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The economy, national safety, and health care are tremendously dependent on the faithful supply of power. The communication technology integration and sensors in power systems have been authorized as a smart grid (SG) that is revolutionizing the model of power generation, distribution, monitoring, and control. To know the Smart Grid compatibility, many problems are required to be directed. The safety of the smart grid is the most challenging function and very crucial difficulties. This paper proposed, a safe demand-side management machine deploying machine learning for the Internet of Things authorized phase is recommended. The propounded demand-side management (DSM) machine protects the effective energy use based on their preferences. A particular flexibility sample was proposed to manage incursion into the smart grid. Anelastic agent prognosticates swindling companies, the ML classifiers are utilized. Promoted power management and intermediate control companies are recommended for processing power data to improve energy usage. The proposed project's effective simulation is implemented to examine the efficiency. The outcome of the analysis discloses that the planned demand-side management (DSM) machine is less susceptible to the incursion and it is sufficient to decrease the smart grid's energy consumption.

Technical efficiency of electric utility is the critical element for its competitiveness in the electricity market and very relevant in the Indian electricity sector presently. This paper is aimed to measure the efficiencies of 30 state owned electric generation utilities/companies for the year 2007-08 by applying DEA models with single input and two outputs. The input used is total cost and outputs are units of energy generated and total energy sold or consumed. Cost benchmarking has been carried out so that cost controls can be implemented. In addition, the target evaluation for input cost has also been done. The result of this model shows that GENCOs are generally inefficient in cost frontier and there is an urgent need for intro inspection. This will help for GENCOs. The result shows that the total average of overall, technical and scale efficiencies are 46%, 75.1% and 60% respectively. This efficiency measurement assists the utilities by identifying their shortcomings, setting targets and trying to reach the set targets.

The objective of this study is to measure the efficiency improvement level and productivity growth of the coal-based thermal power stations in India for regulatory purposes using Stochastic Frontier Analysis, and employing data set covering period of 2002-03 to 20010-11 via power plant specific variables to capture the effect of different characteristics of power plants on inefficiency levels. The results show significant potentials for efficiency improvement in the Indian thermal power stations. Apart from comparing the efficiency and productivity growth among different plants, the study examined the causes of technical inefficiencies, and sources of productivity change, such as technological change and pure technical efficiency change. The application of the parametric production frontier models to Indian power generation industries can be used as a tool for policy makers. Improving the efficiency of coal-based power plants plays a significant role in enhancing the performance of India‘s power sector. It allows for increased consumer benefits through cost reduction, while enhancing energy security and helping reduce local and global pollution through more efficient coal use.

This paper presents a framework for accessing efficiency analysis of State Owned Electric Utilities (SOEUs), which have been mainly responsible for the generation, distribution and transmission of electricity in India. Efficiency Performance of Thirty four state owned generation companies was evaluated using the Data Envelopment Analysis (DEA) for the time periods 2006–07. This efficiency performance of regulated electric utilities of India is very important for future requirement. The result shows that SOEU generation companies are inefficient in operation and need improvement.