Grid integration of renewable energy resources in industry necessitates the need for effective energy management in the modern power system to ensure reliability, efficiency and sustainability. In this article, a comprehensive framework for energy management is proposed for a photovoltaic, wind, battery and grid integrated microgrid, using an Improved Osprey Optimization Algorithm (IOOA) to optimize the system parameters. A quantitative model is formulated to mathematically represent the microgrid, and a logic driven energy management system is proposed to coordinate power exchange and ensure system stability. An optimization problem is formulated for the microgrid to reduce the total generation expenses and emission levels and is solved using IOOA by integrating with the energy management system. The effectiveness of the proposed method is assessed through a comparative analysis with well-established metaheuristics algorithms, namely conventional Osprey Optimization Algorithm, Marine Predators Optimization Algorithm, and Particle Swarm Optimization Algorithm under MATLAB environment. The optimization results indicate that IOOA achieves 17.94%, 38.46% and 47.54% reduction in generation cost and 0.40%, 0.83% and 0.97% reduction in emission compared to the conventional OOA, MPA and PSO respectively. This highlights the effectiveness of the proposed IOOA in optimizing both economic and environmental objectives within the industrial microgrid system that contribute to the advancement of renewable energy integration. Additionally, the sensitivity analysis reveals that variations in solar irradiation, wind speed and load demand will have a significant effect on the system performance.
SingMASBenoMM. Energy consumption analysis and management strategies for the dairy farm in India towards sustainable development. IETE J Res2024; 70(10): 7911–7921. https://doi.org/10.1080/03772063.2024.2359006
2.
HassanQViktorPAl-MusawiTJ, et al.The renewable energy role in the global energy transformations. Renew Energy Focus2024; 48: 100545. https://doi.org/10.1016/j.ref.2024.100545
3.
BhoiSKKasturiKNayakMR. Optimisation of the operation of a microgrid with renewable energy sources and battery storage. e-Prime - Adv Electr Eng Electron Energy2023; 4: 100159. https://doi.org/10.1016/j.prime.2023.100159
TamasigaPOnyeakaHAltaghlibiM, et al.Empowering communities beyond wires: renewable energy microgrids and the impacts on energy poverty and socio-economic outcomes. Energy Rep2024; 12: 4475–4488. https://doi.org/10.1016/j.egyr.2024.10.026
6.
KeshtaHEHassaballahEGAliAA, et al.Multi-level optimal energy management strategy for a grid tied microgrid considering uncertainty in weather conditions and load. Sci Rep2024; 14(1): 10059. https://doi.org/10.1038/s41598-024-59655-7
7.
MasternakCMeunierSBrissetS, et al.Microgrid sizing and energy management using benders decomposition algorithm. Sustain Energy Grids Netw2024; 38: 101314. https://doi.org/10.1016/j.segan.2024.101314
8.
ThirunavukkarasuGSSeyedmahmoudianMJameiE, et al.Role of optimization techniques in microgrid energy management systems—A review. Energy Strategy Rev2022; 43: 100899. https://doi.org/10.1016/j.esr.2022.100899
9.
VeraYEGDufo-LópezRBernal-AgustínJL. Energy management in microgrids with renewable energy sources: a literature review. Appl Sci (Basel)2019; 9(Issue 18): 9183854. https://doi.org/10.3390/app9183854
10.
WamalwaFIshimweA. Optimal energy management in a grid-tied solar PV-battery microgrid for a public building under demand response. Energy Rep2024; 12: 3718–3731. https://doi.org/10.1016/j.egyr.2024.09.054
11.
DashtdarMBajajMHosseinimoghadamSMS. Design of optimal energy management system in a residential microgrid based on smart control. Smart Science2022; 10(1): 25–39. https://doi.org/10.1080/23080477.2021.1949882
12.
KassabFACelikBLocmentF, et al.Optimal sizing and energy management of a microgrid: a joint MILP approach for minimization of energy cost and carbon emission. Renew Energy2024; 224: 120186. https://doi.org/10.1016/j.renene.2024.120186
13.
YoonCParkYSimMK, et al.A quadratic programming-based power dispatch method for a DC-Microgrid. IEEE Access2020; 8: 211924–211936. https://doi.org/10.1109/ACCESS.2020.3039237
14.
CeciliaACarroquinoJRodaV, et al.Optimal energy management in a standalone microgrid, with photovoltaic generation, short-term storage, and hydrogen production. Energies2020; 13(6): 1454. https://doi.org/10.3390/en13061454
15.
AkterAZafirEIDanaNH, et al.A review on microgrid optimization with meta-heuristic techniques: scopes, trends and recommendation. Energy Strategy Rev2024; 51: 101298. https://doi.org/10.1016/j.esr.2024.101298
16.
KarabogaDBasturkB. A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Global Optim2007; 39(3): 459–471. https://doi.org/10.1007/s10898-007-9149-x
17.
YangXSDebS. Cuckoo search via lévy flights. In: 2009 world Congress on nature and biologically Inspired Computing, NABIC 2009 - proceedings, 2009. IEEE. https://doi.org/10.1109/NABIC.2009.5393690
DokerogluTSevincEKucukyilmazT, et al.A survey on new generation metaheuristic algorithms. Comput Ind Eng2019; 137: 106040. https://doi.org/10.1016/j.cie.2019.106040
21.
FathyAAlanaziTMRezkH, et al.Optimal energy management of micro-grid using sparrow search algorithm. Energy Rep2022; 8: 758–773. https://doi.org/10.1016/j.egyr.2021.12.022
22.
MajeedMAPhichisawatSAsgharF, et al.Optimal energy management system for grid-tied microgrid: an improved adaptive genetic algorithm. IEEE Access2023; 11: 3326505. https://doi.org/10.1109/ACCESS.2023.3326505
23.
HaiTZhouJRezvaniA, et al.Optimal energy management strategy for a renewable based microgrid with electric vehicles and demand response program. Elec Power Syst Res2023; 221: 109370. https://doi.org/10.1016/j.epsr.2023.109370
24.
RezaeiMHosseiniSMHNematiB. Presenting an optimal energy management model in microgrids using APSO algorithm. Electr Eng2024; 106(4): 4665–4677. https://doi.org/10.1007/s00202-023-02217-3
25.
BoqtobOel MoussaouiHel MarkhiH, et al.Optimal energy management of microgrid based wind/PV/diesel with integration of incentive-based demand response program. Wind Eng2023; 47(2): 266–282. https://doi.org/10.1177/0309524X221124335
26.
MohammadjafariMEbrahimiRParvin DarabadV. Optimal energy management of a microgrid incorporating a novel efficient demand response and battery storage system. Journal of Electrical Engineering and Technology2020; 15(2): 571–590. https://doi.org/10.1007/s42835-020-00345-5
27.
BaruaSMerabetAAl-DurraA, et al.Lévy arithmetic optimization for energy management of solar wind microgrid with multiple diesel generators for off-grid communities. Appl Energy2024; 371: 123736. https://doi.org/10.1016/j.apenergy.2024.123736
28.
AlharbiAGOlabiAGRezkH, et al.Optimized energy management and control strategy of photovoltaic/PEM fuel cell/batteries/supercapacitors DC microgrid system. Inside Energy2024; 290: 130121. https://doi.org/10.1016/j.energy.2023.130121
29.
AlhasnawiBNAlmutokiSMMHussainFFK, et al.A new methodology for reducing carbon emissions using multi-renewable energy systems and artificial intelligence. Sustain Cities Soc2024; 114: 105721. https://doi.org/10.1016/j.scs.2024.105721
30.
AlbalawiHKassemAMZaidSA, et al.Energy management of an isolated wind/photovoltaic microgrid using cuckoo search algorithm. Intell Autom Soft Comput2022; 34(3): 2051–2066. https://doi.org/10.32604/iasc.2022.026032
31.
AsgharFZahidAHussainMI, et al.A novel solution for optimized energy management systems comprising an AC/DC hybrid microgrid system for industries. Sustainability2022; 14(14): 8788. https://doi.org/10.3390/su14148788
32.
KheiterASouagSChaouchA, et al.Energy management strategy based on marine predators algorithm for grid-connected microgrid. Int J Renew Energy Dev2022; 11(3): 751–765. https://doi.org/10.14710/ijred.2022.42797
33.
HousseinEHIbrahimIEKharrichM, et al.An improved marine predators algorithm for the optimal design of hybrid renewable energy systems. Eng Appl Artif Intell2022; 110: 104722. https://doi.org/10.1016/J.ENGAPPAI.2022.104722
34.
RajakumarPBalasubramaniamPMMunimathanA, et al.Osprey optimization algorithm for distributed generation integration in a radial distribution system for power loss reduction. Sci Rep2025; 15(1): 18775. https://doi.org/10.1038/s41598-025-02411-2
35.
ShaheenMAMHasanienHMUllahZ, et al.Hybrid osprey-parrot optimization algorithm for enhanced parameter identification of supercapacitor model. J Energy Storage2025; 137: 118604. https://doi.org/10.1016/J.EST.2025.118604
36.
AlkuhayliAHasanienHM. Optimized hybrid osprey with PSO control for improved VSC-HVDC-wind power integration. Results Eng2024; 24(103191): 103191. https://doi.org/10.1016/J.RINENG.2024.103191
ManishaKumarP. An improved osprey optimization algorithm to analyse the steady state performance of stainless steel utensil manufacturing unit. Life Cycle Reliability and Safety Engineering. 2025; 2025: 1–18. https://doi.org/10.1007/S41872-025-00319-4
39.
WenXLiuXYuC, et al.IOOA: a multi-strategy fusion improved osprey optimization algorithm for global optimization. Electronic Research Archive2024; 32(3): 2033–2074. https://doi.org/10.3934/ERA.2024093
40.
ZhouLLiuXTianR, et al.A modified osprey optimization algorithm for solving global optimization and engineering optimization design problems. Symmetry2024; 16(9): 1173. https://doi.org/10.3390/SYM16091173
41.
RamliMAMBouchekaraHREHAlghamdiAS. Optimal sizing of PV/wind/diesel hybrid microgrid system using multi-objective self-adaptive differential evolution algorithm. Renew Energy2018; 121: 400–411. https://doi.org/10.1016/j.renene.2018.01.058
42.
Arcos–AvilesDSalazarARodriguezM, et al.Model predictive control-based energy management system for an isolated electro-thermal microgrid in the amazon region of Ecuador. Energy Convers Manag2024; 310: 118479. https://doi.org/10.1016/j.enconman.2024.118479
43.
ToledoCLópez-VicenteRAbadJ, et al.Thermal performance of PV modules as building elements: analysis under real operating conditions of different technologies. Energy Build2020; 223: 110087. https://doi.org/10.1016/j.enbuild.2020.110087
44.
YadavSKumarPKumarA. Optimal design and energy management in isolated renewable sources based microgrid considering excess energy and reliability aspects. J Energy Storage2024; 103: 114320. https://doi.org/10.1016/j.est.2024.114320
45.
SinghSSinghMKaushikSC. Feasibility study of an islanded microgrid in rural area consisting of PV, wind, biomass and battery energy storage system. Energy Convers Manag2016; 128: 178–190. https://doi.org/10.1016/j.enconman.2016.09.046
46.
BabuVVPreetha RoselynJSundaravadivelP. Multi-objective genetic algorithm based energy management system considering optimal utilization of grid and degradation of battery storage in microgrid. Energy Rep2023; 9: 5992–6005. https://doi.org/10.1016/j.egyr.2023.05.067
47.
RoyAOlivierJCAugerF, et al.A combined optimization of the sizing and the energy management of an industrial multi-energy microgrid: application to a harbour area. Energy Convers Manag X2021; 12: 100107. https://doi.org/10.1016/J.ECMX.2021.100107
48.
Tittu GeorgeDXEdwin RajRRajkumarA, et al.Optimal sizing of solar-wind based hybrid energy system using modified dragonfly algorithm for an institution. Energy Convers Manag2023; 283: 116938. https://doi.org/10.1016/j.enconman.2023.116938
49.
ModuBAbdullahMPAlkassemA, et al.Optimal rule-based energy management and sizing of a grid-connected renewable energy microgrid with hybrid storage using levy flight algorithm. Energy Nexus2024; 16: 100333. https://doi.org/10.1016/j.nexus.2024.100333
50.
DehghaniMTrojovskýP. Osprey optimization algorithm: a new bio-inspired metaheuristic algorithm for solving engineering optimization problems. Front Mech Eng2023; 8: 1126450.
