Skip to main content

Advertisement

SpringerLink
Log in

An efficient way to schedule dispersed generators for a microgrid system's economical operation under various power market conditions and grid involvement

  • ORIGINAL ARTICLE
  • Published:
International Journal of System Assurance Engineering and Management Aims and scope Submit manuscript

Abstract

A microgrid is a collection of distributed energy resources (DERs) It might encompass both traditional and unconventional energy sources. The way a microgrid functions determines whether it is categorized as either islanded or grid-connected. The focus of this study is to explore five different optimal scheduling operation scenarios for Distributed Energy Resources (DERs) with the aim of achieving economic benefits for a low voltage microgrid system. The scenarios being studied are developed using several approaches to grid participation and pricing strategies within the power market. The optimization tool used in the research was the Crow Search Algorithm (CSA), a widely recognized and recently developed algorithm known for its speed and efficiency. The results of the study indicate that the microgrid's most profitable operational scenario involved active participation of the grid in the purchase and sale of electricity, utilizing a dynamic pricing approach based on time-of-usage (TOU). Based on numerical data, the Crow Search Algorithm method produced solutions of superior quality in less time compared to several other algorithms with similar designs that were employed in the study. The CSA approach was able to achieve this consistently, regardless of the scale of the problem being addressed.

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

The authors are willing to provide the data upon request, as long as the request is deemed reasonable.

References

  • Alham M, Elshahed M, Ibrahim DK, El Zahab EEDA (2016) A dynamic economic emission dispatch considering wind power uncertainty incorporating energy storage system and demand side management. Renew Energy 96:800–811

    Article  Google Scholar 

  • Askarzadeh A (2016) A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm. Comput Struct 169:1–12

    Article  Google Scholar 

  • Attaviriyanupap P, Kita H, Tanaka E, Hasegawa J (2002) A hybrid EP and SQP for dynamic economic dispatch with nonsmooth fuel cost function. IEEE Trans Power Syst 17:411–416

    Article  Google Scholar 

  • Azizipanah-Abarghooee R (2013) A new hybrid bacterial foraging and simplified swarm optimization algorithm for practical optimal dynamic load dispatch. Int J Electr Power Energy Syst 49:414–429

    Article  Google Scholar 

  • Chauhan A, Prakash S (2023) Optimal parameter estimation of solar photovoltaics through nature inspired metaheuristic and hybrid approaches. IETE Journal of Research pp 1–19

  • Chauhan G, Jain A, Verma N (2017) Solving economic dispatch problem using MiPower by lambda iteration method. In: 2017 1st International Conference on Intelligent Systems and Information Management (ICISIM) (pp. 95–99). IEEE

  • Dai W, Yang Z, Yu J, Cui W, Li W, Li J et al (2021) Economic dispatch of interconnected networks considering hidden flexibility. Energy 223:120054

    Article  Google Scholar 

  • Daniel L, Chaturvedi KT, Kolhe ML (2018) Dynamic economic load dispatch using Levenberg Marquardt algorithm. Energy Procedia 144:95–103

    Article  Google Scholar 

  • Daryani N, Zare K (2018) Multiobjective power and emission dispatch using modified group search optimization method. Ain Shams Eng J 9:319–328

    Article  Google Scholar 

  • Davtalab S, Tousi B, Nazarpour D (2022) Optimized intelligent coordinator for load frequency control in a two-area system with PV plant and thermal generator. IETE J Res 68(5):3876–3886

    Article  Google Scholar 

  • Dey B, Bhattacharyya B (2019) Dynamic cost analysis of a grid connected microgrid using neighborhood based differential evolution technique. Int Trans Electr Energy Syst 29:e2665

    Article  Google Scholar 

  • Dey B, Roy SK, Bhattacharyya B (2019) Solving multi-objective economic emission dispatch of a renewable integrated microgrid using latest bio-inspired algorithms. Eng Sci Technol Int J 22:55–66

    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

    Article  Google Scholar 

  • Dey B, Roy SK, Bhattacharyya B (2018) Neighborhood based differential evolution technique to perform dynamic economic load dispatch on microgrid with renewables. In: 2018 4th international conference on recent advances in information technology (RAIT) (pp. 1–6). IEEE

  • Elsayed W, Hegazy Y, Bendary F, El-Bages M (2016) Modified social spider algorithm for solving the economic dispatch problem. Eng Sci Technol Int J 19:1672–1681

    Google Scholar 

  • Ganjefar S, Tofighi M (2011) Dynamic economic dispatch solution using an improved genetic algorithm with non-stationary penalty functions. Eur Trans Electric Power 21:1480–1492

    Article  Google Scholar 

  • Hassan MH et al (2021) Developing chaotic artificial ecosystem-based optimization algorithm for combined economic emission dispatch. IEEE Access 9:51146–51165

    Article  Google Scholar 

  • Jadoun VK, Pandey VC, Gupta N, Niazi KR, Swarnkar A (2018) Integration of renewable energy sources in dynamic economic load dispatch problem using an improved fireworks algorithm. IET Renew Power Gener 12:1004–1011

    Article  Google Scholar 

  • Khorram-Nia R, Bahmani-Firouzi B, Simab M (2022) Optimal scheduling of reconfigurable microgrids incorporating the PEVs and uncertainty effects. IET Renew Power Gen 16(16):3531–3543

    Article  Google Scholar 

  • Kumar M, Dhillon J (2018) Hybrid artificial algae algorithm for economic load dispatch. Appl Soft Comput 71:89–109

    Article  Google Scholar 

  • Lahon R, Gupta CP (2018) Risk-based coalition of cooperative microgrids in electricity market environment. IET Gener Transm Distrib 12:3230–3241

    Article  Google Scholar 

  • Liu Y, Nair N-KC (2015) A two-stage stochastic dynamic economic dispatch model considering wind uncertainty. IEEE Trans Sustain Energy 7:819–829

    Article  Google Scholar 

  • Lokeshgupta B, Sivasubramani S (2018) Multi-objective dynamic economic and emission dispatch with demand side management. Int J Electr Power Energy Syst 97:334–343

    Article  Google Scholar 

  • Ma H, Yang Z, You P, Fei M (2017) Multi-objective biogeography-based optimization for dynamic economic emission load dispatch considering plug-in electric vehicles charging. Energy 135:101–111

    Article  Google Scholar 

  • Maulik A, Das D (2017) Optimal operation of microgrid using four different optimization techniques. Sustain Energy Technol Assess 21:100–120

    Google Scholar 

  • Rao R (2016) Jaya: a simple and new optimization algorithm for solving constrained and unconstrained optimization problems. Int J Ind Eng Comput 7:19–34

    Google Scholar 

  • Rao RV, Savsani VJ, Vakharia D (2011) Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput Aided Des 43:303–315

    Article  Google Scholar 

  • Roy S (2018) The maximum likelihood optima for an economic load dispatch in presence of demand and generation variability. Energy 147:915–923

    Article  Google Scholar 

  • Sahoo J, Samanta S, Bhattacharyya S (2020) Adaptive PID controller with P&O MPPT algorithm for photovoltaic system. IETE J Res 66(4):442–453

    Article  Google Scholar 

  • Shrivastava A, Nandrajog S (2017) Cost economic power dispatch analysis and solution using optimization technique: CEPD model solution. Int Conf Intell Sustain Syst (ICISS) 2017:1106–1111

    Google Scholar 

  • Singh D, Dhillon J (2019) Ameliorated grey wolf optimization for economic load dispatch problem. Energy 169:398–419

    Article  Google Scholar 

  • Sun Y, Wang Z (2017) Improved particle swarm optimization based dynamic economic dispatch of power system. Procedia Manuf 7:297–302

    Article  Google Scholar 

  • Toopshekan A, Yousefi H, Astaraei FR (2020) Technical, economic, and performance analysis of a hybrid energy system using a novel dispatch strategy. Energy 213:118850

    Article  Google Scholar 

  • Trivedi IN, Thesiya DK, Esmat A, Jangir P (2015) A multiple environment dispatch problem solution using ant colony optimization for micro-grids. Int Conf Power Adv Control Eng (ICPACE) 2015:109–115

    Article  Google Scholar 

  • Trivedi IN, Jangir P, Bhoye M, Jangir N (2018) An economic load dispatch and multiple environmental dispatch problem solution with microgrids using interior search algorithm. Neural Comput Appl 30:2173–2189

    Article  Google Scholar 

  • Victoire TAA, Jeyakumar AE (2005) Reserve constrained dynamic dispatch of units with valve-point effects. IEEE Trans Power Syst 20:1273–1282

    Article  Google Scholar 

  • Wu K, Li Q, Chen Z, Lin J, Yi Y, Chen M (2021) Distributed optimization method with weighted gradients for economic dispatch problem of multi-microgrid systems. Energy 222:119898

    Article  Google Scholar 

  • Xu B, Zhong P-A, Zhao Y-F, Zhu Y-Z, Zhang G-Q (2014) Comparison between dynamic programming and genetic algorithm for hydro unit economic load dispatch. Water Sci Eng 7:420–432

    Google Scholar 

  • Yuan X, Wang L, Yuan Y, Zhang Y, Cao B, Yang B (2008) A modified differential evolution approach for dynamic economic dispatch with valve-point effects. Energy Convers Manage 49:3447–3453

    Article  Google Scholar 

  • Yuan X, Su A, Yuan Y, Nie H, Wang L (2009) An improved PSO for dynamic load dispatch of generators with valve-point effects. Energy 34:67–74

    Article  Google Scholar 

Download references

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Author information

Authors and Affiliations

  1. Department of EEE, GIET University, Gunupur, Odisha, India

    Srikant Misra & P. K. Panigrahi

  2. Department of EE, IIT (Indian School of Mines), Dhanbad, India

    Bishwajit Dey

  3. Department of Electrical Engineering, Adani University, Shantigram, Ahmedabad, India

    Bishwajit Dey

Authors
  1. Srikant Misra
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. P. K. Panigrahi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bishwajit Dey
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Srikant Misra.

Ethics declarations

Conflicts of interest

The authors state that there are no financial or personal connections that could have influenced the research presented in this paper.

Additional information

Publisher's Note

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

Appendix

Appendix

See Table

Table 7 Tuning parameter of the algorithms
Full size table

7

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

Misra, S., Panigrahi, P.K. & Dey, B. An efficient way to schedule dispersed generators for a microgrid system's economical operation under various power market conditions and grid involvement. Int J Syst Assur Eng Manag 14, 1799–1809 (2023). https://doi.org/10.1007/s13198-023-01983-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13198-023-01983-4

Keywords

Search

Navigation