Embedded IoT platforms are frequently deployed in hostile or physically exposed environments, where compromise of the operating system is a realistic threat. In conventional deployments, OpenSSL executes entirely in user space, leaving cryptographic keys and intermediate material potentially exposed in the presence of a compromised OS or privileged malware. This work presents a portable OpenSSL engine that enables secure and transparent offloading of cryptographic operations to OP-TEE, a software stack leveraging a Trusted Execution Environment (TEE) to confine cryptographic key material and security-critical computations within secure-world memory without modifying existing applications or altering the OpenSSL EVP interface. The proposed engine uses a GlobalPlatform Client API implementation as an underlying communication layer and supports both Copy-Based (CB) data transfer and a Shared-Memory (SM) mode, enabling performance tuning under the resource constraints typical of embedded IoT platforms. An experimental evaluation on an NXP i.MX7 industrial gateway shows that secure-world execution introduces bounded overhead dominated by REE–TEE transitions and memory-management operations. For bandwidth-intensive primitives such as SHA-256, SM mode improves throughput by approximately 10–15% over CB transfers. For symmetric encryption algorithms such as AES-256-CBC, SM communication provides a substantial throughput improvement of approximately 40%, indicating that data-movement overhead plays a significant role on embedded platforms. In contrast, asymmetric primitives (e.g. RSA-1024) remain largely insensitive to transfer optimisations because they operate on small, fixed-size operands, making the overall execution time dominated by invocation overheads and internal big-number computations rather than data movement.

Multi-Energy Systems (MES) represent a paradigm in which various energy systems, such as buildings, power grids, and heating networks, are integrated to operate in a cohesive manner. These systems provide a significant opportunity to improve technical, economic, and environmental outcomes. However, the complexity of MES poses challenges in accurately capturing their interdisciplinary interactions using standalone simulations, which often do not adequately model their interconnected nature. Co-simulation frameworks have emerged as a promising solution to this challenge, enabling the coordination of multiple simulators within a single scenario. However, computationally intensive simulators can often hinder scalability, especially as the complexity of the simulations increases. This paper proposes a methodology to apply vertical scalability techniques to simulators and optimise each single-node performance in co-simulation frameworks. Using COESI, a Mosaik-based co-simulation platform, it was possible to systematically test these techniques by increasing the number of model entities a simulator class must manage in a complex MES scenario. This study highlights selective parallelisation as a key strategy for optimising computational efficiency in MES co-simulation, offering insights to scale complex energy systems effectively. The results demonstrate significant performance gains, particularly for CPU-intensive tasks with stateless simulators, such as executing intensive numerical computations.

The ongoing energy transition to reduce carbon emissions presents some of the most formidable challenges the energy sector has ever experienced, requiring a paradigm change that involves diverse players and heterogeneous concerns, including regulations, economic drivers, societal, and environmental aspects. Central to this transition is the adoption of integrated Multi-Energy Systems (MES) to efficiently produce, distribute, store, and convert energy among different vectors. A deep understanding of MES is fundamental to harness the potential for energy savings and foster energy transition towards a low carbon future. Unfortunately, the inherent complexity of MES makes them extremely difficult to analyze, understand, design and optimize. This work proposes a digital twin co-simulation platform that provides a structured basis to design, develop and validate novel solutions and technologies for multi-energy system. The platform will enable the definition of a virtual representation of the real-world (digital twin) as a composition of models (co-simulation) that analyze the environment from multiple viewpoints and at different spatio-temporal scales.

The growing interest in Multi-Energy Systems (MES) leads the scientific community to implement innovative technologies to analyse and simulate these complex systems. Two main research trends are identified in such analysis: i) improve the usability and capability of preexisting reference architectures in the energy field to cope with high-level use case descriptions, and ii) study the interoperability of such reference architectures in order to increase systematic and functional analysis of MES use cases. GAMES is a a general-purpose architectural model for MES engineering application. The aim is twofold: i) GAMES implements an extension of Smart Grid Architecture Model (SGAM) to cope with MES use case descriptions, and ii) it offers a methodology to deal with a systemic description of the use case through a combination of UML and SysML integrated in the proposed architectural model. Furthermore, GAMES will allow the implementation of Domain Specific Language (DSL) and hardware configuration for the specific components described by UML/SysML diagrams. Compared to other solutions, GAMES allows to assess both research trends in a single hierarchical ICT infrastructure.

In this paper, we present a novel distributed framework for real-time management and co-simulation of demand response (DR) in smart grids. Our solution provides a (near-) real-time co-simulation platform to validate new DR-policies exploiting Internet-of-Things approach performing software-in-the-loop. Hence, the behavior of real-world power systems can be emulated in a very realistic way and different DR-policies can be easily deployed and/or replaced in a plug-and-play fashion, without affecting the rest of the framework. In addition, our solution integrates real Internet-connected smart devices deployed at customer premises and along the smart grid to retrieve energy information and send actuation commands. Thus, the framework is also ready to manage DR in a real-world smart grid. This is demonstrated on a realistic smart grid with a test case DR-policy.