EPCN:Solving Electricity-Expansion Problems Efficiently via Decomposition (SEEPED)

该项目的目标是改变电力系统容量扩展问题的建模方式。由于电力系统的性质(横跨大陆，包括数千个节点和分支)以及困扰长期规划决策的许多不确定性，容量扩展问题是大规模和复杂的。这包括大规模的不确定性，如长期需求增长、燃料价格变化、能源政策选择和技术发展；以及小规模的不确定性，如影响实时需求的天气事件以及风能和太阳能的可用性。该项目将开发建模范例，在一个连贯的框架内捕捉规划决策的多个尺度以及影响它们的不确定性。这是一项及时和根本的重要努力，以确保有效地过渡到更可持续和更具弹性的电力系统设计。我们将通过开发两种互补的方法来模拟电力系统容量扩展来解决这一具有挑战性的问题。这两种方法都对多尺度不确定性和决策进行建模，并充分详细地表示系统操作，以满足系统灵活性需求。第一种方法使用多阶段随机最优化方法，在这种方法中，投资决策是在粗略(例如十年)的时间尺度上做出的，运营决策是在精细(例如每小时)的时间尺度上做出的，完全代表了这些决策的时间序列。大规模不确定性在情景树中被显式建模，而小规模不确定性通过不同的运行条件被捕获。另一种方法是自适应稳健随机模型，该模型使规划决策对大规模不确定性具有稳健性或分布稳健性，并考虑随机运行条件。这些复杂和大规模的模型伴随着分解算法。对于多阶段随机优化模型，将采用渐进套期保值算法进行求解，而对于稳健模型，将采用列约束算法进行求解。我们还将探索使用聚类和重要性抽样技术来选择具有代表性的运营期，以在投资时期之间进行建模。我们将使用基于北美电力系统的大规模案例研究来演示所建议的建模技术的用途、易操作性和威力。私人投资督导计划亦会与电力行业人士接触。他们还将利用行业和政府咨询职位来促进行业传播。这些步骤将确保研究结果被行业成员使用，同时行业可以为模型和案例研究的开发提供重要的反馈和输入。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The goal of this project is to transform the manner in which electric power system capacity-expansion problems are modeled. Capacity-expansion problems are large-scale and complex, due to the nature of power systems (spanning continents and including thousands of nodes and branches) and the numerous uncertainties plaguing long-term planning decisions. This includes large-scale uncertainties, such as long-term demand growth, changes in fuel prices, energy-policy choices, and technology development; and small-scale uncertainties, such as weather events affecting real-time demand and wind and solar availability. This project will develop modeling paradigms that capture the multiple scales of planning decisions and the uncertainties that affect them within a coherent framework. This is a timely and fundamentally important endeavor to ensure an efficient transition to more sustainable and resilient power system designs. We will tackle this challenging problem by developing two complementary approaches to modeling power system capacity expansion. Both approaches model multi-scale uncertainties and decisions and represent system operations in sufficient detail to capture system flexibility needs. The first approach uses a multi-stage stochastic optimization approach, in which investment decisions are made at coarse (e.g., decadal) timescales and operating decisions are made at fine (e.g., hourly) timescales, with full representation of the temporal sequence of these decisions. Large-scale uncertainties are modeled explicitly in the scenario tree, while small-scale uncertainties are captured through different operating conditions. The other approach is an adaptive robust stochastic model, in which planning decisions are made to be robust or distributionally robust to large-scale uncertainties and take stochastic operating conditions into account. These complex and large-scale models are accompanied by decomposition algorithms. The progressive hedging algorithm will be adapted to tractably solve the multi-stage stochastic optimization model while column-and-constraint algorithms will be developed for solving the robust model. We will also explore the use of clustering and importance sampling techniques to select representative operating periods to be modeled between investment epochs. The use, tractability, and power of the proposed modeling techniques will be demonstrated using large-scale case studies based on North American power systems. The PIs will also engage with electricity industry members. They will also use industry- and government-advisory positions to advance industry dissemination. These steps will ensure that the results of the research are used by industry members while at the same time industry can provide vital feedback and input to model and case study development.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.