SBIR Phase I: Adaptive Charging Network for EV and Energy Services

这个项目的广泛影响/商业潜力将满足两个社会需求。它将以最低成本为电动汽车（ev）提供大规模充电基础设施，并将提供辅助服务，以帮助整合可再生能源。这一点至关重要，因为发电和运输消耗了美国三分之二的能源，排放了美国一半以上的温室气体。因此，要大幅减少温室气体排放，就需要大规模采用电动汽车和可再生能源发电。CA的任务是到2025年拥有150万辆零排放汽车，目前，全国一半的电动汽车都在CA。据估计，当CA到2025年实现其雄心勃勃的目标时，拟议的技术每年可能节省1.44亿加元的运营成本和11亿美元的资本成本。通过大幅降低大规模电动汽车充电成本，拟议中的技术还将帮助加州每年减少550万吨温室气体排放。因此，该项目将对清洁交通和清洁能源产生影响。这个小企业创新研究（SBIR）第一阶段项目将为智能电动汽车充电器的实时分布式优化和控制开发理论和算法。智能电网生态系统中的多方，从电力批发市场运营商，到公用事业公司，到聚合商，再到个别停车设施运营商，都有自己的个人目标，并根据本地信息做出本地决策，但他们的决策在电网中通过电力流以复杂的方式相互作用。高效解决方案的关键是一组最新的数学技术，将全局问题分解为一组子问题，由各方通过本地消息交换进行通信来解决。该项目将克服的核心挑战涉及优化分解、可扩展性、稳定性、全局最优性和鲁棒性。

The broader impact/commercial potential of this project will address two societal needs. It will enable mass charging infrastructure for electric vehicles (EVs) at minimal costs, and will enable the provisioning of ancillary services to help integrate renewable energy sources. This is critical as electricity generation and transportation consume about 2/3 of all US energy and emit more than 1/2 of all US greenhouse gases. To drastically reduce greenhouse gases will therefore require mass adoption of electric vehicles and renewable generation. CA has a mandate to have 1.5 million zero emission vehicles by 2025 and, currently, half of the nation's EVs are in CA. It has been estimated that the proposed technology can potentially save CA $144M annually in operating costs and $1.1B in capital cost when CA reaches its ambitious goal by 2025. By drastically decreasing the cost of mass EV charging, the proposed technology will also help reduce 5.5 million US tons of greenhouse gases annually in CA. This project will therefore make an impact in both clean transportation and clean energy. This Small Business Innovation Research (SBIR) Phase I project will develop theory and algorithms for real-time distributed optimization and control of smart EV chargers. Multiple parties in the smart grid ecosystem, from electricity wholesale market operator, to utility companies, to aggregators, and individual parking facility operators, have their own individual objectives, and make local decisions based on local information, yet their decisions interact over the grid through power flows in intricate ways. The key to an efficient solution is a set of recent mathematical techniques to decompose the global problem into a set of subproblems, to be solved by individual parties, that communicate through local message exchanges. The core challenges this project will overcome pertain to optimization decomposition, scalability, stability, global optimality, and robustness.