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Power management of PEV using linear programming with solar panels and wind turbines in smart grids

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Abstract

A high generation of solar panels and wind turbines is able to increase voltage magnitudes of electricity grids, whereas uncoordinated procedures of recharging several plug-in electric vehicles (PEV) have capability to decrease them. Accordingly, this paper proposes a novel decentralized algorithm of power management to reschedule charging and discharging events of PEV over appropriate time slots of energy generation and consumption to maintain voltage profiles within their limits. A linear programming model of optimization is utilized to coordinate bi-directional power flows of charging and discharging PEV batteries, while considering solar panels and wind turbines in smart grids. Simulation results of 7 scenarios show that the proposed strategy is able to maintain voltage profiles of power grids within their margins by charging and discharging PEV batteries over specific hours of energy generation and consumption. The proposed algorithm of power management prevents PEV batteries from being overcharged or deeply discharged by keeping their state of charge between upper and lower boundaries. Therefore, the proposed technique has capability to increase PEV hosting capacity of distribution networks without significant upgrading requirements.

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Abbreviations

DMS:

Demand-side management

ESS:

Energy storage systems

G2V:

Grid to vehicle

LV:

Low voltage

PEV:

Plug-in electric vehicles

PV:

Photovoltaic

RES:

Renewable energy sources

SOC:

State of charge

TOU:

Time of use

V2G:

Vehicle to grid

WT:

Wind turbine

\({P}_{\mathrm{PEV}i}\) :

Power of individual PEV in kW.

\({p}_{\mathrm{g}2\mathrm{v}}\) :

Recharging power of PEV battery using G2V mode in kW.

\({p}_{\mathrm{v}2\mathrm{g}}\) :

Discharging power of PEV battery using V2G mode in kW.

\({\mathrm{SOC}}_{i}\) :

State of charge of individual PEV battery in percentage.

\(\mathcal{R}\) :

Rate of energy consumption of PEV during hours of mobility in kWh/km.

\({\mathcal{L}}_{i}\) :

The number of kilometers travelled by PEV per day.

\({E}_{\mathrm{PEV}}\) :

Total capacity of PEV battery in kWh.

\({\eta }_{\mathrm{ch}}\) :

Efficiency of PEV charging tool in percentage.

\({\eta }_{\mathrm{dch}}\) :

Efficiency of the inverter to discharge PEV battery in percentage.

\({P}_{\mathrm{pv}j}\) :

The generated power in an individual solar panel in kW.

\({\phi }_{\mathrm{solar}}\) :

Global component of solar irradiance in W/m2.

\({A}_{\mathrm{pv}}\) :

Total area of individual solar module in m2.

\({\rm T}_{\mathrm{pv}}\) :

A function considering different temperatures of solar operating conditions.

\(k\) :

A factor considering parameters that influence the solar power generation.

\({P}_{\mathrm{wt}j}\) :

Output power generation using WT in kW.

\(\mathcal{V}\) :

Wind speed in meter per second (m/s).

\({\mathcal{V}}_{\fancyscript{r}\mathrm{a}}\), \({\mathcal{V}}_{\mathrm{ci}}\) &\({\mathcal{V}}_{\mathrm{co}}\) :

Rated, cut-in and cut-out values of linear speeds of WT rotational parts in m/s.

\({F}_{\mathrm{PEV}i}\) :

Objective function to be minimized considering recharging-discharging power of PEV batteries.

\({P}_{\mathrm{pv}i}^{pu}\) :

Per unit power of solar panels, considering their rated generation as a base power.

\({P}_{\mathrm{wt}i}^{pu}\) :

Per unit wind generation, considering rated WT output as a base power

\({P}_{\mathrm{load}i}^{pu}\) :

Per unit residential consumption, considering peak demand as a base power.

\({\rm A}\) :

Unity matrix multiplied by decision variables of optimization.

\(\mathrm{Max}\_\mathrm{SOC}\) :

Maximum SOC level of an individual PEV battery among a group of electric vehicles in G2V modes for each time step.

\(\mathrm{Min}\_\mathrm{SOC}\) :

Minimum SOC level of an individual PEV battery among a group of electric vehicles inV2G modes for each time step.

\({V}_{\mathrm{end}}\) :

Voltage magnitudes at the end node of power grids.

\({Z}_{\mathrm{pre}-\mathrm{end}}\) :

Impedance of the section joining the preceding and end nodes of feeders.

\({I}_{\mathrm{pre}-\mathrm{end}}\) :

Amount of current flowing through the section joining the preceding and end nodes of feeders.

\({P}_{\mathrm{end}}\) :

Amount of power at the end node considering power of residential customers, recharging-discharging PEV batteries, and renewable technologies in kW.

\({P}_{\mathrm{res}}\) :

Power consumption of residential customers in kW.

\(M\) :

The total number of plug-in electric vehicles considered in this paper.

\(N\) :

The total number of solar modules considered in this paper.

\(L\) :

The total number of WT considered in this paper.

\(i\) :

Index of plug-in electric vehicles considered in this paper.

\(j\) :

Index of solar modules considered in this paper.

\(l\) :

Index of WT units considered in this paper.

\(t\) :

Index of time steps

References

  1. Iqbal F, Siddiqui AS, Deb T (2017) Study of xEV charging infrastructure and the role of Microgrid and smart grid in its development. Smart Sci 5(2):61–74

    Article  Google Scholar 

  2. Zhou B et al (2016) Smart home energy management systems: concept, configurations, and scheduling strategies. Renew Sustain Energy Rev 61:30–40. https://doi.org/10.1016/j.rser.2016.03.047

    Article  Google Scholar 

  3. Braun GW (2020) State policies for collaborative local renewable integration. Electr J 33(1):106691. https://doi.org/10.1016/j.tej.2019.106691

    Article  Google Scholar 

  4. Zhou S (2021) The effect of smart meter penetration on dynamic electricity pricing: evidence from the United States. Electr J 34(3):106919. https://doi.org/10.1016/j.tej.2021.106919

    Article  MathSciNet  Google Scholar 

  5. Zhiyi Li MY, Bahramirad S, Paaso A, Shahidehpour M (2019) Blockchain for decentralized transactive energy management system in networked microgrids. Electr J 32(4):58–72. https://doi.org/10.1016/j.tej.2019.03.008

    Article  Google Scholar 

  6. Hidalgo S, Angeles M, Ma C (2018) A survey on visual data representation for smart grids control and monitoring. Sustain Energy Grids Netw 16:351–369. https://doi.org/10.1016/j.segan.2018.09.007

    Article  Google Scholar 

  7. Meliani M, El Barkany A, EL Abbassi I, Mahmoudi M (2022) Smart grid challenges in morocco and an energy demand forecasting with smart grid challenges in morocco and an energy demand forecasting with time series. Int J Eng Res Africa 61:195–215. https://doi.org/10.4028/p-2gufv6

    Article  Google Scholar 

  8. Meryem M, Abdellah EB, Ikram EA, Rafik A, Morad M (2022) Energy Management of a Fuzzy Control System in a Microgrid. In: E3S Web of Conferences, vol. 353, p. 02002. [Online]. Available: https://doi.org/10.1051/e3sconf/202235302002

  9. Alonso M, Amaris H, Germain JG, Galan JM (2014) Optimal charging scheduling of electric vehicles in smart grids by heuristic algorithms. Energies 7(4):2449–2475. https://doi.org/10.3390/en7042449

    Article  Google Scholar 

  10. Drude L, Carlos L, Junior P, Rüther R (2014) Photovoltaics ( PV ) and electric vehicle-to-grid ( V2G ) strategies for peak demand reduction in urban regions in Brazil in a smart grid environment. Renew Energy 68:443–451. https://doi.org/10.1016/j.renene.2014.01.049

    Article  Google Scholar 

  11. Üstünsoy F, Sayan HH (2021) Real-time realization of network integration of electric vehicles with a unique balancing strategy. Electr Eng. https://doi.org/10.1007/s00202-021-01259-9

    Article  Google Scholar 

  12. Shemami MS, Alam MS, Asghar MSJ (2018) Fuzzy control assisted vehicle-to-home (V2H) energy management system. Smart Sci 6(2):173–187

    Article  Google Scholar 

  13. Hu W, Su C, Chen Z, Bak-jensen B (2013) Optimal operation of plug-in electric vehicles in power systems with high wind power penetrations. IEEE Trans Sustain Energy 4(3):577–585

    Article  Google Scholar 

  14. Al Essa MJM (2020) Power quality of electrical distribution systems considering PVs, EVs and DSM. J Control Autom Electr Syst 31(6):1520–1532. https://doi.org/10.1007/s40313-020-00637-1

    Article  Google Scholar 

  15. Fachrizal R, Ramadhani UH, Munkhammar J, Widén J (2021) Combined PV–EV hosting capacity assessment for a residential LV distribution grid with smart EV charging and PV curtailment. Sustain Energy Grids Netw 26:100445. https://doi.org/10.1016/j.segan.2021.100445

    Article  Google Scholar 

  16. Uiterkamp MHS, Gerards ME, Hurink JL (2021) Online electric vehicle charging with discrete charging rates. Sustain Energy Grids Netw 25:100423. https://doi.org/10.1016/j.segan.2020.100423

    Article  Google Scholar 

  17. Mulenga E, Bollen MHJ, Etherden N (2021) Distribution networks measured background voltage variations, probability distributions characterization and Solar PV hosting capacity estimations. Electr Power Syst Res 192:106979. https://doi.org/10.1016/j.epsr.2020.106979

    Article  Google Scholar 

  18. Avila-Rojas AE, Jesus PMDO, Alvarez M (2022) Distribution network electric vehicle hosting capacity enhancement using an optimal power flow formulation. Electr Eng 104:1337–1348. https://doi.org/10.1007/s00202-021-01374-7

    Article  Google Scholar 

  19. Vuelvasa J, Ruizb F, Gruossob G (2021) A time-of-use pricing strategy for managing electric vehicle clusters. Sustain Energy Grids Networks 25:100411. https://doi.org/10.1016/j.segan.2020.100411

    Article  Google Scholar 

  20. Mohammed SS, Ahamed TPI, Omar AI (2022) Interruptible charge scheduling of plug-in electric vehicle to minimize charging cost using heuristic algorithm. Electr Eng 104:1425–1440. https://doi.org/10.1007/s00202-021-01398-z

    Article  Google Scholar 

  21. Bakhshinejad A, Tavakoli A, Moghaddam MM (2021) Modeling and simultaneous management of electric vehicle penetration and demand response to improve distribution network performance. Electr Eng 103:325–340. https://doi.org/10.1007/s00202-020-01083-7

    Article  Google Scholar 

  22. Pasaoglu G et al (2012) Driving and parking patterns of European car drivers-a mobility survey. Luxembourg

    Google Scholar 

  23. Element Energy (2013) Pathways to high penetration of electric vehicles Final report for The Committee on Climate Change. Cambridge

  24. National Renewable Energy Laboratory, “Research Data tools,” [Online]. Available: https://www.nrel.gov/research/data-tools-alpha.html. [Accessed: 21-Feb-2018].

  25. Wilcox S, Marion W (2008) Users Manual for TMY3 Data Sets Users Manual for TMY3 Data Sets,” USA

  26. Al Essa MJM (2019) Home energy management of thermostatically controlled loads and photovoltaic-battery systems. Energy 176:742–752. https://doi.org/10.1016/j.energy.2019.04.041

    Article  Google Scholar 

  27. Al Essa MJM (2020) Power management of grid-integrated energy storage batteries with intermittent renewables. J Energy Storage 31:101762. https://doi.org/10.1016/j.est.2020.101762

    Article  Google Scholar 

  28. Al Essa MJM (2023) Energy assessments of a photovoltaic-wind-battery system for residential appliances in Iraq. J Energy Storage 59:106514. https://doi.org/10.1016/j.est.2022.106514

    Article  Google Scholar 

  29. Al Essa MJM, Cipcigan LM (2016) Integration of renewable resources into Low Voltage grids stochastically. In: IEEE international energy conference (ENERGYCON), pp. 1–5. https://doi.org/10.1109/energycon.2016.7514134.

  30. Maleki A, Rosen MA, Pourfayaz F (2017) Optimal operation of a grid-connected hybrid renewable energy system for residential applications. Sustainability 9:1314. https://doi.org/10.3390/su9081314

    Article  Google Scholar 

  31. Rouhani A, Kord H, Mehrabi M (2013) A comprehensive method for optimum sizing of hybrid energy systems using intelligence evolutionary algorithms. Indian J Sci Technol 6(6):4702–4712

    Article  Google Scholar 

  32. Sohoni V, Gupta SC, Nema RK (2016) A critical review on wind turbine power curve modelling techniques and their applications in wind based energy systems. J Energy. https://doi.org/10.1155/2016/8519785

    Article  Google Scholar 

  33. Umetani S, Fukushima Y, Morita H (2016) A linear programming based heuristic algorithm for charge and discharge scheduling of electric vehicles in a building energy management system. Omega. https://doi.org/10.1016/j.omega.2016.04.005

    Article  Google Scholar 

  34. Al Essa MJM (2022) Energy management of space-heating systems and grid-connected batteries in smart homes. Energy Ecol Environ 7(1):1–14. https://doi.org/10.1007/s40974-021-00219-0

    Article  Google Scholar 

  35. The MathWorks, “Solve linear programming problems,” [Online]. Available: https://www.mathworks.com/help/optim/ug/linprog.html?s_tid=srchtitle. [Accessed: 15-Jul-2018]

  36. IEEE Power Energy Society (2011) IEEE Guide for Identifying and Improving Voltage Quality in Power Systems, USA

  37. IEEE Power Energy Society and Power System Analysis Computing and Economics Committee, “The IEEE Test Feeders,” 2015. [Online]. Available: https://ewh.ieee.org/soc/pes/dsacom/testfeeders/. [Accessed: 06-Oct-2017].

  38. Kersting WH (2002) Distribution System Modeling and Analysis. In: Electric p. 2000 N. W. Corporate Blvd., Boca Raton, Florida 33431: CRC Press LLC, pp. 251–285

  39. Al Essa MJM, Cipcigan LM (2016) Reallocating charging loads of electric vehicles in distribution networks. Appl Sci 6(2):53. https://doi.org/10.3390/app6020053

    Article  Google Scholar 

  40. Al Essa M, Cipcigan LM (2015) Effects of Randomly Charging Electric Vehicles on Voltage Unbalance in Micro Grids. In; Universities Power Engineering Conference (UPEC), pp. 1–6. https://doi.org/10.1109/upec.2015.7339906.

  41. Al Essa MJM (2018) Management of charging cycles for grid-connected energy storage batteries. J Energy Storage 18:380–388. https://doi.org/10.1016/j.est.2018.05.019

    Article  Google Scholar 

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  1. Faculty of Engineering, University of Kufa, P.O. Box (21), Kufa, Najaf Governorate, Iraq

    Mohammed Jasim M. Al Essa

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Al Essa, M.J.M. Power management of PEV using linear programming with solar panels and wind turbines in smart grids. Electr Eng 105, 1761–1773 (2023). https://doi.org/10.1007/s00202-023-01763-0

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