Machine Learning for Communication-Cognizant Smart Inverter Control

智能逆变器将太阳能电池板、储能单元和电动汽车连接到主电网，是科普住宅电网中太阳能发电引起的电压波动的有效电网控制机制。然而，在真实的时间中为数百个逆变器选择功率注入是一项艰巨的任务。为了达到网络需求优化解决方案和低于标准的本地控制之间的最佳点，拟议的研究开发了新的算法方案，用于根据通过机器学习（ML）训练的控制规则或策略协调逆变器。该计划促进了对住宅电网通信受限分散控制的理解，并发现了智能电表数据的新用途，可立即应用于配电系统，并与电力公用事业实践相关。对行业的预期好处是先进的物理感知ML解决方案，先进的资产管理以及逆变器，传感器和电网通信的附加值。提高电网可控性有利于社会实现智能电网技术的可靠集成，如分布式太阳能发电，电动汽车和需求响应计划。通过逆变器处理电压偏移延长了电压调节器和电容器的寿命，并推迟了昂贵的电网升级。拟议的研究活动与教育和外联目标相结合。该方案通过关于可再生能源的实践学习活动接触到大学前的女学生，并通过访问居民社区和举办研讨会继续向本科生推广方案。研究结果将被整合到配电网和ML能源系统的（下）研究生课程的课程中，从而教育即将到来的电力工程人员及时使用ML工具。该项目旨在将机器学习（ML）模型集成到最优潮流（OPF）公式中，以开发配电网中的数据自适应和通信受限的智能逆变器控制方案。这是通过完成以下任务来实现的：i）设计算法解决方案，以确保ML模块符合逆变器规范和馈线约束; ii）通过基于模型和无模型设置下的近似和精确AC潮流方程来捕获ML模块之间的物理耦合; iii）提出随机和机会约束的基于ML的OPF方案，以处理太阳能发电和负载的不确定性; iv）设计学习架构，在严格的通信预算下以分布式方式集中训练和实现，以利用大的历史数据，但小的实时数据馈送;以及v）当ML模块由状态驱动以闭合循环时，确保动态稳定性和安全性。其结果将是一套全面的基于ML的逆变器优化解决方案，使用基准馈线的真实数据进行验证。该研究开创了数据驱动的非线性逆变器控制策略的设计，在电力系统，ML，鲁棒控制和随机优化的关系。通过随机约束最优潮流学习逆变器控制规则的原始和潜在的变革思想是这个项目的渗透主题。当OPF和本地控制方案无法适应时变电网条件时，所提出的ML-OPF解决方案利用创造性设计的神经网络拓扑结构，以及基于内核和强化学习的进步，在列车大/操作小数据范式下有效运行。构建通信感知的逆变器控制策略将在严格的通信预算下推进我们对物理或工程系统的约束学习的知识。研究任务探索不同的机器学习工具;考虑基于模型和无模型的设置;处理近似和精确的电网模型;并考虑公用事业和逆变器之间不同的通信能力。该项目独特地结合了配电网、统计学习、鲁棒控制和强化学习方面的专业知识，超越了通信受限的逆变器控制，解决了带宽受限的资源分配问题和网络物理系统控制。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

Smart inverters interfacing solar panels, energy storage units, and electric vehicles to the main electric grid are an effective grid control mechanism to cope with the voltage fluctuations induced by solar generation in residential grids. However, selecting the power injections for hundreds of inverters in real time is a formidable task. To hit the sweet spot between cyber demanding optimization solutions and subpar local control, the proposed research develops novel algorithmic schemes for coordinating inverters based on control rules or policies trained through machine learning (ML). This program advances understanding of communication-limited decentralized control of residential electric grids and discovers novel uses of smart meter data, with immediate applicability to distribution systems and relevance to electric power utility practice. The expected benefits to industry are cutting-edge physics-aware ML solutions, advanced asset management, and value added for inverters, sensors, and grid communications. Enhancing grid controllability benefits society by enabling reliable integration of smart grid technologies, such as distributed solar generation, electric vehicles, and demand-response programs. Handling voltage excursions by inverters extends the lifetime of voltage regulators and capacitors, and postpones costly grid upgrades. The proposed research activities are integrated with educational and outreach objectives. The program reaches pre-college female students through hands-on learning activities on renewable energy sources and continues the PIs' outreach to undergraduate students through visits to residential communities and seminars. The findings will be integrated into the curricula of (under)-graduate courses on distribution grids and ML for energy systems, thus educating the upcoming power-engineering workforce on timely ML tools. This project aspires to integrate machine learning (ML) models into optimal power flow (OPF) formulations to develop data-adaptive and communication-limited smart inverter control schemes in power distribution grids. This is achieved by accomplishing the ensuing tasks: i) devise algorithmic solutions to ensure ML modules comply with inverter specifications and feeder constraints; ii) capture the physical coupling across ML modules through approximate and exact AC power flow equations under both model-based and model-free setups; iii) put forth stochastic and chance-constrained ML-based OPF schemes to deal with the uncertainty of solar generation and loads; iv) design learning architectures that are centrally trained and implemented in a distributed manner on a tight communication budget to exploit big historical data but small real-time data feeds; and v) ensure dynamic stability and safety when ML modules are driven by states to close the loop. The outcome will be a comprehensive suite of ML-based inverter optimization solutions validated using real-world data on benchmark feeders. The proposed research pioneers the design of data-driven nonlinear inverter control policies at the nexus of power systems, ML, robust control, and stochastic optimization. The original and potentially transformative idea of learning inverter control rules through a stochastic constrained OPF is the permeating theme of this project. When OPF and local control schemes fail to adjust to time-varying grid conditions, the proposed ML-OPF solutions exploit creatively designed neural network topologies, as well as advances in kernel-based and reinforcement learning to operate effectively under the train-big/operate-small data paradigm. Building communication-aware inverter control policies will advance our knowledge on constrained learning over a physical or engineered system under a tight communication budget. Research tasks explore different ML tools; account for model-based and model-free setups; handle approximate and exact grid models; and consider varying communication capacities between the utility and inverters. Uniquely combining expertise on distribution grids, statistical learning, robust control, and reinforcement learning, this project goes beyond communication-limited inverter control to bandwidth-limited resource allocation problems and cyber-physical systems control.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.