Digital platform for management of the regional power grid consumption

Abstract

One of the trends in the current energy transition is decentralization of power management and diversification of power generating facilities in terms of both the level and the amount of installed capacity. This puts forward new requirements for interaction of the consumer who owns the pool of electric power equipment with the power supply company. One of the foundations of this interaction is power management and monitoring. An increased number and type of receivers and sources of electricity, and significantly increased data flows on energy consumption necessitates an appropriate digital platform to solve problems of energy consumption management and monitoring. The digital platform determines an appropriate environment for interaction between the consumer and the power supply company, and most significantly, the conditions for improved energy efficiency of the entire process of generation, transmission and consumption of electricity.

Digitalization Processes in Distribution Grids: A Comprehensive Review of Strategies and Challenges

“…The transition to decentralized energy systems accentuates the role of digital platforms in improving energy management and consumer engagement. Studies [36,205] discuss integrating smart technologies and the FEEdBACk project, underscoring the importance of advanced technologies and social science methods in promoting energy-efficient behaviors.…”

Smart Grid Data Management in a Heterogeneous Environment with a Hybrid Load Forecasting Model

“…Moreover, smart homes also allow users to track their energy usage at any time to monitor and control their energy bills. Therefore, smart homes make power consumption very efficient while reducing energy costs [24][25][26][27][28][29][30][31][32]. These existing and potential applications are highly effective in solving some of the energy problems, but there is still a need for more research to handle the energy problems that are more prevalent in developing countries.…”

“…The forecasting techniques can be classified into three main areas, namely long-term load forecasting (LTLF) for yearly observations, medium-term load forecasting (MTLF) for monthly observations, and short-term load forecasting (STLF) for daily or weekly observations. [30][31][32][33][34]. The suitable algorithms, techniques, and observed periods for load forecasting will fully depend on the forecast horizon type and the features of the data.…”