Short term generation scheduling of a Microgrid

Microgrids are low voltage intelligent distribution networks comprising various distributed generators, storage devices and controllable loads which can be operated as interconnected or as islanded system. The optimal generation scheduling is one of the important functions for the Microgrid operation. This paper describes a three-step efficient method for the optimal generation scheduling of a Microgrid in island operation. The first step of the method is to set up an initial feasible solution for thermal unit commitment and the next step is to solve the thermal unit commitment problem. The final step is to optimize the renewable-thermal dispatch based on thermal unit commitment results. Solving the thermal unit commitment problem has more opportunity to minimize the operating cost. Therefore, few algorithms such as Lagrangian relaxation, genetic algorithm and a hybrid algorithm of Lagrangian relaxation and genetic algorithm have been used to find the least operating cost. Microgrid which is considered in the case study, consists of a PV system, a wind plant, 10 thermal units and a battery bank.

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

[2]  A. H. Mantawy,et al.  A genetic-based algorithm for fuzzy unit commitment model , 2000, 2000 Power Engineering Society Summer Meeting (Cat. No.00CH37134).

[3]  A.L. Dimeas,et al.  Agent based control of Virtual Power Plants , 2007, 2007 International Conference on Intelligent Systems Applications to Power Systems.

[4]  Chuan-Ping Cheng,et al.  Unit commitment by Lagrangian relaxation and genetic algorithms , 2000 .

[5]  A. Bakirtzis,et al.  A solution to the unit-commitment problem using integer-coded genetic algorithm , 2004, IEEE Transactions on Power Systems.

[6]  M. Pipattanasomporn,et al.  Intelligent Distributed Autonomous Power Systems (IDAPS) , 2007, 2007 IEEE Power Engineering Society General Meeting.

[7]  W. Ongsakul,et al.  Unit commitment by enhanced adaptive Lagrangian relaxation , 2004, IEEE Transactions on Power Systems.

[8]  S. M. Shahidehpour,et al.  Short term generation scheduling in photovoltaic-utility grid with battery storage , 1997 .

[9]  M. Shahidehpour,et al.  Restructured Electrical Power Systems: Operation: Trading, and Volatility , 2001 .

[10]  D. Srinivasan,et al.  A priority list-based evolutionary algorithm to solve large scale unit commitment problem , 2004, 2004 International Conference on Power System Technology, 2004. PowerCon 2004..

[11]  T. Logenthiran,et al.  Multi-agent coordination for DER in MicroGrid , 2008, 2008 IEEE International Conference on Sustainable Energy Technologies.

[12]  S. M. Shahidehpour,et al.  An intelligent dynamic programming for unit commitment application , 1991 .

[13]  M. H. Wong,et al.  A Hybrid Artificial Neural Network-Dynamic Programming Approach to Unit Commitment , 1998 .

[14]  Zuyi Li,et al.  Market Operations in Electric Power Systems : Forecasting, Scheduling, and Risk Management , 2002 .

[15]  J. Oyarzabal,et al.  Management of microgrids in market environment , 2005, 2005 International Conference on Future Power Systems.