Integration of widely distributed small-scale Renewable Energy Sources like rooftop Photovoltaic panels and emerging loads like plug-in Electric Vehicles would cause more volatility in total net demand of distribution networks. Utility-owned storage units and control devices like tap changers and capacitors may not be sufficient to manage the system in real-time. Exploitation of available flexibility in demand side through aggregators is a new measure that distribution system operators are interested in. In this paper, we present a developed real-time management schema based on Internet of Things solutions which facilitate interactions between system operators and aggregators for ancillary services like power balance at primary substation or voltage regulation at secondary substations. Two algorithms for power balance and voltage regulation are developed based on modified Optimal Power Flow and voltage sensitivity matrix, respectively. To demonstrate the applicability of the schema, we set-up a real-time simulation-based test bed and realised the performance of this approach in a real-like environment using real data of a network with residential buildings.

The impacts of climate change, excessive greenhouse gas emissions, and the current energy crisis have motivated the European Union to adopt mitigation and adaptation strategies. These strategies primarily focus on the building sector due to its crucial role in addressing these issues. Among the strategies, the implementation of resilient technologies for the building envelope, such as vertical greenery systems (VGSs) is gaining ground. The literature analysis shows that existing models are not sufficiently detailed in their description of the overall thermo-physical phenomena of VGSs. The aim of this work is to overcome the research gaps by selecting and improving two mathematical models for green façades and living walls. A dedicated calculation code to estimate the effect of VGSs on a building’s energy performance and indoor thermal comfort has been developed and implemented within the EnergyPlus calculation software (version 23.2). A BESTest Case from ASHRAE 140 was chosen to test the models and to assess benefits of VGSs. The results show that adopting green solutions for the building envelope can contribute to achieving the building’s energy efficiency goals and that the modelling of these technologies can be easily carried out within a dynamic energy simulation of the building.

Integrated modeling and simulation are crucial for optimizing cities' energy planning. Existing sector-specific analyses have implementation limitations in representing interactions across infrastructures, limiting optimization potentials. An integrated framework simulating multiple interacting components from a systemic perspective could reveal efficiency gains, flexibility, and synergies across urban energy networks to guide sustainable energy transitions. Co-simulation approaches are gaining attention for analyzing complex interconnected systems like District Heating (DH). Traditional single-discipline models present limitations in fully representing the interconnectivity between district heating networks and related subsystems such as those in buildings and energy generation. Therefore, we propose a co-simulation based framework to simulate DH system behavior while easily integrating models of other subsystems and Functional Mock-up Unit (FMU) simulators. We tested this Plug&Play modular framework for Demand Side Management (DSM) and Storage-based strategies, evaluating their effectiveness in peak reduction while lowering the temperatures of the network.

Modelling and simulating urban energy facilities and their interactions is becoming a crucial topic in order to better assess policies and decision making in cities. District Heating (DH) is one of the key enabler for energy transition in cities and, in literature, several tools address its analysis and modelling. Usually the focus in analysing energy systems in cities is oriented on the specific fields and thus models and simulation tools are too sectorized and unable to interact in a larger perspective. Therefore, co-simulation paradigm is exponentially gaining attention as well as Multi Agent Systems (MAS). The lack of multidisciplinary approaches holds especially when dealing with DH and the interconnected subsystems that belongs to different domains. Therefore our proposition consists on a MAS-based co-simulation framework to simulate DH system behaviour while easily integrating models of other subsystems (e.g. buildings and storage). We have tested this distributed and modular framework with two case studies to asses a peak reduction strategy exploiting thermal storage. In conclusion, we have showed the flexibility of our tool while analysing benefits of a simple peak reduction strategy.

Multi-Energy-System (MES) integrates diverse energy carriers, digital networks, and human interactions, requiring accurate simulations for efficient urban energy management. Coupled Simulation (Co-Simulation) enables complex systems like MESs to be simulated as a whole by linking and coordinating their individual sub-modules. Current co-simulation approaches face major challenges: connecting heterogeneous models and preparing input data often demand significant manual effort and domain expertise. Most frameworks provide technical interoperability but cannot fully capture each model’s data requirements and interfaces, limiting automation, scalability, and reproducibility. To address these limitations, we present COSIMO, a framework that organizes models, technologies, parameters, and their interconnections in a structured format that software tools can interpret automatically. COSIMO streamlines the creation and management of co-simulation setups, supporting scalable and reproducible urban energy simulations. We demonstrate its effectiveness through scenarios from neighborhood to large urban districts—including photovoltaic systems, battery storage, and occupancy-driven loads—showing improved interoperability, reproducibility, and seamless integration of diverse models and data.