Optimal Location and Sizing of Distributed Generators in DC Networks Using a Hybrid Method Based on Parallel PBIL and PSO

Abstract

This paper addresses the problem of the locating and sizing of distributed generators (DGs) in direct current (DC) grids and proposes a hybrid methodology based on a parallel version of the Population-Based Incremental Learning (PPBIL) algorithm and the Particle Swarm Optimization (PSO) method. The objective function of the method is based on the reduction of the power loss by using a master-slave structure and the consideration of the set of restrictions associated with DC grids in a distributed generation environment. In such a structure, the master stage (PPBIL) finds the location of the generators and the slave stage (PSO) finds the corresponding sizes. For the purpose of comparison, eight additional hybrid methods were formed by using two additional location methods and two additional sizing methods, and this helped in the evaluation of the effectiveness of the proposed solution. Such an evaluation is illustrated with the electrical test systems composed of 10, 21 and 69 buses and simulated on the software, MATLAB. Finally, the results of the simulation demonstrated that the PPBIL–PSO method obtains the best balance between the reduction of power loss and the processing time.

Application of the Multiverse Optimization Method to Solve the Optimal Power Flow Problem in Direct Current Electrical Networks

“…A 900-kcmil conductor (I max ij = 520 A) was selected for the 21-bus test system based on the operation of the base case (without DGs installed). The voltage bounds of both test systems were set at +/− 10% of the nominal voltage [32]. Because this study addresses the OPF problem, the location of the DGs here is the same as that reported in [17]; therefore, they are located at nodes 9, 12, and 16 in the network.…”

“…In this test system, the slack generator produces 4043.1 kW, and the power losses equal 153.85 kW. With respect to the current limits, a 400-kcmil electrical conductor (I max ij = 335 A) was selected for this test system considering a non-telescopic configuration, as well as the same voltage limits used in the 21-bus test system [32]. Finally, the location of the DGs was defined as in [21], i.e., at nodes 26, 61, and 66.…”

Optimal Location and Sizing of DGs in DC Networks Using a Hybrid Methodology Based on the PPBIL Algorithm and the VSA

“…Nonetheless, due to the presence of binary variables within the location problem, these strategies fail to provide an optimal solution, thus requiring the use of specialized software for their implementation. As a result, the complexity of their solution and its implementation costs increase [20].…”

Exact minimization of the energy losses and the CO2 emissions in isolated DC distribution networks using PV sources

“…In the specialized literature, the analyses of DC networks are made in the context of providing electric distribution applications in urban and rural areas [1,10]. The main approaches correspond to: a) optimal location and sizing of distributed generators in DC grids [11], b) optimal location and operation of battery energy storage systems [12][13][14], c) optimal reconfiguration of DC feeders [15], d) and planning of DC networks [1], among others. Based on the importance of these studies, we focus on the problem of the optimal location and sizing of distributed generators in DC networks considering daily load and PV curves to minimize the number of daily energy losses and the harmful greenhouse gas emissions to the atmosphere.…”

“…Section 5 describes the main characteristics of the IEEE 33-and IEEE 69-node test feeders as well as the daily load and PV generation curves, respectively. Section 6 presents the numerical results in both test feeders using the CVX tool and the MOSEK solver in the MATLAB programming environment and their comparisons with the GAMS MINLP solvers [11]. Section 7 shows the main conclusions derived from this study and further works.…”