Research

Main Research Areas

DXON with the partner, Polytechnic University of Catalonia (UPC) aims at developing a rigorous and integrated framework for advanced control and real-time optimization of large-scale and spatially distributed processes that exploits wireless sensor networks as a pervasive and highly reconfigurable information gathering systems, and at validating the approach on real systems.

DXON and UPC develop approaches related to modelling, supervision and control of water and energy systems, and advanced theory, models, methods, and algorithms in designing and maintaining the optimal safety, reliability, and performance and analyzing model uncertainties and system non-linearities and failure behavior related to dynamic complex phenomena for the applied Distributed Fault Diagnosis (FDI, DX), Distributed Fault-Tolerant/Reconfigurable, Cyber Secure & Resilient Control systems.

The research includes model development, analysis, synthesis, comparing with selected different models, and developing programming and optimization methods and applying artificial intelligence (AI), machine learning (ML), deep learning (DL), and reinforcement learning (RL) algorithms, and Distributed and Parallel Computing.

Supervision and Fault-Tolerant Control of Smart Infrastructures based on Advanced Learning and Optimization

Smart infrastructures (SIs), such as water and energy networks, buildings, among others, are most important in modern society because of the services they provide and the essential resources they manage. In SIs, safety and efficiency must be taken into account in all steps of their life cycle, including design, operation and validation of the resulting performance.

DXON with UPC considers research in supervision and fault-tolerant control of SIs by means of two related methodologies: advanced learning and optimization. The use of advanced learning from data is supported by the fact that SIs include sensors and smart-grid technology providing a continuous flow of operational data. First-principles models alone may not capture the complex behaviours of these SIs, so that a combination with learning from operational data is proposed. Optimization-based control is concerned with computing strategies which improve specific performance indicators, such as those related to efficiency and safety. Advanced optimization and learning are proposed for this type of problems in large-scale and complex SIs.

The specific goals of DXON/UPC are grouped in five complementary axes:

(i) Designing learning-based approaches for monitoring, forecasting and control of SIs taking into account interaction of multiple sensors and actuators as well as uncertainty.

(ii) Developing reconfiguration methodologies for fault-tolerant control schemes applied to complex SIs.

(iii) Designing distributed control techniques for SIs based on advanced learning methods and evolutionary game theory.

(iv) Contributing to ontologies definition for platform development in order to provide a flexible solution capable to cover different nature of SIs.

(v) Implementing demonstrators for the design and implementation of the proposed learning-based supervision approaches and analyse the economic, social and environmental impact of the demonstrator results.

The research team has a strong background in two key areas: methodology developments towards optimization-based control of large-scale SIs (especially energy and water infrastructures, e.g., drinking water and sewer networks, canals and rivers, electric networks, clusters of microgrids, oil/gas pipelines, and autonomous vehicles); and fault diagnosis and fault-tolerant control towards the supervision of complex industrial systems. The team also cooperates researchers from Cetaqua Water Technology Centre, a foundation of the water company Aguas de Barcelona (AGBAR), CSIC and UPC, and a reference centre in the application of scientific knowledge to sustainable management of water and environmental systems. This collaboration is essential for the orientation of research towards real industrial and market needs, as well as for maximizing the impact and knowledge transfer towards industry. Realistic demonstration case studies are provided by Cetaqua and industrial partners (EPO). Specifically, the case studies in smart water networks are related to: monitoring for leak detection and localization in water networks, monitoring water quality, water and energy efficiency in water networks and network reconfiguration after fault, such as bursts or contamination events.

Figure 1: Barcelona Drinking Water Distribution Network (WDN)

Efficient Management of Energy Systems including Hybrid Electrochemical Energy Storage (HES) using Digitalization Technologies;

Decarbonization, Sustainable Development, Digitalization

This project deals with the development of efficient management strategies for Distributed Power Generation (DPG/Converters) systems using a hierarchical multi-layer scheme in an evolved context (i.e. including renewable energies (wind, photovoltaics), HES and Combined Heat and Power (CHP)) including digitalisation technologies coming from Communication, Control and Computing (CCC) areas. The rationale is to reduce the costs of the workforce and, also, to remove the necessity of humans to be present in potentially harmful or dangerous locations where the control action takes place, but at the same time providing a resilient, safe and secure operation. Furthermore, emphasis are placed on the monitoring and control based on cloud computing (Control as a Service - CaaS) that allows making use of advanced artificial intelligence algorithms. Such a solution provides benefits in terms of cost, flexibility, ease of modifications and maintenance. Finally, the use of these new digitalisation technologies also possesses specific problems that need to be addressed, such as resilience of control actions, information flow and cybersecurity, that are also targeted in this project in the context of DPG systems (Figure 2).

Figure 2: Multihub distributed energy production and consumption system.

In particular, in this new CCC context some data may be transmitted over the internet –insecurely, whereas others need to be transmitted using industry standards for digital controllers (e.g., Profibus, Modbus, AS-i). The data distribution network has to cooperate with different levels of dispatch and distribution control and management systems. Data transmission aims to control the power generation units, data logging, support the diagnostic systems and the self-diagnosis of intelligent sensors and actuators. It also triggers several security concerns - new integration opportunities mean opening up of IT infrastructure and thus making it more prone to errors and more vulnerable to cyberattacks. Within such a complex structure, cyberattacks may occur at different nodes, different types of communication lines, and different control and management hierarchy. Cyber risks in an energy system could relate to DSN data sharing, implying extended data access by more people and greater data exchange. Much attention should be paid to the threats that are related to and deal with the manipulation of information data that run the supply chains, demand, supply and balance of energy flows, as well as the physical or cyber attack will lead to disabling and/or destroying the operation services in the DPG-HES network. Some contemporary technological approaches and frameworks can be applied to create reliable security threat mitigations and data privacy preservations involving machine learning algorithms, big data processing with artificial intelligence for IoT, Edge AI, and blockchain. Proper understanding and appropriate design of the data transmission network structure is pivotal in ensuring the system's resilience and maximizing the chances of detecting attacks and preventing fault propagation.

Smart Grids (Grid, Micro Grids, Component-Level Analysis) - Distributed Energy Resources Management Systems (DERMS)

Objectives:

Microgrids are the building blocks for the future smart grid, i.e. the means of integrating more renewable sources into the power grid. The main challenges are keeping the microgrid safe, reliable, economical and under full control.

This project proposal addresses the problem of control of DPG using a hierarchical multi-layer approach. In the lower layer, the problem of developing control and supervisory schemes at the component level integrating not only considering the electrical devices, vehicles (EV) but also the storage systems (BESS). In the intermediate layer, the optimal energy management and storage of microgrids considering a flow-energy based modelling approach is addressed using advanced model predictive control approaches able to deal not only with economic performance indexes but also taking into account the health of components using a health-aware control approach that maximizes their lifetime. Finally, in the higher layer, the problem of coordinating several grids for maximizing the overall performance of the grid is considered by using advanced algorithms making use of coordinating mechanisms based on among other game theory or negotiation algorithms. In all these schemes not only the nominal operation of the system are considered but also the operation under unexpected events including faults and attacks. Algorithms for detecting and isolating such unexpected situations that are able to reconfigure the control systems at the different layers are developed increasing the resilience of the smart grid.

To achieve this general goal presented, the following specific objectives were established:

OBJ1. To develop control and supervision strategies at the component level using control oriented-models obtained using physical knowledge and data.

OBJ2. To develop state/parameter estimation algorithms for those component variables/parameters that can not be measured using artificial intelligence algorithms. To explore new methods combining the integration of machine learning including a set-membership description of the uncertainties.

OBJ3. To develop efficient management strategies to deal with uncertainties at microgrid level that consider both the economic and reliable operation (health-aware control) trying to expand the lifetime of the components.

OBJ4. To develop algorithms that allow detecting abnormal events (faults/attacks) at microgrid level and activate some reconfiguration strategy to mitigate their effect.

OBJ5. To develop aggregation modelling approaches that allow the control-oriented modelling of several microgrids connected to the grid.

OBJ6. To develop distributed efficient management strategies using game theory at the grid level that allow the optimal and reliable operation.

OBJ7. To develop supervisory algorithms at the grid level that allow the reconfiguration of the management strategy in case of some abnormal event (faults/attacks) occurs.

OBJ8. To validate the different algorithms developed in the project in lab set-ups and using digital twins and hardware in the loop.

Component level:

This work package addresses the modeling and control of the electrochemical storage elements included in the project. In particular, conventional batteries, redox flow batteries and hydrogen storage systems (HSS) are included. The methods for the online estimation of the main parameters that characterize the behavior of these elements are also developed.

Microgrid level:

The problem of modelling, estimation and control are addressed at the microgrid level. This work package assumes that the control and supervisory systems at the component level are already deployed. This allows dealing with the microgrid from a power-flow perspective. The control system provides to the controllers at the component level flow set-points. On the other hand, if this microgrid is connected to the grid, some coordination policies are imposed to allow the coordinated operation of this microgrid with the rest of microgrids that are also connected.

Grid level:

This work package is focused on proposing novel and state-of-the-art approaches related to modelling, supervision and control of the related energy systems from a grid point of view, understanding the grid as a set of interconnected microgrids together with the power grid interconnection. Such a set of microgrids are properly treated as a virtual power plant (VPP) in order to obtain suitable control-oriented methods, design supervision and non-centralized control approaches aimed with estimation methods to perform a suitable energy dispatch of the whole energy network in a coordinated operation.

Testing and Validation:

The algorithms developed in the previous work packages are tested partly in the laboratory partly using digital twins based on high-fidelity simulators integrated with the algorithms in an emulated real-time manner.