Predictive accommodation of spatiotemporal variability in distributed photovoltaic generation

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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 & weather. I will Discover how variability in PV generation (PVG) can be accommodated within a prosumer framework through distributed control of local PVG, storage, and loads. An intelligent multi-agent system (MAS) will be expressed as transactive energy (TE) agents, with operations supported by services that impact assets in response to dynamic inputs and constraints. This distributed MAS will be informed by probabilistic PVG forecasting. 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 & grid conditions, pricing signals, and prosumer preferences. The dependencies that exist between these uncertain variables will be addressed as a predictive control problem between agents using stochastic programming. Anticipated outcomes are in all smart grid domains. Asset outcomes are the accommodation of dynamic overshadowing of PV panels in the urban environment by module level power electronics (MLPE), and the realization of an intelligent home energy management 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 52 billion by 2023.