Multi-timescale Optimal Scheduling for Carbon-oriented Microgrids Based on an Improved Grey Wolf Optimization Algorithm

ZHENG Hekang; ZHONG Jianwei; CHEN Wei; XIANG Guodong; SHEN Yujun

doi:10.13882/j.cnki.ncdqh.2604F017

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Multi-timescale Optimal Scheduling for Carbon-oriented Microgrids Based on an Improved Grey Wolf Optimization Algorithm

更新时间：2026-07-22

- Multi-timescale Optimal Scheduling for Carbon-oriented Microgrids Based on an Improved Grey Wolf Optimization Algorithm
- Rural Electrification Issue 6, Pages: 42-51(2026)
- 作者机构：
  
  1. 湖北民族大学,湖北,恩施,445000
  2. 国网湖北省电力有限公司恩施供电公司,湖北,恩施,445000
- 作者简介：
- 基金信息：
- DOI：10.13882/j.cnki.ncdqh.2604F017
  CLC： TM73
- Published：2026
- 稿件说明：
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郑和康, 钟建伟, 陈巍, 向国东, 沈宇军. 基于改进灰狼优化算法的碳导向微电网多时间尺度优化调度[J]. 农村电气化, 2026, (6): 42-51.

ZHENG Hekang, ZHONG Jianwei, CHEN Wei, et al. Multi-timescale Optimal Scheduling for Carbon-oriented Microgrids Based on an Improved Grey Wolf Optimization Algorithm[J]. Rural Electrification, 2026, (6): 42-51.
郑和康, 钟建伟, 陈巍, 向国东, 沈宇军. 基于改进灰狼优化算法的碳导向微电网多时间尺度优化调度[J]. 农村电气化, 2026, (6): 42-51. DOI： 10.13882/j.cnki.ncdqh.2604F017.

ZHENG Hekang, ZHONG Jianwei, CHEN Wei, et al. Multi-timescale Optimal Scheduling for Carbon-oriented Microgrids Based on an Improved Grey Wolf Optimization Algorithm[J]. Rural Electrification, 2026, (6): 42-51. DOI： 10.13882/j.cnki.ncdqh.2604F017.

摘要

随着高比例可再生能源的接入，微电网运行面临着源荷不确定性增强与碳减排约束加剧等多方面挑战。针对传统调度方法难以兼顾经济性、低碳性与运行稳定性的问题，文章提出一种基于改进灰狼优化算法的碳导向微电网多时间尺度优化调度方法。首先，构建融合低碳分时电价与过碳奖惩机制的碳导向需求响应模型，引导负荷向低碳时段转移；其次，在日前阶段建立考虑风电、光伏及负荷不确定性的鲁棒优化模型，并引入基于相似原理改进的灰狼优化算法（grey wolf optimizer，GWO）对模型求解；进一步，在日内阶段采用模型预测控制（model predictive control，MPC）实现滚动优化，对日前计划进行动态修正。结果表明，所提方法能够有效削峰填谷并降低碳排放，并在不确定性条件下能够实现更优的综合运行成本与系统稳定性。

Abstract

With the integration of a high proportion of renewable energy

microgrid operation faces multiple challenges

including increased source-load uncertainty and stricter carbon emission reduction constraints. To address the difficulty of traditional scheduling methods in balancing economy

low-carbon performance

and operational stability

this paper proposes a carbon-oriented multi-timescale optimal scheduling method for microgrids based on an improved grey wolf optimization (GWO) algorithm. First

a carbon-oriented demand response model is developed by integrating low-carbon time-of-use tariffs and a carbon-exceedance reward and penalty mechanism. This model guides load transfer to low-carbon periods. Second

a robust optimization model is established in the day-ahead stage considering wind power

photovoltaic power

and load uncertainties. An improved GWO algorithm based on the similarity principle is introduced to solve the model. Furthermore

model predictive control is adopted in the intraday stage to realize rolling optimization and dynamically correct the day-ahead schedule. The results show that the proposed method effectively achieves peak shaving and valley filling

reduces carbon emissions

and improves the overall operating cost and system stability under uncertainty.

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