Resilience of modern power distribution networks with active coordination of EVs and smart restoration

Abstract In this modern era of cyber–physical–social systems, there is a need of dynamic coordination strategies for electric vehicles (EVs) to enhance the resilience of modern power distribution networks (MPDNs). This paper proposes a two‐stage EV coordination framework for MPDN smart restoration. The first stage is to introduce a novel proactive EV prepositioning model to optimize planning prior to a rare event, and thereby enhance the MPDN survivability in its immediate aftermath. The second stage involves creating an advanced spatial–temporal EV dispatch model to maximize the number of available EVs for discharging, thereby improving the MPDN recovery after a rare event. The proposed framework also includes an information system to further enhance MPDN resilience by effectively organizing data exchange among intelligent transportation system and smart charging system, and EV users. In addition, a novel bidirectional geographic graph is proposed to optimize travel plans, covering a large penetration of EVs and considering variations in traffic conditions. The effectiveness is assessed on a modified IEEE 123‐node test feeder with real‐world transportation and charging infrastructure. The results demonstrate a significant improvement in MPDN resilience with smart restoration strategies. The validation and sensitivity analyses evidence a significant superiority of the proposed framework

Uncertain demand prediction for guaranteed automated vehicle fleet performance

Mobility-on-demand (MoD) services offer a convenient and efficient transportation option, using technology to replace traditional modes. However, the flexibility of MoD services also presents challenges in controlling the system. One of the major issues is supply-demand imbalance, caused by uneven stochastic travel demand. To address this, it is crucial to predict the network behavior and proactively adapt to future travel demand.In this thesis, we present a stochastic model predictive controller (SMPC) that accounts for uncertainties in travel demand predictions. Our method make use of Gaussian Process Regression (GPR) to estimate passenger travel demand and predict time patterns with uncertainty bounds. The SMPC integrates these demand predictions into a receding horizon MoD optimization and uses a probabilistic constraining method with a user-defined confidence interval to guarantee constraint satisfaction. This result in a Chance Constrained Model Predictive Control (CCMPC) solution. Our approach has two benefits: incorporating travel demand uncertainty into the MoD optimization and the ability to relax the solution into a simpler Mixed-Integer Linear Program (MILP). Our simulation results demonstrate that this method reduces median customer wait time by 4% compared to using only the mean prediction from GPR. By adjusting the confidence bound, near-optimal performance can be achieved

EcoBlock: Grid Impacts, Scaling, and Resilience

Widespread deployment of EcoBlocks has the potential to transform today's electricity system into one that is more resilient, flexible, efficient and sustainable. In this vision, the system will consist of self- su cient, renewable-powered, block-scale entities that can deliberately adjust their net power exchange and can optimize performance, maintain stability, support each other, or disconnect entirely from the grid as needed. This report is intended as an independent analysis of the potential relationships, both constructive and adverse, between EcoBlocks and the grid

Demand-Responsive Shared Transportation: A Self-Interested Proposal

[EN] With the world population highly increasing, efficient methods of transportation are more necessary than ever. On the other hand, the sharing economy must be explored and applied where possible, aiming to palliate the effects of human development on the environment. In this paper we explore demand-responsive shared transportation as a system with the potential to serve its users' displacement needs while being less polluting. In contrast with previous works, we focus on a distributed proposal that allows each vehicle to retain its private information. Our work describes a partially dynamic system in which the vehicles are self-interested: they decide which users to serve according to the benefit it reports them. With our modelling, the system can be adapted to mobility platforms of autonomous drivers and even simulate the competition among different companies.This work is partially supported by grant RTI2018-095390-B-C31 funded by MCIN/AEI/10.13039/501100011033 and by "ERDF A way of making Europe". Pasqual Marti is supported by grant ACIF/2021/259 funded by the "Conselleria de Innovacion, Universidades, Ciencia y Sociedad Digital de la Generalitat Valenciana".Martí, P.; Jordán, J.; De La Prieta, F.; Billhardt, H.; Julian, V. (2022). Demand-Responsive Shared Transportation: A Self-Interested Proposal. Electronics. 11(1):1-14. https://doi.org/10.3390/electronics1101007811411

Data-driven Methodologies and Applications in Urban Mobility

The world is urbanizing at an unprecedented rate where urbanization goes from 39% in 1980 to 58% in 2019 (World Bank, 2019). This poses more and more transportation demand and pressure on the already at or over-capacity old transport infrastructure, especially in urban areas. Along the same timeline, more data generated as a byproduct of daily activity are being collected via the advancement of the internet of things, and computers are getting more and more powerful. These are shown by the statistics such as 90% of the world’s data is generated within the last two years and IBM’s computer is now processing at the speed of 120,000 GPS points per second. Thus, this dissertation discusses the challenges and opportunities arising from the growing demand for urban mobility, particularly in cities with outdated infrastructure, and how to capitalize on the unprecedented growth in data in solving these problems by ways of data-driven transportation-specific methodologies. The dissertation identifies three primary challenges and/or opportunities, which are (1) optimally locating dynamic wireless charging to promote the adoption of electric vehicles, (2) predicting dynamic traffic state using an enormously large dataset of taxi trips, and (3) improving the ride-hailing system with carpooling, smart dispatching, and preemptive repositioning. The dissertation presents potential solutions/methodologies that have become available only recently thanks to the extraordinary growth of data and computers with explosive power, and these methodologies are (1) bi-level optimization planning frameworks for locating dynamic wireless charging facilities, (2) Traffic Graph Convolutional Network for dynamic urban traffic state estimation, and (3) Graph Matching and Reinforcement Learning for the operation and management of mixed autonomous electric taxi fleets. These methodologies are then carefully calibrated, methodically scrutinized under various performance metrics and procedures, and validated with previous research and ground truth data, which is gathered directly from the real world. In order to bridge the gap between scientific discoveries and practical applications, the three methodologies are applied to the case study of (1) Montgomery County, MD, (2) the City of New York, and (3) the City of Chicago and from which, real-world implementation are suggested. This dissertation’s contribution via the provided methodologies, along with the continual increase in data, have the potential to significantly benefit urban mobility and work toward a sustainable transportation system

Vehicle Dispatching and Routing of On-Demand Intercity Ride-Pooling Services: A Multi-Agent Hierarchical Reinforcement Learning Approach

The integrated development of city clusters has given rise to an increasing demand for intercity travel. Intercity ride-pooling service exhibits considerable potential in upgrading traditional intercity bus services by implementing demand-responsive enhancements. Nevertheless, its online operations suffer the inherent complexities due to the coupling of vehicle resource allocation among cities and pooled-ride vehicle routing. To tackle these challenges, this study proposes a two-level framework designed to facilitate online fleet management. Specifically, a novel multi-agent feudal reinforcement learning model is proposed at the upper level of the framework to cooperatively assign idle vehicles to different intercity lines, while the lower level updates the routes of vehicles using an adaptive large neighborhood search heuristic. Numerical studies based on the realistic dataset of Xiamen and its surrounding cities in China show that the proposed framework effectively mitigates the supply and demand imbalances, and achieves significant improvement in both the average daily system profit and order fulfillment ratio

Optimization strategy of automobile dispatching based on neural network model

As a representative product of the sharing economy era and a powerful supplement to public transportation shared cars have the characteristics of convenience, efficiency, environmental protection, and green travel, and to a certain extent alleviate the contradiction between supply and demand, and solve the problem of long-term idle vehicles and overloaded operation of roads problems. But the uneven distribution of shared cars, the coexistence of no cars, and empty seats will happen. To solve the above problems, this article first analyzes data outliers, data missing values, and data standardization processing on the attached data, and then builds a BP neural network demand prediction model to obtain the distribution of shared car usage in the city