Generation Adjustment Method Based on Bus-Dependent Participation Factor

This paper presents a new generation adjustment method for achieving economical operation of power systems. Uncertainty in the loads and renewable energy sources is inevitable in power systems. The participation factor, which can be calculated using the incremental cost, is generally used to manage such unexpected variations. The incremental cost considers only the generators; however, owing to line loss, the location of the variation may also affect the operation performance. Herein, we introduce a bus-dependent participation factor (BDPF) to precisely reflect the line loss. The BDPF is calculated based on the generation-load incremental cost (GLIC), which is the cost variation for a specific generator and load. The GLIC can fairly reflect line loss changes; in addition, the second-order loss sensitivity is used to improve the accuracy of the BDPF. Using the BDPF, power generation can be instantly adjusted under unexpected variations, after which re-adjustment can be performed, if operation constraint violations exist. The proposed method is tested on the IEEE 14-bus and 118-bus systems, and the simulation results validate its effectiveness.

[1]  Han Yu,et al.  An Optimal Power Flow Algorithm to Achieve Robust Operation Considering Load and Renewable Generation Uncertainties , 2012, IEEE Transactions on Power Systems.

[2]  S. Conti,et al.  Optimal Dispatching of Distributed Generators and Storage Systems for MV Islanded Microgrids , 2012, IEEE Transactions on Power Delivery.

[3]  Babu Narayanan,et al.  POWER SYSTEM STABILITY AND CONTROL , 2015 .

[4]  Gary W. Chang,et al.  Power System Analysis , 1994 .

[5]  R D Zimmerman,et al.  MATPOWER: Steady-State Operations, Planning, and Analysis Tools for Power Systems Research and Education , 2011, IEEE Transactions on Power Systems.

[6]  Ross Baldick,et al.  Integration of $N-1$ Contingency Analysis With Systematic Transmission Capacity Expansion Planning: ERCOT Case Study , 2016, IEEE Transactions on Power Systems.

[7]  T.C. Green,et al.  Fuel consumption minimization of a microgrid , 2005, IEEE Transactions on Industry Applications.

[8]  Rabih A. Jabr,et al.  Robust Multi-Period OPF With Storage and Renewables , 2015, IEEE Transactions on Power Systems.

[9]  Dick Duffey,et al.  Power Generation , 1932, Transactions of the American Institute of Electrical Engineers.

[10]  Philip G. Hill,et al.  Power generation , 1927, Journal of the A.I.E.E..

[11]  P. Kundur,et al.  Power system stability and control , 1994 .

[12]  Abhijit R. Abhyankar,et al.  Real-Time Economic Dispatch Considering Renewable Power Generation Variability and Uncertainty Over Scheduling Period , 2015, IEEE Systems Journal.

[13]  Rabih A. Jabr,et al.  Adjustable Robust OPF With Renewable Energy Sources , 2013, IEEE Transactions on Power Systems.

[14]  Thomas J. Overbye,et al.  An Authenticated Control Framework for Distributed Voltage Support on the Smart Grid , 2010, IEEE Transactions on Smart Grid.

[15]  Jin Zhong,et al.  A Generation Adjustment Methodology Considering Fluctuations of Loads and Renewable Energy Sources , 2016, IEEE Transactions on Power Systems.

[16]  J. Aguado,et al.  Cumulant-based probabilistic optimal power flow (P-OPF) with Gaussian and gamma distributions , 2005, IEEE Transactions on Power Systems.

[17]  Oriol Gomis-Bellmunt,et al.  Trends in Microgrid Control , 2014, IEEE Transactions on Smart Grid.

[18]  F. Bouffard,et al.  Stochastic security for operations planning with significant wind power generation , 2008, 2008 IEEE Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century.

[19]  Shaohua Zhang,et al.  Analysis of Probabilistic Optimal Power Flow Taking Account of the Variation of Load Power , 2008, IEEE Transactions on Power Systems.

[20]  Ming Yang,et al.  Robust economic dispatch considering automatic generation control with affine recourse process , 2016 .

[21]  Bin Wang,et al.  Adjustable Robust Real-Time Power Dispatch With Large-Scale Wind Power Integration , 2015, IEEE Transactions on Sustainable Energy.

[22]  D. Hwang,et al.  Loss sensitivity calculation and analysis , 2003, 2003 IEEE Power Engineering Society General Meeting (IEEE Cat. No.03CH37491).

[23]  Chongqing Kang,et al.  LMP Revisited: A Linear Model for the Loss-Embedded LMP , 2017, IEEE Transactions on Power Systems.

[24]  D. P. Kothari,et al.  Power system optimization , 2004, 2012 2nd National Conference on Computational Intelligence and Signal Processing (CISP).

[25]  H. H. Happ,et al.  Optimal Power Dispatch , 1974 .

[26]  Pu Li,et al.  Probabilistic analysis for optimal power flow under uncertainty , 2010 .

[27]  R. Fischl,et al.  Security constrained economic dispatch with participation factors based on worst case bus load variations , 1977, IEEE Transactions on Power Apparatus and Systems.

[28]  R. Bacher,et al.  Real-time optimal power flow in automatic generation control , 1988 .

[29]  Arthur R. Bergen,et al.  Power Systems Analysis , 1986 .

[30]  Alex D. D. Craik,et al.  Prehistory of Faà di Bruno's Formula , 2005, Am. Math. Mon..