Document Type : Original Article
Authors
University of Tirana, Tirana, Albania
10.52293/SE.1.1.349363
Abstract
In this paper, a new technical and economic analysis for a hybrid system of energy has been performed. The study presents a new procedure for optimal modeling of the hybrid renewable energy system (HRES). The main idea is to optimize the hybrid system configuration based on a multiple-criteria optimization, including three objectives. To simplify the problem and turning it to a single objective problem of optimization, ε-constraints technique has been used. This optimization is done by minimizing the capital cost (CC) and maximizing the electrical power effectiveness and the power supply consistency. To provide a well solution, a new amended design of the rain optimizer has been employed. The method provided a pareto solution with three groups that can be selected based on the decision maker’s purpose.
Keywords
1. Aghajani, G. and N. Ghadimi, Multi-objective energy management in a micro-grid. Energy Reports, 2018. 4: p. 218-225.
2. Akbary, P., et al., Extracting appropriate nodal marginal prices for all types of committed reserve. Computational Economics, 2019. 53(1): p. 1-26.
3. Bagheri, M., et al. A novel wind power forecasting based feature selection and hybrid forecast engine bundled with honey bee mating optimization. in 2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe (EEEIC/I&CPS Europe). 2018. IEEE.
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7. Eslami, M., et al., A New Formulation to Reduce the Number of Variables and Constraints to Expedite SCUC in Bulky Power Systems. Proceedings of the National Academy of Sciences, India Section A: Physical Sciences, 2018: p. 1-11.
8. Cao, Y., et al., Multi-objective optimization of a PEMFC based CCHP system by meta-heuristics. Energy Reports, 2019.
9. Fan, X., et al., High voltage gain DC/DC converter using coupled inductor and VM techniques. IEEE Access, 2020. 8: p. 131975-131987.
10. Tian, M.-W., et al., New optimal design for a hybrid solar chimney, solid oxide electrolysis and fuel cell based on improved deer hunting optimization algorithm. Journal of Cleaner Production, 2020. 249: p. 119414.
11. Firouz, M.H. and N. Ghadimi, Concordant controllers based on FACTS and FPSS for solving wide-area in multi-machine power system. Journal of Intelligent & Fuzzy Systems, 2016. 30(2): p. 845-859.
12. Gao, W., et al., Different states of multi-block based forecast engine for price and load prediction. International Journal of Electrical Power & Energy Systems, 2019. 104: p. 423-435.
13. Yang, Z., et al., Model Parameter Estimation of the PEMFCs Using Improved Barnacles Mating Optimization Algorithm. Energy, 2020: p. 118738.
14. Ye, H., et al., High step-up interleaved dc/dc converter with high efficiency. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 2020: p. 1-20.
15. Yuan, Z., et al., Probabilistic decomposition-based security constrained transmission expansion planning incorporating distributed series reactor. IET Generation, Transmission & Distribution, 2020. 14(17): p. 3478-3487.
16. 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.
17. 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.
18. Khodaei, H., et al., Fuzzy-based heat and power hub models for cost-emission operation of an industrial consumer using compromise programming. Applied Thermal Engineering, 2018. 137: p. 395-405.
19. 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.
20. Liu, J., et al., An IGDT-based risk-involved optimal bidding strategy for hydrogen storage-based intelligent parking lot of electric vehicles. Journal of Energy Storage, 2020. 27: p. 101057.
21. Yin, Z. and N. Razmjooy, PEMFC identification using deep learning developed by improved deer hunting optimization algorithm. Int J Power Energy Syst, 2020. 40(2): p. 189-203.
22. Ramezani, M., et al., A new optimal energy management strategy based on improved multi-objective antlion optimization algorithm: applications in smart home. SN Applied Sciences, 2020. 2(12): p. 1-17.
23. Yuan, Z., et al., A new technique for optimal estimation of the circuit-based PEMFCs using developed Sunflower Optimization Algorithm. Energy Reports, 2020. 6: p. 662-671.
24. Zhi, Y., et al., New approaches for regulation of solid oxide fuel cell using dynamic condition approximation and STATCOM. International Transactions on Electrical Energy Systems, 2021. 31(2): p. e12756.
25. Sun, L., et al., Exergy analysis of a fuel cell power system and optimizing it with Fractional-order Coyote Optimization Algorithm. Energy Reports, 2021. 7: p. 7424-7433.
26. Fei, X., et al., 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.
27. 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.
28. Mahdinia, S., et al., Optimization of PEMFC Model Parameters Using Meta-Heuristics. Sustainability, 2021. 13(22): p. 12771.
29. Cao, Y., et al., Experimental modeling of PEM fuel cells using a new improved seagull optimization algorithm. Energy Reports, 2019. 5: p. 1616-1625.
30. 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.
31. Khan, M.J. and L. Mathew, Fuzzy logic controller-based MPPT for hybrid photo-voltaic/wind/fuel cell power system. Neural Computing and Applications, 2019. 31(10): p. 6331-6344.
32. Bigdeli, N., Optimal management of hybrid PV/fuel cell/battery power system: a comparison of optimal hybrid approaches. Renewable and Sustainable Energy Reviews, 2015. 42: p. 377-393.
33. Jahannoosh, M., et al., New hybrid meta-heuristic algorithm for reliable and cost-effective designing of photovoltaic/wind/fuel cell energy system considering load interruption probability. Journal of Cleaner Production, 2021. 278: p. 123406.
34. Chen, H., et al., Multi-objective optimization of the hybrid wind/solar/fuel cell distributed generation system using Hammersley Sequence Sampling. International Journal of Hydrogen Energy, 2017. 42(12): p. 7836-7846.
35. Nureddin, A.A.M., J. Rahebi, and A. Ab-BelKhair, Power management controller for microgrid integration of hybrid PV/fuel cell system based on artificial deep neural network. International Journal of Photoenergy, 2020. 2020.
36. Li, Y., et al., A Kriging-based bi-objective constrained optimization method for fuel economy of hydrogen fuel cell vehicle. International Journal of Hydrogen Energy, 2019. 44(56): p. 29658-29670.
37. Ramezani, M., et al., A New Improved Model of Marine Predator Algorithm for Optimization Problems. Arabian Journal for Science and Engineering, 2021: p. 1-24.
38. Razmjooy, N., M. Ashourian, and Z. Foroozandeh, Metaheuristics and Optimization in Computer and Electrical Engineering. Springer.
39. Zhao, W., L. Wang, and Z. Zhang, Supply-demand-based optimization: a novel economics-inspired algorithm for global optimization. IEEE Access, 2019. 7: p. 73182-73206.
40. Kaveh, A., M. Khanzadi, and M.R. Moghaddam. Billiards-inspired optimization algorithm; a new meta-heuristic method. in Structures. 2020. Elsevier.
41. Cuevas, E., F. Fausto, and A. González, The Locust Swarm Optimization Algorithm, in New Advancements in Swarm Algorithms: Operators and Applications. 2020, Springer. p. 139-159.
42. Moazzeni, A.R. and E. Khamehchi, Rain optimization algorithm (ROA): A new metaheuristic method for drilling optimization solutions. Journal of Petroleum Science and Engineering, 2020. 195: p. 107512.
43. Wang, C., Modeling and control of hybrid wind/photovoltaic/fuel cell distributed generation systems. 2006: Montana State University.
2. Akbary, P., et al., Extracting appropriate nodal marginal prices for all types of committed reserve. Computational Economics, 2019. 53(1): p. 1-26.
3. Bagheri, M., et al. A novel wind power forecasting based feature selection and hybrid forecast engine bundled with honey bee mating optimization. in 2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe (EEEIC/I&CPS Europe). 2018. IEEE.
4. Cai, W., et al., Optimal bidding and offering strategies of compressed air energy storage: A hybrid robust-stochastic approach. Renewable Energy, 2019. 143: p. 1-8.
5. Dehghani, M., et al., Blockchain-based securing of data exchange in a power transmission system considering congestion management and social welfare. Sustainability, 2021. 13(1): p. 90.
6. Ebrahimian, H., et al., The price prediction for the energy market based on a new method. Economic research-Ekonomska istraživanja, 2018. 31(1): p. 313-337.
7. Eslami, M., et al., A New Formulation to Reduce the Number of Variables and Constraints to Expedite SCUC in Bulky Power Systems. Proceedings of the National Academy of Sciences, India Section A: Physical Sciences, 2018: p. 1-11.
8. Cao, Y., et al., Multi-objective optimization of a PEMFC based CCHP system by meta-heuristics. Energy Reports, 2019.
9. Fan, X., et al., High voltage gain DC/DC converter using coupled inductor and VM techniques. IEEE Access, 2020. 8: p. 131975-131987.
10. Tian, M.-W., et al., New optimal design for a hybrid solar chimney, solid oxide electrolysis and fuel cell based on improved deer hunting optimization algorithm. Journal of Cleaner Production, 2020. 249: p. 119414.
11. Firouz, M.H. and N. Ghadimi, Concordant controllers based on FACTS and FPSS for solving wide-area in multi-machine power system. Journal of Intelligent & Fuzzy Systems, 2016. 30(2): p. 845-859.
12. Gao, W., et al., Different states of multi-block based forecast engine for price and load prediction. International Journal of Electrical Power & Energy Systems, 2019. 104: p. 423-435.
13. Yang, Z., et al., Model Parameter Estimation of the PEMFCs Using Improved Barnacles Mating Optimization Algorithm. Energy, 2020: p. 118738.
14. Ye, H., et al., High step-up interleaved dc/dc converter with high efficiency. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 2020: p. 1-20.
15. Yuan, Z., et al., Probabilistic decomposition-based security constrained transmission expansion planning incorporating distributed series reactor. IET Generation, Transmission & Distribution, 2020. 14(17): p. 3478-3487.
16. 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.
17. 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.
18. Khodaei, H., et al., Fuzzy-based heat and power hub models for cost-emission operation of an industrial consumer using compromise programming. Applied Thermal Engineering, 2018. 137: p. 395-405.
19. 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.
20. Liu, J., et al., An IGDT-based risk-involved optimal bidding strategy for hydrogen storage-based intelligent parking lot of electric vehicles. Journal of Energy Storage, 2020. 27: p. 101057.
21. Yin, Z. and N. Razmjooy, PEMFC identification using deep learning developed by improved deer hunting optimization algorithm. Int J Power Energy Syst, 2020. 40(2): p. 189-203.
22. Ramezani, M., et al., A new optimal energy management strategy based on improved multi-objective antlion optimization algorithm: applications in smart home. SN Applied Sciences, 2020. 2(12): p. 1-17.
23. Yuan, Z., et al., A new technique for optimal estimation of the circuit-based PEMFCs using developed Sunflower Optimization Algorithm. Energy Reports, 2020. 6: p. 662-671.
24. Zhi, Y., et al., New approaches for regulation of solid oxide fuel cell using dynamic condition approximation and STATCOM. International Transactions on Electrical Energy Systems, 2021. 31(2): p. e12756.
25. Sun, L., et al., Exergy analysis of a fuel cell power system and optimizing it with Fractional-order Coyote Optimization Algorithm. Energy Reports, 2021. 7: p. 7424-7433.
26. Fei, X., et al., 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.
27. 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.
28. Mahdinia, S., et al., Optimization of PEMFC Model Parameters Using Meta-Heuristics. Sustainability, 2021. 13(22): p. 12771.
29. Cao, Y., et al., Experimental modeling of PEM fuel cells using a new improved seagull optimization algorithm. Energy Reports, 2019. 5: p. 1616-1625.
30. 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.
31. Khan, M.J. and L. Mathew, Fuzzy logic controller-based MPPT for hybrid photo-voltaic/wind/fuel cell power system. Neural Computing and Applications, 2019. 31(10): p. 6331-6344.
32. Bigdeli, N., Optimal management of hybrid PV/fuel cell/battery power system: a comparison of optimal hybrid approaches. Renewable and Sustainable Energy Reviews, 2015. 42: p. 377-393.
33. Jahannoosh, M., et al., New hybrid meta-heuristic algorithm for reliable and cost-effective designing of photovoltaic/wind/fuel cell energy system considering load interruption probability. Journal of Cleaner Production, 2021. 278: p. 123406.
34. Chen, H., et al., Multi-objective optimization of the hybrid wind/solar/fuel cell distributed generation system using Hammersley Sequence Sampling. International Journal of Hydrogen Energy, 2017. 42(12): p. 7836-7846.
35. Nureddin, A.A.M., J. Rahebi, and A. Ab-BelKhair, Power management controller for microgrid integration of hybrid PV/fuel cell system based on artificial deep neural network. International Journal of Photoenergy, 2020. 2020.
36. Li, Y., et al., A Kriging-based bi-objective constrained optimization method for fuel economy of hydrogen fuel cell vehicle. International Journal of Hydrogen Energy, 2019. 44(56): p. 29658-29670.
37. Ramezani, M., et al., A New Improved Model of Marine Predator Algorithm for Optimization Problems. Arabian Journal for Science and Engineering, 2021: p. 1-24.
38. Razmjooy, N., M. Ashourian, and Z. Foroozandeh, Metaheuristics and Optimization in Computer and Electrical Engineering. Springer.
39. Zhao, W., L. Wang, and Z. Zhang, Supply-demand-based optimization: a novel economics-inspired algorithm for global optimization. IEEE Access, 2019. 7: p. 73182-73206.
40. Kaveh, A., M. Khanzadi, and M.R. Moghaddam. Billiards-inspired optimization algorithm; a new meta-heuristic method. in Structures. 2020. Elsevier.
41. Cuevas, E., F. Fausto, and A. González, The Locust Swarm Optimization Algorithm, in New Advancements in Swarm Algorithms: Operators and Applications. 2020, Springer. p. 139-159.
42. Moazzeni, A.R. and E. Khamehchi, Rain optimization algorithm (ROA): A new metaheuristic method for drilling optimization solutions. Journal of Petroleum Science and Engineering, 2020. 195: p. 107512.
43. Wang, C., Modeling and control of hybrid wind/photovoltaic/fuel cell distributed generation systems. 2006: Montana State University.
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