TY - JOUR
T1 - Incentive Mechanisms for Smart Grid
T2 - State of the Art, Challenges, Open Issues, Future Directions
AU - Bhattacharya, Sweta
AU - Chengoden, Rajeswari
AU - Srivastava, Gautam
AU - Alazab, Mamoun
AU - Javed, Abdul Rehman
AU - Victor, Nancy
AU - Maddikunta, Praveen Kumar Reddy
AU - Gadekallu, Thippa Reddy
N1 - Publisher Copyright:
© 2022 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2022/6
Y1 - 2022/6
N2 - Smart grids (SG) are electricity grids that communicate with each other, provide reliable information, and enable administrators to operate energy supplies across the country, ensuring optimized reliability and efficiency. The smart grid contains sensors that measure and transmit data to adjust the flow of electricity automatically based on supply/demand, and thus, responding to problems becomes quicker and easier. This also plays a crucial role in controlling carbon emissions, by avoiding energy losses during peak load hours and ensuring optimal energy management. The scope of big data analytics in smart grids is huge, as they collect information from raw data and derive intelligent information from the same. However, these benefits of the smart grid are dependent on the active and voluntary participation of the consumers in real-time. Consumers need to be motivated and conscious to avail themselves of the achievable benefits. Incentivizing the appropriate actor is an absolute necessity to encourage prosumers to generate renewable energy sources (RES) and motivate industries to establish plants that support sustainable and green-energy-based processes or products. The current study emphasizes similar aspects and presents a comprehensive survey of the start-of-the-art contributions pertinent to incentive mechanisms in smart grids, which can be used in smart grids to optimize the power distribution during peak times and also reduce carbon emissions. The various technologies, such as game theory, blockchain, and artificial intelligence, used in implementing incentive mechanisms in smart grids are discussed, followed by different incentive projects being implemented across the globe. The lessons learnt, challenges faced in such implementations, and open issues such as data quality, privacy, security, and pricing related to incentive mechanisms in SG are identified to guide the future scope of research in this sector.
AB - Smart grids (SG) are electricity grids that communicate with each other, provide reliable information, and enable administrators to operate energy supplies across the country, ensuring optimized reliability and efficiency. The smart grid contains sensors that measure and transmit data to adjust the flow of electricity automatically based on supply/demand, and thus, responding to problems becomes quicker and easier. This also plays a crucial role in controlling carbon emissions, by avoiding energy losses during peak load hours and ensuring optimal energy management. The scope of big data analytics in smart grids is huge, as they collect information from raw data and derive intelligent information from the same. However, these benefits of the smart grid are dependent on the active and voluntary participation of the consumers in real-time. Consumers need to be motivated and conscious to avail themselves of the achievable benefits. Incentivizing the appropriate actor is an absolute necessity to encourage prosumers to generate renewable energy sources (RES) and motivate industries to establish plants that support sustainable and green-energy-based processes or products. The current study emphasizes similar aspects and presents a comprehensive survey of the start-of-the-art contributions pertinent to incentive mechanisms in smart grids, which can be used in smart grids to optimize the power distribution during peak times and also reduce carbon emissions. The various technologies, such as game theory, blockchain, and artificial intelligence, used in implementing incentive mechanisms in smart grids are discussed, followed by different incentive projects being implemented across the globe. The lessons learnt, challenges faced in such implementations, and open issues such as data quality, privacy, security, and pricing related to incentive mechanisms in SG are identified to guide the future scope of research in this sector.
KW - carbon emissions
KW - energy
KW - renewable energy
KW - Smart Grid
UR - http://www.scopus.com/inward/record.url?scp=85130025937&partnerID=8YFLogxK
U2 - 10.3390/bdcc6020047
DO - 10.3390/bdcc6020047
M3 - Article
AN - SCOPUS:85130025937
SN - 2504-2289
VL - 6
SP - 1
EP - 28
JO - Big Data and Cognitive Computing
JF - Big Data and Cognitive Computing
IS - 2
M1 - 47
ER -