A novel method of relieving congestion in hybrid deregulated market utilizing renewable energy sources

A novel method of relieving congestion in hybrid deregulated market utilizing renewable energy sources

: This paper presents a congestion management technique in the deregulated power sector by optimally using renewable energy sources (RES). The proposed congestion management problem is formulated to minimize the generator rescheduling cost subjected to the real and reactive power balance, thermal line loading limit, and seasonal and day/night constraints of RES. Optimal selection of conventional and renewable participating generators has been identified by using real and reactive power generator sensitivities and the particle swarm optimization algorithm reduces the alteration of rescheduled values of generator power outputs from base case generation levels. The RES participation along with the seasonal and time variation is the pioneering topic in congestion management that has been studied in this work. The practical Indian Tamil Nadu 106-bus system has been analyzed to illustrate the proposed energy-saving technique. The results confirm the benefits of RES as the number of generators required for rescheduling as well as the rescheduling amount have been reduced predominantly when involving RES for rescheduling.

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  • [1] Shahidehpour M, Yamin H, Li Z. Market Operation in Electric Power Systems. 1st ed. New York, NY, USA: Wiley, 2002.
  • [2] Christie RD, Wollenberg BF, Wangensteen I. Transmission management in the deregulated environment. P IEEE 2000; 88: 170-195.
  • [3] Streimikiene D, Alisauskaite-Seskiene I. External costs of electricity generation options in Lithuania. Renew Energ 2014; 64: 215-224.
  • [4] Niimura T, Niioka S, Yokoyama R. Transmission loading relief solutions for congestion management. Electr Pow Syst Res 2003; 67: 73-78.
  • [5] Kumar A, Srivastava SC, Singh SN. A zonal congestion management approach using real and reactive power rescheduling. IEEE T Power Syst 2004; 19: 554-562.
  • [6] Talukdar BK, Sinhaa AK, Mukhopadhyaya S, Bose A. A computationally simple method for cost-efficient generation rescheduling and load shedding for congestion management. Int J Elec Power 2005; 27: 379-388.
  • [7] Liu J, Salama MMA, Mansour R. Identify the impact of distributed resources on congestion management. IEEE T Power Deliver 2005; 20: 1998-2005.
  • [8] Dutta S, Singh SP. Optimal rescheduling of generators for congestion management based on particle swarm optimization. IEEE T Power Syst 2008; 23: 1560-1569.
  • [9] Venkaiah CH, Vinodkumar DM. Fuzzy adaptive bacterial foraging congestion management using sensitivity based optimal active power re-scheduling of generators. Appl Soft Comput 2011; 11: 4921-4930.
  • [10] Hazra J, Sinha AK. Congestion management using multiobjective particle swarm optimization. IEEE T Power Syst 2007; 22: 1726-1734.
  • [11] Senjyu T, Hayashi D, Urasaki N, Funabashi T. Optimum configuration for renewable generating systems in residence using genetic algorithm. IEEE T Energy Conver 2006; 21: 459-466.
  • [12] Dudhani S, Sinha AK, Inamdar SS. Renewable energy sources for peak load demand management in India. Int J Elec Power 2006; 28: 396-400.
  • [13] Førsund FR, Singh B, Jensen T, Larsen C. Phasing in wind-power in Norway: Network congestion and crowding-out of hydropower. Energ Policy 2008; 36: 3514-3520.
  • [14] Babu CA, Ashok S. Optimal utilization of renewable energy-based IPPs for industrial load management. Renew Energ 2009; 34: 2455-2460.
  • [15] Dai R, Mesbahi M. Optimal power generation and load management for off-grid hybrid power systems with renewable sources via mixed-integer programming. Energ Convers Manage 2013; 73: 234-244.
  • [16] Sood YR, Singh R. Optimal model of congestion management in deregulated environment of power sector with promotion of renewable energy sources. Renew Energ 2010; 35: 1828-1836.
  • [17] Singh K, Padhy NP, Sharma J. Congestion management considering hydro–thermal combined operation in a pool based electricity market. Int J Elec Power 2011; 33: 1513-1519.
  • [18] Moreno A, Gilabert MA, Camacho F, Mart´ınez B. Validation of daily global solar irradiation images from MSG over Spain. Renew Energ 2013; 60: 332-342.
  • [19] Karagali I, Badger M, Hahmann AN, Pe˜na A, Hasager CB, Sempreviva AM. Spatial and temporal variability of winds in the Northern European Seas. Renew Energ 2013; 57: 200-210.
  • [20] Ellis A, Nelson R, Engeln EV, Walling R, MacDowell J, Casey L, Seymour E, Peter W, Barker C, Kirby B et al. Reactive power performance requirements for wind and solar plants. In: IEEE 2012 Power and Energy Society General Meeting; 22–26 July 2012; San Diego, CA, USA: IEEE. pp. 1-8.
  • [21] Ackermann T Wind Power in Power Systems 2nd ed New York, NY, USA: Wiley, 2012
  • [22] Dai Y, Ni YX, Shen CM, Wen FS, Han ZX, Wu FF. A study of reactive power marginal price in electricity market. Electr Pow Syst Res 2001; 57: 41-48.
  • [23] Kennedy J, Eberhart R. Particle swarm optimization. In: IEEE 1995 International Conference on Neural Networks; 1995; Perth, Australia. pp. 1942-1948.
  • [24] Ratnaweera A, Halgamuge SK, Watson HC. Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients. IEEE T Evolut Comput 2004; 8: 240-255.
  • [25] Tamil Nadu Electricity Board Statistics at a Glance 2012-2013. Planning Wing of Tamil Nadu Electricity Board Report: Chennai, India, 2013.
  • [26] Power World Simulator Version 17. http://www.powerworld.com/powerworld-simulator-17.
Turkish Journal of Electrical Engineering and Computer Sciences-Cover
  • ISSN: 1300-0632
  • Yayın Aralığı: 6
  • Yayıncı: TÜBİTAK
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