[论文解读] A Conditional Value-at-Risk Based Planning Model for Integrated Energy System with Energy Storage and Renewables

Ang Xuan, Xinwei Shen+2 more|arXiv (Cornell University)|Apr 22, 2021

Integrated Energy Systems Optimization参考文献 33被引用 11

一句话总结

本文提出了一种基于两阶段条件风险价值（CVaR）的综合能源系统（IES）规划模型，整合了可再生能源（RES）和储能系统（ESS）。通过采用Benders分解法与改进的后向场景削减方法，该模型在不同置信水平下优化了投资决策与运行风险，结果表明，ESS与RES的集成可显著降低潜在风险暴露，同时平衡投资成本。

ABSTRACT

Owing to the potential higher energy supply efficiency and operation flexibility, integrated energy system (IES), which usually includes electric power, gas and heating/cooling systems, is considered as one of the primary forms of energy carrier in the future. However, with the increasing complexity of multiple energy devices and systems integration, IES planning is facing a significant challenge in terms of risk assessment. To this end, an energy hub (EH) planning model considering renewable energy sources (RES) and energy storage system (ESS) integration is proposed in this paper, in which the risk is measured by Conditional Value-at-Risk (CVaR). The proposed IES planning model includes two stages: 1) investment planning on equipment types and capacity (e.g., energy converters, distributed RES and ESS) and 2) optimizing the potential risk loss in operation scenarios along with confidence level and risk preference. The problem solving is accelerated by Benders Decomposition and Improved Backward Scenario Reduction Method. The numerical results illustrate the effectiveness of proposed method in balancing the potential operation risk and investment cost. Moreover, the effectiveness of reducing potential operation risk by introducing ESS and RES are also verified.

研究动机与目标

• 为应对因复杂多能耦合而带来的综合能源系统（IES）风险评估挑战。
• 开发一种平衡投资成本与运行风险的IES规划模型，适用于包含可再生能源（RES）与储能系统（ESS）的系统。
• 采用条件风险价值（CVaR）作为风险度量，将风险偏好与置信水平纳入规划过程。
• 通过Benders分解与改进的后向场景削减技术，加速求解计算。

提出的方法

• 构建两阶段随机优化模型：第一阶段用于能源转换器、RES与ESS的投资决策；第二阶段用于运行风险最小化。
• 采用条件风险价值（CVaR）量化最坏情况下的预期损失，体现风险规避型决策行为。
• 应用Benders分解法，将大规模混合整数规划问题分解为Master问题与子问题，以实现高效求解。
• 采用改进的后向场景削减方法，在保留风险分布统计特性的同时减少运行场景数量。
• 集成能源枢纽（EH）建模，以表征IES内电能、天然气与热能之间的转换与交换。
• 通过调节置信水平与风险偏好参数，优化投资成本与风险暴露之间的权衡。

实验结果

研究问题

• RQ1 在高比例可再生能源与储能系统集成的背景下，如何有效量化综合能源系统（IES）规划中的风险？
• RQ2 不同的风险偏好与置信水平对IES中投资与运行决策有何影响？
• RQ3 储能系统（ESS）与可再生能源（RES）在多大程度上可降低IES中的潜在运行风险？
• RQ4 如何提升大规模IES规划问题在随机场景下的计算效率？

主要发现

• 所提出的基于CVaR的模型能有效平衡投资成本与运行风险，结果表明在不同置信水平下风险暴露均有所降低。
• 储能系统（ESS）的集成显著降低了高运行损失的可能性，尤其在高风险场景中表现突出。
• 可再生能源（RES）有助于风险缓解，但其波动性要求与ESS进行协同规划以实现最优性能。
• Benders分解与改进的后向场景削减方法相结合，显著加快了收敛速度，提升了大规模问题的计算效率。
• 数值结果表明，采用CVaR的风险规避型规划相比传统确定性模型，能产生更稳健的投资决策。