Skip to main content
SpringerLink
Log in

Optimal scheduling of battery energy storage train and renewable power generation

  • Original Paper
  • Published:
Electrical Engineering Aims and scope Submit manuscript

Abstract

With the increasing penetration of renewable energy sources (RES), a battery energy storage (BES) Train supply system with flexibility and high cost-effectiveness is urgently needed. In this context, the mobile battery energy storage (BES) Train, as an efficient media of wind energy transfer to the load center with a time–space network (TSN), is proposed to assist the system operations. A real-time operation strategy for this TSN is proposed considering the vehicle routing problem (VRP) of BES Train. To achieve this goal, stochastic scheduling of BES Trains integrated with network constraints along wind power uncertainty is addressed in this work. Autoregressive integrated moving average (ARIMA) models generate power scenarios. Also, consider uncertainties related to wind power and standard deviation to optimize the placement of charging/discharging BES Train station. The uncertain parameter connected to wind power for scenario generations is observed. The proposed mixed-integer linear programming (MILP) to solve the model efficiently is based on tackling the strong coupling between integer and continuous variables. Using GAMS software, the proposed model is simulated on the IEEE six-bus systems, and case studies examine the capability of the suggested approach based on operational, flexibility, and reliability criteria.

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
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

Availability of data and materials

All the data is included in the manuscript.

Abbreviations

ARIMA:

Autoregressive integrated moving average

BES:

Battery energy storage

MILP:

Mixed-integer linear programming

RES:

Renewable energy sources

TSN:

Time–space network

VRP:

Vehicle routing problem

ESS:

Energy storage system

EV:

Electric vehicle

CAES:

Compressed air energy storage

SOC:

State of charge

UC:

Unit commitment

KD:

Kantorovich distance

VOLL:

Value of lost load

\(p,q \in A\) :

Indices arcs and set arcs in the time–space network

\(tb \in TB\) :

Index and set of the BES train unit

\(ts \in TS\) :

Index and set of simulation time span

\(i,j\) :

Index of buses

\(l\) :

Index of network lines

\(w \in W\) :

Index and set of wind power generation

\(t \in T\) :

Index and set of simulation time

\(g \in GU\) :

Index and set of the generation unit

\(sn \in S\) :

Index and set of scenario sample

\({\text{CF}}_{tb} (.)\) :

Charging/discharging cost function of BES train \(tb\)

\(F_{Ch} (.),F_{Dch} (.)\) :

BES Train charging and discharging function

\(F_{g,t}^C (.)\) :

The fuel cost function of unit g

\(E_{tb,t}^{sn}\) :

Energy capacity of the BES train \(tb\) at time \(t\)

\(X_{tb,Ch,t}^{sn} ,X_{tb,Dch,t}^{sn}\) :

Charging/discharging indicator of the BES train \(tb\) at the time \(t\)

\(X_{tb,pq,ts}^{sn}\) :

Status of the BES Train \(tb\) in arc \(pq\) at time span \(ts\)

\(P_{tb,Ch,t}^{sn} ,P_{tb,Dch,t}^{sn}\) :

Charging and discharging active power of the BES train \(tb\)

\(P_{tb,i,t}^{sn}\) :

Up and down flexibility for BES train \(tb\) at bus i

\(P_{g,t}^{sn}\) :

Active power of generation unit g at time \(t\)

\(P_{w,i,t}^{sn} ,P_{w,cur,t}^{sn}\) :

Injected/curtailment of thermal unit g and wind power of unit \(w\)

\(Q_{tb,t}^{sn}\) :

SOC change of the BES train \(tb\) at time \(t\)

\(Q_{tb,Ch,t}^{sn} ,Q_{tb,Dch,t}^{sn}\) :

SOC charging/discharging of the BES train \(tb\) at time \(t\)

\({\text{SR}}_{g,t}^{sn} ,{\text{SR}}_{tb,i,t}^{sn}\) :

Spinning reserve of generation and BES train unit g and \(tb\)

\({\text{SUC}}_{g,t}^{sn} ,\;{\text{SDC}}_{g,t}^{sn}\) :

Start-up/shut down binary variable of unit g

\({\text{CT}}_{tb,pq}\) :

Transportation cost of arc \(p\) on BES train \(tb\)

\(E_{tb,0} ,E_{tb,NS}\) :

Initial and terminal energy of the BES train \(tb\)

\(E_{tb,\min } ,E_{tb,\max }\) :

Minimum and maximum stored energy in BES train \(tb\)

\(X_{tb,p,0}^{sn} ,X_{tb,p,TS}^{sn}\) :

Indicator of initial and terminal state of BES train \(tb\)

\(P_{FD,t}\) :

Forecasted wind power generation at time \(t\)

\(\beta^{sn}\) :

Probability of occurrence of a scenario

\(P_{g,\min } ,P_{g,\max }\) :

Minimum and maximum active power of generation at unit g

\(P_{i,j,\min } ,P_{i,j,\max }\) :

Minimum and maximum active power of network bus i and j

\(P_{tb,\max } ,P_{tb,\min }\) :

Minimum and maximum power exchange rate of the BES train \(tb\)

\({\text{RU}}_g ,{\text{DR}}_g\) :

Ramp-up and ramp-down of generation at unit g

References

  1. Wang S, Li F, Zhang G, Yin C (2023) Analysis of energy storage demand for peak shaving and frequency regulation of power systems with high penetration of renewable energy. Energy 267:126586

    Article  Google Scholar 

  2. Wei X, Ban S, Shi X, Li P, Li Y, Zhu S, Yang C (2023) Carbon and energy storage in salt caverns under the background of carbon neutralization in China. Energy 272:127120

    Article  Google Scholar 

  3. Kittner N, Lill F, Kammen DM (2017) Energy storage deployment and innovation for the clean energy transition. Nat Energy 2(9):1–6

    Article  Google Scholar 

  4. He G, Michalek J, Kar S, Chen Q, Zhang D, Whitacre JF (2021) Utility-scale portable energy storage systems. Joule 5(2):379–392

    Article  Google Scholar 

  5. Gayme D, Topcu U (2012) Optimal power flow with large-scale storage integration. IEEE Trans Power Syst 28(2):709–717

    Article  Google Scholar 

  6. Zhang YJA, Zhao C, Tang W, Low SH (2016) Profit-maximizing planning and control of battery energy storage systems for primary frequency control. IEEE Trans Smart Grid 9(2):712–723

    Article  Google Scholar 

  7. Wang L, Liang DH, Crossland AF, Taylor PC, Jones D, Wade NS (2015) Coordination of multiple energy storage units in a low-voltage distribution network. IEEE Trans Smart Grid 6(6):2906–2918

    Article  Google Scholar 

  8. Khodayar ME, Wu L, Shahidehpour M (2012) Hourly coordination of electric vehicle operation and volatile wind power generation in SCUC. IEEE Trans Smart Grid 3(3):1271–1279

    Article  Google Scholar 

  9. Alvarado-Barrios L, del Nozal AR, Valerino JB, Vera IG, Martínez-Ramos JL (2020) Stochastic unit commitment in microgrids: influence of the load forecasting error and the availability of energy storage. Renew Energy 146:2060–2069

    Article  Google Scholar 

  10. Sun Y, Chen Z, Li Z, Tian W, Shahidehpour M (2018) EV charging schedule in coupled constrained networks of transportation and power system. IEEE Trans Smart Grid 10(5):4706–4716

    Article  Google Scholar 

  11. Habbi M, Vahidinasab V, Priayesh A, Shafie-Khah M, Catalao JPS (2021) An enhanced contingency-based model for joint energy and reserve market operation by considering wind and energy storage systems. IEEE Trans Ind Inf 17(5):3241–3252

    Article  Google Scholar 

  12. Wang C, Fu Y (2016) Fully parallel stochastic security-constrained unit commitment. IEEE Trans Power Syst 31(3):3561–3571

    Article  Google Scholar 

  13. Zhao H, Wu Q, Hu S, Xu H, Rasmussen CN (2015) Review of energy storage system for wind power integration support. Appl Energy 137:545–553

    Article  Google Scholar 

  14. Branco H, Castro R, Lopes AS (2018) Battery energy storage systems as a way to integrate renewable energy in small isolated power systems. Energy Sustain Dev 43:90–99

    Article  Google Scholar 

  15. Ghaljehei M, Ahmadian A, Golkar MA, Amraee T, Elkamel A (2018) Stochastic SCUC considering compressed air energy storage and wind power generation: a techno-economic approach with static voltage stability analysis. Int J Electr Power Energy Syst 100:489–507

    Article  Google Scholar 

  16. Aliasghari P, Zamani-Gargari M, Mohammadi-Ivatloo B (2018) Look-ahead risk-constrained scheduling of wind power integrated system with compressed air energy storage (CAES) plant. Energy 160:668–677

    Article  Google Scholar 

  17. Gupta PP, Jain P, Sharma S, Sharma KC, Bhakar R (2018) Scheduling of energy storage transportation in power system using benders decomposition approach. In: 2018 20th national power systems conference (NPSC)

  18. Daneshvar M, Mohammadi-Ivatloo B, Zare K, Asadi S (2020) Two-stage stochastic programming model for optimal scheduling of the wind-thermal-hydropower- pumped storage system considering the flexibility assessment. Energy 193:116657

    Article  Google Scholar 

  19. Yang JJ, Yang M, Wang MX et al (2020) A deep reinforcement learning method for managing wind farm uncertainties through energy storage system control and external reserve purchasing. Int J Electr Power Energy Syst 119:105928

    Article  Google Scholar 

  20. Ghavidel S, Ghadi MJ, Azizivahed A et al (2019) Risk-constrained bidding strategy for a joint operation of wind power and CAES aggregators. IEEE Trans Sustain Energy 11(1):457–466

    Article  Google Scholar 

  21. Haixiang Z, Mingxin M, Yizhou Z et al (2022) Robust optimal scheduling model for a ‘wind power-concentrating solar power-biomass’ hybrid power plant in the electricity market. Power Syst Protect Control 50(5):1–11

    Google Scholar 

  22. Yunchao S, Dan W, Wei H et al (2021) Research on multi-objective stochastic planning of a micro energy grid with multiple clean energy sources based on scenario construction technology. Power Syst Protect Control 49(3):20–31

    Google Scholar 

  23. Xiao D, Chen H, Cai W, Wei C, Zhao Z (2023) Integrated risk measurement and control for stochastic energy trading of a wind storage system in electricity markets. Protect Control Modern Power Syst 60(8):1–11

    Google Scholar 

  24. Xiao D, Lin Z, Chen H, Hua W, Yan J (2023) Windfall profit-aware stochastic scheduling strategy for industrial virtual power plant with integrated risk-seeking/averse preferences. Appl Energy 357:122460

    Article  Google Scholar 

  25. He H, Ershun Du, Zhang N, Kang C, Wang X (2021) Enhancing the power grid flexibility with battery energy storage transportation and transmission switching. Appl Energy 290:116692

    Article  Google Scholar 

  26. Pozo D, Contreras J, Sauma EE (2014) Unit commitment with ideal and generic energy storage units. IEEE Trans Power Syst 29(6):2974–2984

    Article  Google Scholar 

  27. Guerrero-Mestre V, Dvorkin Y, Fernandez-Blanco R, Ortega-Vazquez MA, Contreras J (2018) Incorporating energy storage into probabilistic security-constrained unit commitment. IET Gener Trans Distrib 12(18):4206–4215

    Article  Google Scholar 

  28. Loisel R (2012) Power system flexibility with electricity storage technologies: a technical economic assessment of a large-scale storage facility. Int J Electr Power Energy Syst 42(1):542–552

    Article  Google Scholar 

  29. Gan W, Shahidehpour M, Yan M et al (2020) Coordinated planning of transportation and electric power networks with the proliferation of electric vehicles. IEEE Trans Smart Grid 11:4005–4016

    Article  Google Scholar 

  30. Mirzaei AM, Hemmati M, Zare K, Mohammadi B, Abapour M, Marzband M, Reza R, Amjad A (2021) Network-constrained rail transportation and power system scheduling with mobile battery energy storage under a multi-objective two-stage stochastic programming. Int J Energy Res 45(13):18827–18845

    Article  Google Scholar 

  31. Ebadi R, Yazdankhah AS, Mohammadi-Ivatloo B, Kazemzadeh R (2021) Coordinated power and train transportation system with transportable battery-based energy storage and demand response: a multi-objective stochastic approach. J Clean Prod 275:2219–2226

    Google Scholar 

  32. Ebadi R, Yazdankhah AS, Kazemzadeh R, Ivatloo BM (2021) Techno-economic evaluation of transportable battery energy storage in robust day-ahead scheduling of integrated power and railway transportation network. Int J Electr Power Energy Syst 126:106606

    Article  Google Scholar 

  33. Sun Y, Li Z, Shahidehpour M, Ai B (2015) Battery-based energy storage transportation for enhancing power system economics and security. IEEE Trans Smart Grid 6(5):2395–2402

    Article  Google Scholar 

  34. Sun Y, Li Z, Tian W, Shahidehpour M (2016) A Lagrangian decomposition approach to energy storage transportation scheduling in power systems. IEEE Trans Power Syst 31(6):4348–4356

    Article  Google Scholar 

  35. Sun Y, Zhong J, Li Z, Tian W, Shahidehpour M (2016) Stochastic scheduling of battery-based energy storage transportation system with the penetration of wind power. IEEE Trans Sust Energy 8(1):135–144

    Article  Google Scholar 

  36. Gut J, Diaz B, Arroyo M, Hinojosa VH (2022) Large scale preventive security-constrained unit commitment considering N-K line outage and transmission losses. IEEE Trans Power Syst 37(3):2032–2041

    Article  Google Scholar 

  37. Gupta PP, Kalkhambkar V, Jain P, Sharma KC, Bhakar R (2022) Battery energy storage train routing and security constrained unit commitment under solar uncertainty. J Energy Storage 55(Part D):105811

    Article  Google Scholar 

  38. Khodayar ME, Wu L, Li Z (2013) Electric vehicle mobility in transmission constrained hourly power generation scheduling. IEEE Trans Smart Grid 4(2):779–788

    Article  Google Scholar 

  39. Gupta PP, Jain P, Sharma KC, Bhakar R (2019) Stochastic scheduling of compressed air energy storage in DC SCUC framework for high wind penetration. IET Gener Trans Distrib 13(13):2747–2760

    Article  Google Scholar 

  40. Ding T, Yang Q, Liu X, Huang C, Yang Y, Wang M, Blaabjerg F (2018) Duality-free decomposition based data-driven stochastic security-constrained unit commitment. IEEE Trans Sust Energy 10(1):82–93

    Article  Google Scholar 

  41. Sharma KC, Jain P, Bhakar R (2013) Wind power scenario generation and reduction in stochastic programming framework. Elect Power Compon Syst 41(3):271–285

    Article  Google Scholar 

  42. Yang F, Yin S, Zhou S, Li D, Fang C, Lin S (2021) Electric vehicle charging current scenario generation based on generative adversarial network combined with clustering algorithm. Int Trans Electr Energy Syst 28:e12971

    Google Scholar 

  43. Prakash V, Sharma KC, Bhakar R, Tiwari HP, Li F (2017) Frequency response constrained modified interval scheduling under wind uncertainty. IEEE Trans on Sust Energy 9(1):302–310

    Article  Google Scholar 

  44. Soroudi A (2017) Power system optimization modeling in GAMS, vol 78. Springer, Switzerland

    Book  Google Scholar 

Download references

Funding

The authors declare that there is no funding for the proposed work.

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, Rajarambapu Institute of Technology, Affiliated to Shivaji University, Uran Islampu, India

    Komal Mohan Todakar, Pranda Prasanta Gupta & Vaiju Kalkhambkar

  2. Department of Electrical Engineering, Dr. B R Ambedkar National Institute of Technology, Jalandhar, India

    Kailash Chand Sharma

Authors
  1. Komal Mohan Todakar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pranda Prasanta Gupta
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Vaiju Kalkhambkar
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kailash Chand Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors have contributed to the paper. Following are the contribution details: KT was involved in problem formulation and modeling. PPG participated in problem formulation and simulations. VK was responsible for result validation and proposed methodology. KCS took part in review of manuscript and proposed methodology.

Corresponding author

Correspondence to Vaiju Kalkhambkar.

Ethics declarations

Conflict of interest

There is no conflict of interest. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Ethical approval

The authors declare that the study does not involve any human subjects and/or animals.

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

Todakar, K.M., Gupta, P.P., Kalkhambkar, V. et al. Optimal scheduling of battery energy storage train and renewable power generation. Electr Eng (2024). https://doi.org/10.1007/s00202-024-02385-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s00202-024-02385-w

Keywords

Search

Navigation