DISTRIBUTION NETWORK OPTIMIZATION BY OPTIMAL SIZING AND PLACEMENT OF D-STATCOM USING TEACHING AND LEARNING BASED OPTIMIZATION ALGORITHM (CASE STUDY: YIRGALEM SUBSTATION)

Abstract

Distribution system is part of an electric power system which links the high voltage transmission networks with the end consumers. This work offers the way of improving the performance of the distribution network by improving voltage profile and reduction of power loss via injecting reactive power through the network. Optimal siting and sizing of custom power devices in power distribution networks maximizes voltage profile, compensates reactive power, minimizes power loss and enhances voltage profile. The search for optimal size and locations of these devices in radial distribution networks is challenging and requiring robust scheduling. This study is conducted with a focus on Aposto feeder of Yirgalem distribution network. The voltage profiles of most buses are not in an acceptable range, and the voltage stability index of the buses shows that network is prone to voltage stability problem. In this study, it is aimed to find the best optimal D-STATCOM sizing and placement by using Teaching and Learning Based Optimization (TLBO). Results obtained have been compared with those of the conventional optimization techniques reported in literature. For the Aposto feeder 62-bus network, the optimal location and size of D-STATCOM were determined at bus 38 with 1019.18 𝑘𝑉𝑎𝑟, at bus 28 with 942.96 𝑘𝑉𝑎𝑟, at bus 39 with 1074 𝑘𝑉𝑎𝑟 and at bus 25 with 1184 𝑘𝑉𝑎𝑟 by the GA, PSO, GREY WOLF and WHALE OPTIMIZATION method respectively. While the TLBO approach obtained the optimal site and size of the D-STATCOM in the network to be bus 51 and 871.4 𝐾𝑉𝑎𝑟 at normal load condition. As stated, the TLBO method performs better in terms of reducing both real and reactive power losses. The real power loss percentage reduction of the test system is 27.09%, 69.19% and 70.24% whereas the reactive power loss percentage reduction is 30.97%, 68.85% and 69.85% for light load, normal load, and heavy load respectively. Also, the minimum voltage level in the worst case is significantly enhanced from 0.93pu to 0.988pu. The model has been formulated to minimize the total cost of the network by determining the optima of the substation locations and power, the load transfers between the demand centers, the feeder routes and the load flow in the network subject to a set of constraints. As per the economic evaluations, the proposed solution is cost-effective. In this research D-STATCOM control is developed based on artificial intelligent (AI) using artificial neural network (ANN), which depends on optimum values obtained by TLBO. Generally, the simulation results show that the proposed technique is effective to maintain all buses voltage magnitude within the IEEE acceptable limit and to reduce power losses significantly