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Transition to a sustainable energy environment results in aggregated generator and load dynamics in the distribution network. State estimation is a key function in building adequate network models for online monitoring and analyzes. The requirements of distribution system state estimation (DSSE) is becoming stringent because of the needs of new system modeling and operation practices associated with integration of distributed energy resources and the adoption of advanced technologies in distribution network. This paper summarizes the state-of-the-art technology, major hurdles, and challenges in DSSE development. The opportunities, paradigm shift, and future research directions that could facilitate the need of DSSE are discussed. [ABSTRACT FROM PUBLISHER]

A stochastic modeling and simulation technique for analyzing impacts of electric vehicles charging demands on distribution network is proposed in this paper. Different from the previous deterministic approaches, the feeder daily load models, electric vehicle start charging time, and battery state of charge used in the impact study are derived from actual measurements and survey data. Distribution operation security risk information, such as over-current and under-voltage, is obtained from three-phase distribution load flow studies that use stochastic parameters drawn from Roulette wheel selection. Voltage and congestion impact indicators are defined and a comparison of the deterministic and stochastic analytical approaches in providing information required in distribution network reinforcement planning is presented. Numerical results illustrate the capability of the proposed stochastic models in reflecting system losses and security impacts due to electric vehicle integrations. The effectiveness of a controlled charging algorithm aimed at relieving the system operation problem is also presented. [ABSTRACT FROM PUBLISHER]

Distribution network designers use a transformer load-management system to estimate and examine the historical and current loads occurring on transformers and test proposed load situations. Due to a lack of measurements at the feeders and distribution transformers, the confidence on the transformer load-management reports was low in the past. Intelligent equipment and advanced information and communication technology are now being integrated into distribution networks at an unprecedented speed. The technology advancement provides an opportunity to enhance transformer load modeling and management. In this paper, a framework using synchronized measurements obtained from different information systems via a utility's enterprise service bus is proposed to calculate the actual load profiles of distribution transformers and reflect changing system conditions. Network analyses resulting from using improved system models based on distribution state estimation allow the system operators and designers to make informed decisions concerning where new loads can be added and when new transformer capacity must be included. [ABSTRACT FROM PUBLISHER]