Real Time Demand Response Modeling for Residential Consumers in Smart Grid Considering Renewable Energy With Deep Learning Approach

Demand response modelling have paved an important role in smart grid at a greater perspective. DR analysis exhibits the analysis of scheduling of appliances for an optimal strategy at the user's side with an effective pricing scheme. In this proposed work, the entire model is done in three different steps. The first step develops strategy patterns for the users considering integration of renewable energy and effective demand response analysis is done. The second step in the process exhibits the learning process of the consumers using Robust Adversarial Reinforcement Learning for privacy process among the users. The third step develops optimal strategy plan for the users for maintaining privacy among the users. Considering the uncertainties of the user's behavioral patterns, typical pricing schemes are involved with integration of renewable energy at the user' side so that an optimal strategy is obtained. The optimal strategy for scheduling the appliances solving privacy issues and considering renewable energy at user' side is done using Robust Adversarial Reinforcement learning and Gradient Based Nikaido-Isoda Function which gives an optimal accuracy. The results of the proposed work exhibit optimal strategy plan for the users developing proper learning paradigm. The effectiveness of the proposed work with mathematical modelling are validated using real time data and shows the demand response strategy plan with proper learning access model. The results obtained among the set of strategy develops 80 % of the patterns created with the learning paradigm moves with optimal DR scheduling patterns. This work embarks the best learning DR pattern created for the future set of consumers following the strategy so privacy among the users can be maintained effectively

A demand response modeling for residential consumers in smart grid environment using game theory based energy scheduling algorithm

In this paper, demand response modeling scheme is proposed for residential consumers using game theory algorithm as Generalized Tit for Tat (GTFT) Dominant Game based Energy Scheduler. The methodology is established as a work flow domain model between the utility and the user considering the smart grid framework. It exhibits an algorithm which schedules load usage by creating several possible tariffs for consumers such that demand is never raised. This can be done both individually and among multiple users of a community. The uniqueness behind the demand response proposed is that, the tariff is calculated for all hours and the load during the peak hours which can be rescheduled is shifted based on the Peak Average Ratio. To enable the vitality of the work simulation results of a general case of three domestic consumers are modeled extended to a comparative performance and evaluation with other algorithms and inference is analyzed

Demand side management scheme in smart grid with cloud computing approach using stochastic dynamic programming

This paper proposes a cloud computing framework in smart grid environment by creating small integrated energy hub supporting real time computing for handling huge storage of data. A stochastic programming approach model is developed with cloud computing scheme for effective demand side management (DSM) in smart grid. Simulation results are obtained using GUI interface and Gurobi optimizer in Matlab in order to reduce the electricity demand by creating energy networks in a smart hub approach

Privacy-Based Demand Response Modeling for Residential Consumers Using Machine Learning with a Cloud–Fog-Based Smart Grid Environment

Demand response modeling in smart grids plays a significant role in analyzing and shaping the load profiles of consumers. This approach is used in order to increase the efficiency of the system and improve the performance of energy management. The use of demand response analysis in determining the load profile enhances the scheduling approach to the user profiles in the residential sector. In accordance with the behavioral pattern of the user’s profile, incentive-based demand response programs can be initiated in the residential sector. In modeling the behavioral pattern of the user’s profile, the machine learning approach is used to analyze the profile patterns. The incentive-based demand response is demonstrated in order to show the importance of maintaining the privacy of residential users, during interactions between demand- and load-profile patterns. In this work, real-time demand response modeling for residential consumers, with incentive schemes, are analyzed. The incentive schemes are proposed in order to show how the privacy of the residential units may be considered, as a result the model is developed with a two-step analysis approach. In the first step, the demand response modeling is performed with the scheduling of appliances on the residential side, by forming hubs in a cloud–fog-based smart grid environment. This process, with an incentive demand response scheme and scheduling of appliances, is performed using an optimal demand response strategy that uses a discounted stochastic game. In the second step, the privacy concerns of the demand response model from the strategy analysis are addressed using a generative adversarial network (GAN) Q-learning model and a cloud computing environment. In this work, the DR strategy model with privacy concerns for residential consumers, along with EV management, is performed in a two-step process and arrives at an optimal strategy. The efficiency and real time analysis proposed in this model are validated with real-time data analysis in simulation studies and with mathematical analysis of the proposed model

A brief insight into the prediction of water vapor transmissibility in highly impermeable hybrid nanocomposites based on bromobutyl/epichlorohydrin rubber blends

The present work proposes a schematic model for predicting the water vapor transmissibility in hybrid nanocomposites based on bromobutyl (BIIR)/epichlorohydrin (CO) rubber blends. Morphology study reveals the exfoliation of nanoclay and development of hybrid nanostructures in the rubber nanocomposites. A unique correlation between water vapor transmissibility and gas (oxygen) permeability through the rubber nanocomposites has been systematically derived. The prediction of relative water vapor transmissibility was achieved by considering the polar path along with the existing tortuous path and has been validated. Interestingly, it is found that the water vapor transmissibility (TW) directly depends on the weight fraction of the polar rubber (ΦP) in the rubber blend and permeability to gas (PG) of the nanocomposites

Future generation 5G wireless networks for smart grid: A comprehensive review

Wireless cellular networks are emerging to take a strong stand in attempts to achieve pervasive large scale obtainment, communication, and processing with the evolution of the fifth generation (5G) network. Both the present day cellular technologies and the evolving new age 5G are considered to be advantageous for the smart grid. The 5G networks exhibit relevant services for critical and timely applications for greater aspects in the smart grid. In the present day electricity markets, 5G provides new business models to the energy providers and improves the way the utility communicates with the grid systems. In this work, a complete analysis and a review of the 5G network and its vision regarding the smart grid is exhibited. The work discusses the present day wireless technologies, and the architectural changes for the past years are shown. Furthermore, to understand the user-based analyses in a smart grid, a detailed analysis of 5G architecture with the grid perspectives is exhibited. The current status of 5G networks in a smart grid with a different analysis for energy efficiency is vividly explained in this work. Furthermore, focus is emphasized on future reliable smart grid communication with future roadmaps and challenges to be faced. The complete work gives an in-depth understanding of 5G networks as they pertain to future smart grids as a comprehensive analysis

An IoT-Based Wristband for Automatic People Tracking, Contact Tracing and Geofencing for COVID-19

The coronavirus disease (COVID-19) pandemic has triggered a huge transformation in the use of existing technologies. Many innovations have been made in the field of contact tracing and tracking. However, studies have shown that there is no holistic system that integrates the overall process from data collection to the proper analysis of the data and actions corresponding to the results. It is critical to identify any contact with infected people and to ensure that they do not interact with others. In this research, we propose an IoT-based system that provides automatic tracking and contact tracing of people using radio frequency identification (RFID) and a global positioning system (GPS)-enabled wristband. Additionally, the proposed system defines virtual boundaries for individuals using geofencing technology to effectively monitor and keep track of infected people. Furthermore, the developed system offers robust and modular data collection, authentication through a fingerprint scanner, and real-time database management, and it communicates the health status of the individuals to appropriate authorities. The validation results prove that the proposed system identifies infected people and curbs the spread of the virus inside organizations and workplaces

Big data analytics and artificial intelligence aspects for privacy and security concerns for demand response modelling in smart grid: A futuristic approach

Next generation electrical grid considered as Smart Grid has completely embarked a journey in the present electricity era. This creates a dominant need of machine learning approaches for security aspects at the larger scale for the electrical grid. The need of connectivity and complete communication in the system uses a large amount of data where the involvement of machine learning models with proper frameworks are required. This massive amount of data can be handled by various process of machine learning models by selecting appropriate set of consumers to respond in accordance with demand response modelling, learning the different attributes of the consumers, dynamic pricing schemes, various load forecasting and also data acquisition process with more cost effectiveness. In connected to this process, considering complex smart grid security and privacy based methods becomes a major aspect and there can be potential cyber threats for the consumers and also utility data. The security concerns related to machine learning model exhibits a key factor based on different machine learning algorithms used and needed for the energy application at a future perspective. This work exhibits as a detailed analysis with machine learning models which are considered as cyber physical system model with smart grid. This work also gives a clear understanding towards the potential advantages, limitations of the algorithms in a security aspect and outlines future direction in this very important area and fastgrowing approach