The electricity trading system based on distributed coordination method in a micro-grid

In this paper, a fully distributed coordination control based on electricity trading system (ETS) is proposed. It aims at simulating price trading mechanism and the interaction process of the electricity price bargain among multiple users in a microgrid. Each user with an intelligent module is considered as an agent and each agent carries out its own price response and decision. The proposed approach adopts the ZigBee technology for communication. Each agent contains a ZigBee module based on the chip CC2530 and embedded C as the development language. The distributed coordination algorithm is proposed for the agents to obtain the optimal power generation or consumption and maintain the supply demand-balance within a microgrid. In the proposed ETS, only local data and information exchange with other adjacent nodes are required, so the computation and communication burdens are evenly allocated to multiple local agents. Hardware simulation results verify the effectiveness of the proposed distributed control strategy.

[1]  A. Selvakumar,et al.  A New Particle Swarm Optimization Solution to Nonconvex Economic Dispatch Problems , 2007, IEEE Transactions on Power Systems.

[2]  Juan C. Vasquez,et al.  Control Strategy for Flexible Microgrid Based on Parallel Line-Interactive UPS Systems , 2009, IEEE Transactions on Industrial Electronics.

[3]  Theodore Tryfonas,et al.  A Distributed Consensus Algorithm for Decision Making in Service-Oriented Internet of Things , 2014, IEEE Transactions on Industrial Informatics.

[4]  Mohammad Bagher Menhaj,et al.  Dynamic average consensus via nonlinear protocols , 2012, Autom..

[5]  Saswati Sarkar,et al.  Dynamic Pricing for Distributed Generation in Smart Grid , 2013, 2013 IEEE Green Technologies Conference (GreenTech).

[6]  Mo-Yuen Chow,et al.  Consensus-based distributed energy management with real-time pricing , 2013, 2013 IEEE Power & Energy Society General Meeting.

[7]  A.L. Dimeas,et al.  Operation of a multiagent system for microgrid control , 2005, IEEE Transactions on Power Systems.

[8]  Sonia Martínez,et al.  Discrete-time dynamic average consensus , 2010, Autom..

[9]  David A. Cartes,et al.  An Intelligent Auction Scheme for Smart Grid Market Using a Hybrid Immune Algorithm , 2011, IEEE Transactions on Industrial Electronics.

[10]  Juan C. Vasquez,et al.  Hierarchical Control of Droop-Controlled AC and DC Microgrids—A General Approach Toward Standardization , 2009, IEEE Transactions on Industrial Electronics.

[11]  S. X. Chen,et al.  Multi-Agent System for Distributed Management of Microgrids , 2015, IEEE Transactions on Power Systems.

[12]  T. Başar,et al.  Dynamic Noncooperative Game Theory , 1982 .

[13]  Mo-Yuen Chow,et al.  Optimal Tradeoff Between Performance and Security in Networked Control Systems Based on Coevolutionary Algorithms , 2012, IEEE Transactions on Industrial Electronics.

[14]  S. C. Srivastava,et al.  A Generalized Approach for DG Planning and Viability Analysis Under Market Scenario , 2013, IEEE Transactions on Industrial Electronics.