Multi-Objective Optimal Energy Consumption Scheduling in Smart Grids

A major source of inefficiency in power grids is the underutilization of generation capacity. This is mainly because load demand during peak hours is much larger than that during off-peak hours. Moreover, extra generation capacity is needed to maintain a security margin above peak load demand. As load demand keeps increasing and two-way communications are enabled by smart meters (SMs), demand response (DR) has been proposed as an alternative to installing new power plants in smart grids. DR makes use of real-time schemes to allow users to modify their load demand patterns according to their energy consumption costs. In particular, when load demand is high, energy consumption cost will be high and users may decide to postpone certain amount of their consumption needs. This strategy may effectively reduce the peak load demand and increases the off-peak demand, and hence could increase existing generation capacity utilization and reduce the need to install extra generation plants. In this paper, we consider a third-party managing the energy consumption of a group of users, and formulate the load scheduling problem as a constrained multi-objective optimization problem (CMOP). The optimization objectives are to minimize energy consumption cost and to maximize a certain utility, which can be conflicting and non-commensurable. We then develop two evolutionary algorithms (EAs) to obtain the Pareto-front solutions and the ε-Pareto front solutions to the CMOP, respectively, which are validated by extensive simulation results.

[1]  David Corne,et al.  The Pareto archived evolution strategy: a new baseline algorithm for Pareto multiobjective optimisation , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[2]  Kalyanmoy Deb,et al.  Evaluating the -Domination Based Multi-Objective Evolutionary Algorithm for a Quick Computation of Pareto-Optimal Solutions , 2005, Evolutionary Computation.

[3]  Marco Laumanns,et al.  A Tutorial on Evolutionary Multiobjective Optimization , 2004, Metaheuristics for Multiobjective Optimisation.

[4]  R. Weiner Lecture Notes in Economics and Mathematical Systems , 1985 .

[5]  Goran Strbac,et al.  Demand side management: Benefits and challenges ☆ , 2008 .

[6]  David E. Goldberg,et al.  A niched Pareto genetic algorithm for multiobjective optimization , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.

[7]  Marco Laumanns,et al.  SPEA2: Improving the strength pareto evolutionary algorithm , 2001 .

[8]  Ding Li,et al.  Auctioning game based Demand Response scheduling in smart grid , 2011, 2011 IEEE Online Conference on Green Communications.

[9]  Kalyanmoy Deb,et al.  Simulated Binary Crossover for Continuous Search Space , 1995, Complex Syst..

[10]  Gary B. Lamont,et al.  Multiobjective optimization with messy genetic algorithms , 2000, SAC '00.

[11]  Shuguang Cui,et al.  Noncooperative Games for Autonomous Consumer Load Balancing over Smart Grid , 2011, GAMENETS.

[12]  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.

[13]  Gary B. Lamont,et al.  Evolutionary Algorithms for Solving Multi-Objective Problems , 2002, Genetic Algorithms and Evolutionary Computation.

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

[15]  Vincent W. S. Wong,et al.  Optimal energy consumption scheduling using mechanism design for the future smart grid , 2011, 2011 IEEE International Conference on Smart Grid Communications (SmartGridComm).

[16]  Martin J. Oates,et al.  The Pareto Envelope-Based Selection Algorithm for Multi-objective Optimisation , 2000, PPSN.

[17]  Marco Laumanns,et al.  Archiving With Guaranteed Convergence And Diversity In Multi-objective Optimization , 2002, GECCO.