Abstract
This paper presents a method to find the optimal size and place of the switched capacitors using a hybrid optimization algorithm. The objective function includes the active and reactive power of power plants, the capital and maintenance costs of capacitor banks, and the cost of active and reactive power losses in distribution lines and transformers. The impact of the load model on the optimal sizing and placement of switched capacitors is studied using three different scenarios: In the first scenario, all loads are voltage-dependent; in the second scenario, only a portion of loads are voltage-dependent; in the third scenario, all loads are voltage-independent. The proposed hybrid algorithm incorporates an outer and two inner optimization layers. The outer layer is executed by a genetic algorithm (GA), while the inner layer is performed by a GA, an exchange market algorithm (EMA), or a particle swarm optimization (PSO). The performance of GA-GA, GA-EMA, and GA-PSO hybrid schemes are compared on an IEEE 33-bus test system. Moreover, IEEE 33-bus and 69-bus networks are used to verify the effectiveness of proposed hybrid scheme against the gravitational search algorithm (GSA), a combination of PSO and GSA (PSOGSA), cuckoo search algorithm (CSA), teaching learning-based optimization (TLBO), and flower pollination algorithm (FPA). The results highlight the advantage of the proposed hybrid optimization scheme over the other optimization algorithms.
Highlights
Minimizing power system losses results in the power system performance improvement from economical and technical points of view
The negative impacts of highly inductive loads can be mitigated by utilizing switched capacitors
The reactive power profile is calculated with 10% probabilistic error using the power factor of the distribution network [28]
Summary
Minimizing power system losses results in the power system performance improvement from economical and technical points of view. Switched capacitors can improve the distribution system voltage profile, reduce the power system losses, and release the system power transfer capacity [3]. In [7], genetic algorithm (GA) is utilized to determine the optimal location and capacity of capacitor banks for reducing the power system loss and improving the voltage profile. Sensitivity analysis and Gravitational Search Algorithm (GSA) are utilized to find the optimal fixed capacitor location for reducing the loss and operational cost of the distribution network in [20]. The majority of the available methods used for optimal placement and sizing of capacitor banks rely on a fixed distribution network load behavior. As opposed to the conventional schemes, in this paper, the optimal location and capacity of capacitors are calculated simultaneously in the form of a bi-level optimization This strategy makes the proposed scheme more robust in finding final optimal solutions.
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