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Energy Management Strategy Based on Marine Predators Algorithm for Grid-Connected Microgrid

Electrical Engineering Department, ECP3M Laboratory, Abdelhamid Ibn Badis University of Mostaganem, 27000 Mostaganem, Algeria

Received: 20 Nov 2021; Revised: 14 Apr 2022; Accepted: 26 Apr 2022; Available online: 8 May 2022; Published: 4 Aug 2022.
Editor(s): H. Hadiyanto
Open Access Copyright (c) 2022 The Author(s). 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 work aims to optimize the economic dispatch problem of a microgrid system in order to cover the load of a commercial building in Algeria. The analyzed microgrid system is connected to the power grid and composed of photovoltaic panels (PV), wind turbine, battery energy storage system (BESS) and diesel generator. To ensure energy balance and the flow of energy, we have implemented an energy management strategy based on Marine Predator Algorithm (MPA) and Multilayer Perceptron Neural Network (MLPNN), which guarantee an optimal economic operation of the system. First, using historical meteorological data, the power generation is forecasted a day-ahead using MLPNN, which allows the optimization of the microgrid operation. Second, the proposed strategy has been studied under three different microgrid configurations. Eventually, the performances of MPA are compared against well-known algorithms. The results indicate that the integration of the PV-BESS microgrid system significantly reduces the daily operating cost up to 34.5%. Due to the availability of wind resources in the studied area, the addition of a wind turbine to the microgrid minimizes the operating cost by 43.96% compared to the operating cost of the power grid. In the case of selling excess energy to the main power grid, the operating cost could be decreased as much as 49.33%.

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Keywords: Hybrid system; Photovoltaic; Wind system; Back propagation algorithm; Artificial intelligence; Deep Learning

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  1. Abhishek, K., Singh, M. P., Ghosh, S., & Anand, A. (2012). Weather Forecasting Model using Artificial Neural Network. Procedia Technology, 4, 311-318. doi: https://doi.org/10.1016/j.protcy.2012.05.047
  2. Adrian Whiteman, S. R., Dennis Akande, Nazik Elhassan, Gerardo Escamilla and Iana Arkhipova, Renewable capacity statistics 2020 International Renewable Energy Agency (IRENA). 2020: Abu Dhabi. p. 66
  3. Al-Zoubi, H., Al-Khasawneh, Y., & Omar, W. (2021). Design and feasibility study of an on-grid photovoltaic system for green electrification of hotels: a case study of Cedars hotel in Jordan. International Journal of Energy and Environmental Engineering. doi: 10.1007/s40095-021-00406-z
  4. Arcos-Aviles, D., Pascual, J., Marroyo, L., Sanchis, P., & Guinjoan, F. (2018). Fuzzy Logic-Based Energy Management System Design for Residential Grid-Connected Microgrids. IEEE Transactions on Smart Grid, 9(2), 530-543. doi: 10.1109/TSG.2016.2555245
  5. Beccali, M., Cellura, M., Lo Brano, V., & Marvuglia, A. (2004). Forecasting daily urban electric load profiles using artificial neural networks. Energy Conversion and Management, 45(18), 2879-2900. doi: https://doi.org/10.1016/j.enconman.2004.01.006
  6. Bochenek, B., Jurasz, J., Jaczewski, A., Stachura, G., Sekuła, P., Strzyżewski, T., . . . Figurski, M. (2021). Day-Ahead Wind Power Forecasting in Poland Based on Numerical Weather Prediction. Energies, 14(8). doi: 10.3390/en14082164
  7. Brenna, M., Corradi, A., Foiadelli, F., Longo, M., & Yaici, W. (2020). Numerical simulation analysis of the impact of photovoltaic systems and energy storage technologies on centralised generation: a case study for Australia. International Journal of Energy and Environmental Engineering, 11(1), 9-31. doi: 10.1007/s40095-019-00330-3
  8. Canales, F. A., Jurasz, J. K., Guezgouz, M., & Beluco, A. (2021). Cost-reliability analysis of hybrid pumped-battery storage for solar and wind energy integration in an island community. Sustainable Energy Technologies and Assessments, 44, 101062. doi: https://doi.org/10.1016/j.seta.2021.101062
  9. Clarke, D. P., Al-Abdeli, Y. M., & Kothapalli, G. (2013). The impact of renewable energy intermittency on the operational characteristics of a stand-alone hydrogen generation system with on-site water production. International Journal of Hydrogen Energy, 38(28), 12253-12265. doi: https://doi.org/10.1016/j.ijhydene.2013.07.031
  10. Copernicus Atmosphere Monitoring Service CAMS, radiation service. Retrieved from http://atmosphere.copernicus.eu/
  11. Crow, M., Gamage, T. T., Liu, Y., Nguyen, T. A., Qiu, X ., McMillin, B. M. (2015). A novel flow invariants-based approach to microgrid management. IEEE Trans. Smart Grid, 6(2), 516-525. doi: 10.1109/TSG.2014.2375064
  12. Dahmoun, M. E.-H., Bekkouche, B., Sudhakar, K., Guezgouz, M., Chenafi, A., & Chaouch, A. (2021). Performance evaluation and analysis of grid-tied large scale PV plant in Algeria. Energy for Sustainable Development, 61, 181-195. doi: https://doi.org/10.1016/j.esd.2021.02.004
  13. Dong, X., Li, X., & Cheng, S. (2020). Energy Management Optimization of Microgrid Cluster Based on Multi-Agent-System and Hierarchical Stackelberg Game Theory. IEEE Access, 8, 206183-206197. doi: 10.1109/ACCESS.2020.3037676
  14. Eid, A., Kamel, S., & Abualigah, L. (2021). Marine predators algorithm for optimal allocation of active and reactive power resources in distribution networks. Neural Computing and Applications, 33(21), 14327-14355. doi: 10.1007/s00521-021-06078-4
  15. Faramarzi, A., Heidarinejad, M., Mirjalili, S., & Gandomi, A. H. (2020). Marine Predators Algorithm: A nature-inspired metaheuristic. Expert Systems with Applications, 152, 113377. doi: https://doi.org/10.1016/j.eswa.2020.113377
  16. Global Modeling and Assimilation Office (GMAO) (2015). Retrieved from http://www.soda-pro.com/web-services/meteo-data/merra
  17. Guezgouz, M., Jurasz, J., & Bekkouche, B. (2019). Techno-Economic and Environmental Analysis of a Hybrid PV-WT-PSH/BB Standalone System Supplying Various Loads. Energies, 12(3). doi: 10.3390/en12030514
  18. Guezgouz, M., Jurasz, J., Bekkouche, B., Ma, T., Javed, M. S., & Kies, A. (2019). Optimal hybrid pumped hydro-battery storage scheme for off-grid renewable energy systems. Energy Conversion and Management, 199, 112046. doi: https://doi.org/10.1016/j.enconman.2019.112046
  19. Guezgouz, M., Jurasz, J., Chouai, M., Bloomfield, H., & Bekkouche, B. (2021). Assessment of solar and wind energy complementarity in Algeria. Energy Conversion and Management, 238, 114170. doi: https://doi.org/10.1016/j.enconman.2021.114170
  20. Hajer, M. A., & Pelzer, P. (2018). 2050—An Energetic Odyssey: Understanding ‘Techniques of Futuring’ in the transition towards renewable energy. Energy Research & Social Science, 44, 222-231. doi: https://doi.org/10.1016/j.erss.2018.01.013
  21. He, L., Wei, Z., Yan, H., Xv, K., Zhao, M., & Cheng, S. (2019, 6-9 Sept. 2019). A Day-ahead Scheduling Optimization Model of Multi-Microgrid Considering Interactive Power Control. Paper presented at the 2019 4th International Conference on Intelligent Green Building and Smart Grid (IGBSG)
  22. Iqbal, T., Khitab, Z., Girbau, F., & Sumper, A. (2018). Energy Management System for Optimal Operatioin of Microgrids Network. Paper presented at the 2018 IEEE International Conference on Smart Energy Grid Engineering (SEGE)
  23. Iqbal, Z., Javaid, N., Iqbal, S., Aslam, S., Khan, Z. A., Abdul, W., . . . Alamri, A. (2018). A Domestic Microgrid with Optimized Home Energy Management System. Energies, 11(4). doi: 10.3390/en11041002
  24. Jurasz, J., & Ciapała, B. (2017). Integrating photovoltaics into energy systems by using a run-off-river power plant with pondage to smooth energy exchange with the power gird. Applied Energy, 198, 21-35. doi: https://doi.org/10.1016/j.apenergy.2017.04.042
  25. Kaabeche, A., Diaf, S., & Ibtiouen, R. (2017). Firefly-inspired algorithm for optimal sizing of renewable hybrid system considering reliability criteria. Solar Energy, 155, 727-738. doi: https://doi.org/10.1016/j.solener.2017.06.070
  26. Karthik, N., Parvathy, A. K., Arul, R., & Padmanathan, K. (2021). Multi-objective optimal power flow using a new heuristic optimization algorithm with the incorporation of renewable energy sources. International Journal of Energy and Environmental Engineering. doi: 10.1007/s40095-021-00397-x
  27. Kilickaplan, A., Dmitrii, B ., Onur, P., Upeksha, C., Arman, A., Christian, B. (2017). An energy transition pathway for Turkey to achieve 100% renewable energy powered electricity, desalination and non-energetic industrial gas demand sectors by 2050. Solar Energy, 158, 218-235
  28. Liu, H., Yu, C., Yu, C., Chen, C., & Wu, H. (2020). A novel axle temperature forecasting method based on decomposition, reinforcement learning optimization and neural network. Advanced Engineering Informatics, 44, 101089. doi: https://doi.org/10.1016/j.aei.2020.101089
  29. Luu Ngoc, A., & Tran, Q.-T. (2015, 26-30 July 2015). Optimal energy management for grid connected microgrid by using dynamic programming method. Paper presented at the 2015 IEEE Power & Energy Society General Meeting
  30. Mellit, A., Pavan, A. M., & Lughi, V. (2021). Deep learning neural networks for short-term photovoltaic power forecasting. Renewable Energy, 172, 276-288. doi: https://doi.org/10.1016/j.renene.2021.02.166
  31. Moran, B. (2016). Microgrid load management and control strategies. Paper presented at the 2016 IEEE/PES Transmission and Distribution Conference and Exposition (T&D)
  32. Nezhad, M.M, Heydari, A., Groppi, D., Cumo, F., & Astiaso Garcia, D. (2020). Wind source potential assessment using Sentinel 1 satellite and a new forecasting model based on machine learning: A case study Sardinia islands. Renewable Energy, 155, 212-224. doi: https://doi.org/10.1016/j.renene.2020.03.148
  33. Naeem, A., & Hassan, N. U. (2020, 7-9 March 2020). Renewable Energy Intermittency Mitigation in Microgrids: State-of-the-Art and Future Prospects. Paper presented at the 2020 4th International Conference on Green Energy and Applications (ICGEA)
  34. Neto, P. J. d. S., Barros, T. A. d. S., Silveira, J. P. C., Filho, E. R., Vasquez, J. C., & Guerrero, J. M. (2020). Power Management Strategy Based on Virtual Inertia for DC Microgrids. IEEE Transactions on Power Electronics, 35(11), 12472-12485. doi: 10.1109/TPEL.2020.2986283
  35. Nimma, K. S., Al-Falahi, M. D. A., Nguyen, H. D ., Jayasinghe, S. D. G., Mahmoud, T. S., & Negnevitsky, M. (2018). Grey Wolf Optimization-Based Optimum Energy-Management and Battery-Sizing Method for Grid-Connected Microgrids. Energies, 11(4). doi: 10.3390/en11040847
  36. Peng, J., Fan, B., & Liu, W. (2021). Voltage-Based Distributed Optimal Control for Generation Cost Minimization and Bounded Bus Voltage Regulation in DC Microgrids. IEEE Transactions on Smart Grid, 12(1), 106-116. doi: 10.1109/TSG.2020.3013303
  37. Shaheen, M. A. M., Yousri, D., Fathy, A., Hasanien, H. M., Alkuhayli, A., & Muyeen, S. M. (2020). A Novel Application of Improved Marine Predators Algorithm and Particle Swarm Optimization for Solving the ORPD Problem. Energies, 13(21). doi: 10.3390/en13215679
  38. Sobhy, M. A., Abdelaziz, A. Y., Hasanien, H. M., & Ezzat, M. (2021). Marine predators algorithm for load frequency control of modern interconnected power systems including renewable energy sources and energy storage units. Ain Shams Engineering Journal. doi: https://doi.org/10.1016/j.asej.2021.04.031
  39. Sonelgaz. Available online: http://www.sonelgaz.dz/ [accessed on 2020, A
  40. Stambouli, A. B., Khiat, Z., Flazi, S., & Kitamura, Y. (2012). A review on the renewable energy development in Algeria: Current perspective, energy scenario and sustainability issues. Renewable and Sustainable Energy Reviews, 16(7), 4445-4460. doi: https://doi.org/10.1016/j.rser.2012.04.031
  41. Suberu, M.Y., Mustafa, M.W., & Bashir, N. (2014). Energy storage systems for renewable energy power sector integration and mitigation of intermittency. Renewable and Sustainable Energy Reviews, 35, 499-514. doi: https://doi.org/10.1016/j.rser.2014.04.009
  42. Tang, X., Deng, W., & Qi, Z. (2014). Investigation of the Dynamic Stability of Microgrid. IEEE Transactions on Power Systems, 29(2), 698-706. doi: 10.1109/TPWRS.2013.2285585
  43. Tayab, U. B., Yang, F., El-Hendawi, M., & Lu, J. (2018, 7-8 Dec. 2018). Energy Management System for a Grid-Connected Microgrid with Photovoltaic and Battery Energy Storage System. Paper presented at the 2018 Australian & New Zealand Control Conference (ANZCC)
  44. Theocharides, S., Venizelou, V., Makrides, G., & Georghiou, G. E. (2018, 10-15 June 2018). Day-ahead Forecasting of Solar Power Output from Photovoltaic Systems Utilising Gradient Boosting Machines. Paper presented at the 2018 IEEE 7th World Conference on Photovoltaic Energy Conversion (WCPEC) (A Joint Conference of 45th IEEE PVSC, 28th PVSEC & 34th EU PVSEC)
  45. Ton, D. T., & Smith, M. A. (2012). The U.S. Department of Energy's Microgrid Initiative. The Electricity Journal, 25(8), 84-94. doi: https://doi.org/10.1016/j.tej.2012.09.013
  46. Tran, T. T., Bateni, S. M., Ki, S. J., & Vosoughifar, H. (2021). A Review of Neural Networks for Air Temperature Forecasting. Water, 13(9). doi: 10.3390/w13091294
  47. Vergara, P. P., Torquato, R., & Silva, L. C. P. d. (2015). Towards a real-time Energy Management System for a Microgrid using a multi-objective genetic algorithm. Paper presented at the 2015 IEEE Power & Energy Society General Meeting
  48. Wang, S., Su, L., & Zhang, J. (2017). MPI based PSO algorithm for the optimization problem in micro-grid energy management system. Paper presented at the 2017 Chinese Automation Congress (CAC)
  49. Zhai, M., Yajie, L., Tao, Z; Yan,Z . (2017). Robust model predictive control for energy management of isolated microgrids. in 2017 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)
  50. Zhai, M., Liu, Y., Zhang, T., & Zhang, Y. (2017). Robust model predictive control for energy management of isolated microgrids. Paper presented at the 2017 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)

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