可扩展的多层级优化方法用于顺序清算的能源市场——以天然气与碳感知机组组合为例
Scalable Multi-Level Optimization for Sequentially Cleared Energy Markets with a Case Study on Gas and Carbon Aware Unit Commitment
AI总结：
本文提出一种结合字典序优化与加权和方法及专用Benders分解的MIMLSF求解技术，用于美国东北部电力-天然气-碳市场中的机组组合，确保仅调度盈利的燃气电厂，平均计算时间降低32.23%，最优性差距降低94.23%。
摘要：
本文研究了具有顺序跟随者的混合整数多层级问题（MIMLSF），这是一种专门的优化模型，旨在通过纳入下层顺序市场清算过程的预期结果来增强上层决策。我们提出了一种新颖的方法，将字典序优化与加权和方法相结合，以渐近方式将MIMLSF近似为单层级问题，能够处理超过三个层级的多层级问题。为了提高计算效率和可扩展性，我们提出了一种专用的Benders分解方法，该方法利用多层级子问题的可分离性。为了展示我们MIMLSF求解技术的实际应用，我们解决了美国东北部综合电力、天然气和碳市场清算框架中的机组组合问题（UC），从而能够将天然气和碳市场的预期成本和收入纳入UC决策。这确保了只有盈利的燃气发电厂（GFPPs）才会被调度，使系统运营商能够做出明智的决策，防止GFPP经济损失，并在电力与天然气系统紧张的情况下降低总运营成本。案例研究不仅证明了MIMLSF模型的适用性，还突出了专用Benders分解技术的计算优势，与最先进的方法相比，平均计算时间减少了32.23%，最优性差距减少了94.23%。
英文摘要：
This paper examines Mixed-Integer Multi-Level problems with Sequential Followers (MIMLSF), a specialized optimization model aimed at enhancing upper-level decision-making by incorporating anticipated outcomes from lower-level sequential market-clearing processes. We introduce a novel approach that combines lexicographic optimization with a weighted-sum method to asymptotically approximate the MIMLSF as a single-level problem, capable of managing multi-level problems exceeding three levels. To enhance computational efficiency and scalability, we propose a dedicated Benders decomposition method with multi-level subproblem separability. To demonstrate the practical application of our MIMLSF solution technique, we tackle a unit commitment problem (UC) within an integrated electricity, gas, and carbon market clearing framework in the Northeastern United States, enabling the incorporation of anticipated costs and revenues from gas and carbon markets into UC decisions. This ensures that only profitable gas-fired power plants (GFPPs) are committed, allowing system operators to make informed decisions that prevent GFPP economic losses and reduce total operational costs under stressed electricity and gas systems. The case study not only demonstrates the applicability of the MIMLSF model but also highlights the computational benefits of the dedicated Benders decomposition technique, achieving average reductions of 32.23% in computing time and 94.23% in optimality gaps compared to state-of-the-art methods.
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