Risk adjustable optimal operation for electricity-hydrogen integrated energy system based on chance constrained goal programming

The electricity-hydrogen integrated energy system (EH-IES) enables synergistic operation of electricity, heat, and hydrogen subsystems, supporting renewable energy integration and efficient multi-energy utilization in future low-carbon societies. However, uncertainties from renewable energy and load...

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Bibliographic Details
Published in:Journal of Central South University Vol. 32; no. 6; pp. 2224 - 2238
Main Authors: Zhou, Xiao-jun, Hu, Jia-ming, Li, Chao-jie, Yang, Chun-hua
Format: Journal Article
Language:English
Published: Changsha Central South University 01.06.2025
Springer Nature B.V
Subjects:
Algorithms
Alternative energy sources
Approximation
Carbon
Constraints
Costs
Efficiency
Electricity
Energy utilization
Engineering
Gases
Goal programming
Hydrogen
Integrated energy systems
Metallic Materials
Natural gas
Nonlinearity
Optimization
Renewable energy
Renewable resources
Risk assessment
Subsystems
Uncertainty
chance constrained goal programming
electricity-hydrogen integrated energy system
机会约束目标规划
电-氢综合能源系统
risk adjustment
state transition algorithm
风险可调
状态转移算法
ISSN:2095-2899, 2227-5223
Online Access:Get full text
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Summary:The electricity-hydrogen integrated energy system (EH-IES) enables synergistic operation of electricity, heat, and hydrogen subsystems, supporting renewable energy integration and efficient multi-energy utilization in future low-carbon societies. However, uncertainties from renewable energy and load variability threaten system safety and economy. Conventional chance-constrained programming (CCP) ensures reliable operation by limiting risk. However, increasing source-load uncertainties that can render CCP models infeasible and exacerbate operational risks. To address this, this paper proposes a risk-adjustable chance-constrained goal programming (RACCGP) model, integrating CCP and goal programming to balance risk and cost based on system risk assessment. An intelligent nonlinear goal programming method based on the state transition algorithm (STA) is developed, along with an improved discretized step transformation, to handle model nonlinearity and enhance computational efficiency. Experimental results show that the proposed model reduces costs while controlling risk compared to traditional CCP, and the solution method outperforms average sample sampling in efficiency and solution quality.
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ISSN:2095-2899
2227-5223
DOI:10.1007/s11771-025-5993-4