Microgrids are small power systems used on a local scale. This kind of systems efficiently integrates distributed generation technologies, energy storage components, and control elements to manage the available electrical energy optimally, regarding the reliability of the supply, environmental considerations, and profitability. The main advantages of using microgrids to supply the energy demand of a load include the reduction of losses of energy due to transmission, enhancement of the reliability and management of the generated electricity, and their ability to operate connected to the main grid or in islanded mode.

At Dr. Trifkovic’s research group we aim to deal with the optimal energy management of a typical microgrid portfolio of resources including renewable resources, conventional energy resources, battery energy storage system (BESS) and utility grid connection. Specifically, one of the objectives is to develop a day-ahead scheduling strategy which maintains the stability of the macrogrid while ensuring the maximum penetration of renewable resources and regular load demand satisfaction at the net minimum cost. Also, we are developing a powerful price forecast model to capture the price peaks in a competitive electricity market to increase the efficiency of the scheduling strategy and maximizing the BESS revenue by participating in the electricity and ancillary services market. Another established objective of the group is to develop a methodology to find the optimal size and geographic placement of different environmentally sustainable energy sources in consideration of government laws, community interests and technical concerns of the location in which the microgrid would be installed. This strategy combines the use of GIS/multicriteria decision analysis and optimization models under different constraints to ensure that the optimal solution fulfills the expectations of any evaluated scenario.

Our goal is to develop a novel, computationally tractable control strategy which enables tight feedback control and optimal operation of complex processes. The main ideas of our approach are: a) development of detailed physics-based units models and their corresponding local controllers (e.g. MPC) at the low level; and b) planning and scheduling at the supervisory level. At the supervisory level, we formulate a stochastic real-time optimization and develop advanced solution strategies for allocating resources to competing tasks over time.

We also aim to optimally design and schedule a bitumen upgrading facility in real time. While optimal designs based on long term forecasts are commonly reported in the literature, simultaneous design and scheduling of an upgrader based on short term forecasts (or the design and schedule as we go paradigm) is new. The main objective is to find the optimal design and schedule routine for the short term, while maximizing profits and satisfying the system energy balance constraints.