Optimal Scheduling of a Microgrid Considering Carbon Capture and Hydrogen Energy Storage

doi:10.12422/j.issn.1672-6952.2026.05.012

Abstract

Abstract:

With the advancement of the global energy transition, reducing carbon emissions and improving energy utilization efficiency have become important objectives in power⁃system optimization.As a new form of power scheduling and management, a microgrid uses distributed energy resources and intelligent dispatching technologies to achieve the optimal allocation and scheduling of energy.An improved non⁃dominated sorting genetic algorithm is proposed,and an optimal scheduling model for a microgrid integrating carbon capture and hydrogen energy storage is established. For the non⁃dominated sorting genetic algorithm Ⅱ (NSGA⁃Ⅱ), the original initial population and parent⁃selection strategy are replaced by a good⁃point⁃set population and linear⁃ranking parent selection. Generational distance is used to quantify the convergence performance before and after algorithm improvement. The contributions of carbon capture and hydrogen energy storage to emission reduction and economic performance are verified, and the levels of curtailed power and convergence performance before and after algorithm improvement are comparatively analyzed. The results show that the improved NSGA⁃Ⅱ algorithm has better convergence and global search capability. A microgrid combining carbon capture with hydrogen storage and hydrogen sales can effectively improve both economic performance and emission⁃reduction capability while satisfying load accommodation requirements, and its convergence is significantly better than that of the original method.

Key words: Microgrid, Carbon capture, Hydrogen energy storage, Optimal scheduling, Carbon emission, NSGA?Ⅱ

摘要：

随着全球能源转型的推进，减少碳排放和提高能源利用效率已成为电力系统优化的重要目标。微电网作为一种新型的电力调度和管理方式，利用分布式能源资源和智能化调度技术，可以有效实现能源的优化配置和调度。提出了改进非支配排序型遗传算法的优化策略，构建结合碳捕集与氢储能的微电网优化调度模型；针对非支配排序的多目标遗传算法（Non⁃Dominated Sorting Genetic Algorithm Ⅱ，NSGA⁃Ⅱ），用佳点集种群与线性排序的父代选择替代原本的初始种群和父代选择策略；用代际距离量化改进前后算法的收敛性能；验证了碳捕集与氢储能对系统减排与经济性的贡献，并对比分析了弃电量水平，以及算法改进前后的收敛性。结果表明，改进后的NSGA⁃Ⅱ算法具有更优秀的收敛性和全局搜索能力；结合碳捕集和储氢售氢的微电网，可在有效提升经济性与减排能力的同时，满足负荷消纳的需求，并且其收敛性明显优于改进前。

关键词: 微电网, 碳捕集, 氢储能, 优化调度, 碳排放, 非支配排序的多目标遗传算法

CLC Number:

TM734

Qin GAO, Ling CHANG, Bo WANG, Qian LI. Optimal Scheduling of a Microgrid Considering Carbon Capture and Hydrogen Energy Storage[J]. Journal of Liaoning Petrochemical University, 2026, 46(5): 86-96.

高钦, 常玲, 王博, 李倩. 计及碳捕集和氢储能的微电网优化调度[J]. 辽宁石油化工大学学报, 2026, 46(5): 86-96.

Figures/Tables 16

Fig.1 Microgrid control block diagram

Fig.2 Population spatial distribution generated by random initialization and good point set initialization

Fig.3 Flowchart of the improved NSGA?Ⅱ algorithm

Fig.4 Monthly and hourly power output curve for a single WD?10 wind turbine

Fig.5 Monthly hourly power output curve for a single MSX?83 photovoltaic panel

Fig.6 Hourly electricity demand curve for a typical day

Table 1 Capacity configuration results

算例	风电机组/台	光伏机组/台	电解槽/台	储氢罐/台
算例一	520	300	无	无
算例二	520	300	无	无
算例三	520	300	63	5
算例四	520	300	58	5

Table 2 Simulation optimization results

算例	碳排放量/t	弃风弃光率	成本/万元	售氢收益/元
算例一	2 775.33	0.166	11 209.2	0
算例二	1 282.99	0.153	11 355.9	0
算例三	2 784.23	0	11 188.9	180 452.1
算例四	1 286.92	0	11 340.6	166 320.3

Fig.7 Best Pareto front of example 4

Fig.8 Daily variation curves for low?wind and high?wind conditions in example 4

Fig.9 Daily carbon emission variation curves under weak wind?solar output for example 3 and example 4

Table 3 The set of Pareto?optimal solutions obtained for example 4 following optimisation using the NSGA?Ⅱ algorithm

序号	碳排放量/t	成本/万元	弃电量/%
1	1 286.926	113.406 9	0
2	1 287.029	113.304 0	0.06
3	1 287.113	113.183 0	0.12
4	1 287.200	113.082 0	0.19
5	1 287.282	112.999 0	0.24
6	1 287.351	112.914 0	0.40
7	1 287.409	112.847 0	0.44
8	1 287.451	112.788 0	0.48
9	1 287.493	112.109 0	0.53
10	1 287.516	112.058 0	0.65

Fig.10 Comparison of daily consumption rate under high wind?solar output for example 2 and example 4

Fig.11 The relation curve of electrolyzer quantity and hydrogen energy revenue

Table 4 Optimization results of the NSGA?Ⅱ algorithm before and after improvement

NSGA⁃Ⅱ算法	迭代次数	代际距离
改进前	267	0.015
改进后	97	0.006

Fig.12 Relationship between generational distance and number of iterations for the NSGA?Ⅱ algorithm before and after improvement

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