1. Introduction
Bipolar DC distribution has attracted the attention of both academia and the industry due to its advantages over traditional AC distribution networks and monopolar DC configurations [
1,
2]. In comparison with AC networks, bipolar DC networks do not require reactive power and frequency control, which makes them easy to control, since the control variable is only the magnitude of the voltage at each pole (positive and negative) in the feeding bus [
3]. With respect to monopolar DC networks, bipolar DC networks can transport two times the power, and they allow interconnecting loads between the positive and negative poles (i.e., two-pole loads) for special applications [
4,
5].
Even though bipolar DC networks are efficient when compared to monopolar DC configurations or easily manageable in comparison with AC conventional systems, their analysis requires specialized tools regarding control and optimization [
6]. In the case of control, efficient nonlinear control methods are required to support constant voltages in the substation while considering the neutral wire and the positive and negative poles and ensuring stability during closed-loop operation for constant power terminals [
7]. The main challenge with respect to control is dealing with the negative resistance effect of the constant power terminals in converters for AC/DC applications [
8]. The optimization area is not the exception, since efficient power flow tools, optimal power flow methods, and so on are required to optimize the grid performance regarding power loss minimization or voltage profile improvements [
2], among other applications. This research focuses on the optimization of bipolar DC configurations in order to deal with power loss minimization. These power losses are mainly caused by imbalances in the monopolar loads (i.e., differences between total positive-to-neutral and negative-to-neutral loads) [
9].
In the current literature, the analysis of bipolar DC networks is still a young field of research, and there are few works that concentrate on it. Ref. [
9] proposed a derivative-free power flow approach to deal with voltage calculation in bipolar DC networks while considering load imbalances and the possibility of working with solidly grounded or floating neutral wire. Numerical results in the 21-bus grid and the 85-bus grid showed the effectiveness of this power flow method regarding processing times, the number of iterations, and convergence. Ref. [
10] solved the power flow problem for bipolar DC networks by considering constant power terminals. A 3-bus system was used for validating the power flow model. This model was developed using the nodal voltage method. However, the authors do not propose any innovative solution alternative, and they just implemented the electrical circuit in the PSCAD/EMTDC software.
Regarding the optimal power flow solution, multiple methodologies have been proposed in the current literature. Ref. [
2] addressed the optimal power flow problem for bipolar DC networks with multiple monopolar and bipolar constant power terminals, with the main advantage that the neutral cable is considered in their formulation. The goal of this paper is to calculate the locational marginal prices of all the nodes in the network. To this effect, the authors relax the hyperbolic relations between voltages and power using linearization methods. This relaxation simplifies the complication of the power flow problem and allows turning it into a linear or quadratic programming model with linear constraints. In [
11], an optimal power flow approach for bipolar DC networks is proposed which involves the classical current injection method using the Newton–Raphson representation. The aim of this research is to minimize the grid voltage imbalances caused by monopolar constant power loads. A quadratic programming model with linear constraints based on the Jacobian matrix is used to solve optimal power flow problems by means of a recursive sequential evaluation. Even though this proposed approach is novel, the authors do not present validations with combinatorial or nonlinear programming methods.
As for optimal pole swapping applications, Ref. [
12] proposed a multi-objective optimization model to redistribute pole-to-neutral loads between positive and negative poles. Nevertheless, they did not consider the hyperbolic relations between voltage and power, and they only worked with resistive loads. This simplification allows obtaining a mixed-integer linear programming model.
Based on the aforementioned state-of-the-art review, it was possible to identify that only [
12] presented a solution methodology for dealing with the optimal pole-swapping problem in bipolar DC grids. However, the model was simplified and does not consider the effect of constant power terminals in its formulation. This allowed identifying a research opportunity regarding the solution of the optimal pole-swapping problem in bipolar DC grids while considering multiple monopolar constant power loads through a master–slave optimization approach. In the master stage, three different metaheuristic optimization methodologies are employed to define the load connection at each node from positive-to-neutral and negative-to-neutral poles. To decide on the connection of these loads to each node a binary codification is implemented, where 0 implies maintaining the initial load connection and 1 means interchanging monopolar loads between poles. The selected metaheuristic techniques selected are (i) the Chu and Beasley genetic algorithm (CBGA), (ii) the sine-cosine algorithm (SCA), and (iii) the black-hole optimizer (BHO). In the slave stage, the triangular-based power flow formulation proposed in [
9] is employed to evaluate the total grid power losses for each possible set of load connections provided by the master stage.
Note that the selection of the three metaheuristic optimizers was made based on the fact that they are different in nature. The CBGA, for instance, was inspired by Darwin’s evolution theory, i.e., it is a nature-inspired optimizer; the SCA corresponds to a combinatorial optimization method that is mathematically inspired by the circular behavior of the sine and cosine trigonometric functions, and the BHO approach is a combinatorial optimization method from the family of physical-inspired algorithms. These selections were made to verify whether the theory that inspired each optimizer influences the final solution of the studied optimization problem. It is worth mentioning that all of these optimizers are population-based algorithms, and their differences lie in the mathematical structure of the evolution rules.
Numerical results in the 21- and 85-bus grids confirm that all the three metaheuristic optimizers find adequate power loss reductions compared to the benchmark cases with reduced processing times. In the 21-bus grids, the reductions with respect to the benchmark case were , and the processing times were lower than 9 s; whereas, for the 85-bus system, the power loss reductions were lower than and the average processing times were lower than 134 s.
The remainder of this research is structured as follows:
Section 2 presents the exact mixed-integer nonlinear programming model that represents the optimal pole-swapping problem in bipolar DC networks with multiple monopolar and bipolar constant power terminals;
Section 3 describes the main aspects of each of the proposed metaheuristic optimizers, i.e., codification, initial population, and evolution rules, among others, and it presents the general power flow formula based on the upper triangular matrix representation;
Section 4 describes the main characteristics of the test feeders, which are composed of 21 and 85 buses with radial structures;
Section 5 presents the numerical validation of the proposed master–slave optimizers, as well as their analysis, comparisons, and discussions; finally,
Section 8 shows the main concluding remarks obtained from this research, as well as some proposals for future work.
8. Conclusions and Future Works
The problem regarding the optimal pole-swapping problem in bipolar DC networks with multiple monopolar and bipolar constant power terminals was addressed in this research through the application of three solution methodologies with a master–slave structure. In the master stage, a metaheuristic optimizer (CBGA, BHO, or SCA) was employed to define the load connection at each node, while the slave stage was entrusted with evaluating the total grid power losses by using a triangular-based power flow formulation specialized for radial bipolar DC networks.
Numerical results in the 21-bus grid showed that the three optimization methods allow reductions of about with respect to the benchmark case, whereas, for the 85-bus grid, these reductions are between and . In both test feeders, the CBGA finds the best objective function value (minimum value): and kW, respectively.
With respect to the voltage profile performance, as expected, the optimal load redistribution in the positive and negative poles with respect to the benchmark case allows noting that the neutral voltage drop was minimized in the 21-bus system, which made the voltage magnitudes of the positive and negative poles similar in comparison with the benchmark case. The initial differences between total monopolar consumptions in the 21- and 85-bus grids were 109 and 1873.42 kW, respectively, whereas, when the optimal solution found with the CBGA was implemented in both test feeders, these imbalances were reduced to 47 and 52.58 kW, respectively.
The statistical analysis of the three metaheuristic optimizers revealed that: (i) the most stable algorithm after 100 consecutive evaluations was the BHO since it showed a lower standard deviation in both test feeders; (ii) with respect to the final objective function value, the most efficient algorithm was the CBGA since it found the minimum value of the power losses in both test feeders.
As future work, the following studies can be conducted: (i) proposing new metaheuristic optimizers to deal with the optimal pole-swapping problem (i.e., the vortex search algorithm, the generalized normal distribution algorithm, and the gradient-based metaheuristic optimizer, among others); (ii) developing a mixed-integer convex optimization model to reformulate the exact MINLP model given in Equations (
1) to (16) in order to ensure that the global optimum is reached via the convex optimization theory.