This paper proposes a new optimization technique that uses Particle Swarm Optimization (PSO) in residential grid-connected photovoltaic systems. The optimization technique targets the sizing of the battery storage system. With the liberation of power systems, the residential grid-connected photovoltaic system can supply power to the grid during peak hours or charge the battery during non-peak hours for later domestic use or for selling back to the grid during peak hours. However, this can only be achieved when the battery energy system in the residential photovoltaic system is optimized. The developed PSO algorithm aims at optimizing the battery capacity that will lower the operation cost of the system. The computational efficiency of the developed algorithm is demonstrated using real PV data from Strathmore University. A comparative study of a PV system with and without battery energy storage is carried out and the simulation results demonstrate that PV system with battery is more efficient when optimized with PSO.

This paper uses Newton–Raphson method for DC power flow analysis of the Addis Ababa light Rail Transit (AALRT). The study focuses onthe line section from Menilik II square station up to Lideta station. First the tractive effort required by the trains for different scenarios such as train movement in a straight line, a curved line, and a line with gradient is computed as the chosen line section contains all these scenarios. Then the total input power will be calculated using computed tractive effort obtained for each scenario and using other input parameters obtained from AALRT, and different papers. The input power for the different loads is computed, and the input power is used to analyse the bus voltage for different loads and train positions. Newton Raphson Method is used to solve the DC Power bus problem assuming that the train requires constant power while moving between two feeding stations. Even if using the rail as the return conductor for DC traction systems has economic advantages, it has some limitations like the rail potential and stray current. A rail potential study is carried out and conclusions are drawn. The result shows that the maximum voltage drop was 0.1 p.u and the train power consumption increases by 136.73 kW as the train takes a gradient of 3.92% and keep increasing again by 29.17kw with a curve resistance (100 meters). The Rail potential moves from 6.0139V to 29.85V proportionally with the variation of the total ground resistance.