Multi-agent network games with applications in smart electric mobility

The growing complexity and globalization of modern society brought to light novel problems and challenges for researchers that aim to model real-life phenomena. Nowadays communities and even single individuals cannot be considered as a closed system, since one's actions create a ripple effect that ends up influencing the action of others. Therefore, the study of decision-making processes over networks became a pivotal topic in the research community. The possible applications are virtually endless and span into many different fields. Two of the most relevant examples are smart mobility and energy management in highly populated cities, where a collection of (partially) noncooperative individuals interact over a network trying to reach an efficient equilibrium point, in the sense of Nash, and share limited resources due to the environment in which they operate. In this work, we approach these problems through the lens of game theory. We use different declinations of this powerful mathematical tool to study several aspects of these themes. We design decentralized iterative algorithms solving generalized network games that generate behavioral rules for the players that, if followed, ensure global convergence. Then, we question the classical assumption of perfect players’ rationality by introducing novel dynamics to model partial rationality and analyzing their properties. We conclude by focusing on the design of optimal policies to regulate smart mobility and energy management. In this case, we create a detailed and more realistic description of the problem and use a nudging mechanism, implemented by means of a semi-decentralized algorithm, to align the users' behavior with the one desired by the policymaker

Generalized Wardrop Equilibrium for Charging Station Selection and Route Choice of Electric Vehicles in Joint Power Distribution and Transportation Networks

This paper presents the equilibrium analysis of a game composed of heterogeneous electric vehicles (EVs) and a power distribution system operator (DSO) as the players, and charging station operators (CSOs) and a transportation network operator (TNO) as coordinators. Each EV tries to pick a charging station as its destination and a route to get there at the same time. However, the traffic and electrical load congestion on the roads and charging stations lead to the interdependencies between the optimal decisions of EVs. CSOs and the TNO need to apply some tolling to control such congestion. On the other hand, the pricing at charging stations depends on real-time distributional locational marginal pricing, which is determined by the DSO after solving the optimal power flow over the power distribution network. This paper also takes into account the local and the coupling/infrastructure constraints of EVs, transportation and distribution networks. This problem is modeled as a generalized aggregative game, and then a decentralized learning method is proposed to obtain an equilibrium point of the game, which is known as variational generalized Wardrop equilibrium. The existence of such an equilibrium point and the convergence of the proposed algorithm to it are proven. We undertake numerical studies on the Savannah city model and the IEEE 33-bus distribution network and investigate the impact of various characteristics on demand and prices

Distributed coordination of flexible devices in power networks

The penetration of new types of devices, such as domestic storage and electric vehicles, offers increasing flexibility on demand side. This will bring both new opportunities and challenges to the operation of power systems. The aim of this thesis is to design novel distributed control strategies for large scale coordination of flexible devices. To this end, flexible devices are modelled as self-interested rational agents that aim at minimizing their individual costs in response to the broadcast price signals. This thesis mainly consists of three parts, considering that the price signals can be designed in different forms, and that flexible devices could operate in different markets (e.g. energy markets, and integrated energy and reserve markets). The first part presents a multi-agent framework for the coordination of large populations of micro-storage devices in energy markets, under the assumption that the electricity price is some monotone increasing function of total power demand. The second part extends the work of the first part through taking into account the topology of power networks: the proposed modelling framework envisages heterogeneous groups of loads that operate at different buses, connected by transmission lines of limited capacity. The locational marginal prices of electricity are used as price signals, which are different in general for each bus and calculated through an optimal power flow problem. In the framework of the third part, it is envisioned that micro-storage devices and electric vehicles participate in an integrated energy-reserve market, and that they can contribute to the provision of reserve by being available to reduce their power consumption. These flexible devices autonomously schedule their operation in response to two kinds of price signals - the locational marginal prices of energy and reserve. Iterative schemes for the coordination of the flexible devices are presented in the three parts. It is proved that the proposed coordination schemes can ensure the convergence to stable market configurations, characterized as aggregative equilibria at which each device cannot further reduce its cost by unilaterally changing its power profile. Distributed implementations of these proposed control strategies are discussed, and their performance is evaluated in simulations on large scale power systems.Open Acces

A Tutorial on Distributed Optimization for Cooperative Robotics: from Setups and Algorithms to Toolboxes and Research Directions

Several interesting problems in multi-robot systems can be cast in the framework of distributed optimization. Examples include multi-robot task allocation, vehicle routing, target protection and surveillance. While the theoretical analysis of distributed optimization algorithms has received significant attention, its application to cooperative robotics has not been investigated in detail. In this paper, we show how notable scenarios in cooperative robotics can be addressed by suitable distributed optimization setups. Specifically, after a brief introduction on the widely investigated consensus optimization (most suited for data analytics) and on the partition-based setup (matching the graph structure in the optimization), we focus on two distributed settings modeling several scenarios in cooperative robotics, i.e., the so-called constraint-coupled and aggregative optimization frameworks. For each one, we consider use-case applications, and we discuss tailored distributed algorithms with their convergence properties. Then, we revise state-of-the-art toolboxes allowing for the implementation of distributed schemes on real networks of robots without central coordinators. For each use case, we discuss their implementation in these toolboxes and provide simulations and real experiments on networks of heterogeneous robots

A Survey on Coordinated Charging Methods for Electric Vehicles

Electric vehicles (EVs) is regarded as one of the most effective ways to reduce oil and gas use. EVs (electric vehicles) have many advantages over ICEVs (internal combustion engine vehicles), including zero pollution, little noise, and exceptional energy efficiency. Even though an EV is known to have a three times higher fuel efficiency than an ICEV, the driving range is often significantly lower because batteries have a lower energy density than gasoline or diesel. Over the next few decades, it is anticipated that the number of electric vehicles will increase significantly due to concerns about pollution and technological advancements in the sector. Utilizing a variety of energy sources will boost energy security while reducing emissions and fuel usage. A paradigm shift has been observed with the switch from internal combustion to electric car technology. For electric vehicles to become widely used, a charging infrastructure must be developed. However, there is a cap on the amount of electricity that can be used to charge the vehicles in a charging station. Rearranging charging times, specifically charging coordination can help optimize the distribution of the available power among the vehicles. In this paper, a review of the various coordinated charging methods has been presented. A detailed comparison of the methods has been done