Wind power development in Eastern Kazakhstan threatens migration of eagles

On the basis of data obtained from ARGOS/GPS and GPS/GSM tracking of 34 eagles (4 Steppe Eagles (Aquila nipalensis) from Central KZ, 1 Steppe Eagle from Southern Ural region, 22 Steppe Eagles, 5 Eastern Imperial Eagles (Aquila heliaca) from the ASR and 2 Greater Spotted Eagles (Aquila clanga) from the from the Altai-Sayan Ecoregion), we have defined the main flyways, terms, and other parameters of migration of eagles through Eastern Kazakhstan. We have outlined the borders of the migration corridor and estimate the number of migrants passing through it. The study highlights the importance of the Karatau ridge for eagles from the vast territories of Russia and Kazakhstan. But we are also concerned about the development of wind farms with horizontal-axis wind turbines that expose ultimate danger for raptors in the Karatau migration corridor. One of them already exists – the Zhanatas Wind-Power Station. Here we calculated the possible negative impact on the eagle population from the existing and projected wind farms of the Karatau ridge and give our recommendations for neutralizing the damage from the development of the electric power industry in Karatau.

Determining the Optimal Offshore Wind Power Station Using a Two-Stage MCDM-Based Spherical Fuzzy Set Approach

In response to challenges from the COVID-19 pandemic and climate change to achieve the goal of ensuring sustainable economic growth, offshore wind power development not only provides a clean and sustainable source of energy but also provides opportunities for economic growth and job creation. Offshore wind energy projects have been promptly suggested in Vietnam as a result of policy advancement, with the country's excellent wind resources. The success of an offshore wind energy project is decided mainly by choosing the best location for offshore wind power station (OWPS) construction, which is a complex multicriteria decision-making (MCDM) problem with the coexistence of conflicting factors. There is a problem with incomplete decision information use and information loss during the decision-making process, and it is easy to overlook the interaction difficulty in a fuzzy environment. To address the complex nature of the prioritization problem posed, this study proposes a hybrid MCDM framework combining the spherical fuzzy analytical hierarchy process (SF-AHP) and weighted aggregated sum product assessment (WASPAS). SF-AHP is used in the first stage to determine the significance levels of OWPS evaluation criteria. WASPAS is then utilized to rank locations of OWPS. A comprehensive set of evaluation criteria developed based on the concept of sustainable development has been recognized by reviewing the literature review and interviewing experts to practice the two-stage MCDM model. A real case study for Vietnam is conducted to test the effectiveness of the proposed method. The best location schemes have been determined by using the decision framework. The results of the sensitivity analysis and a comparison analysis demonstrate that the decision framework is practical and robust. Ultimately, the evaluation criteria and methodology presented in this work can serve as a theoretical foundation for the advancement of offshore wind energy and coastal development.

Optimisation of Energy Accumulation for Renewable Energy Sources

This project focuses on optimisation of energy accumulation for various types of distributed renewable energy sources. The main goal is to prepare charging – discharging strategy depending on actual power consumption and prediction of consumption and production of utilised renewable energy sources for future period. The simulation is based on real long term data measured on photovoltaic system, wind power station and meteo station between 2004 – 2021. The data from meteo station serve as the input for the simulation and prediction of the future production while the data from PV system and wind turbine are used either as actual production or as a verification of the predicted values. Various parameters are used for trimming of the optimisation process. Influence of the charging strategy, discharging strategy, values and shape of the demand from the grid and prices is described on typical examples of the simulations. The main goal is to prepare and verify the system in real conditions with real load chart and real consumption defined by the model building with integrated renewable energy sources. The system can be later used in general installations on commercial or residential buildings.

ECONOMICALLY JUSTIFIED CURRENT DENSITY FOR 10-35 kV CABLES, WHICH CONNECT POWERFUL WIND GENERATORS

At a wind power station, 10-35 kV cable transmission lines are used to connect powerful 1.5-5.5 MW wind turbines. Due to the high cost of “green” electricity, losses in the cable lines of a power station can be too expensive. Therefore, during the design process, it is necessary to choose such a cross-section of cable cores that will ensure the minimum costs to the investor for the entire operation of the wind power station. An analytical dependence is obtained, which makes it possible to calculate an economically justified current density for the cores of such cables for 10, 20, and 35 kV for any electric energy tariff and the duration of the maximum generation of the power station. The economically justified current density for the cables of the electric network of the wind power station is calculated for the current “green” tariff and the tariff that will be introduced in Ukraine from 01.01.2025, with different durations of maximum generation throughout the year. It was established that the cross-sections of cable cores that are selected according to the requirements of current regulatory documents will be 2-3 times smaller than those selected for the obtained value of economically justified current density. The economic current density values given in the article make it possible to choose rational cross-sections cable core during the design of networks of the wind power station. References 4, figures 2. table 1.