Enhancing energy flexibility in cluster of buildings through coordinated energy management

Objective of the activity: 

Exploits the potentialities of Deep Reinforcement Learning for district energy management

Framework of the activity:

District energy management should leverage automated algorithms capable to adapt to a changing environment and to learn from user’s behavior and historical building-related data to optimize, coordinate and control the different actors of the smart grids (e.g., producers, service providers, consumers). However, the computational complexity associated to the district simulation and the application of advanced control strategies limits the application of model-based techniques such as Model Predictive Control (MPC).

In this perspective BAEDA lab is conducting research activities aimed at exploiting data-driven control strategy to lighten the computational complexity of the problem. A novel approach exploits the adaptive and potentially model-free nature of Deep Reinforcement Learning (DRL) to coordinate a cluster of buildings.

Figure: Methodological framework for the testing of Deep Reinforcement learning  control algorithms at district level

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Objective of the activity:

Developing a data-driven framework for advanced control of building energy systems at energy community level.

Framework of the activity:

The overcoming of the traditional way of producing and consuming energy towards a more sustainable energy management has shifted the need of flexibility from the generation side to the demand side. In this context, the Energy Community is the new paradigm where prosumers can acquire a more active role while interacting with the grid by aggregating their loads and generation profiles. Energy Communities can then be seen as a means for optimizing the energy management in smart grids, with positive effects for the members, who can decrease their energy cost, and for the grid, which can benefit from the provided flexibility. Recent studies have proved how coordinated control architecture for energy management in cluster of buildings is effective at achieving such objective. Nonetheless, the development of control strategies and of digital twins at the district level for testing them is particularly demanding due to high complexity of the control problem and its high computational cost. 

To cope with these research challenges BAEDA Lab develops generalizable simulation environments for Energy Communities as virtual testbeds for control strategies. The environments are used for the evaluation of advanced control strategies in terms of achievable energy flexibility and energy cost saving for data-driven energy communities, de facto bridging the gap that is currently characterizing the research.  

Figure: Framework for advanced control of building energy systems at energy community level

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Objective of the activity:

Assessing the advantages of cooperative and coordinated energy management

Framework of the activity:

District Energy Management (DEM) could be achieved in several way, spacing from coordinated management (centralized), in which a group harmonize its individual efforts in pursuit of common goals, or cooperative management (decentralized), where there is a voluntary effort of individuals to work together with the intention of helping each other. However, the architecture structures directly influence scalability, and advantages of specific algorithm.

Considering DEM, BAEDA Lab is studying different agent-based architectures: centralized, hierarchical, distributed to assess pros and cons of every architecture towards a real-world implementation.

Figure: Centralised vs coperative architecture for the development of a energy system controller in cluster of buildings

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Objective of the activity:

Exploiting data-driven techniques to performing IEQ characterization to enhance the energy management at district level.

Framework of the activity:

While district energy management could provide advantages to both users and the grid, the simulation of such complex systems can lead to burdensome and computationally expensive procedure. To address this issue, simplified models have been used, neglecting the possibility to fully exploit the flexibility provided by building thermal mass and indoor setpoint microclimatic conditions.

To overcome this limitation, BAEDA Lab is conducting research activities aimed at using machine learning algorithms for the data-driven modeling and characterization of the built environment. The goal is to capture non-linear dynamics affecting the controlled environment in a fast and reliable way, enabling a more comprehensive implementation of energy management strategies at building district level.

Figure: Methodological framework for the characterization of indoor confort at district level throgh a data-driven approach

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