Integration of Distributed Energy Resources into Electricity Systems and Markets

分布式能源(DER)，包括屋顶太阳能、小型风力涡轮机、家用蓄电池、电动汽车(EV)和需求响应，已经成为配电系统中越来越受欢迎和必不可少的组成部分。然而，DER的增长给系统的运行带来了巨大的挑战，如双向潮流、间歇性发电和意外的频率/电压波动。DES还将对电力市场产生相当大的影响。例如，2020年公布的联邦能源管理委员会(FERC)第222号命令，要求美国的电网运营商建立DER作为市场参与者。因此，电网运营商需要为电力系统和市场的高水平DER渗透做好准备。DER集成的方法已经发展，如DER建模、最优潮流(OPF)和博弈论。在其他方面，申请人开发了需求响应、车辆到电网和电池模型。尽管如此，仍然需要准确和统一的DER模型。对于DER集成，应该开发分布式、随机和凸化的最优潮流模型。此外，有效的动态定价以激励电力市场中的电力参与者变得越来越重要。拟议研究计划的长期目标是安全、可靠和经济地将DER整合到电力系统和市场中。为了达到这一目标，将开发以下学习和优化模型：1)用于建模DER的机器学习；2)用于经济调度、最优规模和DER最优配置的最优潮流；3)用于改善DER集成的配电系统重构方法；4)DER管理系统；以及5)在市场上促进DER的博弈和机制设计方法。短期目标将集中在开发准确和统一的DER、OPF、动态定价和博弈模型。将开发新的学习和优化方法，以实现高效和经济的DER集成。深度学习方法和统一的DER模型可以帮助电网运营商可视化DER，简化DER集成。我们将对提出的最优潮流模型进行定量评估。将确定最适合将高层DER集成到大规模配电系统中的最优潮流模型。电网运营商可以使用这些信息来选择适合其特定应用的最佳模型。使用所提出的方法，可以控制DER，以增加电力系统的稳定性，降低发电成本，推迟基础设施投资，并为加拿大减少温室气体排放的承诺做出贡献。提出的动态电价可以促进电力市场竞争，增加电力业主的收入。此外，还将培训5名高素质人才(HQP)，掌握相关知识、技能和能力，这些都是政府、公用事业、电力行业和学术界高度需要的。我还将努力促进HQP培训中的公平、多样性和包容性(EDI)。

Distributed energy resources (DER), including roof-top solar, small-scale wind turbines, home battery storage, electric vehicles (EV), and demand response, have become increasingly popular and essential components of power distribution systems. However, the growth of DER poses significant challenges to system operation, such as bidirectional power flow, intermittent power generation, and unexpected frequency/voltage fluctuation. DERs will also considerably impact electricity markets. For example, the Federal Energy Regulatory Commission (FERC) Order No. 2222, announced in 2020, requires grid operators in the US to establish DERs as market participants. Therefore, grid operators need to prepare for high-level DER penetration in the electricity system and market. Methods have been developed for DER integration, such as DER modeling, optimal power flow (OPF), and game theory. Among others, the applicant developed demand response, vehicle-to-grid, and battery models. Nonetheless, accurate and unified DER models are needed. Distributed, stochastic, and convexified OPF models should be developed for DER integration. Also, effective dynamic pricing to incentivize DER participants in the electricity market becomes increasingly crucial. The long-term goal of the proposed research program is to safely, reliably, and economically integrate DERs into electricity systems and markets. To reach this goal, the following learning and optimization models will be developed: 1) machine learning for modeling DERs; 2) OPF for economic dispatch, optimal sizing, and optimal placement of DERs; 3) distribution system reconfiguration methods to improve DER integration; 4) DERs management systems; and 5) game and mechanism design methods to promote DERs in markets. The short-term objectives will focus on developing accurate and unified DER, OPF, dynamic pricing, and game models. Novel learning and optimization methods will be developed for efficient and economic DER integration. The deep learning method and unified DER models can help grid operators to visualize DERs and simplify DER integration. The proposed OPF models will be quantitatively evaluated. The most suitable OPF model for high-level DER integration into large-scale power distribution systems will be identified. Grid operators can use this information to select the best model for their specific application. Using the proposed method, DERs can be controlled to increase power system stability, reduce generation cost, delay infrastructure investments, and contribute to Canada's commitment to greenhouse gas emission reduction. The proposed dynamic pricing can promote electricity market competition and increase DER owners' revenue. Also, five highly qualified personnel (HQP) will be trained with relevant knowledge, skills, and abilities, which are in high demand by governments, utilities, power industries, and academia. I will also strive to promote equity, diversity, and inclusion (EDI) in HQP training.