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

该项目更广泛的影响/商业潜力将满足两个社会需求。它将以最低的成本为电动汽车（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约2/3的能源，排放了美国1/2以上的温室气体。因此，要大幅减少温室气体，就需要大规模采用电动汽车和可再生能源发电。 加州的任务是到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加州。据估计，到2025年，当CA实现其雄心勃勃的目标时，拟议的技术可能每年节省1.44亿美元的运营成本和11亿美元的资本成本。通过大幅降低大规模电动汽车充电的成本，该技术还将有助于减少加州每年550万吨的温室气体排放。 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）第一阶段项目将开发用于智能电动汽车充电器实时分布式优化和控制的理论和算法。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，再到聚合器和单个停车设施运营商，都有自己的目标，并根据本地信息做出本地决策，但他们的决策通过电网以复杂的方式进行交互。一个有效的解决方案的关键是一组最近的数学技术，将全球问题分解为一组子问题，由各个当事人解决，通过本地消息交换进行通信。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.