Multi-Agents for Microgrids
- 1 Department of Computer Science and Engineering, Santa Clara University, Santa Clara CA, USA
- 2 Department of Computer Engineering, German Jordanian University, Amman, Jordan
- 3 Electrical Engineering, Delft University of Technology, Delft, The Netherlands
- 4 Department of Computer Engineering, German Jordanian University, Amman, Jordan
- 5 Department of Computer Engineering, German Jordanian University, Amman, Jordan
Abstract
Microgrid systems are built to integrate a generation mix of solar and wind renewable energy resources that are generally intermittent in nature. This paper presents a novel decentralized multi-agent system to securely operate microgrids in real-time while maintaining generation , load balance. Agents provide a normal operation in a grid-connected mode and a contingency operation in an islanded mode for fault handling. Fault handling is especially critical in microgrid operation to simulate possible contingencies and microgrid outages in a real-world scenario. A robust agent design has been implemented using MATLAB - Simulink and Java Agent Development Framework technologies to simulate microgrids with load management and distributed generators control. The microgrid of the German Jordanian University has been used for simulation for Summer and Winter photovoltaic generation and load profiles. The results show agent capabilities to operate microgrid in real-time and its ability to coordinate and adjust generation and load. In a simulated fault incident, agents coordinate and adjust to a normal operation in 0.012 seconds, a negligible time for microgrid restoration. This clearly shows that the multi-agent system is a viable solution to operate MG in real-time.
- Ansari, A., Safari, N. and Chung, C. (2016) Reliability Assessment of Microgrid with Renewable Generation and Prioritized Loads. IEEE Green Energy and Systems Conference, Long Beach, CA, 6-7 November 2016, 1-6. https://doi.org/10.1109/IGESC.2016.7790067
- Yazdanian, M. and Mehrizi-Sani, A. (2014) Distributed Control Techniques in Microgrids. IEEE Transactions on Smart Grid, 5, 2901-2909. https://doi.org/10.1109/TSG.2014.2337838
- Kuo, M. and Lu, S. (2013) Design and Implementation of Real-Time Intelligent Control and Structure Based on Multi-Agent Systems in Microgrids. Energies, 6, 6045-6059. https://doi.org/10.3390/en6116045
- Kantamneni, A., Brown, L., Parker, G. and Weaver, W. (2015) Survey of Multi-Agent Systems for Microgrid Control. Engineering Applications of Artificial Intelligence, 45, 192-203. https://doi.org/10.1016/j.engappai.2015.07.005
- Alfergani, A., Alfaitori, K., Khalil, A. and Buaossa, N. (2018) Control Strategies in Ac Microgrid: A Brief Review. 9th IEEE International Renewable Energy Congress, Hammamet, 20-22 March 2018, 1-6. https://doi.org/10.1109/IREC.2018.8362575
- Khan, M. and Wang, J. (2017) The Research on Multi-Agent System for Microgrid Control and Optimization. Renewable and Sustainable Energy Reviews, 80, 1399-1411. https://doi.org/10.1016/j.rser.2017.05.279
- Sujil, A., Verma, J. and Kumar, R. (2018) Multi Agent System: Concepts, Platforms and Applications in Power Systems. Artificial Intelligence Review, 49, 153-182. https://doi.org/10.1007/s10462-016-9520-8
- Weidlich, A. and Veit, D. (2008) A Critical Survey of Agent-Based Wholesale Electricity Market Models. Energy Economics, 30, 1728-1759. https://doi.org/10.1016/j.eneco.2008.01.003
- Santos, G., Pinto, T., Morais, H., Sousa, T., Pereira, I., Fernandes, R., Prac, I. and Vale, Z. (2015) Multi-Agent Simulation of Competitive Electricity Markets: Autonomous Systems Cooperation for European Market Modeling. Energy Conversion and Management, 99, 387-399. https://doi.org/10.1016/j.enconman.2015.04.042
- Kouluri, M. and Pandey, R. (2011) Intelligent Agent Based Microgrid Control. IEEE 2nd International Conference on Intelligent Agent and Multi-Agent Systems, Chennai, 7-9 September 2011, 62-66.
- Eddy, Y., Gooi, H. and Chen, S. (2014) Multi-Agent System for Distributed Management of Microgrids. IEEE Transactions on Power Systems, 30, 24-34. https://doi.org/10.1109/TPWRS.2014.2322622