A Stochastic Programming Model for the Optimal Allocation of Photovoltaic Distributed Generation in Electrical Distribution Systems Considering Load Variations and Generation Uncertainty

Kheirkhah, Ali Reza; Tabares, Alejandra; Zandrazavi, Seyed Farhad; Leite, Jônatas Boás

doi:10.48011/sbse.v1i1.2247

Cited by 2 publications

(3 citation statements)

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“…The nominal voltage and maximum current of the system are, respectively, 13.8 kV and 200 A. Appendix B of [33] includes additional details on the line and load parameters for the 136-bus distribution network. Additionally, the likelihood of each scenario occurring is 25 percent.…”

Section: Results Of 136-bus Distribution Networkmentioning

confidence: 99%

“…Distribution losses may be minimized by placing capacitors, allocating distributed generation (DGs) resources, and reconfiguring feeders [4]. Approximately 90 percent of power outages occur in the distribution network [5], which generally provides a single source of energy for each customer; therefore, in the power system, the distribution network plays a crucial role in failures [6].…”

Section: Introduction 1backgroundmentioning

confidence: 99%

“…Real-world testing: The proposed model was initially applied to the IEEE 33-bus distribution system to evaluate its performance. To further validate its effectiveness, the model was then tested on a real-world 136-bus distribution network of Três Lagoas located in Brazil [33]. This validation encompassed scenarios both with and without DG units, under scenarios of daily load variations and PV generation uncertainty.…”

mentioning

confidence: 99%

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

Kheirkhah,

Meschini Almeida,

Kagan

et al. 2023

Energies

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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.

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Section: Results Of 136-bus Distribution Networkmentioning

confidence: 99%