机组组合中两种代表性机组聚合公式的反例

中文摘要 AI 辅助

机组聚合消除了机组组合中相同发电机之间的对称性，但仅当聚合公式所允许的每条聚合轨迹都具有可行的机组级实现时，该聚合公式才是可行域精确的。本文表明，两种常用的慢爬坡机组聚合公式，即p-聚类机组组合（PCUC）和紧机组聚合（TUA），无法满足这一要求。我们构造了一个反例，该反例满足两种公式的所有聚合约束，却不存在可行的解聚。该反例揭示了这两种公式共有的局限性：它们不能保证机组级输出分配的跨期一致性。对已发表文献中报告的复制案例进行的实验进一步验证，这种解聚不可行性甚至可能出现在聚合模型的最优解中。本文证明PCUC和TUA仍可能高估爬坡灵活性。开发能够高效求解的、针对相同慢爬坡机组的精确聚合模型仍然是一个挑战。

英文摘要

Unit aggregation removes symmetry among identical generators in unit commitment, but an aggregate formulation is feasible-region exact only if every aggregate trajectory it admits has a feasible unit-level realization. This letter shows that two commonly used aggregation formulations for slow-ramping units, i.e., p-clustered unit commitment (PCUC) and tight unit aggregation (TUA), cannot satisfy this requirement. We construct a counterexample that satisfies all aggregate constraints of both formulations yet admits no feasible disaggregation. This counterexample reveals a limitation common to both formulations: they do not guarantee intertemporal consistency of unit-level output allocations. Experiments on the replication cases reported in published literature further verify that such infeasibility of disaggregation can even occur in optimal solutions of aggregate models. This letter demonstrates that PCUC and TUA can still overestimate ramping flexibility. Developing exact aggregation models that can be solved efficiently for identical slow-ramping units remains a challenge.

发表机构

• Shandong University（山东大学）

机构由 AI 辅助整理，请以论文原文为准。