Integration of alternative sources of energy into a network for distributed generation (DG) requires small-scale power generation technologies located close to the loads served. The move toward on-site distributed power generation has been accelerated because of deregulation and restructuring of the utility industry and the feasibility of alternative energy sources. DG technologies can improve power quality, boost system reliability, reduce energy costs, and defray utility capital investment. This paper presents techno economic analysis of optimally located and sized various DG technologies in a radial distribution system. The impact of DG on the system voltage profile and line losses is also evaluated. This has been accomplished by two parts, part one examine technical benefits of integration of a DG unit to different buses of distribution system and varying DG unit size in a 30 bus radial distribution system. Part two examine the implementation viability of the project; a detailed financial evaluation has been carried out for various DG technologies which are available in the market for commercial use. The results show that there is significant improvement in voltage profile, reduction in line loss and consequently the utility can gain financial benefits when DG is incorporated into the system.

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In this paper, a new algorithm is implemented to solve optimal placement of capacitors in radial distribution systems in two ways that is, optimal placement of fixed or Discrete size of capacitor banks (Variable Locations Discrete sizing Capacitor banks-VLDC) and optimal sizing and placement of capacitors (Variable Locations Continues sizing of Capacitors-VLCC) for real power loss minimization and net savings maximization. The new algorithms Bat Algorithm (BA): search for all possible locations in the system along with the different sizes of capacitors, in which the optimal sizes of capacitor are chosen to be standard sizes that are available in the market. To check the feasibility, the proposed algorithms are applied on standard 34 and 85 bus radial distribution systems. And the results are compared with results of other methods like particle swarm optimization (PSO), harmonic search (HS), genetic algorithm (GA) artificial bee colony (ABC) teaching learning based optimization (TLBO) and plant growth simulation algorithm (PGSA), as available in the literature. The proposed approaches are capable of producing high-quality solutions with good performance of convergence. The entire simulation has been developed in MATLAB R2010a software.

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