The present work introduces an empirically ground agent-based modeling (ABM) framework to assess the spatial and temporal diffusion of rooftop photovoltaic (PV) systems on existing buildings of a city district. The overall ABM framework takes into account social, technical, environmental and economic aspects to evaluate the diffusion of PV technology in the urban context. A city district that includes 18,720 households distributed over 1,290 building blocks and a surface area of 2.47 square kilometers is used to test the proposed ABM framework. Results show how the underlying regulatory framework (i.e., the rules of the internal electricity market) influences the pattern and intensity of adoption, thus realizing different shares of the available potential. Policies that support the establishment of ’prosumers’ within Condominiums (i.e., energy community buildings), and not in single-family houses only, is key to yield high diffusion rates. The installed capacity increases by 80% by switching from the one-to-one configuration to the one-to-many paradigm, i.e., from 5.90 MW of rooftop PV installed on single-family households and/or single PV owners to 10.64 MW in energy community buildings. Moreover, the possibility to spread the auto-generated solar electricity over the load profile of the entire population of a Condominium results in self-consumption rates greater than 50% and self-sufficiency ratios above 20% for the majority of the simulated buildings.

Smart Grid integration plays a crucial role in transitioning towards a climate-neutral future by enabling advanced monitoring, management, and control of renewable energy sources, energy systems, and networks. However, several barriers hinder the integration of these innovative resources and strategies into current power systems due to the inherent complexity of heterogeneous technologies, entities, and actors. To address these challenges, this paper proposes a hybrid multi-model co-simulation infrastructure to analyze innovative Smart Grid scenarios. This infrastructure enables the interconnection of heterogeneous software simulators with real-time hardware simulators within a shared and distributed co-simulation environment, facilitating Hardware-In-the-Loop (HIL) applications. The capabilities and performance of the proposed infrastructure are evaluated using a smart grid scenario focused on implementing a distribution voltage regulation service delivered by distributed resources installed on a building premise. Additionally, the scenario incorporates a physical smart meter device interconnected in HIL with the simulated building energy management system. The scenario results demonstrate the promising performance and low co-simulation latencies of the proposed infrastructure in coupling software and hardware simulators with HIL applications. Overall, the proposed infrastructure has the potential to assist researchers, system operators, and energy stakeholders in evaluating Smart Grid scenarios and designing, developing, and testing new systems, technologies, and business models.

Nowadays, buildings are responsible for large consumption of energy in our cities. Moreover, buildings can be seen as the smallest entity of urban energy systems. On these premises, in this article, we present a flexible and distributed cosimulation platform that exploits a multimodeling approach to simulate and evaluate energy performance in smart buildings. The developed platform exploits the Mosaik cosimulation framework and implements the functional mock-up interface standard in order to couple and synchronize heterogeneous simulators and models. The platform combines in a shared simulation environment: 1) the thermal performance of the building simulated with EnergyPlus; 2) a heat pump integrated with a proportional-integral-derivative control strategy modeled in Modelica to satisfy the heating demand of the building; 3) an electrical energy storage system modeled in MATLAB Simulink; and 4) different Python models used to simulate household occupancy, electrical loads, photovoltaic production, and smart meters, respectively. The platform guarantees a plug-and-play integration of models and simulators, in which one or more models can be easily replaced without affecting the whole simulation engine. Finally, we present a demonstration example to test the functionalities, capability, and usability of the developed platform and discuss future developments of our framework.