Multiobjective genetic algorithm for demand side management of smart grid

Demand side management is one of the most effective methods to control the usage of energy so as to achieve reliability and sustainability in the smart grid. Conventional methods for generating the management scheme are generally based on one objective, which represents only the requirements of energy suppliers or only the energy consumers. In this paper, a multiobjective genetic algorithm (GA) is proposed for extending the optimization problems by considering the objectives from the two conflicting groups and some compromise solutions are provided. The performance of the multiobjective GA is compared with its single objective version by three different cases. The results show that the solutions obtained by the multiobjective GA are more reasonable and better solutions for a single objective can even be found.

[1]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[2]  Jun Zhang,et al.  Clustering-Based Adaptive Crossover and Mutation Probabilities for Genetic Algorithms , 2007, IEEE Transactions on Evolutionary Computation.

[3]  Jiannong Cao,et al.  Multiple Populations for Multiple Objectives: A Coevolutionary Technique for Solving Multiobjective Optimization Problems , 2013, IEEE Transactions on Cybernetics.

[4]  Jun Zhang,et al.  SamACO: Variable Sampling Ant Colony Optimization Algorithm for Continuous Optimization , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[5]  H. Farhangi,et al.  The path of the smart grid , 2010, IEEE Power and Energy Magazine.

[6]  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.

[7]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[8]  Jun Zhang,et al.  Ant colony optimization for enhancing scheduling reliability in wireless sensor networks , 2012, 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[9]  Thillainathan Logenthiran,et al.  Demand Side Management in Smart Grid Using Heuristic Optimization , 2012, IEEE Transactions on Smart Grid.

[10]  Ning Lu,et al.  Appliance Commitment for Household Load Scheduling , 2011, IEEE Transactions on Smart Grid.

[11]  Leandros Tassiulas,et al.  Challenges in demand load control for the smart grid , 2011, IEEE Network.

[12]  Jun Zhang,et al.  An intelligent testing system embedded with an ant colony optimization based test composition method , 2009, 2009 IEEE Congress on Evolutionary Computation.

[13]  Jun Zhang,et al.  Ant Colony Optimization for Software Project Scheduling and Staffing with an Event-Based Scheduler , 2013, IEEE Transactions on Software Engineering.

[14]  H. Vincent Poor,et al.  Scheduling Power Consumption With Price Uncertainty , 2011, IEEE Transactions on Smart Grid.

[15]  Jun Zhang,et al.  Hybrid Genetic Algorithm Using a Forward Encoding Scheme for Lifetime Maximization of Wireless Sensor Networks , 2010, IEEE Transactions on Evolutionary Computation.