Decentralised dispatch of distributed energy resources in smart grids via multi-agent coalition formation

The energy dispatch problem is a fundamental research issue in power distribution networks. With the growing complexity and dimensions of current distribution networks, there is an increasing need for intelligent and scalable mechanisms to facilitate energy dispatch in these networks. To this end, in this paper, we propose a multi-agent coalition formation-based energy dispatch mechanism. This mechanism is decentralised without requiring a central controller or any global information. As this mechanism does not need a central controller, the single point of failure can be avoided and since this mechanism does not require any global information, good scalability can be expected. In addition, this mechanism enables each node in a distribution network to make decisions autonomously about energy dispatch through a negotiation protocol. Simulation results demonstrate the effectiveness of this mechanism in comparison with three recently developed representative mechanisms. We propose a multi-agent coalition formation-based mechanism for efficient energy dispatch in power distribution networks.This mechanism is decentralised and does not need global information.The network structure has influence on the performance of the mechanism.

[1]  Georgios B. Giannakis,et al.  Distributed Optimal Power Flow for Smart Microgrids , 2012, IEEE Transactions on Smart Grid.

[2]  Rayadurgam Srikant,et al.  The Mathematics of Internet Congestion Control , 2003 .

[3]  Andrew W. Moore,et al.  Reinforcement Learning: A Survey , 1996, J. Artif. Intell. Res..

[4]  Rayadurgam Srikant,et al.  The Mathematics of Internet Congestion Control (Systems and Control: Foundations and Applications) , 2004 .

[5]  Manuel Lopes,et al.  Learning exploration strategies in model-based reinforcement learning , 2013, AAMAS.

[6]  Binod Shaw,et al.  Solution of economic dispatch problems by seeker optimization algorithm , 2012, Expert Syst. Appl..

[7]  Minjie Zhang,et al.  A Hybrid Multiagent Framework With Q-Learning for Power Grid Systems Restoration , 2011, IEEE Transactions on Power Systems.

[8]  Minjie Zhang,et al.  Self-Adaptation-Based Dynamic Coalition Formation in a Distributed Agent Network: A Mechanism and a Brief Survey , 2013, IEEE Transactions on Parallel and Distributed Systems.

[9]  Asuman E. Ozdaglar,et al.  Approximate Primal Solutions and Rate Analysis for Dual Subgradient Methods , 2008, SIAM J. Optim..

[10]  Nicholas R. Jennings,et al.  Intelligent agents: theory and practice , 1995, The Knowledge Engineering Review.

[11]  I.S. Baxevanos,et al.  Implementing Multiagent Systems Technology for Power Distribution Network Control and Protection Management , 2007, IEEE Transactions on Power Delivery.

[12]  Steven H. Low,et al.  Optimization flow control—I: basic algorithm and convergence , 1999, TNET.

[13]  Peter Dayan,et al.  Technical Note: Q-Learning , 2004, Machine Learning.

[14]  Vincent W. S. Wong,et al.  Autonomous Demand-Side Management Based on Game-Theoretic Energy Consumption Scheduling for the Future Smart Grid , 2010, IEEE Transactions on Smart Grid.

[15]  Chuangxin Guo,et al.  A multiagent-based particle swarm optimization approach for optimal reactive power dispatch , 2005 .

[16]  Sarvapali D. Ramchurn,et al.  Optimal decentralised dispatch of embedded generation in the smart grid , 2012, AAMAS.

[17]  Claudia V. Goldman,et al.  Self-organization through bottom-up coalition formation , 2003, AAMAS '03.

[18]  A. Rubinstein Perfect Equilibrium in a Bargaining Model , 1982 .

[19]  Günther Palm,et al.  Value-Difference Based Exploration: Adaptive Control between Epsilon-Greedy and Softmax , 2011, KI.

[20]  Giorgio Gallo,et al.  On the supermodular knapsack problem , 1989, Math. Program..

[21]  Sakti Prasad Ghoshal,et al.  Seeker optimisation algorithm: application to the solution of economic load dispatch problems , 2011 .

[22]  Mohammad Ali Abido,et al.  Multiobjective evolutionary algorithms for electric power dispatch problem , 2006, IEEE Transactions on Evolutionary Computation.

[23]  Vladimiro Miranda,et al.  Evolutionary computation in power systems , 1998 .

[24]  Sarit Kraus,et al.  Formation of overlapping coalitions for precedence-ordered task-execution among autonomous agents * , 1996 .

[25]  Balho H. Kim,et al.  A fast distributed implementation of optimal power flow , 1999 .

[26]  Matthias Heger,et al.  Consideration of Risk in Reinforcement Learning , 1994, ICML.

[27]  Salman Mohagheghi,et al.  Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems , 2008, IEEE Transactions on Evolutionary Computation.

[28]  Thomas G. Dietterich What is machine learning? , 2020, Archives of Disease in Childhood.

[29]  Minjie Zhang,et al.  A Multi-Agent Solution to Distribution System Management by Considering Distributed Generators , 2013, IEEE Transactions on Power Systems.

[30]  Q. H. Wu,et al.  Optimal reactive power dispatch using an adaptive genetic algorithm , 1997 .

[31]  Christoforos N. Hadjicostis,et al.  Coordination and Control of Distributed Energy Resources for Provision of Ancillary Services , 2010, 2010 First IEEE International Conference on Smart Grid Communications.

[32]  Sarvapali D. Ramchurn,et al.  Trading agents for the smart electricity grid , 2010, AAMAS.

[33]  Stephen P. Boyd,et al.  Message Passing for Dynamic Network Energy Management , 2012, ArXiv.

[34]  Francisco J. Prieto,et al.  A Decomposition Methodology Applied to the Multi-Area Optimal Power Flow Problem , 2003, Ann. Oper. Res..

[35]  Reid G. Smith,et al.  The Contract Net Protocol: High-Level Communication and Control in a Distributed Problem Solver , 1980, IEEE Transactions on Computers.

[36]  G. Andersson,et al.  Decentralized Optimal Power Flow Control for Overlapping Areas in Power Systems , 2009, IEEE Transactions on Power Systems.

[37]  Sebastian Thrun,et al.  Efficient Exploration In Reinforcement Learning , 1992 .

[38]  Chaohua Dai,et al.  Seeker Optimization Algorithm for Optimal Reactive Power Dispatch , 2009, IEEE Transactions on Power Systems.

[39]  Ross Baldick,et al.  Coarse-grained distributed optimal power flow , 1997 .

[40]  Peter Dayan,et al.  Q-learning , 1992, Machine Learning.

[41]  Ulrich Endriss,et al.  Monotonic concession protocols for multilateral negotiation , 2006, AAMAS '06.

[42]  Marco Wiering,et al.  Explorations in efficient reinforcement learning , 1999 .

[43]  Christoforos N. Hadjicostis,et al.  Distributed algorithms for control of demand response and distributed energy resources , 2011, IEEE Conference on Decision and Control and European Control Conference.

[44]  G. Cohen Auxiliary problem principle and decomposition of optimization problems , 1980 .

[45]  Somesh Jha,et al.  Multi-Agent Coordination through Coalition Formation , 1997, ATAL.

[46]  M.E. Baran,et al.  A Multiagent-Based Dispatching Scheme for Distributed Generators for Voltage Support on Distribution Feeders , 2007, IEEE Transactions on Power Systems.

[47]  J. K. Kok,et al.  Intelligence in Electricity Networks for Embedding Renewables and Distributed Generation , 2010 .

[48]  Sarvapali D. Ramchurn,et al.  Putting the 'smarts' into the smart grid , 2012, Commun. ACM.

[49]  M. Kalantar,et al.  Optimal reactive power dispatch based on harmony search algorithm , 2011 .

[50]  Bo An,et al.  Strategic agents for multi-resource negotiation , 2011, Autonomous Agents and Multi-Agent Systems.

[51]  Christoforos N. Hadjicostis,et al.  Decentralized optimal dispatch of distributed energy resources , 2012, 2012 IEEE 51st IEEE Conference on Decision and Control (CDC).

[52]  Bo An,et al.  Automated negotiation with decommitment for dynamic resource allocation in cloud computing , 2010, AAMAS.

[53]  M. Lehtonen,et al.  Distributed agent-based State estimation for electrical distribution networks , 2005, IEEE Transactions on Power Systems.