Long-Term Planning of Electrical Distribution Grids: How Load Uncertainty and Flexibility Affect the Investment Timing

Marie-Cécile Alvarez-Hérault (), Jean-Pierre Dib, Oana Ionescu () and Bertrand Raison
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Marie-Cécile Alvarez-Hérault: G2Elab-SYREL - G2Elab-SYstèmes et Réseaux ELectriques - G2ELab - Laboratoire de Génie Electrique de Grenoble - CNRS - Centre National de la Recherche Scientifique - UGA - Université Grenoble Alpes - Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology - UGA - Université Grenoble Alpes, G2ELab - Laboratoire de Génie Electrique de Grenoble - CNRS - Centre National de la Recherche Scientifique - UGA - Université Grenoble Alpes - Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology - UGA - Université Grenoble Alpes
Jean-Pierre Dib: G2Elab-SYREL - G2Elab-SYstèmes et Réseaux ELectriques - G2ELab - Laboratoire de Génie Electrique de Grenoble - CNRS - Centre National de la Recherche Scientifique - UGA - Université Grenoble Alpes - Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology - UGA - Université Grenoble Alpes, G2ELab - Laboratoire de Génie Electrique de Grenoble - CNRS - Centre National de la Recherche Scientifique - UGA - Université Grenoble Alpes - Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology - UGA - Université Grenoble Alpes
Oana Ionescu: GAEL - Laboratoire d'Economie Appliquée de Grenoble - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement - UGA - Université Grenoble Alpes - Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology - UGA - Université Grenoble Alpes, G2Elab-SYREL - G2Elab-SYstèmes et Réseaux ELectriques - G2ELab - Laboratoire de Génie Electrique de Grenoble - CNRS - Centre National de la Recherche Scientifique - UGA - Université Grenoble Alpes - Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology - UGA - Université Grenoble Alpes
Bertrand Raison: G2Elab-SYREL - G2Elab-SYstèmes et Réseaux ELectriques - G2ELab - Laboratoire de Génie Electrique de Grenoble - CNRS - Centre National de la Recherche Scientifique - UGA - Université Grenoble Alpes - Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology - UGA - Université Grenoble Alpes, G2ELab - Laboratoire de Génie Electrique de Grenoble - CNRS - Centre National de la Recherche Scientifique - UGA - Université Grenoble Alpes - Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology - UGA - Université Grenoble Alpes

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Abstract: Due to the rise of smart grids, new players and services are emerging and can have an impact on the decision-making process in distribution networks, which, over the past decades, were only driven by linear demand growth with a low level of uncertainties. Nowadays, the evolution of distribution networks and investment decisions (conductors and transformers) can no longer be based solely on deterministic assumptions of load evolution since there is a high level of uncertainties related to the development of electrical loads such as electric vehicles. In this paper, we focus on the uncertainty of the peak power, key elements for triggering an investment, and the flexibility to choose between different topologies of electric networks. To solve this problem, we apply a real option approach and provide an analytical model with closed-form solutions that allows a full treatment of the dynamic aspects of the decision to reconsider the topology of the network. Moreover, through a comparative statics analysis, we infer the sensitivity of the option value to modify the network with respect to the volatility of the peak power, the associated investment cost or other types of costs of power losses, the growth rate, or the discount rate.

Keywords: Real options; Distribution network; Irreversible investment; Uncertainty (search for similar items in EconPapers)
Date: 2022
Note: View the original document on HAL open archive server: https://hal.science/hal-04051093v1
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Published in Energies, 2022, 15 (16), pp.6084. ⟨10.3390/en15166084⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04051093

DOI: 10.3390/en15166084

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