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This paper deals with the stochastic placement and sizing of the distribution static compensator in the distribution systems considering uncertainty. The proposed stochastic framework utilises the point estimate method to consider the forecasting uncertainty of the active and reactive loads in the load flow equations. The objective functions to be investigated are total active power losses and the voltage profile simultaneously. In order to reach a proper compromise to satisfy all the objective functions, an interactive fuzzy satisfying approach is employed. Also, since the problem investigated is a type of discrete, nonlinear, multi-objective optimisation problem, a new modified optimisation technique is proposed to escape from the local optima as well as the premature convergence. Finally, the satisfying performance of the proposed method is examined on the 86-bus IEEE distribution system. [ABSTRACT FROM PUBLISHER]

In the new competitive electricity markets, the necessity of appropriate load forecasting tools for accurate scheduling is completely evident. The model which is utilised for the forecasting purposes determines how much the forecasted results would be dependable. In this regard, this paper proposes a new hybrid forecasting method based on the wavelet transform, autoregressive integrated moving average (ARIMA) and artificial neural network (ANN) for short-term load forecasting. In the proposed model, the autocorrelation function and the partial autocorrelation function are utilised to see the stationary or non-stationary behaviour of the load time series. Then, by the use of Akaike information criterion, the appropriate order of the ARIMA model is found. Now, the ARIMA model would capture the linear component of the load time series and the residuals would contain only the nonlinear components. The nonlinear part would be decomposed by the discrete wavelet transform into its sub-frequencies. Several ANNs are applied to the details and approximation components of the residuals signal to predict the future load sample. Finally, the outputs of the ARIMA and ANNs are summed. The empirical results show that the proposed hybrid method can improve the load forecasting accuracy suitably. [ABSTRACT FROM PUBLISHER]