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Selection of appropriate climatic variables for prediction of electricity demand is critical as it affects the accuracy of the prediction. Different climatic variables may have different impacts on the electricity demand due to the varying geographical conditions. This paper uses multicollinearity and backward elimination processes to select the most appropriate variables and develop a multiple regression model for monthly forecasting of electricity demand. The former process is employed to reduce the collinearity between the explanatory variables by excluding the predictor which has highly linear relationship with the other independent variables in the dataset. In the next step, involving backward elimination regression analysis, the variables with coefficients that have a low level of significance are removed. A case study has been reported in this paper by acquiring the data from the state of New South Wales, Australia. The data analyses have revealed that the climatic variables such as temperature, humidity, and rainy days predominantly affect the electricity demand of the state of New South Wales. A regression model for monthly forecasting of the electricity demand is developed using the climatic variables that are dominant. The model has been trained and validated using the time series data. The monthly forecasted demands obtained using the proposed model are found to be closely matched with the actual electricity demands highlighting the fact that the prediction errors are well within the acceptable limits. Abstract: Selection of appropriate climatic variables for prediction of electricity demand is critical as it affects the accuracy 10 of the prediction. Different climatic variables may have different impacts on the electricity demand due to the varying 11 geographical conditions. This paper uses multicollinearity and backward elimination processes to select the most appropriate 12 variables and develop a multiple regression model for monthly forecasting of electricity demand. The former process is 13 employed to reduce the collinearity between the explanatory variables by excluding the predictor which has highly linear 14 relationship with the other independent variables in the dataset. In the next step, involving backward elimination regression 15 analysis, the variables with coefficients that have a low level of significance are removed. A case study has been reported in this 16paper by acquiring the data from the state of New South Wales, Australia. The data analyses have revealed that the climatic 17 variables such as temperature, humidity, and rainy days predominantly affect the electricity demand of the state of New South 18Wales. A regression model for monthly forecasting of the electricity demand is developed using the climatic variables that are 19 dominant. The model has been trained and validated using the time series data. The monthly forecasted demands obtained using 20 the proposed model are found to be closely matched with the actual electricity demands highlighting the fact tha...

Voltage regulation in distribution systems is typically performed with the aid of multiple voltage regulating devices, such as on-load tap changer and step voltage regulators. These devices are conventionally tuned and locally coordinated using Volt/VAR optimization strategies in accordance with the time-graded operation. However, in case of distribution systems with distributed generation (DG), there could be a possibility of simultaneous responses of DG and multiple voltage regulators for correcting the target bus voltage, thereby resulting in operational conflicts. This paper proposes an online voltage control strategy for a realistic distribution system containing a synchronous machine-based renewable DG unit and other voltage regulating devices. The proposed strategy minimizes the operational conflicts by prioritizing the operations of different regulating devices while maximizing the voltage regulation support by the DG. It is tested on an interconnected medium voltage distribution system, present in New South Wales, Australia, through time-domain simulation studies. The results have demonstrated that voltage control for a distribution feeder can effectively be achieved on a real-time basis through the application of the proposed control strategy.

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The rapid growth of grid-connected embedded generation is changing the operational characteristics of power distribution networks. Amongst a range of issues being reported in the research, the effect of these changes on so-called 'traditional protection systems' has not gone without attention. Looking to the future, the possibility of microgrid systems and deliberate islanding of sections of the network will require highly flexible distribution management systems and a re-design of protection strategies. This paper explores the envisaged protection issues concerned with large penetrations of embedded generation in distribution networks extending into auto-reclosure and protection device coordination. A critical review of recently reported protection strategies for grid-connected only and microgrid operation is also undertaken. The outcome is a list of recommendations to achieve microgrid protection adequacy in future networks. AbstractThe rapid growth of grid-connected embedded generation is changing the operational characteristics of power distribution networks. Amongst a range of issues being reported in the research, the effect of these changes on so-called 'traditional protection systems' has not gone without attention. Looking to the future, the possibility of microgrid systems and deliberate islanding of sections of the network will require highly flexible distribution management systems and a re-design of protection strategies.This paper explores the envisaged protection issues concerned with large penetrations of embedded generation in distribution networks extending into auto-reclosure and protection device coordination. A critical review of recently reported protection strategies for grid-connected only and microgrid operation is also undertaken. The outcome is a list of recommendations to * Corresponding author Tel. +61 2 42392397 NOTICE: this is the authors' version of a work that was accepted for publication. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. achieve microgrid protection adequacy in future networks.