Day‐ahead dispatch strategy for integrated system of wind/photovoltaic/pumped‐storage/gas‐turbine‐power/energy storage based on multi‐frequency scale of PWP

Summary A day-ahead dispatch strategy for the integrated system of wind/photovoltaic/pumped-storage/gas-turbine-power/energy storage is proposed based on the multi-frequency scale power of wind/photovoltaic (PWP). First, filter analysis of the PWP is carried out according to the control target of the system to get the components of PWP under different frequency scales; thus, the power output of different compensatory plants can be scheduled. Second, an improved particle swarm optimization algorithm is utilized to parallel optimize costs of different plants and calculate the overall power output of the integrated system. Finally, based on different dispatch modes, considering system stability and the penetration ability of wind/photovoltaic, the per-unit generation cost of the integrated system is optimized and the power output is scheduled. Simulation results show that, compared with traditional optimization models and algorithms, the proposed strategy is capable of smoothing the power fluctuation caused by large-scale wind/photovoltaic integration, as well as realizing economical, efficient and environmentally friendly operation of the system. Copyright © 2014 John Wiley & Sons, Ltd.

[1]  Eiichi Tanaka,et al.  An Evolutionary Programming Solution to the Unit Commitment Problem , 1997 .

[2]  K. Clark,et al.  Hybrid wind and advanced gas turbine farms: firm dispatchable power for weak grids , 2005, IEEE Power Engineering Society General Meeting, 2005.

[3]  Ziyad M. Salameh,et al.  Sizing of a stand-alone hybrid wind-photovoltaic system using a three-event probability density approximation , 1996 .

[4]  T. Sasaki,et al.  Study on load frequency control using redox flow batteries , 2004, IEEE Transactions on Power Systems.

[5]  Weidou Ni,et al.  Framework design of a hybrid energy system by combining wind farm with small gas turbine power plants , 2010 .

[6]  Anastasios G. Bakirtzis,et al.  A genetic algorithm solution to the unit commitment problem , 1996 .

[7]  Marco Dorigo,et al.  Distributed Optimization by Ant Colonies , 1992 .

[8]  Christine Elizabeth Schoppe.  Wind and Pumped-Hydro Power Storage: Determining Optimal Commitment Policies with Knowledge Gradient Non-Parametric Estimation , 2010 .

[9]  R. Eberhart,et al.  Comparing inertia weights and constriction factors in particle swarm optimization , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[10]  J. A. Carta,et al.  Technical–economic analysis of wind-powered pumped hydrostorage systems. Part I: model development , 2005 .

[11]  Saman K. Halgamuge,et al.  Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients , 2004, IEEE Transactions on Evolutionary Computation.

[12]  Djamila Diaf,et al.  A methodology for optimal sizing of autonomous hybrid PV/wind system , 2007 .

[13]  Jun Zhang,et al.  A Novel Set-Based Particle Swarm Optimization Method for Discrete Optimization Problems , 2010, IEEE Transactions on Evolutionary Computation.

[14]  S.A. Lone,et al.  Integrating a Redox Flow Battery System with a Wind-Diesel Power System , 2006, 2006 International Conference on Power Electronic, Drives and Energy Systems.

[15]  I. Suzuki,et al.  High power and long life lithium-ion battery for backup power sources , 2003, The 25th International Telecommunications Energy Conference, 2003. INTELEC '03..

[16]  Yoshikazu Fukuyama,et al.  A Hybrid Particle Swarm Optimization for Distribution State Estimation , 2002, IEEE Power Engineering Review.

[17]  A. T. Holen,et al.  Operation planning of hydrogen storage connected to wind power operating in a power market , 2006, IEEE Transactions on Energy Conversion.

[18]  J. A. Carta,et al.  Technical-economic analysis of wind-powered pumped hydrostorage systems. Part II: Model application to the island of El Hierro , 2005 .