In this study, a methodology for determining the solar and wind DG (distributed generation) capacity is proposed by using sequential optimisation method considering a seasonal variation of load demand, seasonal solar, and wind variations. The load demand profile is collected from state load dispatch centre; whereas solar data are collected from Indian Institute of Technology Kharagpur and wind data are collected from a weather station. Along with the DGs, the shunt capacitors are also placed to improve the voltage profile and energy loss reduction with different scenarios. For this purpose, minimisation of a multi-objective function is considered. The proposed methodology is applied to a 69-bus distribution network. The results for different scenarios show the substantial reduction in the annual energy loss and improvement of voltage profile. The result also shows that maximum amount of profit is gained with both renewable DGs and shunt capacitors. The impact of load growth on the distribution network with and without renewable DGs and shunt capacitors are compared. The analysis reveals that with integrating the renewable DGs and shunt capacitors in the system, the distribution network can take load growth for few more years without violating the system constraints.
This study presents the uncertainty analysis of a distribution network (DNR) caused by unit power output control (UPC) distributed generations (DGs) (solar, wind) and load demand by point estimate method (PEM) based on mixed-discretebased particle swarm optimisation (MDPSO) technique. The uncertainties are taken care by the feeder flow control (FFC) DGs which make the DNR power independent of the main grid, which means the DNR does not exchange any power with the main grid at any load level. To analyse the situation, the load flow technique is modified with introducing PQVδ and zero bus in the system. 2m + 1 and the higher-order PEM methods are applied in this study for uncertainty analysis. The FFC and the UPC DGs are placed and sized by the MDPSO algorithm. The uncertainty analysis of the system is done based on different objective functions and test cases which are the combinations of active power loss, voltage deviation, and the DG operation cost. The proposed method is applied to the 69-bus DNR, and the results are compared with teaching learning-based meta-heuristic optimisation method. The cumulative distribution function and probability density function of the output random variable are approximate with Gram-Charlier expansion method.
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