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<p>Integrated energy systems (IES) are crucial for addressing energy crises and environmental concerns. However, increasing energy demand and uncertainties from renewable energy sources and fluctuating energy prices pose significant challenges to IES capacity expansion planning and operation. This paper proposes a multistage and multiscale stochastic mixed integer programming (MM-SMIP) model for IES capacity expansion, incorporating unit retirement, and comparing chance constraints and Conditional Value-at-Risk (CVaR) for risk management. The model considers uncertainties across multiple time scales, integrating detailed short-term economic dispatch into long-term planning. A modified nested cross decomposition algorithm with parallel computing is employed for solution. Numerical studies demonstrate the model’s effectiveness in optimizing IES capacity expansion under multiscale uncertainties and different risk management approaches.</p>

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Risk-constrained integrated energy system capacity expansion with multiscale uncertainties

  • 期刊论文

  • 发表日期：

    2025年7月5日

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  • Fen Qi,
  • Zhouchun Huang,
  • Yuping Huang

摘要

Integrated energy systems (IES) are crucial for addressing energy crises and environmental concerns. However, increasing energy demand and uncertainties from renewable energy sources and fluctuating energy prices pose significant challenges to IES capacity expansion planning and operation. This paper proposes a multistage and multiscale stochastic mixed integer programming (MM-SMIP) model for IES capacity expansion, incorporating unit retirement, and comparing chance constraints and Conditional Value-at-Risk (CVaR) for risk management. The model considers uncertainties across multiple time scales, integrating detailed short-term economic dispatch into long-term planning. A modified nested cross decomposition algorithm with parallel computing is employed for solution. Numerical studies demonstrate the model’s effectiveness in optimizing IES capacity expansion under multiscale uncertainties and different risk management approaches.