Autoregressive Models of the Process of Post-War Recovery and Post-War Development of the Renewable Sources of Energy in Ukraine
Keywords
Abstract
The paper [1] published the “Draft Recovery Plan for Ukraine: Energy,” developed in 2022 by the National Council for the Restoration of Ukraine. In our work [2], to align the “Draft Plan…” with the realities of the military situation, we synthesized mathematical models to represent the restoration and development processes of Ukraine's electric power industry as a whole, taking into account the stochastic nature of these processes. In our work [3], we further refined the “Draft Plan…” to address the military realities specific to the electric power industry. We developed mathematical models for the restoration and development of renewable electricity sources (RES) in Ukraine. This task was accomplished using various data, tables, and graphs characterizing RES as presented in [1]. However, we expanded the definition of RES beyond solar (SPP), wind (WPP), and biogas (BPP) power plants, as defined in [1], to include hydroelectric (HPP) and pumped storage (PSH) power plants, which were excluded from the RES category in [1]. In [3], we assumed that Ukraine's war with its aggressive neighbor would continue into 2024 and 2025, introducing a stochastic element into restoration plans—both for pre-war infrastructure and assets restored during the war. We also presumed that the restoration and development of RES would continue to exhibit stochastic characteristics over an extended period. For this reason, we proposed using second-order autoregressive models in the form of ARPKC(2,0,1), starting from the zero time point in 2023. However, in [3], the idea of employing autoregressive models was only mentioned in a general formulation. A year after completing [3], during the ongoing war between Ukraine and Russia, Ukraine’s overall electricity infrastructure and RES, in particular, suffered further destruction [4]. This necessitated the synthesis of autoregressive models in a fully operational form, suitable for use by electrical engineers in design projects. This idea is implemented in the present publication as a Python program. The foundational prerequisites for this program were initially presented in our report at the International Scientific Conference held in Kyiv in November 2024, with extended abstracts published in [5].
