Home energy management with PSO in smart grid

In this paper, a real-time optimal appliance usage strategy is proposed based on binary particle swarm algorithm, participated by both energy suppliers and end users. Under the multi end-users and time-of-use electricity prices circumstances, the total electricity bills can be generated with different appliance usage pattern: random, single optimal objective and double optimal objectives, resulting in different load curves. Considering the characteristics of the appliances and living habits, the appliances are classified into three categories. Matlab is used as the simulation tool to perform the experiments to prove that the load shifting, energy saving and energy supply efficiency enhancement can be achieved with multi-objectives with particle swarm optimization.

[1]  Chin Kim Gan,et al.  A review of recent development in smart grid and micro-grid laboratories , 2012, 2012 IEEE International Power Engineering and Optimization Conference Melaka, Malaysia.

[2]  Wolfgang Ketter,et al.  Demand side management—A simulation of household behavior under variable prices , 2011 .

[3]  Lingfeng Wang,et al.  A demand side management based simulation platform incorporating heuristic optimization for management of household appliances , 2012 .

[4]  Ana Morais,et al.  Demand side management using fuzzy inference , 2001, 10th IEEE International Conference on Fuzzy Systems. (Cat. No.01CH37297).

[5]  Massoud Pedram,et al.  Minimizing the Electricity Bill of Cooperative Users under a Quasi-Dynamic Pricing Model , 2010, 2010 First IEEE International Conference on Smart Grid Communications.

[6]  Lazaros G. Papageorgiou,et al.  Optimal Scheduling of Smart Homes Energy Consumption with Microgrid , 2011 .

[7]  P.R. Babu,et al.  Neural Network and DSM Techniques Applied to a Industrial Consumer a Case Study , 2007, 2007 Compatibility in Power Electronics.

[8]  Alessandro Di Giorgio,et al.  An event driven Smart Home Controller enabling consumer economic saving and automated Demand Side Management , 2012 .

[9]  C. Kang,et al.  Demand side management in China , 2010, IEEE PES General Meeting.

[10]  Yadwinder Singh Brar,et al.  Multiobjective load dispatch using Particle Swarm Optimization , 2013, 2013 IEEE 8th Conference on Industrial Electronics and Applications (ICIEA).

[11]  H. T. Mouftah,et al.  TOU-Aware Energy Management and Wireless Sensor Networks for Reducing Peak Load in Smart Grids , 2010, 2010 IEEE 72nd Vehicular Technology Conference - Fall.

[12]  Guoqing Xu,et al.  Regulated Charging of Plug-in Hybrid Electric Vehicles for Minimizing Load Variance in Household Smart Microgrid , 2013, IEEE Transactions on Industrial Electronics.

[13]  Maurizio Delfanti,et al.  House energy demand optimization in single and multi-user scenarios , 2011, 2011 IEEE International Conference on Smart Grid Communications (SmartGridComm).