Stochastic Gradient Descent Optimization Model for Demand Response in a Connected Microgrid

Abstract

Smart power grid is a user friendly system that transforms the traditional electric grid to the one that operates in a co-operative and reliable manner. Demand Response (DR) is one of the important components of the smart grid. The DR programs enable the end user participation by which they can communicate with the electricity service provider and shape their daily energy consumption patterns and reduce their consumption costs. The increasing demands of electricity owing to growing population stresses the need for optimal usage of electricity and also to look out alternative and cheap renewable sources of electricity. The solar and wind energy are the promising sources of alternative energy at present because of renewable nature and low cost implementation. The proposed work models a smart home with renewable energy units. The random nature of the renewable sources like wind and solar energy brings an uncertainty to the model developed. A stochastic dual descent optimization method is used to bring optimality to the developed model. The proposed work is validated using the simulation results. From the results it is concluded that proposed work brings a balanced usage of the grid power and the renewable energy units. The work also optimizes the daily consumption pattern thereby reducing the consumption cost for the end users of electricity.

Design strategies towards the enhancement of short-term forecasting in the energy sector

“…Firstly, simple linear models such as ordinary least squares linear regression [172], ridge [173], lasso [174], stochastic gradient descent (SGD) [175] and Huber [176] estimators search for the line of best fit that optimally describes the relationship between the dependent and independent variables. Linear models are commonly used in large-scale forecasting tasks due to their low computational cost and interpretability.…”