An elegant emergence of optimal siting and sizing of multiple distributed generators used for transmission congestion relief

After the implementation of deregulation in a power system, an appreciable volume of renewable energy sources is used to generate electric power. Even though they are intended to improve the reliability of the power system, the unpredictable outages of generators or transmission lines, an impulsive increase in demand, and failures of other equipment lead to congestion in one or more transmission lines. There are several ways to alleviate this transmission congestion, such as the installation of new generation facilities in the place where the demand is high, the addition of a new transmission facility, generation rescheduling, and curtailment of load demand processes. Among the above methods generation rescheduling and load shedding are normally preferred, since the other methods require additional investments. However, some critical cases require improved methods to alleviate congestion. With the extensive application of distributed generation (DG), congestion management is also accomplished by the optimal placement of multiple DG units. It is well known that incorrect sizes and improper locations of DG undoubtedly create higher power losses and an undesirable voltage profile. Hence, this research effort employs the line flow sensitivity index to establish the optimal location of DG units and genetic algorithm-based optimization for determining the optimal sizes of DG units. The objective of this research is to minimize the total losses and real power flow performance index and to improve the voltage shape of the modified IEEE 30-bus test system. The results of this proposed approach are encouraging and help in anticipating higher efficiency by satisfying all the objectives.

An elegant emergence of optimal siting and sizing of multiple distributed generators used for transmission congestion relief

After the implementation of deregulation in a power system, an appreciable volume of renewable energy sources is used to generate electric power. Even though they are intended to improve the reliability of the power system, the unpredictable outages of generators or transmission lines, an impulsive increase in demand, and failures of other equipment lead to congestion in one or more transmission lines. There are several ways to alleviate this transmission congestion, such as the installation of new generation facilities in the place where the demand is high, the addition of a new transmission facility, generation rescheduling, and curtailment of load demand processes. Among the above methods generation rescheduling and load shedding are normally preferred, since the other methods require additional investments. However, some critical cases require improved methods to alleviate congestion. With the extensive application of distributed generation (DG), congestion management is also accomplished by the optimal placement of multiple DG units. It is well known that incorrect sizes and improper locations of DG undoubtedly create higher power losses and an undesirable voltage profile. Hence, this research effort employs the line flow sensitivity index to establish the optimal location of DG units and genetic algorithm-based optimization for determining the optimal sizes of DG units. The objective of this research is to minimize the total losses and real power flow performance index and to improve the voltage shape of the modified IEEE 30-bus test system. The results of this proposed approach are encouraging and help in anticipating higher efficiency by satisfying all the objectives.

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  • El-Khattam W, Hegazy YG, Salama MMA. An integrated distributed generation optimization model for distribution system planning. IEEE T Power Syst 2005; 20: 1158–1165.
  • Wang C, Nehrir MH. Analytical approaches for optimal placement of distributed generation sources in power systems. IEEE T Power Syst 2004; 19: 2068–2076.
  • Keane A, O’Malley M. Optimal allocation of embedded generation on distribution networks. IEEE T Power Syst ; 20: 1640–1646.
  • Sasiraja RM, Suresh Kumar V, Sudha S. A heuristic approach for optimal location and sizing of multiple DGs in radial distribution system. Appl Mech Mater 2014; 626: 227–233.
  • Singh RK, Goswami SK. Optimum siting and sizing of distributed generations in radial and networked systems. Electr Pow Compo Sys 2009; 37: 127–145.
  • Gautam D, Mithulananthan N. Optimal DG placement in deregulated electricity market. Electr Pow Syst Res 2007; : 1627–1636.
  • Ghosh S, Ghoshal SP, Ghosh S. Optimal sizing and placement of distributed generation in a network system. Int J Elec Power 2010; 32: 849–856.
  • G¨ozel T, Hocaoglu MH. An analytical method for the sizing and siting of distributed generators in radial systems. Electr Pow Syst Res 2009; 79: 912–918.
  • Elnashar MM, Shatshat RE, Salama MMA. Optimum siting and sizing of a large distributed generator in a mesh connected system. Electr Pow Syst Res 2010; 80: 670–697.
  • Tuan LA, Bhattacharya K, Daalder J. Transmission congestion management in bilateral markets: an interruptible load auction solution. Electr Pow Syst Res 2005; 74: 379–389.
  • Xu D, Girgis AA. Optimal load shedding strategy in power systems with distributed generation. In: IEEE 2001
  • Power Engineering Society Winter Meeting; 28 January–1 February 2001; Columbus, Ohio, USA. New York, NY, USA: IEEE. pp. 788–793.
  • Muthulakshmi K, Babulal CK. Relieving transmission congestion by optimal rescheduling of generators using PSO. Appl Mech Mater 2014; 626: 213–218.
  • Yesuratnam G, Thukaram D. Congestion management in open access based on relative electrical distances using voltage stability criteria. Electr Power Sys Res 2007; 77: 1608–1618.
  • Afkousi MP, Abbaspour ATF, Rashidinejad M, Lee KY. Optimal placement and sizing of distributed resources for congestion management considering cost/benefit analysis. In: IEEE 2010 Power and Energy Society General
  • Meeting; 25–29 July 2010; Minneapolis, MN, USA. New York, NY, USA: IEEE. pp. 1–7.
  • Singh AK, Parida SK. Congestion management with distributed generation and its impact on electricity market. Int J Elec Power 2013; 48: 39–47.
  • Zimmerman RD, Murillo-Sanchez CE. MATPOWER: A MATLAB Power System Simulation Package. 5th ed. Tempe, AZ, USA: Arizona State University, 2014.
Turkish Journal of Electrical Engineering and Computer Science-Cover
  • ISSN: 1300-0632
  • Yayın Aralığı: Yılda 6 Sayı
  • Yayıncı: TÜBİTAK
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