基于随机MPC与多目标遗传算法的多储能电站调频功率双层优化策略

李翠萍; 谷长旭; 齐军; 李晗; 任杰; 严干贵; 李军徽

doi:10.19799/j.cnki.2095-4239.2026.0659

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基于随机MPC与多目标遗传算法的多储能电站调频功率双层优化策略

储能XXXX | 更新时间：2026-10-16

- 基于随机MPC与多目标遗传算法的多储能电站调频功率双层优化策略
- Bi-Level Optimization Strategy for Frequency-Regulation Power Allocation Among Multiple Energy Storage Power Stations Based on Stochastic MPC and a Multi-Objective Genetic Algorithm
- 储能科学与技术 2026年页码：1-13
- 作者机构：
  
  1.现代电力系统仿真控制与绿色电能新技术教育部重点实验室（东北电力大学），吉林省吉林市 132012
  2.内蒙古电力（集团）有限责任公司电力调度控制分公司呼和浩特 010020
- 作者简介：
  
  李翠萍（1982—），女，博士，副教授，研究方向为储能技术在电力系统中的应用，E-mail：licuipingabc@163.com；
  李军徽，教授，研究方向为新能源发电联网运行关键技术和储能技术的应用，E-mail：lijunhui@neepu.edu.cn。
- 基金信息：
  
  内蒙古自治区科学技术厅揭榜挂帅项目(2022JBGS0044)
- DOI：10.19799/j.cnki.2095-4239.2026.0659
  中图分类号： TM 73
- 收稿：2026-07-28，
  
  修回：2026-10-15，
  
  网络首发：2026-10-16，
- 稿件说明：
移动端阅览
李翠萍, 谷长旭, 齐军, 等. 基于随机MPC与多目标遗传算法的多储能电站调频功率双层优化策略[J]. 储能科学与技术, XXXX, XX(XX): 1-13.

LI Junhui, GU Changxu, QI Jun, et al. Bi-Level Optimization Strategy for Frequency-Regulation Power Allocation Among Multiple Energy Storage Power Stations Based on Stochastic MPC and a Multi-Objective Genetic Algorithm[J]. Energy Storage Science and Technology, XXXX, XX(XX): 1-13.
李翠萍, 谷长旭, 齐军, 等. 基于随机MPC与多目标遗传算法的多储能电站调频功率双层优化策略[J]. 储能科学与技术, XXXX, XX(XX): 1-13. DOI： 10.19799/j.cnki.2095-4239.2026.0659.

LI Junhui, GU Changxu, QI Jun, et al. Bi-Level Optimization Strategy for Frequency-Regulation Power Allocation Among Multiple Energy Storage Power Stations Based on Stochastic MPC and a Multi-Objective Genetic Algorithm[J]. Energy Storage Science and Technology, XXXX, XX(XX): 1-13. DOI： 10.19799/j.cnki.2095-4239.2026.0659.

摘要

针对高比例新能源电网中风电场、火电机组与多座储能电站联合调频的功率分配问题，提出一种融合随机模型预测控制和多目标遗传算法的双层分配方法。上层综合考虑各调频资源调频成本和风机动态减载，结合AGC指令的预测及误差信息，采用边界缩进方法处理储能荷电状态机会约束，以应对预测误差对储能荷电状态及调频功率分配的影响；同时，根据储能荷电状态动态调整风机参与调频的程度，完成风-火-储调频功率的分配；下层根据预测时域内各储能电站荷电状态接近运行边界的程度构建自适应权重，并将其引入SOC均衡目标函数，增强对边界风险较高电站的调节作用；以调频成本最低和加权SOC均衡为优化目标，采用多目标遗传算法求取帕累托解集，并结合自适应权重计算综合评价值以选取最优解，形成兼顾成本与SOC状态的储能电站差异化功率指令。仿真结果表明，所提策略能够在保障调频经济性的前提下，显著降低储能整体SOC越界风险、减少风机减载量，并有效均衡各储能电站的SOC，提升系统持续调频能力。

Abstract

A bi-level allocation method integrating stochastic model predictive control (SMPC)with a multi-objective genetic algorithm (MOGA) is developed for power allocation among wind farms

thermal power units

and multiple energy storage power stations participating in coordinated frequency regulation in power grids with a high penetration of renewable energy. The upper layer considers the frequency-regulation costs of different regulation resources and dynamic wind-turbine deloading. It combines automatic generation control (AGC) command forecasts with forecast-error information and applies a boundary-tightening method to handle chance constraints on the state of charge (SOC) of energy storage

thereby addressing the effects of forecast errors on storage SOC and frequency-regulation power allocation. Meanwhile

the participation level of wind turbines in frequency regulation is dynamically adjusted according to the SOC of the energy storage system

and the frequency-regulation power among wind power

thermal power

and energy storage is allocated. In the lower layer

adaptive weights are constructed according to the extent to which the predicted SOC of each energy storage power station approaches its operating boundaries over the prediction horizon. The adaptive weights are incorporated into the SOC-balancing objective function to strengthen the regulation of power stations exposed to higher boundary risks. With frequency-regulation cost minimization and weighted SOC balancing as the optimization objectives

MOGA is used to obtain a Pareto solution set. The optimal solution is then selected from the solution set by calculating a comprehensive evaluation value based on the adaptive weights

thereby realizing optimized frequency-regulation power allocation among the energy storage power stations. Simulation results show that the proposed strategy significantly reduces the overall SOC boundary-violation risk of energy storage and wind-turbine deloading while maintaining the economic performance of frequency regulation

and effectively improves SOC balance among the energy storage power stations

thereby enhancing the sustained frequency regulation capability of the system.

关键词

Keywords

references

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