It is our pleasure to invite you to participate in the IFAC Workshop Control for Smart Cities – CSC 2022, which aims to bring together researchers and practitioners from industry and academia to give an overview of the state of the art, to present new research results and to exchange ideas and experiences in the field of international stability.
Control for Smart City is a multidisciplinary research area and involves collaboration with Control Systems specialists, Information Technology professionals, Urban Development Engineers and Architects, Civil engineers, Environment researchers, Transport and Automotive specialists, Water and Energy professionals, etc. Special Measurements, Networking, Communications, Decision Making, Operations Research, Control Systems, Mathematical Modelling, and Optimization technics are advanced instruments based on emerging Big Data and Artificial Intelligence (AI) technologies directed to create a healthy, energy efficient, and convenient environment to improve the quality of life of the citizens.
Whilst this workshop is particularly interested to attract papers which address cost-oriented issues, the IPC will consider any papers within the scope of TC 9.3. The scope includes, but is not limited to:
Mariagrazia Dotoli is a Full Professor in Automatic Control at Politecnico di Bari, Italy, where she is also the Founder and Coordinator of the Industry 4.0 PhD Program. She was the Vice Rector for research of Politecnico di Bari and a member elect of the Academic Senate. She has been a visiting scholar at the Paris 6 University, France, and at the Technical University of Denmark. Her research interests include modeling, identification, management, control and diagnosis of smart cities, energy systems, manufacturing systems, logistics systems, traffic networks, discrete event systems. She is a Senior Editor of the IEEE TRANS. ON AUTOMATION SCIENCE AND ENGINEERING and an Associate Editor of the IEEE TRANS. ON SYSTEMS MAN AND CYBERNETICS: SYSTEMS. She served in the organization of many well-reputed international conferences. Currently, she is the General chair of the 2024 IEEE Conference on Automation Science and Engineering. She is author of 200+ international publications, including 1 textbook (in Italian) and 80+ international journal papers. Her h-index in Google Scholar equals 40.
In this talk several decision and control tools that address the emerging need for intelligent energy management of smart cities are presented. A smart city is a sustainable and efficient urban centre that provides a high quality of life to its inhabitants through optimal management of its resources. Energy management is one of the most demanding issues within such urban centres, due to the complexity of energy systems and their vital role. Therefore, to increase energy smartness, cities should improve present systems and implement new solutions in a coordinated way and through an optimal approach, by profiting from the synergies among all the involved urban actors. From the one hand, policy makers such as urban planners have a crucial role in actively identifying strategic plans and associated operational implementation to make urban infrastructure and facilities more energy efficient and environmentally friendly in a cost-effective manner. On the other hand, also small end-users such as smart homes are key enablers in the transition towards a low-carbon energy sector, ensuring the efficient and sustainable use of natural resources from a consumers’ perspective.
Against this background, this talk presents the results of a research published in a series of journal papers developed by the Decision and Control Laboratory research group at Politecnico di Bari (Italy) together with a series of initiatives and projects dedicated to energy efficiency, reduction of CO2 emissions and the increase of citizens’ quality of life.
In the first part, this talk deals with optimization tools devoted to the strategic management of urban energy systems, i.e., solutions that effectively measure the city energy performance and proficiently support the decision maker in determining the optimal action plans for a long term energy efficiency-oriented strategy. Indeed, several ICT-based planning, management, and policy tools are being experienced worldwide to improve the city energy efficiency and environmental sustainability. In most of the related works, the city energy management is addressed from the perspective of a single urban sector. There is an apparent lack of methods that look at the collection of existing urban subsystems in an integrated way rather than on a subsystem by subsystem basis. Hence, a hierarchical model for an integrated urban energy decision process, reflecting the smart city system of systems view, is presented.
In the second part, this talk deals with decision and control techniques for the operational management of smart energy users. In particular, the optimal scheduling of energy activities of a group of interconnected homes and the optimal charging of a fleet of electric vehicles (EVs) are addressed.
As for the energy scheduling of smart homes, this talk deals with a multi-user scenario: nearby networked users optimally share renewable energy sources and/or storage systems to jointly take advantage of the locally harvested and stored energy. There is an emerging need for enabling micro-grids to make cooperative decisions, to exchange and trade power. Consequently, this talk presents decentralized and distributed optimization techniques, such as game-theoretic and decomposition methodologies, for large-scale residential energy systems, where individuals, as well as aggregations of, smart users make use of energy scheduling systems to optimally manage the use of electrical appliances, plan the energy production and supplying, and program the storage systems charging/discharging.
As for the energy scheduling of electric vehicles, this talk presents novel distributed and decentralized control strategies for the optimal charging of a large-scale fleet of EVs with congestion constraints on the overall network and on the single grid components. It may occur that several EVs are concentrated in a narrow area supplied by undersized capacity feeder and line, implying a break-down of the related distribution subsystem due to over-demand. Hence, the problem of optimally scheduling an EV fleet charging considering congestion constraints on the overall network and on single components is addressed without relying on a central decision maker. The resulting charging scheduling problem aims at ensuring a cost-optimal profile of the aggregated energy demand and at satisfying the resource constraints depending both on power grid components’ capacity and EV locations in the distribution network. The presented solution approaches rely on iterative algorithms based on duality, waterfilling, and consensus theory.
Karl H. Johansson is Professor with the School of Electrical Engineering and Computer Science at KTH Royal Institute of Technology in Sweden and Director of Digital Futures. He received MSc degree in Electrical Engineering and PhD in Automatic Control from Lund University. He has held visiting positions at UC Berkeley, Caltech, NTU, HKUST Institute of Advanced Studies, and NTNU. His research interests are in networked control systems and cyber-physical systems with applications in transportation, energy, and automation networks. He is President of the European Control Association and member of the IFAC Council, and has served on the IEEE Control Systems Society Board of Governors and the Swedish Scientific Council for Natural Sciences and Engineering Sciences. He has received several best paper awards and other distinctions from IEEE, IFAC, and ACM. He has been awarded Swedish Research Council Distinguished Professor, Wallenberg Scholar with the Knut and Alice Wallenberg Foundation, Future Research Leader Award from the Swedish Foundation for Strategic Research, the triennial IFAC Young Author Prize, and IEEE Control Systems Society Distinguished Lecturer. He is Fellow of the IEEE and the Royal Swedish Academy of Engineering Sciences.
While the long-term benefits of introducing connected and automated vehicles into road traffic are widely understood to be revolutionary, there is much debate about whether its early stages will cause an increase in congestion and issues related to human-driven vehicles. Notwithstanding, connected vehicles acting as mobile sensors and actuators could enable traffic predictions and control at a scale never before possible, and thereby a much more efficient and sustainable use of the available road infrastructure and energy resources. In this talk, we will present how new freight transport technology based on automated truck platoons can be the backbone for such a system. Novel system architectures, sensing and communication technologies, optimization and learning algorithms together with extensive experimental evaluations will be discussed. How vehicles platoons can influence traffic flows by acting as a moving bottleneck will be shown together with traffic models suitable for designing traffic control systems. It will be argued that these models are possible to learn automatically from data gathered from vehicles acting as traffic flow sensors. Experiments show that relatively few connected vehicles are enough to mitigate congestion and improve traffic conditions significantly. The presentation will be based on joint work with several existing and former students and postdocs and Swedish automotive industry.
Qing-Shan Jia received the B.S. degree in automation in July 2002 and the Ph.D. degree in control science and engineering in July 2006, both from Tsinghua University, Beijing, China. He is a Full Professor in the Center for Intelligent and Networked Systems (CFINS), Department of Automation, Tsinghua University, where he currently serves as the associate director. He was the vice dean of Tsinghua Global Innovation eXchange (GIX) institute from 2016 to 2019 and the vice chair of Department of Automation at Tsinghua University from 2015 to 2018. He was a postdoc at Harvard University in 2006, a visiting scholar at the Hong Kong University of Science and Technology in 2010, and at Laboratory for Information and Decision Systems, Massachusetts Institute of Technology in 2013. His research interest is to develop an integrated data-driven, statistical, and computational approach to find designs and decision-making policies which have simple structures and guaranteed good performance. His work relies on strong collaborations with experts in manufacturing systems, energy systems, autonomous systems, and experts in manufacturing systems, energy systems, autonomous systems, and smart cities. He is currently the executive editorsmart cities. He is currently the executive editor--inin--chief of chief of Results in Results in Control and Optimization, and an associate editor (AE) of Science China Control and Optimization, and an associate editor (AE) of Science China Information Sciences. He was an AE of IEEE Transactions on Automatic Control Information Sciences. He was an AE of IEEE Transactions on Automatic Control (2015(2015--2021), IEEE Control Systems Letters (20192021), IEEE Control Systems Letters (2019--2021), IEEE Transactions on 2021), IEEE Transactions on Automation Science aAutomation Science and Engineering (2012nd Engineering (2012--2017), and Discrete Event Dynamic 2017), and Discrete Event Dynamic Systems Systems –– Theory and Applications (2012Theory and Applications (2012--2016). He served the Discrete Event 2016). He served the Discrete Event Systems Technical Committee chair in IEEE Control Systems Society (2012Systems Technical Committee chair in IEEE Control Systems Society (2012--2015), 2015), and the coand the co--chair for Smart Buildings Technicalchair for Smart Buildings Technical Committee in IEEE Robotics and Committee in IEEE Robotics and Automation Society (2012Automation Society (2012--2021). He is currently the Control for Smart Cities 2021). He is currently the Control for Smart Cities Technical Committee chair in International Federation of Automatic Control, Technical Committee chair in International Federation of Automatic Control, and the Beijing Chapter Chair of IEEE Control Systems Society. He is a and the Beijing Chapter Chair of IEEE Control Systems Society. He is a member member of the 11of the 11thth Chinese Automation Association Technical Committee on Control Chinese Automation Association Technical Committee on Control Theory (2018Theory (2018--2022) and the 12022) and the 1stst Chinese Automation Association Technical Chinese Automation Association Technical Committee on Information Security of Industrial Systems (2016Committee on Information Security of Industrial Systems (2016--2020). 2020).
Cyber physical energy system (CPES) is where information and energy merges together to improve the overall system performance including economic, comfort, and safety aspects. Artificial intelligence which are enabled by internet of things (IoT), big data, and cloud computing, has a big role in the optimization of CPES. In this talk, we focus on event-based reinforcement learning (eRL) which makes decisions according to events instead of states.
This method provides a scalable solution for large-scale multi-stage decision making problem in which an accurate model may not be available. The performance of this method will be demonstrated by examples in smart buildings, smart micro-grid of buildings, and smart cities, and in particular on the problem of stochastic matching between the renewable power generation and the uncertain charging demand from the plug-in electric vehicles (PHEVs) in a city. We will also discuss extensions of this method to distributed optimization. We hope this work sheds light to the optimization of CPES.
CSC 2022 will take place in Hotel Vila List, Sozopol, Bulgaria. Sozopol is an ancient seaside town located 35 km south of airport Burgas on the southern Bulgarian Black Sea Coast.
Sozopol is a lovely place to spend a few days with its long sandy beaches, cobbled streets, numerous traditional restaurants and historical features. Sozopol is with easy travel connections to major cities and airports of Bulgaria – Sofia, Burgas, Varna, Plovdiv.
Participation with Extended Abstracts: €100
Student fee: €100 (with student participation only)
The above fees are for one workshop paper. For a second paper accepted for presentation at the workshop, the authors should pay an extra fee of €100 (for extended abstract - the authors should pay an extra fee of €50)
For participants without papers: €50
Form of payment: Bank transfer.
Details for bank transfer:
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