RESEARCH ARTICLE
An intelligent decentralized energy management strategy for the optimal electric vehicles' charging in low-voltage islanded microgrids
Vasileios Boglou,
Christos-Spyridon Karavas,
Athanasios Karlis,
Konstantinos Arvanitis,
Corresponding Author
Vasileios Boglou
Department of Electrical and Computer Engineering, School of Engineering, Democritus University of Thrace, Xanthi, Greece
Correspondence
Vasileios Boglou, Department of Electrical and Computer Engineering, School of Engineering, Democritus University of Thrace, Xanthi, 67100, Greece.
Email: [email protected]
Search for more papers by this authorChristos-Spyridon Karavas
Department of Natural Resources Development and Agricultural Engineering, School of Environment and Agricultural Engineering, Agricultural University of Athens, Athens, Greece
Research, Technology & Development Department, Independent Power Transmission Operator (IPTO) S.A., Athens, Greece
Search for more papers by this authorAthanasios Karlis
Department of Electrical and Computer Engineering, School of Engineering, Democritus University of Thrace, Xanthi, Greece
Search for more papers by this authorKonstantinos Arvanitis
Department of Natural Resources Development and Agricultural Engineering, School of Environment and Agricultural Engineering, Agricultural University of Athens, Athens, Greece
Search for more papers by this authorVasileios Boglou,
Christos-Spyridon Karavas,
Athanasios Karlis,
Konstantinos Arvanitis,
Corresponding Author
Vasileios Boglou
Department of Electrical and Computer Engineering, School of Engineering, Democritus University of Thrace, Xanthi, Greece
Correspondence
Vasileios Boglou, Department of Electrical and Computer Engineering, School of Engineering, Democritus University of Thrace, Xanthi, 67100, Greece.
Email: [email protected]
Search for more papers by this authorChristos-Spyridon Karavas
Department of Natural Resources Development and Agricultural Engineering, School of Environment and Agricultural Engineering, Agricultural University of Athens, Athens, Greece
Research, Technology & Development Department, Independent Power Transmission Operator (IPTO) S.A., Athens, Greece
Search for more papers by this authorAthanasios Karlis
Department of Electrical and Computer Engineering, School of Engineering, Democritus University of Thrace, Xanthi, Greece
Search for more papers by this authorKonstantinos Arvanitis
Department of Natural Resources Development and Agricultural Engineering, School of Environment and Agricultural Engineering, Agricultural University of Athens, Athens, Greece
Search for more papers by this authorSummary
The expected significant growth in global electricity demand, followed by the adoption of green energy sources, led to the modernization of the energy distribution grids, such as the development of microgrids that can improve the reliability of the electric power system. Meanwhile, the increased integration of electric vehicles is expected to have a negative impact on power quality, normal operation, and investment costs of the microgrid. In this paper, a microgrid topology was studied for the islanded operation of a low-voltage distribution network. The deployment of such energy management systems is essential to guarantee the safe and reliable operation of isolated microgrids. Hence, a decentralized energy management system, based on multi-agent systems, was developed for the efficient charging of electric vehicles, by expanding a state-of-the-art fuzzy logic controller-based energy management strategy, in combination with a charging power controller, based on fuzzy cognitive maps. According to the authors' best knowledge, this is the first time that fuzzy cognitive maps theory is introduced in EVs' charging management problems. The performance of the proposed multi-agent decentralized energy management system presented significant reduction in the investment costs of the microgrid, as compared with the use of a sole fuzzy logic controller-based energy management strategy. The total cost of the microgrid for a 20-year investment period is decreased by approximately 8.8%. Furthermore, the incorporation of the fuzzy cognitive maps-based charging power controller leads to a significant amount of chargeable EVs. The mean chargeable EVs increased by 31%. Finally, the proposed energy management system reduces the peak load and load variances approximately 17% and 29%, respectively, without shifting and delaying the charging of the EVs. Hence, the proposed novel charging management system offers an intelligence approach for islanding distribution grids including the high penetration of electric vehicles by presenting operational and financial benefits.
CONFLICT OF INTEREST
The authors declare no conflict of interest.
Open Research
DATA AVAILABILITY STATEMENT
Data available on request from the authors
REFERENCES
- 1 IEA. World Energy Outlook 2019. Paris, France: IEA; 2019 https://www.iea.org/reports/world-energy-outlook-2019/ [accessed December 1, 2020]
- 2Gielen D, Boshell F, Saygin D, Bazilian MD, Wagner N, Gorini R. The role of renewable energy in the global energy transformation. Energy Strat Rev. 2019; 24(June 2018): 38-50. https://doi.org/10.1016/j.esr.2019.01.006
- 3 IEA. Energy Technology Perspectives 2020. Paris, France: IEA; 2020 https://www.iea.org/reports/energy-technology-perspectives-2020 [accessed December 1, 2020]
- 4Chen C, Wang J, Qiu F, Zhao D. Resilient distribution system by microgrids formation after natural disasters. IEEE Trans Smart Grid. 2016; 7(2): 958-966. https://doi.org/10.1109/TSG.2015.2429653
- 5Held M, Baumann M. Assessment of the environmental impacts of electric vehicle concepts. Towards Life Cycle Sustain Manag. Dordrecht, Netherlands: Springer; 2011; 535-546. https://doi.org/10.1007/978-94-007-1899-9
10.1007/978-94-007-1899-9_52 Google Scholar
- 6Ayadi F, Colak I, Garip I, Bulbul HI. Impacts of renewable energy resources in smart grid. 8th International Conference on Smart Grid, IcSmartGrid 2020 Piscataway, NJ: IEEE; 2020: 183-188. https://doi.org/10.1109/icSmartGrid49881.2020.9144695
10.1109/icSmartGrid49881.2020.9144695 Google Scholar
- 7Nassar IA, Hossam K, Abdella MM. Economic and environmental benefits of increasing the renewable energy sources in the power system. Energy Rep. 2019; 5: 1082-1088. https://doi.org/10.1016/j.egyr.2019.08.006
- 8Wang Q, Zhan L. Assessing the sustainability of renewable energy: an empirical analysis of selected 18 European countries. Sci Total Environ. 2019; 692: 529-545. https://doi.org/10.1016/j.scitotenv.2019.07.170
- 9Mahesh A, Sandhu KS. Hybrid wind/photovoltaic energy system developments: critical review and findings. Renew Sustain Energy Rev. 2015; 52: 1135-1147. https://doi.org/10.1016/j.rser.2015.08.008
- 10Burd JTJ, Moore AE, Ezzat H, Kirchain R, Roth R. Improvements in electric vehicle battery technology influence vehicle lightweighting and material substitution decisions. Appl Energy. 2021; 283: 116269. https://doi.org/10.1016/j.apenergy.2020.116269
- 11Tuchnitz F, Ebell N, Schlund J, Pruckner M. Development and evaluation of a smart charging strategy for an electric vehicle Fleet based on reinforcement learning. Appl Energy. 2021; 285: 116382. https://doi.org/10.1016/j.apenergy.2020.116382
- 12 IEA. Global EV Outlook 2019. Paris, France: IEA; 2019 https://www.iea.org/reports/global-ev-outlook-2019, 2019. [accessed December 1, 2020]
- 13Sun X, Li Z, Wang X, Li C. Technology development of electric vehicles: a review. Energies. 2019; 13(1): 1-29. https://doi.org/10.3390/en13010090
- 14Bilgin B, Magne P, Malysz P, et al. Making the case for electrified transportation. IEEE Trans Transport Electrif. 2015; 1(1): 4-17. https://doi.org/10.1109/TTE.2015.2437338
- 15Ghosh A. Possibilities and challenges for the inclusion of the electric vehicle (EV) to reduce the carbon footprint in the transport sector: a review. Energies. 2020; 13(10): 2602. https://doi.org/10.3390/en13102602
- 16Winyuchakrit P, Sukamongkol Y, Limmeechokchai B. Do Electric vehicles really reduce GHG emissions in Thailand? Energy Procedia. 2017; 138: 348-353.
10.1016/j.egypro.2017.10.137 Google Scholar
- 17Raugei M, Hutchinson A, Morrey D. Can electric vehicles significantly reduce our dependence on non-renewable energy? Scenarios of compact vehicles in the UK as a case in point. J Clean Prod. 2018; 201: 1043-1051.
- 18Protogeropoulos C, Tselepis S, Neris A. Research issues on stand-alone PV/hybrid systems: state-of-art and future technology perspectives for the integration of μgrid topologies on local Island grids. Conference record of the 2006 IEEE 4th world conference on photovoltaic. Energy Convers. 2006; WCPEC-4(2): 2277-2282. https://doi.org/10.1109/WCPEC.2006.279627
10.1109/WCPEC.2006.279627 Google Scholar
- 19Hirsch A, Parag Y, Guerrero J. Microgrids: a review of technologies, key drivers, and outstanding issues. Renew Sustain Energy Rev. 2018; 90(March): 402-411. https://doi.org/10.1016/j.rser.2018.03.040
- 20Lidula NWA, Rajapakse AD. Microgrids research: a review of experimental microgrids and test systems. Renew Sustain Energy Rev. 2011; 15(1): 186-202. https://doi.org/10.1016/j.rser.2010.09.041
- 21Hatziargyriou N, Asano H, Iravani R, Marnay C. Microgrids. IEEE Power Energy Mag. 2007; 5(4): 78-94.
- 22Groppi D, Pfeifer A, Garcia DA, Krajačić G, Duić N. A review on energy storage and demand side management solutions in smart energy islands. Renew Sustain Energy Rev. 2021; 135(April 2020): 110183. https://doi.org/10.1016/j.rser.2020.110183
- 23Procopiou AT, Quiros-Tortos J, Ochoa LF. HPC-based probabilistic analysis of LV networks with EVs: impacts and control. IEEE Trans Smart Grid. 2017; 8(3): 1479-1487. https://doi.org/10.1109/TSG.2016.2604245
- 24Lopes JAP, Soares FJ, Almeida PMR. Integration of electric vehicles in the electric power system. Proc IEEE. 2011; 99(1): 168-183. https://doi.org/10.1109/JPROC.2010.2066250
- 25Lopes JAP, Soares FJ, Almeida RMR. Identifying management procedures to deal with connection of electric vehicles in the grid. Proceedings of 2009 IEEE Bucharest PowerTech, Bucharest, Romania, 28 June-2 July 2009. Piscataway, NJ: IEEE; 2009: 1-8.
10.1109/PTC.2009.5282155 Google Scholar
- 26Buchholz BM, Styczynski ZA. Smart Grids: Grundlagen und Technologien der elektrischen Netze der Zukunft. 2nd ed. Berlin, Germany: VDE Verlag; 2019.
- 27Elmouatamid A, Ouladsine R, Bakhouya M, El Kamoun N, Khaidar M, Zine-Dine K. Review of control and energy management approaches in micro-grid systems. Energies. 2021; 14(1): 168. https://doi.org/10.3390/en14010168
- 28Shayeghi H, Shahryari E, Moradzadeh M, Siano P. A survey on microgrid energy management considering flexible energy sources. Energies. 2019; 12(11): 1-26. https://doi.org/10.3390/en12112156
- 29Tahir MF, Haoyong C, Mehmood K, Ali N, Bhutto JA. Integrated energy system modeling of China for 2020 by incorporating demand response, heat pump and thermal storage. IEEE Access. 2019; 7: 40095-40108. https://doi.org/10.1109/ACCESS.2019.2905684
- 30Mao Y, He B, Wang D, et al. Microgrid group control method based on deep learning under cloud edge collaboration. Wirel Commun Mobile Comput. 2021; 2021: 6635638. https://doi.org/10.1155/2021/6635638
- 31Karavas CS, 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: 166-179. https://doi.org/10.1016/j.enconman.2015.06.021
- 32Karavas CS, Arvanitis KG, Papadakis G. Optimal technical and economic configuration of photovoltaic powered reverse osmosis desalination systems operating in autonomous mode. Desalination. 2019; 466(April): 97-106. https://doi.org/10.1016/j.desal.2019.05.007
- 33Fioriti D, Giglioli R, Poli D, Lutzemberger G, Vanni A, Salza P. Optimal sizing of a mini-grid in developing countries, taking into account the operation of an electrochemical storage and a fuel tank. 2017 6th International Conference on Clean Electrical Power: Renewable Energy Resources Impact, ICCEP 2017. Piscataway, NJ: IEEE; 2017: 320-326. https://doi.org/10.1109/ICCEP.2017.8004834
10.1109/ICCEP.2017.8004834 Google Scholar
- 34Kirthiga MV, Daniel SA. Optimal sizing of hybrid generators for autonomous operation of a micro-grid. 2010 IEEE 26th convention of electrical and electronics engineers in Israel. IEEEI. 2010; 2010: 864-868. https://doi.org/10.1109/EEEI.2010.5662085
10.1109/EEEI.2010.5662085 Google Scholar
- 35Brenna M, Foiadelli F, Leone C, Longo M. Electric vehicles charging technology review and optimal size estimation. J Electr Eng Technol. 2020; 15: 2539-2552. https://doi.org/10.1007/s42835-020-00547-x
- 36Hassan T, Cheema KM, Mehmood K, Tahir MF, Milyani AH, Akhtar M. Optimal control of high-power density hybrid electric vehicle charger. Energy Rep. 2021; 7: 194-207. https://doi.org/10.1016/j.egyr.2020.12.021
- 37Amjad M, Ahmad A, Rehmani MH, Umer T. A review of EVs charging: from the perspective of energy optimization, optimization approaches, and charging techniques. Transport Res Part D Transport Environ. 2018; 62(March): 386-417. https://doi.org/10.1016/j.trd.2018.03.006
- 38Nayak A, Rana R, Mishra S. Frequency regulation by electric vehicle during grid restoration using adaptive optimal control. IFAC-PapOnLine. 2019; 52(4): 270-275. https://doi.org/10.1016/j.ifacol.2019.08.210
10.1016/j.ifacol.2019.08.210 Google Scholar
- 39Bampoulas A, Karlis A. Provision of frequency regulation by a residential microgrid integrating PVs, energy storage and electric vehicle. In Proceedings of the 2017 IEEE International Conference on Environment and Electrical Engineering and 2017 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe), Milan, Italy. Piscataway, NJ: IEEE; 2017: 1-6. https://doi.org/10.1109/EEEIC.2017.7977472
10.1109/EEEIC.2017.7977472 Google Scholar
- 40Singh S, Jagota S, Singh M. Energy management and voltage stabilization in an islanded microgrid through an electric vehicle charging station. Sustain Cities Soc. 2018; 41: 679-694. https://doi.org/10.1016/j.scs.2018.05.055
- 41Amin A, Tareen WUK, Usman M, et al. A review of optimal charging strategy for electric vehicles under dynamic pricing schemes in the distribution charging network. Sustainability (Switzerland). 2020; 12(23): 1-28. https://doi.org/10.3390/su122310160
- 42Zia MF, Elbouchikhi E, Benbouzid M. Microgrids energy management systems: a critical review on methods, solutions, and prospects. Appl Energy. 2018; 222(April): 1033-1055. https://doi.org/10.1016/j.apenergy.2018.04.103
- 43Babic J, Carvalho A, Ketter W, Podobnik V. Evaluating policies for parking lots handling electric vehicles. IEEE Access. 2017; 6: 944-961. https://doi.org/10.1109/ACCESS.2017.2777098
- 44Qian 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(2): 802-810. https://doi.org/10.1109/TPWRS.2010.2057456
- 45Boglou V, Karavas CS, Arvanitis K, Karlis A. A fuzzy energy management strategy for the coordination of electric vehicle charging in low voltage distribution grids. Energies. 2020; 13(14): 3709. https://doi.org/10.3390/en13143709
- 46Held L, Suriyah MR, Junge E, Lossau S. Impact of electric vehicle charging on low-voltage grids and the potential of battery storage as temporary equipment during grid reinforcement. Proceedings of the E-Mobility Integration Symposium, Berlin, Germany, 23; Darmstadt, Germany: Energynautics GmbH; 2017.
- 47Mocci S, Natale N, Pilo F, Ruggeri S. Multi-agent control system to coordinate optimal electric vehicles charging and demand response actions in active distribution networks. IET Conf Publ. Savoy Place, London, UK: IET; 2014;( CP651): 1-6. https://doi.org/10.1049/cp.2014.0841
10.1049/cp.2014.0841 Google Scholar
- 48Xydas E, Marmaras C, Cipcigan LM. A multi-agent based scheduling algorithm for adaptive electric vehicles charging. Appl Energy. 2016; 177: 354-365. https://doi.org/10.1016/j.apenergy.2016.05.034
- 49Jordán J, Palanca J, del Val E, Julian V, Botti V. A multi-agent system for the dynamic emplacement of electric vehicle charging stations. Appl Sci (Switzerland). 2018; 8(2): 313. https://doi.org/10.3390/app8020313
- 50Karfopoulos EL, Hatziargyriou ND. A multi-agent system for controlled charging of a large population of electric vehicles. IEEE Trans Power Syst. 2013; 28(2): 1196-1204. https://doi.org/10.1109/TPWRS.2012.2211624
- 51Ugirumurera J, Haas ZJ. Optimal capacity sizing for completely green charging systems for electric vehicles. IEEE Trans Transport Electrif. 2017; 3(3): 565-577. https://doi.org/10.1109/TTE.2017.2713098
- 52Yoon SG, Kang SG. Economic microgrid planning algorithm with electric vehicle charging demands. Energies. 2017; 10(10): 1487. https://doi.org/10.3390/en10101487
- 53Clairand JM, Rodríguez-García J, Álvarez-Bel C. Electric vehicle charging strategy for isolated systems with high penetration of renewable generation. Energies. 2018; 11(11): 3188. https://doi.org/10.3390/en11113188
- 54Luna-Rubio R, Trejo-Perea M, Vargas-Vázquez D, Ríos-Moreno GJ. Optimal sizing of renewable hybrids energy systems: a review of methodologies. Sol Energy. 2012; 86(4): 1077-1088. https://doi.org/10.1016/j.solener.2011.10.016
- 55Qiang T, Xie M, Yang K, Luo Y, Zhou D, Song Y. A decision function based smart charging and discharging strategy for electric vehicle in smart grid. Mobile Netw Appl. 2019; 24(5): 1722-1731. https://doi.org/10.1007/s11036-018-1049-4
- 56Olle S, Binding C. Flexible charging optimization for electric vehicles considering distribution grid constraints. IEEE Trans Smart Grid. 2011; 3(1): 26-37. https://doi.org/10.1109/TSG.2011.2168431
- 57Wang Q, Liu X, Du J, Kong F. Smart charging for electric vehicles: a survey from the algorithmic perspective. IEEE Commun Surv Tutor. 2016; 18(2): 1500-1517. https://doi.org/10.1109/COMST.2016.2518628
- 58Gan L, Topcu U, Low SH. Stochastic distributed protocol for electric vehicle charging with discrete charging rate. IEEE Power and Energy Society General Meeting. Piscataway, NJ: IEEE; 2012; 1–8. https://doi.org/10.1109/PESGM.2012.6344847.
- 59Nour M, Chaves-Ávila JP, Magdy G, Sánchez-Miralles Á. Review of positive and negative impacts of electric vehicles charging on electric power systems. Energies. 2020; 13(18): 4675. https://doi.org/10.3390/en13184675
- 60Das A, Chimonyo KB, Kumar TR, Gourishankar S, Rani C. Vertical axis and horizontal axis wind turbine—a comprehensive review. 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS). Piscataway, NJ: IEEE; 2017: 2660-2669. https://doi.org/10.1109/ICECDS.2017.8389937
10.1109/ICECDS.2017.8389937 Google Scholar
- 61Jiang L, Cui S, Sun P, Wang Y, Yang C. Comparison of monocrystalline and polycrystalline solar modules. 2020 IEEE 5th Information Technology and Mechatronics Engineering Conference (ITOEC). Piscataway, NJ: IEEE; 2020: 341-344. https://doi.org/10.1109/ITOEC49072.2020.9141722
10.1109/ITOEC49072.2020.9141722 Google Scholar
- 62Ahmadian A, Mohammadi-Ivatloo B, Elkamel A. A review on plug-in electric vehicles: introduction, current status, and load modeling techniques. J Modern Power Syst Clean Energy. 2019; 8(3): 412-425. https://doi.org/10.35833/MPCE.2018.000802
- 63 Electric Vehicle Database. Ev-database.org [accessed on September 20, 2020].
- 64Russell S. Artificial intelligence. A Modern Approach Author: Stuart Russell Peter Norvig. Hoboken, NJ: Prentice Hall Pa; 2020.
- 65Kosko B. Fuzzy cognitive maps. Int J Man-Machine Stud. 1986; 24(1): 65-75. https://doi.org/10.1016/S0020-7373(86)80040-2
- 66Papageorgiou EI, Salmeron JL. A review of fuzzy cognitive maps research during the last decade. IEEE Trans Fuzzy Syst. 2013; 21(1): 66-79. https://doi.org/10.1109/TFUZZ.2012.2201727
- 67Stylios CD, Groumpos PP. Mathematical formulation of fuzzy cognitive maps. Proceedings of the 7th Mediterranean Conference on Control and Automation. Vol 2014. Nicosia, Cyprus: Mediterranean Control Association; 1999: 2251-2261.
- 68Kottas TL, Boutalis YS, Christodoulou MA. Fuzzy cognitive network: a general framework. Intell Decis Technol. 2007; 1(4): 183-196. https://doi.org/10.3233/IDT-2007-1402
10.3233/IDT-2007-1402 Google Scholar
- 69Karlis AD, Kottas TL, Boutalis YS. A novel maximum power point tracking method for PV systems using fuzzy cognitive networks (FCN). Electr Power Syst res. 2007; 77(3–4): 315-327. https://doi.org/10.1016/j.epsr.2006.03.008
- 70Karavas CS, Arvanitis K, Papadakis G. A game theory approach to multi-agent decentralized energy management of autonomous polygeneration microgrids. Energies. 2017; 10(11): 1756. https://doi.org/10.3390/en10111756
- 71Abdmouleh Z, Gastli A, Ben-Brahim L, Haouari M, Al-Emadi NA. Review of optimization techniques applied for the integration of distributed generation from renewable energy sources. Renew Energy. 2017; 113: 266-280. https://doi.org/10.1016/j.renene.2017.05.087
- 72Lee KY, Park JB. Application of particle swarm optimization to economic dispatch problem: advantages and disadvantages. 2006 IEEE PES Power Systems Conference and Exposition, PSCE 2006—Proceedings. Piscataway, NJ: IEEE; 2006: 188-192. https://doi.org/10.1109/PSCE.2006.296295
10.1109/PSCE.2006.296295 Google Scholar
- 73Grau Unda I, Papadopoulos P, Skarvelis-Kazakos S, Cipcigan LM, Jenkins N, Zabala E. Management of electric vehicle battery charging in distribution networks with multi-agent systems. Electr Power Syst res. 2014; 110: 172-179. https://doi.org/10.1016/j.epsr.2014.01.014
- 74Ko YD, Jang YJ. The optimal system design of the online electric vehicle utilizing wireless power transmission technology. IEEE Trans Intell Transport Syst. 2013; 14(3): 1255-1265. https://doi.org/10.1109/TITS.2013.2259159
- 75Tian W, He J, Jiang J, Niu L, Wang X. Multi-objective optimization of charging dispatching for electric vehicle battery swapping station based on adaptive mutation particle swarm optimization. Power Syst Technol. 2012; 36(11): 25-29.
- 76Marra F, Yang GY, Traholt C, Larsen E, Rasmussen CN, You S. Demand profile study of battery electric vehicle under different charging options. 2012 IEEE Power and Energy Society General Meeting. Piscataway, NJ: IEEE; 2012; 1-7. https://doi.org/10.1109/PESGM.2012.6345063
10.1109/PESGM.2012.6345063 Google Scholar
- 77Held L, Märtz A, Krohn D, et al. The influence of electric vehicle charging on low voltage grids with characteristics typical for Germany. World Electr Veh J. 2019; 10(4): 1-20. https://doi.org/10.3390/wevj10040088
10.3390/wevj10040088 Google Scholar
- 78Rivera J., & Jacobsen H. A. (2017). On the effects of distributed electric vehicle network utility maximization in low voltage feeders. arXiv, arXiv:1706.10074v1. https://arxiv.org/abs/1706.10074v1 [accessed on December 20, 2019].
- 79Hannan MA, Lipu MSH, Hussain A, Mohamed A. A review of lithium-ion battery state of charge estimation and management system in electric vehicle applications: challenges and recommendations. Renew Sustain Energy Rev. 2017; 78(August 2016): 834-854. https://doi.org/10.1016/j.rser.2017.05.001
- 80 IEEE PES distribution systems analysis subcommittee radial test feeders. https://site.ieee.org/pes-testfeeders/resources/ [accessed on December 20, 2019].
- 81Engler A, Meinhardt M, Rothert M, Wollny M. New generation of V/f-statics controlled battery inverter Sunny Island–the key component for AC coupled hybrid systems and mini grids. In Proceedings of the 14th International photovoltaic science and engineering conference (PVSEC-14). Bangkok Thailand: PVSEC-14; 2004.
- 82McKenna R, Hollnaicher S, Fichtner W. Cost-potential curves for onshore wind energy: a high-resolution analysis for Germany. Appl Energy. 2014; 115(2014): 103-115. https://doi.org/10.1016/j.apenergy.2013.10.030
- 83Padrón I, Avila D, Marichal GN, Rodríguez JA. Assessment of hybrid renewable energy systems to supplied energy to autonomous desalination systems in two islands of the Canary Archipelago. Renew Sustain Energy Rev. 2019; 101: 221-230. https://doi.org/10.1016/j.rser.2018.11.009
- 84 Volkswagen. (2020). Astypalea: smart, sustainable island. https://www.volkswagenag.com/en/news/stories/2020/11/astypalea-smart-sustainable-island.html
- 85 EKathimerini. (2021). Chalki to become a ‘green’ island. https://www.ekathimerini.com/news/1163886/chalki-to-become-a-green-island/
