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

EPCN：通过分解有效解决电力膨胀问题（SEEPED）

基本信息

批准号：
1808169
负责人：
Ramteen Sioshansi
金额：
$ 29.92万
依托单位：
Ohio State University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-06-01 至 2023-05-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1808169&HistoricalAwards=false
关键词：
EPCN Solving Electricity Expansion Problems

项目摘要

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.

本计画的目标是改变电力系统容量扩充问题的模式。由于电力系统的性质（跨越大陆，包括数千个节点和分支）以及长期规划决策中的众多不确定性，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，如影响实时需求以及风能和太阳能可用性的天气事件。该项目将开发建模范例，捕捉规划决策的多个尺度和影响它们的不确定性在一个连贯的框架。这是一项及时且具有根本重要性的奋进，以确保向更具可持续性和弹性的电力系统设计的有效过渡。我们将通过开发两种互补的方法来建模电力系统容量扩展来解决这个具有挑战性的问题。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，以满足系统灵活性的需求。第一种方法使用多阶段随机优化方法，其中粗略地做出投资决策（例如，十年的）时间尺度和操作决策是精细地做出的（例如，每小时）的时间尺度，与这些决定的时间顺序的完整表示。大规模的不确定性在情景树中明确建模，而小规模的不确定性则通过不同的操作条件来捕获。另一种方法是自适应鲁棒随机模型，在该模型中，规划决策对大规模的不确定性具有鲁棒性或分布鲁棒性，并考虑随机操作条件。这些复杂和大规模的模型伴随着分解算法。逐步对冲算法将适应于可追踪地求解多阶段随机优化模型，而列和约束算法将被开发用于求解鲁棒模型。我们还将探索使用聚类和重要性抽样技术来选择投资时期之间的代表性经营期进行建模。所提出的建模技术的使用，易处理性和权力将使用基于北美电力系统的大规模案例研究。PI还将与电力行业成员进行接触。他们还将利用行业和政府咨询职位来推动行业传播。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投入。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

期刊论文数量（23）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Influence of the number of decision stages on multi-stage renewable generation expansion models

决策阶段数对多阶段可再生能源发电扩展模型的影响

DOI：
10.1016/j.ijepes.2020.106588
发表时间：
2021
期刊：
International Journal of Electrical Power & Energy Systems
影响因子：
5.2
作者：
Domínguez, R.;Carrión, M.;Conejo, A.J.
通讯作者：
Conejo, A.J.

Equilibria in Electricity and Natural Gas Markets With Strategic Offers and Bids

DOI：
10.1109/tpwrs.2019.2947646
发表时间：
2020-05
期刊：
IEEE Transactions on Power Systems
影响因子：
6.6
作者：
Sheng Chen;A. Conejo;R. Sioshansi;Zhi-nong Wei
通讯作者：
Sheng Chen;A. Conejo;R. Sioshansi;Zhi-nong Wei

Unit Commitment With an Enhanced Natural Gas-Flow Model

增强天然气流量模型的装置承诺

DOI：
10.1109/tpwrs.2019.2908895
发表时间：
2019
期刊：
IEEE Transactions on Power Systems
影响因子：
6.6
作者：
Chen, Sheng;Conejo, Antonio J.;Sioshansi, Ramteen;Wei, Zhinong
通讯作者：
Wei, Zhinong

Comparing Electric Water Heaters and Batteries as Energy-Storage Resources for Energy Shifting and Frequency Regulation

比较电热水器和电池作为能量转移和频率调节的储能资源

DOI：
10.1109/oajpe.2022.3231834
发表时间：
2023
期刊：
IEEE Open Access Journal of Power and Energy
影响因子：
3.8
作者：
Mansouri, Mahan A.;Sioshansi, Ramteen
通讯作者：
Sioshansi, Ramteen

Using In-Home Energy Storage to Improve the Resilience of Residential Electricity Supply

利用家庭储能提高住宅电力供应的弹性

DOI：
10.1109/oajpe.2023.3298701
发表时间：
2023
期刊：
IEEE Open Access Journal of Power and Energy
影响因子：
3.8
作者：
Hunter-Rinderle, Rachel;Fong, Matthew Y.;Yang, Baihua;Xian, Haoshu;Sioshansi, Ramteen
通讯作者：
Sioshansi, Ramteen