engleski

Receding horizon model predictive control for smart management of microgrids under the day-ahead electricity market.

In this paper a Model Predictive Control is applied for investigating the management of grid- connected microgrid consisting of renewable energy sources, unit demand, unit storage and plug-in vehicles. The method optimizes the behaviour of the microgrid system for a predefined time horizon but applies only the solution for the first hour. This procedure is repeated for each hour of microgrid operation. The objective is to maximize the net profit for the system owner. Optimization procedure is under the influence of environment (state) variables, which are generally unknown, but predictable. These are: wind speed, solar insolation and consumer needs for electricity and mobility. Applicability of the method is directly related to uncertainty in input data. Optimization is performed in stochastic programming framework. Input of market electricity price is assumed to be deterministic under day-ahead market. The proposed method is verified for a hypothetical case study. Results are showing potential benefits in optimization of the microgrid management with the proposed method.

Model Predictive Control (MPC); Microgrids; Energy Storage; Renewable Energy Sources (RES)

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