CDI-Type II: Energy Policy, Investment, and Pricing Analysis Driven by Computational Steering

CDI-Type II：计算指导驱动的能源政策、投资和定价分析

• 批准号: 1029337
• 负责人: Ramteen Sioshansi
• 金额: $ 167.5万
• 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: 2010
• 资助国家: 美国
• 起止时间: 2010-10-01 至 2016-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1029337&HistoricalAwards=false
• 关键词: CDI; Type; II; Energy; Policy

1029337SioshansiAbstractConcerns about the U.S.'s reliance on fossil fuels have increased the incentive to reduce this dependence. Increased renewable energy, energy storage, and plug-in hybrid vehicles (PHEVs) and electric vehicles (EVs) are often suggested as parts of the solution. However, widespread use of these technologies will have a profound effect on the U.S. power grid. It is therefore important to understand economic and policy questions regarding the interplay of PHEVs, EVs, renewable energy, energy storage, distributed generation, and the grid. Integrated models that allow the simulation of policies under different sets of assumptions are crucial to understanding the complexity and emergent behaviors of such systems. The intersection of energy, environmental, and national security issues involve a complex set of trade-offs among initial cost, local air quality, climate change, technology, and energy independence. Without understanding the interactions of the different scales and different sectors of energy production, only partial and overly simplistic solutions can be analyzed. This project will create a comprehensive model of next-generation power systems that integrates the economic decisions faced by both individuals and the power grid?called the Integrated Computational System for Energy Pricing and Policy (ICS-EPP). The main project outcome will be the ICS-EPP model that includes a typical power grid (such as that of the U.S.) and autonomous sub-models of individuals to efficiently simulate different scenarios of the evolution of such a system under different economic policies and technology options. The ICS-EPP will be designed so that important questions can be addressed, such as the cost of adding PHEVs and EVs to the grid, the value of vehicle to grid services, or the impacts of different pricing structures on energy use and investment. The comprehensive nature of ICS-EPP permits examination of the side effects of various actions: changes in emissions and demand for generating capacity, for example. More important, as policy-makers debate various mechanisms to reduce GHG emissions and reduce dependence on fossil fuels, they will have available a more accurate view of the cost and efficacy of various policy options. State and local governments can examine the value of incentivizing different technologies such as PHEV charging stations, household solar panels, and/or integrated heat and power units. This model will permit policy makers and analysts to conduct virtual experiments of the interaction of households, energy technologies, and the power system at a new level of detail. The project leverages a broad collaboration that includes engineers, economists, computational scientists, as well as automotive and electric power industry leaders.Intellectual Merit:This project relies on computational steering to knit together and solve models of households and the power grid into a large-scale agent-based model. Such modeling has not been attempted, due to the size and computational power needed to accommodate the subtleties of the markets and solve such a model. The work is based on the premise that computational modeling and simulation can serve as an effective means of evaluating the potential effects of different economic policies. This will require advances in a number of areas: (a) the ability to integrate models of diverse systems based on different simulation methodologies into an integrated whole; (b) techniques for information from large simulation results that can be matched against qualitative objectives; and (c) running large, heterogeneous models at multiple time scales on currently prevalent High Performance Computing systems.Broader Impact:The success of this project will enable paradigm-shifting advances in energy policy analysis, as decisionmakers can examine the results, in multiple dimensions (price; GHG, SOx, and NOx emissions; petroleum consumption; household expenditures; etc.), of policies that target or affect energy, such as tax credits for PHEV purchase or charging infrastructure, GHG emissions taxes or caps, or the synergies of integrating PHEVs with solar and wind generation. Also, a low-level computational steering toolkit that can be applied to other domain areas will be created. Research will also be integrated into economics, engineering, and computer science curricula in the form of interdisciplinary courses.Program Officer: Bruce HamiltonCBET DivisionDirectorate for EngineeringOn Behalf of CDI Working GroupDate: August 9, 2010

1029337 Sioshansi Abstract对美国的担忧对化石燃料的依赖增加了减少这种依赖的动力。增加可再生能源、储能、插电式混合动力汽车（PHEV）和电动汽车（EV）通常被认为是解决方案的一部分。然而，这些技术的广泛使用将对美国电网产生深远的影响。因此，重要的是要了解有关PHEV，EV，可再生能源，储能，分布式发电和电网相互作用的经济和政策问题。允许在不同假设下模拟政策的集成模型对于理解此类系统的复杂性和紧急行为至关重要。 能源、环境和国家安全问题的交叉点涉及初始成本、当地空气质量、气候变化、技术和能源独立性之间的一系列复杂权衡。 如果不了解能源生产的不同规模和不同部门之间的相互作用，就只能分析部分和过于简单的解决方案。该项目将创建一个综合模型的下一代电力系统，集成的经济决策所面临的个人和电网？能源定价和政策综合计算系统（ICS-EPP）。 主要项目成果将是ICS-EPP模型，其中包括一个典型的电网（如美国）。以及个体的自主子模型，以有效地模拟这种系统在不同经济政策和技术选择下的不同演变情景。ICS-EPP的设计将能够解决重要问题，例如将插电式电动汽车和电动汽车添加到电网的成本、车辆对电网服务的价值，或者不同定价结构对能源使用和投资的影响。ICS-EPP的综合性质允许检查各种行动的副作用：例如，排放量和发电能力需求的变化。更重要的是，随着决策者就减少温室气体排放和减少对化石燃料依赖的各种机制展开辩论，他们将对各种政策选择的成本和效率有更准确的看法。州和地方政府可以研究激励不同技术的价值，例如PHEV充电站，家用太阳能电池板和/或集成热电机组。该模型将允许政策制定者和分析人员在一个新的细节水平上对家庭、能源技术和电力系统的相互作用进行虚拟实验。 该项目利用了广泛的合作，包括工程师，经济学家，计算科学家，以及汽车和电力行业的领导者。智力优势：该项目依靠计算转向将家庭和电网的模型编织在一起并解决成一个大规模的基于代理的模型。由于需要适应市场的微妙之处并解决这样的模型所需的规模和计算能力，这种建模尚未尝试。这项工作的前提是，计算建模和模拟可以作为一种有效的手段，评估不同的经济政策的潜在影响。这将需要在若干领域取得进展：（a）将基于不同模拟方法的不同系统的模型集成为一个综合整体的能力;（B）从大型模拟结果中获取信息的技术，这些信息可以与定性目标相匹配;（c）在当前流行的高性能计算系统上以多个时间尺度运行大型异构模型。该项目的成功将推动能源政策分析的范式转变，因为决策者可以从多个方面（价格;温室气体、硫氧化物和氮氧化物排放;石油消费;家庭支出等）审查结果，针对或影响能源的政策，例如PHEV购买或充电基础设施的税收抵免，温室气体排放税或上限，或将PHEV与太阳能和风能发电相结合的协同效应。此外，还将创建一个可应用于其他领域的低级计算指导工具包。研究还将以跨学科课程的形式融入经济学、工程学和计算机科学课程。项目官员：布鲁斯汉密尔顿CBET分部工程理事会代表CDI工作组日期：2010年8月9日

项目成果

Modeling short-run electricity demand with long-term growth rates and consumer price elasticity in commercial and industrial sectors

• DOI: 10.1016/j.energy.2012.07.059
• 发表时间: 2012-10
• 期刊: Energy
• 影响因子: 9
• 作者: Amy Pielow;R. Sioshansi;M. C. Roberts

Market equilibria and interactions between strategic generation, wind, and storage

• DOI: 10.1016/j.apenergy.2017.10.035
• 发表时间: 2017-11
• 期刊: Applied Energy
• 影响因子: 11.2
• 作者: A. Shahmohammadi;R. Sioshansi;A. Conejo;S. Afsharnia

A highly resolved modeling technique to simulate residential power demand

• DOI: 10.1016/j.apenergy.2013.02.057
• 发表时间: 2013-07
• 期刊: Applied Energy
• 影响因子: 11.2
• 作者: M. Muratori;M. C. Roberts;R. Sioshansi;V. Marano;G. Rizzoni

Hierarchical Clustering to Find Representative Operating Periods for Capacity-Expansion Modeling

• DOI: 10.1109/tpwrs.2017.2746379
• 发表时间: 2018-05
• 期刊: IEEE Transactions on Power Systems
• 影响因子: 6.6
• 作者: Yixian Liu;R. Sioshansi;A. Conejo

User-Steered Energy Generation and Consumption Multimodel Simulation for Pricing and Policy Development

用于定价和政策制定的用户引导的能源生成和消耗多模型模拟

• DOI: 10.1109/mcse.2013.48
• 发表时间: 2014
• 期刊: Computing in Science & Engineering
• 影响因子: 2.1
• 作者: Smith, Harrison B.;Pielow, Amy;Jayakumar, Adithya;Muratori, Matteo;Yurkovich, B.J.;Sioshansi, Ramteen;Krishnamurthy, Ashok;Rizzoni, Giorgio;Roberts, Matthew C.
• 通讯作者: Roberts, Matthew C.