Published January 27, 2022 | Version v1
Journal article Open

A Specialized Long-Term Distribution System Expansion Planning Method With the Integration of Distributed Energy Resources

  • 1. São Paulo State University (UNESP), Ilha Solteira, São Paulo, Brazil
  • 2. São Paulo State University (UNESP), Ilha Solteira, São Paulo, Brazil School of Energy Engineering, UNESP, Rosana, São Paulo, Brazil
  • 3. Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development (GECAD), Polytechnic Institute of Porto (ISEP/IPP), Porto, Portugal

Description

The electrical distribution system (EDS) has undergone major changes in the last decade due to the increasing integration of distributed generation (DG), particularly renewable energy DG. Since renewable energy resources have uncertain generation, energy storage systems (ESSs) in the EDS can reduce the impact of those uncertainties. Besides, electric vehicles (EVs) have been increasing in recent years leveraged by environmental concerns, bringing new challenges to the operation and planning of the EDS. In this context, new approaches for the distribution system expansion planning (DSEP) problem should consider the distributed energy resources (DG units, ESSs, and EVs) and address environmental impacts. This paper proposes a mixed-integer linear programming model for the DSEP problem considering DG units, ESSs, and EV charging stations, thus incorporating the environmental impact and uncertainties associated with demand (conventional and EVs) and renewable generation. In contrast to other approaches, the proposed model includes the simultaneous optimization of investments in substations, circuits, and distributed energy resources, including environmental aspects (CO 2 emissions). The optimization method was developed in the modeling language AMPL and solved via CPLEX. Tests carried out with a 24-node system illustrate its effectiveness as a valuable tool that can assist EDS planners in the integration of distributed energy resources.

Notes

This work was supported in part by the Brazilian Federal Agency for Support and Evaluation of Graduate Education (CAPES), in the scope of the Program CAPES-PrInt, under Grant 88887.310463/2018-00 and Grant 88887.570741/2020-00; in part by the Brazilian National Council for Scientific and Technological Development (CNPq) under Grant 313047/2017-0; in part by the São Paulo Research Foundation (FAPESP) through CENERGETIC Research Project under Grant 2015/21972-6, Grant 2017/02831-8, Grant 2018/23617-7, Grant 2018/20990-9, and Grant 2018/08008-4; in part by the Fundo Europeu de Desenvolvimento Regional (FEDER) Funds through the Operational Program for Competitiveness and Internationalization (COMPETE2020) under Project POCI-01-0145-FEDER-028983; and in part by the National Funds through the Fundação para a Ciência e Tecnologia (FCT) Portuguese Foundation for Science and Technology under Project PTDC/EEI-EEE/28983/2017 (CENERGETIC), Project CEECIND/02814/2017, and Project UIDB/000760/2020.

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Additional details

Funding

Fundação para a Ciência e Tecnologia
PTDC/EEI-EEE/28983/2017 - Coordinated energy resource management under uncertainty considering electric vehicles and demand flexibility in distribution networks PTDC/EEI-EEE/28983/2017
Fundação para a Ciência e Tecnologia
CEECIND/02814/2017/CP1417/CT0002 - Not available CEECIND/02814/2017/CP1417/CT0002