Skip to main content

Advertisement

Springer Nature Link
Log in

An efficient short-term energy management system for a microgrid with renewable power generation and electric vehicles

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

According to statistical reports, thermal power plants have long played a critical role in supplying electricity using fossil fuels. However, due to the high investment and operation costs of these power plants and their destructive effects on the environment, renewable energy sources (RESs) in power networks have been considered an effective alternative to traditional power plants. Optimal scheduling of microgrid in smart grids has a significant impact on reducing energy consumption, environmental pollutants and end-user's energy prices. In this paper, the influence of plug-in hybrid electric vehicles (PHEVs) charging on microgrids' optimal operation is evaluated. To assess the behaviour of PHEVs, three various charging patterns consisting of uncontrolled, controlled and smart charging methods are considered. Uncertainties due to forecast error in the PHEVs, loads, prices and renewable source output power are also considered in microgrid modelling energy management. To deal with the optimisation scheduling of microgrid considering uncertainty, a modified harmony search (MHS) algorithm is used. The suggested scheme is validated using simulations and without the PHEV charging effects compared with the conventional methods.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig.8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

Explore related subjects

Discover the latest articles and news from researchers in related subjects, suggested using machine learning.

Abbreviations

t start :

Start time of PHEV charging

B Gi :

Bids of the DGs

B sj :

Bids of storage devices

S G :

Start-up or shutdown costs for ith DG

S sj :

Start-up or shutdown costs for jth storage

P Grid :

Active power

P Grid :

Utility bid

X :

Control vector

n :

State variables

N g :

Total number of generation units

N s :

Total number of storage units

P g :

Power vector including active powers

U g :

State vector

P Gi(t):

Real power outputs of ith generator

P sj(t):

Real power outputs jth storage

P Lk :

Value of the load level k

N k :

Load levels

Δt :

Available energy in the battery at each time slot

W ess,t :

Intertemporal feature

t :

Current number of iterations

P S, charge :

Charging power

P S, discharge :

Discharging power

max:

Upper bounds

min:

Lower bounds

η charge :

Efficiencies in the charging modes

η discharge :

Efficiencies in the discharging modes

X r :

Random position vector

X w :

Worst frog position between (0, 1)

X b :

Best frog position between (0, 1

References

  1. Rezvani A, Gandomkar M, Izadbakhsh M, Ahmadi A (2015) Environmental/economic scheduling of a micro-grid with renewable energy resources. J Clean Prod 15(87):216–226

    Article  Google Scholar 

  2. Izadbakhsh M, Gandomkar M, Rezvani A, Ahmadi A (2015) Short-term resource scheduling of a renewable energy based micro grid. Renew Energy 1(75):598–606

    Article  Google Scholar 

  3. Ali ZM, Quynh NV, Dadfar S, Nakamura H (2020) Variable step size perturb and observe MPPT controller by applying θ-modified krill herd algorithm-sliding mode controller under partially shaded conditions. J Cleaner Prod 271:122243

    Article  Google Scholar 

  4. Quynh NV, Ali ZM, Alhaider MM, Rezvani A, Suzuki K (2020) Optimal energy management strategy for a renewable-based microgrid considering sizing of battery energy storage with control policies. Int J Energy Res 45(4):5766–5780

    Article  Google Scholar 

  5. Abdel-Akher M, Ali ZM, Eid A (2014) Continuous charging coordination of PHEVs for voltage profile and stability improvements of unbalanced distribution systems. In2014 16th international conference on harmonics and quality of power (ICHQP) May 25. IEEE, pp 49–53

  6. Moghaddam AA, Seifi A, Niknam T, Pahlavani MR (2011) Multi-objective operation management of a renewable MG (micro-grid) with back-up micro-turbine/fuel cell/battery hybrid power source. Energy 36(11):6490–6507

    Article  Google Scholar 

  7. Moghaddam AA, Seifi A, Niknam T (2012) Multi-operation management of a typical micro-grids using particle swarm optimization: a comparative study. Renew Sustain Energy Rev 16(2):1268–1281

    Article  Google Scholar 

  8. Liu G, Xu Y, Tomsovic K (2015) Bidding strategy for Microgrid in day-ahead market based on hybrid stochastic/robust optimisation. IEEE Trans Smart Grid 7(1):227–237

    Article  Google Scholar 

  9. Pan Z, Quynh NV, Ali ZM, Dadfar S, Kashiwagi T (2020) Enhancement of maximum power point tracking technique based on PV-Battery system using hybrid BAT algorithm and fuzzy controller. J Clean Prod 274:123719

    Article  Google Scholar 

  10. Bahramara S, Moghaddam MP, Haghifam MR (2016) A bi-level optimisation model for operation of distribution networks with micro-grids. Int J Electr Power Energy Syst 1(82):169–178

    Article  Google Scholar 

  11. Han X, Zhang H, Yu X, Wang L (2016) Economic evaluation of grid-connected micro-grid system with photovoltaic and energy storage under different investment and financing models. Appl Energy 15(184):103–118

    Article  Google Scholar 

  12. Ferruzzi G, Cervone G, Delle Monache L, Graditi G, Jacobone F (2016) Optimal bidding in a Day-Ahead energy market for Micro Grid under uncertainty in renewable energy production. Energy 1(106):194–202

    Article  Google Scholar 

  13. Kamankesh H, Agelidis VG, Kavousi-Fard A (2016) Optimal scheduling of renewable micro-grids considering plug-in hybrid electric vehicle charging demand. Energy 1(100):285–297

    Article  Google Scholar 

  14. Guo S, Abbassi R, Jerbi H, Rezvani A, Suzuki K (2021) Efficient maximum power point tracking for a photovoltaic using hybrid shuffled frog-leaping and pattern search algorithm under changing environmental conditions. J Clean Prod 11:126573

    Article  Google Scholar 

  15. Parastegari M, Hooshmand RA, Khodabakhshian A, Zare AH (2015) Joint operation of wind farm, photovoltaic, pump-storage and energy storage devices in energy and reserve markets. Int J Electr Power Energy Syst 1(64):275–284

    Article  Google Scholar 

  16. Parastegari M, Hooshmand RA, Khodabakhshian A, Forghani Z (2013) Joint operation of wind farms and pump-storage units in the electricity markets: Modeling, simulation and evaluation. Simul Model Pract Theory 1(37):56–69

    Article  Google Scholar 

  17. Luo L, Abdulkareem SS, Rezvani A, Miveh MR, Samad S, Aljojo N, Pazhoohesh M (2020) Optimal scheduling of a renewable based microgrid considering photovoltaic system and battery energy storage under uncertainty. J Energy Storage. 28:101306

    Article  Google Scholar 

  18. Fathabadi H (2015) Utilization of electric vehicles and renewable energy sources used as distributed generators for improving characteristics of electric power distribution systems. Energy 1(90):1100–1110

    Article  Google Scholar 

  19. Hannan MA, Azidin FA, Mohamed A (2012) Multi-sources model and control algorithm of an energy management system for light electric vehicles. Energy Convers Manage 1(62):123–130

    Article  Google Scholar 

  20. Zhang K, Xu L, Ouyang M, Wang H, Lu L, Li J, Li Z (2014) Optimal decentralised valley-filling charging strategy for electric vehicles. Energy Convers Manage 1(78):537–550

    Article  Google Scholar 

  21. Noori M, Tatari O (2016) Development of an agent-based model for regional market penetration projections of electric vehicles in the United States. Energy 1(96):215–230

    Article  Google Scholar 

  22. Rostami MA, Kavousi-Fard A, Niknam T (2015) Expected cost minimisation of smart grids with plug-in hybrid electric vehicles using optimal distribution feeder reconfiguration. IEEE Trans Ind Inf 11(2):388–397

    Article  Google Scholar 

  23. Tan X, Li Q, Wang H (2013) Advances and trends of energy storage technology in Microgrid. Int J Electr Power Energy Syst 44(1):179–191

    Article  Google Scholar 

  24. Druitt J, Früh WG (2012) Simulation of demand management and grid balancing with electric vehicles. J Power Sources 15(216):104–116

    Article  Google Scholar 

  25. Bahramara S, Golpîra H (2018) Robust optimisation of micro-grids operation problem in the presence of electric vehicles. Sustain Urban Areas 1(37):388–395

    Google Scholar 

  26. Tabatabaee S, Mortazavi SS, Niknam T (2017) Stochastic scheduling of local distribution systems considering high penetration of plug-in electric vehicles and renewable energy sources. Energy 15(121):480–490

    Article  Google Scholar 

  27. Aluisio B, Conserva A, Dicorato M, Forte G, Trovato M (2017) Optimal operation planning of V2G-equipped Microgrid in the presence of EV aggregator. Electric Power Syst Res 1(152):295–305

    Article  Google Scholar 

  28. Rodrigues YR, de Souza AZ, Ribeiro PF (2018) An inclusive methodology for Plug-in electrical vehicle operation with G2V and V2G in smart microgrid environments. Int J Electr Power Energy Syst 1(102):312–323

    Article  Google Scholar 

  29. Shamshirband M, Salehi J, Gazijahani FS (2018) Decentralised trading of plug-in electric vehicle aggregation agents for optimal energy management of smart renewable penetrated microgrids with the aim of CO2 emission reduction. J Clean Prod 1(200):622–640

    Article  Google Scholar 

  30. Mortaz E, Vinel A, Dvorkin Y (2019) An optimisation model for siting and sizing of vehicle-to-grid facilities in a microgrid. Appl Energy 15(242):1649–1660

    Article  Google Scholar 

  31. Zhu Q, Tang X (2021) An ameliorated harmony search algorithm with hybrid convergence mechanism. IEEE Access 8(9):9262–9276

    Article  Google Scholar 

  32. Tabatabaee S, Mortazavi SS, Niknam T (2016) Stochastic energy management of renewable micro-grids in the correlated environment using unscented transformation. Energy 15(109):365–377

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the Deanship of Scientific Research (DSR) at King Fahd University of Petroleum & Minerals (KFUPM) under Project DF191006.

Author information

Authors and Affiliations

  1. Systems Engineering Department, King Fahd University of Petroleum & Minerals, Dhahran, 31261, Saudi Arabia

    Mujahed AL-Dhaifallah

  2. College of Engineering at Wadi Addawaser, Prince Sattam Bin Abdulaziz University, Wadi Addawaser, 11991, Saudi Arabia

    Ziad M. Ali

  3. Electrical Engineering Department, Faculty of Engineering, Aswan University, Aswan, Egypt

    Ziad M. Ali

  4. Electrical Engineering Department, College of Engineering, Al Jouf University, Sakaka, 42421, Saudi Arabia

    Mohana Alanazi

  5. Department of Electrical Engineering, Islamic Azad University, Saveh, Iran

    Sajjad Dadfar

  6. Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran

    Mohammad Hosein Fazaeli

Authors
  1. Mujahed AL-Dhaifallah
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Ziad M. Ali
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Mohana Alanazi
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Sajjad Dadfar
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Mohammad Hosein Fazaeli
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Mohammad Hosein Fazaeli.

Ethics declarations

Conflict of interest

The authors declare that there is no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

AL-Dhaifallah, M., Ali, Z.M., Alanazi, M. et al. An efficient short-term energy management system for a microgrid with renewable power generation and electric vehicles. Neural Comput & Applic 33, 16095–16111 (2021). https://doi.org/10.1007/s00521-021-06247-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-021-06247-5

Keywords