Abstract
In microgrids a major share of the energy production comes from renewable energy sources such as photovoltaic panels or wind turbines. The intermittent nature of these types of producers along with the fluctuation in energy demand can destabilize the grid if not dealt with properly. This paper presents a multi-agent-based energy management approach for a non-isolated microgrid with solar and wind units and in the presence of demand response, considering uncertainty in generation and load. More specifically, a modified version of the lightning search algorithm, along with the weighted objective function of the current microgrid cost, based on different scenarios for the energy management of the microgrid, is proposed. The probability density functions of the solar and wind power outputs, as well as the demand of the households, have been used to determine the amount of uncertainty and to plan various scenarios. We also used a particle swarm optimization algorithm for the microgrid energy management and compared the optimization results obtained from the two algorithms. The simulation results show that uncertainty in the microgrid normally has a significant effect on the outcomes, and failure to consider it would lead to inaccurate management methods. Moreover, the results confirm the excellent performance of the proposed approach.
Highlights
The lifestyle of today leads to a substantial growth in the demand for energy in residential areas.To fulfill this increasing demand, it is inevitable that there is a need to increase the generation capacity of the current energy grid
Several methods have been used for the energy management under uncertainty, among which, the most suitable one is the use of metaheuristic algorithms with an appropriate objective function
A weighting objective function based on the cost of the microgrid is presented, in which each scenario is weighted according to its probability of occurrence
Summary
The lifestyle of today leads to a substantial growth in the demand for energy in residential areas. To fulfill this increasing demand, it is inevitable that there is a need to increase the generation capacity of the current energy grid. Economic and environmental benefits of distributed generation (DG) with the priority on renewable energy resource usage makes it a much better alternative compared to building expensive and polluting fossil fuel power plants that are far away from many consumers. Energy management in microgrids considering the uncertainties in generation/demand has always been of paramount importance to researchers in this field [3,4,5,6]
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