Medium-term forecasting of power generation by hydropower plants in isolated power systems under climate change

Date
2022-11
Authors
Safaraliev, Murodbek
Kiryanova, Natalya
Matrenin, Pavel
Dmitriev, Stepan
Kokin, Sergey
Kamalov, Firuz
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier Ltd
Abstract
Reliable operation of power systems (PS), including those with a significant share of hydropower plants (HPPs) in the energy balance, largely depends on the accuracy of forecasting power generation. The importance of power generation forecasts increases with the development of renewable power generation, which is stochastic by nature. Those kinds of tasks are complicated by the lack of reliable information on metrological data and estimated energy consumption, which is also stochastic. In the medium-term forecasting (MTF) of power generation by HPPs, the seasonality of changes in flow and inflow of water should be taken into account, which significantly affects the reserves and regulatory capabilities of the power system as a whole. This work discusses the problem of constructing a model for MTF of power generation HPP in isolated power systems (IPS), taking into account such atmospheric parameters as air temperature, wind speed and humidity. To address constant climatic changes, this paper suggests implementing machine learning models. The proposed approach is characterized by a high degree of autonomy and learning automation. The paper provides a comparative study of the machine learning models such as polynomial model with Tikhonov's regularization (LR), k-nearest neighbors (kNN), multilayer perceptron (MLP), ensembles of decision trees, adaptive boosting of linear models (ABLR), etc. Computational experiments have shown that the machine learning approach yields the results of sufficient quality, which allows to use them for forecasting of power generation HPP in isolated power systems under conditions of climate change. The Adaptive Boosting Linear Regression model is the simplest and most reliable machine learning model that has proven itself well in the tasks with a relatively small amount of training samples. © 2022 The Author(s)
Description
This article is licensed under Creative Commons License and full text is openly accessible in CUD Digital Repository. The version of the scholarly record of this work is published in Energy Reports (2022), available online at: https://doi.org/10.1016/j.egyr.2022.09.164
Keywords
Climate change, Ensemble models, GBAO, Hydropower plant, Isolated power system, Medium-term forecasting of power generation, Temperature
Citation
Safaraliev, M., Kiryanova, N., Matrenin, P., Dmitriev, S., Kokin, S., & Kamalov, F. (2022). Medium-term forecasting of power generation by hydropower plants in isolated power systems under climate change. Energy Reports, 8, 765-774. doi:10.1016/j.egyr.2022.09.164
DOI