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Open AccessArticle

An Innovative Stochastic Multi-Agent-Based Energy Management Approach for Microgrids Considering Uncertainties

1
Institute of Computer Science and Business Information Systems, University of Duisburg-Essen, 45127 Essen, Germany
2
Electrical and Computer Engineering Faculty, Semnan University, Semnan 35131-19111, Iran
3
Department of Computer Science and Software Engineering, Swinburne University of Technology, Melbourne, VIC 3122, Australia
4
Systems Research Institute, Polish Academy of Sciences, 01-447 Warsaw, Poland
*
Author to whom correspondence should be addressed.
Inventions 2019, 4(3), 37; https://doi.org/10.3390/inventions4030037
Received: 30 June 2019 / Revised: 21 July 2019 / Accepted: 25 July 2019 / Published: 29 July 2019
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. View Full-Text
Keywords: multi-agent systems; energy management; microgrids; optimization; AI techniques; lightning search algorithm multi-agent systems; energy management; microgrids; optimization; AI techniques; lightning search algorithm
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Ghorbani, S.; Unland, R.; Shokouhandeh, H.; Kowalczyk, R. An Innovative Stochastic Multi-Agent-Based Energy Management Approach for Microgrids Considering Uncertainties. Inventions 2019, 4, 37.

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