Battery Energy Storage System (BESS) arbitrage is a topic of growing interest given the widespread use of storage systems by end users and the recent developments in European electricity market policies. The optimal operation and management of a storage unit depend on multiple inputs, i.e., load demand (connected to the BESS), the wholesale electricity price, and the production from power plants. Genetic Algorithms (GAs), which belong to the family of meta-heuristic algorithms, can be used for the optimal dispatch of the BESS over a short time window, i.e., a timespan over which the forecasted data of demand, production, and energy prices can be considered reliable. The value of GAs for BESS optimization can be ascribed to three key factors: their simple implementation, their effectiveness, and the variety of parameters that can be customized to better suit the optimization problem. Additionally, GAs do not require any training data (which can be often difficult to obtain, especially when considering real data), in contrast to Deep Reinforcement Learning algorithms. In this article, a comparison of different GAs is made to provide more insight when evaluating such algorithms for BESS short-term optimization when connecting the storage systems with a commercial end-user type and a photovoltaic (PV) power plant, both located in northern Italy, for four different typical days. GAs performance is evaluated in terms of the quality of the optimal solution obtained and the execution time. Additionally, the effects of installing a BESS are evaluated by comparing the obtained economic results with the benefits obtained from a single PV plant.

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.

Electric Vehicles (EVs) provide an alternative to traditional mobility and a sustainable means of transportation. As a result, electric vehicle sales are increasing across Europe, prompting researchers to wonder about the impact of EVs on smart grids. The proposed framework simulates users’ activities, highly characterising individual behaviour using Time Use Survey (TUS) data to estimate EV usage and consumption. Then, for each trip, the routes between origin and destination are determined, simulating in separate modules i) the driving behaviour, ii) the motion of the EV and its discharge considering spatial data and iii) the charge considering users’ preference. Thanks to the spatial information openly available, it is possible to characterise the simulation and improve EV consumption estimation. Different scenarios are analysed to demonstrate the versatility of the proposed framework by exploiting its modularity. The individuals’ heterogeneity is considered by using an agent-oriented approach. Furthermore, the simulation proceeds on a time-step basis to enable the use of the simulator in a co-simulation environment for future purposes, such as the integration of power networks. The results indicate that achieving a high realism with limited, i.e. containing scarce data for the problem under study, is feasible, enabling researchers to make informed decisions about future mobility.

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.