considering economic dispatch. Energies 2019:12. https://doi.org/10.3390/en12081457.

[153] Mohammadzadeh A, Rathinasamy S. Energy management in photovoltaic battery hybrid systems: A novel type-2 fuzzy control. Int J Hydrog Energy 2020;45: 20970-82. https://doi.org/10.1016/j.ijhydene.2020.05.187.

[154] Jafari M, Malekjamshidi Z, Zhu J, Khooban M-H. A novel Predictive fuzzy logic- based energy management system for grid-connected and off-grid operation of residential smart microgrids. IEEE J Emerg Sel Top Power Electron 2020;8: 1391-404. https://doi.org/10.1109/JESTPE.2018.2882509.

[155] Tifour B, Moussa B, Ahmed H, Camel T. Monitoring and energy management approach for a fuel cell hybrid electric vehicle. Diagnostyka 2020;21:15-29. https://doi.org/10.29354/diag/123996.

[156] Keshtkar A, Arzanpour S. An adaptive fuzzy logic system for residential energy management in smart grid environments. Appl Energy 2017;186:68-81. https://doi.org/10.1016/j.apenergy.2016.11.028.

[157] Venayagamoorthy GK, Sharma RK, Gautam PK, Ahmadi A. Dynamic energy management system for a smart microgrid. IEEE Trans Neural Networks Learn Syst 2016;27:1643-56. https://doi.org/10.1109/TNNLS.2016.2514358.

[158] Gamez Urias ME, Sanchez EN, Ricalde LJ. Electrical microgrid Optimization via a new recurrent neural network. IEEE J Syst J 2015;9:945-53. https://doi.org/10.1109/JSYST.2014.2305494.

[159] Garcia P, Garcia CA, Fernandez LM, Llorens F, Jurado F. ANFIS-based control of a grid-connected hybrid system integrating renewable energies, hydrogen and batteries. IEEE Trans Ind Informatics 2014;10:1107-17. https://doi.org/10.1109/TII.2013.2290069.

[160] Bogaraj T, Kanakaraj J. A novel energy management scheme using anfis for independent microgrid. Int J Renew Energy Res 2016;6:735-46. https://doi.org/10.1234/ijrjer.v6i3.458.

[161] Zou Y, Liu T, Liu D, Sun F. Reinforcement learning-based real-time energy management for a hybrid tracked vehicle. Appl Energy 2016;171:372-82. https://doi.org/10.1016/j.apenergy.2016.03.082.

[162] Liu Y, Zhang D, Gooi HB. Optimization strategy based on deep reinforcement learning for home energy management. CSEE J Power Energy Syst 2020;6: 572-82. https://doi.org/10.17775/SEEJPEES.2019.02890.

[163] Ghorbani S, Rahmani R, Unland R. Multi-agent Autonomous Decision Making in Smart Micro-Grids' Energy Management: A Decentralized Approach 2017: 223-37. https://doi.org/10.1007/978-3-319-64798-2_14.

[164] Anvari-Moghaddam A, Rahimi-Kian A, Mirian MS, Guerrero JM. A multi-agent based energy management solution for integrated buildings and microgrid system. Appl Energy 2017;203:41-56. https://doi.org/10.1016/j.apenergy.2017.06.007.

[165] Karavas C-S, Kyriakarakos G, Arvanitis KG, Papadakis G. A multi-agent decentralized energy management system based on distributed intelligence for the design and control of autonomous polygeneration microgrids. Energy Convers Manag 2015;103:166-79. https://doi.org/10.1016/j.enconman.2015.06.021.

[166] Wu J, Wei Z, Li W, Wang Y, Li Y, Sauer D. Battery thermal- and health-constrained energy Management for Hybrid Electric bus based on soft actor-critic DRL algorithm. IEEE Trans Ind Informatics 2021:1. https://doi.org/10.1109/TII.2020.3014599.

[167] Wu J, Wei Z, Liu K, Quan Z, Li Y. Battery-involved energy Management for Hybrid Electric bus Based on expert-assistance deep deterministic policy gradient algorithm. IEEE Trans Veh Technol 2020;69:12786-96. https://doi.org/10.1109/TVT.2020.3025627.

[168] Byrne RH, Nguyen TA, Copp DA, Chalamala BR, Guky I. Energy management and Optimization methods for grid energy storage systems. IEEE Access 2018;6: 13231-60. https://doi.org/10.1109/ACCESS.2017.2741578.

[169] Wang X, Huang Y, Guo F, Zhao W. Energy Management Strategy based on Dynamic Programming Considering Engine Dynamic Operating Conditions Optimization. 2020 39th Chinese control Conf., IEEE. 2020. p. 5485-92. https://doi.org/10.23919/CCC50068.2020.9188933.

[170] Zhang X, Ren X, Zheng C, Yuan Y, Diao L. Dynamic Programming in energy management Strategy for optimizing multi-energy drive system. Lect Notes Electr Eng 2020;638:471-80. https://doi.org/10.1007/978-981-15-2862-0_45.

[171] Zhang S, Xiong R. Adaptive energy management of a plug-in hybrid electric vehicle based on driving pattern recognition and dynamic programming. Appl Energy 2015;155:68-78. https://doi.org/10.1016/j.apenergy.2015.06.003.

[172] Tsikalakis AG, Hatziarygiou ND. Centralized control for optimizing microgrids operation. IEEE Trans Energy Convers 2011;23:241-8. https://doi.org/10.1109/PES.2011.6039737.

[173] Panwar LK, Konda SR, Verma A, Panigrahi BK, Kumar R. Operation window constrained strategic energy management of microgrid with electric vehicle and distributed resources. IET Gener Transm Distrib 2017;11:615-26. https://doi.org/10.1049/iet-gtd.2016.0654.

[174] Leroy T, Vidal-Naquet F, Tona P. Stochastic Dynamic Programming based energy management of HEV's: an experimental validation. IFAC Proc 2014;47:4813-8. https://doi.org/10.3182/20140824-6-2A-1003.01868.

[175] Panday A, Bansal HO. Energy management strategy for hybrid electric vehicles using genetic algorithm. J Renew Sustain Energy 2016;8:015701. https://doi.org/10.1063/1.4938552.

[176] Lü X, Wu Y, Lian J, Zhang Y, Chen C, Wang P, et al. Energy management of hybrid electric vehicles: A review of energy optimization of fuel cell hybrid power system based on genetic algorithm. Energy Convers Manag 2020;205:112474. https://doi.org/10.1016/j.enconman.2020.112474.

[177] Alhasnawi BN, Jasim BH, Bureš V, Sedhom BE, Alhasnawi AN, Abbassi R, et al. A novel economic dispatch in the stand-alone system using improved butterfly optimization algorithm. Energy Strateg Rev 2023;49:101135. https://doi.org/10.1016/j.esr.2023.101135.

[178] Alhasnawi BN, Jasim BH, Siano P, Alhelou HH, Al-Hinai A. A novel solution for day-ahead scheduling problems using the IoT-based bald eagle search Optimization algorithm. Inventions 2022;7:48. https://doi.org/10.3390/inventions7030048.

[179] Alhasnawi BN, Jasim BH, Jasim AM, Bureš V, Alhasnawi AN, Homod RZ, et al. A multi-objective improved cockroach swarm algorithm approach for apartment energy management systems. Information 2023;14:521. https://doi.org/10.3390/info14100521.

[180] Shi W, Li N, Chu CC, Gadh R. Real-time energy Management in Microgrids. IEEE Trans Smart Grid 2017;8:228-38. https://doi.org/10.1109/TSG.2015.2462294.

[181] Motapon SN, Dessaint L-A, Al-Haddad K. A robust H2-consumption-minimization- based energy management Strategy for a fuel cell hybrid emergency power system of more electric aircraft. IEEE Trans Ind Electron 2014;61:6148-56. https://doi.org/10.1109/TIE.2014.2308148.

[182] Lei Z, Qin D, Hou L, Peng J, Liu Y, Chen Z. An adaptive equivalent consumption minimization strategy for plug-in hybrid electric vehicles based on traffic information. Energy 2020;190:116409. https://doi.org/10.1016/j.energy.2019.116409.

[183] Borhan H, Member S, Vahidi A, Phillips AM, Kuang ML, Kolmanovsky IV, et al. MPC-based energy Management of a Power-Split Hybrid Electric Vehicle. IEEE Trans Control Syst Technol 2012;20:593-603. https://doi.org/10.1016/j.powsour.2016.11.106.

[184] Huang Y, Wang H, Khajepour A, He H, Ji J. Model predictive control power management strategies for HEVs: A review. J Power Sources 2017;341:91-106. https://doi.org/10.1016/j.powsour.2016.11.106.

[185] Peng H, Li J, Löwenstein L, Hameyer K. A scalable, causal, adaptive energy management strategy based on optimal control theory for a fuel cell hybrid railway vehicle. Appl Energy 2020:267. https://doi.org/10.1016/j.apenergy.2020.114987.

[186] Dhiilf M, Jawadi S, Lashab A, Guerrero JM, Cherif A. An efficient external energy maximization-based energy management Strategy for a battery/supercapacitor of a Micro grid system. Int J Comput Sci Netw Secur 2020;20:196-203.

[187] Olatomiwa L, Mekhilef S, Ismail MS, Moghavvemi M. Energy management strategies in hybrid renewable energy systems based on a review. Renew Sust Energ Rev 2016;62:821-35. https://doi.org/10.1016/j.rser.2016.05.040.

[188] Vivas FJ, De las Heras A, Segura F, Andújar JM. A review of energy management strategies for renewable hybrid energy systems with hydrogen backup. Renew Sust Energ Rev 2018;82:126-55. https://doi.org/10.1016/j.rser.2017.09.014.

[189] Rathor SK, Saxena D. Energy management system for smart grid: an overview and key issues. Int J Energy Res 2020;44:4067-109. https://doi.org/10.1002/er.4883.

[190] Dursun E, Kilic O. Comparative evaluation of different power management strategies of a stand-alone PV/wind/PEMFC hybrid power system. Int J Electr Power Energy Syst 2012;34:81-9. https://doi.org/10.1016/j.ijepes.2011.08.025.

[191] Pascual J, Barricarte J, Sanchis P, Marroyo L. Energy management strategy for a renewable-based residential microgrid with generation and demand forecasting. Appl Energy 2015;158:12-25. https://doi.org/10.1016/j.apenergy.2015.08.040.

[192] Abedi S, Alimardani A, Gharehpetian GB, Riahy GH, Hosseinian SH. A comprehensive method for optimal power management and design of hybrid RES-based autonomous energy systems. Renew Sust Energ Rev 2012;16:1577-87. https://doi.org/10.1016/j.rser.2011.11.030.

[193] Feroldi D, Degliuomini LN, Basualdo M. Energy management of a hybrid system based on wind-solar power sources and bioethanol. Chem Eng Res Des 2013;91: 1440-55. https://doi.org/10.1016/j.chedr.2013.03.007.

[194] Bruni G, Cordiner S, Mulone V, Rocco V, Spagnolo F. A study on the energy management in domestic micro-grids based on model Predictive control strategies. Energy Convers Manag 2015;102:50-8. https://doi.org/10.1016/j.enconman.2015.01.067.

[195] Basir Khan MR, Jidin R, Pasupuleti J. Multi-agent based distributed control architecture for microgrid energy management and optimization. Energy Convers Manag 2016;112:288-307. https://doi.org/10.1016/j.enconman.2016.01.011.

[196] Brka A, Kothapalli G, Al-Abdeli YM. Predictive power management strategies for stand-alone hydrogen systems: lab-scale validation. Int J Hydrog Energy 2015;40: 9907-16. https://doi.org/10.1016/j.ijhydene.2015.06.081.

[197] Kyriakarakos G, Dounis AI, Arvanitis KG, Papadakis G. A fuzzy logic energy management system for polygeneration microgrids. Renew Energy 2012;41: 315-27. https://doi.org/10.1016/j.renene.2011.11.019.

[198] Ferahtia S, Djerioui A, Zeghlache S, Houari A. A hybrid power system based on fuel cell, photovoltaic source and supercapacitor. SN Appl Sci 2020;2:940. https://doi.org/10.1007/s42452-020-2709-0.

[199] Sethlaolo D, Xia X. Combined residential demand side management strategies with coordination and economic analysis. Int J Electr Power Energy Syst 2016;79: 150-60. https://doi.org/10.1016/j.ijepes.2016.01.016.

[200] Moghaddam AA, Seifi A, Niknam T, Alizadeh Pahlavani MR. Multi-objective operation management of a renewable MG (micro-grid) with back-up micro- turbine/fuel cell/battery hybrid power source. Energy 2011;36:6490-507. https://doi.org/10.1016/j.energy.2011.09.017.

[201] Mohammadi S, Soleymani S, Mozafari B. Scenario-based stochastic operation management of MicroGrid including wind, photovoltaic, Micro-turbine, fuel cell and energy storage devices. Int J Electric Power Energy Syst 2014;54:525-35. https://doi.org/10.1016/j.ijepes.2013.08.004.
considering economic dispatch. Energies 2019:12. https://doi.org/10.3390/en12081457.

[153] Mohammadzadeh A, Rathinasamy S. Energy management in photovoltaic battery hybrid systems: A novel type-2 fuzzy control. Int J Hydrog Energy 2020;45: 20970-82. https://doi.org/10.1016/j.ijhydene.2020.05.187.

[154] Jafari M, Malekjamshidi Z, Zhu J, Khooban M-H. A novel Predictive fuzzy logic- based energy management system for grid-connected and off-grid operation of residential smart microgrids. IEEE J Emerg Sel Top Power Electron 2020;8: 1391-404. https://doi.org/10.1109/JESTPE.2018.2882509.

[155] Tifour B, Moussa B, Ahmed H, Camel T. Monitoring and energy management approach for a fuel cell hybrid electric vehicle. Diagnostyka 2020;21:15-29. https://doi.org/10.29354/diag/123996.

[156] Keshtkar A, Arzanpour S. An adaptive fuzzy logic system for residential energy management in smart grid environments. Appl Energy 2017;186:68-81. https:// doi.org/10.1016/j.apenergy.2016.11.028.

[157] Venayagamoorthy GK, Sharma RK, Gautam PK, Ahmadi A. Dynamic energy management system for a smart microgrid. IEEE Trans Neural Networks Learn Syst 2016;27:1643-56. https://doi.org/10.1109/TNNLS.2016.2514358.

[158] Gamez Urias ME, Sanchez EN, Ricalde LJ. Electrical microgrid Optimization via a new recurrent neural network. IEEE J Syst J 2015;9:945-53. https://doi.org/ 10.1109/JSYST.2014.2305494.

[159] Garcia P, Garcia CA, Fernandez LM, Llorens F, Jurado F. ANFIS-based control of a grid-connected hybrid system integrating renewable energies, hydrogen and batteries. IEEE Trans Ind Informatics 2014;10:1107-17. https://doi.org/10.1109/ TII.2013.2290069.

[160] Bogaraj T, Kanakaraj J. A novel energy management scheme using anfis for independent microgrid. Int J Renew Energy Res 2016;6:735-46. https://doi.org/ 10.1234/ijrjer.v6i3.458.

[161] Zou Y, Liu T, Liu D, Sun F. Reinforcement learning-based real-time energy management for a hybrid tracked vehicle. Appl Energy 2016;171:372-82. https://doi.org/10.1016/j.apenergy.2016.03.082.

[162] Liu Y, Zhang D, Gooi HB. Optimization strategy based on deep reinforcement learning for home energy management. CSEE J Power Energy Syst 2020;6: 572-82. https://doi.org/10.17775/SEEJPEES.2019.02890.

[163] Ghorbani S, Rahmani R, Unland R. Multi-agent Autonomous Decision Making in Smart Micro-Grids' Energy Management: A Decentralized Approach 2017: 223-37. https://doi.org/10.1007/978-3-319-64798-2_14.

[164] Anvari-Moghaddam A, Rahimi-Kian A, Mirian MS, Guerrero JM. A multi-agent based energy management solution for integrated buildings and microgrid system. Appl Energy 2017;203:41-56. https://doi.org/10.1016/j. apenergy.2017.06.007.

[165] Karavas C-S, Kyriakarakos G, Arvanitis KG, Papadakis G. A multi-agent decentralized energy management system based on distributed intelligence for the design and control of autonomous polygeneration microgrids. Energy Convers Manag 2015;103:166-79. https://doi.org/10.1016/j.enconman.2015.06.021.

[166] Wu J, Wei Z, Li W, Wang Y, Li Y, Sauer D. Battery thermal- and health-constrained energy Management for Hybrid Electric bus based on soft actor-critic DRL algorithm. IEEE Trans Ind Informatics 2021:1. https://doi.org/10.1109/ TII.2020.3014599.

[167] Wu J, Wei Z, Liu K, Quan Z, Li Y. Battery-involved energy Management for Hybrid Electric bus Based on expert-assistance deep deterministic policy gradient algorithm. IEEE Trans Veh Technol 2020;69:12786-96. https://doi.org/10.1109/ TVT.2020.3025627.

[168] Byrne RH, Nguyen TA, Copp DA, Chalamala BR, Guky I. Energy management and Optimization methods for grid energy storage systems. IEEE Access 2018;6: 13231-60. https://doi.org/10.1109/ACCESS.2017.2741578.

[169] Wang X, Huang Y, Guo F, Zhao W. Energy Management Strategy based on Dynamic Programming Considering Engine Dynamic Operating Conditions Optimization. 2020 39th Chinese control Conf., IEEE. 2020. p. 5485-92. https:// doi.org/10.23919/CCC50068.2020.9188933.

[170] Zhang X, Ren X, Zheng C, Yuan Y, Diao L. Dynamic Programming in energy management Strategy for optimizing multi-energy drive system. Lect Notes Electr Eng 2020;638:471-80. https://doi.org/10.1007/978-981-15-2862-0_45.

[171] Zhang S, Xiong R. Adaptive energy management of a plug-in hybrid electric vehicle based on driving pattern recognition and dynamic programming. Appl Energy 2015;155:68-78. https://doi.org/10.1016/j.apenergy.2015.06.003.

[172] Tsikalakis AG, Hatziarygiou ND. Centralized control for optimizing microgrids operation. IEEE Trans Energy Convers 2011;23:241-8. https://doi.org/10.1109/ PES.2011.6039737.

[173] Panwar LK, Konda SR, Verma A, Panigrahi BK, Kumar R. Operation window constrained strategic energy management of microgrid with electric vehicle and distributed resources. IET Gener Transm Distrib 2017;11:615-26. https://doi. org/10.1049/iet-gtd.2016.0654.

[174] Leroy T, Vidal-Naquet F, Tona P. Stochastic Dynamic Programming based energy management of HEV's: an experimental validation. IFAC Proc 2014;47:4813-8. https://doi.org/10.3182/20140824-6-2A-1003.01868.

[175] Panday A, Bansal HO. Energy management strategy for hybrid electric vehicles using genetic algorithm. J Renew Sustain Energy 2016;8:015701. https://doi. org/10.1063/1.4938552.

[176] Lü X, Wu Y, Lian J, Zhang Y, Chen C, Wang P, et al. Energy management of hybrid electric vehicles: A review of energy optimization of fuel cell hybrid power system based on genetic algorithm. Energy Convers Manag 2020;205:112474. https:// doi.org/10.1016/j.enconman.2020.112474.

[177] Alhasnawi BN, Jasim BH, Bureš V, Sedhom BE, Alhasnawi AN, Abbassi R, et al. A novel economic dispatch in the stand-alone system using improved butterfly optimization algorithm. Energy Strateg Rev 2023;49:101135. https://doi.org/ 10.1016/j.esr.2023.101135.

[178] Alhasnawi BN, Jasim BH, Siano P, Alhelou HH, Al-Hinai A. A novel solution for day-ahead scheduling problems using the IoT-based bald eagle search Optimization algorithm. Inventions 2022;7:48. https://doi.org/10.3390/ inventions7030048.

[179] Alhasnawi BN, Jasim BH, Jasim AM, Bureš V, Alhasnawi AN, Homod RZ, et al. A multi-objective improved cockroach swarm algorithm approach for apartment energy management systems. Information 2023;14:521. https://doi.org/ 10.3390/info14100521.

[180] Shi W, Li N, Chu CC, Gadh R. Real-time energy Management in Microgrids. IEEE Trans Smart Grid 2017;8:228-38. https://doi.org/10.1109/TSG.2015.2462294.

[181] Motapon SN, Dessaint L-A, Al-Haddad K. A robust H2-consumption-minimization- based energy management Strategy for a fuel cell hybrid emergency power system of more electric aircraft. IEEE Trans Ind Electron 2014;61:6148-56. https://doi.org/10.1109/TIE.2014.2308148.

[182] Lei Z, Qin D, Hou L, Peng J, Liu Y, Chen Z. An adaptive equivalent consumption minimization strategy for plug-in hybrid electric vehicles based on traffic information. Energy 2020;190:116409. https://doi.org/10.1016/j. energy.2019.116409.

[183] Borhan H, Member S, Vahidi A, Phillips AM, Kuang ML, Kolmanovsky IV, et al. MPC-based energy Management of a Power-Split Hybrid Electric Vehicle. IEEE Trans Control Syst Technol 2012;20:593-603. https://doi.org/10.1016/j.powsour.2016.11.106.

[184] Huang Y, Wang H, Khajepour A, He H, Ji J. Model predictive control power management strategies for HEVs: A review. J Power Sources 2017;341:91-106. https://doi.org/10.1016/j.powsour.2016.11.106.

[185] Peng H, Li J, Löwenstein L, Hameyer K. A scalable, causal, adaptive energy management strategy based on optimal control theory for a fuel cell hybrid railway vehicle. Appl Energy 2020:267. https://doi.org/10.1016/j. apenergy.2020.114987.

[186] Dhiilf M, Jawadi S, Lashab A, Guerrero JM, Cherif A. An efficient external energy maximization-based energy management Strategy for a battery/supercapacitor of a Micro grid system. Int J Comput Sci Netw Secur 2020;20:196-203.

[187] Olatomiwa L, Mekhilef S, Ismail MS, Moghavvemi M. Energy management strategies in hybrid renewable energy systems based on a review. Renew Sust Energ Rev 2016;62:821-35. https://doi.org/10.1016/j.rser.2016.05.040.

[188] Vivas FJ, De las Heras A, Segura F, Andújar JM. A review of energy management strategies for renewable hybrid energy systems with hydrogen backup. Renew Sust Energ Rev 2018;82:126-55. https://doi.org/10.1016/j.rser.2017.09.014.

[189] Rathor SK, Saxena D. Energy management system for smart grid: an overview and key issues. Int J Energy Res 2020;44:4067-109. https://doi.org/10.1002/ er.4883.

[190] Dursun E, Kilic O. Comparative evaluation of different power management strategies of a stand-alone PV/wind/PEMFC hybrid power system. Int J Electr Power Energy Syst 2012;34:81-9. https://doi.org/10.1016/j.ijepes.2011.08.025.

[191] Pascual J, Barricarte J, Sanchis P, Marroyo L. Energy management strategy for a renewable-based residential microgrid with generation and demand forecasting. Appl Energy 2015;158:12-25. https://doi.org/10.1016/j.apenergy.2015.08.040.

[192] Abedi S, Alimardani A, Gharehpetian GB, Riahy GH, Hosseinian SH. A comprehensive method for optimal power management and design of hybrid RES-based autonomous energy systems. Renew Sust Energ Rev 2012;16:1577-87. https://doi.org/10.1016/j.rser.2011.11.030.

[193] Feroldi D, Degliuomini LN, Basualdo M. Energy management of a hybrid system based on wind-solar power sources and bioethanol. Chem Eng Res Des 2013;91: 1440-55. https://doi.org/10.1016/j.chedr.2013.03.007.

[194] Bruni G, Cordiner S, Mulone V, Rocco V, Spagnolo F. A study on the energy management in domestic micro-grids based on model Predictive control strategies. Energy Convers Manag 2015;102:50-8. https://doi.org/10.1016/j. enconman.2015.01.067.

[195] Basir Khan MR, Jidin R, Pasupuleti J. Multi-agent based distributed control architecture for microgrid energy management and optimization. Energy Convers Manag 2016;112:288-307. https://doi.org/10.1016/j.enconman.2016.01.011.

[196] Brka A, Kothapalli G, Al-Abdeli YM. Predictive power management strategies for stand-alone hydrogen systems: lab-scale validation. Int J Hydrog Energy 2015;40: 9907-16. https://doi.org/10.1016/j.ijhydene.2015.06.081.

[197] Kyriakarakos G, Dounis AI, Arvanitis KG, Papadakis G. A fuzzy logic energy management system for polygeneration microgrids. Renew Energy 2012;41: 315-27. https://doi.org/10.1016/j.renene.2011.11.019.

[198] Ferahtia S, Djerioui A, Zeghlache S, Houari A. A hybrid power system based on fuel cell, photovoltaic source and supercapacitor. SN Appl Sci 2020;2:940. https://doi.org/10.1007/s42452-020-2709-0.

[199] Sethlaolo D, Xia X. Combined residential demand side management strategies with coordination and economic analysis. Int J Electr Power Energy Syst 2016;79: 150-60. https://doi.org/10.1016/j.ijepes.2016.01.016.

[200] Moghaddam AA, Seifi A, Niknam T, Alizadeh Pahlavani MR. Multi-objective operation management of a renewable MG (micro-grid) with back-up micro- turbine/fuel cell/battery hybrid power source. Energy 2011;36:6490-507. https://doi.org/10.1016/j.energy.2011.09.017.

[201] Mohammadi S, Soleymani S, Mozafari B. Scenario-based stochastic operation management of MicroGrid including wind, photovoltaic, Micro-turbine, fuel cell and energy storage devices. Int J Electric Power Energy Syst 2014;54:525-35. https://doi.org/10.1016/j.ijepes.2013.08.004.