DOI: https://doi.org/10.14710/ijred.2021.38909

Solving Multi-Objective Energy Management of a DC Microgrid using Multi-Objective Multiverse Optimization

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

Received: 7 Jun 2021; Revised: 25 Jul 2021; Accepted: 5 Aug 2021; Available online: 18 Aug 2021; Published: 1 Nov 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.

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Abstract

This paper deals with the multi-objective optimization dispatch (MOOD) problem in a DC microgrid. The aim is to formulate the MOOD to simultaneously minimize the operating cost, pollutant emission level of (NOx, SO2 and CO2) and the power loss of conversion devices.  Taking into account the equality and inequality constraints of the system. Two approaches have been adopted to solve the MOOD issue. The scalarization approach is first introduced, which combines the weighted sum method with price penalty factor to aggregate objective functions and obtain Pareto optimal solutions. Whilst, the Pareto approach is based on the implementation of evolutionary multi-objective optimization solution. Single and multi-objective versions of multi-verse optimizer algorithm are, respectively, employed in both approaches to handle the MOOD. For each time step, a fuzzy set theory is selected to find the best compromise solution in the Pareto optimal set. The simulation results reveal that the Pareto approach achieves the best performances with a considerable decrease of 28.96 $/day in the daily operating cost, a slight reduction in the power loss of conversion devices from 419.79 kWh to 419.29 kWh, and in less computational time. While, it is noticing a small increment in the pollutant emission level from 11.54 kg/day to 12.21 kg/day, for the daily microgrid operation. This deviation can be fully covered when comparing the cost related to the treatment of these pollutants, which is only 5.55 $/day, to the significant reduction in the operating cost obtained using the Pareto approach.

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Keywords: Multi-objective optimization dispatch; microgrid; weighted sum method; price penalty factor; multi-verse optimizer

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Section: Original Research Article
Language : EN
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  1. Abdullah, M. N., Bakar, A. H. A., Rahim, N. A. and Mokhlis, H. (2015). Modified Particle Swarm Optimization for Economic-Emission Load Dispatch of Power System Operation, Turkish Journal of Electrical Engineering & Computer Sciences, 23(Sup.1), 2304–2318; doi: 10.3906/elk-1307-204
  2. Aghajani, G. and Ghadimi, N. (2018). Multi-Objective Energy Management in a Micro-grid, Energy Reports, 4, 218–225; doi: 10.1016/j.egyr.2017.10.002
  3. Alazemi, F. Z. and Hatata, A. Y. (2019). Ant Lion Optimizer for Optimum Economic Dispatch Considering Demand Response as a Visual Power Plant, Electric Power Components and Systems, 47(6–7), 629–643; doi: 10.1080/15325008.2019.1602799
  4. Aluisio, B., Dicorato, M., Forte, G. and Trovato, M. (2017). An Optimization Procedure for Microgrid Day-ahead Operation in the Presence of CHP Facilities, Sustainable Energy, Grids and Networks, 11,34–45; doi: 10.1016/j.segan.2017.07.003
  5. Alvarado-Barrios, L., Rodríguez del Nozal, A., Tapia, A., Martínez-Ramos, J. L. and Reina, D. G. (2019). An Evolutionary Computational Approach for the Problem of Unit Commitment and Economic Dispatch in Microgrids under Several Operation Modes, Energies, 12(11), 2143; doi: 10.3390/en12112143
  6. Augusto, O. B., Bennis, F. and Caro, S. (2012). A new method for decision making in multi-objective optimization problems, Pesquisa Operacional, 32(2), 331–369; doi: 10.1590/S0101-74382012005000014
  7. Cheng, J., Dongliang, D., Cheng, X., Yang, L. and Cui, S. (2020). Probabilistic Microgrid Energy Management with Interval Predictions, Energies, 13(12), 3116; doi: 10.3390/en13123116
  8. Contreras, S. F., Cortes, C. A. and Myrzik, J. M. A. (2019). Optimal Microgrid Planning for Enhancing Ancillary Service Provision, Journal of Modern Power Systems and Clean Energy, 7(4), 862–875; doi: 10.1007/s40565-019-0528-3
  9. Dey, A. and Choudhury, A. B. (2017). A comparative study between scalarization approach and Pareto approach for multi-objective optimization problem using Genetic Algorithm (MOGA) formulated based on superconducting fault current limiter, in 1st IEEE International Conference on Power Electronics, Intelligent Control and Energy Systems, ICPEICES 2016. Institute of Electrical and Electronics Engineers Inc. doi: 10.1109/ICPEICES.2016.7853238
  10. Dey, B., Shivam, K. and Bhattacharyya, B. (2019). Energy management of various microgrid test systems using swarm evolutionary algorithms, in Distributed Energy Resources in Microgrids: Integration, Challenges and Optimization. Elsevier, 323–341; doi: 10.1016/B978-0-12-817774-7.00012-0
  11. Fathy, A. and Rezk, H. (2018). Multi-verse optimizer for identifying the optimal parameters of PEMFC model, Energy, 143, 634–644; doi: 10.1016/j.energy.2017.11.014
  12. Feng, J., Zhang, J., Wang, C., Jiang, R. and Xu, M. (2020). Multi-Objective Economic Scheduling of a Shipboard Microgrid Based on Self-Adaptive Collective Intelligence DE Algorithm, IEEE Access, 8, 73204–73219; doi: 10.1109/ACCESS.2020.2988530
  13. Ghiasi, M., Niknam, T., Dehghani, M., Siano, P., Alhelou, H. H. and Al-Hinai, A. (2021). Optimal Multi-Operation Energy Management in Smart Microgrids in the Presence of RESs Based on Multi-Objective Improved DE Algorithm: Cost-Emission Based Optimization, Applied Sciences, 11(8), 3661; doi: 10.3390/app11083661
  14. Gunantara, N. (2018). A review of multi-objective optimization: Methods and its applications, Cogent Engineering., 5(1), 1–16; doi: 10.1080/23311916.2018.1502242
  15. Haseeb, M., Kazmi, S. A. A., Malik, M. M., Ali, S., Bukhari, S. B. A. and Shin, D. R. (2020). Multi Objective Based Framework for Energy Management of Smart Micro-Grid, IEEE Access, 8, 220302–220319; doi: 10.1109/ACCESS.2020.3041473
  16. Hatata, A. Y. and Hafez, A. A. (2019). Ant Lion Optimizer Versus Particle Swarm and Artificial Immune System for Economical and Eco‐Friendly Power System Operation, International Transactions on Electrical Energy Systems, 29(4); doi: 10.1002/etep.2803
  17. Hou, H., Xue, M., Xu, Y., Xiao, Z., Deng, X., Xu, T., Liu, P. and Cui, R. (2020). Multi-Objective Economic Dispatch of a Microgrid Considering Electric Vehicle and Transferable Load, Applied Energy, 262, 114489; doi: 10.1016/j.apenergy.2020.114489
  18. Kamboj, V. K., Bhadoria, A. and 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
  19. Lagouir, M., Badri, A. and Sayouti, Y. (2021). Multi-Objective Optimization Dispatch Based Energy Management of A Microgrid Running Under Grid Connected and Standalone Operation Mode, International Journal of Renewable Energy Development, 10(2), 333–343; doi: 10.14710/IJRED.2021.34656
  20. Liu, H., Ji, Y., Zhuang, H. and Wu, H. (2015). Multi-Objective Dynamic Economic Dispatch of Microgrid Systems Including Vehicle-to-Grid, Energies, 8(5), 4476–4495; doi: 10.3390/en8054476
  21. Lokeshgupta, B. and Sivasubramani, S. (2018). Multi-objective dynamic economic and emission dispatch with demand side management, International Journal of Electrical Power and Energy Systems, 97, 334–343; doi: 10.1016/j.ijepes.2017.11.020
  22. Mirjalili, S., Jangir, P., Mirjalili, S. Z., Saremi, S. and Trivedi, I. N. (2017). Optimization of problems with multiple objectives using the multi-verse optimization algorithm, Knowledge-Based Systems, 134, 50–71; doi: 10.1016/j.knosys.2017.07.018
  23. Mirjalili, S., Mirjalili, S. M. and Hatamlou, A. (2016). Multi-Verse Optimizer: a nature-inspired algorithm for global optimization, Neural Computing and Applications, 27(2), 495–513; doi: 10.1007/s00521-015-1870-7
  24. Mohamed, F. A. and 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
  25. Mondal, S., Bhattacharya, A. and nee Dey, S. H. (2013). Multi-Objective Economic Emission Load Dispatch Solution Using gravitational Search algorithm and considering wind Power Penetration, International Journal of Electrical Power & Energy Systems, 44(1), 282–292; doi: 10.1016/j.ijepes.2012.06.049
  26. Moradi, H., Esfahanian, M., Abtahi, A. and 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/j.energy.2018.01.016
  27. Murty, V. V. S. N. and 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
  28. Nemati, M., Braun, M. and 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
  29. Rodríguez del Nozal, A., Tapia, A., Alvarado-Barrios, L. and Reina, D. G. (2020). Application of genetic algorithms for unit commitment and economic dispatch problems in microgrids, in Studies in Computational Intelligence. Springer, 139–167; doi: 10.1007/978-3-030-33820-6_6
  30. Stafford, R. (2021). Random Vectors with Fixed Sum, MATLAB Central File Exchange. Available at: https://www.mathworks.com/matlabcentral/fileexchange/9700-random-vectors-with-fixed-sum (Accessed: 7 July 2021)
  31. Sundaram, A. (2020). Multiobjective multi-verse optimization algorithm to solve combined economic, heat and power emission dispatch problems, Applied Soft Computing Journal, 91, 106195; doi: 10.1016/j.asoc.2020.106195
  32. Taha, M. S., Abdeltawab, H. H. and 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
  33. Van, T. P., Snasel, V. and Nguyen, T. T. (2020). Antlion Optimization Algorithm for Optimal Non-Smooth Economic Load Dispatch, International Journal of Electrical and Computer Engineering (IJECE), 10(2), 1187–1199; doi: 10.11591/ijece.v10i2.pp1187-1199
  34. Wang, B., Sechilariu, M. and Locment, F. (2012). Intelligent DC microgrid with smart grid communications: Control strategy consideration and design, IEEE Transactions on Smart Grid, 3(4), 2148–2156; doi: 10.1109/TSG.2012.2217764
  35. Wang, T., He, X. and Deng, T. (2019). Neural Networks for Power Management Optimal Strategy in Hybrid Microgrid, Neural Computing and Applications, 31(7), 2635–2647; doi: 10.1007/s00521-017-3219-x
  36. Wang, Z., Zhu, Q., Huang, M. and 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
  37. Wu, H., Liu, X. and Ding, M. (2014). Dynamic Economic Dispatch of a Microgrid: Mathematical Models and Solution Algorithm, International Journal of Electrical Power & Energy Systems, 63, 336–346; doi: 10.1016/j.ijepes.2014.06.002
  38. Wu, X., Cao, W., Wang, D. and Ding, M. (2019). A Multi-Objective Optimization Dispatch Method for Microgrid Energy Management Considering the Power Loss of Converters, Energies, 12(11), 2160; doi: 10.3390/en12112160
  39. Yuan, X., Zhang, B., Wang, P., Liang, J., Yuan, Y., Huang, Y. and Lei, X. (2017). Multi-Objective Optimal Power Flow based on Improved Strength Pareto Evolutionary Algorithm, Energy, 122, 70–82; doi: 10.1016/j.energy.2017.01.071
  40. Zhang, Q., Ding, J., Shen, W., Ma, J. and Li, G. (2020). Multiobjective Particle Swarm Optimization for Microgrids Pareto Optimization Dispatch, Mathematical Problems in Engineering, 2020; doi: 10.1155/2020/5695917
  41. Zhang, Y., Gong, D. W. and Ding, Z. (2012). A bare-bones multi-objective particle swarm optimization algorithm for environmental/economic dispatch, Information Sciences, 192, 213–227; doi: 10.1016/j.ins.2011.06.004

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