2,290 research outputs found

    Forecasting Recharging Demand to Integrate Electric Vehicle Fleets in Smart Grids

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    Electric vehicle fleets and smart grids are two growing technologies. These technologies provided new possibilities to reduce pollution and increase energy efficiency. In this sense, electric vehicles are used as mobile loads in the power grid. A distributed charging prioritization methodology is proposed in this paper. The solution is based on the concept of virtual power plants and the usage of evolutionary computation algorithms. Additionally, the comparison of several evolutionary algorithms, genetic algorithm, genetic algorithm with evolution control, particle swarm optimization, and hybrid solution are shown in order to evaluate the proposed architecture. The proposed solution is presented to prevent the overload of the power grid

    Sustainability of Autonomous Vehicles: An Agent-based Simulation of the Private Passenger Sector

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    Initiatives such as the European Green Deal establish mandatory objectives for climate neutrality by 2050. To realize this, predominant sectors like the transportation industry necessitate substantial improvements in emission efficiency, as transportation accounts for 20% of global CO2 emissions. Remarkably, 41 % of that are attributed to passenger transport alone. Amidst technological progression, shared autonomous vehicles (SAV) are projected to be a vital instrument in reducing greenhouse gas emissions in the private passenger sector. Hence, this study investigates the potential sustainability advantages of SAV introduction to the private passenger car sector. We draw upon an agent-based simulation model to avoid building upon theoretic populations and generic simulation approaches, not appropriately allowing to derive realistic SAV simulations. Agent-based simulations account for individual agent optimisation and are posed to be especially applicable to model traffic. More precisely, we build upon a calibrated model which incorporates real world commuter and travel statistics of Berlin. As current research frequently uses outdated travel data, this study generates a projection of three levels of travel demand of the wider area of Berlin in 2050. Additionally, we identify the need for more research on sustainability effects considering multiple levels of potential SAV introduction, as AV and SAV adoption is associated with high uncertainty. Regulatory interventions posed to be a solution to steer SAV adoption effectively. Therefore, we introduce three levels of SAV-exclusive car-based traffic zones in our simulation scenarios. The three level of zones range from the inner city of Berlin to the entire simulation, including Berlin and Brandenburg. Lastly, we identify the need for more comprehensive sustainability analyses, focusing on more than single parameters such as tailpipe emissions. Consequently, we compare driving-related emissions and energy consumption as well as the total expected life-cycle greenhouse gas impact of all SAV introduction and demand forecasting scenarios. Our findings reveal that SAV introduction increases the total passenger travel duration up to 62.1% and the passenger travel distance of up to 15.2% due to added wait time and detours. This effect is particularly noticeable in large SAV-exclusive zones. However, occupancy rates increase simultaneously, causing total vehicles kilometers to stay consistent. We observe an initial rise of 0.8% to 2.8% in vehicle kilometers considering an unchanged population, while smaller SAV-exclusive zones see the highest increase. In turn, when including our travel demand forecast which accounts for increased population size and travel density, SAV introduction reduces total vehicle kilometers by 0.5% to 3.6%, related to higher SAV occupancy. Lastly, accounting for SAV-related efficiency increases, we conclude savings in total life-cycle CO2 emissions ranging from 0.4% to 9.6% and energy consumption ranging from 1.5% to 12.2% across all scenarios. When combined with a fully electric SAV fleet, the potential for emission reduction increases to 59.0%, and for energy consumption reduction to 74.7%

    On green routing and scheduling problem

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    The vehicle routing and scheduling problem has been studied with much interest within the last four decades. In this paper, some of the existing literature dealing with routing and scheduling problems with environmental issues is reviewed, and a description is provided of the problems that have been investigated and how they are treated using combinatorial optimization tools

    Forecasting Recharging Demand to Integrate Electric Vehicle Fleets in Smart Grids

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    Electric vehicle fleets and smart grids are two growing technologies. These technologies provided new possibilities to reduce pollution and increase energy efficiency. In this sense, electric vehicles are used as mobile loads in the power grid. A distributed charging prioritization methodology is proposed in this paper. The solution is based on the concept of virtual power plants and the usage of evolutionary computation algorithms. Additionally, the comparison of several evolutionary algorithms, genetic algorithm, genetic algorithm with evolution control, particle swarm optimization, and hybrid solution are shown in order to evaluate the proposed architecture. The proposed solution is presented to prevent the overload of the power grid

    Self-Regulating Demand and Supply Equilibrium in Joint Simulation of Travel Demand and a Ride-Pooling Service

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    This paper presents the coupling of a state-of-the-art ride-pooling fleet simulation package with the mobiTopp travel demand modeling framework. The coupling of both models enables a detailed agent- and activity-based demand model, in which travelers have the option to use ride-pooling based on real-time offers of an optimized ride-pooling operation. On the one hand, this approach allows the application of detailed mode-choice models based on agent-level attributes coming from mobiTopp functionalities. On the other hand, existing state-of-the-art ride-pooling optimization can be applied to utilize the full potential of ride-pooling. The introduced interface allows mode choice based on real-time fleet information and thereby does not require multiple iterations per simulated day to achieve a balance of ride-pooling demand and supply. The introduced methodology is applied to a case study of an example model where in total approximately 70,000 trips are performed. Simulations with a simplified mode-choice model with varying fleet size (0–150 vehicles), fares, and further fleet operators’ settings show that (i) ride-pooling can be a very attractive alternative to existing modes and (ii) the fare model can affect the mode shifts to ride-pooling. Depending on the scenario, the mode share of ride-pooling is between 7.6% and 16.8% and the average distance-weighed occupancy of the ride-pooling fleet varies between 0.75 and 1.17

    Adopting different wind-assisted ship propulsion technologies as fleet retrofit: An agent-based modeling approach

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    The maritime shipping industry will increasingly switch to low carbon fuels and adopt energy saving technologies (ESTs) to achieve the industry target of decarbonization. Among ESTs, deck equipment, including those based on wind propulsion technologies (WPTs), represents the largest potential fuel savings and a source of increasing innovation initiatives by industry actors. Previous contributions to WPT innovation have addressed barriers and drivers for increased adoption in the industry but failed to consider the specific aspects of the fleet retrofitting market. Through an agent-based simulation model, this work studies the effects of different policy and market scenarios (subsidies, fuel prices, and networking) on the adoption of WPT retrofitting solutions. The proposed model incorporates two decision steps for each vessel to adopt the technology (acquiring awareness of the technology, and a utility decision process to determine the WPT option). The study also expands on previous knowledge by modeling three WPT options and by integrating real world data of technology costs and their fuel savings as well as vessel features. Insights from simulations allow to identify the most convenient policies as well as the potential of alternative models to reduce introduction barriers (e.g., product-service business models).Interreg North Sea Region project WASP: Wind Assisted Ship Propulsion, "Run Wind Propulsion Technology real life trials on sea going ships in operation, showcase proven concepts, market adaptation, green sea transport" 38-2-6-19Spanish Ministry of Science, Andalusian GovernmentEuropean Commission RYC-2016-19800ERDF under CONFIA PID2021-122916NB-I00ERDF under SIMARK P18-TP-447

    Integrated assessment modelling as a positive science: private passenger road transport policies to meet a climate target well below 2 0C

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    This is the final version. Available from Springer Verlag via the DOI in this record.Transport generates a large and growing component of global greenhouse gas emissions contributing to climate change. Effective transport emissions reduction policies are needed in order to reach a climate target well below 2 ∘C. Representations of technology evolution in current integrated assessment models (IAM) make use of systems optimisations that may not always provide sufficient insight on consumer response to realistic policy packages for extensive use in policy-making. Here, we introduce FTT: transport, an evolutionary technology diffusion simulation model for road transport technology, as an IAM sub-component, which features sufficiently realistic features of consumers and of existing technological trajectories that enables to simulate the impact of detailed climate policies in private passenger road transport. Integrated to the simulation-based macroeconometric IAM E3ME-FTT, a plausible scenario of transport decarbonisation is given, defined by a detailed transport policy package, that reaches sufficient emissions reductions to achieve the 2 ∘C target of the Paris Agreement.Engineering and Physical Sciences Research Council (EPSRC)Natural Environment Research Council (NERC)Economic and Social Research Council (ESRC
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