Solution of the Optimal Reactive Power Flow Problem Using a Discrete-Continuous CBGA Implemented in the DigSILENT Programming Language

The problem of the optimal reactive power flow in transmission systems is addressed in this research from the point of view of combinatorial optimization. A discrete-continuous version of the Chu & Beasley genetic algorithm (CBGA) is proposed to model continuous variables such as voltage outputs in generators and reactive power injection in capacitor banks, as well as binary variables such as tap positions in transformers. The minimization of the total power losses is considered as the objective performance indicator. The main contribution in this research corresponds to the implementation of the CBGA in the DigSILENT Programming Language (DPL), which exploits the advantages of the power flow tool at a low computational effort. The solution of the optimal reactive power flow problem in power systems is a key task since the efficiency and secure operation of the whole electrical system depend on the adequate distribution of the reactive power in generators, transformers, shunt compensators, and transmission lines. To provide an efficient optimization tool for academics and power system operators, this paper selects the DigSILENT software, since this is widely used for power systems for industries and researchers. Numerical results in three IEEE test feeders composed of 6, 14, and 39 buses demonstrate the efficiency of the proposed CBGA in the DPL environment from DigSILENT to reduce the total grid power losses (between 21.17% to 37.62% of the benchmark case) considering four simulation scenarios regarding voltage regulation bounds and slack voltage outputs. In addition, the total processing times for the IEEE 6-, 14-, and 39-bus systems were 32.33 s, 49.45 s, and 138.88 s, which confirms the low computational effort of the optimization methods directly implemented in the DPL environment.

Voltage Control in Transmission Grids Considering Uncertainties of Renewable Energy Sources_preprint.pdf

Power traders and system operators need to balance the uncertain generation of renewable energy sources by adapting the dispatch of conventional power plants. This poses a challenge to voltage control in power system operation. Accordingly, this paper presents a method to determine voltage magnitude probability densities which are integrated into an optimal reactive power flow to consider an uncertain active power generation. First, the probability densities are determined by Monte Carlo simulations including a unit commitment problem to derive the dispatch of conventional power plants. Second, uncertainty restrictions are used to create soft constraints for the optimal reactive power flow, which mitigates the risk of voltage limit violations. By adapting the soft constraints slack costs, the consideration of uncertainties can be prioritized in relation to other objectives, such as the reduction of active power losses, or reactive power costs.<br>

Voltage Control in Transmission Grids Considering Uncertainties of Renewable Energy Sources_preprint.pdf

Power traders and system operators need to balance the uncertain generation of renewable energy sources by adapting the dispatch of conventional power plants. This poses a challenge to voltage control in power system operation. Accordingly, this paper presents a method to determine voltage magnitude probability densities which are integrated into an optimal reactive power flow to consider an uncertain active power generation. First, the probability densities are determined by Monte Carlo simulations including a unit commitment problem to derive the dispatch of conventional power plants. Second, uncertainty restrictions are used to create soft constraints for the optimal reactive power flow, which mitigates the risk of voltage limit violations. By adapting the soft constraints slack costs, the consideration of uncertainties can be prioritized in relation to other objectives, such as the reduction of active power losses, or reactive power costs.<br>

Optimal Reactive Power Flow for Large-Scale Power Systems Using an Effective Metaheuristic Algorithm

In this paper, stochastic fractal search method (SFS) is employed for solving the optimal reactive power flow (ORPF) problem with a target of optimizing total active power losses (TPL), voltage deviation (VD), and voltage stability index (VSI). SFS is an effective metaheuristic algorithm consisting of diffusion process and two update processes. ORPF is a complex problem giving challenges to applied algorithms by taking into account many complex constraints such as operating voltage from generators and loads, active and reactive power generation of generators, limit of capacitors, apparent power limit from branches, and tap setting of transformers. For verifying the performance, solutions of IEEE 30 and 118-bus system with TPL, VD, and VSI objectives are found by the SFS method with different control parameter settings. Result comparisons indicate that SFS is more favorable than other methods about finding effective solutions and having faster speed. As a result, it is suggested that SFS should be used for ORPF problem, and modifications performed on SFS are encouraged for better results.

A Sequential Hybridization of Genetic Algorithm and Particle Swarm Optimization for the Optimal Reactive Power Flow

In this paper, the problem of the Optimal Reactive Power Flow (ORPF) in the Algerian Western Network with 102 nodes is solved by the sequential hybridization of metaheuristics methods, which consists of the combination of both the Genetic Algorithm (GA) and the Particle Swarm Optimization (PSO). The aim of this optimization appears in the minimization of the power losses while keeping the voltage, the generated power, and the transformation ratio of the transformers within their real limits. The results obtained from this method are compared to those obtained from the two methods on populations used separately. It seems that the hybridization method gives good minimizations of the power losses in comparison to those obtained from GA and PSO, individually, considered. However, the hybrid method seems to be faster than the PSO but slower than GA.