1,720,952 research outputs found
Intention-Aware Routing to Minimise Delays at Electric Vehicle Charging Stations
No full textEn-route charging stations allow electric vehicles to greatly extend their range. However, as a full charge takes a considerable amount of time, there may be significant waiting times at peak hours. To address this problem, we propose a novel navigation system, which communicates its intentions (i.e., routing policies) to other drivers. Using these intentions, our system accurately predicts congestion at charging stations and suggests the most efficient route to its user. We achieve this by extending existing time-dependent stochastic routing algorithms to include the battery's state of charge and charging stations. Furthermore, we describe a novel technique for combining historical information with agent intentions to predict the queues at charging stations. Through simulations we show that our system leads to a significant increase in utility compared to existing approaches that do not explicitly model waiting times or use intentions, in some cases reducing waiting times by over 80% and achieving near-optimal overall journey times.Software and Computer TechnologyElectrical Engineering, Mathematics and Computer Scienc
Online mechanism design for scheduling non-preemptive jobs under uncertain supply and demand
No full textWe design new algorithms for the problem of allocating uncertain flexible, and multi-unit demand online given uncertain supply, in order to maximise social welfare. The algorithms can be seen as extensions of the expectation and consensus algorithms from the domain of online scheduling. The problem is especially relevant to the future smart grid, where uncertain output from renewable generators and conventional supply need to be integrated and matched to flexible, non-preemptive demand. To deal with uncertain supply and demand, the algorithms generate multiple scenarios which can then be solved offline. Furthermore, we use a novel method of reweighting the scenarios based on their likelihood whenever new information about supply becomes available. An additional improvement allows the selection of multiple non-preemptive jobs at the same time. Finally, our main contribution is a novel online mechanism based on these extensions, where it is in the agents' best interest to truthfully reveal their preferences. The experimental evaluation of the extended algorithms and different variants of the mechanism show that both achieve more than 85% of the offline optimal economic efficiency. Importantly, the mechanism yields comparable efficiency, while, in contrast to the algorithms, it allows for strategic agents
Acceptance conditions in automated negotiation
No full textIn every negotiation with a deadline, one of the negotiating parties has to accept an offer to avoid a break off. A break off is usually an undesirable outcome for both parties, therefore it is important that a negotiator employs a proficient mechanism to decide under which conditions to accept. When designing such conditions one is faced with the acceptance dilemma: accepting the current offer may be suboptimal, as better offers may still be presented. On the other hand, accepting too late may prevent an agreement from being reached, resulting in a break off with no gain for either party. Motivated by the challenges of bilateral negotiations between automated agents and by the results and insights of the automated negotiating agents competition (ANAC), we classify and compare state-of-the-art generic acceptance conditions. We focus on decoupled acceptance conditions, i.e. conditions that do not depend on the bidding strategy that is used. We performed extensive experiments to compare the performance of acceptance conditions in combination with a broad range of bidding strategies and negotiation domains. Furthermore we propose new acceptance conditions and we demonstrate that they outperform the other conditions that we study. In particular, it is shown that they outperform the standard acceptance condition of comparing the current offer with the offer the agent is ready to send out. We also provide insight in to why some conditions work better than others and investigate correlations between the properties of the negotiation environment and the efficacy of acceptance conditions.MediamaticsElectrical Engineering, Mathematics and Computer Scienc
Modelling an electricity price and journey time trade-off routing decision for electric vehicles
No full textThis paper introduces an electricity price extension to the intention-aware routing system (IARS) for electric vehicles (EV). The existing intention-aware routing system is used to route electric vehicles who require to charge en-route through a road network. To achieve the objective of minimising the average journey time, the intentions of EVs and waiting times at charging stations are com- municated. Instead of only minimizing travel time, the model extension presented in this paper makes it possible to express a decision trade-off between price and time. A vehicle computes its routing policy such that the combined utility of price and time is as high as possible. In this paper the performance of IARS with a price extension is compared to a greedy maximising algorithm (MAX) in several settings. The increase in utility by using IARS depends on the population of electric vehicles. However, in most experiments conducted in this research IARS achieves a significantly higher average utility.CSE3000 Research ProjectComputer Science and Engineerin
The influence of a charging station’s location on its profitability [paper]
No full textWith the increasing number of electric vehicles onthe road, the routing problem has become morecomplex. As charging electric vehicles takes longerthan fueling non-electric vehicles, congestion canoccur at charging stations. This might lead to theshortest route not being the fastest route, due tolong waiting times at the stations. By commu-nicating the intentions of each vehicle, they canspread out over multiple stations. This paper in-vestigates the effect of such a routing system on theprofitability of charging stations in comparison toa more simple shortest-path algorithm. In particu-lar, the influence of a charging station’s location onits profitability has been researched for both rout-ing algorithms. In order to do this, a pricing modelhas been developed to extend the routing mod-els used for both the shortest-path algorithm andthe intention-based routing algorithm. Through-out several simulations, it became clear that for theshortest-path algorithm, more centralised stationsobtain a higher profit, whereas for the intention-based routing algorithm there were no significantdifferences in profitability between the more cen-tral stations, and the ones on longer routes.CSE3000 Research ProjectComputer Science and Engineerin
Evaluation and Comparison of Scheduling Strategies for the Scheduling of Electric Vehicles at Capacity-Constrained Charging Stations
No full textAs the popularity of electric vehicles (EVs) increases, congestion at charging becomes a more imminent problem. Congestion at a charging station can lead to long waiting queues and failure of EV owners to charge their vehicles fully before their departure from the station. To combat this issue, this paper explores several candidate scheduling strategies that can be applied for the charging prioritization of EVs at a single station. Through extensive simulations, the efficacy of these strategies is studied under three performance metrics. From the set of strategies studied, we find that earliest deadline first (EDF) and shortest job first (SJF) are the best options in the case that adherence to deadline or a shorter waiting time is most valued, respectively.CSE3000 Research ProjectComputer Science and Engineerin
Efficient Routing Decisions for Electric Vehicles in a Congested Network
No full textThe Electric Vehicle Routing Problem (E-VRP) is an extension of the infamous Vehicle Routing Problem which asks which routing decisions an electric vehicle needs to take in order to traverse the network efficiently. Many extensions of this problem have been subject to research in the last decade and now that electric vehicles are starting to pop up in more and more cities, questions are asked about how an electric vehicle should decide which charging station to charge at to minimize their lateness. This problem is of growing significance due to the growth of the amount of electric vehicles and the disruptions which are caused by the longer recharging times of an electrical vehicle when compared to fossil fuel-based vehicles. Since there are countless possibilities and variables to consider in this problem (e.g. the price of electricity or the distance to a charging station), research should be conducted to see which kind of algorithm most satisfies the need of the end-user. To address this problem, this paper proposes several algorithms and compares them to each other based on algorithmic efficiency, average travel time of the vehicles and possible disadvantages when using the algorithms. Through simulations we show that the IARS algorithm as proposed in an earlier paper leads to the overall best performance, but that it lacks efficiency in terms of algorithmical complexity. We also show that when using a shortest path algorithm, the addition of a greedy geometric spanner significantly decreases the time complexity of the algorithm, in some cases reducing the average timespan of the simulation of 1 day by as much as 34%.CSE3000 Research ProjectComputer Science and Engineerin
Accepting Optimally in Automated Negotiation with Incomplete Information (abstract)
No full textIntelligent SystemsElectrical Engineering, Mathematics and Computer Scienc
Intention Aware Routing System with variable station pricing
No full textIntention Aware Routing System is a route-planning algorithm for electric vehicles that minimizes overall travel time by taking into consideration congestion at charging stations. This paper extends this algorithm to allow choices to be made based on prices at charging stations. The goal of this paper is to find a way to minimize maximum congestion while maximizing overall profit across the stations.CSE3000 Research ProjectComputer Science and Engineerin
Battery Degradation in Control Algorithms for Redistribution of Benefits in a Community Energy Project
No full textIn a community energy project, batteries are the asset with the shortest lifespan and are therefore key contributors to cost. Understanding the influence of the battery state of health model on a control algorithm designed for redistribution of benefits in terms of financial gains in a community energy project can help elongate battery lifetime and reduce need for replacement hence minimising costs and reaping environmental benefits. Battery depreciation is predominantly stimulated by cyclic degradation and thus incurred costs are compared by simulating degradation curves for different battery storage systems in terms of chemistry and capacity. Costs are calculated by applying battery models to the control algorithm proposed by Norbu et al. (2021), which factors in cyclic degradation using the rainflow counting algorithm. The experiment explores the influences on cost of different battery chemistry types and capacities. Results demonstrate that lithium-ion batteries, which are the current norm in utility-scale applications, incur the lowest costs. Specifically, lithium manganeseoxide batteries appear to be most effective. Additionally, costs tend to decrease with increasing capacity until a minima corresponding to the optimal battery capacity.CSE3000 Research ProjectComputer Science and Engineerin
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