DSM in an area consisting of residential, commercial and industrial load in smart grid

Abstract

With the latest introduction of the demand side management (DSM) in smart grids, the power distribution units are able to modify the load schedules of the consumers. This involves a co-operative interaction of the utility and the consumers so as to achieve customer load modifications in which the customer, utility and society all are benefited. The interaction is performed with the help of the devices known as the smart meter. This paper shows the use of game theory and logical mathematical expressions in order to achieve the objectives. The objectives are to minimize the peak to average ratio (PAR) and the energy cost. The outcome of the game between supplier and customers helps to shape the load profile. The design proposed in this paper is very user-friendly and is based on simple logarithmic programming computations. In this paper, residential, commercial and industrial types of loads are taken into account. A basic 24 h load schedule along with the fluctuating prices at each hour of the day is forecasted by the supplier of the various shiftable and non-shiftable loads and then that schedule is conveyed to the user. The users are encouraged to shift their high load devices to off-peak hours which will not only reduce their electricity costs but also substantially reduce the PAR in the load demand.

Key words： demand side management (DSM); smart grids; peak to average ratio (PAR); smart meters and logarithmic price function

References

1 Wen L. The application of temporal pattern clustering algorithms in DSM. In: 2006 6th International Conference on Intelligent Systems Design and Applications. Jinan, China, 2006, 569–573

2 Pedrasa A A, Spooner T D, MacGill I F. Scheduling of demand side resources using binary particle swarm optimization. IEEE Transactions on Power Systems, 2009, 24(3): 1173–1181

3 Cárdenas J J, Garcia A, Romeral J L, Andrade F. A genetic algorithm approach to optimization of power peaks in an automated warehouse. In: Proceedings of the 35th IEEE Industrial Electronics Society Congress. IEEE Press, 2009, 3297–3302

4 Bakker V, Bosman M G C, Molderink A, Hurink J L, Smit G J M. Demand side load management using a three step optimization methodology. In: 1st IEEE International Conference on Smart Grid Communications. Gaithersburg, USA, 2010, 431–436

5 Samadi P, Mohsenian-Rad A H, Schober R, Wong V W S, Jatskevich J. Optimal real-time pricing algorithm based on utility maximization for smart grid. In: IEEE International Conference on Smart Grid Communications, Gaithersburg, USA, 2010, 415–420

6 Logenthiran T, Srinivasan D, Shun T Z. Demand side management in smart grid using heuristic optimization. IEEE Transactions on Smart Grid, 2012, 3(3): 1244–1252

7 Et-Tolba El H, Maaroufiand M, Ouassaid M. Demand side management algorithms and modeling in smart grids. In: 2013 International Renewable and Sustainable Energy Conference. Ouarzazate, Morocco, 2013, 531–536

8 Fadlullah Z, Quan D M, Kato N, Stojmenovic I. GTES: an optimized game-theoretic demand side management scheme for smart grid. IEEE Systems Journal, 2014, 8(2): 588–597

9 Mohsenian-Rad A H, Wong V W S, Jatskevich J, Schober R, Leon-Garcia A. Autonomous demand-side management based on game-theoretic energy consumption scheduling for the future smart grid. IEEE Transactions on Smart Grid, 2010, 1(3): 320–331

10 Mohsenian-Rad A H, Leon-Garcia A. Optimal residential load control with price prediction in real-time electricity pricing environments. IEEE Transactions on Smart Grid, 2010, 1(2): 120–133

11 Chuang A S, Gellings C W. Demand-side integration in a restructured electric power industry. In: 42nd International Conference on Large High Voltage Electric Systems. Paris, France, 2008