This paper presents a case study on transmission pricing practices. Now a days, restructuring of the power system market through deregulation is gaining attention as technical and economical benefits at generator and user. In India, after the Electricity Act 2003, restructuring has been introduced in the Indian power system market. Researchers are continuously working towards improvement of deregulation based on restructuring to improve transmission pricing practices and calculations in a better way. Therefore, this paper presents an overview of MW- Mile and postage stamp methods to estimate the transmission cost. Further, the North Indian practical power system of 37 bus test system has been analyzed by reverse, absolute and dominant Mw-Mile methods. The results obtained to be expected for deregulated power market.
The development of renewable resources and the deregulation of the market have made forecasting energy demand more critical in recent years. Advanced intelligent models are created to ensure accurate power projections for several time horizons to address new difficulties. Intelligent forecasting algorithms are a fundamental component of smart grids and a powerful tool for reducing uncertainty in order to make more cost- and energy-efficient decisions about generation scheduling, system reliability and power optimization, and profitable smart grid operations. However, since many crucial tasks of power operators, such as load dispatch, rely on short-term forecasts, prediction accuracy in forecasting algorithms is highly desired. This essay suggests a model for estimating Denmark’s power use that can precisely forecast the month’s demand. In order to identify factors that may have an impact on the pattern of a number of unique qualities in the city direct consumption of electricity. The current paper also demonstrates how to use an ensemble deep learning technique and Random forest to dramatically increase prediction accuracy. In addition to their ensemble, we showed how well the individual Random forest performed.