Abstract

Abstract
Hydrogen is becoming a key energy carrier in the transition toward decarbonization, as electrolysis creates strong interdependencies between electricity and hydrogen markets. Accurately representing strategic behaviour in these coupled markets is essential, yet current models fail to capture price-responsive bidding. To address this, a joint hybrid Cournot-Linear Supply Function Equilibria (CLSFE) model is developed and reformulated as an equivalent optimization problem, enabling tractable large-scale analysis. The model is applied to the Iberian system for 2030 and compared with perfect competition and Cournot benchmarks. Results show that hydrogen prices are lowest under CLSFE, with a reduction of about 44% relative to perfect competition and 10% to Cournot, while hydrogen demand increases by up to 58%. Electrolytic hydrogen production rises up to 92%, displacing grey hydrogen and reducing hydrogen-sector emissions. However, renewable self-curtailment reaches 82 TWh, indicating increased market power. These results highlight cross-sector trade-offs and support market design and policy analysis.

Abstract
As connected and autonomous vehicles become more complex, protecting them against advanced cyber-attacks becomes more difficult. This paper proposes a hardware-based authentication mechanism to protect sensor data acquisition in vehicular networks and embedded systems. The scheme ensures that only authentic sensors and microcontrollers can communicate with the processing unit by adding trusted hardware components, such as trusted platform modules (TPM) and physical unclonable functions (PUF). The system uses lightweight embedded platforms, such as ESP32 and Raspberry Pi with ROS 2, which allow scalable and tamper-proof real-time communication. An exhaustive study of cryptography and hardware-based authentication systems is presented, from elliptic curve cryptography to chaotic map functions, to put the proposed solution in a proper perspective. The system is modularly extensible and adaptable to new technologies, such as blockchains and new encryption protocols, and provides a robust and future-proof solution for securing vehicles and sensor-based IoT sets.

Abstract

Abstract
Renewable Energy Communities (RECs) are a promising driver for energy system decarbonization, engaging end consumers in energy markets, promoting local renewable generation and affordability, and enhancing community resilience. This paper presents a methodology for planning REC by optimally sizing distributed energy resources (DERs), including conventional resources such as photovoltaic (PV) systems and battery energy storage systems (BESSs), but also power-to-hydrogen (P2H) systems composed of an electrolyzer (EC), hydrogen tank, and fuel cell (FC). A mixed-integer linear programming (MILP) model is used to minimize DER investments and REC operational costs, using a reduced input data set based on clustering techniques to decrease complexity and improve computational efficiency. Then, a detailed MILP operation model is used to assess the REC performance with the newly sized DERs under different scenarios using conventional indicators such as the net present value (NPV) and the payback period. A sensitivity analysis is also conducted on electricity costs, EC costs and the hydrogen selling price for an external combined heat and power (CHP) client. Results show that integrating P2H systems increases NPV and provides new revenue opportunities, thereby reinforcing their role as a complementary technology to PV and BESS within RECs.

Abstract
The rise of Connected and Autonomous Vehicles (CAVs) brings distinctive features, challenges, and opportunities which require security frameworks for real-time threat mitigation. This paper presents a Security Operations Center (SOC) designed for the Internet of Vehicles (IoV). This SOC design merges Intrusion Detection Systems (IDS) and Security Information and Event Management (STEM) systems to monitor vehicle communications and adjacent infrastructure, classify network traffic as usual or malicious, and ensure threat detection and safe mitigation. The framework also addresses the General Data Protection Regulation (GDPR), complying with data protection laws by enabling user controls and granting the right to be forgotten. Our approach is intended to be a first step in providing a comprehensive solution in network security for intelligent transportation systems, offering a foundation for future implementation and exploration of secure vehicle environments.