IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v213y2020ics0360544220319393.html
   My bibliography  Save this article

The trade-off between demand growth and renewables: A multiperiod electricity planning model under CO2 emission constraints

Author

Listed:
  • Rego, Erik Eduardo
  • Costa, Oswaldo L.V.
  • Ribeiro, Celma de Oliveira
  • Lima Filho, Roberto Ivo da R.
  • Takada, Hellinton
  • Stern, Julio

Abstract

Under the Paris Agreement, each participant country established its Nationally Determined Contribution aiming at reducing its CO2 emissions. This makes the trade-off between the electricity capacity expansion planning to meet the increase of demand and the reduction of greenhouse gas emissions a challenge, specially for developing countries, which require a higher rate of economic growth. To consider this trade-off, and identify the feasibility of the targets of the electricity expansion planning, a multiperiod optimization model is proposed considering the seasonality of supply and demand and the peak period demand. The goal is to minimize the total cost, satisfying demand constraints, the maximum CO2 emission constraints and the power expansion supply restrictions for each source. An analysis of the Brazilian electricity matrix for the years 2020–2033 is performed considering two scenarios for the growth of the demand and two scenarios for the CO2 target emissions The numerical simulations indicate that the present Brazilian electricity expansion planning seems adequate to meet the Nationally Determined Contribution only under a mild economic growth rate scenario. A higher economic growth rate would require a stronger economic policy related to the power expansions of the renewable sources.

Suggested Citation

  • Rego, Erik Eduardo & Costa, Oswaldo L.V. & Ribeiro, Celma de Oliveira & Lima Filho, Roberto Ivo da R. & Takada, Hellinton & Stern, Julio, 2020. "The trade-off between demand growth and renewables: A multiperiod electricity planning model under CO2 emission constraints," Energy, Elsevier, vol. 213(C).
    • Handle: RePEc:eee:energy:v:213:y:2020:i:c:s0360544220319393
    DOI: 10.1016/j.energy.2020.118832
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544220319393
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2020.118832?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. Koltsaklis, Nikolaos E. & Nazos, Konstantinos, 2017. "A stochastic MILP energy planning model incorporating power market dynamics," Applied Energy, Elsevier, vol. 205(C), pages 1364-1383.
    2. Staffell, Iain & Pfenninger, Stefan, 2018. "The increasing impact of weather on electricity supply and demand," Energy, Elsevier, vol. 145(C), pages 65-78.
    3. Yu, L. & Li, Y.P. & Huang, G.H., 2016. "A fuzzy-stochastic simulation-optimization model for planning electric power systems with considering peak-electricity demand: A case study of Qingdao, China," Energy, Elsevier, vol. 98(C), pages 190-203.
    4. Bastidas, Daniel & Mc Isaac, Florent, 2019. "Reaching Brazil's Nationally Determined Contributions: An assessment of the key transitions in final demand and employment," Energy Policy, Elsevier, vol. 135(C).
    5. Morris, Peter & Vine, Desley & Buys, Laurie, 2015. "Application of a Bayesian Network complex system model to a successful community electricity demand reduction program," Energy, Elsevier, vol. 84(C), pages 63-74.
    6. Costa, Oswaldo L.V. & de Oliveira Ribeiro, Celma & Rego, Erik Eduardo & Stern, Julio Michael & Parente, Virginia & Kileber, Solange, 2017. "Robust portfolio optimization for electricity planning: An application based on the Brazilian electricity mix," Energy Economics, Elsevier, vol. 64(C), pages 158-169.
    7. Senatla, Mamahloko & Nchake, Mamello & Taele, Benedict M. & Hapazari, Innocent, 2018. "Electricity capacity expansion plan for Lesotho – implications on energy policy," Energy Policy, Elsevier, vol. 120(C), pages 622-634.
    8. Paim, Maria-Augusta & Dalmarco, Arthur R. & Yang, Chung-Han & Salas, Pablo & Lindner, Sören & Mercure, Jean-Francois & de Andrade Guerra, José Baltazar Salgueirinho Osório & Derani, Cristiane & Bruce , 2019. "Evaluating regulatory strategies for mitigating hydrological risk in Brazil through diversification of its electricity mix," Energy Policy, Elsevier, vol. 128(C), pages 393-401.
    9. Lara, Cristiana L. & Mallapragada, Dharik S. & Papageorgiou, Dimitri J. & Venkatesh, Aranya & Grossmann, Ignacio E., 2018. "Deterministic electric power infrastructure planning: Mixed-integer programming model and nested decomposition algorithm," European Journal of Operational Research, Elsevier, vol. 271(3), pages 1037-1054.
    10. Herter, Karen & Wayland, Seth, 2010. "Residential response to critical-peak pricing of electricity: California evidence," Energy, Elsevier, vol. 35(4), pages 1561-1567.
    11. Lee, Jui-Yuan, 2017. "A multi-period optimisation model for planning carbon sequestration retrofits in the electricity sector," Applied Energy, Elsevier, vol. 198(C), pages 12-20.
    12. Jaber Valinejad & Mousa Marzband & Michael Elsdon & Ameena Saad Al-Sumaiti & Taghi Barforoushi, 2019. "Dynamic Carbon-Constrained EPEC Model for Strategic Generation Investment Incentives with the Aim of Reducing CO 2 Emissions," Energies, MDPI, vol. 12(24), pages 1-35, December.
    13. Chen, Fang & Huang, Guohe & Fan, Yurui, 2015. "A linearization and parameterization approach to tri-objective linear programming problems for power generation expansion planning," Energy, Elsevier, vol. 87(C), pages 240-250.
    14. Park, Heejung & Baldick, Ross, 2016. "Multi-year stochastic generation capacity expansion planning under environmental energy policy," Applied Energy, Elsevier, vol. 183(C), pages 737-745.
    15. Florent MCISAAC & Daniel BASTIDAS, 2019. "Reaching Brazil's Nationally Determined Contributions: An Assessment of the Key Transitions in Final Demand and Employment," Working Paper 911644f9-625d-496f-8ecf-8, Agence française de développement.
    16. Quiroga, Daniela & Sauma, Enzo & Pozo, David, 2019. "Power system expansion planning under global and local emission mitigation policies," Applied Energy, Elsevier, vol. 239(C), pages 1250-1264.
    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. Tanoto, Yusak & Haghdadi, Navid & Bruce, Anna & MacGill, Iain, 2021. "Reliability-cost trade-offs for electricity industry planning with high variable renewable energy penetrations in emerging economies: A case study of Indonesia’s Java-Bali grid," Energy, Elsevier, vol. 227(C).
    2. Hailin Mu & Zhewen Pei & Hongye Wang & Nan Li & Ye Duan, 2022. "Optimal Strategy for Low-Carbon Development of Power Industry in Northeast China Considering the ‘Dual Carbon’ Goal," Energies, MDPI, vol. 15(17), pages 1-22, September.
    3. Shin, Hansol & Kim, Wook, 2023. "Comparison of the centralized and decentralized environmentally constrained economic dispatch methods of coal-fired generators: A case study for South Korea," Energy, Elsevier, vol. 275(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. Chandra Ade Irawan & Peter S. Hofman & Hing Kai Chan & Antony Paulraj, 2022. "A stochastic programming model for an energy planning problem: formulation, solution method and application," Annals of Operations Research, Springer, vol. 311(2), pages 695-730, April.
    2. Yu, L. & Li, Y.P. & Huang, G.H., 2016. "A fuzzy-stochastic simulation-optimization model for planning electric power systems with considering peak-electricity demand: A case study of Qingdao, China," Energy, Elsevier, vol. 98(C), pages 190-203.
    3. Cahen-Fourot, Louison & Campiglio, Emanuele & Godin, Antoine & Kemp-Benedict, Eric & Trsek, Stefan, 2021. "Capital stranding cascades: The impact of decarbonisation on productive asset utilisation," Energy Economics, Elsevier, vol. 103(C).
    4. Skolfield, J. Kyle & Escobedo, Adolfo R., 2022. "Operations research in optimal power flow: A guide to recent and emerging methodologies and applications," European Journal of Operational Research, Elsevier, vol. 300(2), pages 387-404.
    5. Magacho, Guilherme & Espagne, Etienne & Godin, Antoine & Mantes, Achilleas & Yilmaz, Devrim, 2023. "Macroeconomic exposure of developing economies to low-carbon transition," World Development, Elsevier, vol. 167(C).
    6. Carvalho, N.B. & Berrêdo Viana, D. & Muylaert de Araújo, M.S. & Lampreia, J. & Gomes, M.S.P. & Freitas, M.A.V., 2020. "How likely is Brazil to achieve its NDC commitments in the energy sector? A review on Brazilian low-carbon energy perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    7. Hsiao, Chih-Tung & Liu, Chung-Shu & Chang, Dong-Shang & Chen, Chun-Cheng, 2018. "Dynamic modeling of the policy effect and development of electric power systems: A case in Taiwan," Energy Policy, Elsevier, vol. 122(C), pages 377-387.
    8. Antoine GODIN & Paul Hadji-Lazaro, 2020. "Demand-induced transition risks: A systemic approach applied to South Africa," Working Paper 1ec2dacf-58b9-4235-8d35-4, Agence française de développement.
    9. Patricio Castillo & Matias Aguad & Álvaro Lorca & Samuel Cordova & Matias Negrete-Pincetic, 2024. "Coal Phase-Out and Carbon Tax Analysis with Long-Term Planning Models: A Case Study for the Chilean Electric Power System," Energies, MDPI, vol. 17(21), pages 1-31, October.
    10. Fitiwi, Desta Z. & Lynch, Muireann & Bertsch, Valentin, 2020. "Enhanced network effects and stochastic modelling in generation expansion planning: Insights from an insular power system," Socio-Economic Planning Sciences, Elsevier, vol. 71(C).
    11. Flores-Quiroz, Angela & Strunz, Kai, 2021. "A distributed computing framework for multi-stage stochastic planning of renewable power systems with energy storage as flexibility option," Applied Energy, Elsevier, vol. 291(C).
    12. Yu, L. & Li, Y.P. & Huang, G.H. & Fan, Y.R. & Yin, S., 2018. "Planning regional-scale electric power systems under uncertainty: A case study of Jing-Jin-Ji region, China," Applied Energy, Elsevier, vol. 212(C), pages 834-849.
    13. Radhanon Diewvilai & Kulyos Audomvongseree, 2021. "Generation Expansion Planning with Energy Storage Systems Considering Renewable Energy Generation Profiles and Full-Year Hourly Power Balance Constraints," Energies, MDPI, vol. 14(18), pages 1-25, September.
    14. Gregor Semieniuk & Emanuele Campiglio & Jean‐Francois Mercure & Ulrich Volz & Neil R. Edwards, 2021. "Low‐carbon transition risks for finance," Wiley Interdisciplinary Reviews: Climate Change, John Wiley & Sons, vol. 12(1), January.
    15. Pereira, Andrés & Sauma, Enzo, 2020. "Power systems expansion planning with time-varying CO2 tax," Energy Policy, Elsevier, vol. 144(C).
    16. Grottera, Carolina & Naspolini, Giovanna Ferrazzo & La Rovere, Emilio Lèbre & Schmitz Gonçalves, Daniel Neves & Nogueira, Tainan de Farias & Hebeda, Otto & Dubeux, Carolina Burle Schmidt & Goes, Georg, 2022. "Energy policy implications of carbon pricing scenarios for the Brazilian NDC implementation," Energy Policy, Elsevier, vol. 160(C).
    17. Fitiwi, Desta & Lynch, Muireann Á. & Bertsch, Valentin, 2019. "Optimal development of electricity generation mix considering fossil fuel phase-out and strategic multi-area interconnection," Papers WP616, Economic and Social Research Institute (ESRI).
    18. Scott, Ian J. & Botterud, Audun & Carvalho, Pedro M.S. & Silva, Carlos A. Santos, 2020. "Renewable energy support policy evaluation: The role of long-term uncertainty in market modelling," Applied Energy, Elsevier, vol. 278(C).
    19. Mirian Bortoluzzi & Marcelo Furlan & Simone Geitenes Colombo & Tatiele Martins Amaral & Celso Correia de Souza & José Francisco dos Reis Neto & Josimar Fernandes de França, 2021. "Combining Value-Focused Thinking and PROMETHEE Techniques for Selecting a Portfolio of Distributed Energy Generation Projects in the Brazilian Electricity Sector," Sustainability, MDPI, vol. 13(19), pages 1-19, October.
    20. Yu, L. & Li, Y.P. & Huang, G.H. & Fan, Y.R. & Nie, S., 2018. "A copula-based flexible-stochastic programming method for planning regional energy system under multiple uncertainties: A case study of the urban agglomeration of Beijing and Tianjin," Applied Energy, Elsevier, vol. 210(C), pages 60-74.

    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:energy:v:213:y:2020:i:c:s0360544220319393. 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: http://www.journals.elsevier.com/energy .

    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.