Title :
Incremental Welfare Consensus Algorithm for Cooperative Distributed Generation/Demand Response in Smart Grid
Author :
Rahbari-Asr, Navid ; Ojha, Unnati ; Ziang Zhang ; Mo-Yuen Chow
Author_Institution :
Dept. of Electr. & Comput. Eng., North Carolina State Univ., Raleigh, NC, USA
Abstract :
In this paper, we introduce the incremental welfare consensus algorithm for solving the energy management problem in a smart grid environment populated with distributed generators and responsive demands. The proposed algorithm is distributed and cooperative such that it eliminates the need for a central energy-management unit, central price coordinator, or leader. The optimum energy solution is found through local peer-to-peer communications among smart devices. Each distributed generation unit is connected to a local price regulator, as is each consumer unit. In response to the price of energy proposed by the local price regulators, the power regulator on each generation/consumer unit determines the level of generation/consumption power needed to optimize the benefit of the device. The consensus-based coordination among price regulators drives the behavior of the overall system toward the global optimum, despite the greedy behavior of each unit. The primary advantages of the proposed approach are: 1) convergence to the global optimum without requiring a central controller/coordinator or leader, despite the greedy behavior at the individual level and limited communications; and 2) scalability in terms of per-node computation and communications burden.
Keywords :
demand side management; distributed power generation; peer-to-peer computing; power engineering computing; power generation economics; smart power grids; central energy-management unit; cooperative distributed generation-demand response; incremental welfare consensus algorithm; peer-to-peer communications; power generation-consumption; price regulators; smart devices; smart grid; Algorithm design and analysis; Cost function; Distributed algorithms; Energy management; Optimization; Power demand; Distributed algorithms; distributed control; energy management; optimization methods;
Journal_Title :
Smart Grid, IEEE Transactions on
DOI :
10.1109/TSG.2014.2346511