A closed-loop energy price controlling method for real-time energy balancing in a smart grid energy market

Future smart grids will require a flexible, observable, and controllable network for reliable and efficient energy delivery under uncertain generation and demand conditions. One of the mechanisms for efficient and reliable energy generation is dynamic demand-responsive generation management based on energy price adjustments that creates a balance in energy markets. This study presents a closed-loop PID (proportional–integral–derivative) controller-based price control method for autonomous and real-time balancing of energy demand and generation in smart grid electricity markets. The PID control system can regulate energy prices online to respond dynamically and instantaneously to the varying energy demands of grid consumers. Independent energy suppliers in the smart grid decide whether to sell their energy to the grid according to the energy prices declared by the closed-loop PID controller system. Energy market simulations demonstrate that PID-controlled energy price regulation can effectively maintain an energy balance for hourly demand fluctuations of consumers.

[1]  Sergio Ulgiati,et al.  Assessing the environmental performance and sustainability of bioenergy production in Sweden: A life cycle assessment perspective , 2012 .

[2]  Vincent W. S. Wong,et al.  Optimal Real-Time Pricing Algorithm Based on Utility Maximization for Smart Grid , 2010, 2010 First IEEE International Conference on Smart Grid Communications.

[3]  Nikos D. Hatziargyriou,et al.  Integrating distributed generation into electric power systems: A review of drivers, challenges and opportunities , 2007 .

[4]  Albert Molderink,et al.  Domestic energy management methodology for optimizing efficiency in Smart Grids , 2009, 2009 IEEE Bucharest PowerTech.

[5]  A. J. López,et al.  The electricity prices in the European Union. The role of renewable energies and regulatory electric market reforms , 2012 .

[6]  Sarah McCormack,et al.  Validated Real-time Energy Models for Small-Scale Grid-Connected PV-Systems , 2010 .

[7]  Henrik Lund,et al.  Large-scale integration of wind power into different energy systems , 2005 .

[8]  Brian Vad Mathiesen,et al.  Large-scale integration of wind power into the existing Chinese energy system , 2011 .

[9]  Koen Steemers,et al.  Can microgrids make a major contribution to UK energy supply , 2006 .

[10]  M. P. Moghaddam,et al.  Optimal real time pricing in an agent-based retail market using a comprehensive demand response model , 2011 .

[11]  Peter Xiaoping Liu,et al.  Dynamic pricing for demand-side management in the smart grid , 2011, 2011 IEEE Online Conference on Green Communications.

[12]  Gaetano Zizzo,et al.  Smart renewable generation for an islanded system. Technical and economic issues of future scenarios , 2012 .

[13]  Michael A. Johnson,et al.  PID CONTROL: NEW IDENTIFICATION AND DESIGN METHODS , 2008 .

[14]  Taher Niknam,et al.  Multi-objective operation management of a renewable MG (micro-grid) with back-up micro-turbine/fuel , 2011 .

[15]  J. Oyarzabal,et al.  Agent based micro grid management system , 2005, 2005 International Conference on Future Power Systems.

[16]  B.F. Wollenberg,et al.  Toward a smart grid: power delivery for the 21st century , 2005, IEEE Power and Energy Magazine.

[17]  Brian Vad Mathiesen,et al.  From electricity smart grids to smart energy systems – A market operation based approach and understanding , 2012 .

[18]  Thomas A. Weber Optimal Control Theory with Applications in Economics , 2011 .

[19]  Tore Hägglund,et al.  The future of PID control , 2000 .

[20]  Martin J. Leahy,et al.  Facilitation of renewable electricity using price based appliance control in Irelands electricity m , 2011 .

[21]  Z. Vale,et al.  Demand response in electrical energy supply: An optimal real time pricing approach , 2011 .

[22]  Jayakrishnan Radhakrishna Pillai,et al.  Comparative analysis of hourly and dynamic power balancing models for validating future energy scena , 2011 .

[23]  Michael D. Intriligator Applications of optimal control theory in economics , 2004, Synthese.

[24]  Hamed Mohsenian Rad,et al.  Optimal Residential Load Control With Price Prediction in Real-Time Electricity Pricing Environments , 2010, IEEE Transactions on Smart Grid.

[25]  Poul Alberg Østergaard,et al.  Comparing electricity, heat and biogas storages’ impacts on renewable energy integration , 2012 .

[26]  David G. Hull,et al.  Optimal Control Theory for Applications , 2003 .

[27]  Angel A. Bayod-Rújula,et al.  Future development of the electricity systems with distributed generation , 2009 .

[28]  Jean-Michel Glachant,et al.  Energy efficiency actions related to the rollout of smart meters for small consumers, application to , 2010 .

[29]  Abdulkerim Karabiber,et al.  A user-mode distributed energy management architecture for smart grid applications , 2012 .

[30]  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.

[31]  Sean P. Meyn,et al.  A Control Theorist's Perspective on Dynamic Competitive Equilibria in Electricity Markets , 2011 .

[32]  C. Fitzpatrick,et al.  Demand side management of electric car charging: Benefits for consumer and grid , 2012 .

[33]  A. Rajendra Prasad,et al.  Optimization of integrated photovoltaic–wind power generation systems with battery storage , 2006 .

[34]  James Scott,et al.  A review of multi-criteria decision-making methods for bioenergy systems , 2012 .

[35]  Masood Parvania,et al.  Demand Response Scheduling by Stochastic SCUC , 2010, IEEE Transactions on Smart Grid.