Welcome to the Dutch Digital Energy Seminar website!


Introduction

Mailing List


    To register for our mailing list, please follow the link:

    https://lists.cwi.nl/mailman/listinfo/digital-energy-list

    Note that the mailing list will be used sparingly, only for posting seminar-related announcements, such as upcoming talks. You can adjust your preferences or unsubscribe any time. For any technical issues, please send an email to Valentin Robu: v.robucwi.nl.

Organisation


    All talks are announced in advance through our mailing list. Note that in order to allow for questions and a free discussion between the speaker and participants, the seminars are not recorded (but the slides can be made available on request, if the speaker agrees).

    Upcoming Seminars


    Marco Gerards (University of Twente) - 13th December 2023, 14:00-15:00 CET

    Energy management in distribution grids
    Abstract: Many Dutch distribution grids reached their capacity and in some regions new houses and companies can no longer be connected to the grid. Next to that, renewable energy is not always used efficiently and sometimes its production needs to be curtailed. Energy management methods and algorithms can help to address these problems by matching demand with the production of renewable energy. The main challenges in this field are: a lack of detailed measurements in the low voltage grid, the difficulty to predict production/consumption at a house level, and the involvement of consumers. As the energy management algorithms need to run on resource constrained networked embedded devices, the algorithms need to be efficient and robust. This talk introduces our multi-disciplinary research group, several of our energy management algorithms, and discusses the mathematical properties and practical application of the algorithms.

    Desen Kirli (University of Edinburgh) - Jan/Feb 2024 (Time and date TBA)

    Data and Digitalisation in Net Zero Energy Systems
    Abstract: TBA

    Previous Speakers


    Brinn Hekkelman (CWI Amsterdam)

    Fair Mechanisms for Smart Grid Congestion Management
    Abstract: With the transition to a more distributed and intermittent energy system centered around prosumers, local distribution grids are undergoing significant changes. One of the primary challenges for these local grids is maintaining grid stability, which requires constant balancing of supply and demand. Because local grids were not designed for distributed energy generation and large loads such as electric vehicle charging, their limited capacity is now leading to congestion. As a result, the consequences for supply-demand balancing and congestion management are falling increasingly with the individual prosumers. This immediately raises the question: how to fairly distribute these consequences? To this end we focus on supply-demand matching mechanisms for fair congestion management. We represent the local networks, populated with prosumers, by radial multi-agent commodity flow systems. Given agents’ desired prosumptions, we then compute congestion solutions for the local network based on different notions of fairness. We provide corresponding algorithmic mechanisms to compute the fair solutions, and rigorously prove some additional properties of these mechanisms such as individual rationality and incentive compatibility. We find that notions of fairness regarding congested commodity flow networks can either focus on local or global fairness, and that the network topology plays an important role. Furthermore, we find that the mix of producers and consumers (prosumers) requires slight adaptation of notions of fairness, with agents envying one group while welcoming the other. Finally, we find that it is possible to combine notions of fairness with welfare optimization in a congestion aftermarket. We let individual agents decide which of the two is more important and protect their fair shares by making aftermarket participation optional.

    Merlinda Andoni (University of Glasgow)

    Strategic decision-making on low-carbon technology and network capacity investments using game theory
    Abstract: Rapid adoption of renewable technologies has in many areas led to undesired curtailment. This means that not only renewable production is wasted, but often curtailment comes with high costs for renewable energy developers and energy end-users. A long-term solution to dealing with curtailment is increasing the network capacity. However, grid upgrades can be costly leading to a need for attracting private investment in network reinforcement. In this work, we design and evaluate a game-theoretic framework to study strategic interactions between private profit-maximising players that invest in network, renewable generation and storage capacity. Specifically, we study the case where grid capacity is developed by a private renewable investor, but line access is shared with competing renewable and storage investors, thus enabling them to export energy and access electricity demand. A practical demonstration of the underlying methodology is shown for a real-world grid reinforcement project in the UK. The methodology provides a realistic mechanism to analyse investor decision-making and investigate feasible tariffs that encourage distributed renewable investment, with sharing of grid access.

    Sergio Grammatico (Delft University of Technology)

    Game-Theoretic Peer-to-Peer Energy Trading in Distribution Grids
    Abstract: In future distribution grids, prosumers (i.e., energy consumers with storage and/or production capabilities) will trade energy with each other and with the main grid. To ensure an efficient and safe operation of energy trading, in this paper, we formulate a peer-to-peer energy market of prosumers as a generalized aggregative game, in which a network operator is only responsible for the operational constraints of the system. We design a distributed market-clearing mechanism with convergence guarantee to an economically-efficient and operationally-safe configuration (i.e., a variational generalized Nash equilibrium). Numerical studies on the IEEE 37-bus testcase show the scalability of the proposed approach and suggest that active participation in the market is beneficial for both prosumers and the network operator.

    Weiqi Hua (University of Oxford)

    Peer-to-Peer Energy Trading: Barriers and Opportunities
    Abstract: Electric power systems are transitioning towards a decentralised paradigm with the engagement of active prosumers (both producers and consumers) through using distributed multi-energy sources e.g., roof-top solar panels and electrified heating sources. Accommodating the new role of prosumers requires a flexible structure of local energy markets. The innovation of peer-to-peer energy trading enables prosumers to directly exchange energy in local markets for the energy bill saving, local energy balance, and grid resilience. This speak will analyse the barriers and opportunities of the peer-to-peer energy trading from the perspectives of regulators, power system operators, market operators, communities, individual prosumers, and enabling technologies. Potential implementations of the peer-to-peer energy trading will be instantiated by two studies in addressing the research questions of: 1) How to couple local energy markets and carbon markets through exploiting the peer-to-peer energy trading in achieving the net-zero energy transition; 2) How to use the peer-to-peer energy trading to unlock the flexibility provision from heating systems.

    Speaker Bio: Weiqi Hua is a postdoc researcher in the Department of Engineering Science at the University of Oxford, UK. He received his PhD and Master degrees at the University of Durham, UK, in 2020 and 2017, respectively, and then took the postdoctoral position at the Centre for Integrated Energy Generation and Supply (CIREGS), Cardiff University, UK, from 2020 to 2021. He was the visiting researcher to the Hellenic Telecommunications Organisation (OTE), Greece, in 2019, and visiting researcher to the Chinese Academy of Sciences, China, in 2018 and 2019. His research interests include energy system modelling, renewable energy integration, energy policy and economics, machine learning for energy system analytics, and peer-to-peer energy trading.

    Na Li (Harvard University)

    Learning and control for residential demand response
    Residential loads have great potential to enhance the efficiency and reliability of electricity systems via demand response (DR) programs. One major challenge in residential DR is to handle the unknown and uncertain customer behaviors, which are further influenced by time-varying environmental factors. In this talk, we present a set of learning and control methods for regulating loads in residential demand response (DR) by modeling it as a multi-period stochastic optimization problem. Machine learning techniques including both offline and online learning tools are employed to learn the unknown thermal dynamics model and customer opt-out behavior model, respectively. Based on the Thompson sampling framework, we propose an online DR control algorithm to learn customer behaviors and make real-time load control schemes. This algorithm considers the influence of various environmental factors on customer behaviors and is implemented in a distributed fashion to preserve the privacy of customers. This work is based on our collaboration with an industry IoT company, ThinkEco Inc. If time allows, we will briefly present some of our other projects on real-time learning in power systems.
    Joint work with Xin Chen, Yingying Li, Yutong Nie, Ran Qin, and Jun Shimada (Founder/CTO of ThinkEco Inc.
    Speaker bio: Na Li is a Gordon McKay professor in Electrical Engineering and Applied Mathematics at Harvard University. She received her Bachelor's degree in Mathematics from Zhejiang University in 2007 and a Ph.D. degree in Control and Dynamical systems from California Institute of Technology in 2013. She was a postdoctoral associate at Massachusetts Institute of Technology 2013-2014. Her research lies in the control, learning, and optimization of networked systems, including theory development, algorithm design, and applications to real-world cyber-physical societal systems. She received NSF career award (2016), AFSOR Young Investigator Award (2017), ONR Young Investigator Award(2019), Donald P. Eckman Award (2019), McDonald Mentoring Award (2020), along with some other awards.

    Alessandro Zocca (Vrije Universiteit Amsterdam)

    Improving Power Systems Reliability Via Adaptive Grid Partitioning
    Transmission line failures in power systems propagate and cascade non-locally, making it even more challenging to optimally and reliably operate these complex networks. I will present a comprehensive framework based on spectral graph theory that fully and rigorously captures how multiple simultaneous failures propagate, both for non-cut and cut set outages. A highlight of this theory is that specific network substructures, named bridge-blocks, prevent line failures from propagating globally. I will first introduce an adaptive network topology reconfiguration paradigm that uses a two-stage algorithm to increase the network robustness against line failures. The first stage aims to identify optimal clusters using spectral methods and the second stage refines the network structure by means of optimal line switching actions. Secondly, I will introduce a novel adaptive control strategy that leverages the properties the network bridge-block decomposition in combination with a frequency regulation method called unified control to effectively stopping cascading failures with minimal disruption. Such a strategy greatly improves overall reliability in terms of the N-k security standard, and localizes the impact of initial failures in the majority of the simulated contingencies. Lastly, I will also show how the network bridge-block decomposition can be exploited as a less severe emergency measure alternative to controlled islanding.
    This talk is based on joint work with Chen Liang, Steven Low, Adam Wierman, and Leon Lan.
    Speaker bio: Alessandro Zocca is tenure-track assistant professor position in the Department of Mathematics at the Vrije Universiteit Amsterdam since October 2019. He received his B.Sc. in Mathematics from the University of Padua in 2010, his M.A.St. in Mathematics from the University of Cambridge in 2011, and his Ph.D. in Mathematics from TU Eindhoven in 2015. He worked as postdoctoral researcher first at CWI Amsterdam (2016-2017) and then at California Institute of Technology (2017-2019), where he was supported by his personal NWO Rubicon grant. His work lies mostly in the area of applied probability and optimization, but has deep ramifications in areas as diverse as operations research, graph theory, algorithm design, and control theory. His research focuses on dynamics and rare events on large-scale networked systems affected by uncertainty, with a strong emphasis on applications to power systems reliability.

    Peter Palensky (Delft University of Technology)

    Cyber-physical security of Power Grids
    The power system - man's largest machine - receives increasing attention from questionable parties. Activist hackers, terrorists, digital vandals, state player attacks: they all aim at the digital assets of modern power systems in order to impact the physical half of it. Industrial control systems for power systems are one element in this landscape that is targeted. This talk will introduce you to cyber-physical power systems and explain which threats we have to deal with now and in future. It will also explain some known attacks of recent years and demonstrate you how to hack digital power system protection and that this could lead to cascading outages - more prominently known as blackouts.

    Wolf Ketter (University of Cologne) and John Collins (University of Minnesota)

    Exploring paths to a sustainable energy future: From simulation to the real world
    We can all imagine a future powered by sustainable energy, and many of us engaged in research related to sustainability. But too often our work fails to find traction in the real world. Energy infrastructure is essential to our society, and the reality on the ground is that markets, grid operations, policies and energy users will affect the rollout of renewables like solar and wind and the ‘beneficial electrification’ of transport and climate control in numerous ways that remain unclear. We would like to clear the fog just a bit. Power TAC simulates retail electricity markets in a sustainable energy future. Co-founders John Collins and Wolf Ketter have used it to explore the use of demand flexibility to maintain supply/demand balance in high-renewable scenarios. But storage and demand flexibility could also be used to manage congestion in distribution grids. Power TAC’s next evolution will be in co-simulation: exploring the relationship between market and grid in a coordinated, dual-platform simulation with other ‘open energy system models’ such as PandaPower or Power Matcher. John is also an elected Director of an electric cooperative in Wisconsin, USA. In this interactive seminar, John will explore insights derived from a decade of Power TAC competitions and what the team hopes to learn from co-simulation efforts. In dialogue with participants, he will compare the vision of the future with reality on the ground in an electric cooperative that is focused on safety, cost, and reliability for its members.