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

Abstract

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.

1. Introduction

Against the backdrop of global fossil energy depletion and environmental pollution, the low-carbon transformation of energy is imperative [1]. Integrated energy systems (IES) with multi-energy complementarity and coordination are pivotal in energy decarbonization. However, conventional IES like combined cooling, heat, and power (CCHP) systems primarily integrate electricity, cooling, heat, and natural gas—though cleaner than coal/oil, these still emit greenhouse gases [2, 3]. Limited energy storage and renewable integration further reduce operational flexibility, constraining distribution-level IES development.

Hydrogen energy, a green secondary energy source, enables large-scale storage and transportation. Studies demonstrate hydrogen’s role in multi-energy systems: LI et al [4] proposed power-to-hydrogen-and-heat (P2HH) devices with thermal-electrical-hydrogen coordination for efficient network dispatching, while PAN et al [5] optimized seasonal hydrogen storage to mitigate P2HH start-stop losses and enhance renewable penetration. Hydrogen also supports clean mobility via fuel cell vehicles and integrated energy-transport systems [6]. Therefore, for zero-carbon vision, the aforementioned studies indicate that developing an electricity-hydrogen integrated energy system (EH-IES) that combines CCHP with hydrogen production and storage technologies is imperative.

However, the increasing diversity of supply-side resources and demand-side loads introduces multifaceted uncertainties in EH-IES, challenging its safe and reliable operation. Establishing an uncertainty-aware optimization model is thus critical. Common approaches include robust optimization (RO) [7], scenario-based stochastic optimization (SO) [8], and chance-constrained programming (CCP) [9−12]. RO employs uncertainty intervals without probabilistic information, adopting min-max(-min) formulations to address worst-case scenarios. SO approximates probability distributions via discretized scenarios, demanding high scenario representativeness and scalability. CCP ensures constraint satisfaction at predefined probability levels. However, existing studies predominantly adopt fixed risk/confidence levels. Notably, as source-load uncertainties grow, fixed risk level will lead to the following problems: 1) Fixed risk levels render solutions infeasible due to mismatches between subjective renewable consumption intervals and objective safety limits, thereby making target risk appetite impractical. 2) Growing uncertainties hinder rational trade-offs across multiple chance constraints under a single fixed risk level. 3) CCP’s inherent rigidity forces strict risk-level adherence, escalating costs via over-reliance on reserves for low-probability extremes.