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

Joao Soares,John F. Franco,Fernando Lezama,Tayenne D. De Lima

doi:10.1109/access.2022.3146799

Abstract

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 <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> 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.

Highlights

The automotive industry has been going through a moment of transformations with the increase of electric vehicles (EVs)
Uncertainties related to conventional demand, EV charging demand, and renewable generation are represented in the model through two-stage Stochastic Programming
FUNCTION The objective function of the problem is presented in (10) and minimizes the present value of the expected total cost, being composed by the following costs: 1) Investments (Ip) (11) associated with substation, circuits, distributed generation (DG) units, EV charging stations (EVCSs), and energy storage systems (ESSs); 2) Operational costs (Op) (12) related to the energy supplied by substations, jointly with distributed energy resources maintenance costs, and CO2 emissions costs

Summary

INTRODUCTION

The automotive industry has been going through a moment of transformations with the increase of electric vehicles (EVs). Such formulation considers the simultaneous optimization of multi-period investments in substations, circuits, renewable and non-renewable DG units, EVCSs, and ESSs. The joint optimization of these investments has not been addressed yet. OBJECTIVE FUNCTION The objective function of the problem is presented in (10) and minimizes the present value of the expected total cost, being composed by the following costs: 1) Investments (Ip) (11) associated with substation, circuits, DG units, EVCSs, and ESSs; 2) Operational costs (Op) (12) related to the energy supplied by substations, jointly with distributed energy resources maintenance costs, and CO2 emissions costs These costs are calculated for each period p.

CONSTRAINTS The proposed model has the following types of constraints

TESTS AND RESULTS

CONCLUSIONS