Reinforcement Learning-Based Dynamic Power Management of a Battery-Powered System Supplying Multiple Active Modes

Abstract-This paper addresses the problem of extending battery service lifetime in a portable electronic system while maintaining an acceptable performance degradation level. The proposed dynamic power management (DPM) framework is based on model-free reinforcement learning (RL) technique. In this DPM framework, the Power Manager (PM) adapts the system operating mode to the actual battery state of charge. It uses RL technique to accurately define the optimal battery voltage threshold value and use it to specify the system active mode. In addition, the PM automatically adjusts the power management policy by learning the optimal timeout value. Moreover, the SoC and latency tradeoffs can be precisely controlled based on a userdefined parameter. Experiments show that the proposed method outperforms existing methods by 35% in terms of saving battery service lifetime. Keywords-Dynamic power management; reinforcement learning, extending battery lifetime; battery-powered system design

Network resource allocation policies with energy transfer capabilities

During the last decades, mobile network operators have witnessed an exponential increase in the traffic demand, mainly due to the high request of services from a huge amount of users. The trend is of a further increase in both the traffic demand and the number of connected devices over the next years. The traffic load is expected to have an annual growth rate of 53% for the mobile network alone, and the upcoming industrial era, which will connect different types of devices to the mobile infrastructure including human and machine type communications, will definitely exacerbate such an increasing trend. The current directions anticipate that future mobile networks will be composed of ultra dense deployments of heterogeneous Base Stations (BSs), where BSs using different transmission powers coexist. Accordingly, the traditional Macro BSs layer will be complemented or replaced with multiple overlapping tiers of small BSs (SBSs), which will allow extending the system capacity. However, the massive use of Information and Communication Technology (ICT) and the dense deployment of network elements is going to increase the level of energy consumed by the telecommunication infrastructure and its carbon footprint on the environment. Current estimations indicates that 10% of the worldwide electricity generation is due to the ICT industry and this value is forecasted to reach 51% by 2030, which imply that 23% of the carbon footprint by human activity will be due to ICT. Environmental sustainability is thus a key requirement for designing next generation mobile networks. Recently, the use of Renewable Energy Sources (RESs) for supplying network elements has attracted the attention of the research community, where the interest is driven by the increased efficiency and the reduced costs of energy harvesters and storage devices, specially when installed to supply SBSs. Such a solution has been demonstrated to be environmentally and economically sustainable in both rural and urban areas. However, RESs will entail a higher management complexity. In fact, environmental energy is inherently erratic and intermittent, which may cause a fluctuating energy inflow and produce service outage. A proper control of how the energy is drained and balanced across network elements is therefore necessary for a self-sustainable network design. In this dissertation, we focus on energy harvested through solar panels that is deemed the most appropriate due to the good efficiency of commercial photovoltaic panels as well as the wide availability of the solar source for typical installations. The characteristics of this energy source are analyzed in the first technical part of the dissertation, by considering an approach based on the extraction of features from collected data of solar energy radiation. In the second technical part of the thesis we introduce our proposed scenario. A federation of BSs together with the distributed harvesters and storage devices at the SBS sites form a micro-grid, whose operations are managed by an energy management system in charge of controlling the intermittent and erratic energy budget from the RESs. We consider load control (i.e., enabling sleep mode in the SBSs) as a method to properly manage energy inflow and spending, based on the traffic demand. Moreover, in the third technical part, we introduce the possibility of improving the network energy efficiency by sharing the exceeding energy that may be available at some BS sites within the micro-grid. Finally, a centralized controller based on supervised and reinforcement learning is proposed in the last technical part of the dissertation. The controller is in charge of opportunistically operating the network to achieve efficient utilization of the harvested energy and prevent SBSs blackout.Durante las últimas décadas, los operadores de redes móviles han sido testigos de un aumento exponencial en la demanda de tráfico, principalmente debido a la gran solicitud de servicios de una gran cantidad de usuarios. La tendencia es un aumento adicional tanto en la demanda de tráfico como en la cantidad de dispositivos conectados en los próximos años. Se espera que la carga de tráfico tenga una tasa de crecimiento anual del 53% solo para la red móvil, y la próxima era industrial, que conectará diferentes tipos de dispositivos a la infraestructura móvil, definitivamente exacerbará tal aumento. Las instrucciones actuales anticipan que las redes móviles futuras estarán compuestas por despliegues ultra densos de estaciones base (BS) heterogéneas. En consecuencia, la capa tradicional de Macro BS se complementará o reemplazará con múltiples niveles superpuestos de pequeños BS (SBS), lo que permitirá ampliar la capacidad del sistema. Sin embargo, el uso masivo de la Tecnología de la Información y la Comunicación (TIC) y el despliegue denso de los elementos de la red aumentará el nivel de energía consumida por la infraestructura de telecomunicaciones y su huella de carbono en el medio ambiente. Las estimaciones actuales indican que el 10% de la generación mundial de electricidad se debe a la industria de las TIC y se prevé que este valor alcance el 51% para 2030, lo que implica que el 23% de la huella de carbono por actividad humana se deberá a las TIC. La sostenibilidad ambiental es, por lo tanto, un requisito clave para diseñar redes móviles de próxima generación. Recientemente, el uso de fuentes de energía renovables (RES) para suministrar elementos de red ha atraído la atención de la comunidad investigadora, donde el interés se ve impulsado por el aumento de la eficiencia y la reducción de los costos de los recolectores y dispositivos de almacenamiento de energía, especialmente cuando se instalan para suministrar SBS. Se ha demostrado que dicha solución es ambiental y económicamente sostenible tanto en áreas rurales como urbanas. Sin embargo, las RES conllevarán una mayor complejidad de gestión. De hecho, la energía ambiental es inherentemente errática e intermitente, lo que puede causar una entrada de energía fluctuante y producir una interrupción del servicio. Por lo tanto, es necesario un control adecuado de cómo se drena y equilibra la energía entre los elementos de la red para un diseño de red autosostenible. En esta disertación, nos enfocamos en la energía cosechada a través de paneles solares que se considera la más apropiada debido a la buena eficiencia de los paneles fotovoltaicos comerciales, así como a la amplia disponibilidad de la fuente solar para instalaciones típicas. Las características de esta fuente de energía se analizan en la primera parte técnica de la disertación, al considerar un enfoque basado en la extracción de características de los datos recopilados de radiación de energía solar. En la segunda parte técnica de la tesis presentamos nuestro escenario propuesto. Una federación de BS junto con los cosechadores distribuidos y los dispositivos de almacenamiento forman una microrred, cuyas operaciones son administradas por un sistema de administración de energía a cargo de controlar el presupuesto de energía intermitente y errático de las RES. Consideramos el control de carga como un método para administrar adecuadamente la entrada y el gasto de energía, en función de la demanda de tráfico. Además, en la tercera parte técnica, presentamos la posibilidad de mejorar la eficiencia energética de la red al compartir la energía excedente que puede estar disponible en algunos sitios dentro de la microrred. Finalmente, se propone un controlador centralizado basado en aprendizaje supervisado y de refuerzo en la última parte técnica de la disertación. El controlador está a cargo de operar la red para lograr una utilización eficiente de energía y previene el apagón de SB

Bridges Structural Health Monitoring and Deterioration Detection Synthesis of Knowledge and Technology

INE/AUTC 10.0

Optimal energy management for a grid-tied solar PV-battery microgrid: A reinforcement learning approach

There has been a shift towards energy sustainability in recent years, and this shift should continue. The steady growth of energy demand because of population growth, as well as heightened worries about the number of anthropogenic gases released into the atmosphere and deployment of advanced grid technologies, has spurred the penetration of renewable energy resources (RERs) at different locations and scales in the power grid. As a result, the energy system is moving away from the centralized paradigm of large, controllable power plants and toward a decentralized network based on renewables. Microgrids, either grid-connected or islanded, provide a key solution for integrating RERs, load demand flexibility, and energy storage systems within this framework. Nonetheless, renewable energy resources, such as solar and wind energy, can be extremely stochastic as they are weather dependent. These resources coupled with load demand uncertainties lead to random variations on both the generation and load sides, thus challenging optimal energy management. This thesis develops an optimal energy management system (EMS) for a grid-tied solar PV-battery microgrid. The goal of the EMS is to obtain the minimum operational costs (cost of power exchange with the utility and battery wear cost) while still considering network constraints, which ensure grid violations are avoided. A reinforcement learning (RL) approach is proposed to minimize the operational cost of the microgrid under this stochastic setting. RL is a reward-motivated optimization technique derived from how animals learn to optimize their behaviour in new environments. Unlike other conventional model-based optimization approaches, RL doesn't need an explicit model of the optimization system to get optimal solutions. The EMS is modelled as a Markov Decision Process (MDP) to achieve optimality considering the state, action, and reward function. The feasibility of two RL algorithms, namely, conventional Q-learning algorithm and deep Q network algorithm, are developed, and their efficacy in performing optimal energy management for the designed system is evaluated in this thesis. First, the energy management problem is expressed as a sequential decision-making process, after which two algorithms, trading, and non-trading algorithm, are developed. In the trading algorithm case, excess microgrid's energy can be sold back to the utility to increase revenue, while in the latter case constraining rules are embedded in the designed EMS to ensure that no excess energy is sold back to the utility. Then a Q-learning algorithm is developed to minimize the operational cost of the microgrid under unknown future information. Finally, to evaluate the performance of the proposed EMS, a comparison study between a trading case EMS model and a non-trading case is performed using a typical commercial load curve and PV generation profile over a 24- hour horizon. Numerical simulation results indicated that the algorithm learned to select an optimized energy schedule that minimizes energy cost (cost of power purchased from the utility based on the time-varying tariff and battery wear cost) in both summer and winter case studies. However, comparing the non-trading EMS to the trading EMS model operational costs, the latter one decreased cost by 4.033% in the summer season and 2.199% in the winter season. Secondly, a deep Q network (DQN) method that uses recent learning algorithm enhancements, including experience replay and target network, is developed to learn the system uncertainties, including load demand, grid prices and volatile power supply from the renewables solve the optimal energy management problem. Unlike the Q-learning method, which updates the Q-function using a lookup table (which limits its scalability and overall performance in stochastic optimization), the DQN method uses a deep neural network that approximates the Q- function via statistical regression. The performance of the proposed method is evaluated with differently fluctuating load profiles, i.e., slow, medium, and fast. Simulation results substantiated the efficacy of the proposed method as the algorithm was established to learn from experience to raise the battery state of charge and optimally shift loads from a one-time instance, thus supporting the utility grid in reducing aggregate peak load. Furthermore, the performance of the proposed DQN approach was compared to the conventional Q-learning algorithm in terms of achieving a minimum global cost. Simulation results showed that the DQN algorithm outperformed the conventional Q-learning approach, reducing system operational costs by 15%, 24%, and 26% for the slow, medium, and fast fluctuating load profiles in the studied cases

Energy sustainable paradigms and methods for future mobile networks: A survey

In this survey, we discuss the role of energy in the design of future mobile networks and, in particular, we advocate and elaborate on the use of energy harvesting (EH) hardware as a means to decrease the environmental footprint of 5G technology. To take full advantage of the harvested (renewable) energy, while still meeting the quality of service required by dense 5G deployments, suitable management techniques are here reviewed, highlighting the open issues that are still to be solved to provide eco-friendly and cost-effective mobile architectures. Several solutions have recently been proposed to tackle capacity, coverage and efficiency problems, including: C-RAN, Software Defined Networking (SDN) and fog computing, among others. However, these are not explicitly tailored to increase the energy efficiency of networks featuring renewable energy sources, and have the following limitations: (i) their energy savings are in many cases still insufficient and (ii) they do not consider network elements possessing energy harvesting capabilities. In this paper, we systematically review existing energy sustainable paradigms and methods to address points (i) and (ii), discussing how these can be exploited to obtain highly efficient, energy self-sufficient and high capacity networks. Several open issues have emerged from our review, ranging from the need for accurate energy, transmission and consumption models, to the lack of accurate data traffic profiles, to the use of power transfer, energy cooperation and energy trading techniques. These challenges are here discussed along with some research directions to follow for achieving sustainable 5G systems.Comment: Accepted by Elsevier Computer Communications, 21 pages, 9 figure

Combined power system planning and policy proposition for future electric vehicle charging infrastructure

This thesis was submitted for the award of Master of Philosophy and was awarded by Brunel University LondonIn order to achieve the UK Government’s legally bound framework of greenhouse gas reduction targets, the transport sector is undergoing drastic changes. The key action taken by the Department of Transport in addressing the issue was the introduction of Ultra-low emission vehicles (ULEV) concept. Office for Low Emission Vehicles (OLEV) was introduced to support early market for ULEV and development of efficient recharging network through Plugged-in Places programme. The massive deployment of EV charging stations across GB will have direct impact on GB power system as they require electricity supply for their operation. It is therefore deemed necessary to carry out investigations on the capacity of the network assets to handle this load and to develop policies to manage the future EV charging infrastructure efficiently. This thesis provides an overview of the EV technology introducing various technicalities behind EVs and the associated charging stations. The extended theory about interoperability between EVs and power networks is also presented. Investigation of an 11kV networked site and 66/11 kV networked area is performed to determine their potential in accommodating future EV charging infrastructure. A methodology has been proposed to carry out investigations in 11kV networked site. For analysis purpose both the real networks are modelled in detail using power system analysis software Electrical Transient Analyzer Programme (ETAP). Scottish and Southern Energy (SSE) and Northern Power Grid (NPG) are the owners of the distribution networks respectively. Collaboration with DNOs has taken place to collect the existing network data. Finally, a university based EV charging bays management policy has been proposed

Toward Holistic Energy Management Strategies for Fuel Cell Hybrid Electric Vehicles in Heavy-Duty Applications

The increasing need to slow down climate change for environmental protection demands further advancements toward regenerative energy and sustainable mobility. While individual mobility applications are assumed to be satisfied with improving battery electric vehicles (BEVs), the growing sector of freight transport and heavy-duty applications requires alternative solutions to meet the requirements of long ranges and high payloads. Fuel cell hybrid electric vehicles (FCHEVs) emerge as a capable technology for high-energy applications. This technology comprises a fuel cell system (FCS) for energy supply combined with buffering energy storages, such as batteries or ultracapacitors. In this article, recent successful developments regarding FCHEVs in various heavy-duty applications are presented. Subsequently, an overview of the FCHEV drivetrain, its main components, and different topologies with an emphasis on heavy-duty trucks is given. In order to enable system layout optimization and energy management strategy (EMS) design, functionality and modeling approaches for the FCS, battery, ultracapacitor, and further relevant subsystems are briefly described. Afterward, common methodologies for EMS are structured, presenting a new taxonomy for dynamic optimization-based EMS from a control engineering perspective. Finally, the findings lead to a guideline toward holistic EMS, encouraging the co-optimization of system design, and EMS development for FCHEVs. For the EMS, we propose a layered model predictive control (MPC) approach, which takes velocity planning, the mitigation of degradation effects, and the auxiliaries into account simultaneously

Innovation in Energy Systems

It has been a little over a century since the inception of interconnected networks and little has changed in the way that they are operated. Demand-supply balance methods, protection schemes, business models for electric power companies, and future development considerations have remained the same until very recently. Distributed generators, storage devices, and electric vehicles have become widespread and disrupted century-old bulk generation - bulk transmission operation. Distribution networks are no longer passive networks and now contribute to power generation. Old billing and energy trading schemes cannot accommodate this change and need revision. Furthermore, bidirectional power flow is an unprecedented phenomenon in distribution networks and traditional protection schemes require a thorough fix for proper operation. This book aims to cover new technologies, methods, and approaches developed to meet the needs of this changing field