Short-term net feeder load forecasting of microgrid considering weather conditions

In this paper, an approach of feeder net load forecasting is proposed for mirogrid operation. Firstly, the output of intermittent renewable energy sources are took into account as a negative load and give the net feeder load definition. Then the feeder load patterns are established according to weather conditions and different solar terms that may reflect the change of season. The forecasting model of back-propagation network is developed with improved Levenberg-Maruardt (LM) training algorithm. The optimized solution of the developed model can accurately forecast the minutely net feeder loads. The validity of the proposed approach for a simplified microgrid is shown by the simulation results.

[1]  George W. Irwin,et al.  A New Jacobian Matrix for Optimal Learning of Single-Layer Neural Networks , 2008, IEEE Transactions on Neural Networks.

[2]  Anurag K. Srivastava,et al.  Controls for microgrids with storage: Review, challenges, and research needs , 2010 .

[3]  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.

[4]  P. J. Werbos,et al.  Backpropagation: past and future , 1988, IEEE 1988 International Conference on Neural Networks.

[5]  Mohammad Bagher Menhaj,et al.  Training feedforward networks with the Marquardt algorithm , 1994, IEEE Trans. Neural Networks.

[6]  Hao Yu,et al.  Improved Computation for Levenberg–Marquardt Training , 2010, IEEE Transactions on Neural Networks.

[7]  L. Clotea,et al.  Smart electrical energy storage system for small power wind turbines , 2010, 2010 12th International Conference on Optimization of Electrical and Electronic Equipment.

[8]  F. Galiana,et al.  Stochastic Security for Operations Planning With Significant Wind Power Generation , 2008, IEEE Transactions on Power Systems.

[9]  Daniel S. Kirschen,et al.  Estimating the Spinning Reserve Requirements in Systems With Significant Wind Power Generation Penetration , 2009, IEEE Transactions on Power Systems.

[10]  Jiann-Ming Wu,et al.  Multilayer Potts Perceptrons With Levenberg–Marquardt Learning , 2008, IEEE Transactions on Neural Networks.

[11]  Stavros J. Perantonis,et al.  Two highly efficient second-order algorithms for training feedforward networks , 2002, IEEE Trans. Neural Networks.

[12]  Athula D. Rajapakse,et al.  Microgrids research: A review of experimental microgrids and test systems , 2011 .

[13]  Jian Ma,et al.  Incorporating Uncertainty of Wind Power Generation Forecast Into Power System Operation, Dispatch, and Unit Commitment Procedures , 2011, IEEE Transactions on Sustainable Energy.