Explore the vast range of titles available.

This report provides some practical guidance on how utilities can define their own smart grid vision, identify priorities, and structure investment plans. While most of these strategic aspects apply to any area of the electricity grid, the document focuses on the segment of distribution. The guidance includes key building blocks that are needed to modernize the distribution grid and provides examples of grid modernization projects. Potential benefits that can be achieved (in monetary terms) for a given investment range are also discussed. The concept of the smart grid is relevant to any grid regardless of its stage of development. What varies are the magnitude and type of the incremental steps toward modernization that will be required to achieve a specific smart grid vision. Importantly, a utility that is at a relatively low level of grid modernization may leap frog one or more levels of modernization to achieve some of the benefits offered by the highest levels of grid modernization. Smart grids impact electric distribution systems significantly and sometimes more than any other part of the electric power grid. In developing countries, modernizing the distribution grid promises to benefit the operation of electric distribution utilities in many and various ways. These benefits include improved operational efficiency (reduced losses, lower energy consumption, amongst others), reduced peak demand, improved service reliability, and ability to accommodate distributed generating resources without adversely impacting overall power quality. Benefits of distribution grid modernization also include improved asset utilization (allowing operators to 'squeeze' more capacity out of existing assets) and workforce productivity improvement. These benefits can provide more than enough monetary gain for electric utility stakeholders in developing countries to offset the cost of grid modernization. Finally the report describes some funding and regulatory issues that may need to be taken into account when developing smart grid plans.

The development of deep space exploration and manned technology has led to an increasing demand for radiation-resistant materials, making the development of effective anti-radiation materials crucial. This article reviews the various types of space radiation-resistant materials and their radiation shielding mechanisms, as well as the relationship between material radiation resistance and internal composition, providing the new ideas for the selection and application of novel radiation-resistant materials with good radiation protection performance, high attenuation coefficients, thin thickness, and low density in spacecrafts.

This paper presents mathematical models and solution methods for assigning time windows to customers in vehicle routing problems with stochastic travel times. The goal is to design minimum-cost routes in terms of traveling distance and to simultaneously minimize the expected penalty costs associated with the assignment of time windows. Focus is given on how to evaluate the flexibility of time windows and to incorporate service reliability into the routing plans. Two mathematical models are proposed for the stochastic time window assignment problem that treat the travel times as continuous and discrete random variables, and take into account lateness and earliness penalties, penalties for the time window widths, and chance constraints to model the probability of serving each customer within the assigned time window so as to meet service reliability requirements. A hierarchical solution approach is proposed. At the first level, the master vehicle routing problem is solved via an adaptive large neighborhood search metaheuristic to optimize the routing cost, while at the second level the subordinate time window assignment subproblems are solved optimally. Various computational experiments are conducted to assess the performance of the proposed algorithmic framework, to understand time window flexibility, to study the trade-off between service reliability and routing costs, and to demonstrate the effectiveness and applicability of optimal time window assignment policies in an urban transportation environment with uncertain travel times.

The transition towards full network virtualization will see services for smart ecosystems including smart metering, healthcare and transportation among others, being deployed as Service Function Chains (SFCs) comprised of an ordered set of virtual network functions. However, since such services are usually deployed in remote cloud networks, the SFCs may transcend multiple domains belonging to different Infrastructure Providers (InPs), possibly with differing policies regarding billing and Quality-of-service (QoS) guarantees. Therefore, efficiently allocating the exhaustible network resources to the different SFCs while meeting the stringent requirements of the services such as delay and QoS among others, remains a complex challenge, especially under limited information disclosure by the InPs. In this work, we formulate the SFC deployment problem across multiple domains focusing on delay constraints, and propose a framework for SFC orchestration which adheres to the privacy requirements of the InPs. Then, we propose a reinforcement learning (RL)-based algorithm for partitioning the SFC request across the different InPs while considering service reliability across the participating InPs. Such RL-based algorithms have the intelligence to infer undisclosed InP information from historical data obtained from past experiences. Simulation results, considering both online and offline scenarios, reveal that the proposed algorithm results in up to 10% improvement in terms of acceptance ratio and provisioning cost compared to the benchmark algorithms, with up to more than 90% saving in execution time for large networks. In addition, the paper proposes an enhancement to a state-of-the-art algorithm which results in up to 5% improvement in terms of provisioning cost.

In cloud-edge-end scenarios, how to achieve rational resource allocation, implement effective service deployment, and ensure high service quality has become a hot research topic in academic domains. Service providers usually deploy services by considering the characteristics of different geographical regions, which helps to meet the diverse needs of users in different regions and optimize resource allocation and utilization. However, due to the widespread distribution of users and limited server resources, providing all types of services to users in every geographical region is not feasible. In addition, edge servers are prone to operational failures caused by software anomalies, hardware malfunctions, and malicious attacks, which will decrease service reliability. To address the problems above, this paper proposes a metric for service priorities based on user demands and regional characteristics for different geographical regions. Building upon this foundation, a Multi-Service Geographic region Deployment based on Priority (MS-GD-P) is proposed. This method takes user coverage and service reliability into consideration, which facilitates users’ needs for multiple services in different geographical regions. Experimental results on real datasets demonstrate that MS-GD-P outperforms baseline methods in user coverage and service reliability.

To understand the impact of competition on organizational service reliability decisions, this study investigates whether firms in the airline industry consider competitors' actions when making their service reliability decisions. Using data from the U.S. Bureau of Transportation Statistics on flight cancellation rates and average length of flight delays, the authors use two complementary approaches, a simultaneous equation model and a discrete game framework, to examine competitive influence on firm decisions on the level of service reliability. The authors find that competitive effects are asymmetric and differ by the type of firm and its competitors—full-service versus low-cost airlines—as well by level of market concentration. The authors show that internal initiatives, such as on-time bonuses, can substantially improve service reliability but require the firm to account for competitive reactions. Ignoring competitive effects leads to an overestimation of the impact of these programs on service reliability levels.

The share of wind power in power systems is increasing dramatically, and this is happening in parallel with increased penetration of solar photovoltaics, storage, other inverter-based technologies, and electrification of other sectors. Recognising the fundamental objective of power systems, maintaining supply–demand balance reliably at the lowest cost, and integrating all these technologies are significant research challenges that are driving radical changes to planning and operations of power systems globally. In this changing environment, wind power can maximise its long-term value to the power system by balancing the needs it imposes on the power system with its contribution to addressing these needs with services. A needs and services paradigm is adopted here to highlight these research challenges, which should also be guided by a balanced approach, concentrating on its advantages over competitors. The research challenges within the wind technology itself are many and varied, with control and coordination internally being a focal point in parallel with a strong recommendation for a holistic approach targeted at where wind has an advantage over its competitors and in coordination with research into other technologies such as storage, power electronics, and power systems.

The use of electric vehicles (EVs) to provide different grid services is becoming possible due to the increased penetration levels, mileage efficiencies, and useable battery sizes of EVs. One such application is providing reliability-as-a-service (RaaS) during short-term power outages. Instead of using a dedicated backup power source, EVs can be contracted to provide RaaS, which is an environmentally friendly solution with benefits for both building owners and EV owners. However, the presence of EVs at a particular location during different hours of the day and the availability of energy from EVs is uncertain. Therefore, in this study, a suitability analysis is performed concerning the use of EVs to provide RaaS for different types of buildings. First, the National Household Travel Survey (NHTS) survey data are used to estimate driver behavior, such as arrival/departure times, daily mileage, and traveling duration. Then, the usable battery size and mileage efficiency of EVs is extracted from the database of commercially available EVs. Based on these parameters, the daily energy consumption and available energy of EVs to provide RaaS are estimated. A suitability analysis is conducted for residential, commercial/industrial, and mixed buildings for both weekdays and holidays. The participation ratio of EV owners is varied between 10 and 90%, and nine cases are simulated for commercial/industrial buildings and multi-unit residential buildings. Similarly, the ratio of home-based EVs is varied between 5 and 50%, and 10 cases are tested for mixed buildings. The analysis shows that mixed buildings are the most suitable, while commercial/industrial buildings are the least suitable for using EVs to provide RaaS. To this end, an index is proposed to analyze and determine the desired ratio of EVs to be contracted from homes and workplaces for mixed buildings. Finally, the impact of EV fleet size on the available energy for RaaS is also analyzed.

Modern power and transportation systems are subject to high requirements for reliability and performance in performing their specified functions. At the same time, these requirements are constantly increasing with the increasing complexity of technology and the introduction of electronics and computer technology into its structure. This is fully applicable to energy and transportation infrastructure, including electric vehicles. The complexity of the systems and increasing requirements for them have led to the fact that the problem of increasing their operational reliability has acquired great importance. The article presents a review of methods and justification of ensuring a high level of reliability and serviceability of technical systems as one of the most important tasks in the creation and operation of complex systems, such as modern energy and transportation systems. It is shown that a significant reserve in solving the problem of increasing the reliability and performance of technical systems is the information on failures and malfunctions of these systems obtained from the field of operation. The methodology of collection and processing of statistical information on failures of vehicles described by different distribution laws is outlined.

Public transportation is often disrupted by disturbances, such as the uncertain travel time caused by road congestion. Therefore, the operators need to take real-time measures to guarantee the service reliability of transit networks. In this paper, we investigate a dynamic scheduling problem in a transit network, which takes account of the impact of disturbances on bus services. The objective is to minimize the total travel time of passengers in the transit network. A two-layer control method is developed to solve the proposed problem based on a hybrid control strategy. Specifically, relying on conventional strategies (e.g., holding, stop-skipping), the hybrid control strategy makes full use of the idle standby buses at the depot. Standby buses can be dispatched to bus fleets to provide temporary or regular services. Besides, deep reinforcement learning (DRL) is adopted to solve the problem of continuous decision-making. A long short-term memory (LSTM) method is added to the DRL framework to predict the passenger demand in the future, which enables the current decision to adapt to disturbances. The numerical results indicate that the hybrid control strategy can reduce the average headway of the bus fleet and improve the reliability of bus service.