Skip to main content

Advertisement

SpringerLink
Log in

Energy management strategy based on short-term resource scheduling of a renewable energy-based microgrid in the presence of electric vehicles using θ-modified krill herd algorithm

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

Abstract

Providing of energy is one of the most important issues for each country. Also, environmental issues due to fossil fuel depletion are other serious concern of them. In this regard, moving toward energy sustainability is a constructive solution for each country. This paper studies the short-term planning of generating units in renewable energy-based distribution networks equipped with plug-in electric vehicles (PEVs). PEVs can cause problems for distributed energy sources in the electrical grid, as well as power units inside the grid. So, to overcome this problem, an efficient stochastic programming technique is designed to allow the control entity to control the charging behavior of PEVs for managing power units. In this paper, to obtain the least total cost, a new method is suggested to decrease the reliability expenses. In other words, the vehicle-2-grid (V2G) is applied to decrease the operating. On the other hand, a novel stochastic flow using the unscented transform is suggested to improve the model of the severe uncertainty due to the wind power, photovoltaic (PV) and charging/discharging power of PEVs. In this research work, a novel and efficient optimization algorithm called ‘θ-modified krill herd (θ-MKH)” is used as an applicable technique to optimize the microgrid (MG) operation. This algorithm is useful and has many advantages like the runaway from the local optima with fast converging in comparison with other methods. Also, the satisfactory efficiency of the suggested randomized manner is validated on an MG connected to the main grid.

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

Access this article

Log in via an institution

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

Similar content being viewed by others

Abbreviations

PEVs:

Plug-in electric vehicles

V2G:

Vehicle-2-grid

PV:

Photovoltaic

θ-MKH:

θ-modified krill herd

MG:

Microgrid

RES:

Renewable energy sources

DG:

Distributed generation

DERs:

Distributed energy resources

FCs:

Fuel cells

MT:

Microturbines

LV:

Low-voltage

EVs:

Electric vehicles

VPPs:

Virtual power plants

MILP:

Mixed-integer linear programming

DRPs:

Demand response programs

UML:

Unified modeling language

MPC:

Model predictive control

MGCC:

MG central control

ST:

Start-up

SD:

Shut-down

ENS:

Energy not supplied

C DG,k :

The price of energy, supplied by DG units at each hour

C Grid :

The price, relating to transacting energy with the utility grid at each hour

C ENS :

The cost that should be tolerated as a result of load curtailment at node i ($/kW)

N DG :

The total number of DGs, existing in the network

N Cus :

Total number of customers with satisfied load demand

La(i) :

The average load demand at node i

Cost DG :

The cost of energy generation by DG units.

\(P_{(DG,k)}^{t}\) :

Power generation of DG unit k at time interval t

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

The power charged/discharged by the PEV fleet v at each time interval t

DoD i & DoD f :

The initial value of DOD and final value of DOD during a discharge cycle respectively

\(V_{r,i}^{K}\) :

The velocity of the ith

\(V_{ind\;i}^{k}\) :

Induction motion

θi :

Phase vector

M nk :

Mean value of the krill population

Np :

The size of population

References

  1. Razmjoo A, Kaigutha LG, Rad MV, Marzband M, Davarpanah A, Denai M (2020) A Technical analysis investigating energy sustainability utilizing reliable renewable energy sources to reduce CO2 emissions in a high potential area. Renew Energ 164:46–57

    Article  Google Scholar 

  2. Ahmed EM, Aly M, Elmelegi A, Alharbi AG, Ali ZM (2019) Multifunctional distributed MPPT controller for 3P4W grid-connected PV systems in distribution network with unbalanced loads. Energies 12(24):4799

    Article  Google Scholar 

  3. Gandoman FH, Ahmadi A, Van den Bossche P, Van Mierlo J, Omar N, Nezhad AE, Mavalizadeh H, Mayet C (2019) Status and future perspectives of reliability assessment for electric vehicles. Reliab Eng Syst Saf 183:1–16

    Article  Google Scholar 

  4. 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 

  5. Chen W, Shao Z, Wakil K, Aljojo N, Samad S, Rezvani A (2020) An efficient day-ahead cost-based generation scheduling of a multi-supply microgrid using a modified krill herd algorithm. J Clean Prod 1(272):122364

    Article  Google Scholar 

  6. Yin N, Abbassi R, Jerbi H, Rezvani A, Müller M (2020) A day-ahead joint energy management and battery sizing framework based on θ-modified krill herd algorithm for a renewable energy-integrated microgrid. J Clean Prod 1:124435

    Google Scholar 

  7. Mahmoud K, Abdel-Nasser M, Mustafa E, Ali ZM (2020) Improved salp-swarm optimizer and accurate forecasting model for dynamic economic dispatch in sustainable power systems. Sustainability 12(2):576

    Article  Google Scholar 

  8. Liu C, Abdulkareem SS, Rezvani A, Samad S, Aljojo N, Foong LK, Nishihara K (2020) Stochastic scheduling of a renewable-based microgrid in the presence of electric vehicles using modified harmony search algorithm with control policies. Sustain Cities Soc 3:102183

    Article  Google Scholar 

  9. Mostafa MH, Aleem SH, Ali SG, Abdelaziz AY, Ribeiro PF, Ali ZM (2020) Robust energy management and economic analysis of microgrids considering different battery characteristics. IEEE Access 18(8):54751–54775

    Article  Google Scholar 

  10. Li Y, Mohammed SQ, Nariman GS, Aljojo N, Rezvani A, Dadfar S (2020) Energy management of microgrid considering renewable energy sources and electric vehicles using the backtracking search optimization algorithm. J Energ Resour Technol. https://doi.org/10.1115/1.4046098

    Article  Google Scholar 

  11. Shayegan-Rad A, Badri A, Zangeneh A (2017) Day-ahead scheduling of virtual power plant in joint energy and regulation reserve markets under uncertainties. Energy 121:114–125

    Article  Google Scholar 

  12. Mkahl R, Nait-Sidi-Moh A, Gaber J, Wack M (2017) An optimal solution for charging management of electric vehicles fleets. Electr Power Syst Res 146:177–188

    Article  Google Scholar 

  13. Jannati J, Nazarpour D (2017) Optimal energy management of the smart parking lot under demand response program in the presence of the electrolyser and fuel cell as hydrogen storage system. Energy Convers Manage 138:659–669

    Article  Google Scholar 

  14. Timpner J, Wolf L (2013) Design and evaluation of charging station scheduling strategies for electric vehicles. IEEE Trans Intell Transp Syst 15(2):579–588

    Article  Google Scholar 

  15. Clemente M, Fanti MP, Iacobellis G, Ukovich W (2013) A discrete-event simulation approach for the management of a car sharing service. In2013 IEEE International Conference on Systems, Man, and Cybernetics 2013(pp. 403–408). IEEE

  16. Yang H, Pan H, Luo F, Qiu J, Deng Y, Lai M, Dong ZY (2016) Operational planning of electric vehicles for balancing wind power and load fluctuations in a microgrid. IEEE Trans Sustain Energy 8(2):592–604

    Article  Google Scholar 

  17. Kong F, Xiang Q, Kong L, Liu X (2016) On-line event-driven scheduling for electric vehicle charging via park-and-charge. In2016 IEEE Real-Time Systems Symposium (RTSS) (pp. 69–78). IEEE

  18. Di Giorgio A, Liberati F, Canale S (2013) IEC 61851 compliant electric vehicle charging control in Smartgrids. In21st Mediterranean Conference on Control and Automation (pp. 1329–1335). IEEE

  19. Di Giorgio A, Liberati F (2014) Near real time load shifting control for residential electricity prosumers under designed and market indexed pricing models. Appl Energy 128:119–132

    Article  Google Scholar 

  20. Weckx S, D’Hulst R, Claessens B, Driesensam J (2014) Multiagent charging of electric vehicles respecting distribution transformer loading and voltage limits. IEEE Trans on Smart Grid 5(6):2857–2867

    Article  Google Scholar 

  21. Azar AG, Jacobsen RH (2016) Agent-based charging scheduling of electric vehicles. In2016 IEEE Online Conference on Green Communications (OnlineGreenComm) (pp. 64–69). IEEE

  22. Fanti MP, Mangini AM, Pedroncelli G, Ukovich W (2014) A framework for the distributed management of charging operations. In2014 IEEE International Electric Vehicle Conference (IEVC) 2014 Dec 17 (pp. 1–7). IEEE

  23. Zhou Y, Kumar R, Tang S (2018) Incentive-based distributed scheduling of electric vehicle charging under uncertainty. IEEE Trans Power Syst 34(1):3–11

    Article  Google Scholar 

  24. Ghaedi A, Dehnavi SD, Fotoohabadi H (2016) Probabilistic scheduling of smart electric grids considering plug-in hybrid electric vehicles. J Intell Fuzzy Syst 31(3):1329–1340

    Article  Google Scholar 

  25. Mortazavi SMB, Shiri N, Javadi MS, Dehnavi SD (2015) Optimal planning and management of hybrid vehicles in smart grid. Ciência e Natura 37:253–263

    Article  Google Scholar 

  26. Aghaei J, Nezhad AE, Rabiee A, Rahimi E (2016) Contribution of plug-in hybrid electric vehicles in power system uncertainty management. Renew Sustain Energy Rev 59:450–458

    Article  Google Scholar 

  27. Gandomi AH, Alavi AH (2012) Krill herd: a new bio-inspired optimization algorithm. Commun Nonlinear Sci Numer Simulat 17:4831–4845

    Article  MathSciNet  Google Scholar 

  28. 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. https://doi.org/10.1002/er.6198

    Article  Google Scholar 

  29. 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 1(28):101306

    Article  Google Scholar 

  30. Kavousi-Fard A, Rostami MA, Niknam T (2015) Reliability-oriented reconfiguration of vehicle-to-grid networks. IEEE Trans Industr Inf 11(3):682–691

    Article  Google Scholar 

  31. Aien M, Fotouhi-Firuzabad M, Aminfar F (2012) Probabilistic load flow in correlated uncertain environment using unscented transformation. IEEE Trans Power Sys 27(4):2233–2241

    Article  Google Scholar 

  32. Marzband M, Yousefnejad E, Sumper A, Domínguez-García JL (2016) Real time experimental implementation of optimum energy management system in standalone microgrid by using multi-layer ant colony optimization. Int J Electr Power Energy Syst 75:265–274

    Article  Google Scholar 

  33. Kavousi-Fard A, Abunasri A, Zare A, Hoseinzadeh R (2014) Impact of plug-in hybrid electric vehicles charging demand on the optimal energy management of renewable micro-grids. Energy 15(78):904–915

    Article  Google Scholar 

  34. Brown CT, Liebovitch LS, Glendon R (2007) Lévy flights in Dobe Ju/’hoansi foraging patterns. Human Ecol 35(1):129–138

    Article  Google Scholar 

Download references

Acknowledgement

This project was funded by King Abdulaziz University, Jeddah, Saudi Arabia and King Abdullah City for Atomic and Renewable Energy, Riyadh, Saudi Arabia under grant no. (KCR-KFL-09-20). Therefore, the authors gratefully acknowledge their technical and financial support.

Author information

Authors and Affiliations

  1. Department of Computer Science, Prince Sattam bin Abdulaziz University, As Sulayyil, 11991, Saudi Arabia

    Abdallah Aldosary

  2. Center of Research Excellence in Renewable Energy and Power Systems, and Department of Electrical and Computer Engineering, Faculty of Engineering, K. A. CARE Energy Research and Innovation Center, King Abdulaziz University, Jeddah, 21589, Saudi Arabia

    Muhyaddin Rawa

  3. College of Engineering At Wadi Addawaser, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia

    Ziad M. Ali

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

    Ziad M. Ali

  5. Department of Mechanical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Mohsen latifi

  6. Escola Tècnica Superior d’Enginyeria Industrial de Barcelona (ETSEIB), Universitat Politècnica de Catalunya (UPC), 08028, Barcelona, Spain

    Armin Razmjoo

  7. Institute of Research and Development, Duy Tan University, Da Nang, 550000, Viet Nam

    Alireza Rezvani

  8. Faculty of Electrical–Electronic Engineering, Duy Tan University, Da Nang, 550000, Viet Nam

    Alireza Rezvani

Authors
  1. Abdallah Aldosary
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Muhyaddin Rawa
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ziad M. Ali
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mohsen latifi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Armin Razmjoo
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Alireza Rezvani
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alireza Rezvani.

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.

Appendix

Appendix

Data of the two fleets of PEVs.

Fleet #

Capacity (kWh)

Min

Max

1

263

1973

2

219

1644

Fleet #

Charging/discharging rate (kW)

Min

Max

1

7.3

496

2

7.3

292

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Aldosary, A., Rawa, M., Ali, Z.M. et al. Energy management strategy based on short-term resource scheduling of a renewable energy-based microgrid in the presence of electric vehicles using θ-modified krill herd algorithm. Neural Comput & Applic 33, 10005–10020 (2021). https://doi.org/10.1007/s00521-021-05768-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-021-05768-3

Keywords

Search

Navigation