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This paper presents some analytical studies on the performance of grid connected solar photovoltaic system with different parameters in Rajasthan. The various factors that are considered for performance evaluation are: solar irradiation, ambience, tilt angle, orientation and shading. Rajasthan is rich in solar energy with varying ambience. Thus, due to change in climatic condition the PV system performance varies from place to place to a considerable extent. This influences the energy yield as well as economics of such system. In this paper a performance evaluation have been carried out for a 5 MW p crystalline silicon solar photovoltaic power plant in Rajasthan. As a part of the plant design, considerations have been taken in account on aesthetic, safety and cost aspects. The optimised annual energy yield of the 5 MW p is in the order of 8 GWh at a tilt angle of 22 o , average ambient temperature of 26 o Cand relative humidity (RH) of 60%. The minimum yield observed was about 7 GWhat 28 o tilt angle, average ambient temperature of 20 o C and RH of 75%. These studies justified on the implementation of photovoltaic system in urban areas of developing countries where peak load demand is very high.

This article presents a multi-objective economic environmental/emission dispatch (EED) of variable head hydro-wind-thermal power system. The combination of NOx emission, SO2 emission, and fuel cost are minimized for non-smooth hydrothermal plants while satisfying various operational constraints like non-smooth fuel cost, penalty coefficient, and wind power uncertainty. The objectives—cost, NOx emission, and SO2 emission—are optimized at the same time. In this research, the non-dominated sorting genetic algorithm-II (NSGA-II) has been employed for solving the given problem where the total cost, NOx emission level, and SO2 emission level are optimized at the same time while satisfying all the operational constraints. The simulation results that are obtained by applying the two test systems on the proposed scheme have been evaluated against strength pareto evolutionary algorithm 2 (SPEA 2).

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