Predictive accommodation of spatiotemporal variability in distributed photovoltaic generation

为了实现对全人类至关重要的可持续能源目标，我们必须提高光伏发电在配电网中的渗透率。然而，光伏发电功率随时间波动。这项工作的本质是通过研究受不确定变量影响的这些TE代理的社区协调，如作为交易代理的本地能源管理系统（HEMS），来了解日内操作范围内的预测调节潜力。服务贡献是使用阴天预报器生成PVG小时内预报的时间顺序概率模型。在操作上，本研究提供了一个分布式智能电网控制系统，具有完整的协议支持，将电力系统的不同级别联系在一起，形成一个可互操作的整体。

In order to achieve sustainable energy goals, which are of importance to all humanity, we must increase photovoltaic (PV) penetration into the distribution grid. However, PV power fluctuates with time g. The nature of the work is to understand the predictive accommodation potential across intraday operating horizons by studying the community coordination of these TE agents subject to uncertain variables, like local energy nagement system (HEMS) as a transactive agent. The service contribution is of overcast sky predictors used to generate chronological probability models for PVG intrahour forecasting. Operationally, this research provides a distributed smart grid control system, with full protocol support, that ties the different levels of the electrical system together into one interoperable whole. Benefits to the research field are in smart grid technology convergence. The MLPE approach yields a PVG model that gives the greatest breadth to accommodation studies for building-integrated PV, while an open source HEMS with forward-looking inverter, storage and load control benefits the energy management field. Probability prediction in PVG is matured through the fusion of energy forecasting problems. Operationally, this research accelerates smart grid evolution toward a robust transactive energy internet. Benefits to Canada transcend the energy sector. The research will give prosumers greater choice and control, while creating an energy services industry that mediates between them and distribution service operators. Advances in asset technologies will enable commodity solutions that aid in removing bottlenecks to increased PV penetration. Advances in energy forecasting will provide greater operational certainty over critical intraday time horizons, enhancing energy security at lower cost. This program addresses an emerging approach to smart grid evolution, and will inform policy, market design, and business models while training critically needed engineers. The global smart grid market is expected to grow annually by 20%, reaching USD