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Optimal EV Battery Storage Exploitation for Energy Conservation in Low Voltage Distribution Network

  • Research Article-Electrical Engineering
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Abstract

The storage capacity of Electric Vehicles (EVs) can be used to improve energy management in low-voltage distribution networks. The research throughout the previous decade has concentrated on several control strategies for grid auxiliary assistance via Vehicle-to-Grid (V2G) and Grid-to-Vehicle (G2V). This article focuses on energy management in distribution networks using EVs, solar photovoltaic (PV), and diesel generators (DG). To begin with, Water Filling Algorithm (WFA) is utilized to disperse EV storage optimally in each zone of energy needed for load flattening. The goal here is to decrease grid reliance while minimizing overall energy costs. After that, a win–win plan is formed between the EV owner and the grid operator to lower EV charging costs and increase EV storage usage for grid support. A multi-Objective Optimization problem is formulated with two objectives: load flattening and voltage regulation. This is solved by Multi-Objective Genetic Algorithm (MOGA) where the decision variable is EV power transaction, Optimal Power Transaction (OPT). ANFIS-based EV ranking technique has been designed to achieve these two aims concurrently. The influence of Optimal Energy Distribution (OED) has been studied in different scenarios. ANFIS prioritization is investigated in several scenarios, as well as the impact on overall EV power availability and Cost of Charging (CoC).

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Abbreviations

\(C_{t}^{i}\) :

Energy cost for EV during charging ($/kWh)

\(C_{L,i}\) :

Battery replacement cost ($)

\(C_{t,c}^{e}\) :

Price of electricity at tth interval ($/kWh)

\({\mathrm{C}}_{\mathrm{pev},\mathrm{i}}^{\mathrm{bdc}}\) :

EV battery cost equivalent of degradation ($/kWh)

\({\mathrm{C}}_{\mathrm{b},\mathrm{i}}\) :

EV battery cost ($)

\(DoD_{i}\) :

DoD of EV battery (% of Capacity)

\(E_{{{\text{pev}}}}^{{{\text{cap}},\;i}}\) :

Storage capacity of EV battery (kWh)

\(E_{{{\text{pev}}}}^{t,\;i}\) :

Amount energy charged/discharged in ‘tth interval (kWh)

\(E_{{{\text{pev}},\;t = 1}}^{{{\text{avail}},\;z,\;{\text{ch}}}}\) :

Total energy available at the first interval of zone z

\(E_{{{\text{pev}},\;t = 1}}^{{{\text{avail}},\;z,\;dc}}\) :

Available storage for discharging at starting of z

\(E_{{{\text{need}}}}^{z}\) :

Amount of energy need in zone ‘z’

\(E_{{{\text{spec}}}}^{{{\text{grid}}}}\) :

Amount of energy authenticated in the given interval

\(E_{{{\text{need}}}}^{{t,\;{\text{pev}}}}\) :

Energy need from EVs in tth interval

\(E_{{{\text{need}}}}^{{t,\;{\text{grid}}}}\) :

Amount of energy need in tth interval

\(E_{{{\text{pev}}}}^{{{\text{old}},\;i}}\) :

EV storage level before departure

\(L_{c,i}\) :

Maximum number charging cycles of EV battery

\({\mathrm{L}}_{\mathrm{pev}}^{\mathrm{i},\mathrm{t}}\) :

EV laxity tth interval (intervals)

\(P_{{{\text{spec}}}}^{{{\text{grid}}}}\) :

Authenticated power from grid (kW)

\(P_{{{\text{need}}}}^{{t,\;{\text{grid}}}}\) :

Amount of power need from grid at tth interval (kW)

\(P_{solar}^{t}\) :

Power obtained from solar PV at tth interval (kW)

\(P_{load}^{t}\) :

Load demand at tth interval (kW)

\(P_{pev}^{t,ch}\) :

Power required for charging EVs at tth interval (kW)

\(P_{{{\text{rate}}}}^{i,\;t}\) :

Charging rate of ith EV at tth interval (kW)

\({\mathrm{P}}_{\mathrm{rate}}^{\mathrm{i},\mathrm{t},\mathrm{ref}}\) :

EV reference charge rate (kW)

Pp-to :

Probability of starting trip by an EV

Pp-from :

Probability of arriving from trip

\({\mathrm{PEV}}_{\mathrm{avail}}^{\mathrm{t},\mathrm{i}}\) :

Probability of EV availability at home

\({\mathrm{SoC}}_{\mathrm{min}}^{\mathrm{i}}\) :

Minimum SoC to be maintained

\({\mathrm{SoC}}_{\mathrm{pev}}^{\mathrm{i},\mathrm{t}}\) :

SoC at tth interval

\({\mathrm{SoC}}_{\mathrm{max}}^{\mathrm{i}}\) :

Maximum SoC to be maintained

\({\mathrm{SoC}}_{\mathrm{depart}}^{\mathrm{i},\mathrm{t}={\mathrm{t}}_{\mathrm{d}}}\) :

SoC to be maintained for trip

\(SoC_{{i,\;{\text{pev}}}}^{{{\text{new}}}}\) :

SoC at the time of arrival from the trip

\({\mathrm{t}}_{\mathrm{d}}^{\mathrm{i}}\) :

Departure time (interval)

\({\mathrm{T}}_{\mathrm{ch}}^{\mathrm{i}}\) :

Charging time required to reach maximum SoC (intervals)

References

  1. Godina, R.; Rodrigues, E.M.G.; Paterakis, N.G.; Erdinc, O.; Catalão, J.P.S.: Innovative impact assessment of electric vehicles charging loads on distribution transformers using real data. Energy Convers. Manag. 120, 206–216 (2016)

    Google Scholar 

  2. Lazkano, I.; Nøstbakken, L.; Pelli, M.: From fossil fuels to renewables: the role of electricity storage. Eur. Econ. Rev. 99, 113–129 (2017)

    Google Scholar 

  3. Zhou, C., et al.: Modeling of the cost of EV battery wear due to V2G application in power systems. IEEE Trans. Energy Convers. 26(4), 1041–1050 (2011)

    Google Scholar 

  4. Gnann, T., et al.: What is the market potential of plug-in electric vehicles as commercial passenger cars? A case study from Germany. Transp. Res. Part D Transp. Environ. 37, 171–187 (2015)

    Google Scholar 

  5. Khoucha, F., et al.: Integrated energy management of a plug-in electric vehicle in residential distribution systems with renewables. In: 2015 IEEE 24th International Symposium on Industrial Electronics (ISIE), IEEE (2015)

  6. Venter, G.; Sobieszczanski-Sobieski, J.: Particle swarm optimization. AIAA J. 41(8), 1583–1589 (2003)

    Google Scholar 

  7. Zhao, J., et al.: A novel preventive control approach for mitigating voltage collapse. In: 2006 IEEE Power Engineering Society General Meeting, IEEE (2006)

  8. Yang, J.; He, L.; Siyao, Fu.: An improved PSO-based charging strategy of electric vehicles in electrical distribution grid. Appl. Energy 128, 82–92 (2014)

    Google Scholar 

  9. Soares, F.J.; Almeida, P.M.R.; Lopes, J.A.P.: Quasi-real-time management of electric vehicles charging. Electr. Power Syst. Res. 108, 293–303 (2014)

    Google Scholar 

  10. Torres, S., et al.: Electric vehicles and power quality in low voltage networks: real data analysis and modeling. Appl. Energy 305, 117718 (2022)

    Google Scholar 

  11. Lund, H.; Kempton, W.: Integration of renewable energy into the transport and electricity sectors through V2G. Energy Policy 36(9), 3578–3587 (2008)

    Google Scholar 

  12. Debnath, U.K.; Ahmad, I.; Habibi, D.: Quantifying economic benefits of second-life batteries of grindable vehicles in the smart grid. Int. J. Electr. Power Energy Syst. 63, 577–587 (2014)

    Google Scholar 

  13. Ashique, R.H., et al.: Integrated photovoltaic-grid dc fast-charging system for electric vehicle: a review of the architecture and control. Renew. Sustain. Energy Rev. 69, 1243–1257 (2017)

    Google Scholar 

  14. Meng, J., et al.: Dynamic frequency response from electric vehicles considering traveling behavior in the Great Britain power system. Appl. Energy 162, 966–979 (2016)

    Google Scholar 

  15. Wang, M., et al.: Active power regulation for large-scale wind farms through an efficient power plant model of electric vehicles. Appl. Energy 185, 1673–1683 (2017)

    Google Scholar 

  16. Amini, M.H; Islam, A.: Allocation of electric vehicles' parking lots in distribution network. ISGT 2014, IEEE (2014)

  17. Gao, S., et al.: Integrated energy management of plug-in electric vehicles in power grid with renewables. IEEE Trans. Veh. Technol. 63(7), 3019–3027 (2014)

    Google Scholar 

  18. Alam, M.J.E.; Muttaqi, K.M.; Sutanto, D.: A controllable local peak-shaving strategy for effective utilization of PEV battery capacity for distribution network support. IEEE Trans. Ind. Appl. 51(3), 2030–2037 (2014)

    Google Scholar 

  19. Sortomme, E.; El-Sharkawi, M.A.: Optimal charging strategies for unidirectional vehicle-to-grid. IEEE Trans. Smart Grid 2(1), 131–138 (2010)

    Google Scholar 

  20. Zhou, X.S.; Huang, T.S.: Edge-based structural features for content-based image retrieval. Pattern Recognit. Lett. 22(5), 457–468 (2001)

    MATH  Google Scholar 

  21. Mou, Y., et al.: Decentralized optimal demand-side management for PHEV charging in a smart grid. IEEE Trans. Smart Grid 6(2), 726–736 (2015)

    Google Scholar 

  22. Hu, W.; He, L.; Chen, J.: A bi-layer optimization-based schedule considering large-scale electric vehicles. Power Syst. Prot. Control 44(21), 22–28 (2016)

    Google Scholar 

  23. Neyestani, N., et al. Modeling the optimal behavior of PEV parking lots in energy and reserve market. In: IEEE PES Innovative Smart Grid Technologies, Europe. IEEE (2014)

  24. Neyestani, N., et al. Modeling the PEV traffic pattern in an urban environment with parking lots and charging stations. In: 2015 IEEE Eindhoven PowerTech, IEEE (2015)

  25. Mullan, J., et al.: Modelling the impacts of electric vehicle recharging on the Western Australian electricity supply system. Energy Policy 39(7), 4349–4359 (2011)

    Google Scholar 

  26. Park, W.-J.; Song, K.-B.; Park, J.-W.: Impact of electric vehicle penetration-based charging demand on load profile. J. Electr. Eng. Technol. 8(2), 244–251 (2013)

    Google Scholar 

  27. El-Zonkoly, A.; dos Santos Coelho, L.: Optimal allocation, sizing of PHEV parking lots in distribution system. Int. J. Electr. Power Energy Syst. 67, 472–477 (2015)

    Google Scholar 

  28. Su, W.; Chow, M.Y.: Performance evaluation of a PHEV parking station using particle swarm optimization. In: 2011 IEEE Power and Energy Society General Meeting, IEEE (2011)

  29. Ma, Z.; Callaway, D.S.; Hiskens, I.A.: Decentralized charging control of large populations of plug-in electric vehicles. IEEE Trans. Control Syst. Technol. 21(1), 67–78 (2011)

    Google Scholar 

  30. Zeng, W.; Gibeau, J.; Chow, M.Y.: Economic benefits of plug-in electric vehicles using V2G for grid performance-based regulation service. In: IECON 2015–41st Annual Conference of the IEEE Industrial Electronics Society, IEEE (2015)

  31. Wang, Lu.; Sharkh, S.; Chipperfield, A.: Optimal coordination of vehicle-to-grid batteries and renewable generators in a distribution system. Energy 113, 1250–1264 (2016)

    Google Scholar 

  32. Jiang, B.; Fei, Y.: Dynamic residential demand response and distributed generation management in smart microgrid with hierarchical agents. Energy Procedia 12, 76–90 (2011)

    Google Scholar 

  33. Yang, Z., et al.: Joint scheduling of large-scale appliances and batteries via distributed mixed optimization. IEEE Trans. Power Syst. 30(4), 2031–2040 (2014)

    Google Scholar 

  34. Nunna, HK; and Srinivasan, D.: A multi-agent system for energy management in smart microgrids with distributed energy storage and demand response. In: 2016 IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES), IEEE (2016)

  35. Dagdougui, H., et al.: Global energy management system for cooperative networked residential green buildings. IET Renew. Power Gener. 10(8), 1237–1244 (2016)

    Google Scholar 

  36. Eppstein, M.J., et al.: An agent-based model to study market penetration of plug-in hybrid electric vehicles. Energy Policy 39(6), 3789–3802 (2011)

    Google Scholar 

  37. Sudhakar, A.; Kumar, B.M.: Maximum exploitation of electric vehicle storage in smart micro grids with stochastic renewable sources. J. Energy Storage 48, 104029 (2022)

    Google Scholar 

  38. López, K.L.; Gagné, C.; Gardner, M.-A.: Demand-side management using deep learning for smart charging of electric vehicles. IEEE Trans. Smart Grid 10(3), 2683–2691 (2018)

    Google Scholar 

  39. Yao, L.; Lim, W.H.; Tsai, T.S.: A real-time charging scheme for demand response in electric vehicle parking station. IEEE Trans. Smart Grid 8(1), 52–62 (2016)

    Google Scholar 

  40. Wei, Z.; Li, Y.; Cai, L.: Electric vehicle charging scheme for a park-and-charge system considering battery degradation costs. IEEE Trans. Intell. Veh. 3(3), 361–373 (2018)

    Google Scholar 

  41. Hafez, O.; Bhattacharya, K.: Integrating EV charging stations as smart loads for demand response provisions in distribution systems. IEEE Trans. Smart Grid 9(2), 1096–1106 (2016)

    Google Scholar 

  42. Alsabbagh, A.; Yin, H.; Ma, C.: Distributed electric vehicles charging management with social contribution concept. IEEE Trans. Ind. Inf. 16(5), 3483–3492 (2019)

    Google Scholar 

  43. Arias, N.B.; Franco, J.F.; Lavorato, M.; Romero, R.: Metaheuristic optimization algorithms for the optimal coordination of plug-in electric vehicle charging in distribution systems with distributed generation. Electr. Power Syst. Res. 142, 351–361 (2017)

    Google Scholar 

  44. Reddy, K.R.; Meikandasivam, S.; Vijayakumar, D.: A novel strategy for maximization of plug-In electric vehicle’s storage utilization for grid support with consideration of customer flexibility. Electr. Power Syst. Res. 170(158), 175 (2019)

    Google Scholar 

  45. Lam, A.Y.; Leung, K.C.; Li, V.O.: Capacity estimation for vehicle-to-grid frequency regulation services with smart charging mechanism. IEEE Trans. Smart Grid 7(1), 156–166 (2015)

    Google Scholar 

  46. Zivkovic, Z.; van der Heijden, F.: Recursive unsupervised learning of finite mixture models. IEEE Trans Pattern Anal. Mach. Intell. 26(5), 651–656 (2004)

    Google Scholar 

  47. Ilea, V., et al.: Voltage control methodologies in active distribution networks. Energies 13(12), 3293 (2020)

    Google Scholar 

  48. Baran, M.E.; Felix, FWu.: Network reconfiguration in distribution systems for loss reduction and load balancing. IEEE Power Eng. Rev. 9(4), 101–102 (1989)

    Google Scholar 

  49. Abbey, C.; Joos, G.: Supercapacitor energy storage for wind energy applications. IEEE Trans. Ind. Appl. 43(3), 769–776 (2007)

    Google Scholar 

  50. Wang, Zhu, et al.: Integration of plug-in hybrid electric vehicles into energy and comfort management for smart building. Energy Build 47, 260–266 (2012)

    Google Scholar 

  51. Fano, R.: Transmission of Information. MIT Press, Cambridge (1961)

    MATH  Google Scholar 

  52. Holland, John H.: Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. MIT Press, Cambridge (1992)

    Google Scholar 

  53. Jang, J.S.R.: ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man Cybern. 23(3), 665–685 (1993)

    Google Scholar 

  54. Kemal, M.S., et al.: A real-time open access platform towards proof of concept for smart grid applications. J. Commun. Navig. Sens. Serv. 2017(1), 49–74 (2018)

    Google Scholar 

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Authors and Affiliations

  1. Puducherry Technological University, Puducherry, India

    A. Sudhakar & B. Mahesh Kumar

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Correspondence to A. Sudhakar.

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Sudhakar, A., Mahesh Kumar, B. Optimal EV Battery Storage Exploitation for Energy Conservation in Low Voltage Distribution Network. Arab J Sci Eng 48, 14517–14536 (2023). https://doi.org/10.1007/s13369-023-07728-6

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