skip to main content

Multi-Objective Optimization Dispatch Based Energy Management of A Microgrid Running Under Grid Connected and Standalone Operation Mode

Department of Electrical Engineering, EEA&TI Laboratory, Faculty of Science and Technology (FSTM), Hassan II University of Casablanca, BP 146 Mohammedia 20650, Morocco

Received: 28 Nov 2020; Revised: 25 Dec 2020; Accepted: 10 Jan 2021; Available online: 15 Jan 2021; Published: 1 May 2021.
Editor(s): H. Hadiyanto
Open Access Copyright (c) 2021 The Authors. Published by Centre of Biomass and Renewable Energy (CBIORE)
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Citation Format:

This paper presents a novel optimization approach for a day-ahead power management and control of a DC microgrid (MG). The multi-objective optimization dispatch (MOOD) problem involves minimizing the overall operating cost, pollutant emission levels of (NOx, SO2 and CO2) and the power loss cost of the conversion devices. The weighted sum method is selected to convert the multi-objective optimization problem into a single optimization problem. Then, analytic hierarchy process (AHP) method is applied to determine the weight coefficients, according to the preference of each objective function. The system’s performance is evaluated under both grid connected and standalone operation mode, considering power balancing, high level penetration of renewable energy, optimal scheduling of charging/discharging of battery storage system, control of load curtailment and the system technical constraints. Ant lion optimizer (ALO) method is considered for handling MOOD, and the performance of the proposed algorithm is compared with other known heuristic optimization techniques.  The simulation results prove the effectiveness and the capability of the developed approach to deal better with the coordinated control and optimization dispatch problem.They also revealed that economically running the MG system under grid connected mode can reduce the overall cost by around 4.70% compared to when it is in standalone operation mode.

Fulltext View|Download
Keywords: microgrid; multi-objective optimization dispatch; operating cost; pollutant emission levels; power loss cost; weighted sum method; analytic hierarchy process; renewable energy; ant lion optimizer; heuristic optimization techniques

Article Metrics:

  1. Aghajani, G., & Ghadimi, N. (2018). Multi-objective energy management in a micro-grid. Energy Reports, 4, 218–225; doi: 10.1016/j.egyr.2017.10.002
  2. Al-Sakkaf, S., Kassas, M., Khalid, M., & Abido, M. A. (2019). An Energy management system for residential autonomous dc microgrid using optimized fuzzy logic controller considering economic dispatch. Energies, 12, 1-25
  3. Alazemi, F. Z., & Hatata A. Y. (2019). Ant lion optimizer for optimum economic dispatch considering demand response as a visual power plant. Electric Power Components and Systems; doi: 10.1080/15325008.2019.1602799
  4. Alvarado-Barrios, L., Rodríguez del Nozal, A., Tapia, A., Martínez-Ramos, J. L., & Reina, D. (2019). An evolutionary computational approach for the problem of unit commitment and economic dispatch in microgrids under several operation modes. Energies, 12, 2143; doi: 10.3390/en12112143
  5. Augusto, O., Bennis, F., & Caro, S. (2012). A New method for decision making in multi-objective optimization problems. Sociedade Brasileira de Pesquisa Operacional, 32 (2), 331–369
  6. Contreras, S. F., Cortes, C. A, & Myrzik, J. M. A. (2019). Optimal microgrid planning for enhancing ancillary service provision. Journal of Modern Power Systems and Clean Energy, 7, 862–875; doi: 10.1007/s40565-019-0528-3
  7. García, P., García, C. A., Fernández, L. M., F. Llorens, & Jurado, F. (2014). ANFIS-based control of a grid-connected hybrid system integrating renewable energies, hydrogen and batteries. IEEE Transactions on Industrial Informatics, 10 (2), 1107-1117; doi: 10.1109/TII.2013.2290069
  8. Garcia, P., Torreglosa, J. P., Fernandez, L. M., & Jurado, Fr. (2013). Optimal energy management system for standalone wind turbine/photovoltaic/hydrogen/battery hybrid system with supervisory control based on fuzzy logic. International Journal of Hydrogen Energy, 38 (33), 14146-14158; doi: 10.1016/j.ijhydene.2013.08.106
  9. Hatata, A. Y., & Hafez, A. A. (2019). Ant lion optimizer versus particle swarm and artificial immune system for economical and eco‐friendly power system operation. International Transaction on Electrical Energy Systems; doi: 10.1002/etep.2803
  10. Jiang, Q., Xue, M., & Geng, G. (2013). Energy management of microgrid in grid-connected and stand-alone modes. IEEE Transactions On Power Systems, 28 (3), 3380-3389; doi: 0.1109/TPWRS.2013.2244104
  11. Jin, X., Mu, Y., Jia, H., Wu, J., Jiang, T., & Yu, X. (2017). Dynamic economic dispatch of a hybrid energy microgrid considering building based virtual energy storage system. Applied Energy, 194, 386-398; doi: 10.1016/j.apenergy.2016.07.080
  12. Kamboj, V. K., Bhadoria, A., & Bath, S. K. (2017). Solution of non-convex economic load dispatch problem for small-scale power systems using ant lion optimizer. Neural Computing and Applications, 28, 2181–2192; doi: 10.1007/s00521-015-2148-9
  13. Karaboga, D., & Akay, B. (2009). A Comparative study of artificial bee colony algorithm. Applied Mathematics and Computation, 214 (1), 108-132; doi: 10.1016/j.amc.2009.03.090
  14. Kiptoo, M. K., Lotfy, M. E., Adewuyi, O. B., conteh, A., howlader, A. M., & Senjyu, T. (2020). Integrated approach for optimal techno-economic planning for high renewable energy-based isolated microgrid considering cost of energy storage and demand response strategies. Energy Conversion and Management, 215; doi: 10.1016/j.enconman.2020.112917
  15. Kyriakarakos, G., Dounis, A. I., Arvanitis, K. G., & Papadakis G., (2012). A fuzzy logic energy management system for polygeneration microgrids. Renewable Energy, 41, 315-327; doi: 10.1016/j.renene.2011.11.019
  16. Lagouir, M., Badri, A., Sayouti, Y. (2019). Development of an intelligent energy management system with economic dispatch of a standalone microgrid. Journal of Electrical Systems, 15 (4) 568-581
  17. Liu, H., Ji, Y., Zhuang, H., & Wu, H. (2015). Multi-Objective dynamic economic dispatch of microgrid systems including vehicle-to-grid. Energies, 8, 4476-4495. DOI: 10.3390/en8054476
  18. Meng, X. B., Gao, X. Z., Liu, Y., & Zhang, H. (2015). A novel bat algorithm with habitat selection and doppler effect in echoes for optimization. Expert Systems with Applications, 42 (17-18), 6350-6364; doi: 10.1016/j.eswa.2015.04.026
  19. Mirjalili, S. (2015). The Ant Lion Optimizer. Advances in Engineering Software, 83, 80–98; doi: 10.1016/j.advengsoft.2015.01.010
  20. Mohamed, F. A., & Koivo, H. N. (2012). Online management genetic algorithms of microgrid for residential application. Energy Conversion and Management, 64, 562–568; doi: 10.1016/j.enconman.2012.06.010
  21. Moradi, H., Esfahanian, M., Abtahi, A., & Zilouchian, A. (2018). Optimization and energy management of a standalone hybrid microgrid in the presence of battery storage system. Energy, 147, 226-238; doi: 10.1016/
  22. Murty, V. V. S. N., & Kumar, A. (2020). Multi-objective energy management in microgrids with hybrid energy sources and battery energy storage systems. Protection and Control of Modern Power Systems, 5 (2); doi: 10.1186/s41601-019-0147-z
  23. Nemati, M., Braun, M., & Tenbohlen, S. (2018). Optimization of unit commitment and economic dispatch in microgrids based on genetic algorithm and mixed integer linear programming. Applied Energy, 210, 944-963; doi: 10.1016/j.apenergy.2017.07.007
  24. Nwulu, N. I., & Xia, Xi. (2015). Multi-objective dynamic economic emission dispatch of electric power generation integrated with game theory based demand response programs. Energy Conversion and Management, 89, 963–974; doi: 10.1016/j.enconman.2014.11.001
  25. Olivares, D. E., Cañizares, C. A., & Kazerani, M., (2011). A Centralized optimal energy management system for microgrids. IEEE Power and Energy Society General Meeting; doi: 10.1109/PES.2011.6039527
  26. Parisio, A., Rikos, E., & Glielmo, L., (2014). A Model predictive control approach to microgrid operation optimization. IEEE Transactions on Control Systems Technology, 22 (5), 1813-1827; doi: 10.1109/TCST.2013.2295737
  27. Reddy, S. S. (2017). Optimal power flow with renewable energy resources including storage. Electrical Engineering, 99, 685-695; doi: 10.1007/s00202-016-0402-5
  28. Reddy, S. S., & Momoh, J. A. (2015). Realistic and transparent optimum scheduling strategy for hybrid power system. IEEE Transactions on Smart Grid, 6 (6), 3114-3125; doi: 10.1109/TSG.2015.2406879
  29. Shen, J., Jiang, Ch., Liu, Y., & Wang, X. (2016). A Microgrid energy management system and risk management under an electricity market environment. IEEE Access, 4, 2349-2356
  30. Taha, M. S., Abdeltawab, H., Ha., & Mohamed, Y. A. I. (2018). An Online energy management system for a grid-connected hybrid energy source. IEEE Journal of Emerging and Selected Topics in Power Electronics, 6 (4), 2015 –2030; doi: 10.1109/JESTPE.2018.2828803
  31. Triantaphyllou, E., & Mann, S. H. (1995). Using the analytic hierarchy process for decision making in engineering applications: some challenges. International Journal of Industrial Engineering: Applications and Practice, 2 (1), 35-44
  32. Vivas, F. J., Segura, F., Andújar, J. M., Palacio, A., Saenz, J. L., Isorna, F., & López, E. (2020). Multi-objective fuzzy logic-based energy management system for microgrids with battery and hydrogen energy storage system. Electronics, 9 (7), 1074; doi: 10.3390/electronics9071074
  33. Wang, T., He, X., & Deng, T. (2017). Neural Networks for power management optimal strategy in hybrid microgrid. Neural Computer & Application Journal, 31 (7), 2635-2647
  34. Wang. Z., Zhu, Q., Huang, M., & Yang, B. (2017). Optimization of economic/environmental operation management for microgrids by using hybrid fireworks algorithm. International Transactions on Electrical Energy Systems, 27 (12); doi: 10.1002/etep.2429
  35. Wu, H., Liu, X., & Ding, M. (2014). Dynamic economic dispatch of a microgrid: Mathematical models and solution algorithm. Electrical Power and Energy Systems, 63, 336-346; doi: 10.1016/j.ijepes.2014.06.002
  36. Wu, H., Zhuang, H., Zhang, W., & Ding, M. (2016). Optimal allocation of microgrid considering economic dispatch based on hybrid weighted bilevel planning method and algorithm improvement. Electrical Power and Energy Systems, 75, 28-37; doi: 10.1016/j.ijepes.2015.08.011
  37. Wu, X., Cao, W., Wang, D., & Ding, M. (2019). Multi-objective optimization dispatch method for microgrid energy management considering the power loss of converters, Energies.12(11), 2160,
  38. Yuan, X., Zhang, B., Wang, P., Liang, J., Yuan, Y., Huang, Y., & Lei, X. (2017). Multi-objective optimal power flow based on improved strength Pareto evolutionary algorithm. Energy, 122, 70–82; doi: 10.1016/

Last update:

  1. Research on life cycle low carbon optimization method of multi-energy complementary distributed energy system: A review

    Changrong Liu, Hanqing Wang, ZhiYong Wang, Zhiqiang Liu, Yifang Tang, Sheng Yang. Journal of Cleaner Production, 336 , 2022. doi: 10.1016/j.jclepro.2022.130380
  2. Optimization of Management Mode of Small- and Medium-Sized Enterprises Based on Decision Tree Model

    Yuzhu Diao, Qing Zhang, Miaochao Chen. Journal of Mathematics, 2021 , 2021. doi: 10.1155/2021/2815086
  3. A comprehensive review on optimization of hybrid renewable energy systems using various optimization techniques

    M. Thirunavukkarasu, Yashwant Sawle, Himadri Lala. Renewable and Sustainable Energy Reviews, 176 , 2023. doi: 10.1016/j.rser.2023.113192
  4. Solving Multi-Objective Energy Management of a DC Microgrid using Multi-Objective Multiverse Optimization

    Marouane Lagouir, Abdelmajid Badri, Yassine Sayouti. International Journal of Renewable Energy Development, 10 (4), 2021. doi: 10.14710/ijred.2021.38909

Last update: 2023-09-23 17:15:12

No citation recorded.