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CVaR constrained planning of renewable generation with consideration of system inertial response, reserve services and demand participation

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  • Inzunza, Andrés
  • Moreno, Rodrigo
  • Bernales, Alejandro
  • Rudnick, Hugh

Abstract

Integration of renewable generation can lead to both diversification of energy sources (which can improve the overall economic performance of the power sector) and cost increase due to the need for further resources to provide flexibility and thus secure operation from unpredictable, variable and asynchronous generation. In this context, we propose a cost-risk model that can properly plan generation and determine efficient technology portfolios through balancing the benefits of energy source diversification and cost of security of supply through the provision of various generation frequency control and demand side services, including preservation of system inertia levels. We do so through a scenario-based cost minimization framework where the conditional value at risk (CVaR), associated with costs under extreme scenarios of fossil fuel prices combined with hydrological inflows, is constrained. The model can tackle problems with large data sets (e.g. 8760 hours and 1000 scenarios) since we use linear programming and propose a Benders-based method adapted to deal with CVaR constraints in the master problem. Through several analyses, including the Chilean main electricity system, we demonstrate the effects of renewables on hedging both fossil fuel and hydrological risks; effects of security of supply on costs, risks and renewable investment; and the importance of demand side services in limiting risk exposure of generation portfolios through encouraging risk mitigating renewable generation investment.

Suggested Citation

  • Inzunza, Andrés & Moreno, Rodrigo & Bernales, Alejandro & Rudnick, Hugh, 2016. "CVaR constrained planning of renewable generation with consideration of system inertial response, reserve services and demand participation," Energy Economics, Elsevier, vol. 59(C), pages 104-117.
    • Handle: RePEc:eee:eneeco:v:59:y:2016:i:c:p:104-117
    DOI: 10.1016/j.eneco.2016.07.020
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    Cited by:

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    7. Nie, S. & Li, Y.P. & Liu, J. & Huang, Charley Z., 2017. "Risk management of energy system for identifying optimal power mix with financial-cost minimization and environmental-impact mitigation under uncertainty," Energy Economics, Elsevier, vol. 61(C), pages 313-329.
    8. Bergen, Matías & Muñoz, Francisco D., 2018. "Quantifying the effects of uncertain climate and environmental policies on investments and carbon emissions: A case study of Chile," Energy Economics, Elsevier, vol. 75(C), pages 261-273.
    9. Domínguez, Ruth & Vitali, Sebastiano & Carrión, Miguel & Moriggia, Vittorio, 2021. "Analysing decarbonizing strategies in the European power system applying stochastic dominance constraints," Energy Economics, Elsevier, vol. 101(C).
    10. Paolo Falbo & Carlos Ruiz, 2021. "Joint optimization of sales-mix and generation plan for a large electricity producer," Papers 2110.02016, arXiv.org.
    11. Iwona Gorzeń-Mitka & Monika Wieczorek-Kosmala, 2023. "Mapping the Energy Sector from a Risk Management Research Perspective: A Bibliometric and Scientific Approach," Energies, MDPI, vol. 16(4), pages 1-32, February.
    12. 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.
    13. Gonzalez, Jose M. & Tomlinson, James E. & Harou, Julien J. & Martínez Ceseña, Eduardo A. & Panteli, Mathaios & Bottacin-Busolin, Andrea & Hurford, Anthony & Olivares, Marcelo A. & Siddiqui, Afzal & Er, 2020. "Spatial and sectoral benefit distribution in water-energy system design," Applied Energy, Elsevier, vol. 269(C).
    14. Ambrosius, Mirjam & Egerer, Jonas & Grimm, Veronika & van der Weijde, Adriaan H., 2022. "Risk aversion in multilevel electricity market models with different congestion pricing regimes," Energy Economics, Elsevier, vol. 105(C).
    15. Vijaya Dixit & Manoj Kumar Tiwari, 2020. "Project portfolio selection and scheduling optimization based on risk measure: a conditional value at risk approach," Annals of Operations Research, Springer, vol. 285(1), pages 9-33, February.
    16. Falbo, Paolo & Ruiz, Carlos, 2019. "Optimal sales-mix and generation plan in a two-stage electricity market," Energy Economics, Elsevier, vol. 78(C), pages 598-614.
    17. Inzunza, Andrés & Muñoz, Francisco D. & Moreno, Rodrigo, 2021. "Measuring the effects of environmental policies on electricity markets risk," Energy Economics, Elsevier, vol. 102(C).
    18. Lasantha Meegahapola & Pierluigi Mancarella & Damian Flynn & Rodrigo Moreno, 2021. "Power system stability in the transition to a low carbon grid: A techno‐economic perspective on challenges and opportunities," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 10(5), September.
    19. Acevedo, Giancarlo & Bernales, Alejandro & Flores, Andrés & Inzunza, Andrés & Moreno, Rodrigo, 2021. "The effect of environmental policies on risk reductions in energy generation," Journal of Economic Dynamics and Control, Elsevier, vol. 126(C).

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    More about this item

    Keywords

    Mean-risk electricity generation investment; Generation technologies portfolios; Frequency response and reserves; Power system economics; Power system security;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities
    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General
    • C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty

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