Coordinated power demand management at residential or domestic levels allows energy participants to efficiently manage load profiles, increase efficiency and reduce operational cost. In this paper, a hierarchical coordination framework optimally using photovoltaic (PV) units, battery-energy-storage-systems (BESs) electric vehicles (EVs) is presented. The bidirectional flow of EV with vehicle gr...

Battery Energy Storage Systems (BESS) can nowadays be used for many applications on the electric distribution grids. They can lead to significant technical and economic benefits such as continuity of service, improvement of power quality levels, smart load management and power flow control between different interconnected electric networks. Their applications can be implemented thanks to effici...

Electric load forecasting is a key element for management and operation of the electric grid. In this study we introduce ensemble re-forecast methods that take an initial forecast and produce a better prediction by extracting information from the structured errors. The models in the ensemble rely upon the real-time information obtained from load measurements and estimates over a state-wide doma...

Electric load forecasting is a key to the efficient management of power supply system. Load forecasting, which involves estimation of future load according to the previous load data. This paper presents a pragmatic methodology for short term load forecasting (STLF) using proposed hybrid method of wavelet transform (WT) and artificial neural network (ANN). It is a two stage prediction system whi...

In distribution networks, failure to smooth the load curve leads to voltage drop and power quality loss. In this regard, electric vehicle batteries can be used to smooth the load curve. However, to persuade vehicle owners to share their vehicle batteries, we must also consider the owners' profits. A challenging problem is that existing methods do not take into account the vehicle owner demands ...

Stochastic noises have a great adverse effect on the prediction accuracy of electric power load. Modeling online and filtering real-time can effectively improve measurement accuracy. Firstly, pretreating and inspecting statistically the electric power load data is essential to characterize the stochastic noise of electric power load. Then, set order for the time series model by Akaike informati...

review and classification of electric load forecasting (lf) techniques based on artificial neuralnetworks (ann) is presented. a basic anns architectures used in lf reviewed. a wide range of annoriented applications for forecasting are given in the literature. these are classified into five groups:(1) anns in short-term lf, (2) anns in mid-term lf, (3) anns in long-term lf, (4) hybrid anns inlf,...