Abstract
This study provides a comprehensive review of how game theory can be applied to model and optimize dynamic coalitions in contemporary energy markets. With the increasing decentralization of energy systems driven by technologies such as solar photovoltaics, home energy storage, and electric vehicles, consumers have begun to play a more active and influential role in the market. In this new context, where cooperative and collective decision-making is gaining importance, game theory emerges as a valuable tool for analyzing and structuring these interactions. The primary objective of this work is to systematically review existing models, assess their methodological strengths and limitations, and identify open research gaps that hinder their applicability to real-world settings. By synthesizing the current state-of-the-art, this study aims to highlight pathways toward the development of more realistic and effective models that capture the dynamic and interdependent behaviors of energy consumers and the coalitions they form. Ultimately, this review seeks to provide an updated overview of this growing field, serving both as a basis for future research and as a foundation for the design of solutions that promote fairer, more efficient, and more participatory energy markets, especially for small-scale consumers, who now have greater voice and power of choice. © 2026 IEEE.

Abstract
The expansion of distributed renewable energy technologies like photovoltaic systems and electric vehicle (EV) charging stations and its active power electronics causes direct current (DC) emissions in the electrical AC distribution grids which are currently not monitored. Although emissions in the harmonic frequency range (50 Hz to 2 kHz) are well standardized, for DC emissions no standards are defined yet. However, failure to detect and standardize DC emissions do have a significant negative impact on the public grid through grid disturbances or quality losses. Measurement and protection systems are affected as well as thermal effects on cables or transformers are given. In this study the DC current emissions of EV charging stations and PV systems are analyzed and characterized at the reconstructed electrical distribution grid of the university of applied sciences Technikum Vienna. The results show that the DC current emissions depend on the design of the active power used in V2G chargers and photovoltaic systems and how they vary at different power levels (low power vs. high power). The research indicates that the emissions can reach up to 200 mA from a single EV charger. © 2025 IEEE.

Abstract
The main objective of this work is to identify the most efficient methodologies for transmitting energy generated onshore to offshore gas extraction platforms. Initially, a case study was carried out to determine the most suitable transmission method, AC or DC, with DC being the most suitable. Subsequently, theoretical research was carried out into the type of cable that should be used in this project, resulting in static and dynamic cables. The authors also propose a mathematical optimization model to determine which type of cable section should be used, minimizing the installation cost, power losses and ensuring that the voltage drop is lower than the regulatory limits. Two scenarios were considered in this optimization, one taking power losses into account and the other not. A more positive result was found with the 1000 mm2 section cable, because even though the initial investment is higher, the financial return is superior. © 2025 IEEE.

Abstract
Artificial intelligence techniques offer promising potential for accurately predicting solar intensity, enabling more efficient management of renewable energy resources. This research addresses the main methods for predicting solar radiation using artificial intelligence techniques. Good solar radiation forecasting is crucial for the optimization of solar energy systems and for the efficient management of renewable energy resources. This study explores the use of different artificial intelligence (AI) methods, including Artificial Neural Networks (ANN), Support Vector Machines (SVM), decision trees, linear regression and fuzzy logic, to predict solar radiation based on meteorological data such as temperature, wind speed and direction, and solar radiation. The Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and correlation coefficient are used to evaluate the performance of the various models tested. The results show that AI models, especially ANNs, outperform traditional solar radiation forecasting approaches in terms of accuracy and efficiency. © 2025 Elsevier B.V., All rights reserved.

Abstract
Photovoltaic (PV) and battery energy storage system (BESS) capacities are among the fastest-growing renewable energy technologies worldwide. The optimal sizing of these technologies is crucial to achieving a cost-effective integration into existing energy systems and increase competitiveness. However, existing models often neglect cyclic (dynamic) BESS degradation and replacement costs, and assume a calendar aging (static) system lifetime, use low-resolution consumption and solar irradiation data, or determine the optimal size of only one component. This contribution proposes a cost optimization model for the size of a grid-connected PV-BESS system including cyclic battery degradation based on its intensity of use. The model considers the most relevant technical parameters of PV and BESS, including state of charge (SoC), round-trip efficiency, depth of discharge (DoD), and self-discharge rate, and the lifetime based on a maximum number of cycles. The energy flows of the system are based on the principle that PV generation initially covers consumption directly, surplus energy is used to charge the BESS, deficits are covered by discharging the BESS, and any remaining demand is drawn from the grid or surplus electricity is fed into the grid to generate revenue. The model is validated on the basis of a real-world use case, a single-family house in Vienna, Austria, with the hourly load profile and PV generation on site available. The results indicate that assumptions about calendarbased BESS degradation lead to shorter replacement periods and lower available BESS capacity compared to the cyclic degradation model, leading to higher costs for assumptions with calendarbased degradation. © 2025 Elsevier B.V., All rights reserved.

Abstract
With the European Union's requirement for reducing the amount of energy generated from non-renewable sources, there is a need for increased production of energy from renewable sources such as solar and wind power, among others. Due to the stochastic nature of natural resources that serve as these renewable energy sources, it necessitates adaptation by electrical energy systems. Predicting these resources is crucial for better planning and management of electrical energy systems. This paper aims to forecast wind speed using machine learning models, specifically comparing AutoRegressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) models. The results show that the LSTM is able to reach a Root Mean Square Error (RMSE) and a Mean Absolute Error (MAE) of 3.145 and 2.245, respectively, while the ARIMA achieves a higher error of 3.460 and 3.031, respectively. The results allows to conclude that the LSTM model shows a more effective performance, with a lower error rate, due to its ability to recognize patterns over longer periods. © 2025 Elsevier B.V., All rights reserved.