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A Short-Term Distribution Network Planning Strategy to Incentivize Higher Penetrations of Independent Renewable-Based Distributed Generation Projects

Grant number: 19/24677-6
Support type:Scholarships abroad - Research Internship - Post-doctor
Effective date (Start): March 01, 2021
Effective date (End): February 28, 2022
Field of knowledge:Engineering - Electrical Engineering - Power Systems
Principal researcher:José Roberto Sanches Mantovani
Grantee:Ozy Daniel Melgar Dominguez
Supervisor abroad: Gianfranco Chicco
Home Institution: Faculdade de Engenharia (FEIS). Universidade Estadual Paulista (UNESP). Campus de Ilha Solteira. Ilha Solteira , SP, Brazil
Research place: Politecnico di Torino, Italy  
Associated to the scholarship:18/12422-0 - Short-Term Planning of Real Electric Power Distribution Systems Using Equivalent Models, BP.PD


In view of the ongoing integration of renewable-based distributed generation (DG) in electric distribution networks (EDNs), distribution companies are facing the need to develop innovative planning techniques to allow the integration of increasing levels of DG in the available network infrastructure. Such projects for renewable DG sources could be submitted as investment proposals by independent investors or by the distribution companies; however, in some cases, from an EDN perspective, these proposals may not comply with operating requirements established by the regulated entities. In this context, distribution companies should determine the appropriate DG capacities to comply with the operational and technical constraints of the network. Notwithstanding, short-term investment actions could be carried out to upgrade the distribution network and, consequently, enable higher penetrations of renewable-based DG sources. On this basis, the benefits of allocating these independent DG projects at targeted network locations, from the economic and operational perspective of the distribution companies, would be analyzed. In this research project, a planning strategy based on bi-lateral economic and technical agreements between independent DG investors and distribution companies is proposed. This strategy should encourage investments in DG projects at targeted network locations, through the use of incentive programs. On the other hand, to enable higher penetrations of DG, traditional short-term planning options such as upgrading circuits, allocating devices to control the voltage magnitude and reactive power compensation, among others, should be investigated to upgrade the system. In this regard, the proposed approach involves the participation of independent DG investors in a dynamic interaction with distribution companies as an important element in the network-planning problem. In addition, the proposed strategy should determine the lowest cost investment solution obtained from a combination of traditional planning alternatives and DG investment projects. Environmental issues with an emphasis on the mitigation of carbon emissions should be considered and formulated in the mathematical problem to compensate the environmental impact. This planning problem will be represented by a mixed-integer linear programming model that must represent technical, economic, environmental and operating conditions for both parties (distribution companies and DG investors). Higher uncertainty levels are associated with the operation of renewable-based DG units and the demand consumption of the EDN. Thereby, this formulation should be extended to an uncertainty-based optimization model. Considering the development of this planning strategy to solve large-scale distribution systems, the technical and operational viability to apply a simplification algorithm to represent such large-scale systems on equivalent systems of small- and medium-scale should be analyzed. The objective is to develop a computational support tool for the decision-making process in order to plan and promote an efficient EDN with a low environmental impact aiming at a practical application. Several study cases should be conducted under different conditions to demonstrate the efficiency and robustness of the proposed planning strategy.

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