Forecasting Recharging Demand to Integrate Electric Vehicle Fleets in Smart Grids

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

Electrification of Urban Freight Transport - a Case Study of the Food Retailing Industry

Decarbonisation is a major challenge for the coming decades, for all industries, including the transport sector. Battery electric vehicles are a potential solution for the transport sector to reduce its carbon impact. Asides from the question whether there is sufficient supply of electric vehicles for freight transport, it is also unclear whether battery-powered trucks meet the practical requirements, especially in terms of their driving range. To investigate this, synthetic tours were generated by solving a Vehicle Routing Problem (VRP). This also generates the fleet size and composition depending on a set of different vehicle types. The network with underlying traffic conditions comes from an publicly available transport model. The generated tours are then simulated with an open-source transport simulation (MATSim), for both diesel and battery electric vehicles (BEVs). In a sensitivity study, two different purchase prices were considered for calculating vehicle costs. The case study uses a model of the food retailing industry for the city of Berlin. 56% of the tours can be driven without recharging. When recharged one time, 90% of the tours are suitable for BEVs. The costs for transporting the goods will increase by 17 to 23% depending on the assumption for the purchase prices for the BEVs. Using a well-to-wheel calculation, the electrification of all tours leads to a reduction of greenhouse gas (GHG) emissions by 26 to 96% depending on the assumed electricity production.DFG, 398051144, Analyse von Strategien zur vollständigen Dekarbonisierung des urbanen Verkehr

A simheuristic for routing electric vehicles with limited driving ranges and stochastic travel times

Green transportation is becoming relevant in the context of smart cities, where the use of electric vehicles represents a promising strategy to support sustainability policies. However the use of electric vehicles shows some drawbacks as well, such as their limited driving-range capacity. This paper analyses a realistic vehicle routing problem in which both driving-range constraints and stochastic travel times are considered. Thus, the main goal is to minimize the expected time-based cost required to complete the freight distribution plan. In order to design reliable Routing plans, a simheuristic algorithm is proposed. It combines Monte Carlo simulation with a multi-start metaheuristic, which also employs biased-randomization techniques. By including simulation, simheuristics extend the capabilities of metaheuristics to deal with stochastic problems. A series of computational experiments are performed to test our solving approach as well as to analyse the effect of uncertainty on the routing plans.Peer Reviewe

Smart Procurement Of Naturally Generated Energy (SPONGE) for PHEV's

In this paper we propose a new engine management system for hybrid vehicles to enable energy providers and car manufacturers to provide new services. Energy forecasts are used to collaboratively orchestrate the behaviour of engine management systems of a fleet of PHEV's to absorb oncoming energy in an smart manner. Cooperative algorithms are suggested to manage the energy absorption in an optimal manner for a fleet of vehicles, and the mobility simulator SUMO is used to show simple simulations to support the efficacy of the proposed idea.Comment: Updated typos with respect to previous versio

Modeling and coordinated control for integrating electric vehicles into the power grid

This paper introduces a framework for the integration of renewable energy generation units and electric vehicle into smart grid, which takes into account the setting up of the PHEV recharging infrastructure and modern power system. The impact of recharging a large amount of PHEVs on the existing power system is estimated considering the PHEV characteristics and the driving pattern of the vehicle owners. Three scenarios for uncontrolled and controlled charging are derived to investigate the impact in terms of power quality. The simulation results show the necessity to coordinate the PHEV recharging with the power network condition. Therefore an optimal algorithm is also designed to minimize the power losses based on the hierarchical structure of the proposed framework. The aggregation of PHEVs is expected to act as a controllable load or resource. Both of the battery charging and discharging are comprised in the optimal algorithm to achieve better performance in the V2G operation. © 2011 IEEE.published_or_final_versionThe 2011 International Conference on Electrical Machines and Systems (ICEMS 2011), Beijing, China, 20-23 August 2011. In Proceedings of ICEMS, 2011, p. 1-

An approach to potential evaluation of a contactless energy supply infrastructure for occasional recharging in production related, non-automated material handling

Significant advances have been made in the research and development of electric vehicles (EV’s). Along with the major challenge of energy storage, being also addressed is the efficient design of system energy transfer and consumption. This has had the effect of fundamentally changing perspectives across the mobility and transportation sector. Applied predominantly to road-going vehicles, the industrial context of non-road Electric Vehicles (nrEV’s) and specifically the use of manned electric forklift trucks integrated within the production related materials handling system has, to-date, received far less attention. The overarching aim of this research is to examine the impact and potential for the use of contactless occasional recharging of nrEV’s integrated within a manufacturing line, recognising the need to balance the (sometimes competing) demands of delivering sustainable production while exercising environmental responsibility. Meeting the objectives of this research resulted in the development of a location allocation model for electric charging station determination based on a fundamental understanding of the nature and quality of process inherent key performance indicators (KPI’s) as well as comprehensive process and energy monitoring while considering both Lean and Green Management perspectives. The integration of the generated knowledge and information into a generally valid simulation tool for occasional charging system implementation allows to more thoroughly investigate the impact from occasional charging to overall efficiency and sustainability to be realised. An investigation into relevant literature identified the need for specifically generated energy consumption data and confirmed the need for an energy optimisation model specific to the area of production related materials handling. Empirical data collected from repeated standardised materials handling operations within a selected production related materials handling environment resulted in the development of the Standard Energy Consumption Activity tool (SECA). Further work within this pilot study confirmed the tool as capable of generating reliable and valid data and confirmed the SECA tool as a generally applicable benchmark for energy consumption determination in material handling based on fractional process functions. Integrating this approach into a comprehensive process analysis and charging infrastructure optimisation resulted in the development of an Excel-based simulation model. The (Occasional Charging Station Location Model) OCSLM is based upon Maximal Covering Location Modelling and an endogenous covering distance definition in order to simulate process related potentials and optimal charging system implementation allocations, the target being to increase vehicles usable battery energy. A comprehensive case study based upon six individual and one combined data set confirmed the general and wider applicability of the OCSLM model while the application of the model provides a set of novel results. The application demonstrated a theoretical increase in usable battery energy of between 40% and 60% and within the same case study the impact of technology implementation identified that a reduction in battery and system cost of between 5% and 45% can be realised. However, the use of contactless power transfer resulted in an increase in CO2 emissions of up to 6.89% revealing a negative impact to overall ecology from the use of this energy transfer system. Depending on the availability of fast connecting, contact based energy transmission systems, the approach and results of OCSLM have shown to be directly applicable to contact based systems with resulting CO2 emissions decreasing by 0.94% at an energy transfer efficiency of 96%. Further novelty, of benefit to both academic and industry practice, was realised through the framework and information of the research with the provision of SECA as a process function-based and generally applicable energy consumption standard, OCSLM as a Maximal Covering Location Modell with a focus on occasional charging based on an endogenous covering distance and integrating detailed energy and process monitoring into electric charging station allocation, and the methodology for the application of this approach for fast connecting contactless and contact charging models and cases

Implementing a hybrid series bus with gas turbine device - a preliminary study

This paper presents the implementation of an hybrid series Bus with a gas turbine, as thermal engine. The hybridization methodology for transforming city buses, substituting the original gasoline/diesel engine with a micro gas turbine device (intended as range extender), into a series hybrid vehicle has investigated and its feasibility analyzed. The study was conducted by the university of Rome “Sapienza” in collaboration with several enterprises. The idea is to design a hybrid power train that can be installed in a typical city bus, which means that all systems and components will be influenced by the limited space available. In this paper the details of the mechanical and electrical realization of the power train will be discussed. The hybrid system also includes consideration on the battery pack and the vehicle management logic. The proposed solution obtains a reduction in fuel consumption higher than 20%, in comparison with normal commercial fleet

Electric Vehicles: Charging into the Future

Electric vehicle drives offer a number of advantages over conventional internal combustion engines, especially in terms of lower local emissions, higher energy efficiency, and decreased dependency upon oil. Yet there are significant barriers to the rapid adoption of electric cars, including the limitations of battery technology, high purchase costs, and the lack of recharging infrastructure. With intelligently controlled charging operations, the energy needs of potential electric vehicle fleets could be covered by existing German power plants without incurring large price fluctuations. Over the long term, electric vehicles could represent a sustainable technology path. In the short to mid-term, however, exceedingly optimistic expectations should be avoided, especially with respect to the reduction of greenhouse gas emissions. Electric vehicles as such will not be able to solve all current problems of transportation policy. Yet they may constitute an important component of a larger roadmap for sustainable transportation.Transportation, Electric vehicles, Electricity markets