430925 Power Management of Microgrids: Multiparametric Programming Approach to Improved Handling of System Uncertainties

Wednesday, November 11, 2015: 12:48 PM
Salon E (Salt Lake Marriott Downtown at City Creek)
Eva Umeozor and Milana Trifkovic, Chemical & Petroleum Engineering, University of Calgary, Calgary, AB, Canada

Local power quality and environmental concerns associated with the conventional energy systems have spurred a move towards an improved grid system consisting of distributed, hybrid energy sources (renewable and non-renewable) and loads – microgrid. In the face of external uncertainties arising from electricity demand, price, and the availability of renewable resources, the microgrid system is required to operate seamlessly and optimally with or without interactions with a central grid network. This study presents a multi-parametric programming (MPP) based operational strategy for efficient and tractable integration and management of the distributed energy sources in a residential level microgrid. The hybrid system comprises of renewable (solar photovoltaic and wind turbine) and conventional systems (microturbine and utility grid connection). The overall problem is formulated using multi-parametric mixed-integer linear programming (mp-MILP) via parameterizations of the uncertain coordinates of the wind and solar resources. This results in a bi-level optimization problem, where choice of the parameterization scheme is made at the upper level while system operation decisions are made at the lower level. The mp-MILP formulation leads to significant improvements in uncertainty management, solution quality and computational burden; by easing dependency on the need for accurate meteorological information and removing the limitations of multiple computational cycles of the traditional online optimization techniques. The problem is solved offline on a day-ahead basis, allowing online implementation to be achieved on hourly system state updates. We consider two microgrid arrangements where one operates under a fixed feed-in-tariff (FIT) contract with a local distribution company, while the other goes through the price bidding system to take advantage of the time-of-use electricity price dynamics through the energy trading system (ETS). In the first scenario, renewables feed-in incentives discountenance any energy storage requirement. For the second scenario, the variable electricity pricing system calls for smart economic decisions where renewable energy surpluses to local load are stored in a battery when selling price is low, and subsequently sold when price is high. These two case studies of microgrid energy systems are used to assess the performance of the MPP algorithm. Lastly, the algorithm is applied to an experimental microgrid located in Sarnia, Ontario. Our preliminary results evidence the feasibility and the effectiveness of the proposed approach.

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