A Hybrid PSO-DE Algorithm for Smart Home Energy Management

Home energy management system is an important part in smart home, smart home is assumed to be equipped with smart meter which smart control of generators, storages, and demand response programs. In this paper, we study a versatile convex optimization framework for the automatic energy management of various household loads in a smart home. The scheduling algorithm determines how energy resources available to the end-users considering a number of constraints. Hence, a hybrid PSO-DE algorithm approach is proposed. We devise a model accounting for a typical household user, and present computational results showing that it can be efficiently solved in real-life instances.

[1]  Robert Schober,et al.  Optimal and autonomous incentive-based energy consumption scheduling algorithm for smart grid , 2010, 2010 Innovative Smart Grid Technologies (ISGT).

[2]  Hassan Ghasemi,et al.  Residential Microgrid Scheduling Based on Smart Meters Data and Temperature Dependent Thermal Load Modeling , 2014, IEEE Transactions on Smart Grid.

[3]  Yong Wang,et al.  Hybridizing particle swarm optimization with differential evolution for constrained numerical and engineering optimization , 2010, Appl. Soft Comput..

[4]  Maurice Clerc,et al.  The particle swarm - explosion, stability, and convergence in a multidimensional complex space , 2002, IEEE Trans. Evol. Comput..

[5]  Ville Tirronen,et al.  Recent advances in differential evolution: a survey and experimental analysis , 2010, Artificial Intelligence Review.

[6]  R. Storn,et al.  Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series) , 2005 .

[7]  Juan M. Morales,et al.  Real-Time Demand Response Model , 2010, IEEE Transactions on Smart Grid.

[8]  J.S. Katz,et al.  Educating the Smart Grid , 2008, 2008 IEEE Energy 2030 Conference.

[9]  Leandro dos Santos Coelho,et al.  Coevolutionary Particle Swarm Optimization Using Gaussian Distribution for Solving Constrained Optimization Problems , 2006, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[10]  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.

[11]  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.

[12]  C.M. Colson,et al.  A review of challenges to real-time power management of microgrids , 2009, 2009 IEEE Power & Energy Society General Meeting.

[13]  Qing-Shan Jia,et al.  Energy-Efficient Buildings Facilitated by Microgrid , 2010, IEEE Transactions on Smart Grid.

[14]  Na Li,et al.  Optimal demand response based on utility maximization in power networks , 2011, 2011 IEEE Power and Energy Society General Meeting.

[15]  I. Mansouri,et al.  Energy consumption in UK households: Impact of domestic electrical appliances , 1996 .

[16]  Sung-Kwan Joo,et al.  Electric vehicle charging method for smart homes/buildings with a photovoltaic system , 2013, IEEE Transactions on Consumer Electronics.

[17]  Iain MacGill,et al.  Coordinated Scheduling of Residential Distributed Energy Resources to Optimize Smart Home Energy Services , 2010, IEEE Transactions on Smart Grid.

[18]  M. Guay,et al.  Minmax dynamic optimization over a finite-time horizon for building demand control , 2008, 2008 American Control Conference.

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

[20]  Furong Li,et al.  Demand response in the UK's domestic sector , 2009 .

[21]  Abbe E. Forman,et al.  Smart Grid , 2013, Int. J. E Politics.