An Enhanced Simulated Annealing Scheduling Approach for Smart Meter Technology

(*) Corresponding author

Authors' affiliations

DOI's assignment:
the author of the article can submit here a request for assignment of a DOI number to this resource!
Cost of the service: euros 10,00 (for a DOI)


For residential electricity consumers, there are power loads which need to be switched on for a time between two predefined time instants. The electricity price could be different between peak and off-peak time. Customers can minimize their own energy expense if the smart grid could calculate and schedule the electrical equipments’ operation times which are determined by their power consumptions and operation time constraints.
We call these services Demand Response (DR) services. In the Smart Grid environments, the Advanced Metering Infrastructure (AMI) could automatically schedule the operation time of each equipment to minimize the residential overall power consumption while satisfying the equipment’s operation time constraint i.e. the equipment needs to be started at a time between two predefined time instants, and the system power load constraint i.e. the power system is not overloaded at any time instant. The paper formulates the situation as an optimization problem and proposes an enhanced Simulated Annealing (SA) based algorithm to find the optimum schedule arrangement for all the tasks. The simulation results show that the SA based scheduling algorithm can efficiently and optimally minimize customers’ electricity cost

Copyright © 2014 Praise Worthy Prize - All rights reserved.


Smart Grid; Advanced Metering Infrastructure; Demand Response; Simulated Annealing

Full Text:



J. M. T. Flick, Securing the smart grid : Next generation power grid security, Syngress Press, pp. 1–312, Oct. 2007.

M. P. S. Han, A method of detecting the manipulation message to provide a reliable demand response service, International Conference on Advanced Communication Technology, vol. 1, pp. 77–80, Apr. 2012.

E.-S. M.Albadi, A summary of demand response in electricity markets, Electrical Power Systems Research Journal, vol. 78, no. 11, pp. 1989– 1996, Nov. 2008.

M.Parajpe, Security and privacy in demad response systems in smart grid, California state university PhD Thesis, pp. 1–203, Sept. 2010.

L. Cormen, Chapter 17 greedy algorithms, Introduction to Algorithms, 2001, pp. 329–332.

N. P. A. Trivedi, Improved multi-objective evolutionary algorithm for day-ahead thermal generation scheduling, IEEE Congress on Evolutionary Computation, vol. 1, pp. 2170–2177, Jun. 2011.

Ahamed, A simulated annealing algorithm for demand response, IEEE PES International Conference and Exhibition on Innovative Smart Grid Technologies, vol. 1, pp. 1–4, Dec. 2011.

G. D. A. Agnetis, Appliance operation scheduling for electricity consumption optimization, in IEEE Conference on Decision and Control and European Control Conference, Jun. 2011, pp. 5899–5904.

S. Kazarlis, A genetic algorithm solution to the unit commitment problem, in IEEE Transactions on Power Systems, Aug. 1996, pp. 83–92.

D. M. D. Dasgupta, Thermal unit commitment using genetic algorithms, in IEE Proceedings-Generation, Transmission and Distribution, vol. 141, Aug. 1994, pp. 459–465.

G. Dudek, Unit commitment by genetic algorithm with specialized search operators, Electric Power Systems Research, vol. 72, pp. 299–308, Feb. 2004.

K. S. S. Valsan, “Hopfield neural network approach to the solution of economic dispatch and unit commitment,” in International Conference on Intelligent Sensing and Information Processing, vol. 1, Jan. 2004, pp. 311–316.

G. Zwe-Lee, “Discrete particle swarm optimization algorithm for unit commitment,” IEEE Power Engineering Society General Meeting, Jul. 2003.

L. Jin, “An improved binary particle swarm optimization for unit commitment problem,” Asia-Pacific Power and Energy Engineering Conference, vol. 4, pp. 884–893, Mar. 2010.

T. Senjyu, “A fast technique for unit commitment problem by extended priority list,” IEEE Transactions on Power Systems, vol. 18, pp. 882– 888, May. 2003.

H. Daneshi, “Mixed integer programming method to solve security constrained unit commitment with restricted operating zone limits,” in IEEE International Conference on Electro/Information Technology, May. 2008, pp. 187–192.

A. Cohen, “A branch and bound algorithm for unit commitment,” IEEE Transactions on Power Apparatus and Systems, vol. 102, pp. 444–451, 1983.

S. Wang, “Short-term generation scheduling with transmission and environmental constraints using an augmented lagrangian relaxation,” IEEE Transactions on Power Systems, vol. 10, pp. 1294 – 1301, Aug. 1995.

N. Ongsakul, “Unit commitment by enhanced adaptive lagrangian relaxation,” IEEE Transactions on Power Systems, vol. 19, pp. 620–628, Feb. 2004.

D. Simopoulos, “Reliability constrained unit commitment using simulated annealing,” in IEEE Transactions on Power Systems, vol. 21, Nov. 2006, pp. 1699 – 1706.

A. Viana, “Simulated annealing for the unit commitment problem,” in IEEE Porto Power Tech Proceedings, vol. 2, May. 2001, pp. 650–655.

Y. Suzannah, “An enhanced simulated annealing approach to unit commitment,” International Journal of Electrical Power and Energy Systems, vol. 20, pp. 359–368, 1998.

T. Victoire and A. Jeyakumar, “An improved simulated annealing method for the combinatorial sub-problem of the profit-based unit commitment problem,” Evolutionary Computation in Combinatorial Optimization, vol. 3448, pp. 234–245, Aug. 2005.

S. Lodi, “Two-dimensional Bin packing problems,” in Paradigms of Combinatorial Optimization, Aug. 2010, pp. 107–129.

P. Martello, Knapsack problems: algorithms and computer implementations. John Wiley & Sons Inc, pp. 145-174, 1990.

T. Kawabe, “Chaotic noise and iterative simulated annealing for tsp,” Proceedings of the 41st SICE Annual Conference, vol. 5, Aug. 2002, pp. 3106–3109.

W. Teukolsky, “Section 10.12. simulated annealing methods,” in Numerical Recipes: The Art of Scientific Computing, Spet. 2007, pp. 456–489.

P. Black, “greedy algorithm,” Dictionary of Algorithms and Data Structures, Feb. 2005, pp. 10–18.

Mohamad Nikkhah, Mojdehi Ahad, A Genetic Based Algorithm for Intentional Islanding of Distribution Network to Maximize Social Welfare, (2009) International Review on Modelling & Simulations (IREMOS), 2 (5), pp. 525-531.

Bouslama-Bouabdallah, S., Tagina, M., A fault detection and isolation fuzzy system optimized by genetic algorithms and simulated annealing, (2010) International Review on Modelling and Simulations (IREMOS), 3 (2), pp. 212-219.


  • There are currently no refbacks.

Please send any question about this web site to
Copyright © 2005-2024 Praise Worthy Prize