A comprehensive review of demand response strategies and the role of emergent technologies for sustainable home energy management systems

被引:5
作者
Ajitha A. [1 ,2 ]
Radhika S. [1 ]
机构
[1] Department of Electrical and Electronics Engineering, BITS-Pilani, Hyderabad
[2] Department of Electrical and Electronics Engineering, Anurag Group of Institutions, Hyderabad
关键词
Artificial intelligence; demand response; demand side management; internet of things; rebound peaks; smart grid;
D O I
10.1080/01430750.2023.2233522
中图分类号
学科分类号
摘要
Growing electricity demand shoots the challenge of maintaining a supply-demand balance, especially during peak hours. Failure of this stimulates the system’s stability problems and may lead to a blackout. This raised the need for new power generation installations associated with huge costs. Instead, the Demand-side Management (DSM) techniques were adopted to balance the existing infrastructure for a sustainable energy supply. They motivate optimal, reliable and efficient ways of energy utilisation. This paper presents an overview of DSM and its subcategories, benefits and potential challenges in its implementation with a focus on the residential sector. This also highlights rebound peaks caused by Demand Response and the need for renewable power resources at consumer premises for better load management. Moreover, DR techniques can be best implemented with emerging networking and communication technologies, such as the Internet of Things, Artificial Intelligence and Blockchain. Hence the paper highlights their future scope and importance in residential DR under the smart grid paradigm. © 2023 Informa UK Limited, trading as Taylor & Francis Group.
引用
收藏
页码:2262 / 2282
页数:20
相关论文
共 148 条
[1]  
Aalami H., Yousefi G.R., Moghadam M.P., Demand Response Model Considering EDRP and TOU Programs, 2008 IEEE/PES Transmission and Distribution Conference and Exposition, (2008)
[2]  
Abubakar I., Khalid S.N., Mustafa M.W., Shareef H., Mustapha M., Application of Load Monitoring in Appliances’ Energy Management–A Review, Renewable and Sustainable Energy Reviews, 67, pp. 235-245, (2017)
[3]  
Affonso C.M., da Silva L.C.P., Potential Benefits of Implementing Load Management to Improve Power System Security, International Journal of Electrical Power & Energy Systems, 32, 6, pp. 704-710, (2010)
[4]  
Afzal M., Huang Q., Amin W., Umer K., Raza A., Naeem M., Blockchain Enabled Distributed Demand Side Management in Community Energy System with Smart Homes, IEEE Access, 8, pp. 37428-37439, (2020)
[5]  
Aghaei J., Alizadeh M.-I., Demand Response in Smart Electricity Grids Equipped with Renewable Energy Sources: A Review, Renewable and Sustainable Energy Reviews, 18, pp. 64-72, (2013)
[6]  
Ahmad T., Chen H., Utility Companies Strategy for Short-term Energy Demand Forecasting Using Machine Learning Based Models, Sustainable Cities and Society, 39, pp. 401-417, (2018)
[7]  
Ahmed F., Javaid N., Manzoor A., Judge M.A., Feroze F., Khan Z.A., Cost and Comfort Based Optimization of Residential Load in Smart Grid, International Conference on Emerging Internetworking, Data & Web Technologies, (2017)
[8]  
Ahmed M.S., Mohamed A., Homod R.Z., Shareef H., Hybrid LSA-ANN Based Home Energy Management Scheduling Controller for Residential Demand Response Strategy, Energies, 9, 9, (2016)
[9]  
Ahmed M.S., Mohamed A., Khatib T., Shareef H., Homod R.Z., Ali J.A., Real Time Optimal Schedule Controller for Home Energy Management System Using New Binary Backtracking Search Algorithm, Energy and Buildings, 138, pp. 215-227, (2017)
[10]  
Ajitha A., Goel M., Assudani M., Radhika S., Goel S., Design and Development of Residential Sector Load Prediction Model During COVID-19 Pandemic Using LSTM Based RNN, Electric Power Systems Research, 212, (2022)