Methodological Approaches to Building the Scenarios of Inflows into Reservoirs When Modeling Long-Term Regimes of Hydroelectric Power Plants
The paper highlights the problems in current planning and approaches to building the scenarios for the long-term operation of hydroelectric power plants (HPPs) and their cascades as part of energy and water management systems. The study considers advanced global climate models to assess the future water availability in rivers, lakes, and reservoirs. The long-term forecasting GeoGIPSAR system developed at Melentiev Energy Systems Institute SB RAS, which includes the methods for analyzing spatially distributed climatic data, is presented. The approach proposed to build the long-term scenarios of water inflows into the reservoirs of hydroelectric power plants and temperature conditions is described. The multivariate neural network designed to obtain the interval estimates of the studied indices, such as the net inflow of water into the reservoir for individual months or the temperature conditions for specific points or a selected region, is presented. Results achieved by applying the developed approach in practice are shown when building the predictive scenarios of water inflows into Lake Baikal and the reservoirs of the Angara–Yenisei cascade HPPs for the years 2021 and 2022. Estimates of projected electricity generation for individual HPPs are given for the minimum, maximum, and most probable inflow scenarios. The developed system makes it possible to improve the quality of projected estimates compared to the statistical data, both for individual hydroelectric power plants and for the entire power system. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Библиографическая ссылка
Nikitin V.M., Abasov N.V., Berezhnykh T.V., Osipchuk E.N. Methodological Approaches to Building the Scenarios of Inflows into Reservoirs When Modeling Long-Term Regimes of Hydroelectric Power Plants // Lecture Notes in Mechanical Engineering. 2022. P.253-261. ISBN (print): 9789811693755. DOI: 10.1007/978-981-16-9376-2_25
