A comparative study on hybrid GA-PSO performance for stand-alone hybrid energy systems optimization

Yıl 2024, Cilt: 42 Sayı: 5, 1410 - 1438, 04.10.2024

Aykut Fatih Güven , Nuran Yörükeren

Öz

As global energy demands surge and the environmental implications of fossil fuel dependence become more pronounced, there is an urgent need to transition toward more sustainable and eco-friendly energy alternatives. This underscores the dire need for sustainable, secure, and environmentally friendly energy solutions. To this end, efficient energy management strategies combined with the optimal design of hybrid renewable energy systems are paramount for ju-diciously harnessing renewable resources. In such systems, wind turbines, photovoltaic panels, diesel generators, and battery storage must be meticulously sized to ensure cost-efficiency, en-vironmental sensitivity, and resilience against unpredictable load variations. Addressing these design challenges, our study emphasized the significance of strategic efficiency, prudential com-ponent selection, and system dependability. We designed an off-grid hybrid renewable energy system, incorporating photovoltaic panels, wind turbines, battery storage, and diesel generators, to meet the annual energy requirements of a university campus. After recording data for a full year, which included metrics on solar radiation, wind speed, ambient temperature, and campus load, we developed a model founded on comprehensive energy management strategies. This model aims to identify optimal design parameters, reduce annual costs, achieve sustainable en-ergy benchmarks, and ensure a harmonious power exchange between system components. For optimization, we used an array of algorithms, notably the genetic algorithms, particle swarm optimization, gravity search algorithms, and hybrid algorithms, such as the hybrid genetic al-gorithm-particle swarm optimization and the hybrid gravity search algorithm-particle swarm optimization, supplemented by the HOMERPro software. Our findings revealed that the inte-gration of photovoltaic panels with battery storage led to an annual system cost of $671,474.98, a levelized cost of energy of $0.1800, a total net present cost of $10,898,221.74, and a renewable energy fraction of 100%. It became evident that the hybrid genetic algorithm combined with particle swarm optimization, when aligned with astute energy management strategies, was more effective in determining optimal design parameters than other methodologies. Through this re-search, we offer profound insights into the dynamics of hybrid renewable energy systems, serv-ing as a guide for pragmatic design and tangible implementation.

Anahtar Kelimeler

Cost of Energy, Genetic Algorithm Partical Swarm Optimization, Energy Management System Optimal Sizing Design, Stand-Alone Renewable Microgrid System

Kaynakça

• REFERENCES
• [1] Pinto R, Henriques ST, Brockway PE, Heun MK, Sousa T. The rise and stall of world electricity efficiency: 1900–2017, results and insights for the renewables transition. Energy 2023;269.
• [2] Effatpanah SK, Ahmadi MH, Aungkulanon P, Maleki A, Sadeghzadeh M, Sharifpur M, Lingen C. Comparative analysis of five widely-used multi-criteria decision-making methods to evaluate clean energy technologies: A case study. Sustainability 2022;14:140314. [CrossRef]
• [3] Uddin MN, Biswas MM, Nuruddin S. Techno-economic impacts of floating PV power generation for remote coastal regions. Sustain Energy Technol Assess 2022;51:101930. [CrossRef]
• [4] Qashou Y, Samour A, Abumunshar M. Does the real estate market and renewable energy induce carbon dioxide emissions? Novel evidence from Turkey. Energies 2022;15:1–13. [CrossRef]
• [5] Dino IG, Meral Akgül C. Impact of climate change on the existing residential building stock in Turkey: An analysis on energy use, greenhouse gas emissions and occupant comfort. Renew Energy 2019;141:828–846. [CrossRef]
• [6] Zhang W, Maleki A. Modeling and optimization of a stand-alone desalination plant powered by solar/wind energies based on back-up systems using a hybrid algorithm. Energy 2022;254:124341. [CrossRef]
• [7] Zhou J, Xu Z. Optimal sizing design and integrated cost-benefit assessment of stand-alone microgrid system with different energy storage employing chameleon swarm algorithm: A rural case in Northeast China. Renew Energy 2023;202:1110–1137. [CrossRef]
• [8] Yu X, Li W, Maleki A, Rosen MA, Komeili Birjandi A, Tang L. Selection of optimal location and design of a stand-alone photovoltaic scheme using a modified hybrid methodology. Sustain Energy Technol Assess 2021;45:101071. [CrossRef]
• [9] Cao Y, Taslimi MS, Dastjerdi SM, Ahmadi P, Ashjaee M. Design, dynamic simulation, and optimal size selection of a hybrid solar/wind and battery-based system for off-grid energy supply. Renew Energy 2022;187:1082–1099. [CrossRef]
• [10] Zeljković Č, Mršić P, Erceg B, Lekić Đ, Kitić N, Matić P. Optimal sizing of photovoltaic-wind-diesel-battery power supply for mobile telephony base stations. Energy 2022;242:122545. [CrossRef]
• [11] Mahmoudi SM, Maleki A, Rezaei Ochbelagh D. A novel method based on fuzzy logic to evaluate the storage and backup systems in determining the optimal size of a hybrid renewable energy system. J Energy Storage 2022;49:104015. [CrossRef]
• [12] Ma Q, Huang X, Wang F, Xu C, Babaei R, Ahmadian H. Optimal sizing and feasibility analysis of grid-isolated renewable hybrid microgrids: Effects of energy management controllers. Energy 2022;240:122503. [CrossRef]
• [13] Xu Y, Huang S, Wang Z, Ren Y, Xie Z, Guo J, et al. Optimization based on tabu search algorithm for optimal sizing of hybrid PV/energy storage system: Effects of tabu search parameters. Sustain Energy Technol Assess 2022;53:102662. [CrossRef]
• [14] Yi H, Yang X. A metaheuristic algorithm based on simulated annealing for optimal sizing and techno-economic analysis of PV systems with multi-type of battery energy storage. Sustain Energy Technol Assess 2022;53:102724. [CrossRef]
• [15] Aziz AS, Tajuddin MFN, Hussain MK, Adzman MR, Ghazali NH, Ramli MAM, Zidane TEK. A new optimization strategy for wind/diesel/battery hybrid energy system. Energy 2022;239:122458. [CrossRef]
• [16] Dufo-López R, Champier D, Gibout S, Lujano-Rojas JM, Domínguez-Navarro JA. Optimisation of off-grid hybrid renewable systems with thermoelectric generator. Energy Convers Manag 2019;196:1051–1067. [CrossRef]
• [17] Yu J, Ryu JH, Lee IB. A stochastic optimization approach to the design and operation planning of a hybrid renewable energy system. Appl Energy 2019;247:212–220. [CrossRef]
• [18] Kang W, Chen M, Lai W, Luo Y. Distributed real-time power management for virtual energy storage systems using dynamic price. Energy. 2021;216:119069. [CrossRef]
• [19] Fares D, Fathi M, Mekhilef S. Performance evaluation of metaheuristic techniques for optimal sizing of a stand-alone hybrid PV/wind/battery system. Appl Energy 2022;305:117823. [CrossRef]
• [20] Jasim AM, Jasim BH, Baiceanu FC, Neagu BC. Optimized sizing of energy management system for off-grid hybrid solar/wind/battery/biogasifier/diesel microgrid system. Mathematics 2023;11:1248. [CrossRef]
• [21] Afolabi T, Farzaneh H. Optimal design and operation of an off-grid hybrid renewable energy system in Nigeria’s rural residential area, using fuzzy logic and optimization techniques. Sustainability 2023;15:3862. [CrossRef]
• [22] Rangel N, Li H, Aristidou P. An optimisation tool for minimising fuel consumption, costs and emissions from Diesel-PV-Battery hybrid microgrids. Appl Energy 2023;335:120748. [CrossRef]
• [23] Pires ALG, Rotella Junior P, Rocha LCS, Peruchi RS, Janda K, Miranda R. Environmental and financial multi-objective optimization: Hybrid wind-photovoltaic generation with battery energy storage systems. J Energy Storage. 2023;66:107425. [CrossRef]
• [24] Al Hariri A, Selimli S, Dumrul H. Effectiveness of heat sink fin position on photovoltaic thermal collector cooling supported by paraffin and steel foam: An experimental study. Appl Therm Eng 2022;213:118784. [CrossRef]
• [25] Khalili Z, Sheikholeslami M. Investigation of innovative cooling system for photovoltaic solar unit in existence of thermoelectric layer utilizing hybrid nanomaterial and Y-shaped fins. Sustain Cities Soc 2023;93:104543. [CrossRef]
• [26] Jasim AM, Jasim BH, Neagu BC, Alhasnawi BN. Efficient optimization algorithm-based demand-side management program for smart grid residential load. Axioms 2023;12:33. [CrossRef]
• [27] Akdamar I, Dumrul H, Selimli S, Yilmaz S. Energetic and exergetic analyses of experimentally investigated hybrid solar air heater. J Energy Eng 2023;149:1–11. [CrossRef]
• [28] Amuta EO, Orovwode H, Wara ST, Agbetuyi AF, Matthew S, Esisio EF. Materials Today: Proceedings hybrid power microgrid optimization and assessment for an off-grid location in Nigeria. Mater Today Proc. 2023. [CrossRef]
• [29] El-Khozondar HJ, El-batta F, El-Khozondar RJ, Nassar Y, Alramlawi M, Alsadi S. Standalone hybrid PV/wind/diesel-electric generator system for a COVID-19 quarantine center. Environ Prog Sustain Energy 2022;42. [CrossRef]
• [30] Kelly E, Medjo Nouadje BA, Tonsie Djiela RH, Kapen PT, Tchuen G, Tchinda R. Off grid PV/Diesel/Wind/Batteries energy system options for the electrification of isolated regions of Chad. Heliyon 2023;9. [CrossRef]
• [31] Zhang W, Maleki A, Rosen MA. A heuristic-based approach for optimizing a small independent solar and wind hybrid power scheme incorporating load forecasting. J Clean Prod 2019;241:117920. [CrossRef]
• [32] Abdelkader A, Rabeh A, Mohamed Ali D, Mohamed J. Multi-objective genetic algorithm based sizing optimization of a stand-alone wind/PV power supply system with enhanced
• battery/supercapacitor hybrid energy storage. Energy 2018;163:351–363. [CrossRef]
• [33] Zhang Z, Wen K, Sun W. Optimization and sustainability analysis of a hybrid diesel-solar-battery energy storage structure for zero energy buildings at various reliability conditions. Sustain Energy Technol Assess 2022;55:102913. [CrossRef]
• [34] Güven AF, Samy MM. Performance analysis of autonomous green energy system based on multi and hybrid metaheuristic optimization approaches. Energy Convers Manag 2022;269:116058. [CrossRef]
• [35] Yousri D, Farag HEZ, Zeineldin H, El-Saadany EF. Integrated model for optimal energy management and demand response of microgrids considering hybrid hydrogen-battery storage systems. Energy Convers Manag 2023;280:116809. [CrossRef]
• [36] Kharrich M, Selim A, Kamel S, Kim J. An effective design of hybrid renewable energy system using an improved Archimedes optimization algorithm: A case study of Farafra, Egypt. Energy Convers Manag 2023;283:116907. [CrossRef]
• [37] Nadimi R, Goto M, Tokimatsu K. The impact of diesel operation time constraint on total cost of diesel-based hybrid renewable power system simulation model. Renew Energy Focus 2022;44:40– 55. [CrossRef]
• [38] Sanni SO, Oricha JY, Oyewole TO, Bawonda FI. Analysis of backup power supply for unreliable grid using hybrid solar PV/diesel/biogas system. Energy 2021;227:120506. [CrossRef]
• [39] Merabet A, Al-Durra A, El-Saadany EF. Energy management system for optimal cost and storage utilization of renewable hybrid energy microgrid. Energy Convers Manag 2022;252:115116. [CrossRef]
• [40] Mishra S, Saini G, Saha S, Chauhan A, Kumar A, Maity S. A survey on multi-criterion decision parameters, integration layout, storage technologies, sizing methodologies and control strategies for integrated renewable energy system. Sustain Energy Technol Assess 2022;52:102246. [CrossRef]
• [41] Yahiaoui A, Tlemçani A. Superior performances strategies of different hybrid renewable energy systems configurations with energy storage units. Wind Eng 2022;46:1471–1486. [CrossRef]
• [42] Amupolo A, Nambundunga S, Chowdhury DSP, Grün G. Techno-economic feasibility of off-grid renewable energy electrification schemes: A case study of an informal settlement in Namibia. Energies 2022;15:4235. [CrossRef]
• [43] Ahmed EEE, Demirci A, Tercan SM. Optimal sizing and techno-enviro-economic feasibility assessment of solar tracker-based hybrid energy systems for rural electrification in Sudan. Renew Energy 2023;205:1057–1070. [CrossRef]
• [44] Maisanam AKS, Biswas A, Sharma KK. Integrated socio-environmental and techno-economic factors for designing and sizing of a sustainable hybrid renewable energy system. Energy Convers Manag 2021;247:114709. [CrossRef]
• [45] Hamanah WM, Abido MA, Alhems LM. Optimum sizing of hybrid PV, wind, battery and diesel system using lightning search algorithm. Arab J Sci Eng 2020;45:1871–1883. [CrossRef]
• [46] Omotoso HO, Al-Shaalan AM, Farh HMH, Al-Shamma’a AA. Techno-economic evaluation of hybrid energy systems using artificial ecosystem-based optimization with demand side management. Electronics 2022;11:204. [CrossRef]
• [47] Talla Konchou FA, Djeudjo Temene H, Tchinda R, Njomo D. Techno-economic and environmental design of an optimal hybrid energy system for a community multimedia centre in Cameroon. SN Appl Sci 2021;3:127. [CrossRef] [48] Ogunjuyigbe ASO, Ayodele TR, Akinola OA. Optimal allocation and sizing of PV/Wind/Split-diesel/Battery hybrid energy system for minimizing life cycle cost, carbon emission and dump energy of remote residential building. Appl Energy 2016;171:153–171. [CrossRef]
• [49] Bajpai P, Dash V. Hybrid renewable energy systems for power generation in stand-alone applications: A review. Renew Sustain Energy Rev 2012;16:2926–2939. [CrossRef]
• [50] Singh S, Chauhan P, Singh NJ. Capacity optimization of grid connected solar/fuel cell energy system using hybrid ABC-PSO algorithm. Int J Hydrogen Energy 2020;45:10070–10088. [CrossRef]
• [51] Holland JH. Even their creators do not fully understand genetic algorithms. Sci Am 1992;267:66–73. [CrossRef]
• [52] Rashedi E, Nezamabadi-pour H, Saryazdi S. GSA: A gravitational search algorithm. Inf Sci 2009;179:2232–2248. [CrossRef]
• [53] Wang JS, Song J Di. Function optimization and parameter performance analysis based on gravitation search algorithm. Algorithms 2016;9:3. [CrossRef]
• [54] Mirjalili S, Hashim SZM. A new hybrid PSOGSA algorithm for function optimization. Int Conf Comput Inf Appl Tianjin: 2010. p. 374–377. [CrossRef]
• [55] Duman S, Yorukeren N, Altas IH. A novel modified hybrid PSOGSA based on fuzzy logic for non-convex economic dispatch problem with valve-point effect. Int J Electr Power Energy Syst 2014;64:121–135. [CrossRef]
• [56] Alajmi A, Wright J. Selecting the most efficient genetic algorithm sets in solving unconstrained building optimization problem. Int J Sustain Built Environ 2014;3:18–26. [CrossRef]
• [57] Armstrong JS. Combining forecasts. In: Armstrong JS, editor. Principles of Forecasting. Int Ser Oper Res Manag Sci 2001;30. [CrossRef]
• [58] Pesaran HAM, Nazari-Heris M, Mohammadi-Ivatloo B, Seyedi H. A hybrid genetic particle swarm optimization for distributed generation allocation in power distribution networks. Energy 2020;209:118218. [CrossRef]
• [59] Ünler A. Improvement of energy demand forecasts using swarm intelligence: The case of Turkey with projections to 2025. Energy Policy 2008;36:1937–1944. [CrossRef]
• [60] Younes M, Benhamida F. Genetic algorithm-particle swarm optimization (GA-PSO) for economic load dispatch. Przegląd Elektrotechniczny 2011;369–372. [61] Sahoo BM, Pandey HM, Amgoth T. GAPSO-H: A hybrid approach towards optimizing the cluster based routing in wireless sensor network. Swarm Evol Comput 2021;60:100772.[CrossRef]