A New List of Research related to renewables energy senario generation
By using a set of possible power generation scenarios, renewables producers and system operators are able to make decisions that take uncertainties into account, such as stochastic economic dispatch/unit commitment, optimal operation of wind and storage systems, and trading strategies (e.g., see [4], [5], [6] and the references within). Currently, most methods adopted a model-based approach [7], [8], [9], [10]. An explicit probabilistic model is first fitted from historical data, then it is sampled to generate new scenarios [11], [12], [13], [14], [15]. Some of these methods may also require pre-processing of data. For example, ARMA models may require the input data that are marginally distributed as Gaussian, so preprocessing is generally required.
[9] T. Wang, H.-D. Chiang, and R. Tanabe, “Toward a flexible scenario generation tool for stochastic renewable energy analysis,” in 2016 Power Systems Computation Conference (PSCC), 2016, pp. 1–7.
[10] J. E. B. Iversen and P. Pinson, “RESGen: Renewable Energy Scenario Generation Platform,” in Proceedings of IEEE PES General Meeting, 2016.
[13] X.-Y. Ma, Y.-Z. Sun, and H.-L. Fang, “Scenario generation of wind power based on statistical uncertainty and variability,” IEEE Transactions on Sustainable Energy, vol. 4, no. 4, pp. 894–904, 2013.