IDEAS home Printed from https://ideas.repec.org/a/eee/juipol/v79y2022ics0957178722001059.html
   My bibliography  Save this article

Optimizing Brazil's regulated electricity market in the context of time-of-use rates and prosumers with energy storage systems

Author

Listed:
  • Costa, Vinicius B.F.
  • Bonatto, Benedito D.
  • Silva, Patrícia F.

Abstract

The modeling of the regulated electricity market is essential since it allows the calculation of optimal rates by the regulatory agency, resulting in maximum socioeconomic welfare. Besides that, it is also possible to predict consumer behavior based on socioeconomic models. Therefore, under rate readjustments, energy shifting can be estimated and encouraged by the regulatory agency. This paper proposes major modifications to the optimized tariff model, originally developed for constant rates and grids without distributed energy resources, to model static time-of-use rates, distributed generation, and energy storage, enabling regulated electricity market optimization.

Suggested Citation

  • Costa, Vinicius B.F. & Bonatto, Benedito D. & Silva, Patrícia F., 2022. "Optimizing Brazil's regulated electricity market in the context of time-of-use rates and prosumers with energy storage systems," Utilities Policy, Elsevier, vol. 79(C).
  • Handle: RePEc:eee:juipol:v:79:y:2022:i:c:s0957178722001059
    DOI: 10.1016/j.jup.2022.101441
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0957178722001059
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.jup.2022.101441?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Yunusov, Timur & Torriti, Jacopo, 2021. "Distributional effects of Time of Use tariffs based on electricity demand and time use," Energy Policy, Elsevier, vol. 156(C).
    2. Haesum Ali & Akhtar Hussain & Van-Hai Bui & Jinhong Jeon & Hak-Man Kim, 2019. "Welfare Maximization-Based Distributed Demand Response for Islanded Multi-Microgrid Networks Using Diffusion Strategy," Energies, MDPI, vol. 12(19), pages 1-18, September.
    3. Li, Yuanyuan & Li, Junxiang & He, Jianjia & Zhang, Shuyuan, 2021. "The real-time pricing optimization model of smart grid based on the utility function of the logistic function," Energy, Elsevier, vol. 224(C).
    4. Leticia dos Santos Benso Maciel & Benedito Donizeti Bonatto & Hector Arango & Lucas Gustavo Arango, 2020. "Evaluating Public Policies for Fair Social Tariffs of Electricity in Brazil by Using an Economic Market Model," Energies, MDPI, vol. 13(18), pages 1-20, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Costa, Vinicius Braga Ferreira da & Bonatto, Benedito Donizeti, 2023. "Cutting-edge public policy proposal to maximize the long-term benefits of distributed energy resources," Renewable Energy, Elsevier, vol. 203(C), pages 357-372.
    2. Pinto, G.X.A. & Naspolini, H.F. & Rüther, R., 2024. "Assessing the economic viability of BESS in distributed PV generation on public buildings in Brazil: A 2030 outlook," Renewable Energy, Elsevier, vol. 225(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Emad M. Ahmed & Rajarajeswari Rathinam & Suchitra Dayalan & George S. Fernandez & Ziad M. Ali & Shady H. E. Abdel Aleem & Ahmed I. Omar, 2021. "A Comprehensive Analysis of Demand Response Pricing Strategies in a Smart Grid Environment Using Particle Swarm Optimization and the Strawberry Optimization Algorithm," Mathematics, MDPI, vol. 9(18), pages 1-24, September.
    2. Asfand Yar Ali & Akhtar Hussain & Ju-Won Baek & Hak-Man Kim, 2020. "Optimal Operation of Networked Microgrids for Enhancing Resilience Using Mobile Electric Vehicles," Energies, MDPI, vol. 14(1), pages 1-20, December.
    3. Eduardo Correia & Rodrigo Calili & José Francisco Pessanha & Maria Fatima Almeida, 2023. "Definition of Regulatory Targets for Electricity Non-Technical Losses: Proposition of an Automatic Model-Selection Technique for Panel Data Regressions," Energies, MDPI, vol. 16(6), pages 1-22, March.
    4. Ussama Assad & Muhammad Arshad Shehzad Hassan & Umar Farooq & Asif Kabir & Muhammad Zeeshan Khan & S. Sabahat H. Bukhari & Zain ul Abidin Jaffri & Judit Oláh & József Popp, 2022. "Smart Grid, Demand Response and Optimization: A Critical Review of Computational Methods," Energies, MDPI, vol. 15(6), pages 1-36, March.
    5. Zhang, Li & Gao, Yan & Zhu, Hongbo & Tao, Li, 2022. "Bi-level stochastic real-time pricing model in multi-energy generation system: A reinforcement learning approach," Energy, Elsevier, vol. 239(PA).
    6. Lin, Jin & Dong, Jun & Liu, Dongran & Zhang, Yaoyu & Ma, Tongtao, 2022. "From peak shedding to low-carbon transitions: Customer psychological factors in demand response," Energy, Elsevier, vol. 238(PA).
    7. Costa, Vinicius Braga Ferreira da & Bonatto, Benedito Donizeti, 2023. "Cutting-edge public policy proposal to maximize the long-term benefits of distributed energy resources," Renewable Energy, Elsevier, vol. 203(C), pages 357-372.
    8. Stute, Judith & Klobasa, Marian, 2024. "How do dynamic electricity tariffs and different grid charge designs interact? - Implications for residential consumers and grid reinforcement requirements," Energy Policy, Elsevier, vol. 189(C).
    9. Penelope Buckley, 2021. "A Systematic Review of Qualitative Studies on Residential Consumer Experience with Smart Meters and Dynamic Pricing," Post-Print hal-03335199, HAL.
    10. Mier, Mathias & Siala, Kais & Govorukha, Kristina & Mayer, Philip, 2023. "Collaboration, decarbonization, and distributional effects," Applied Energy, Elsevier, vol. 341(C).
    11. Xu, Bo & Wang, Jiexin & Guo, Mengyuan & Lu, Jiayu & Li, Gehui & Han, Liang, 2021. "A hybrid demand response mechanism based on real-time incentive and real-time pricing," Energy, Elsevier, vol. 231(C).
    12. Quentin Raillard-Cazanove & Edward Barbour, 2022. "Analysis of Smart Meter Electricity Consumption Data for PV Storage in the UK," Energies, MDPI, vol. 15(10), pages 1-15, May.
    13. Hye-Jeong Lee & Beom Jin Chung & Sung-Yoon Huh, 2023. "Consumer Preferences for Smart Energy Services Based on AMI Data in the Power Sector," Energies, MDPI, vol. 16(9), pages 1-20, May.
    14. Amrutha Raju Battula & Sandeep Vuddanti & Surender Reddy Salkuti, 2021. "Review of Energy Management System Approaches in Microgrids," Energies, MDPI, vol. 14(17), pages 1-32, September.
    15. Joel Alpízar-Castillo & Laura Ramirez-Elizondo & Pavol Bauer, 2022. "Assessing the Role of Energy Storage in Multiple Energy Carriers toward Providing Ancillary Services: A Review," Energies, MDPI, vol. 16(1), pages 1-31, December.
    16. Tostado-Véliz, Marcos & Kamel, Salah & Hasanien, Hany M. & Turky, Rania A. & Jurado, Francisco, 2022. "Uncertainty-aware day-ahead scheduling of microgrids considering response fatigue: An IGDT approach," Applied Energy, Elsevier, vol. 310(C).
    17. Li, Ningning & Gao, Yan, 2023. "Real-time pricing based on convex hull method for smart grid with multiple generating units," Energy, Elsevier, vol. 285(C).
    18. Yuan, Guanxiu & Gao, Yan & Ye, Bei, 2021. "Optimal dispatching strategy and real-time pricing for multi-regional integrated energy systems based on demand response," Renewable Energy, Elsevier, vol. 179(C), pages 1424-1446.
    19. Lee, Hyun-Suk, 2024. "Automated tariff design for energy supply–demand matching based on Bayesian optimization: Technical framework and policy implications," Energy Policy, Elsevier, vol. 188(C).
    20. Patrick Schembri & Hynd Remita, 2021. "Énergies « nouvelles » et société," Post-Print hal-03394500, HAL.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:juipol:v:79:y:2022:i:c:s0957178722001059. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.sciencedirect.com/journal/utilities-policy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.