Multivariate probabilistic forecasting and its performance’s impacts on long-term dispatch of hydro-wind hybrid systems

Yi Zhang, Chuntian Cheng, Rui Cao, Gang Li, Jianjian Shen and Xinyu Wu

Applied Energy, 2021, vol. 283, issue C, No S0306261920316378

Abstract: There are two difficulties in long-term optimal dispatch of hydro-wind hybrid systems. First, monthly runoffs and wind speeds have the dynamic characteristics such as variability, instability, seasonality, heteroscedasticity, linear and nonlinear dynamic correlations. Second, hydro-wind hybrid systems have highly non-convex nonlinear constraints. To overcome the problem, this research develops a novel X-12 seasonal adjustment, vector autoregressive integrated moving average (VARIMA), component generalized autoregressive conditional heteroscedasticity (C-GARCH) and dynamic copula mixed model to estimate the joint probability distribution of runoffs and wind speeds. And then, this paper builds a multistage stochastic mixed-integer linear programming (MILP) with the help of several linearization methods. Finally, the paper compares several probabilistic forecasting models’ performances and analyzes their impacts on the dispatch of the hydro-wind hybrid system under different hydrological years. A hydro-wind hybrid system in southwest China is taken as an example. The case study leads to the following conclusions: 1) the more sufficient to capture the dynamic characteristics of variables, the higher benefit will be; 2) it is necessary to increase the scale of scenario tree to reduce the electricity shortfall during the dry year; 3) serious spilled water can be caused by insufficient interregional transmission capacity under the wet year and it is the most appropriate to expand the capacity to 8000 MW; 4) the model proposed in this paper can increase the economic benefit by 0.466×109CNY, 1.775×109CNY and 0.400×109CNY during the normal, dry and wet year, respectively.

Keywords: Multivariate probabilistic forecasting; Long-term dispatch; Hydro-wind hybrid system; Non-convex nonlinear constraints; Multistage stochastic MILP; Probabilistic forecasting model's performance (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

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DOI: 10.1016/j.apenergy.2020.116243

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