Document Type : Original Article
Authors
1 School of Management, Universiti Sains Malaysia, Minden, Penang 11800, Malaysia
2 School of Economics, Anyang Normal University, Anyang, 455000, China
10.52293/SE.1.1.183195
Abstract
A hybrid PV/battery/DG energy production system is configured and optimized in this study for powering a network of a remote rural of china. The target of system design is to minimize the system’s fuel costs subject to the load demanded (LD) and several limitations. Thus, the concept comprises a problem of optimization which has been solved by a new optimization method using the Sequential Quadratic Programming (SQP) and a modified version of Sparrow Search Optimizer (MSSO). The achievements of the suggested technique are evaluated in various seasons and also in weekends and weekdays to indicate their impact on the operating cost of the PV/Diesel BESS system. The achievements show that in summer and winter, the costs of weekday are lower toward costs of weekend fuel. Moreover, the fuel cost of summer is lower than the fuel costs of winter that is due to lower demand in summer and also the more summer radiation levels mean less usage of auxiliary sources.
Keywords
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40. Hove, T. and H. Tazvinga, A techno-economic model for optimising component sizing and energy dispatch strategy for PV-diesel-battery hybrid power systems. Journal of Energy in Southern Africa, 2012. 23(4): p. 18-28.
2. 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.
3. TARAFDAR, H.M. and N. GHADIMI, Radial basis neural network based islanding detection in distributed generation. 2014.
4. Shamel, A. and N. Ghadimi, Hybrid PSOTVAC/BFA technique for tuning of robust PID controller of fuel cell voltage. 2016.
5. 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.
6. 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.
7. Mirzapour, F., et al., A new prediction model of battery and wind-solar output in hybrid power system. Journal of Ambient Intelligence and Humanized Computing, 2019. 10(1): p. 77-87.
8. Hosseini, H., et al., A novel method using imperialist competitive algorithm (ICA) for controlling pitch angle in hybrid wind and PV array energy production system. International Journal on Technical and Physical Problems of Engineering (IJTPE), 2012(11): p. 145-152.
9. 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.
10. Cao, Y., et al., Multi-objective optimization of a PEMFC based CCHP system by meta-heuristics. Energy Reports, 2019. 5: p. 1551-1559.
11. Meng, Q., et al., A single-phase transformer-less grid-tied inverter based on switched capacitor for PV application. Journal of Control, Automation and Electrical Systems, 2020. 31(1): p. 257-270.
12. 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.
13. Thiagarajan, Y., et al., DIGITAL GARBAGE BIN MONITORING SYSTEM (DGBMS): A SMART GARBAGE MONITORING AND MANAGEMENT CYBER-PHYSICAL SYSTEM. database. 3: p. 4.
14. Razmjooy, N., et al., Comparison of lqr and pole placement design controllers for controlling the inverted pendulum. Journal of World’s Electrical Engineering and Technology, 2014. 2322: p. 5114.
15. 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.
16. 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.
17. 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.
18. Hagh, M.T., H. Ebrahimian, and N. Ghadimi, Hybrid intelligent water drop bundled wavelet neural network to solve the islanding detection by inverter-based DG. Frontiers in Energy, 2015. 9(1): p. 75-90.
19. 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.
20. Ghadimi, N., et al., A new prediction model based on multi-block forecast engine in smart grid. Journal of Ambient Intelligence and Humanized Computing, 2018. 9(6): p. 1873-1888.
21. Xu, Z., Sheykhahmad, et al., Computer-aided diagnosis of skin cancer based on soft computing techniques. Open Medicine, 2020. 15(1): p. 11.
22. Namadchian, A., M. Ramezani, and N. Razmjooy, A new meta-heuristic algorithm for optimization based on variance reduction of guassian distribution. Majlesi Journal of Electrical Engineering, 2016. 10(4): p. 49.
23. Liu, Q., et al., Computer-aided breast cancer diagnosis based on image segmentation and interval analysis. Automatika, 2020. 61(3): p. 496-506.
24. Khalilpour, R., et al. Optimal control of DC motor using invasive weed optimization (IWO) algorithm. in Majlesi Conference on Electrical Engineering, Majlesi New Town, Isfahan, Iran. 2011.
25. 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.
26. do Nascimento, D.A., et al., Sustainable adoption of connected vehicles in the Brazilian landscape: policies, technical specifications and challenges. Transactions on Environment and Electrical Engineering, 2019. 3(1): p. 44-62.
27. 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.
28. Yin, Z. and N. Razmjooy, PEMFC identification using deep learning developed by improved deer hunting optimization algorithm. International Journal of Power and Energy Systems, 2020. 40(2).
29. Yanda, L., et al., Optimal Arrangement of a Micro-CHP System in the Presence of Fuel Cell-Heat Pump based on Metaheuristics. International Journal of Ambient Energy, 2020(just-accepted): p. 1-24.
30. Fodhil, F., A. Hamidat, and O. Nadjemi, Potential, optimization and sensitivity analysis of photovoltaic-diesel-battery hybrid energy system for rural electrification in Algeria. Energy, 2019. 169: p. 613-624.
31. Rezk, H., et al., Optimization and Energy Management of Hybrid Photovoltaic-Diesel-Battery System to Pump and Desalinate Water at Isolated Regions. IEEE Access, 2020. 8: p. 102512-102529.
32. Ndwali, P.K., J.G. Njiri, and E.M. Wanjiru, Optimal Operation Control of Microgrid Connected Photovoltaic-Diesel Generator Backup System Under Time of Use Tariff. Journal of Control, Automation and Electrical Systems, 2020: p. 1-14.
33. Sanusi, Y.S., H. Dandajeh, and H. Mustapha, Techno-economic analysis of hybrid diesel generator/PV/battery power system for telecommunication application. ATBU Journal of Science, Technology and Education, 2020. 8(3): p. 77-91.
34. Cai, W., et al., Optimal sizing and location based on economic parameters for an off-grid application of a hybrid system with photovoltaic, battery and diesel technology. Energy, 2020: p. 117480.
35. Ali, E., S. Abd Elazim, and A. Abdelaziz, Ant Lion Optimization Algorithm for optimal location and sizing of renewable distributed generations. Renewable Energy, 2017. 101: p. 1311-1324.
36. Collares-Pereira, M. and A. Rabl, The average distribution of solar radiation-correlations between diffuse and hemispherical and between daily and hourly insolation values. Solar energy, 1979. 22(2): p. 155-164.
37. Xue, J. and B. Shen, A novel swarm intelligence optimization approach: sparrow search algorithm. Systems Science & Control Engineering, 2020. 8(1): p. 22-34.
38. Zhang, G., et al., Optimal Parameter Extraction of PEM Fuel Cells by Meta-heuristics. International Journal of Ambient Energy, 2020(just-accepted): p. 1-22.
39. 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.
40. Hove, T. and H. Tazvinga, A techno-economic model for optimising component sizing and energy dispatch strategy for PV-diesel-battery hybrid power systems. Journal of Energy in Southern Africa, 2012. 23(4): p. 18-28.
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