IFAC Workshop Control for Smart Cities – CSC 2022

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.

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.

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.