Power supply-demand balance in a Smart Grid: An information sharing model for a market mechanism

Abstract In the future, global energy balance of a Smart Grid system can be achieved by its agents deciding on their own power demand and production (locally) and the exchange of these decisions. In this paper, we develop a network model that describes how the information of power imbalance of individual agents can be exchanged in the system. Compared to existing network models with hierarchical structures, our developed model, together with a market mechanism, achieve the power balance in the system in a completely distributed way. Additionally, dynamics, constraints and forecasts of each agent can be conveniently involved.

[1]  A. R. Khattak,et al.  The Power to Deliver: Trends in Smart Grid Solutions , 2012, IEEE Power and Energy Magazine.

[2]  Marc Timme,et al.  Self-organized synchronization in decentralized power grids. , 2012, Physical review letters.

[3]  Andrej Jokić,et al.  Price-based optimal control of electrical power systems , 2005 .

[4]  Rudy R. Negenborn,et al.  Multi-agent model predictive control with applications to power networks , 2007 .

[5]  Bart De Schutter,et al.  Demand Response With Micro-CHP Systems , 2011, Proceedings of the IEEE.

[6]  David Q. Mayne,et al.  Constrained model predictive control: Stability and optimality , 2000, Autom..

[7]  David A. Starrett,et al.  Shadow Pricing in Economics , 2000, Ecosystems.

[8]  B. Roossien,et al.  Energy Pattern Generator Understanding the effect of user behaviour on energy systems , 2009 .

[9]  Gerard J. M. Smit,et al.  Management and Control of Domestic Smart Grid Technology , 2010, IEEE Transactions on Smart Grid.

[10]  P. P. J. van den Bosch,et al.  Price-Based Optimal Control of Power Flow in Electrical Energy Transmission Networks , 2007, HSCC.

[11]  Anders Rantzer,et al.  Dynamic dual decomposition for distributed control , 2009, 2009 American Control Conference.

[12]  Jacquelien M. A. Scherpen,et al.  Distributed MPC applied to power demand side control , 2013, 2013 European Control Conference (ECC).

[13]  Eduardo F. Camacho,et al.  Model predictive control in the process industry , 1995 .

[14]  M. Kezunovic,et al.  The Big Picture: Smart Research for Large-Scale Integrated Smart Grid Solutions , 2012, IEEE Power and Energy Magazine.

[15]  Anders Rantzer,et al.  Distributed Model Predictive Control with suboptimality and stability guarantees , 2010, 49th IEEE Conference on Decision and Control (CDC).

[16]  J. K. Kok,et al.  PowerMatcher: multiagent control in the electricity infrastructure , 2005, AAMAS '05.

[17]  Jacquelien M. A. Scherpen,et al.  Distributed MPC for controlling μ-CHPs in a network , 2012, 2012 American Control Conference (ACC).

[18]  David W. Coit,et al.  Electric power system generation expansion plans considering the impact of Smart Grid technologies , 2012 .

[19]  E. Seneta.  Non-negative Matrices and Markov Chains , 2008 .

[20]  Jacquelien M. A. Scherpen,et al.  Distributed Control of the Power Supply-Demand Balance , 2013, IEEE Transactions on Smart Grid.

[21]  Stephen P. Boyd,et al.  Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.

[22]  Jay H. Lee,et al.  Model predictive control: past, present and future , 1999 .

[23]  Anders Rantzer,et al.  On Prize Mechanisms in linear quadratic team theory , 2007, 2007 46th IEEE Conference on Decision and Control.