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The accurate training of a wind power forecasting (WPF) model for a newly built wind farm is difficult because of limited historical data. This study established a multitask learning architecture wherein the WPF in different wind farms represents an independent task. Subsequently, a novel short-term WPF model based on a multitask learning architecture was proposed. In this model, a multitask Gaussian process is used to capture the intertask conjunction, which contributed to the training of each task. The proposed methodological framework employs dependencies from other tasks wherein older wind farms contain substantial historical data to enhance the performance of tasks in which there is a newly built wind farm. Several numerical experiments were conducted using datasets from seven independent wind farms in Australia. The results show that the proposed scheme not only obtains improved point forecasting results but also produces better probabilistic forecasting results, thus demonstrating the superiority of the proposed method.

Pump as turbine (PAT) is a common method of energy recovery, however, vortices are a negative phenom for these units. The objective of this research is to study the effect of vortex motion on the hydraulic loss of pump as turbine, and establishing the correlation mechanism between vortex intensity and turbulence loss. The research method adopts theoretical analysis and model test and numerical simulation. Based on the entropy production theory, the hydraulic loss and the turbulent dissipation in boundary layer induced by vortex motion are studied, revealing the influence of vortices on the energy loss. Results show that the vortex motion can be decomposed into a synchronous component v_sy and a rotational component v_ro, among them, the rotational component v_ro meets to Biot-Savart Law. The turbulent dissipation rate in the boundary layer is closely to the vortex motion, which can characterize the boundary turbulence height. Turbulent flow induced by vortex can propagate in the flow channel of the unit, causing lot of additional hydraulic loss. In the end, a mathematical model between entropy production (Sk) induced by vortex and vortex strength (¿k) was established, indicating that Sk changes with ¿k in the form of a quadratic function.

Wind energy projects provide clean energy so that they should be increased to reach the sustainable development goals of the countries. However, current decision-making process should be improved for the effectiveness of these projects. Thus, critical factors should be considered to understand the significant indicators of the performance of the wind energy projects. This article aims to determine the factors that should be considered when deciding on wind energy investments. In this context, 9 different criteria belonging to 3 dimensions (project, firm, market) are determined based on literature review. Later, an analysis is carried out by using hesitant interval-valued intuitionistic fuzzy (IVIF) Decision Making Trial and Evaluation Laboratory (DEMATEL) to identify the most important factors. Furthermore, 4 different investment strategies in Boston Consultancy Group (BCG) matrix have been determined as alternatives. To determine which of these strategies is suitable for wind energy investments, the hesitant IVIF multi-objective optimization on the basis of ratio analysis (MOORA) method has been considered. Additionally, a comparative evaluation is also performed by using technique for order preference by similarity to ideal solution (TOPSIS) methodology. Similarly, sensitivity analysis is also made by considering 9 different cases. The analysis results of different methodologies are quite similar which shows the coherency and reliability of the findings. It is concluded that firm-based factors play the most significant role. It is also identified that technical development, financial performance and organizational effectiveness are the most significant criteria to make investment decision on wind energy projects. Furthermore, due to the market growth potential, it is recommended that wind energy investors increase their investments and strengthen their position in the market.

The current study investigates if environmental taxation in Turkey significantly impacts stock returns of renewable energy producers. Renewable energy-related firms trading Istanbul Stock Exchange and the overall energy stock index are selected and analyzed using time series methods. Results generally confirm the long-term effects of taxation on renewable energy stock prices. This study demonstrates that variations in national envi ronmental regulations and energy taxation result in significant variations in energy stock performance. It is also found that foreign direct investments in Turkey negatively impact energy stock markets. Implications are pro vided at the end of this study.

With the rapid increasing of wind power generation in the power system, the coordinated dispatch of active and reactive power for each wind turbine (WT) in the wind farm (WF) becomes the critical issue for the safe and stable of power grid. Considering the time-varying characteristic of the WF, this can be regarded as a decision-making problem under uncertainty. To this end, this study formulates the active and reactive power dispatch problem of WF as a Markov decision process (MDP) allowing for the system uncertainty, e. g. wind speed, reactive power demand and wake effect. Then, an agent is trained via deep reinforcement learning algorithm (DRL) to solve the MDP to obtain the optimal dispatch policy with the minimizing levelized production cost (LPC) target. Finally, the proposed method is tested on an 80 MW WF and some benchmark methods are utilized to act as comparison examples. Simulation results show that, compared with other methods, the proposed dispatch strategy can provide more appropriate active and reactive reference for each wind turbine to extend lifetime of WF, resulting in less LPC.

The new PV technologies need to be analysed in the long term to obtain mature results. Technologies such as tandem amorphous silicon and Cadmium Telluride were installed in the same conditions ten years ago. Now, the results of their operation are presented with the aim to offer real data to the market.

This study investigates Iraq’s challenging electricity landscape, exacerbated by the cumulative impacts of four wars, leading to daily power outages. The reliance on neighborhood diesel generators (NDG) as a temporary fix is critically assessed, with a strong expert consensus via the Delphi method advocating for a transition to solar photovoltaic (SPV) panels. The Delphi survey involved 20 experts, with 85% agreeing on the necessity of this transition, and high consensus (90% or higher) achieved on key questions regarding the inadequacy of NDG and the suitability of SPV as a replacement. The scarcity of local load data prompted the adaptation of Spain’s load profiles to Iraq using the innovative Rosetta transform, identifying the optimal number of SPV panels needed for low, base, and high consumption scenarios as 7, 9, and 11 panels, respectively. In a first approach to the viability of such an SPV installation, it is deduced that the minimum prices per kWh should be between $0.106 and $0.078, depending on the scenario, for it to be viable, well above the current prices in Iraq. A deeper analysis was then performed to evaluate Hybrid Microgrid Systems (HMGS) integrating SPVs, batteries and gasoline generators, both off-grid and grid-connected, and taking into account NDG-related savings. This analysis evidenced the viability of a grid-connected HMGS, leveraging SPV and battery storage, as the most economically viable solution, achieving payback periods up to 3.6 years in the best case. This research underscores the need for a policy shift towards sustainable energy solutions in Iraq and similar contexts, highlighting the technical and economic advantages of adopting clean, renewable energy systems over traditional NDG, and paving the way for a sustainable energy future. This work is partially supported by Grant C-ING-288-UGR23 funded by Consejería de Universidad, Investigación e Innovación and by ERDF Andalusia Program 2021–2027