Document Type : Original/Review Paper


Department of Electrical Engineering, Faculty of Engineering, Urmia University, Urmia, Iran.


This paper develops an energy management approach for a multi-microgrid (MMG) taking into account multiple objectives involving plug-in electric vehicle (PEV), photovoltaic (PV) power, and a distribution static compensator (DSTATCOM) to improve power provision sharing. In the proposed approach, there is a pool of fuzzy microgrids granules that they compete with each other to prolong their lives while monitored and evaluated by the specific fuzzy sets. In addition, based on the hourly reconfiguration of microgrids (MGs), granules learn to dispatch cost-effective resources. To promote interactive service, a well-defined, multi-objective approach is derived from fuzzy granulation analysis to improve power quality in MMGs. A combination of the meta-heuristic approach of genetic algorithm (GA) and particle swarm optimization (PSO) eliminates the computational difficulty of the nonlinearity and uncertainty analysis of the system and improves the precision of the results. The proposed approach is successfully applied to a 69-bus MMG test with results reported in terms of stored energy improvement, daily voltage profile improvement, MMG operations, and cost reduction.


Main Subjects

[1] Xu, Z., Yang, P., Zheng, C., Zhang, Y., Peng, J., & Zeng, Z. (2017). Analysis on the organization and Development of multi-microgrids, Renewable and Sustainable Energy Reviews, 2017/06/30/ 2017.

[2] Pisei, S., Choi, J.-Y., Lee, W.-P., & Won, D.-J. (2017). Optimal Power Scheduling in Multi-Microgrid System Using Particle Swarm Optimization, J Electr Eng Technol, vol. 12 pp. 1329-1339, 2017.

[3] Hoke, A., Brissette, A., Chandler, S., Pratt, A. & Maksimovic, D. (2013). Look-ahead Economic Dispatch of Microgrids with Energy Storage, Using Linear Programming, in Technologies for Sustainability (SusTech), 2013 1st IEEE Conference on 2013, pp. 155-161.

[4] Chen, C.,  Duan, S., Cai, T., Liu, B., & Hu, G. (2011). Smart Energy Management System for Optimal Microgrid Economic Operation, IET Renewable Power Generation, vol. 5, pp. 258-267, 2011.

[5] Bae, I.-S. & Kim, J.-O. (2012). Phasor Discrete Particle Swarm Optimization Algorithm to Configure Micro-grids, Journal of Electrical Engineering and Technology, vol. 7, pp. 9-16, 2012.

[6] J. Vasiljevska, Lopes, J. A. P. & Matos, M. A. (2009). Multi-Microgrid Impact Assessment Using Multi Criteria Decision Aid Methods, in IEEE Bucharest Power Tech Conference, Romania, 2009, pp. 1-8.

[7] Farzin, H., Ghorani, R., Fotuhi-Firuzabad, M., & Moeini-Aghtaie, M. (2017). A Market Mechanism to Quantify Emergency Energy Transactions Value in a Multi-Microgrid System, IEEE Transactions on Sustainable Energy pp. 1-1, 2017.

[8] Valipour, K. & Ghasemi, A. (2017). Using a new modified harmony search algorithm to solve multi-objective reactive power dispatch in deterministic and stochastic models, Journal of AI and Data Mining, vol. 5, pp. 89-100, 2017.

[9] Song, N. O., Lee, J. H., & Kim, H. M. (2016). Optimal Electric and Heat Energy Management of Multi-Microgrids with Sequentially-Coordinated Operations, Energies, vol. 9, pp. 1-18, 2016.

[10] Chiu, W. Y., Sun, H., & Poor, H. V. (2015). A Multiobjective Approach to Multi-microgrid System Design, IEEE Transactions on Smart Grid, vol. 6, pp. 2263 - 2272 2015.

[11] Hussain, A., Bui, V. H. &, Kim, H. M. (2016). Robust Optimization-Based Scheduling of Multi-Microgrids Considering Uncertainties, Energies, vol. 9, pp. 1-21, 2016.

[12] Yoo, H. J. , Nguyen, T. T. & H. M., Kim. (2017). Multi-Frequency Control in a Stand-Alone Multi-Microgrid System Using a Back-To-Back Converter, Energies, vol. 10, pp. 1-18, 2017.

[13] Shahnia, F., Bourbour, S. & Ghosh, A., (2017). Coupling Neighboring Microgrids for Overload Management Based on Dynamic Multicriteria Decision-Making, IEEE Transactions on Smart Grid, vol. 8, pp. 969-983, 2017.

[14] Bourbour, S. & Shahnia, F. (2016). A suitable mechanism for the interconnection phase of temporary coupling of adjacent microgrids, in 2016 IEEE Innovative Smart Grid Technologies - Asia (ISGT-Asia), 2016, pp. 624-629.

[15] Shekari, T., Golshannavaz, S., & Aminifar, F. (2017). Techno-Economic Collaboration of PEV Fleets in Energy Management of Microgrids, IEEE Transactions on Power Systems, 2017.

[16] Hamidi, A., Golshannavaz, S. & Nazarpour, D. (2017). D-FACTS Cooperation in Renewable Integrated Microgrids: A Linear Multi-Objective Approach, IEEE Transactions on Sustainable Energy, 2017.

[17] Grimaldi, E. A., Gandelli, A., Grimaccia, F., Mussetta, M. & Zich, R. E. (2006). GSO: A new integrated evolutionary procedure for high dimension electromagnetic problems, in 2006 International Waveform Diversity & Design Conference, 2006, pp. 1-4.

[18] Arefifar, S. A., Ordonez. M. & Mohamed, Y. A-R I., Energy Management in Multi-Microgrid Systems—Development and Assessment, IEEE Transactions on Power Systems vol. 32, pp. 910-922 2017.