Sergio Grammatico (Delft University of Technology) - 18th May 2022
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) - 30th March 2022
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) - 12th January 2022
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.
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.