International Journal of Electrical Power & Energy Systems 

Abstract: This paper presents an efficient and reliable evolutionary-based approach to solve the optimal power flow (OPF) problem. The proposed approach employs particle swarm optimization (PSO) algorithm for optimal settings of OPF problem control variables. Incorporation of PSO as a derivative-free optimization technique in solving OPF problem significantly relieves the assumptions imposed on the optimized objective functions. The proposed approach has been examined and tested on the standard IEEE 30-bus test system with different objectives that reflect fuel cost minimization, voltage profile improvement, and voltage stability enhancement. The proposed approach results have been compared to those that reported in the literature recently. The results are promising and show the effectiveness and robustness of the proposed approach.

Abstract: This paper proposes an analytical expression to calculate the optimal size and an effective methodology to identify the corresponding optimum location for DG placement for minimizing the total power losses in primary distribution systems. The analytical expression and the methodology are based on the exact loss formula. The effect of size and location of DG with respect to loss in the network is also examined in detail. The proposed methodology was tested and validated in three distribution test systems with varying size and complexity. Results obtained from the proposed methodology are compared with that of the exhaustive load flows and loss sensitivity method. Results show that the loss sensitivity factor based approach may not lead to the best placement for loss reduction.

Abstract: Distributed generation (DG) sources are becoming more prominent in distribution systems due to the incremental demands for electrical energy. Locations and capacities of DG sources have profoundly impacted on the system losses in a distribution network. In this paper, a novel combined genetic algorithm (GA)/particle swarm optimization (PSO) is presented for optimal location and sizing of DG on distribution systems. The objective is to minimize network power losses, better voltage regulation and improve the voltage stability within the frame-work of system operation and security constraints in radial distribution systems. A detailed performance analysis is carried out on 33 and 69 bus systems to demonstrate the effectiveness of the proposed methodology.

Abstract: This paper presents a methodology for optimal distributed generation (DG) allocation and sizing in distribution systems, in order to minimize the electrical network losses and to guarantee acceptable reliability level and voltage profile. The optimization process is solved by the combination of genetic algorithms (GA) techniques with methods to evaluate DG impacts in system reliability, losses and voltage profile. The fitness evaluation function that drives the GA to the solution is the relation between the benefit obtained by the installation of DG units and the investment and operational costs incurred in their installation. The losses and voltage profile evaluation is based on a power flow method for radial networks with the representation of dispersed generators. The reliability indices are evaluated based on analytical methods modified to handle multiple generations. The results obtained with the proposed methodology for hypothetical systems found in the literature and actual distribution systems demonstrate its applicability.

Abstract: The paper presents a simple and efficient method for solving radial distribution networks. The proposed method involves only the evaluation of a simple algebraic expression of voltage magnitudes and no trigonometric functions as opposed to the standard load flow case. Thus, computationally the proposed method is very efficient and it requires less computer memory. The proposed method can easily handle different types of load characteristics. Several Indian rural distribution networks have been successfully solved by using the proposed method.