No Arabic abstract
The objective behind this project is to maximize the efficiency of land space, to decrease the driver stress and frustration, along with a considerable reduction in air pollution. Our contribution is in the form of an automatic parking system that is controlled by cellular phones. The structure is a hexagon shape that uses conveyor belts, to transport the vehicles from the entrance into the parking spaces over an elevating platform. The entrance gate includes length-measuring sensors to determine whether the approaching vehicle is eligible to enter. Our system is controlled through a microcontroller, and using cellular communications to connect to the customer. The project can be applied to different locations and is capable of capacity extensions.
As an environment-friendly substitute for conventional fuel-powered vehicles, electric vehicles (EVs) and their components have been widely developed and deployed worldwide. The large-scale integration of EVs into power grid brings both challenges and opportunities to the system performance. On one hand, the load demand from EV charging imposes large impact on the stability and efficiency of power grid. On the other hand, EVs could potentially act as mobile energy storage systems to improve the power network performance, such as load flattening, fast frequency control, and facilitating renewable energy integration. Evidently, uncontrolled EV charging could lead to inefficient power network operation or even security issues. This spurs enormous research interests in designing charging coordination mechanisms. A key design challenge here lies in the lack of complete knowledge of events that occur in the future. Indeed, the amount of knowledge of future events significantly impacts the design of efficient charging control algorithms. This article focuses on introducing online EV charging scheduling techniques that deal with different degrees of uncertainty and randomness of future knowledge. Besides, we highlight the promising future research directions for EV charging control.
Industrial Control Systems (ICS) are evolving with advances in new technology. The addition of wireless sensors and actuators and new control techniques means that engineering practices from communication systems are being integrated into those used for control systems. The two are engineered in very different ways. Neither engineering approach is capable of accounting for the subtle interactions and interdependence that occur when the two are combined. This paper describes our first steps to bridge this gap, and push the boundaries of both computer communication system and control system design. We present The Separator testbed, a Cyber-Physical testbed enabling our search for a suitable way to engineer systems that combine both computer networks and control systems.
This paper develops a model-free volt-VAR optimization (VVO) algorithm via multi-agent deep reinforcement learning (MADRL) in unbalanced distribution systems. This method is novel since we cast the VVO problem in unbalanced distribution networks to an intelligent deep Q-network (DQN) framework, which avoids solving a specific optimization model directly when facing time-varying operating conditions of the systems. We consider statuses/ratios of switchable capacitors, voltage regulators, and smart inverters installed at distributed generators as the action variables of the DQN agents. A delicately designed reward function guides these agents to interact with the distribution system, in the direction of reinforcing voltage regulation and power loss reduction simultaneously. The forward-backward sweep method for radial three-phase distribution systems provides accurate power flow results within a few iterations to the DQN environment. Finally, the proposed multi-objective MADRL method realizes the dual goals for VVO. We test this algorithm on the unbalanced IEEE 13-bus and 123-bus systems. Numerical simulations validate the excellent performance of this method in voltage regulation and power loss reduction.
The explosion of advancements in artificial intelligence, sensor technologies, and wireless communication activates ubiquitous sensing through distributed sensors. These sensors are various domains of networks that lead us to smart systems in healthcare, transportation, environment, and other relevant branches/networks. Having collaborative interaction among the smart systems connects end-user devices to each other which enables achieving a new integrated entity called Smart Cities. The goal of this study is to provide a comprehensive survey of data analytics in smart cities. In this paper, we aim to focus on one of the smart cities important branches, namely Smart Mobility, and its positive ample impact on the smart cities decision-making process. Intelligent decision-making systems in smart mobility offer many advantages such as saving energy, relaying city traffic, and more importantly, reducing air pollution by offering real-time useful information and imperative knowledge. Making a decision in smart cities in time is challenging due to various and high dimensional factors and parameters, which are not frequently collected. In this paper, we first address current challenges in smart cities and provide an overview of potential solutions to these challenges. Then, we offer a framework of these solutions, called universal smart cities decision making, with three main sections of data capturing, data analysis, and decision making to optimize the smart mobility within smart cities. With this framework, we elaborate on fundamental concepts of big data, machine learning, and deep leaning algorithms that have been applied to smart cities and discuss the role of these algorithms in decision making for smart mobility in smart cities.
In this article we describe our efforts of extending demand-side control concepts to the application in portable electronic devices, such as laptop computers, mobile phones and tablet computers. As these devices feature built-in energy storage (in the form of batteries) and the ability to run complex control routines, they are ideal for the implementation of smart charging concepts. We developed a prototype of a smart laptop charger that controls the charging process depending on the locally measured frequency of the electricity grid. If this technique is incorporated into millions of devices in UK households, this will contribute significantly to the stability of the electricity grid, help to mitigate the power production fluctuations from renewable energy sources and avoid the high cost of building and maintaining conventional power plants as standby reserve.