Skip to main content
Log in

Modeling Uncertainty Energy Price Based on Interval Optimization and Energy Management in the Electrical Grid

  • Research
  • Published:
Operations Research Forum Aims and scope Submit manuscript

Abstract

Energy providers are faced with the challenge of effectively managing electrical energy systems amidst uncertainties. This study focuses on the management and dispatch of energy demand in the electricity microgrid, employing an interval optimization strategy to address electricity price uncertainties. The demand response program (DRP) incentive modeling is utilized to implement demand dispatch. To mitigate the impact of electricity price uncertainties, an incentive modeling approach based on offering reduced electricity demand during peak periods is proposed. The interval optimization approach is employed to minimize operational costs, with the epsilon constraint-based fuzzy method utilized to solve and address the problem. The effectiveness of the proposed modeling approach under conditions of uncertainty is demonstrated through the use of the microgrid in various case studies and numeric simulations. The participation of the DRP leads to minimizing the average and deviation costs by 9.5% and 6.5% in comparison with non-participation.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

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

Availability of Data and Materials

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Abbreviations

t, T :

Time (hour)

m, M :

Micro turbine (MT) index (–)

NC :

Consumers’ number

D eq , D RE :

Consumers’ demand and demand reduced by DRP (kW)

Ψ RE :

Offer price for DRP ($)

C RE :

DRP price ($)

A, B, C :

MT fuel cost ($)

P m , P PM :

MT power and PM power (kW)

c PM :

Price of electricity in PM ($)

Θ RE :

Binary variable of the DRP

τ RE , κ RE :

Binary variable for before and after DRP

References

  1. Han L, Yu HH (2023) An empirical study from Chinese energy firms on the relationship between executive compensation and corporate performance. Nurture 17(3):378–393. https://doi.org/10.55951/nurture.v17i3.356

    Article  Google Scholar 

  2. Rehan R (2022) Investigating the capital structure determinants of energy firms. Edelweiss Appl Sci Technol 6(1):1–14. https://doi.org/10.55214/25768484.v6i1.301

    Article  Google Scholar 

  3. Tolmachev Y, Starodubceva SV (2022) Flow batteries with solid energy boosters. J Electrochem Sci Eng 12(4):731–766. https://doi.org/10.5599/jese.1363

    Article  Google Scholar 

  4. Norouzi N, Bozorgian A (2023) Energy and exergy analysis and optimization of a pentageneration (cooling, heating, power, water and hydrogen). Iran J Chem Chem Eng. https://doi.org/10.30492/ijcce.2023.560423.5529

    Article  Google Scholar 

  5. Aalami H, Moghaddam M, Yousefi G (2010) Demand response modeling considering interruptible/curtailable loads and capacity market programs. Appl Energy 87(1):243–250

    Article  Google Scholar 

  6. Chamandoust H et al (2022) Optimal hybrid participation of customers in a smart micro grid based on day ahead electrical market. Artif Intell Rev 55:5891–5915. https://doi.org/10.1007/s10462-022-10154-z

    Article  Google Scholar 

  7. Aikhuele D (2023) Development of a statistical reliability-based model for the estimation and optimization of a spur gear system. J Comput Cogn Eng 2(2):168–174. https://doi.org/10.47852/bonviewJCCE2202153

    Article  Google Scholar 

  8. Chugo D, Muramatsu S, Yokota S, She JH, Hashimoto H (2022) Stand-up assistive devices allowing patients to perform voluntary movements within the safety movement tolerance. J Artif Intell Technol 2(4):164–173

    Google Scholar 

  9. Siano P (2014) Demand response and smart grids-a survey. Renew Sustain Energy Rev 30:461–478

    Article  Google Scholar 

  10. Chen H, Wu H, Kan T, Zhang J, Li H (2023) Low-carbon economic dispatch of integrated energy system containing electric hydrogen production based on VMD-GRU short-term wind power prediction. Int J Electr Power Energy Syst 154:109420. https://doi.org/10.1016/j.ijepes.2023.109420

    Article  Google Scholar 

  11. Qi H et al (2021) Optimisation of a smart energy hub with integration of combined heat and power, demand side response and energy storage. Energy 234:121268

    Article  Google Scholar 

  12. Bakis R, Koc C, Bayazit Y, Cabuk SN (2020) Application of geographic information system to select dam location for hydropower. Int J Sustain Energy Environ Res 9(1):56–72

    Google Scholar 

  13. Ajam M, Mohammadiun H, Dibaee Bonab MH, Mohammadiun M (2022) Energy, exergy, and economic analyses of a combined heat and power generation system with a gas turbine and a horizontal axis wind turbine. Iran J Chem Chem Eng 41(6):2100–2120

    Google Scholar 

  14. Lak Kamari M, Isvand H, Alhuyi Nazari M (2020) Applications of multi-criteria decision-making (MCDM) methods in renewable energy development: a review. Renew Energy Res Appl 1(1):47–54

    Google Scholar 

  15. Zhang L, Sun C, Cai G, Koh LH (2023) Charging and discharging optimization strategy for electric vehicles considering elasticity demand response. eTransportation 18:100262. https://doi.org/10.1016/j.etran.2023.100262

    Article  Google Scholar 

  16. Morales-España G, Martínez-Gordón R, Sijm J (2022) Classifying and modelling demand response in power systems. Energy 242:122544. https://doi.org/10.1016/j.energy.2021.122544

    Article  Google Scholar 

  17. Shen L, Li Z, Sun Y (2016) Performance evaluation of conventional demand response at building-group-level under different electricity pricings. Energy Build 128:143–154. https://doi.org/10.1016/j.enbuild.2016.06.082

    Article  Google Scholar 

  18. Nezhadkian M et al (2023) A model for new product development in business companies based on grounded theory approach and fuzzy method. J Comput Cogn Eng 2(2):124–132. https://doi.org/10.47852/bonviewJCCE2202260

    Article  Google Scholar 

  19. Jiang J, Zhang L, Wen X, Valipour E, Nojavan S (2022) Risk-based performance of power-to-gas storage technology integrated with energy hub system regarding downside risk constrained approach. Int J Hydrogen Energy 47(93):39429–39442. https://doi.org/10.1016/j.ijhydene.2022.09.115

    Article  Google Scholar 

  20. Okampo EJ, Nwulu N (2021) Techno-economic evaluation of reverse osmosis desalination system considering emission cost and demand response. Sustain Energy Technol Assess 46:101252. https://doi.org/10.1016/j.seta.2021.101252

    Article  Google Scholar 

  21. Chen L, Tang H, Wu J, Li C, Wang Y (2022) A robust optimization framework for energy management of CCHP users with integrated demand response in electricity market. Int J Electr Power Energy Syst 141:108181. https://doi.org/10.1016/j.ijepes.2022.108181

    Article  Google Scholar 

  22. Torriti J, Hassan MG, Leach M (2010) Demand response experience in Europe: policies, programmes and implementation. Energy 35:1575–1583. https://doi.org/10.1016/j.energy.2009.05.021

    Article  Google Scholar 

  23. Nawaz A, Zhou M, Wu J, Long C (2022) A comprehensive review on energy management, demand response, and coordination schemes utilization in multi-microgrids network. Appl Energy 323:119596. https://doi.org/10.1016/j.apenergy.2022.119596

    Article  Google Scholar 

  24. O׳Connell N, Pinson P, Madsen H, O׳Malley M (2014) Benefits and challenges of electrical demand response: a critical review. Renew Sustain Energy Rev 39:686–699. https://doi.org/10.1016/j.rser.2014.07.098

    Article  Google Scholar 

  25. Mittelviefhaus M et al (2021) Optimal investment and scheduling of residential multi-energy systems including electric mobility: a cost-effective approach to climate change mitigation. Appl Energy 301:117445

    Article  Google Scholar 

  26. Ghilardi LM et al (2021) Co-optimization of multi-energy system operation, district heating/cooling network and thermal comfort management for buildings. Appl Energy 302:117480

    Article  Google Scholar 

  27. Gholinejad HR et al (2020) A hierarchical energy management system for multiple home energy hubs in neighborhood grids. J Build Eng 28:101028

    Article  Google Scholar 

  28. Bai L, Li F, Cui H, Jiang T, Sun H, Zhu J (2016) Interval optimization based operating strategy for gas-electricity integrated energy systems considering demand response and wind uncertainty. Appl Energy 167:270–279

    Article  Google Scholar 

  29. Chamandoust H et al (2020) Multi-objective operation of smart stand-alone microgrid with the optimal performance of customers to improve economic and technical indices. J Energy Storage 31:101738

    Article  Google Scholar 

  30. Cao B, Dong W, Lv Z, Gu Y, Singh S, Kumar P (2020) Hybrid microgrid many-objective sizing optimization with fuzzy decision. IEEE Trans Fuzzy Syst 28(11):2702–2710. https://doi.org/10.1109/TFUZZ.2020.3026140

    Article  Google Scholar 

  31. Chamandoust H et al (2021) Energy management of a smart autonomous electrical grid with a hydrogen storage system. Int J Hydrogen Energy 46(34):17608–17626

    Article  Google Scholar 

  32. https://cmte.ieee.org/pes-testfeeders/resources/

  33. Mirjalili S, Lewis A (2015) Novel performance metrics for robust multi-objective optimization algorithms. Swarm Evol Comput 21:1–23

    Article  Google Scholar 

  34. Chamandoust H et al (2017) Optimal hybrid system design based on renewable energy resources. Proceedings of the IEEE Smart Grid Conference (SGC). https://doi.org/10.1109/SGC.2017.8308878

    Book  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Contributions

All authors have equal contributions including conceptualization, methodology, software, data curation, formal analysis, writing—review and editing, and writing—original draft.

Corresponding author

Correspondence to Julio César Machaca Mamani.

Ethics declarations

Ethics Approval and Consent to Participate

There are no human subjects in this manuscript and informed consent is not applicable.

Competing Interests

The authors declare no competing interests.

Additional information

This article is part of the Topical Collection on Mathematical Models and Optimization for Environmental Engineering and Sustainable Technologies

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

Mamani, J.C.M., Carrasco-Choque, F., Paredes-Calatayud, E.F. et al. Modeling Uncertainty Energy Price Based on Interval Optimization and Energy Management in the Electrical Grid. Oper. Res. Forum 5, 4 (2024). https://doi.org/10.1007/s43069-023-00289-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s43069-023-00289-2

Keywords

Navigation