A New Approach for Optimal Sizing of DG based on statistical STLF

The paper proposes a new approach for the optimal sizing of the distributed generator based on a statistical short-term load forecasting technique. The curve fitting method is used to perform the short-term load forecasting. Shorttermloadforecastingin thispaperis donebyconsideringthesensibilityofthenetwork loadtothe temperature, humidity, daytypeparameters(THD)andprevious load andalso ensuringthatforecasting theload with these parameters canbestbedonebythecurve fittingmethod.Theanalysis oftheloaddatarecognizethatthe loadpattern isnotonlydependent ontemperaturebutalso is dependenton humidityandday type. Auniquenormwitha, b,c anddconstants basedonthehistorydatahasbeenproposed for the STLFusingtheconceptofcurvefittingtechnique. The optimal sizing of the DG is obtained based on the results of the STLF done. The technique is implemented on a real time dataset. The algorithms implementing this forecasting and optimal DG Sizing techniques have been programmed using MATLAB. The simulation results show the robustness and suitability of the proposed norm for the STLF as the forecasting accuracies are less than 3% for almost all the day types and all the seasons. The reduction in the overall system losses proves the suitability of the developed technique for the optimal sizing of the distributed generator.