计及源荷双重不确定性的高速路域虚拟电厂运行优化策略

李欣; 宋金金

doi:10.11930/j.issn.1004-9649.202509067

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计及源荷双重不确定性的高速路域虚拟电厂运行优化策略

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- 计及源荷双重不确定性的高速路域虚拟电厂运行优化策略
- Operation optimization strategy for highway-domain virtual power plants considering dual uncertainties of source and loads
- 中国电力 2026年59卷第4期页码：79-93
- 作者机构：
  
  兰州交通大学新能源与动力工程学院,甘肃,兰州,730070
- 作者简介：
- 基金信息：
  
  国家自然科学基金资助项目（52567023）;甘肃省交通厅揭榜挂帅项目（JT-JJ-2023-008）。
- DOI：10.11930/j.issn.1004-9649.202509067
  中图分类号：
- 网络首发：2026-04-20，
  
  纸质出版：2026
- 稿件说明：
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李欣, 宋金金. 计及源荷双重不确定性的高速路域虚拟电厂运行优化策略[J]. 中国电力, 2026, 59(4): 79-93.

LI Xin, SONG Jinjin. Operation optimization strategy for highway-domain virtual power plants considering dual uncertainties of source and loads[J]. 2026, 59(4): 79-93.
李欣, 宋金金. 计及源荷双重不确定性的高速路域虚拟电厂运行优化策略[J]. 中国电力, 2026, 59(4): 79-93. DOI： 10.11930/j.issn.1004-9649.202509067.

LI Xin, SONG Jinjin. Operation optimization strategy for highway-domain virtual power plants considering dual uncertainties of source and loads[J]. 2026, 59(4): 79-93. DOI： 10.11930/j.issn.1004-9649.202509067.

摘要

中国高速公路路网的快速扩张加剧了交通能源消耗与碳排放问题，亟需通过虚拟电厂（virtual power plant，VPP）实现高速路域分布式能源的高效就地消纳，开展计及源荷不确定性的高速路域VPP运行优化策略对降低VPP运行成本具有重要意义。针对分布式能源出力与电动汽车充电需求不确定性的问题，基于马尔科夫链蒙特卡洛法采样生成多时间尺度场景集，提出马尔科夫链蒙特卡洛法与基于概率距离削减的方法结合的场景生成-削减混合框架进行场景生成与削减。以高速路域VPP运行成本最小为目标建立高速路域VPP运行优化模型，采用基于约束策略优化的高速路域VPP智能调度算法进行求解优化。算例结果表明，所提优化方案使高速路域VPP运行成本降低，验证了所提方案具有良好的经济性。

Abstract

The rapid expansion of China's highway network has exacerbated the problems of transportation energy consumption and carbon emissions. It is therefore imperative to achieve efficient local consumption of distributed energy resources in highway domains through virtual power plants (VPPs). To address the "source-load mismatch" problem caused by the volatility of distributed energy output and the randomness of electric vehicle (EV) charging demand

it is of great significance to develop an optimal operational strategy for highway-domain VPPs that account for source-load uncertainty

with the goal of reducing the VPPs' operating costs. To tackle the uncertainty in distributed energy output and EV charging demand

this paper firstly employs the Markov Chain Monte Carlo (MCMC) method to generate a multi-timescale scenario set. It then proposes a hybrid framework combining MCMC and the probability distance-based reduction method for scenario generation and reduction. Subsequently

an optimization model for highway-domain VPPs is established with the objective of minimizing its operating costs

and a smart scheduling algorithm based on constrained proximal policy optimization (C-PPO) is proposed to solve this optimization model. Case study results show that the proposed optimization scheme reduces the operating cost of the highway-domain VPPs

validating the favorable economic performance of the proposed solution.

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