Executive Summary : | There is a high demand for trustworthy energy management to offer the user - privacy, transparency in trading, incentivized trading, security of energy trading and supply information, metering and reporting the power quality measures, for avoiding energy crisis though energy and demand prediction. To fulfill these needs, investigators propose to develop a smart controller which will help in optimally utilizing energy resources in micro grid, by taking generation information, Batteries state of charge, load data and grid connectivity status as inputs and applies energy resource optimization to make a decision on power export/import to/from the grid. Also, power quality measurements like voltage swell/sag, imbalance, power factor, harmonic distortions, etc., will be analysed. The power supply details from each of the energy generations and power quality measurements from important nodes are collected and sent to the distributed ledger technology (DLT) for trustworthy management. As per investigators' observation, ethereum is well suited for the given application. However, it has the problem of scalability and delayed transaction, and thus, the functionalities of ethereum will be enhanced through feasible consensus mechanism and block/transaction format. For trustworthy and incentivized energy trading and distribution, we run different smart contracts over the enhanced DLT by considering the power auctioning policies and incentivized game theory. As a promising technology, DLT can help in achieving users' privacy, transparency in energy trading, security and high availability of trading and supplied energy related information to address the future queries, energy metering and reporting for the users with power quality notification. These data will enable us to perform a detailed analysis on patterns of energy generation, demand and other aspects of these resources. Investigators will be using power transmission data from various regions of India and applying data analytics methods to understand and identify day wise energy demand and generation behavior. This will provide insights about the dynamic behavior of energy demand in different regions for predictive preparedness to avoid energy crisis. Suitable analytics method will be applied over the energy demand and generation data on DLT for run time load and demand prediction. Energy distribution information is applied to machine learning techniques for visual representation of information as per the different user queries about the supplied power quality, energy usage, etc. Such metering and reporting service over the DLT integrated with cloud will help in improving the trust among users. |
