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Optimal Probabilistic Allocation of Photovoltaic Distributed Generation: Proposing a Scenario-Based Stochastic Programming Model

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  • Ali Reza Kheirkhah

    (Department of Electrical Engineering, Sao Paulo State University—UNESP, Ilha Solteira 15385-000, SP, Brazil
    Department of Energy Engineering and Electrical Automation, University of Sao Paulo—USP, Sao Paulo 05508-020, SP, Brazil)

  • Carlos Frederico Meschini Almeida

    (Department of Energy Engineering and Electrical Automation, University of Sao Paulo—USP, Sao Paulo 05508-020, SP, Brazil)

  • Nelson Kagan

    (Department of Energy Engineering and Electrical Automation, University of Sao Paulo—USP, Sao Paulo 05508-020, SP, Brazil)

  • Jonatas Boas Leite

    (Department of Electrical Engineering, Sao Paulo State University—UNESP, Ilha Solteira 15385-000, SP, Brazil)

Abstract

The recent developments in the design, planning, and operation of distribution systems indicate the need for a modern integrated infrastructure in which participants are managed through the perceptions of a utility company in an economic network (e.g., energy loss reduction, restoration, etc.). The penetration of distributed generation units in power systems are growing due to their significant influence on the key attributes of power systems. As a result, the placement, type, and size of distributed generations have an essential role in reducing power loss and lowering costs. Power loss minimization, investment and cost reduction, and voltage profile improvement combine to form a conceivable goal function for distributed generation allocation in a constrained optimization problem, and they require a complex procedure to control them in the most appropriate way while satisfying network constraints. Such a complex decision-making procedure can be solved by adjusting the dynamic optimal power flow problem to the associated network. The purpose of the present work is to handle the distributed generation allocation problem for photovoltaic units, attempting to reduce energy and investment costs while accounting for generation unpredictability as well as load fluctuation. The problem is analyzed under various scenarios of solar radiation through a stochastic programming technique because of the intense uncertainty of solar energy resources. The formulation of photovoltaic distributed generation allocation is represented as a mixed-integer second-order conic programming problem. The IEEE 33-bus and real-world 136-bus distribution systems are tested. The findings illustrate the efficacy of the proposed mathematical model and the role of appropriate distributed generation allocation.

Suggested Citation

  • Ali Reza Kheirkhah & Carlos Frederico Meschini Almeida & Nelson Kagan & Jonatas Boas Leite, 2023. "Optimal Probabilistic Allocation of Photovoltaic Distributed Generation: Proposing a Scenario-Based Stochastic Programming Model," Energies, MDPI, vol. 16(21), pages 1-18, October.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:21:p:7261-:d:1267443
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    References listed on IDEAS

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    1. Spyros Giannelos & Stefan Borozan & Marko Aunedi & Xi Zhang & Hossein Ameli & Danny Pudjianto & Ioannis Konstantelos & Goran Strbac, 2023. "Modelling Smart Grid Technologies in Optimisation Problems for Electricity Grids," Energies, MDPI, vol. 16(13), pages 1-15, June.
    2. Straub, Ludwig & Ulbricht, Robert, 2015. "Endogenous Uncertainty and Credit Crunches," TSE Working Papers 15-604, Toulouse School of Economics (TSE), revised Dec 2017.
    3. Antonio J. Conejo & Miguel Carrión & Juan M. Morales, 2010. "Decision Making Under Uncertainty in Electricity Markets," International Series in Operations Research and Management Science, Springer, number 978-1-4419-7421-1, July-Dece.
    4. Diego Mendoza Osorio & Javier Rosero Garcia, 2023. "Convex Stochastic Approaches for the Optimal Allocation of Distributed Energy Resources in AC Distribution Networks with Measurements Fitted to a Continuous Probability Distribution Function," Energies, MDPI, vol. 16(14), pages 1-27, July.
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