Optimization of social welfare in P2P community microgrid with efficient decentralized energy management and communication-efficient power trading
Authors:
- Jawad HussainJHJawad Hussain
- Qi HuangQHQi Huang
- Jian LiJLJian Li
- Zhenyuan ZhangZZZhenyuan Zhang
- Fazal HussainFHFazal Hussain
- Syed Adrees AhmedSASyed Adrees Ahmed
- Kashif ManzoorKMKashif Manzoor
- 16 January 2024
- journal article
- research article
- Published by Elsevier in Journal of Energy Storage
- Vol. 81
Abstract
No abstract availableKeywords
Funding Information
- National Natural Science Foundation of China (U23A20651)
This publication has 34 references indexed in Scilit:
- Demand Response for Home Energy Management Using Reinforcement Learning and Artificial Neural NetworkIEEE Transactions on Smart Grid, 2019
- A Hierarchical Real-Time Balancing Market Considering Multi-Microgrids With Distributed Sustainable ResourcesIEEE Transactions on Sustainable Energy, 2018
- Consensus-Based Approach to Peer-to-Peer Electricity Markets With Product DifferentiationIEEE Transactions on Power Systems, 2018
- Multiclass Energy Management for Peer-to-Peer Energy Trading Driven by Prosumer PreferencesIEEE Transactions on Power Systems, 2018
- Energy Collectives: A Community and Fairness Based Approach to Future Electricity MarketsIEEE Transactions on Power Systems, 2018
- Demand Response: Social Welfare Maximization in an Unbundled Energy Market Case Study for the Low-Voltage Networks of a Distribution Network Operator in The NetherlandsIEEE Transactions on Industry Applications, 2016
- Demand Response Management for Residential Smart Grid: From Theory to PracticeIEEE Access, 2015
- A Collaborative Design of Aggregated Residential Appliances and Renewable Energy for Demand Response ParticipationIEEE Transactions on Industry Applications, 2015
- A stochastic framework for the grid integration of wind power using flexible load approachEnergy Conversion and Management, 2014
- Distributed Optimization and Statistical Learning via the Alternating Direction Method of MultipliersFoundations and Trends in Machine Learning, 2011