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、家用电池存储、电动汽车（EV）和需求响应，已经变得越来越流行，成为配电系统的重要组成部分。然而，DER的增长给系统运行带来了重大挑战，如双向潮流、间歇性发电和意外的频率/电压波动。DERs也将对电力市场产生相当大的影响。例如，联邦能源监管委员会（FERC）于2020年宣布的第2222号命令要求美国的电网运营商将DERs建立为市场参与者。因此，电网运营商需要为分布式电源在电力系统和市场中的高水平渗透做好准备。研究人员已经开发了许多方法，如DER建模、最优潮流（OPF）和博弈论。其中，申请人开发了需求响应、车辆到电网和电池模型。然而，准确和统一的DER模型是必要的。分布式、随机和凸化OPF模型应该用于DER集成。此外，有效的动态定价以激励电力市场中的分布式发电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DERs集成到电力系统和市场中。为了实现这一目标，将开发以下学习和优化模型：1)用于DERs建模的机器学习；2)经济调度、最优规模和最优布局的OPF；3)配电系统重构方法提高DER集成；4) DERs管理制度；5)在市场中推广DERs的博弈和机制设计方法。短期目标将集中于开发准确统一的DER、OPF、动态定价和博弈模型。新的学习和优化方法将被开发出来，以实现高效和经济的DER集成。深度学习方法和统一的DER模型可以帮助网格操作员可视化DER，简化DER集成。将对拟议的OPF模型进行定量评价。将确定最适合将高级DER集成到大型配电系统中的OPF模型。网格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。使用该方法，可以控制DERs，以提高电力系统的稳定性，降低发电成本，延迟基础设施投资，并有助于加拿大减少温室气体排放的承诺。所提出的动态电价可以促进电力市场竞争，增加分布式电网业主的收益。另外，还将培养政府、公共事业、电力行业、学术界急需的5名高素质人才（HQP）。我还将努力在HQP培训中促进公平、多元化和包容性。

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.