ECR Webinar- Control for Clean Energy
Date: 26th February 2025
Time: 14:00 – 17:30
Location: Online. The Zoom link will be sent before the event.
Registration: Register at this link
About the Seminar
The aim of this online event is to connect and showcase early career researchers working at the intersection of control theory and clean energy. The event will be split into two sessions. Session 1 will focus on systems-level problems where control theory can be used to improve the efficiency and resilience of future power systems. Session 2 will zoom down to the device level and explore some emerging methods and toolboxes used to control, manage, and estimate lithium-ion battery systems. It is hoped this event will help stimulate the early career researcher community in this area and lead to new collaborations and perspectives.
Agenda
| Session 1 | Control of power systems | |
| 2:00 – 2:10 | Introduction | |
| 2:10 – 2:30 | Online End-to-End Learning-Based Predictive Control for Microgrid Energy Management | Francesca Boem |
| 2:30 – 2:50 | Data-Driven Optimal Voltage Performance Index Tracking in Active Distribution Networks | Jonathan Mayo-Maldonado |
| 2:50 – 3:10 | Plug-and-play control: A study of interconnectedness | Pablo Balidivieso |
| 3:10 – 3:30 | Panel discussion: Controlling the grid of the future | Speakers + others. |
| Break (10 min) | ||
| Session 2 | Control theory for lithium-ion batteries | |
| 3:40 – 3:55 | Depreciation Cost is a Poor Proxy for Revenue Lost to Aging in Grid Storage Optimization | Volkan Kumtepeli |
| 3:55 – 4:10 | PyBop: Python Battery Optimisation and Parameterisation | Brady Planden |
| 4:10 – 4:25 | Physics-based battery model parametrisation from impedance data | Noel Hallemans |
| 4:25 – 4:40 | A battery parameter library for control | Yuanbo Nie |
| 4:40 – 5:00 | Panel discussion: The next 15 years of battery control research | |
| 5:00 – 5:30 | Poster showcase + video 4min each x5 | Available in advance |
About the Speakers
Dr Francesca Boem
Francesca Boem received the MSc degree (cum laude) in Management Engineering in 2009 and the PhD degree in Information Engineering in 2013, both from the University of Trieste, Italy. She was Post-Doc at the University of Trieste with the Machine Learning Group from 2013 to 2014. From 2014 to 2018, she was Research Associate with the Control and Power research group at the Department of Electrical and Electronic Engineering, Imperial College London, UK. From 2015 to 2018 she was part of the team at Imperial College working on the flagship EU H2020-WIDESPREAD-TEAMING project for the development of the EU KIOS Research and Innovation Centre of Excellence in Cyprus. Dr Boem was awarded the Imperial College Research Fellowship in 2018, and in the same year she has been appointed as a Lecturer in the Department of Electronic and Electrical Engineering at University College London, UK. She also is Honorary Lecturer at Imperial College London. In 2022 she has been awarded as PI the EPSRC New Investigator Award and a NNTI Joint Lab – Base Exploratory project in 2023. She currently is Associate Professor with the EEE Department at UCL and the Director of UCL’s MSc in Integrated Machine Learning Systems. Her current research interests include distributed fault diagnosis and fault-tolerant control methods for large-scale networked systems, safety and security of cyber-physical systems, learning-based control. Dr Boem is member of the IFAC Technical Committees 1.5 (Networked Systems) and 6.4 (“Fault Detection, Supervision & Safety of Technical Processes – SAFEPROCESS”) and Associate Editor for the IEEE Systems Journal, the EUCA European Journal of Control, the IEEE CSS Letters, and for the IEEE Control System Society, IFAC and EUCA Conference Editorial Boards.
Title: Online Learning-Based Predictive Control with uncertainty estimation for Resilient Microgrid Energy Management
Abstract of talk: To enhance fault resilience in microgrid systems at the energy management level, this talk presents a novel proactive scheduling algorithm, based on uncertainty modelling thanks to a specifically designed neural network-based MPC controller. The algorithm is trained and deployed online and it adaptively estimates uncertainties in predicting future load demands and other relevant profiles. We integrate the novel learning algorithm with a stochastic model predictive control, enabling the microgrid to store sufficient energy to adaptively deal with possible faults. Experimental results show that a reliable estimation of the unknown profiles’ mean and variance is obtained, improving the robustness of proactive scheduling strategies against uncertainties.
Dr Jonathan C. Mayo-Maldonado
Jonathan C. Mayo-Maldonado (Senior Member, IEEE) received the B.S. and M.Eng. degrees in electrical engineering from the Instituto Tecnologico de Ciudad Madero, Madero, Mexico, in 2008 and 2010, respectively, and the Ph.D. degree in electrical and electronic engineering from the University of Southampton, Southampton, U.K., in 2015. From 2015 to 2021, he was a Faculty Member with the Department of Electrical Engineering, Tecnologico de Monterrey, Monterrey, Mexico. He is currently a Lecturer with the Electrical Machines and Drives, The University of Sheffield, Sheffield, U.K. His research interests include system and control theory, power electronics, and smart grid technologies. He was a recipient of the Doctoral Control and Automation Dissertation Prize by the Institute of Engineering and Technology, in 2015, for his thesis titled Switched Linear Differential Systems.
Title: Data-Driven Optimal Voltage Performance Index Tracking in Active Distribution Networks
Abstract of talk: This paper presents a data-driven dynamic voltage regulation approach that coordinates medium-voltage distributed energy resources (DERs) and distribution static synchronous compensators (D-STATCOMs) in active distribution networks. Using data generated by distribution phasor measurement units (D-PMUs), a data-driven voltage performance index is calculated and a control method is proposed to ensure optimal voltage performance across network nodes. This control method requires minimal data, using only voltage and reactive power measurements to generate control signals. The performance of this approach is validated through simulation tests on IEEE-33 node test feeders, demonstrating efficient voltage regulation under challenging conditions such as solar energy integration, grid faults, and topology changes. The paper also explores additional contributions to system identification and digital twin techniques. This work highlights the potential of data-driven control in distribution networks for effective Volt/Var control, using modern smart-grid technologies for improved grid management.
Dr Pablo Balidivieso
Pablo R Baldivieso received his Ph.D in robust distributed control from the University of Sheffield in 2018. He has worked as a postdoctoral researcher in the Control And Power Systems (CAPS) laboratory and University Technology Centre (UTC) at the University of Sheffield.
His previous education comprises a degree in Electronic Engineering at Escuela Militar de Ingeniería, Pure Mathematics at Universidad Mayor de San Andrés in Bolivia, and an MSc in Control Systems at the University of Sheffield.
Currently, he is a lecturer in Data Science and AI for engineering at the University of Sheffield UK.
Title of talk: A Robust solution to the plug-and-play problem: A study of interconnectedness
Abstract of talk: Networked systems are a collection of dynamical systems interconnected via physical or digital links. These networks are ubiquitous int he modern world and range from power networks to the financial systems.A particular feature of these networks is their size changes over time: new elements are added, and old elements are removed.
The control of these systems presents a significant challenge. Classical techniques are monolithic, and changes in size or the interconnection structure may require a complete redesign of controllers. The plug-and-play paradigm necessitates controllers with reconfiguration capabilities to adapt to such structural changes. In contrast, coalitional control offers a control methodology inherently capable of reconfiguration.
In this talk, we discuss how the reconfiguration properties of a coalitional controller arise via partitioning the network’s set of elements. We endow each of these elements with robust distributed predictive controllers designed to withstand interconnections through invariant sets. We show that this controller allows us to conclude the asymptotic stability of the equilibrium point and constraint satisfaction of the overall network.
Dr Volkan Kumtepeli
Dr. Volkan Kumtepeli is a Postdoctoral Researcher at the Battery Intelligence Lab within the Department of Engineering Science at the University of Oxford, having joined in February 2021. He obtained his BSc degree in Electrical Engineering from Yıldız Technical University in Istanbul, Turkey, and later completed his PhD at the Energy Research Institute, Nanyang Technological University, Singapore. His current research focuses on advanced energy system modelling, particularly exploring the intersection of optimisation and learning-based methods. He is a strong advocate for open-source software and dedicated to fostering a collaborative and inclusive research environment.
Title of talk: Depreciation Cost is a Poor Proxy for Revenue Lost to Aging in Grid Storage Optimization
Abstract of talk: Dispatch of a grid energy storage system for arbitrage is typically formulated into a rolling-horizon optimization problem that includes a battery aging model within the cost function. Quantifying degradation as a depreciation cost in the objective can increase overall profits by extending lifetime. However, depreciation is just a proxy metric for battery aging; it is used because simulating the entire system life is challenging due to computational complexity and the absence of decades of future data. In cases where the depreciation cost does not match the loss of possible future revenue, different optimal usage profiles result and this reduces overall profit significantly compared to the best case (e.g., by 30-50%). Representing battery degradation perfectly within the rolling-horizon optimization does not resolve this – in addition, the economic cost of degradation throughout life should be carefully considered. For energy arbitrage, optimal economic dispatch requires a trade-off between overuse, leading to high return rate but short lifetime, vs. underuse, leading to a long but not profitable life. We reveal the intuition behind selecting representative costs for the objective function, and propose a simple moving average filter method to estimate degradation cost. Results show that this better captures peak revenue, assuming reliable price forecasts are available.
Dr Brady Planden
Dr Brady Planden is a Postdoctoral Research Associate at the University of Oxford. He received his Ph.D. from Oxford Brookes University with a thesis entitled “Improvements on physics-informed models for lithium batteries”, which focused on reduced order electrochemical modelling techniques. Brady completed his Bachelor of Engineering at the University of Victoria. Brady currently does the following:
- Develops software for electrochemical modelling (PyBOP, LiiBRA, and Galv)
- Conducting research on new lithium-ion battery chemistries witin the EU’s IntelLiGent Consortium
- Works as a post-doc in the Battery Intelligence Lab. This includes mentoring, research IT, and teaching.
Title of talk: PyBOP: Optimise and Parameterise Battery Models
Abstract of talk: PyBOP (Python Battery Optimisation and Parameterisation) is a versatile computational framework designed for the parameterisation and optimisation of battery models. It provides a robust set of tools that cater to both Bayesian and frequentist methodologies, offering flexibility to the battery researchers. By integrating example workflows, PyBOP ensures a smooth user experience, helping users navigate the complexities of battery model parameterisation
Dr Noel Hallemans
Noël was born in Brussels in December 1996. He received his MEng degree (2019) and the prize for best master thesis from the Vrije Universiteit Brussel and Université Libre de Bruxelles. Noël obtained his PhD degree (2023) from the Vrije Universiteit Brussel and University of Warwick with the dissertation entitled “Frequency domain data-driven modelling as a tool for monitoring the impedance of electrochemical systems beyond linearity and stationarity”. He was supervised by professor Rik Pintelon and modelled lithium-ion batteries.
In 2024 Noël moved to Oxford where he is now postdoctoral research assistant at the control group.
Title of talk: Physics-based battery model parametrisation from impedance data
Abstract of talk: Non-invasive parametrisation of physics-based battery models can be performed by fitting the model to electrochemical impedance spectroscopy (EIS) data containing features related to the different physical processes. However, this requires an impedance model to be derived, which may be complex to obtain analytically. We have developed the open-source software PyBaMM-EIS that provides a fast method to compute the impedance of any PyBaMM model at any operating point using automatic differentiation. Using PyBaMM-EIS, we investigate the impedance of the single particle model, single particle model with electrolyte (SPMe), and Doyle-Fuller-Newman model, and identify the SPMe as a parsimonious option that shows the typical features of measured lithium-ion cell impedance data. We provide a grouped parameter SPMe and analyse the features in the impedance related to each parameter. Using the open-source software PyBOP, we estimate 18 grouped parameters both from simulated impedance data and from measured impedance data from a LG M50LT lithium-ion battery. The parameters that directly affect the response of the SPMe can be accurately determined and assigned to the correct electrode. Crucially, parameter fitting must be done simultaneously to data across a wide range of states-of-charge. Overall, this work presents a practical way to find the parameters of physics-based models
Dr Yuanbo Nie
Dr Yuanbo Nie is a Lecturer in the Department of Automatic Control and Systems Engineering, University of Sheffield. He received an MSc degree in Aerospace Engineering from the Delft University of Technology, and MSc degree in Advanced Computational Methods from Imperial College London, and a Ph.D. degree in Aeronautics from Imperial College London (Thesis: Numerical Optimal Control with Applications in Aerospace) in 2021.
Between 2012 and 2013, he was with the Institute of System Dynamics and Control, German Aerospace Center (DLR), and between 2021 and 2022, he was a post-doctoral research associate with the Rolls-Royce Control, Monitoring and Systems Engineering University Technology Centre.