Document Type : Original Article
Author
University of Tirana, Tirana, Albania
10.52293/SE.1.1.259269
Abstract
This paper addresses one of the most important challenges in utilization of renewable energy source the design of a system that incorporates this type of energy sources, which is the size determination of system component. A meta-model of hybrid renewable power system is studied here which can include many sorts of renewable and nonrenewable energy sources as well as energy storage. This system is then optimized in terms of component size to supply the load with minimum overall cost. In addition, a novel optimization algorithm is proposed here named modified marine predators optimization technique which addresses the shortcomings observed in classic and metaheuristic optimization algorithm which are slow convergence, local optima, and immature convergence. Moreover, a real-world case study in an isolated location is analyzed here and a hybrid renewable power system with diesel generator, PV panels, and battery and the proposed optimization algorithm are implemented to determine optimum component size. The results suggest that, due to high investment cost, only a battery with very small capacity is economically advisable. Utilization of the proposed methodology here can help the system designers and operators to increase renewable energy penetration with higher reliability and lower costs.
Keywords
1. Al‐Ghussain, L., Global warming: review on driving forces and mitigation. Environmental Progress & Sustainable Energy, 2019. 38(1): p. 13-21.
2. Ahadi, A., H. Hayati, and S.M.M. Aval, Reliability evaluation of future photovoltaic systems with smart operation strategy. Frontiers in Energy, 2016. 10(2): p. 125-135.
3. Zhang, J., et al., Adaptation to salinity: Response of biogas production and microbial communities in anaerobic digestion of kitchen waste to salinity stress. Journal of Bioscience and Bioengineering, 2020.
4. Aghajani, G. and N. Ghadimi, Multi-objective energy management in a micro-grid. Energy Reports, 2018. 4: p. 218-225.
5. Akbary, P., et al., Extracting appropriate nodal marginal prices for all types of committed reserve. Computational Economics, 2019. 53(1): p. 1-26.
6. SAGALA, R., et al., Environment-friendly education as a solution to against global warming: A case study at Sekolah Alam Lampung, Indonesia. Journal for the Education of Gifted Young Scientists, 2019. 7(2): p. 87-97.
7. Alizadeh, E., et al., Investigation of contact pressure distribution over the active area of PEM fuel cell stack. International Journal of Hydrogen Energy, 2016. 41(4): p. 3062-3071.
8. Patil, B.V., et al., Decentralized nonlinear model predictive control of a multimachine power system. International Journal of Electrical Power & Energy Systems, 2019. 106: p. 358-372.
9. Gollou, A.R. and N. Ghadimi, A new feature selection and hybrid forecast engine for day-ahead price forecasting of electricity markets. Journal of Intelligent & Fuzzy Systems, 2017. 32(6): p. 4031-4045.
10. Hamian, M., et al., A framework to expedite joint energy-reserve payment cost minimization using a custom-designed method based on mixed integer genetic algorithm. Engineering Applications of Artificial Intelligence, 2018. 72: p. 203-212.
11. Cao, Y., et al., Experimental modeling of PEM fuel cells using a new improved seagull optimization algorithm. Energy Reports, 2019. 5: p. 1616-1625.
12. Cao, Y., et al., Multi-objective optimization of a PEMFC based CCHP system by meta-heuristics. Energy Reports, 2019. 5: p. 1551-1559.
13. Hosseini Firouz, M. and N. Ghadimi, Optimal preventive maintenance policy for electric power distribution systems based on the fuzzy AHP methods. Complexity, 2016. 21(6): p. 70-88.
14. Das, S. and A.K. Akella, Power flow control of PV-wind-battery hybrid renewable energy systems for stand-alone application. International Journal of Renewable Energy Research (IJRER), 2018. 8(1): p. 36-43.
15. Singh, R., et al., Multi-objective optimization of hybrid renewable energy system using reformed electric system cascade analysis for islanding and grid connected modes of operation. IEEE Access, 2018. 6: p. 47332-47354.
16. Fan, X., et al., Multi-objective optimization for the proper selection of the best heat pump technology in a fuel cell-heat pump micro-CHP system. Energy Reports, 2020. 6: p. 325-335.
17. Fei, X., R. Xuejun, and N. Razmjooy, Optimal configuration and energy management for combined solar chimney, solid oxide electrolysis, and fuel cell: a case study in Iran. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 2019: p. 1-21.
18. Gong, W. and N. razmjooy, A new optimisation algorithm based on OCM and PCM solution through energy reserve. International Journal of Ambient Energy, 2020: p. 1-14.
19. Guo, Y., et al., An optimal configuration for a battery and PEM fuel cell-based hybrid energy system using developed Krill herd optimization algorithm for locomotive application. Energy Reports, 2020. 6: p. 885-894.
20. Mir, M., et al., Employing a Gaussian Particle Swarm Optimization method for tuning Multi Input Multi Output‐fuzzy system as an integrated controller of a micro‐grid with stability analysis. Computational Intelligence, 2020. 36(1): p. 225-258.
21. Razmjooy, N., V.V. Estrela, and H.J. Loschi, A Study on Metaheuristic-Based Neural Networks for Image Segmentation Purposes, in Data Science. 2019, CRC Press. p. 25-49.
22. Razmjooy, N., V.V. Estrela, and H.J. Loschi, Entropy-Based Breast Cancer Detection in Digital Mammograms Using World Cup Optimization Algorithm. International Journal of Swarm Intelligence Research (IJSIR), 2020. 11(3): p. 1-18.
23. Jafari, M. and Z. Malekjamshidi, Optimal energy management of a residential-based hybrid renewable energy system using rule-based real-time control and 2D dynamic programming optimization method. Renewable Energy, 2020. 146: p. 254-266.
24. Yu, D., et al., System identification of PEM fuel cells using an improved Elman neural network and a new hybrid optimization algorithm. Energy Reports, 2019. 5: p. 1365-1374.
25. Leng, H., et al., A new wind power prediction method based on ridgelet transforms, hybrid feature selection and closed-loop forecasting. Advanced Engineering Informatics, 2018. 36: p. 20-30.
26. Liu, Y., W. Wang, and N. Ghadimi, Electricity load forecasting by an improved forecast engine for building level consumers. Energy, 2017. 139: p. 18-30.
27. Vaccari, M., et al., A sequential linear programming algorithm for economic optimization of hybrid renewable energy systems. Journal of Process Control, 2019. 74: p. 189-201.
28. Nejad, H.C., et al., Reliability based optimal allocation of distributed generations in transmission systems under demand response program. Electric Power Systems Research, 2019. 176: p. 105952.
29. Razmjooy, N., F.R. Sheykhahmad, and N. Ghadimi, A hybrid neural network–world cup optimization algorithm for melanoma detection. Open Medicine, 2018. 13(1): p. 9-16.
30. Song, K., et al., Pontryagin’s minimum principle-based real-time energy management strategy for fuel cell hybrid electric vehicle considering both fuel economy and power source durability. Energy, 2020: p. 118064.
31. Shamel, A. and N. Ghadimi, Hybrid PSOTVAC/BFA technique for tuning of robust PID controller of fuel cell voltage. 2016.
32. Konneh, D.A., et al., A multi-criteria decision maker for grid-connected hybrid renewable energy systems selection using multi-objective particle swarm optimization. Sustainability, 2019. 11(4): p. 1188.
33. Mayer, M.J., A. Szilágyi, and G. Gróf, Environmental and economic multi-objective optimization of a household level hybrid renewable energy system by genetic algorithm. Applied Energy, 2020. 269: p. 115058.
34. Duman, A.C. and Ö. Güler, Techno-economic analysis of off-grid PV/wind/fuel cell hybrid system combinations with a comparison of regularly and seasonally occupied households. Sustainable Cities and Society, 2018. 42: p. 107-126.
35. Ahadi, A., et al., A new method for estimating the longevity and degradation of photovoltaic systems considering weather states. Frontiers in Energy, 2016. 10(3): p. 277-285.
36. Elkadeem, M., et al., A systematic decision-making approach for planning and assessment of hybrid renewable energy-based microgrid with techno-economic optimization: A case study on an urban community in Egypt. Sustainable Cities and Society, 2020. 54: p. 102013.
37. Ramesh, M. and R.P. Saini, Dispatch strategies based performance analysis of a hybrid renewable energy system for a remote rural area in India. Journal of Cleaner Production, 2020: p. 120697.
38. Ghenai, C., et al., Hybrid solar PV/PEM fuel cell/diesel generator power system for cruise ship: a case study in Stockholm, Sweden. Case Studies in Thermal Engineering, 2019. 14: p. 100497.
39. Faramarzi, A., et al., Marine predators algorithm: A nature-inspired Metaheuristic. Expert Systems with Applications, 2020: p. 113377.
40. Yousri, D., et al., A Robust Strategy Based on Marine Predators Algorithm for Large Scale Photovoltaic Array Reconfiguration to Mitigate the Partial Shading Effect on the Performance of PV System. IEEE Access, 2020. 8: p. 112407-112426.
41. Abdel-Basset, M., et al., Energy-Aware Marine Predators Algorithm for Task Scheduling in IoT-based Fog Computing Applications. IEEE Transactions on Industrial Informatics, 2020.
42. Al-Qaness, M.A., et al., Marine Predators Algorithm for Forecasting Confirmed Cases of COVID-19 in Italy, USA, Iran and Korea. International Journal of Environmental Research and Public Health, 2020. 17(10): p. 3520.
43. Ahmed, K., B. Al-Khateeb, and M. Mahmood, Application of chaos discrete particle swarm optimization algorithm on pavement maintenance scheduling problem. Cluster Computing, 2019. 22(2): p. 4647-4657.
2. Ahadi, A., H. Hayati, and S.M.M. Aval, Reliability evaluation of future photovoltaic systems with smart operation strategy. Frontiers in Energy, 2016. 10(2): p. 125-135.
3. Zhang, J., et al., Adaptation to salinity: Response of biogas production and microbial communities in anaerobic digestion of kitchen waste to salinity stress. Journal of Bioscience and Bioengineering, 2020.
4. Aghajani, G. and N. Ghadimi, Multi-objective energy management in a micro-grid. Energy Reports, 2018. 4: p. 218-225.
5. Akbary, P., et al., Extracting appropriate nodal marginal prices for all types of committed reserve. Computational Economics, 2019. 53(1): p. 1-26.
6. SAGALA, R., et al., Environment-friendly education as a solution to against global warming: A case study at Sekolah Alam Lampung, Indonesia. Journal for the Education of Gifted Young Scientists, 2019. 7(2): p. 87-97.
7. Alizadeh, E., et al., Investigation of contact pressure distribution over the active area of PEM fuel cell stack. International Journal of Hydrogen Energy, 2016. 41(4): p. 3062-3071.
8. Patil, B.V., et al., Decentralized nonlinear model predictive control of a multimachine power system. International Journal of Electrical Power & Energy Systems, 2019. 106: p. 358-372.
9. Gollou, A.R. and N. Ghadimi, A new feature selection and hybrid forecast engine for day-ahead price forecasting of electricity markets. Journal of Intelligent & Fuzzy Systems, 2017. 32(6): p. 4031-4045.
10. Hamian, M., et al., A framework to expedite joint energy-reserve payment cost minimization using a custom-designed method based on mixed integer genetic algorithm. Engineering Applications of Artificial Intelligence, 2018. 72: p. 203-212.
11. Cao, Y., et al., Experimental modeling of PEM fuel cells using a new improved seagull optimization algorithm. Energy Reports, 2019. 5: p. 1616-1625.
12. Cao, Y., et al., Multi-objective optimization of a PEMFC based CCHP system by meta-heuristics. Energy Reports, 2019. 5: p. 1551-1559.
13. Hosseini Firouz, M. and N. Ghadimi, Optimal preventive maintenance policy for electric power distribution systems based on the fuzzy AHP methods. Complexity, 2016. 21(6): p. 70-88.
14. Das, S. and A.K. Akella, Power flow control of PV-wind-battery hybrid renewable energy systems for stand-alone application. International Journal of Renewable Energy Research (IJRER), 2018. 8(1): p. 36-43.
15. Singh, R., et al., Multi-objective optimization of hybrid renewable energy system using reformed electric system cascade analysis for islanding and grid connected modes of operation. IEEE Access, 2018. 6: p. 47332-47354.
16. Fan, X., et al., Multi-objective optimization for the proper selection of the best heat pump technology in a fuel cell-heat pump micro-CHP system. Energy Reports, 2020. 6: p. 325-335.
17. Fei, X., R. Xuejun, and N. Razmjooy, Optimal configuration and energy management for combined solar chimney, solid oxide electrolysis, and fuel cell: a case study in Iran. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 2019: p. 1-21.
18. Gong, W. and N. razmjooy, A new optimisation algorithm based on OCM and PCM solution through energy reserve. International Journal of Ambient Energy, 2020: p. 1-14.
19. Guo, Y., et al., An optimal configuration for a battery and PEM fuel cell-based hybrid energy system using developed Krill herd optimization algorithm for locomotive application. Energy Reports, 2020. 6: p. 885-894.
20. Mir, M., et al., Employing a Gaussian Particle Swarm Optimization method for tuning Multi Input Multi Output‐fuzzy system as an integrated controller of a micro‐grid with stability analysis. Computational Intelligence, 2020. 36(1): p. 225-258.
21. Razmjooy, N., V.V. Estrela, and H.J. Loschi, A Study on Metaheuristic-Based Neural Networks for Image Segmentation Purposes, in Data Science. 2019, CRC Press. p. 25-49.
22. Razmjooy, N., V.V. Estrela, and H.J. Loschi, Entropy-Based Breast Cancer Detection in Digital Mammograms Using World Cup Optimization Algorithm. International Journal of Swarm Intelligence Research (IJSIR), 2020. 11(3): p. 1-18.
23. Jafari, M. and Z. Malekjamshidi, Optimal energy management of a residential-based hybrid renewable energy system using rule-based real-time control and 2D dynamic programming optimization method. Renewable Energy, 2020. 146: p. 254-266.
24. Yu, D., et al., System identification of PEM fuel cells using an improved Elman neural network and a new hybrid optimization algorithm. Energy Reports, 2019. 5: p. 1365-1374.
25. Leng, H., et al., A new wind power prediction method based on ridgelet transforms, hybrid feature selection and closed-loop forecasting. Advanced Engineering Informatics, 2018. 36: p. 20-30.
26. Liu, Y., W. Wang, and N. Ghadimi, Electricity load forecasting by an improved forecast engine for building level consumers. Energy, 2017. 139: p. 18-30.
27. Vaccari, M., et al., A sequential linear programming algorithm for economic optimization of hybrid renewable energy systems. Journal of Process Control, 2019. 74: p. 189-201.
28. Nejad, H.C., et al., Reliability based optimal allocation of distributed generations in transmission systems under demand response program. Electric Power Systems Research, 2019. 176: p. 105952.
29. Razmjooy, N., F.R. Sheykhahmad, and N. Ghadimi, A hybrid neural network–world cup optimization algorithm for melanoma detection. Open Medicine, 2018. 13(1): p. 9-16.
30. Song, K., et al., Pontryagin’s minimum principle-based real-time energy management strategy for fuel cell hybrid electric vehicle considering both fuel economy and power source durability. Energy, 2020: p. 118064.
31. Shamel, A. and N. Ghadimi, Hybrid PSOTVAC/BFA technique for tuning of robust PID controller of fuel cell voltage. 2016.
32. Konneh, D.A., et al., A multi-criteria decision maker for grid-connected hybrid renewable energy systems selection using multi-objective particle swarm optimization. Sustainability, 2019. 11(4): p. 1188.
33. Mayer, M.J., A. Szilágyi, and G. Gróf, Environmental and economic multi-objective optimization of a household level hybrid renewable energy system by genetic algorithm. Applied Energy, 2020. 269: p. 115058.
34. Duman, A.C. and Ö. Güler, Techno-economic analysis of off-grid PV/wind/fuel cell hybrid system combinations with a comparison of regularly and seasonally occupied households. Sustainable Cities and Society, 2018. 42: p. 107-126.
35. Ahadi, A., et al., A new method for estimating the longevity and degradation of photovoltaic systems considering weather states. Frontiers in Energy, 2016. 10(3): p. 277-285.
36. Elkadeem, M., et al., A systematic decision-making approach for planning and assessment of hybrid renewable energy-based microgrid with techno-economic optimization: A case study on an urban community in Egypt. Sustainable Cities and Society, 2020. 54: p. 102013.
37. Ramesh, M. and R.P. Saini, Dispatch strategies based performance analysis of a hybrid renewable energy system for a remote rural area in India. Journal of Cleaner Production, 2020: p. 120697.
38. Ghenai, C., et al., Hybrid solar PV/PEM fuel cell/diesel generator power system for cruise ship: a case study in Stockholm, Sweden. Case Studies in Thermal Engineering, 2019. 14: p. 100497.
39. Faramarzi, A., et al., Marine predators algorithm: A nature-inspired Metaheuristic. Expert Systems with Applications, 2020: p. 113377.
40. Yousri, D., et al., A Robust Strategy Based on Marine Predators Algorithm for Large Scale Photovoltaic Array Reconfiguration to Mitigate the Partial Shading Effect on the Performance of PV System. IEEE Access, 2020. 8: p. 112407-112426.
41. Abdel-Basset, M., et al., Energy-Aware Marine Predators Algorithm for Task Scheduling in IoT-based Fog Computing Applications. IEEE Transactions on Industrial Informatics, 2020.
42. Al-Qaness, M.A., et al., Marine Predators Algorithm for Forecasting Confirmed Cases of COVID-19 in Italy, USA, Iran and Korea. International Journal of Environmental Research and Public Health, 2020. 17(10): p. 3520.
43. Ahmed, K., B. Al-Khateeb, and M. Mahmood, Application of chaos discrete particle swarm optimization algorithm on pavement maintenance scheduling problem. Cluster Computing, 2019. 22(2): p. 4647-4657.
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