A Review on Demand Side Management and Case Study Using Optimization Tool

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dc.contributor.author Islam, Shafayetul
dc.contributor.author Alvy, Shadman
dc.contributor.author Ahmed, Shadman
dc.contributor.author Leon, Mahmudur Rahman
dc.date.accessioned 2020-12-25T06:45:44Z
dc.date.available 2020-12-25T06:45:44Z
dc.date.issued 2019-11-15
dc.identifier.citation [1] [Ontario, 2006] “Energy Conservation Committee Report and Recommendations, Reducing electricity consumption in houses”, Ontario Home Builders Assoc., 2006 [2] [Macedo et al., 2015] Macedo M, Galo J, de Almeida L, Lima AdC, “Demand side management using artificial neural networks in a smart grid environment”, Renew Sustain Energy, 41:128-33, 2015. [3] 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: Proceedings of the 2010 IEEE smart grid communications; 4–6 Oct. 2010, pp. 431–436. [4] Rosso A,Ma J, Kirschen D S,Ochoa L F. Assessing the contribution of demand side management to power system flexibility. In: Proceedings of the 50th IEEE conference on decision and control and European control conference (CDC- ECC); 12–15 Dec. 2011, pp.4361– 4365. [5]Agneessens J, Vandoorn T, Meersman B, Vandevelde L. The use of binary particle swarm optimization to obtain a demand side management system. In: Proceedings of the IEEE IET conference on renewable power generation (RPG 2011); Sept. 2011. [6] Logenthiran Thillainathan, Srinivasan Dipti, Shun Tan Zong. Demand side management in smart grid using heuristic optimization. IEEE Trans Smart Grid, 2012; 3(3): 1244–52 [7] Molitor C, Cali D, Streblow R, Ponci F, Muller D, Monti A. New energy concepts and related information technologies: dual demand side management. In: Proceedings of the IEEE PES innovative smart grid technologies (ISGT); 16–20 Jan. 2012, pp. 1–6. [8] Natarajan Venkat, Closepet Amit S. Demand-side approaches to manage electricity outages in developing countries. In: Proceedings of the 2nd IEEE ENERGYCON conference & exhibition, ICT for Energy Symposium; 9–12 Sept. 2012 pp. 829– 835. [9] Vande Ven Peter M, Hegde Nidhi, Massoulié Laurent, Salonidis Theodoros. Optimal control of end-user energy storage. IEEE Trans Smartgrid 2013; 4(2): 789–97. [10] Croft Aaron, Boys John, Covic Grant. Net energy stored control for residential demand-side management. In: Proceedings of the 39th annual conference of the IEEE industrial electronics society, IECON; 2013. 56 [11] Kinhekar Nandkishor, Padhy Narayana Prasad, Gupta Hari Om. Demand side management for residential consumers. In: IEEE power and energy society general meeting (PES); 2013. [12] Kunwar N, YashK, Kumar R. Area-load based pricing in DSM through ANN and heuristic scheduling. IEEE Trans Smart Grid 2013; 4(3): 1275–81. [13] Chavali P, Yang Peng, Nehorai A. A distributed algorithm of appliance scheduling for home energy management system. IEEE Trans Smart Grid 2014; 5(1): 282–90. [14] Bae Hyoungchel, Yoon Jongha, Lee Yunseong, LeeJuho, Kim Taejin, Yu Jeongseok, etal. User-friendly demand side management for smart grid net- works. In: ICOIN. 84 Heukseok-ro, Dongjak-gu: School of Computer Science and Engineering, Chung-Ang University; International Conference on Information Networking, 10–12 Feb. 2014, vol. pp. 481–485. [15] Dario Javor, Aleksandar Janjić. Using Optimization Tools for Solving Demand Side Management Problems. In: Proceedings of the eNergetics. 2016. [16] Ying Li, Boon Loong Ng, Mark Trayer, and Lingjia Liu, “Automated Residential Demand Response: Algorithmic Implications of Pricing Models,” IEEE Trans. Smart Grid., Vol. 3, No. 4, pp. 1712-1721, Dec 2012. [17] K. Spees and L. Lave, “Impacts of responsive load in pjm: Load shifting and real time pricing,” Energy J., vol. 29, no. 2, pp. 101–122, 2008. [18] P. Cappers, C. Goldman, and D. Kathan, “Demand response in U.S. electricity markets: Empirical evidence,” Lawrence Berkeley National Lab, Tech. Rep. LBNL-2124E, 2009. [19] M. R. Ander berg, Cluster Analysis for Applications. New York, NY, USA: Academic, 1973. [20] M. R. Ander berg, Cluster Analysis for Applications. New York ,NY, USA: Academic, 1973. [21] J. A. Hartigan, Clustering Algorithms. New York, NY, USA: Wiley, 1975. [22] H. C. Romes burg, Cluster Analysis for Researchers. Raleigh, NC, USA: Lulu Press, 2007. [23] J. E. Aronson and G. Klein, “A clustering algorithm for computer-assisted process organization,” Dec. Sci., vol. 29, no. 4, pp. 730–745, 1989. 57 [24] G. Klein and J. E. Aronson, “Optimal clustering: Amodel and method,” Naval Res. Log. Quart., vol. 28, pp. 447–461, 1991. [25] A. Kusiak, “Analysis of integer programming formulations of clustering problems,” Image Vision Comput., vol. 2, no. 1, pp. 35–40, 1984. [26] M. R. Rao, “Cluster analysis with mathematical programming,” J. Amer. Statist. Assoc., Theory Meth., vol. 66, no. 335, pp. 622–626, 1971. [27] P. Hansen and B. Jaumard, “Cluster analysis and mathematical programming,” Math. Prog., vol. 79, pp. 191–215, 1997. [28] J. MacQueen, “Some methods for classification and analysis of multivariate observations,” in Proc. 5th Berkeley Symp. Math., Stat., Prob., 1967, vol. 1, pp. 281–297, Univ. California Press. [29] J. MacQueen, “Some methods for classification and analysis of multivariate observations,” in Proc. 5th Berkeley Symp. Math., Stat., Prob., 1967, vol. 1, pp. 281–297, Univ. California Press. [30] K.-H. Ng and G. B. Sheblé, “Direct load control-A profit-based load management using linear programming,” IEEE Trans. Power Syst., vol. 13, no. 2, pp. 688–694, May 1998. [31] F. C. Schweppe, B. Daryanian, and R. D. Tabors, “Algorithms for a spot price responding residential load controller,” IEEE Trans. Power Syst., vol. 4, no. 2, pp. 507–516, May 1989. [32] F. P. Sener, "System planning using existing flexibility," IEEE Trans. on Power Systems, vol. 11, no. 4, pp. 1874-1878, Nov. 1996. [33] G. Xu, C. Kang, G. Yang, and Z. Wang, "A novel flexibility evaluating approach for power system planning under deregulated environment," in Proc. 2006 International Conference on Power System Technology (PowerCon), pp. 1-6. [34] A. Tuohy, P. Meibom, E. Denny, and M. O'Malley, "Unit commitment for systems with significant wind penetration," IEEE Trans. on Power Systems, vol. 24, no. 2, pp. 592-601, May 2009. [35] C.-L. Su and D. Kirschen, "Quantifying the effect of demand response on electricity markets," IEEE Trans. on Power Systems, vol. 24, no. 3, pp. 1199-1207, Aug. 2009. 58 [36] R. Tyagi, J. Black, and J. Petersen, "Scheduling demand response events with constraints on total number of events per year," in Proc. 2010 IEEE Energy Conversion Congress and Exposition. [37] A. Kowli and G. Gross, "Quantifying the variable effects of systems with demand response resources," in Proc. 2010 Bulk Power System Dynamics and Control Symposium (iREP). [38] “NOBEL.” [Online]. Available: http://www.ict-nobel.eu/. [Accessed: 15-Jul-2011]. [39] Hussein. A. Attia, “Mathematical Formulation of the Demand Side Management (DSM) Problem and its Optimal Solution”, In Proceedings of the 14th International Middle East Power Systems Conference (MEPCON’10), Cairo University, Egypt, December 19-21, 2010, Paper ID 314. [40] Dario Javor, Aleksandar Janjić. “Using Optimization Tools for Solving Demand Side Management Problems” , In proceeding of e Nergetics, 2016 en_US
dc.identifier.uri http://hdl.handle.net/123456789/734
dc.description Supervised by Dr. Ashik Ahmed Assistant Professor, Department of Electrical and Electronic Engineering, Islamic University of Technology(IUT) en_US
dc.description.abstract Utilities around the world are now considering Demand Side Management (DSM) in their strategic planning. The costs of constructing and operating a new capacity generation unit are increasing everyday, which force the utilities to search for another alternative without any additional constraints on customers comfort level or quality of delivered product. DSM encompasses a broad range of utility initiated activities to encourage end users to willingly modify their electricity consumption without any impact on service quality or customer satisfaction. It was found that an objective function reflecting the user electricity expenses did widely serve the best for both the electric utility as well as the end user. From a utility point of view, benefits are meterized as freed capacity, deferred investment or increased revenues. Other developed target objective functions such as maximizing the load factor or the utility revenues did serve to achieve its targets but without much impact on end user electricity expenses or even increased ones. Demand side management (DSM) is one of the important functions in a smart grid that allows customers to make informed decisions regarding their energy consumption, and helps the energy providers reduce the peak load demand and reshape the load profile. This results in increased sustainability of the smart grid, as well as reduced overall operational cost and carbon emission levels. Most of the existing demand side management strategies used in traditional energy management systems employ system specific techniques and algorithms. In addition, the existing strategies handle only a limited number of controllable loads of limited types. This research presents a review on different prevalent demand side management strategies and case study based on peak clipping & load shifting technique for demand side management. Simulations were carried out on a load profile which contains a variety of loads in different hours of the day. The simulation results show that the proposed demand side management strategy achieves substantial savings, while reducing the peak load demand of the smart grid. en_US
dc.language.iso en en_US
dc.publisher Department of Electrical and Electronic Engineering, Islamic University of Technology,Board Bazar, Gazipur, Bangladesh en_US
dc.title A Review on Demand Side Management and Case Study Using Optimization Tool en_US
dc.type Thesis en_US


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