发表机构
MIT Energy Initiative; Department of Electrical Engineering & Computer Science, and Laboratory for Information & Decision Systems, Massachusetts Institute of Technology（麻省理工学院能源倡议; 麻省理工学院电气工程和计算机科学系及信息与决策系统实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文扩展GenX模型，纳入基于不动点定理的网络约束，以在保持可处理性的同时解决电网大规模容量扩展优化中网络约束建模的问题，通过ISO新英格兰电网案例验证其效果优于直接嵌入ACOPF的方法。
AI 中文摘要
容量扩展建模在优化新一代发电、储能及输电设施部署中发挥关键作用，通常应用于国家和区域层面。为支持长期规划，这些模型需考虑大量能源技术、政策，以及数十年的天气与需求数据。因此，实际的容量扩展模型会成为包含数亿变量与约束的高维优化问题，求解极具挑战性。应对这一复杂性的常见策略是省略非线性、非凸的交流最优潮流（ACOPF）约束，转而使用线性化功率平衡方程或传输公式。尽管这些简化提升了可处理性，但限制了我们对潮流及电网物理特性如何影响发电、储能与输电基础设施投资决策的理解。本文通过扩展GenX容量扩展模型以纳入基于不动点定理的网络约束，解决了这一缺口。这些约束嵌入了基于ACOPF的考量，同时保持了规划模型的可处理性，几乎保留了传输公式的维度，且仅导致运行时间的适度增加。该方法比直接嵌入ACOPF成本低得多，因此适用于大规模容量规划问题。我们以ISO新英格兰电网为案例研究，将该方法与原始基于传输的GenX模型，以及纳入完整ACOPF约束的非线性、非凸版本进行对比。
英文摘要
Capacity expansion modeling plays a critical role in optimizing the deployment of new generation, storage, and transmission, typically at national and regional levels. To support long-term planning, these models consider a large set of energy technologies and policies, along with decades of weather and demand data. Realistic capacity expansion models thus become high-dimensional optimization problems, with hundreds of millions of variables and constraints, which are challenging to solve. A common strategy to address this complexity is to omit non-linear, non-convex AC optimal power flow (ACOPF) constraints and instead use linearized power balance equations or transport formulations. While these simplifications improve tractability, they limit our understanding of how power flow and the physical properties of power networks impact investment decisions across generation, storage, and transmission infrastructure. This paper addresses this gap by extending the GenX capacity expansion model to incorporate fixed point theorem-based network constraints. These embed ACOPF-based considerations while maintaining the tractability of the planning model, nearly preserving the dimensionality of the transport formulation and incurring only modest runtime increases. This approach is much cheaper than embedding ACOPF directly, making it appropriate for large-scale capacity planning problems. We compare our approach to the original transport-based GenX model as well as a non-linear, non-convex version that incorporates the full ACOPF constraints, for a case study of the ISO New England grid.
Journal refWeng, Yu, et al. "Modeling of network constraints in large-scale capacity expansion optimization of power grids." Electric Power Systems Research 263 (2027): 113779
DOI：10.1016/j.epsr.2026.113779
