The use of plug-in hybrid electric vehicles (PHEVs) provides a way to address energy and environmental issues. Integrating a large number of PHEVs with advanced control and storage capabilities can enhance the flexibility of the distribution grid. This study proposes an innovative energy management strategy (EMS) using an Iterative map-based self-adaptive crystal structure algorithm (SaCryStAl) specifically designed for microgrids with renewable energy sources (RESs) and PHEVs. The goal is to optimize multi-objective scheduling for a microgrid with wind turbines, micro-turbines, fuel cells, solar photovoltaic systems, and batteries to balance power and store excess energy. The aim is to minimize microgrid operating costs while considering environmental impacts. The optimization problem is framed as a multi-objective problem with nonlinear constraints, using fuzzy logic to aid decision-making. In the first scenario, the microgrid is optimized with all RESs installed within predetermined boundaries, in addition to grid connection. In the second scenario, the microgrid operates with a wind turbine at rated power. The third case study involves integrating plug-in hybrid electric vehicles (PHEVs) into the microgrid in three charging modes: coordinated, smart, and uncoordinated, utilizing standard and rated RES power. The SaCryStAl algorithm showed superior performance in operation cost, emissions, and execution time compared to traditional CryStAl and other recent optimization methods. The proposed SaCryStAl algorithm achieved optimal solutions in the first scenario for cost and emissions at 177.29 €ct and 469.92 kg, respectively, within a reasonable time frame. In the second scenario, it yielded optimal cost and emissions values of 112.02 €ct and 196.15 kg, respectively. Lastly, in the third scenario, the SaCryStAl algorithm achieves optimal cost values of 319.9301 €ct, 160.9827 €ct and 128.2815 €ct for uncoordinated charging, coordinated charging and smart charging modes respectively. Optimization results reveal that the proposed SaCryStAl outperformed other evolutionary optimization algorithms, such as differential evolution, CryStAl, Grey Wolf Optimizer, particle swarm optimization, and genetic algorithm, as confirmed through test cases.

Centre for Smart Grid Technologies School of Electrical Engineering Vellore Institute of Technology Chennai Tamilnadu 600 127 India

Department of Electrical and Electronics Engineering Hindustan Institute of Technology and Science Chennai Tamilnadu 603 103 India

Department of Electrical Engineering SRM Institute of Science and Technology Kattankulathur Tamilnadu 603203 India

Electrical Engineering Department Graphic Era Dehradun 248002 India

ENET Centre VSB Technical University of Ostrava 708 00 Ostrava Czech Republic

Graphic Era Hill University Dehradun 248002 India

Hourani Center for Applied Scientific Research Al Ahliyya Amman University Amman Jordan

Zobrazit více v PubMed

Rekioua D, Mokrani Z, Kakouche K, Rekioua T, Oubelaid A, Logerais PO, Ali E, Bajaj M, Berhanu M, Ghoneim SSM. Optimization and intelligent power management control for an autonomous hybrid wind turbine photovoltaic diesel generator with batteries. Sci. Rep. 2023;13:21830. doi: 10.1038/s41598-023-49067-4. PubMed DOI PMC

Yang C, Kumar Nutakki TU, Alghassab MA, Alkhalaf S, Alturise F, Alharbi FS, Elmasry Y, Abdullaev S. Optimized integration of solar energy and liquefied natural gas regasification for sustainable urban development: Dynamic modeling, data-driven optimization, and case study. J. Clean. Prod. 2024;447:141405. doi: 10.1016/j.jclepro.2024.141405. DOI

Zhou X, Cai Y, Li X. Process arrangement and multi-aspect study of a novel environmentally-friendly multigeneration plant relying on a geothermal-based plant combined with the goswami cycle booted by kalina and desalination cycles. Energy. 2024;299:131381. doi: 10.1016/j.energy.2024.131381. DOI

Ravindran MA, Nallathambi K, Vishnuram P, Rathore RS, Bajaj M, Rida I, Alkhayyat A. A novel technological review on fast charging infrastructure for electrical vehicles: Challenges, solutions, and future research directions. Alex. Eng. J. 2023;82:260–290. doi: 10.1016/j.aej.2023.10.009. DOI

Cao B, Dong W, Lv Z, Gu Y, Singh S, Kumar P. Hybrid microgrid many-objective sizing optimization with fuzzy decision. IEEE Trans. Fuzzy Syst. 2020;28:2702–2710. doi: 10.1109/TFUZZ.2020.3026140. DOI

Panda S, Mohanty S, Rout PK, Sahu BK, Parida SM, Samanta IS, Bajaj M, Piecha M, Blazek V, Prokop L. A comprehensive review on demand side management and market design for renewable energy support and integration. Energy Rep. 2023;10:2228–2250. doi: 10.1016/j.egyr.2023.09.049. DOI

Li P, Hu J, Qiu L, Zhao Y, Ghosh BK. A distributed economic dispatch strategy for power-water networks. IEEE Trans. Control Netw. Syst. 2022;9:356–366. doi: 10.1109/TCNS.2021.3104103. DOI

Duan Y, Zhao Y, Hu J. An initialization-free distributed algorithm for dynamic economic dispatch problems in microgrid: Modeling, optimization and analysis. Sustain. Energy, Grids Netw. 2023;34:101004. doi: 10.1016/j.segan.2023.101004. DOI

Kumar BA, Jyothi B, Rathore RS, Singh AR, Kumar BH, Bajaj M. A novel framework for enhancing the power quality of electrical vehicle battery charging based on a modified Ferdowsi converter. Energy Rep. 2023;10:2394–2416. doi: 10.1016/j.egyr.2023.09.070. DOI

Shirkhani M, Tavoosi J, Danyali S, Sarvenoee AK, Abdali A, Mohammadzadeh A, Zhang C. A review on microgrid decentralized energy/voltage control structures and methods. Energy Rep. 2023;10:368–380. doi: 10.1016/j.egyr.2023.06.022. DOI

Sahoo GK, Choudhury S, Rathore RS, Bajaj M, Dutta AK. Scaled conjugate-artificial neural network-based novel framework for enhancing the power quality of grid-tied microgrid systems. Alex. Eng. J. 2023;80:520–541. doi: 10.1016/j.aej.2023.08.081. DOI

Wang C, Wang Y, Wang K, Dong Y, Yang Y. An improved hybrid algorithm based on biogeography/complex and metropolis for many-objective optimization. Math. Probl. Eng. 2017;2017:1–14. doi: 10.1155/2017/2462891. DOI

Sahoo GK, Choudhury S, Rathore RS, Bajaj M. A novel prairie dog-based meta-heuristic optimization algorithm for improved control, better transient response, and power quality enhancement of hybrid microgrids. Sensors. 2023;23:5973. doi: 10.3390/s23135973. PubMed DOI PMC

Lei Y, Yanrong C, Hai T, Ren G, Wenhuan W. DGNet: An adaptive lightweight defect detection model for new energy vehicle battery current collector. IEEE Sens. J. 2023;23:29815–29830. doi: 10.1109/JSEN.2023.3324441. DOI

Azaroual M, Hermann DT, Ouassaid M, Bajaj M, Maaroufi M, Alsaif F, Alsulamy S. Optimal solution of peer-to-peer and peer-to-grid trading strategy sharing between prosumers with grid-connected photovoltaic/wind turbine/battery storage systems. Int. J. Energy Res. 2023;2023:1–17. doi: 10.1155/2023/6747936. DOI

Hou M, Zhao Y, Ge X. Optimal scheduling of the plug-in electric vehicles aggregator energy and regulation services based on grid to vehicle. Int. Trans. Electr. Energy Syst. 2017;27:e2364. doi: 10.1002/etep.2364. DOI

Choudhury S, Varghese GT, Mohanty S, Kolluru VR, Bajaj M, Blazek V, Prokop L, Misak S. Energy management and power quality improvement of microgrid system through modified water wave optimization. Energy Rep. 2023;9:6020–6041. doi: 10.1016/j.egyr.2023.05.068. DOI

Iqbal S, Habib S, Khan NH, Ali M, Aurangzeb M, Ahmed EM. Electric vehicles aggregation for frequency control of microgrid under various operation conditions using an optimal coordinated strategy. Sustainability. 2022;14:3108. doi: 10.3390/su14053108. DOI

Khosravi N, Baghbanzadeh R, Oubelaid A, Tostado-Véliz M, Bajaj M, Hekss Z, Echalih S, Belkhier Y, Houran MA, Aboras KM. A novel control approach to improve the stability of hybrid AC/DC microgrids. Appl. Energy. 2023;344:121261. doi: 10.1016/j.apenergy.2023.121261. DOI

Karthik N, Parvathy AK, Rajagopalan A, Baskar S. A review of optimization techniques applied to solve unit commitment problem in microgrid. Indones. J. Electr. Eng. Comput. Sci. 2019;15:1161. doi: 10.11591/ijeecs.v15.i3.pp1161-1169. DOI

Khan NH, Wang Y, Jamal R, Iqbal S, Elbarbary ZMS, Alshammari NF, Ebeed M, Jurado F. A novel modified artificial rabbit optimization for stochastic energy management of a grid-connected microgrid: A case study in China. Energy Rep. 2024;11:5436–5455. doi: 10.1016/j.egyr.2024.05.018. DOI

Nagarajan K, Rajagopalan A, Angalaeswari S, Natrayan L, Mammo WD. Combined economic emission dispatch of microgrid with the incorporation of renewable energy sources using improved mayfly optimization algorithm. Comput. Intell. Neurosci. 2022;2022:1–22. doi: 10.1155/2022/6461690. PubMed DOI PMC

Sahri Y, Tamalouzt S, Belaid SL, Bajaj M, Ghoneim SSM, Zawbaa HM, Kamel S. Performance improvement of hybrid system based DFIG-Wind/PV/Batteries connected to DC and AC grid by applying intelligent control. Energy Rep. 2023;9:2027–2043. doi: 10.1016/j.egyr.2023.01.021. DOI

Dhivya, S., Arul, R., Maheswari, S., Kanimozhi, R., Karthik, N. Optimal scheduling and sizing of energy storage system using hybrid algorithm for electric vehicles. In 2022 International Virtual Conference on Power Engineering Computing and Control: Developments in Electric Vehicles and Energy Sector for Sustainable Future (PECCON), 2022: pp. 1–5. 10.1109/PECCON55017.2022.9851152

Abraham DS, Chandrasekar B, Rajamanickam N, Vishnuram P, Ramakrishnan V, Bajaj M, Piecha M, Blazek V, Prokop L. Fuzzy-based efficient control of DC microgrid configuration for PV-energized EV charging station. Energies. 2023;16:2753. doi: 10.3390/en16062753. DOI

Pachauri N, Thangavel V, Suresh V, Kantipudi MP, Kotb H, Tripathi RN, Bajaj M. A robust fractional-order control scheme for PV-penetrated grid-connected microgrid. Mathematics. 2023;11:1283. doi: 10.3390/math11061283. DOI

Chatterjee S, Mohammed AN, Mishra S, Sharma NK, Selim A, Bajaj M, Rihan M, Kamel S. Optimal real-time tuning of autonomous distributed power systems using modern techniques. Front. Energy Res. 2023;11:1055845. doi: 10.3389/fenrg.2023.1055845. DOI

Mohanty S, Panda S, Parida SM, Rout PK, Sahu BK, Bajaj M, Zawbaa HM, Kumar NM, Kamel S. Demand side management of electric vehicles in smart grids: A survey on strategies, challenges, modeling, and optimization. Energy Rep. 2022;8:12466–12490. doi: 10.1016/j.egyr.2022.09.023. DOI

Zandrazavi SF, Guzman CP, Pozos AT, Quiros-Tortos J, Franco JF. Stochastic multi-objective optimal energy management of grid-connected unbalanced microgrids with renewable energy generation and plug-in electric vehicles. Energy. 2022;241:122884. doi: 10.1016/j.energy.2021.122884. DOI

Panda S, Mohanty S, Rout PK, Sahu BK, Bajaj M, Zawbaa HM, Kamel S. Residential demand side management model, optimization and future perspective: A review. Energy Rep. 2022;8:3727–3766. doi: 10.1016/j.egyr.2022.02.300. DOI

Sharma S, Sood YR, Sharma NK, Bajaj M, Zawbaa HM, Turky RA, Kamel S. Modeling and sensitivity analysis of grid-connected hybrid green microgrid system. Ain Shams Eng. J. 2022;13:101679. doi: 10.1016/j.asej.2021.101679. DOI

Abdalla AN, Nazir MS, Tiezhu Z, Bajaj M, Sanjeevikumar P, Yao L. Optimized economic operation of microgrid: Combined cooling and heating power and hybrid energy storage systems. J. Energy Resour. Technol. 2021;143:070906. doi: 10.1115/1.4050971. DOI

Dashtdar M, Bajaj M, Hosseinimoghadam SMS. Design of optimal energy management system in a residential microgrid based on smart control. Smart Sci. 2022;10:25–39. doi: 10.1080/23080477.2021.1949882. DOI

Dashtdar M, Nazir MS, Hosseinimoghadam SMS, Bajaj M, Goud BS. Improving the sharing of active and reactive power of the islanded microgrid based on load voltage control. Smart Sci. 2022;10:142–157. doi: 10.1080/23080477.2021.2012010. DOI

Amoussou I, Tanyi E, Agajie T, Khan B, Bajaj M. Optimal sizing and location of grid-interfaced PV, PHES, and ultra capacitor systems to replace LFO and HFO based power generations. Sci. Rep. 2024;14:8591. doi: 10.1038/s41598-024-57231-7. PubMed DOI PMC

Mohapatra B, Sahu BK, Pati S, Bajaj M, Blazek V, Prokop L, Misak S. Optimizing grid-connected PV systems with novel super-twisting sliding mode controllers for real-time power management. Sci. Rep. 2024;14:4646. doi: 10.1038/s41598-024-55380-3. PubMed DOI PMC

Kumar BA, Jyothi B, Singh AR, Bajaj M, Rathore RS, Tuka MB. Hybrid genetic algorithm-simulated annealing based electric vehicle charging station placement for optimizing distribution network resilience. Sci. Rep. 2024;14:7637. doi: 10.1038/s41598-024-58024-8. PubMed DOI PMC

Deghfel N, Badoud AE, Merahi F, Bajaj M, Zaitsev I. A new intelligently optimized model reference adaptive controller using GA and WOA-based MPPT techniques for photovoltaic systems. Sci. Rep. 2024;14:6827. doi: 10.1038/s41598-024-57610-0. PubMed DOI PMC

Hou H, Xue M, Xu Y, Xiao Z, Deng X, Xu T, Liu P, Cui R. Multi-objective economic dispatch of a microgrid considering electric vehicle and transferable load. Appl. Energy. 2020;262:114489. doi: 10.1016/j.apenergy.2020.114489. DOI

Hu J, Zou Y, Soltanov N. A multilevel optimization approach for daily scheduling of combined heat and power units with integrated electrical and thermal storage. Expert Syst. Appl. 2024;250:123729. doi: 10.1016/j.eswa.2024.123729. DOI

Huang Z, Guo Z, Ma P, Wang M, Long Y, Zhang M. Economic-environmental scheduling of microgrid considering V2G-enabled electric vehicles integration. Sustain. Energy, Grids Netw. 2022;32:100872. doi: 10.1016/j.segan.2022.100872. DOI

Jiao F, Zou Y, Zhang X, Zou R. Multi-objective optimal energy management of microgrids including plug-in electric vehicles with the vehicle to grid capability for energy resources scheduling. Proc. Inst. Mech. Eng. Part A J. Power Energy. 2021;235:563–580. doi: 10.1177/0957650920942998. DOI

Mei Y, Li B, Wang H, Wang X, Negnevitsky M. Multi-objective optimal scheduling of microgrid with electric vehicles. Energy Rep. 2022;8:4512–4524. doi: 10.1016/j.egyr.2022.03.131. DOI

Beyazıt MA, Taşcıkaraoğlu A, Catalão JPS. Cost optimization of a microgrid considering vehicle-to-grid technology and demand response. Sustain. Energy, Grids Netw. 2022;32:100924. doi: 10.1016/j.segan.2022.100924. DOI

Dharavat N, Sudabattula SK, Velamuri S, Mishra S, Sharma NK, Bajaj M, Elgamli E, Shouran M, Kamel S. Optimal allocation of renewable distributed generators and electric vehicles in a distribution system using the political optimization algorithm. Energies. 2022;15:6698. doi: 10.3390/en15186698. DOI

Hai T, Zhou J, Alazzawi AK, Muranaka T. Management of renewable-based multi-energy microgrids with energy storage and integrated electric vehicles considering uncertainties. J. Energy Storage. 2023;60:106582. doi: 10.1016/j.est.2022.106582. DOI

Hai T, Zhou J, Muranaka K. Energy management and operational planning of renewable energy resources-based microgrid with energy saving. Electr. Power Syst. Res. 2023;214:108792. doi: 10.1016/j.epsr.2022.108792. DOI

Nodehi M, Zafari A, Radmehr M. A new energy management scheme for electric vehicles microgrids concerning demand response and reduced emission. Sustain. Energy, Grids Netw. 2022;32:100927. doi: 10.1016/j.segan.2022.100927. DOI

Eghbali N, Hakimi SM, Hasankhani A, Derakhshan G, Abdi B. A scenario-based stochastic model for day-ahead energy management of a multi-carrier microgrid considering uncertainty of electric vehicles. J. Energy Storage. 2022;52:104843. doi: 10.1016/j.est.2022.104843. DOI

Taghizadegan N, Cheshmeh Khavar S, Abdolahi A, Arasteh F, Ghoreyshi R. Dominated GSO algorithm for optimal scheduling of renewable microgrids with penetration of electric vehicles and energy storages considering DRP. Int. J. Ambient Energy. 2022;43:6380–6391. doi: 10.1080/01430750.2021.2019110. DOI

Amrutha Raju B, Sandeep V. A chance constraint microgrid energy management with phase balancing using electric vehicle demand aggregation. Energy Sources Part A Recover Util. Environ. Eff. 2023;45:111–139. doi: 10.1080/15567036.2022.2164812. DOI

Arunkumar AP, Kuppusamy S, Muthusamy S, Pandiyan S, Panchal H, Nagaiyan P. An extensive review on energy management system for microgrids. Energy Sources Part A Recover. Util. Environ. Eff. 2022;44:4203–4228. doi: 10.1080/15567036.2022.2075059. DOI

Rajagopalan A, Nagarajan K, Montoya OD, Dhanasekaran S, Kareem IA, Perumal AS, Lakshmaiya N, Paramasivam P. Multi-objective optimal scheduling of a microgrid using oppositional gradient-based grey wolf optimizer. Energies. 2022;15:9024. doi: 10.3390/en15239024. DOI

Kumar RP, Karthikeyan G. A multi-objective optimization solution for distributed generation energy management in microgrids with hybrid energy sources and battery storage system. J. Energy Storage. 2024;75:109702. doi: 10.1016/j.est.2023.109702. DOI

Ghasemi E, Ranjbaran A, Pourhossein J. Designing multi-objective electric and thermal energy management system of microgrid in the presence of controllable loads and electric vehicles. Electr. Eng. 2024;106:1519–1532. doi: 10.1007/s00202-023-01823-5. DOI

Li M, Aksoy M, Samad S. Optimal energy management and scheduling of a microgrid with integrated electric vehicles and cost minimization. Soft Comput. 2024;28:2015–2034. doi: 10.1007/s00500-023-09168-8. DOI

Wang Y, Wang B, Farjam H. Multi-objective scheduling and optimization for smart energy systems with energy hubs and microgrids. Eng. Sci. Technol. Int. J. 2024;51:101649. doi: 10.1016/j.jestch.2024.101649. DOI

Huang A, Mao Y, Chen X, Xu Y, Wu S. A multi-timescale energy scheduling model for microgrid embedded with differentiated electric vehicle charging management strategies. Sustain. Cities Soc. 2024;101:105123. doi: 10.1016/j.scs.2023.105123. DOI

Seyednouri SR, Safari A, Farrokhifar M, Ravadanegh SN, Quteishat A, Younis M. Day-ahead scheduling of multi-energy microgrids based on a stochastic multi-objective optimization model. Energies. 2023;16:1802. doi: 10.3390/en16041802. DOI

Hai T, Zhou J, Rezvani A, Le BN, Oikawa H. Optimal energy management strategy for a renewable based microgrid with electric vehicles and demand response program. Electr. Power Syst. Res. 2023;221:109370. doi: 10.1016/j.epsr.2023.109370. DOI

Ahmed I, Rehan M, Basit A, Tufail M, Hong K-S. A dynamic optimal scheduling strategy for multi-charging scenarios of plug-in-electric vehicles over a smart grid. IEEE Access. 2023;11:28992–29008. doi: 10.1109/ACCESS.2023.3258859. DOI

Cavus M, Allahham A, Adhikari K, Zangiabadi M, Giaouris D. Energy management of grid-connected microgrids using an optimal systems approach. IEEE Access. 2023;11:9907–9919. doi: 10.1109/ACCESS.2023.3239135. DOI

Ankar SJ, Pinkymol KP. Optimal sizing and energy management of electric vehicle hybrid energy storage systems with multi-objective optimization criterion. IEEE Trans. Veh. Technol. 2024 doi: 10.1109/TVT.2024.3372137. DOI

Yang M, Cui Y, Wang J. Multi-Objective optimal scheduling of island microgrids considering the uncertainty of renewable energy output. Int. J. Electr. Power Energy Syst. 2023;144:108619. doi: 10.1016/j.ijepes.2022.108619. DOI

Karimi H, Jadid S, Hasanzadeh S. Optimal-sustainable multi-energy management of microgrid systems considering integration of renewable energy resources: A multi-layer four-objective optimization. Sustain. Prod. Consum. 2023;36:126–138. doi: 10.1016/j.spc.2022.12.025. DOI

Asaad A, Ali A, Mahmoud K, Shaaban MF, Lehtonen M, Kassem AM, Ebeed M. Multi-objective optimal planning of EV charging stations and renewable energy resources for smart microgrids. Energy Sci. Eng. 2023;11:1202–1218. doi: 10.1002/ese3.1385. DOI

Huang Y, He G, Pu Z, Zhang Y, Luo Q, Ding C. Multi-objective particle swarm optimization for optimal scheduling of household microgrids. Front. Energy Res. 2024;11:1354869. doi: 10.3389/fenrg.2023.1354869. DOI

Guan Z, Wang H, Li Z, Luo X, Yang X, Fang J, Zhao Q. Multi-objective optimal scheduling of microgrids based on improved particle swarm algorithm. Energies. 2024;17:1760. doi: 10.3390/en17071760. DOI

Wu Z, Zou Y, Zheng F, Liang N. Research on optimal scheduling strategy of microgrid considering electric vehicle access. Symmetry (Basel). 2023;15:1993. doi: 10.3390/sym15111993. DOI

Yu Z, Dou Z, Zhao Y, Xie R, Qiao M, Wang Y, Liu L. Grid scheduling strategy considering electric vehicles participating in multi-microgrid interaction. J. Electr. Eng. Technol. 2023;18:1557–1572. doi: 10.1007/s42835-022-01294-x. DOI

Abid MS, Apon HJ, Hossain S, Ahmed A, Ahshan R, Lipu MSH. A novel multi-objective optimization based multi-agent deep reinforcement learning approach for microgrid resources planning. Appl. Energy. 2024;353:122029. doi: 10.1016/j.apenergy.2023.122029. DOI

Hao J, Huang T, Xu Q, Sun Y. Robust optimal scheduling of microgrid with electric vehicles based on Stackelberg game. Sustainability. 2023;15:16682. doi: 10.3390/su152416682. DOI

Cui F, Lin X, Zhang R, Yang Q. Multi-objective optimal scheduling of charging stations based on deep reinforcement learning. Front. Energy Res. 2023;10:1042882. doi: 10.3389/fenrg.2022.1042882. DOI

Song J, Mingotti A, Zhang J, Peretto L, Wen H. Fast iterative-interpolated DFT phasor estimator considering out-of-band interference. IEEE Trans. Instrum. Meas. 2022;71:1–14. doi: 10.1109/TIM.2022.3203459. DOI

Talatahari S, Azizi M, Tolouei M, Talatahari B, Sareh P. Crystal structure algorithm (CryStAl): A metaheuristic optimization method. IEEE Access. 2021;9:71244–71261. doi: 10.1109/ACCESS.2021.3079161. DOI

Tavazoei MS, Haeri M. Comparison of different one-dimensional maps as chaotic search pattern in chaos optimization algorithms. Appl. Math. Comput. 2007;187:1076–1085. doi: 10.1016/j.amc.2006.09.087. DOI

Karaboga D, Akay B. A comparative study of artificial bee colony algorithm. Appl. Math. Comput. 2009;214:108–132. doi: 10.1016/j.amc.2009.03.090. DOI

Fathy A. Bald eagle search optimizer-based energy management strategy for microgrid with renewable sources and electric vehicles. Appl. Energy. 2023;334:120688. doi: 10.1016/j.apenergy.2023.120688. DOI

Zhang X, Wang Z, Lu Z. Multi-objective load dispatch for microgrid with electric vehicles using modified gravitational search and particle swarm optimization algorithm. Appl. Energy. 2022;306:118018. doi: 10.1016/j.apenergy.2021.118018. DOI

Liang J, Feng J, Fang Z, Lu Y, Yin G, Mao X, Wu J, Wang F. An energy-oriented torque-vector control framework for distributed drive electric vehicles. IEEE Trans. Transp. Electrif. 2023;9:4014–4031. doi: 10.1109/TTE.2022.3231933. DOI

Zhang X, Wang Y, Yuan X, Shen Y, Lu Z. Adaptive dynamic surface control with disturbance observers for battery/supercapacitor-based hybrid energy sources in electric vehicles. IEEE Trans. Transp. Electrif. 2023;9:5165–5181. doi: 10.1109/TTE.2022.3194034. DOI

Zhang X, Lu Z, Yuan X, Wang Y, Shen X. L2-gain adaptive robust control for hybrid energy storage system in electric vehicles. IEEE Trans. Power Electron. 2021;36:7319–7332. doi: 10.1109/TPEL.2020.3041653. DOI

Zhang X, Gong L, Zhao X, Li R, Yang L, Wang B. Voltage and frequency stabilization control strategy of virtual synchronous generator based on small signal model. Energy Rep. 2023;9:583–590. doi: 10.1016/j.egyr.2023.03.071. DOI

Yan C, Zou Y, Wu Z, Maleki A. Effect of various design configurations and operating conditions for optimization of a wind/solar/hydrogen/fuel cell hybrid microgrid system by a bio-inspired algorithm. Int. J. Hydrogen Energy. 2024;60:378–391. doi: 10.1016/j.ijhydene.2024.02.004. DOI

Zheng S, Hai Q, Zhou X, Stanford RJ. A novel multi-generation system for sustainable power, heating, cooling, freshwater, and methane production: Thermodynamic, economic, and environmental analysis. Energy. 2024;290:130084. doi: 10.1016/j.energy.2023.130084. DOI

Li S, Zhao X, Liang W, Hossain MT, Zhang Z. A fast and accurate calculation method of line breaking power flow based on taylor expansion. Front. Energy Res. 2022;10:943946. doi: 10.3389/fenrg.2022.943946. DOI

Fang Z, Liang J, Tan C, Tian Q, Pi D, Yin G. Enhancing robust driver assistance control in distributed drive electric vehicles through integrated AFS and DYC technology. IEEE Trans. Intell. Veh. 2024 doi: 10.1109/TIV.2024.3368050. DOI

Ghaedi A, Dehnavi SD, Fotoohabadi H. Probabilistic scheduling of smart electric grids considering plug-in hybrid electric vehicles. J. Intell. Fuzzy Syst. 2016;31:1329–1340. doi: 10.3233/IFS-162199. DOI

Song J, Mingotti A, Zhang J, Peretto L, Wen H. Accurate damping factor and frequency estimation for damped real-valued sinusoidal signals. IEEE Trans. Instrum. Meas. 2022;71:1–4. doi: 10.1109/TIM.2022.3220300. DOI

Qian K, Zhou C, Allan M, Yuan Y. Modeling of load demand due to EV battery charging in distribution systems. IEEE Trans. Power Syst. 2011;26:802–810. doi: 10.1109/TPWRS.2010.2057456. DOI

Fathy A, Abdelaziz AY. Single and multi-objective operation management of micro-grid using krill herd optimization and ant lion optimizer algorithms. Int. J. Energy Environ. Eng. 2018;9:257–271. doi: 10.1007/s40095-018-0266-8. DOI

Aghajani G, Ghadimi N. Multi-objective energy management in a micro-grid. Energy Rep. 2018;4:218–225. doi: 10.1016/j.egyr.2017.10.002. DOI

Aghajani GR, Shayanfar HA, Shayeghi H. Presenting a multi-objective generation scheduling model for pricing demand response rate in micro-grid energy management. Energy Convers. Manag. 2015;106:308–321. doi: 10.1016/j.enconman.2015.08.059. DOI

Fang Z, Wang J, Liang J, Yan Y, Pi D, Zhang H, Yin G. Authority allocation strategy for shared steering control considering human-machine mutual trust level. IEEE Trans. Intell. Veh. 2024;9:2002–2015. doi: 10.1109/TIV.2023.3300152. DOI

Liang J, Feng J, Lu Y, Yin G, Zhuang W, Mao X. A direct yaw moment control framework through robust T-S fuzzy approach considering vehicle stability margin. IEEE/ASME Trans. Mechatron. 2024;29:166–178. doi: 10.1109/TMECH.2023.3274689. DOI

Li Z, Wang Y, Zhang R, Ding F, Wei C, Lu J-G. A LiDAR-OpenStreetMap matching method for vehicle global position initialization based on boundary directional feature extraction. IEEE Trans. Intell. Veh. 2024 doi: 10.1109/TIV.2024.3393229. DOI

Bai X, He Y, Xu M. Low-thrust reconfiguration strategy and optimization for formation flying using Jordan normal form. IEEE Trans. Aerosp. Electron. Syst. 2021;57:3279–3295. doi: 10.1109/TAES.2021.3074204. DOI

Venkatesh P, Gnanadass R, Padhy NP. Comparison and application of evolutionary programming techniques to combined economic emission dispatch with line flow constraints. IEEE Trans. Power Syst. 2003;18:688–697. doi: 10.1109/TPWRS.2003.811008. DOI

Liang J, Lu Y, Wang F, Feng J, Pi D, Yin G, Li Y. ETS-based human-machine robust shared control design considering the network delays. IEEE Trans. Autom. Sci. Eng. 2024 doi: 10.1109/TASE.2024.3383094. DOI

Chen J, Xu J, Zhang Y, Zhao J, Hou J, Wang Y. Geometrical state-plane-based synchronous rectification scheme for LLC converter in EVs. IEEE Trans. Transp. Electrif. 2024 doi: 10.1109/TTE.2024.3383208. DOI

Karthik N, Parvathy AK, Arul R, Padmanathan K. Artificial Intelligence and Evolutionary Computations in Engineering Systems. Springer; 2022. A new heuristic algorithm for economic load dispatch incorporating wind power; pp. 47–65.

Karthik, N., Rajagopalan, A., Prakash, V. R., Montoya, O. D., Sowmmiya, U. & Kanimozhi, R. (2023). Environmental economic load dispatch considering demand response using a new heuristic optimization algorithm. In AI Techniques for Renewable Source Integration and Battery Charging Methods in Electric Vehicle Applications, pp. 220-242. 10.4018/978-1-6684-8816-4.ch013

Nagarajan K, Rajagopalan A, Selvaraj P, Ravi HK, Kareem IA. Demand response-integrated economic emission dispatch using improved remora optimization algorithm. In: Global IGI, editor. AI Approaches to Smart Sustain and Power Systems. 2024. pp. 120–140.

Biswas PP, Suganthan PN, Amaratunga GA. Optimal power flow solutions incorporating stochastic wind and solar power. Energy Convers. Manag. 2017;148:1194–1207. doi: 10.1016/j.enconman.2017.06.071. DOI

Abdullah, M., Javaid, N., Chand, A., Khan, Z. A., Waqas, M. & Abbas, Z. Multi-objective optimal power flow using improved multi-objective multi-verse algorithm. In Web, Artificial Intelligence and Network Applications: Proceedings of the Workshops of the 33rd International Conference on Advanced Information Networking and Applications (WAINA-2019), Vol. 33 (pp. 1071–1083, Springer International Publishing, 2019).

Abdullah, M., Javaid, N., Khan, I. U., Khan, Z. A., Chand, A. & Ahmad, N. Optimal power flow with uncertain renewable energy sources using flower pollination algorithm. In Advanced Information Networking and Applications: Proceedings of the 33rd International Conference on Advanced Information Networking and Applications (AINA-2019), Vol. 33 (pp. 95–107, Springer International Publishing, 2020).

Biswas PP, Suganthan PN, Qu BY, Amaratunga GA. Multiobjective economic-environmental power dispatch with stochastic wind-solar-small hydro power. Energy. 2018;150:1039–1057. doi: 10.1016/j.energy.2018.03.002. DOI

Karthik N, Parvathy AK, Arul R, et al. Multi-objective optimal power flow using a new heuristic optimization algorithm with the incorporation of renewable energy sources. Int. J. Energy Environ. Eng. 2021 doi: 10.1007/s40095-021-00397-x. DOI

Bai X, Xu M, Li Q, Yu L. Trajectory-battery integrated design and its application to orbital maneuvers with electric pump-fed engines. Adv. Sp. Res. 2022;70:825–841. doi: 10.1016/j.asr.2022.05.014. DOI

Rong Y, Xu Z, Liu J, Liu H, Ding J, Liu X, Luo W, Zhang C, Gao J. Du-bus: A realtime bus waiting time estimation system based on multi-source data. IEEE Trans. Intell. Transp. Syst. 2022;23:24524–24539. doi: 10.1109/TITS.2022.3210170. DOI

Oubelaid A, Kakouche K, Belkhier Y, Khosravi N, Taib N, Rekioua T, Bajaj M, Rekioua D, Tuka MB. New coordinated drive mode switching strategy for distributed drive electric vehicles with energy storage system. Sci. Rep. 2024;14:6448. doi: 10.1038/s41598-024-56209-9. PubMed DOI PMC

Punyavathi R, Pandian A, Singh AR, Bajaj M, Tuka MB, Blazek V. Sustainable power management in light electric vehicles with hybrid energy storage and machine learning control. Sci. Rep. 2024;14:5661. doi: 10.1038/s41598-024-55988-5. PubMed DOI PMC

Naoussi SRD, Saatong KT, Molu RJJ, Mbasso WF, Bajaj M, Louzazni M, Berhanu M, Kamel S. Enhancing MPPT performance for partially shaded photovoltaic arrays through backstepping control with Genetic algorithm-optimized gains. Sci. Rep. 2024;14:3334. doi: 10.1038/s41598-024-53721-w. PubMed DOI PMC

Nagarajan K, Rajagopalan A, Bajaj M, Sitharthan R, Dost Mohammadi SA, Blazek V. Optimizing dynamic economic dispatch through an enhanced Cheetah-inspired algorithm for integrated renewable energy and demand-side management. Sci. Rep. 2024;14:3091. doi: 10.1038/s41598-024-53688-8. PubMed DOI PMC

Fathy A, Rezk H, Ferahtia S, Ghoniem RM, Alkanhel R. An efficient honey badger algorithm for scheduling the microgrid energy management. Energy Rep. 2023;9:2058–2074. doi: 10.1016/j.egyr.2023.01.028. DOI

Improved Lyrebird optimization for multi microgrid sectionalizing and cost efficient scheduling of distributed generation

Comprehensive framework for smart residential demand side management with electric vehicle integration and advanced optimization techniques

A hybrid demand-side policy for balanced economic emission in microgrid systems

Machine learning-based energy management and power forecasting in grid-connected microgrids with multiple distributed energy sources