模型模拟结果的影响。结果显示，该模型可生成适应气候变化的VPP最优运行策略，降低系统运行成本，提升VPP运行效益。
Abstract:In order to effectively cope with climate change and promote the healthy development of virtual power plant, an uncertainty operation optimization model of virtual power plant (VPP) adapted to climate change was proposed based on the providing regional climate for impact studies (PRECIS), BP neural network prediction model and interval optimization algorithm. PRECIS was used to simulate the changes in meteorological factors such as temperature,wind speed and radiation under different carbon emission scenarios in 2025. The BP neural network model was used to predict the power generation of photovoltaic power plants based on the simulation results of PRICES. The interval optimization algorithm was coupled with the power generation prediction results to reduce the impact caused by the influence of photovoltaic power generation uncertainty on the simulation results of the optimization model. The results show that the model can not only generate the optimal operation strategy of VPP under climate change, but also reduce operating costs and improve economic benefits.
标题：气候变化条件下基于智能预测模型的虚拟电厂不确定性运行优化研究
title:Study on Uncertainty Operation Optimization of Virtual Power Plant Based on Intelligent Prediction Model Under Climate Change
作者：贾晓强, 杨永标, 杜姣, 甘海庆, 杨楠
authors:Xiaoqiang JIA, Yongbiao YANG, Jiao DU, Haiqing GAN, Nan YANG
关键词：虚拟电厂(VPP),区域气候模型(PRECIS),BP神经网络,不确定性优化,气候变化,
keywords:virtual power plant (VPP),providing regional climate for impact studies (PRECIS),BP neural network,uncertainty optimization,climate change,
发表日期：2023-12-31
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