This paper presents a formulation to determine the appropriate power dispatch of an energy storage system, whose available energy is dependent on the charging/discharging pattern from previous time periods. The implementation structure is consistent with current dispatch algorithms used in microgrids, and the algorithm can be used in either grid-connected or islanded modes of operation. The proposed approach employs a backcasting algorithm to estimate the net stored energy value, against which the current cost of energy is compared to determine how the storage system should be used to perform arbitrage. The contribution of this work is a means to include the time-dependent resource in traditional economic dispatch algorithms to reduce the cost of energy in a microgrid while enabling the arbitrage algorithm to continuously adapt to changing market conditions. Results show that the backcasting algorithm is able to reduce the average cost of energy by 8.14% and can reduce the average cost of energy by up to 72.3% of the ideal reduction, as determined by a perfect forecasting dispatch.
Many benefits can be achieved through the implementation of a Microgrid controller, such as minimized cost, reduction in peak power, power smoothing, greenhouse gas emission reduction, and increased reliability of service. However, most Microgrid controllers found in the literature and in the industry optimize a single objective, which either exacerbates or does not solve the problems with integrating a high penetration of renewable energy. This paper presents a methodology of formulating a multi-objective optimization so that each objective is quantified through valuation functions that can be specific to every Microgrid. The proposed approach attains a Pareto optimal solution by directly comparing the quantified valuation functions and solving as if it were a single-objective optimization problem. Three cases of controllers are presented and compared: a base case system with no controller, a single-objective optimization that optimizes the cost of energy, and a multi-objective optimization that optimizes five identified benefits. Results show that the proposed controller can mitigate the negative impacts of volatile generation to levels below that of the system load.