Skip to main content
SpringerLink
Log in

Optimal planning and operation of power grid with electric vehicles considering cost reduction

  • Optimization
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

Given the ever-growing electricity consumption and environmental anxiety with the predominant usage of conventional fuels in power plants, it is crucial to explore suitable alternatives to address these issues. Renewable energy sources (RESs) have emerged as the preferred choice for meeting energy requirements due to their minimal pollution. This study proposes a new idea to minimize operational costs and achieve the most cost-effective grid with minimum cost. Meanwhile, the transportation sector is gradually replacing conventional fossil-cars with electric ones, specifically plug-in electric vehicles (PEVs) and plug-in hybrid electric vehicles (PHEVs), which have gained significant consideration. These vehicles can join to the main grid and engage in energy exchange through grid-to-vehicle (G2V) and vehicle-to-grid (V2G) technologies. Additionally, the concept of microgrid (MG) is proposed to optimize the potential of PEVs through smart infrastructure. Using the V2G capability, the operating costs are reduced, providing opportunities to incorporate PEVs into the network. Therefore, effective operation of MGs becomes highly significant. This paper suggests management of a MG consisting of PEVs and RESs. The approach utilizes a stochastic programming technique called unscented transformation (UT). The problem is addressed as a single-objective stochastic optimization problem with the aim of minimizing the operation cost. The proposed approach employs the hybrid whale optimization algorithm and pattern search (HWOA–PS) to solve the stochastic problem. The obtained outcomes are compared with those of other approaches to validate its effectiveness.

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

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Data availability

Enquiries about data availability should be directed to the authors.

Abbreviations

RES:

Renewable energy sources

WTs:

Wind turbines

DGs:

Distributed generators

PEVs:

Electric vehicles

PV:

Photovoltaic

PEVs:

Plug-in electric vehicles

G2V:

Grid-to-vehicle

FCSs:

Fuel cell systems

DSOs:

Distribution operators

V2G:

Vehicle-to-grid

CS:

Charging station

DOD:

Depth of discharge

MG:

Microgrid

ENS:

Energy not supplied

UT:

Unscented transformation

A i :

Amplitude of the frequency

DoDi & DoDf :

The original and final depth of discharge

a,b :

Wöhler curve’s parameter

\({E}_{v}^{{\text{ini}}}\) & \({E}_{v}^{{\text{fin}}}\) :

The initial and concluding energy levels of fleet v

d :

The size or scale of the problem.

\({E}_{v}^{{\text{min}}}\) & \({E}_{v}^{{\text{max}}}\) :

The maximum and minimum energy bounds

E bat :

The stored energy of battery (kWh)

N DG :

The total number of DGs

N br /N bus :

The number of branches/buses of the network

N Cus :

The overall count of consumers being supplied

Nv :

Total number of electric vehicles

Np :

The population sizes

Ndis :

The total count of battery discharge cycles

m :

The count of uncertain variables

Nc :

Cycle life

rand:

Operator for generating random values

T :

Scheduling period

ε :

Charging and discharging efficiencies

CostGrid :

The cost of supplying energy by the upstream system

C Bat :

The cost of interrupting load at bus i ($/kW)

CostPEV :

The aggregated costs related to PEVs

C Grid :

The 1-h time resolution energy price provided by the grid, V2G technology

CostENS :

The cost of energy not served of the customers

C PEV :

The 1-h time resolution energy price provided by the grid, V2G technology

CostDG :

The cost operation of distributed generators (DGs)

C ENS :

The 1-h time resolution cost associated with V2G technology

Ct DG,k :

The cost of battery investment in US dollars ($)

\({E}_{D,v}^{t}\) :

The energy consumed by fleet v for driving at hour t

\({P}_{v}^{t}\) :

The rate of charging or discharging power for fleet v at time t

\({E}_{v}^{t}\) :

The available energy for fleet v at hour t

\({P}_{i}^{t}\) &\({Q}_{i}^{t}\) :

The powers injected to bus i at time t

\({P}_{DG,k}^{t}\) :

The output of DG k at time t

\({S}_{ij}^{t}\) &\({S}_{ij}^{{\text{max}}}\) :

The apparent power and maximum active power from bus i to bus j at time t, respectively

\({P}_{{\text{Grid}}}^{t}{ \& P}_{{\text{Grid}}}^{{\text{max}}}\) :

The hourly power exchange and the peak electricity transaction with the external grid

\({U}_{v}^{t}\) :

The functioning condition of fleet v's link with the grid at time t

\({{P}_{d,v}^{t} \& P}_{c,v}^{t}\) :

Discharging and charging capacity of fleet

\({V}_{i}^{t}\) :

The voltage magnitude at bus i and at time t

References

  • Abdalla AN, Nazir MS, Tiezhu Z, Bajaj M, Sanjeevikumar P, Yao L (2021) Optimized economic operation of microgrid: combined cooling and heating power and hybrid energy storage systems. J Energy Res Technol 143(7):070906

    Google Scholar 

  • Afrakhte H, Bayat P (2020) A contingency based energy management strategy for multi-microgrids considering battery energy storage systems and electric vehicles. J Energy Storage 27:101087

    Google Scholar 

  • Aghdam FH, Mudiyanselage MW, Mohammadi-Ivatloo B, Marzband M (2023) Optimal scheduling of multi-energy type virtual energy storage system in reconfigurable distribution networks for congestion management. Appl Energy 333:120569

    Google Scholar 

  • Ahmad F, Ashraf I, Iqbal A, Marzband M, Khan I (2022) A novel AI approach for optimal deployment of EV fast charging station and reliability analysis with solar based DGs in distribution network. Energy Rep 8:11646–11660

    Google Scholar 

  • Ahmadi SE, Kazemi-Razi SM, Marzband M, Ikpehai A, Abusorrah A (2023) Multi-objective stochastic techno-economic-environmental optimization of distribution networks with G2V and V2G systems. Electric Power Syst Res 218:109195

    Google Scholar 

  • Aien M, Fotuhi-Firuzabad M, Aminifar F (2012) Probabilistic load flow in correlated uncertain environment using unscented transformation. IEEE Trans Power Syst 27(4):2233–2241

    Google Scholar 

  • Alipour M, Mohammadi-Ivatloo B, Zare K (2014) Stochastic risk-constrained short-term scheduling of industrial cogeneration systems in the presence of demand response programs. Appl Energy 136:393–404

    Google Scholar 

  • Balasubramaniam K, Saraf P, Hadidi R, Makram EB (2016) Energy management system for enhanced resiliency of microgrids during islanded operation. Electr Power Syst Res 137:133–141

    Google Scholar 

  • Camacho-Gómez C, Jiménez-Fernández S, Mallol-Poyato R, Del Ser J, Salcedo-Sanz S (2019) Optimal design of microgrid’s network topology and location of the distributed renewable energy resources using the Harmony search algorithm. Soft Comput 23(15):6495–6510

    Google Scholar 

  • Chanda S, Srivastava AK (2016) Defining and enabling resiliency of electric distribution systems with multiple microgrids. IEEE Trans Smart Grid 7(6):2859–2868

    Google Scholar 

  • Chen PH, Chang HC (1995) Large-scale economic dispatch by genetic algorithm. IEEE Trans Power Syst 10(4):1919–1926

    Google Scholar 

  • Coello CA, Pulido GT, Lechuga MS (2004) Handling multiple objectives with particle swarm optimization. IEEE Trans Evolut Comput 8(3):256–279

    Google Scholar 

  • Coello Coello CA (1999) A comprehensive survey of evolutionary-based multiobjective optimization. Knowl Inf Syst 1(3):269–308

    Google Scholar 

  • Davatgaran V, Saniei M, Mortazavi SS (2019) Smart distribution system management considering electrical and thermal demand response of energy hubs. Energy 169:38–49

    Google Scholar 

  • Della Cioppa A, De Stefano C, Marcelli A (2004) On the role of population size and niche radius in fitness sharing. IEEE Trans Evolut Comput 8(6):580–592

    Google Scholar 

  • Dey B, Bhattacharyya B, Srivastava A, Shivam K (2020) Solving energy management of renewable integrated microgrid systems using crow search algorithm. Soft Comput 24(14):10433–10454

    Google Scholar 

  • Fieldsend JE, Everson RM, Singh S (2003) Using unconstrained elite archives for multiobjective optimization. IEEE Trans Evolut Comput 7(3):305–323

    Google Scholar 

  • Figueroa-Candia M, Felder FA, Coit DW (2018) Resiliency-based optimization of restoration policies for electric power distribution systems. Electric Power Syst Res 161:188–198

    Google Scholar 

  • Gazijahani FS, Salehi J (2018) Integrated DR and reconfiguration scheduling for optimal operation of microgrids using Hong’s point estimate method. Int J Electr Power Energy Syst 99:481–492

    Google Scholar 

  • He Y, Venkatesh B, Guan L (2012) Optimal scheduling for charging and discharging of electric vehicles. IEEE Trans Smart Grid 3(3):1095–1105

    Google Scholar 

  • Hosseinnezhad V, Rafiee M, Ahmadian M, Siano P (2018) Optimal island partitioning of smart distribution systems to improve system restoration under emergency conditions. Int J Electr Power Energy Syst 97:155–164

    Google Scholar 

  • Hussain A, Bui VH, Kim HM (2019) Microgrids as a resilience resource and strategies used by microgrids for enhancing resilience. Appl Energy 240:56–72

    Google Scholar 

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

    Google Scholar 

  • Khadanga RK, Padhy S, Panda S, Kumar A (2018) Design and analysis of multi-stage PID controller for frequency control in an islanded micro-grid using a novel hybrid whale optimization-pattern search algorithm. Int J Numer Model Electron Netw Devices Fields 31(5):e2349

    Google Scholar 

  • Khalili T, Hagh MT, Zadeh SG, Maleki S (2019) Optimal reliable and resilient construction of dynamic self-adequate multi-microgrids under large-scale events. IET Renew Power Gener 13(10):1750–1760

    Google Scholar 

  • Khalili R, Khaledi A, Marzband M, Nematollahi AF, Vahidi B, Siano P (2023) Robust multi-objective optimization for the Iranian electricity market considering green hydrogen and analyzing the performance of different demand response programs. Appl Energy 334:120737

    Google Scholar 

  • Khodaei A (2014) Resiliency-oriented microgrid optimal scheduling. IEEE Trans Smart Grid 5(4):1584–1591

    Google Scholar 

  • Li W, Zhang T, Wang R (2018) Energy management model of charging station micro-grid considering random arrival of electric vehicles. In: 2018 IEEE international conference on energy internet (ICEI). IEEE, pp 29–34

  • Liao GC (2011) A novel evolutionary algorithm for dynamic economic dispatch with energy saving and emission reduction in power system integrated wind power. Energy 36(2):1018–1029

    Google Scholar 

  • Manshadi SD, Khodayar ME (2015) Resilient operation of multiple energy carrier microgrids. IEEE Trans Smart Grid 6(5):2283–2292

    Google Scholar 

  • Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67

    Google Scholar 

  • 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

    Google Scholar 

  • Mousavizadeh S, Bolandi TG, Haghifam MR, Moghimi M, Lu J (2020) Resiliency analysis of electric distribution networks: a new approach based on modularity concept. Int J Electr Power Energy Syst 117:105669

    Google Scholar 

  • Muttaqi KM, Nezhad AE, Aghaei J, Ganapathy V (2014) Control issues of distribution system automation in smart grids. Renew Sustain Energy Rev 37:386–396

    Google Scholar 

  • Nasiri J, Khiyabani FM (2018) A whale optimization algorithm (WOA) approach for clustering. Cogent Math Stat 5(1):1483565

    MathSciNet  Google Scholar 

  • Nezhad AE, Nardelli PH, Sahoo S, Ghanavati F (2022a) Scheduling of energy hub resources using robust chance-constrained optimization. IEEE Access 10:129738–129753

    Google Scholar 

  • Nezhad AE, Rahimnejad A, Nardelli PH, Gadsden SA, Sahoo S, Ghanavati F (2022b) A shrinking horizon model predictive controller for daily scheduling of home energy management systems. IEEE Access 10:29716–29730

    Google Scholar 

  • Nezhad AE, Ghanavati F, Ahmarinejad A (2022c) Determining the optimal operating point of CHP units with nonconvex characteristics in the context of combined heat and power scheduling problem. IETE J Res 68(4):2609–2621

    Google Scholar 

  • Park JB, Lee KS, Shin JR, Lee KY (2005) A particle swarm optimization for economic dispatch with nonsmooth cost functions. IEEE Trans Power Syst 20(1):34–42

    Google Scholar 

  • Rabiee A, Sadeghi M, Aghaeic J, Heidari A (2016) Optimal operation of microgrids through simultaneous scheduling of electrical vehicles and responsive loads considering wind and PV units uncertainties. Renew Sustain Energy Rev 57:721–739

    Google Scholar 

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

    Google Scholar 

  • Sayed AR, Wang C, Bi T (2019) Resilient operational strategies for power systems considering the interactions with natural gas systems. Appl Energy 241:548–566

    Google Scholar 

  • Soleymani S, Ranjbar AM, Shirani AR (2007) New approach for strategic bidding of Gencos in energy and spinning reserve markets. Energy Convers Manag 48(7):2044–2052

    Google Scholar 

  • Tehrani NH, Shrestha GB, Wang P (2013) Vehicle-to-grid service potential with price based PEV charging/discharging. In: 2013 IEEE power & energy society general meeting. IEEE, pp 1–5

  • Wang Z, Wang J (2015) Self-healing resilient distribution systems based on sectionalization into microgrids. IEEE Trans Power Syst 30(6):3139–3149

    Google Scholar 

  • Zadsar M, Haghifam MR, Miri Larimi SM (2017) Approach for self-healing resilient operation of active distribution network with microgrid. IET Gener Transm Distrib 11(18):4633–4643

    Google Scholar 

  • Zhang B, Li Q, Wang L, Feng W (2018) Robust optimization for energy transactions in multi-microgrids under uncertainty. Appl Energy 217:346–360

    Google Scholar 

  • Zhu J, Yuan Y, Wang W (2020) An exact microgrid formation model for load restoration in resilient distribution system. Int J Electr Power Energy Syst 116:105568

    Google Scholar 

Download references

Acknowledgements

This work was supported by the 2024 Key Research and Scientific Research Capability Improvement Project in Qiannan Normal University for Nationalities (2024zdzk06), National Natural Science Foundation of China (No. 61862051), the Science and Technology Foundation of Guizhou Province (No. ZK[2022]549), the Natural Science Foundation of Education of Guizhou province (No. [2019]203, No. KY[2019]067), and the Funds of Qiannan Normal University for Nationalities (No. qnsy2019rc09) and the Al-Mustaqbal University.

Funding

This study was not funded by any institution or organization.

Author information

Authors and Affiliations

  1. School of Electronics and Information Engineering, Ankang University, Ankang, China

    Tao Hai

  2. School of Computer and Information, Qiannan Normal University for Nationalities, Duyun, Guizhou, 558000, China

    Tao Hai

  3. Cyber Security Department, College of Sciences, Al-Mustaqbal University, Babylon, 51001, Iraq

    Muammer Aksoy

  4. Computer Information Systems Department, Ahmed Bin Mohammed Military College, P.O. Box 22988, Doha, Qatar

    Muammer Aksoy

  5. Solar Energy and Power Electronic Co, Ltd, Istanbul, Turkey

    Mehrdad Khaki

Authors
  1. Tao Hai
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Muammer Aksoy
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mehrdad Khaki
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Muammer Aksoy or Mehrdad Khaki.

Ethics declarations

Conflict of interest

The author declares that he has no conflict of interest.

Research involving human participants and/or animals

This article does not contain any studies with human participants performed by any of the authors.

Informed consent

The processes of program coding, numerical execution, and statistical analysis were based on personal computers. All authors agreed to publish this paper, if accepted.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hai, T., Aksoy, M. & Khaki, M. Optimal planning and operation of power grid with electric vehicles considering cost reduction. Soft Comput 28, 7161–7179 (2024). https://doi.org/10.1007/s00500-023-09597-5

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-023-09597-5

Keywords

Search

Navigation