A MOPSO method for Direct Load Control in Smart Grid

We model electricity demand of a city in a complex way including appliances and users.We propose a scalable real time method for managing the demand.We implement this method using a multi-objective criteria optimisation algorithm.A solution for managing loads is found within the time requirements.Demand can be reduced up to 20% by controlling three types of appliances. In recent years, power grids have been evolving to decentralised production and control. Direct Load Control (DLC) methods are oriented to manage loads on the demand side. A DLC method based on Multi-Objective Particle Swarm Optimisation (MOPSO) algorithm is described. This method sets up the operation of the appliances when a power restriction must be accomplished. Since the method must operate in real-time, the calculations are distributed. The operation of appliances is obtained by dividing the power restriction among neighbourhoods and calculating multiple local optimisations. The method has been experimentally evaluated through simulations.

[1]  Gilbert M. Masters,et al.  Renewable and Efficient Electric Power Systems , 2004 .

[2]  Walid G. Morsi,et al.  A novel demand side management program using water heaters and particle swarm optimization , 2010, 2010 IEEE Electrical Power & Energy Conference.

[3]  C.A. Coello Coello,et al.  MOPSO: a proposal for multiple objective particle swarm optimization , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[4]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[5]  O. Weck,et al.  A COMPARISON OF PARTICLE SWARM OPTIMIZATION AND THE GENETIC ALGORITHM , 2005 .

[6]  Sameer Alam,et al.  Multi-Objective Optimization in Computational Intelligence: Theory and Practice , 2008 .

[7]  Panagiotis D. Christofides,et al.  Distributed Supervisory Predictive Control of Distributed Wind and Solar Energy Systems , 2013, IEEE Transactions on Control Systems Technology.

[8]  M. Pedrasa,et al.  Scheduling of Demand Side Resources Using Binary Particle Swarm Optimization , 2009, IEEE Transactions on Power Systems.

[9]  Regina Lamedica,et al.  A bottom-up approach to residential load modeling , 1994 .

[10]  P. Palensky,et al.  Modeling domestic housing loads for demand response , 2008, 2008 34th Annual Conference of IEEE Industrial Electronics.

[11]  Qingfu Zhang,et al.  Multiobjective evolutionary algorithms: A survey of the state of the art , 2011, Swarm Evol. Comput..

[12]  M. Negnevitsky,et al.  Demand response in the retail market: Benefits and challenges , 2009, 2009 Australasian Universities Power Engineering Conference.

[13]  Farhad Shahnia,et al.  Plug In Electric Vehicles in Smart Grids , 2015 .

[14]  Carlos A. Coello Coello,et al.  Improving PSO-Based Multi-objective Optimization Using Crowding, Mutation and epsilon-Dominance , 2005, EMO.

[15]  Hani Hagras,et al.  An intelligent agent based approach for energy management in commercial buildings , 2008, 2008 IEEE International Conference on Fuzzy Systems (IEEE World Congress on Computational Intelligence).

[16]  Pavel Kordík,et al.  Building Automation Simulator and Control Strategy for Intelligent and Energy Efficient Home , 2009, 2009 Third UKSim European Symposium on Computer Modeling and Simulation.

[17]  Panagiotis D. Christofides,et al.  A distributed control framework for smart grid development: Energy/water system optimal operation and electric grid integration , 2011 .

[18]  Hortensia Amaris,et al.  Integration of renewable energy sources in smart grids by means of evolutionary optimization algorithms , 2012, Expert Syst. Appl..

[19]  Carlos A. Coello Coello,et al.  A Comprehensive Survey of Evolutionary-Based Multiobjective Optimization Techniques , 1999, Knowledge and Information Systems.

[20]  Martin Lukasiewycz,et al.  Opt4J: a modular framework for meta-heuristic optimization , 2011, GECCO '11.

[21]  Steven D. Silver,et al.  Small world network model of personal consumption: Demand-side management in an expert system , 2008, Expert Syst. Appl..

[22]  Jasbir S. Arora,et al.  Survey of multi-objective optimization methods for engineering , 2004 .

[23]  Willett Kempton,et al.  Vehicle-to-grid power implementation: From stabilizing the grid to supporting large-scale renewable energy , 2005 .

[24]  M Reyes Sierra,et al.  Multi-Objective Particle Swarm Optimizers: A Survey of the State-of-the-Art , 2006 .

[25]  David W. Corne,et al.  Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy , 2000, Evolutionary Computation.

[26]  Michael N. Vrahatis,et al.  Multi-Objective Particles Swarm Optimization Approaches , 2008 .