Performance analysis of heuristic optimization algorithms for demand side energy scheduling with TOU pricing

  • Authors

    • Nilima R. Das SOA(Deemed to be University)
    • Satyananda C.Rai
    • Ajit K.Nayak
    2018-12-05
    https://doi.org/10.14419/ijet.v7i4.18306
  • APSO, IQPSOS, NQPSO, PSO, TOUP.

  • The major objective of the paper is to find a suitable optimization algorithm which can manage the energy consumption behaviour of a consumer in presence of time of use (TOU) pricing tariff so that the demand for energy during peak hours as well as the cost of energy for the consumer is minimized. A mathematical model has been presented to describe the proposed demand management system and a comparative assessment of the performance of different heuristic optimization algorithms for optimization of daily energy consumption of a household has also been made. The algorithm PSO and some of its variants are taken for comparison. The comparative assessment of the algorithms reveals that the NQPSO optimization algorithm which is a quantum based variant of PSO is the best among the discussed algorithms and can be implemented in a residential sector for energy optimization. From the comparison of energy costs with or without optimization it becomes apparent that the projected heuristic based optimization should be used to have an optimized schedule for the operations of the appliances at a household. As a result the individuals are motivated to be a part of the demand side energy management programs which finally leads to a reliable and stable grid system.

     

     

  • References

    1. [1] Y. Y. Hsu and C. C Su, “Dispatch of direct load control using dynamic programmingâ€, IEEE Transactions on Power System, Vol 6,No 3,(1991), pp.1056–1061. https://doi.org/10.1109/59.119246.

      [2] J. Kennedy and R. Eberhart, “Particle swarm optimizationâ€, in Proceedings of the IEEE International Conference on Neural Networks,(1995),pp.1942–1948. https://doi.org/10.1109/ICNN.1995.488968.

      [3] C. N. Kurucz, D. Brandt and S. Sim, “A linear programming model for reducing system peak through customer load control programsâ€, IEEE Transactions on Power System, Vol 11,No 4,( 1996), pp.1817–1824. https://doi.org/10.1109/59.544648 .

      [4] K.H. Ng and G. B. Sheble, “Direct load control-A profit-based load management using linear programmingâ€, IEEE Transactions on Power System, Vol 13, No 2, (1998), pp.688–694. https://doi.org/10.1109/59.667401.

      [5] Z.N. Popovic and D. S. Popovic, “Direct load control as a market-based program in deregulated power industriesâ€, Proceedings of IEEE Bologna Power Tech Conference, Vol 3, (2003), pp.1-4.

      [6] J. Sun, B. Feng and W.B. Xu, “Particle swarm optimization with particles having quantum behaviorâ€, IEEE Proceedings of Congress on Evolutionary Computation, (2004), pp.325–331.

      [7] J. Sun, W.B. Xu and B. Feng, “A global search strategy of quantum-behaved particle swarm optimizationâ€, Cybernetics and Intelligent Systems Proceedings of the 2004 IEEE Conference, (2004), pp.111–116.

      [8] L. Yao, W. C. Chang and R. L. Yen, “An iterative deepening genetic algorithm for scheduling of direct load controlâ€, IEEE Transactions on Power System, Vol 20, No. 3, (2005),pp.1414–1421. https://doi.org/10.1109/TPWRS.2005.852151.

      [9] M. Xi, J. Sun and W. Xu, “An improved quantum-behaved particle swarm optimization algorithm with weighted mean best positionâ€, Applied Mathematics and Computation, Vol 205, No. 2, (2008), pp.751–759. https://doi.org/10.1016/j.amc.2008.05.135.

      [10] Z.H. Zhan, J. Zhang, Y. Li and H.S-H. Chung, “Adaptive Particle Swarm Optimizationâ€, IEEE Transactions on Systems, Man, and Cybernetics, Vol 39, No. 6, (2009), pp. 1362–1381. https://doi.org/10.1109/TSMCB.2009.2015956.

      [11] G. K. Venayagamoorthy, “Potentials and Promises of Computational Intelligence for Smart Gridsâ€, Power & Energy Society General Meeting, PES '09. IEEE Xplore, (2009), pp.1-6.

      [12] T. Logenthiran, D. Srinivasan and A. M. Khambadkone, “Multi-agent system for energy resource scheduling of integrated microgrids in a distributed systemâ€, Electrical Power System Research, Vol 81, No. 1, (2011), pp.138–148. https://doi.org/10.1109/TSMCB.2009.2015956.

      [13] Z. Zhu, J. Tang, S. Lambotharan, W.H. Chin, Z. Fan, “An Integer Linear Programming Based Optimization for Home Demand-side Management in Smart Gridâ€, Innovative Smart Grid Technologies (ISGT), 2012 IEEE PES, (2012), pp.1-5.

      [14] P. Samadi, H. Mohsenian-Rad, R. Schober and W. S. Wong, “Advanced Demand Side Management for the Future Smart Grid Using Mechanism Designâ€, IEEE Transactions on smart grid, Vol 3, No. 3, (2012), pp.1170-1180. https://doi.org/10.1109/TSG.2012.2203341.

      [15] X. Fu, W. Liu, B. Zhang and H. Deng, “Quantum Behaved Particle Swarm Optimization with Neighbourhood Search for Numerical Optimizationâ€, Mathematical Problems in Engineering, pp.1-10, 2013.

      [16] S. Bu and F. R. Yu, “A Game-Theoretical Scheme in the Smart Grid with Demand-Side Management: Towards a Smart Cyber-Physical Power Infrastructureâ€, IEEE Transactions, Vol 1, No. 1, (2013), pp.22-32. https://doi.org/10.1109/TETC.2013.2273457.

      [17] E. Davoodi, M. T. Hagh, and S. G. Zadeh, “A hybrid Improved Quantum-behaved Particle Swarm Optimization–Simplex method (IQPSOS) to solve power system load flow problemsâ€, Applied Soft Computing, Vol 21, (2014), pp.171–179. https://doi.org/10.1016/j.asoc.2014.03.004.

      [18] Yi Liu, C. Yuen, S. Huang, N. U. Hassan, X. Wang and S. Xie, “Peak-to-Average Ratio Constrained Demand-Side Management with Consumer’s Preference in Residential Smart Gridâ€, IEEE journal of selected topics in signal processing, Vol 8, No.6, (2014), pp.1084-1097. https://doi.org/10.1109/JSTSP.2014.2332301.

      [19] C. Zhao,S. Dong, F. Li and Y. Song, “Optimal Home Energy Management System with Mixed Types of Loadsâ€, CSEE journal of Power and Energy Systems, Vol 1, No. 4, (2015), pp.29-36.

      [20] A. Barbatoa, A. Caponea, L. Chenb, F. Martignonb, S. Paris, “A Distributed Demand-Side Management Framework for the Smart Gridâ€, Elsevier Computer Communications, Vol 57, (2015), pp.13-24. https://doi.org/10.1016/j.comcom.2014.11.001.

      [21] H.K. Nguyen, J. B. Song and Z. Han, “Distributed Demand Side Management with Energy Storage in Smart Gridâ€, IEEE Transactions on Parallel and Distributed Systems, Vol 26, No. 12, (2015), pp. 3346-3357. https://doi.org/10.1109/TPDS.2014.2372781.

      [22] J. S. Vardakas, N. Zorba and C. V. Verikoukis, “A Survey on Demand Response Programs in Smart Grids: Pricing Methods and Optimization Algorithmsâ€, IEEE Communication Surveys & Tutorials, Vol 17, No 1, (2015), pp.152-178. https://doi.org/10.1109/COMST.2014.2341586.

      [23] F. Ye, Y. Qian and R. Q. Hu, “A real time Information Based Demand-side Management System in Smart Gridâ€, IEEE Transactions on Parallel and Distributed Systems, Vol 27, No. 2, (2016), pp.329-339. https://doi.org/10.1109/TPDS.2015.2403833.

      [24] B. Hayes, I. Melatti, T. Mancini, M. Prodanovic and E. Tronci, “Residential Demand Management using Individualised Demand Aware Price Policiesâ€, IEEE Transactions On Smart Grid, Vol 8, No. 3, (2017), pp.1284-1294. https://doi.org/10.1109/TSG.2016.2596790.

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    R. Das, N., C.Rai, S., & K.Nayak, A. (2018). Performance analysis of heuristic optimization algorithms for demand side energy scheduling with TOU pricing. International Journal of Engineering & Technology, 7(4), 3835-3842. https://doi.org/10.14419/ijet.v7i4.18306