CPS: Synergy: Collaborative Research: The Sharing Economy for Electricity Services in Connected Communities

迫在眉睫的环境问题、能源供应安全问题和核电安全问题推动了全球对可再生能源的兴趣。美国清洁能源挑战呼吁各州和社区合作，到2020年将太阳能发电扩大到140千兆瓦。今天对可再生能源的投资是在公用事业规模的太阳能发电厂和风力发电场，以及小规模的分布式屋顶光伏(PV)。大型太阳能发电厂比屋顶光伏更便宜，但考虑到输电基础设施的成本，这一优势就会减弱。慷慨的税收抵免和净计量补贴是分布式光伏大幅增长的主要原因。在净计量下，公用事业公司被要求以零售价回购多余的发电量。但税收抵免正在逐步取消，公用事业公司强烈反对净计量政策，因为它们允许光伏所有者避免支付基础设施成本，并对公用事业商业模式构成生存威胁。分布式光伏发电的增长可能会减速。该项目旨在通过使互联社区共享电力服务来维持和加快分布式光伏投资的未来增长。核心论点是，共享光伏所有权和运营可以刺激对分布式光伏的更大投资，而补贴最少，不需要净计量，参与者可以公平地支付基础设施、储备和可靠性成本。我们的研究将使互联社区能够有效利用资源、减少排放，并支持我们的集体可持续发展目标。它将在没有补贴的情况下刺激分布式光伏的更深层次渗透，同时为电力服务在共享经济中定义新的创业机会。该项目将通过新的跨学科课程将教育和研究结合起来，这些课程结合了技术、经济、政策和电力系统。这项研究广泛适用于其他共享服务，包括电力储存、建筑能源管理和交通网络。具体地说，我们将(A)开发共享电力服务所需的基础设施，(B)分析各种电价和补贴设计下家庭的投资决策，(C)构建预测消费者对激励措施反应的行为模型，以及(D)基于数据对共享进行实证评估。在我们的共享架构愿景中，代理之间通过基于云的监控系统进行交互。这个系统管理约束，接受对共享资源的供需出价，出清市场，并公布价格。我们架构的一个关键元素是软件定义的能力流，以在对等匹配平台下将共享扩展到数百万客户。我们将利用博弈论方法和微观经济工具，分析电力服务共享经济中的投资决策，为能源领域的共享提供商业案例。招募客户分享他们的资源是一项关键的研究挑战。在这里，我们将应用现代机器学习方法来识别、建模和定位合适的客户。最后，我们将使用数据分析方法，为基于城市规模的数据共享提供一个令人信服的案例。

Pressing environmental problems, energy supply security issues, and nuclear power safety concerns drive the worldwide interest in renewable energy. The US Clean Energy Challenge calls for a partnership of states and communities to expand solar to 140GW by 2020. Investment in renewables today is in utility-scale solar plants and wind farms, as well as small-scale distributed rooftop photovoltaics (PV). Large solar plants are cheaper than rooftop PV, but this advantage is diminished when considering transmission infrastructure costs. Generous tax credits and net metering subsidies are responsible for much of the dramatic growth of distributed PV. Under net metering, utilities are mandated to buy back excess generation at retail prices. But tax credits are being phased out, and utilities strenuously oppose net metering policies as they allow PV owners to avoid paying for infrastructure costs and pose an existential threat to utility business models. The growth of distributed PV generation may decelerate. This project aims to sustain and accelerate future growth in distributed PV investments by enabling connected communities to share electricity services. The central thesis is that shared PV ownership and operations can spur greater investment in distributed PV with minimal subsidy, without net metering, and with participants fairly paying for infrastructure, reserves and reliability costs. Our research will enable connected communities to efficiently use resources, reduce emissions, and support our collective sustainability goals. It will spur deeper penetration of distributed PV without subsidy, while defining new entrepreneurial opportunities in the sharing economy for electricity services. The project will integrate education and research through new interdisciplinary courses that combine technology, economics, policy, and power systems. This research is broadly applicable to other shared services including electricity storage, building energy management, and transportation networks. Specifically, we will (a) develop the infrastructure necessary for sharing electricity services, (b) analyze investment decisions of households under various tariff and subsidy designs, (c) construct behavioral model that predict consumer response to incentives, and (d) conduct an empirical assessment of sharing grounded in data. In our architectural vision for sharing, agents interact with each other through a cloud based supervisory system. This system manages constraints, accepts supply and demand bids for shared resources, clears the market, and publishes prices. A key element of our architecture is software-define-power-flow to scale sharing to millions of clients under a peer-to-peer matching platform. We will make the business case for sharing in the energy sector using game-theoretic methods and micro-economic tools to analyze investment decisions in a sharing economy for electricity services. Recruiting clients to share their resources is a key research challenge. Here, we will apply modern machine learning methods to identify, model, and target suitable clients. Finally, we will use data analytics methods to make a compelling case for sharing based on city-scale data.