Optimal Scheduling of Island Integrated Energy System Using Improved Multi-Objective Particle Swarm Optimization Algorithm

DAI Hongwei; HU Qingyi; SUN Jing; WANG Jinlong; YANG Yu

doi:10.7652/xjtuxb202606016

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Optimal Scheduling of Island Integrated Energy System Using Improved Multi-Objective Particle Swarm Optimization Algorithm

—————————————————————— | 更新时间：2026-06-12

- Optimal Scheduling of Island Integrated Energy System Using Improved Multi-Objective Particle Swarm Optimization Algorithm
- Journal of Xi'an Jiaotong University Vol. 60, Issue 6, Pages: 188-200(2026)
- 作者机构：
  
  江苏海洋大学计算机工程学院，222005，江苏连云港
  青岛科技大学数据科学学院，266061，山东青岛
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb202606016
  CLC： TK01+9
- Received：06 October 2025，
  
  Published：10 June 2026
- 稿件说明：
移动端阅览
DAI Hongwei, HU Qingyi, SUN Jing, et al. Optimal Scheduling of Island Integrated Energy System Using Improved Multi-Objective Particle Swarm Optimization Algorithm[J]. Journal of Xi'an Jiaotong University, 2026, 60(6): 188-200.
DOI：

DAI Hongwei, HU Qingyi, SUN Jing, et al. Optimal Scheduling of Island Integrated Energy System Using Improved Multi-Objective Particle Swarm Optimization Algorithm[J]. Journal of Xi'an Jiaotong University, 2026, 60(6): 188-200. DOI： 10.7652/xjtuxb202606016.

摘要

为解决海岛多样化用能需求的有效供给问题，弥补传统调度模型难以兼顾多负荷需求与可再生能源消纳的不足，提出一种包含电、热、冷、水、氢5种负荷的海岛综合能源系统优化调度方法。以最小化综合经济成本、最大化可再生能源出力为优化目标，考虑能量平衡、储能限制、转换效率等约束，构建海岛综合能源系统优化调度模型。针对传统多目标粒子群算法的缺陷，提出一种改进多目标粒子群算法（IMOPSO），引入自适应惯性权重更新机制与飞行参数动态调整策略，以增强算法的全局搜索能力与局部开发能力，同时结合领导者择优选择机制与变异算子，提高非支配解集的多样性和分布均匀性，从而优化算法性能。标准测试函数对比实验表明：所提出的IMOPSO算法的世代距离、逆世代距离指标均表现最好，超体积指标在多个函数上取得最高值，证明其在多样性、分布性和收敛性的显著优势。夏季和冬季两个典型日场景的验证表明，与传统多目标粒子群算法和其他对比算法相比，所提算法在综合经济成本指标上分别降低了1.80%、4.37%以上，在能源出力指标上分别提高了18.52%、1.60%以上，能满足海岛多类型负荷需求。

Abstract

To address the challenge of meeting diverse energy demands on islands while overcoming shortcomings of traditional scheduling models in balancing multi-load demands with renewable energy absorption，an optimal scheduling approach for an island integrated energy system（IIES）is proposed.The IIES incorporates 5 load types：electricity，heat energy，cold energy，water，and hydrogen. Subject to such constraints as energy balance，energy storage limits，and conversion efficiencies，an optimal scheduling model for IIES is established to minimize overall economic cost and maximize renewable energy output.To remedy deficiencies of the traditional multi-objective particle swarm optimization（MOPSO）algorithm，an improved multi-objective particle swarm optimization（IMOPSO）algorithm is proposed.The improvements include introducing an adaptive inertia weight update mechanism and a dynamic adjustment strategy for flight parameters to enhance both global search and local exploitation capabilities of the algorithm.In addition，the diversity and uniform distribution of the non-dominated solution set are improved by integrating the merit-based selection mechanism of leaders and mutation operator，thereby boosting algorithmic performance.Comparative experiments using standard test functions show that the proposed IMOPSO algorithm achieves the best results on generational distance and inverted generational distance，and attains the highest hypervolume across multiple functions，demonstrating its significant advantages in diversity，distribution，and convergence.Validation for two typical daily scenarios-summer and winter-indicates that，compared with the traditional MOPSO algorithm and other comparative algorithms，the proposed IMOPSO reduces overall economic cost by more than 1.80%and 4.37%，while increasing energy output by over 18.52%and 1.60%，respectively，thereby meeting diverse energy demands on islands.

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