The evolution of the power systems towards the smart grid paradigm is strictly dependent on the modernization of distribution grids. To achieve this target, new infrastructures, technologies and applica- tions are increasingly required. This paper presents a smart metering infrastructure that unlocks a large set of possible services aimed at the automation and management of distribution grids. The proposed architecture is based on a cloud solution, which allows the communication with the smart meters from one side and provides the needed interfaces to the distribution grid services on the other one. While a large number of applications can be designed on top of the cloud, in this paper the focus will be on a real-time distributed state estimation algorithm that enables the automatic reconfiguration of the grid. The paper will present the key role of the cloud solution for obtaining scalability, interoperability and flexibility, and for enabling the integration of different services for the automation of the distribution system. The distributed state estimation algorithm and the automatic network reconfiguration will be presented as an example of coordinated operation of different distribution grid services through the cloud

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.

While the initial aim of smart meters is to provide energy readings for billing purposes, the availability of these measurements could open new opportunities for the management of future distribution grids. This paper presents a multilevel state estimator that exploits the smart meter measurements for monitoring both low and medium voltage grids. The goal of this paper is to present an architecture that is able to efficiently integrate smart meter measurements and to show the accuracy performance achievable if the use of real-time smart meter measurements for state estimation purposes was enabled. The design of the state estimator applies the uncertainty propagation theory for the integration of the data at different hierarchical levels. The coordination of the estimation levels is realized through a cloud-based infrastructure, which also provides the interface to auxiliary functions and the access to the estimation results for other distribution grid management applications. A mathematical analysis is performed to characterize the estimation algorithm in terms of accuracy and to show the performance achievable at different levels of the distribution grid when using the smart meter data. Simulations are presented, which validate the analytical results and demonstrate the operation of the multilevel estimator in coordination with the cloud-based platform.

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.

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.