The building sector is responsible for a significant amount of energy consumption and greenhouse gas (GHG) emissions. Thus, the monitoring, control and optimization of energy consumption in buildings will play a critical role in the coming years in improving energy efficiency in the building sector and in reducing greenhouse gas emissions. However, while there are a significant number of studies on how to make buildings smarter and manage energy through smart devices, there is a need for more research on integrating buildings with legacy equipment and systems. It is therefore vital to define mechanisms to improve the use of energy efficiency in existing buildings. This study proposes a new architecture (PHOENIX architecture) for integrating legacy building systems into scalable energy management systems with focus also on user comfort in the concept of interoperability layers. This interoperable and intelligent architecture relies on Artificial Intelligence/Machine Learning (AI/ML) and Internet of Things (IoT) technologies to increase building efficiency, grid flexibility and occupant well-being. To validate the architecture and demonstrate the impact and replication potential of the proposed solution, five demonstration pilots have been utilized across Europe. As a result, by implementing the proposed architecture in the pilot sites, 30 apartments and four commercial buildings with more than 400 devices have been integrated into the architecture and have been communicating successfully. In addition, six Trials were performed in a commercial building and five key performance indicators (KPIs) were measured in order to evaluate the robust operation of the architecture. Work is still ongoing for the trials and the KPIs’ analysis after the implementation of PHOENIX architecture at the rest of the pilot sites.

<p>In this work, a holistic energy management methodology comprising forecasting and scheduling algorithms was developed. The algorithms aim at maximizing the customer benefits during normal operation as well as supplying critical infrastructure during grid events. Four separate real-world trials were conducted over a period of three months in the context of a demonstration in an MV customer with results showing that energy costs may be reduced to up to 27.3% while also dropping the system’s peak load demand by up to 16.7%. In terms of islanding capabilities, the trials demonstrate the system's ability to support the local network during blackouts and suggested the need for a formalization and standardization of the sizing process of any equipment with blackout capabilities within the planning of LV/MV systems. Additional use cases that aim at unveiling the flexibility offered to aggregators were examined and results showed that the proposed scheme can support flexibility services during Demand Response (DR) events.</p>

<p>In this work, a holistic energy management methodology comprising forecasting and scheduling algorithms was developed. The algorithms aim at maximizing the customer benefits during normal operation as well as supplying critical infrastructure during grid events. Four separate real-world trials were conducted over a period of three months in the context of a demonstration in an MV customer with results showing that energy costs may be reduced to up to 27.3% while also dropping the system’s peak load demand by up to 16.7%. In terms of islanding capabilities, the trials demonstrate the system's ability to support the local network during blackouts and suggested the need for a formalization and standardization of the sizing process of any equipment with blackout capabilities within the planning of LV/MV systems. Additional use cases that aim at unveiling the flexibility offered to aggregators were examined and results showed that the proposed scheme can support flexibility services during Demand Response (DR) events.</p>

Studies on forecasting and optimal exploitation of renewable resources (especially within microgrids) were already introduced in the past. However, in several research papers, the constraints regarding integration within real applications were relaxed, i.e., this kind of research provides impractical solutions, although they are very complex. In this paper, the computational components (such as photovoltaic and load forecasting, and resource scheduling and optimization) are brought together into a practical implementation, introducing an automated system through a chain of independent services aiming to allow forecasting, optimization, and control. Encountered challenges may provide a valuable indication to make ground with this design, especially in cases for which the trade-off between sophistication and available resources should be rather considered. The research work was conducted to identify the requirements for controlling a set of flexibility assets—namely, electrochemical battery storage system and electric car charging station—for a semicommercial use-case by minimizing the operational energy costs for the microgrid considering static and dynamic parameters of the assets.