Skip to main content
SpringerLink
Log in

Short-term economic dispatch incorporating renewable power generations with plug-in electric vehicles considering emissions reduction

  • Published:
Environment, Development and Sustainability Aims and scope Submit manuscript

Abstract

Given the increasing worldwide energy demand and the growing environmental apprehension regarding the utilization of fossil fuels in power systems, it is critical to implement viable alternatives to resolve this issue. Subsequently, renewable energy sources (RESs), which generate minimal pollution, have become the predominant choice for fulfilling energy requirements. By employing an innovative problem formulation approach, this study proposes reducing costs to attain the most economical overall cost for the grid. Concurrently, there has been a shift in the transportation industry from traditional vehicles propelled by fossil fuels to those that are electrified. Plug-in electric vehicles (PEVs) and plug-in hybrid electric vehicles (PHEVs) have emerged as prominent alternatives and are being embraced at an accelerated rate. By linking to the power grid and utilizing vehicle-to-grid (V2G) and grid-to-vehicle (G2V) innovations, these vehicles can either receive or return energy to the grid. Microgrids (MGs), which are an emerging concept in power systems, seek to optimize the performance of electric vehicles (EVs) through their integration with intelligent infrastructure and to encourage the incorporation of renewable energy sources (RESs). To facilitate the integration of PEVs into the network, the vehicle-to-grid (V2G) capacity is effectively utilized to reduce operational costs. This underscores the criticality of the resource management problem at MG. Unscented transformation (UT), an efficient stochastic programming technique, is utilized in this study's optimization framework to optimize the energy management of MGs (including PEVs and RESs) for the upcoming day. Approaching this issue as a stochastic optimization problem with the sole objective of minimizing the overall operational cost. The problem at hand is resolved by utilizing the modified water strider (MWS) algorithm, an efficient approach inspired by nature. Its efficacy is assessed through a comparative analysis with other documented techniques.

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

Similar content being viewed by others

Data availability

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

Abbreviations

A i :

Frequency amplitude (Hz)

a,b :

Wӧhler curve parameters

C ENS :

At the ith bus, the cost of reducing load ($/kW)

C Bat :

A battery's initial investment ($)

Ct DG ,k :

The cost of energy produced by DGs per hour ($/kWh)

d :

Investigated problem's dimension

DoD i & DoD f :

Both the beginning and ending depths of discharge (DOD) in percentage terms

Cd :

The deterioration cost of the battery ($/kWh)

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

Fleet v's energy to drive at time t (kWh)

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

Fleet v's initial and ultimate energy availability (kWh)

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

Energy stored in the PEV fleet, max and min (kWh).

Iter :

Counter for iterations

La(i) :

Load demand average at node i (kW)

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

The hour-long duration of the ith component's annual outage.

m :

The quantity of uncertain factors

N br /N bus :

Collection of the system's nodes and branches

Nv :

Collection of electric vehicles

Ndis :

The number of battery cycles

Nc :

Life cycle

N DG :

Collection of DGs.

Np :

Size of the population

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

Grid and microgrid power transactions per hour, with an upper limit (kW)

\({P}_{c,v}^{max}\, \& \,{P}_{c,v}^{min}\) :

Power requirements for charging fleet v, highest and lowest (kW)

\({P}_{d,v}^{max}\, \& \,{P}_{d,v}^{min}\) :

Power requirements for discharging fleet v, highest and lowest (kW)

rand :

The function that generates random numbers

\({V}_{i}^{min}\) & \({V}_{i}^{max}\) :

The minimum and maximum values for the magnitude of the voltage at node i (V)

W k :

K, the weighting factor associated with the point in the sample

Y ij & θij :

The impedance's magnitude and phase across nodes i and j

ηc & ηd :

Rates of efficiency while charging and discharging, expressed as a percentage

μ :

The average of the uncertain parameter's value

Cost Grid :

Cost of exchanging energy with the power grid ($).

Cost PEV :

The total cost associated with all of the plug-in electric vehicles ($)

Cost ENS :

Cost of energy not supplied to consumers ($)

Cost DG :

Costs associated with distributed generator generation ($)

Cost :

The whole system cost ($)

C v :

Per hour cost of V2G ($/MWh)

E bat :

Energy available from the battery (kWh)

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

Energy available in the fleet v at time t (kWh)

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

Power generated by DG k at time t (kW)

N Cus :

Collection of customers serviced

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

Power required to discharge the fleet v and charge it (kW)

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

Amount of reactive power and active power inputted onto bus i at time t (kW)

T :

The time a certain value is given for the state of charge (SOC)

References

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

    Article  Google Scholar 

  • Alipour, M., Mohammadi-Ivatloo, B., Moradi-Dalvand, M., & Zare, K. (2017). Stochastic scheduling of aggregators of plug-in electric vehicles for participation in energy and ancillary service markets. Energy, 118, 1168–1179.

    Article  Google Scholar 

  • Areekkara, S., Kumar, R., & Bansal, R. C. (2021). An intelligent multi agent based approach for autonomous energy management in a Microgrid. Electric Power Components and Systems., 49(1–2), 18–31.

    Article  Google Scholar 

  • Arias, A., Granada, M., & Castro, C. A. (2017). Optimal probabilistic charging of electric vehicles in distribution systems. IET Electrical Systems in Transportation., 7(3), 246–251.

    Article  Google Scholar 

  • Awad, A. S., Shaaban, M. F., El-Fouly, T. H., El-Saadany, E. F., & Salama, M. M. (2016). Optimal resource allocation and charging prices for benefit maximization in smart PEV-parking lots. IEEE Transactions on Sustainable Energy., 8(3), 906–915.

    Article  Google Scholar 

  • Battistelli, C., Baringo, L., & Conejo, A. J. (2012). Optimal energy management of small electric energy systems including V2G facilities and renewable energy sources. Electric Power Systems Research., 92, 50–59.

    Article  Google Scholar 

  • Dallinger, D., Link, J., & Büttner, M. (2014). Smart grid agent: Plug-in electric vehicle. IEEE Transactions on Sustainable Energy., 5(3), 710–717.

    Article  Google Scholar 

  • Faddel, S., Al-Awami, A. T., & Abido, M. A. (2017). Fuzzy optimization for the operation of electric vehicle parking lots. Electric Power Systems Research., 145, 166–174.

    Article  Google Scholar 

  • Han, S., Han, S., Sezaki, K.(2011). Optimal control of the plug-in electric vehicles for V2G frequency regulation using quadratic programming. InISGT 2011 (pp. 1–6). IEEE.

  • Heydari, A., Nezhad, M. M., Keynia, F., Fekih, A., Shahsavari-Pour, N., Garcia, D. A., & Piras, G. (2023). A combined multi-objective intelligent optimization approach considering techno-economic and reliability factors for hybrid-renewable microgrid systems. Journal of Cleaner Production., 383, 135249.

    Article  Google Scholar 

  • Heydarian-Forushani, E., Golshan, M. E., & Shafie-khah, M. (2016). Flexible interaction of plug-in electric vehicle parking lots for efficient wind integration. Applied Energy., 179, 338–349.

    Article  Google Scholar 

  • Jian, L., Zheng, Y., Xiao, X., & Chan, C. C. (2015). Optimal scheduling for vehicle-to-grid operation with stochastic connection of plug-in electric vehicles to smart grid. Applied Energy., 146, 150–161.

    Article  Google Scholar 

  • Kabatepe, B., & Türkay, M. (2017). A bi-criteria optimization model to analyze the impacts of electric vehicles on costs and emissions. Computers & Chemical Engineering., 102, 156–168.

    Article  CAS  Google Scholar 

  • Kang, Q., Feng, S., Zhou, M., Ammari, A. C., & Sedraoui, K. (2017). Optimal load scheduling of plug-in hybrid electric vehicles via weight-aggregation multi-objective evolutionary algorithms. IEEE Transactions on Intelligent Transportation Systems., 18(9), 2557–2568.

    Article  Google Scholar 

  • Kaveh, A., Eslamlou, A.D. (2020). Water strider algorithm: A new metaheuristic and applications. InStructures (Vol. 25, pp. 520–541). Elsevier.

  • 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, 78, 904–915.

    Article  Google Scholar 

  • Kavousi-Fard, A., Rostami, M. A., & Niknam, T. (2015). Reliability-oriented reconfiguration of vehicle-to-grid networks. IEEE Transactions on Industrial Informatics., 11(3), 682–691.

    Article  Google Scholar 

  • Kazemi, M. A., Sedighizadeh, M., Mirzaei, M. J., & Homaee, O. (2016). Optimal siting and sizing of distribution system operator owned EV parking lots. Applied Energy., 179, 1176–1184.

    Article  Google Scholar 

  • Khalili, T., Hagh, M. T., Zadeh, S. G., & Maleki, S. (2019). Optimal reliable and resilient construction of dynamic self-adequate multi-microgrids under large-scale events. IET Renewable Power Generation., 13(10), 1750–1760.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Li, Y., Li, K., Yang, Z., Yu, Y., Xu, R., & Yang, M. (2022). Stochastic optimal scheduling of demand response-enabled microgrids with renewable generations: An analytical-heuristic approach. Journal of Cleaner Production., 330, 129840.

    Article  Google Scholar 

  • Moghaddam, A. A., Seifi, A., Niknam, T., & Pahlavani, M. R. (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), 6490–507.

    Article  Google Scholar 

  • Moon, S. K., & Kim, J. O. (2017). Balanced charging strategies for electric vehicles on power systems. Applied Energy., 189, 44–54.

    Article  Google Scholar 

  • Morais, H., Sousa, T., Vale, Z., & Faria, P. (2014). Evaluation of the electric vehicle impact in the power demand curve in a smart grid environment. Energy Conversion and Management., 82, 268–282.

    Article  Google Scholar 

  • Moselimizadeh, E., Rastegar, M., Shabani, F., & Hassan, A. M. (2022). Optimal structuring of microgrid distributed state estimation. Electric Power Components and Systems., 49(15), 1278–1288.

    Article  Google Scholar 

  • Mousavizadeh, S., Bolandi, T. G., Haghifam, M. R., Moghimi, M., & Lu, J. (2020). Resiliency analysis of electric distribution networks: A new approach based on modularity concept. International Journal of Electrical Power & Energy Systems., 117, 105669.

    Article  Google Scholar 

  • Neyestani, N., Damavandi, M. Y., Shafie-Khah, M., Bakirtzis, A. G., & Catalao, J. P. (2016). Plug-in electric vehicles parking lot equilibria with energy and reserve markets. IEEE Transactions on Power Systems., 32(3), 2001–2016.

    Article  Google Scholar 

  • Nurre, S. G., Bent, R., Pan, F., & Sharkey, T. C. (2014). Managing operations of plug-in hybrid electric vehicle (PHEV) exchange stations for use with a smart grid. Energy Policy, 67, 364–377.

    Article  Google Scholar 

  • Ortega-Vazquez, M. A. (2014). Optimal scheduling of electric vehicle charging and vehicle-to-grid services at household level including battery degradation and price uncertainty. IET Generation, Transmission & Distribution., 8(6), 1007–1016.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Shao, S., Pipattanasomporn, M., & Rahman, S. (2012). Grid integration of electric vehicles and demand response with customer choice. IEEE Transactions on Smart Grid., 3(1), 543–550.

    Article  Google Scholar 

  • Soares, J., Morais, H., Sousa, T., Vale, Z., & Faria, P. (2013). Day-ahead resource scheduling including demand response for electric vehicles. IEEE Transactions on Smart Grid., 4(1), 596–605.

    Article  Google Scholar 

  • Sortomme, E., & El-Sharkawi, M. A. (2011). Optimal scheduling of vehicle-to-grid energy and ancillary services. IEEE Transactions on Smart Grid., 3(1), 351–359.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Tizhoosh, H.R.(2005) Opposition-based learning: a new scheme for machine intelligence. InInternational conference on computational intelligence for modelling, control and automation and international conference on intelligent agents, web technologies and internet commerce (CIMCA-IAWTIC'06) (Vol. 1, pp. 695–701). IEEE.

  • Tomić, J., & Kempton, W. (2007). Using fleets of electric-drive vehicles for grid support. Journal of Power Sources., 168(2), 459–468.

    Article  Google Scholar 

  • Wang, X., & Liang, Q. (2015). Energy management strategy for plug-in hybrid electric vehicles via bidirectional vehicle-to-grid. IEEE Systems Journal., 11(3), 1789–1798.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Yao, W., Zhao, J., Wen, F., Xue, Y., & Ledwich, G. (2013). A hierarchical decomposition approach for coordinated dispatch of plug-in electric vehicles. IEEE Transactions on Power Systems., 28(3), 2768–2778.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Humanities, University of Glasgow, Glasgow, Scotland, UK

    Jiayu Chen

  2. School of Eonomics and Management, Hebei University of Science and Technology, Shijiazhuang, Hebei, China

    Xiangjun Liu

  3. Renewable Product and TCSC-AI Mangament, Ankara, Turkey

    Iman Karam

Authors
  1. Jiayu Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiangjun Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Iman Karam
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Xiangjun Liu or Iman Karam.

Ethics declarations

Conflict of interest

Authors declares that he has 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

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

Chen, J., Liu, X. & Karam, I. Short-term economic dispatch incorporating renewable power generations with plug-in electric vehicles considering emissions reduction. Environ Dev Sustain (2024). https://doi.org/10.1007/s10668-024-04899-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10668-024-04899-6

Keywords

Search

Navigation