Research on 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/xjtuxb*********

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

更新时间：2025-12-23

- Research on Optimal Scheduling of Island Integrated Energy System Using Improved Multi-Objective Particle Swarm Optimization Algorithm
- JOURNAL OF XI’AN JIAOTONG UNIVERSITY (2025)
- 作者机构：
  
  1.江苏海洋大学计算机工程学院，222005，江苏连云港
  2.青岛科技大学数据科学学院，212013，山东青岛
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb*********
  CLC： TK01+9
- Received：06 October 2025，
  
  Revised：2025-12-16，
  
  Accepted：23 December 2025，
- 稿件说明：
移动端阅览
DAI Hongwei, HU Qingyi, SUN Jing, et al. Research on Optimal Scheduling of Island Integrated Energy System Using Improved Multi-Objective Particle Swarm Optimization Algorithm[J/OL]. JOURNAL OF XI’AN JIAOTONG UNIVERSITY, 2025.
DOI：

DAI Hongwei, HU Qingyi, SUN Jing, et al. Research on Optimal Scheduling of Island Integrated Energy System Using Improved Multi-Objective Particle Swarm Optimization Algorithm[J/OL]. JOURNAL OF XI’AN JIAOTONG UNIVERSITY, 2025. DOI： 10.7652/xjtuxb*********.

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，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，从而改进多目标粒子群算法优化性能。标准测试函数对比实验表明：改进算法的世代距离、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，改进多目标粒子群算法在综合经济成本指标上分别降低1.80%、4.37%以上，在能源出力指标上分别提高18.52%、1.60%以上，能满足海岛多类型负荷需求。

Abstract

To address the effective supply of multiple energy needs on islands and overcome the shortcomings of traditional scheduling models in balancing multi-load requirements with renewable energy absorption

this paper proposes an optimized scheduling method for island integrated energy system (IIES) including five types of loads: electricity

heat

cold

water

and hydrogen. The optimization objectives include minimizing comprehensive economic costs and maximizing renewable energy output. Considering energy balance

energy storage constraints

and conversion efficiency

an integrated energy system optimization model is established. To address the shortcomings of the traditional multi-objective particle swarm optimization (MOPSO) algorithm

an adaptive inertia weight update mechanism and dynamic adjustment strategy for flight parameters are introduced to enhance the global search ability and local exploitation ability of the algorithm. Meanwhile

by integrating the leader selection mechanism and mutation operator

the diversity and uniform distribution of the non-dominated solution set are improved

thereby enhancing the performance of the MOPSO algorithm. Comparative experiments using standard test functions demonstrate that the improved algorithm achieves the best performance in generational distance and inverted generational distance

and achieves the highest hyper-volume values across multiple functions

proving its significant advantages in diversity

distribution

and convergence. Validation through summer and winter typical day scenarios shows that the improved MOPSO algorithm reduces comprehensive economic costs by 1.80% and 4.37% or more

while increasing energy output by 18.52% and 1.60% or more

respectively

thereby meeting multiple energy needs on islands.

关键词

Keywords

references

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