• Energy Research
• Open Access close
• Transport Research close

A quasi-static analysis and sensitivity investigation of two different mooring configurations—a single anchor leg mooring (SALM) and a three-legged catenary anchor leg system (CALM)—is presented. The analysis aims to indicate what can be expected in terms of requirements for the mooring system size and stiffness. The two mooring systems were designed for the same reference load case, corresponding to a horizontal design load at the wave energy converter (WEC) of 2000 kN and a water depth of 30 m. This reference scenario seems to be representative for large WECs operating in intermediate water depths, such as Weptos, Wave Dragon and many others, including reasonable design safety factors. Around this reference scenario, the main influential parameters were modified in order to investigate their impact on the specifications of the mooring system, e.g. the water depth, the horizontal design load, and a mooring design parameter.

This paper analyses different charging strategies for a fleet of electric vehicles. Along with increasing the realism of the strategies, the opportunity for acting on the regulating market is also included. We test the value of a vehicle owner that can choose when and how to charge; by presenting a model of four alternative charging strategies. We think of them as increasing in sophistication from dumb via delayed to deterministic and stochastic model-based charging. We show that 29% of the total savings from ‘dumb’ are due to delayed charging and that substantial additional gains come charging optimally in response to predicted spot prices, and – in some settings – additional gains from using the up and down regulating prices. Particularly, strategies are chosen from uncontrolled charging through deterministic optimization, to modelling the charging and bidding problem with stochastic programming. We show that all vehicle owners will benefit from acting more intelligently on the energy market. Furthermore, the high value of the stochastic solution shows that, in case the regulating price differs from the expected, the solution to the deterministic problem becomes infeasible. International Journal of Sustainable Energy Planning and Management, Vol 7 (2015)

AbstractThis paper presents a Variable Speed Limit (VSL) control algorithm for simultaneously maximizing the mobility, safety and environmental benefit in a Connected Vehicle environment. Development of Connected Vehicle (CV)/Autonomous Vehicle (AV) technology has the potential to provide essential data at the microscopic level to provide a better understanding of real-time driver behavior. This paper investigated a VSL control algorithm using a microscopic approach by focusing on individual driver’s behavior (e.g., acceleration and deceleration) through the use of Model Predictive Control (MPC) approach. A multi-objective optimization function was formulated with the aim of finding a balanced trade-off among mobility, safety and sustainability. A microscopic traffic flow prediction model was used to calculate Total Travel Time (TTT); a surrogate safety measure Time To Collision (TTC) was used to measure instantaneous safety; and, a microscopic fuel consumption model (VT-Micro) was used to measure the environmental impact. Real-time driver’s compliance to the posted speed limit was used to adjust the optimal speed limit values. A sensitivity analysis was conducted to compare the performance of the developed approach for different weights in the objective function and for two different percentages of CV. The results showed that with 100% penetration rate, the developed VSL approach outperformed the uncontrolled scenario consistently, resulting in up to 20% of total travel time reductions, 6–11% of safety improvements and 5–16% reduction in fuel consumptions. Our findings revealed that the scenario which optimized for safety alone, resulted in more optimum improvements as compared to the multi-criteria optimization. Thus, one can argue that in case of 100% penetration rates of CVs, optimizing for safety alone is enough to achieve simultaneous and optimum improvements in all measures. However, mixed results were obtained in case of lower % penetration rate which showed higher collision risk when optimizing for only mobility or fuel consumption. This indicates that with such % penetration rate, multi-criteria optimization is crucial to realize optimum and balanced benefits for the examined measures.

Mobility plays a crucial role in wellbeing and quality of life. It enables access to services and resources necessary for economic and cultural development and is essential for social inclusion. Peer-to-peer carsharing (P2PCS) represents an effective solution with respect to the sustainability goals indicated by the European New Green Deal and in light of the accessibility objectives of the National Strategy for Internal Areas. For this reason, we estimated the potential supply and demand of P2PCS in Friuli-Venezia Giulia (FVG), an Italian region characterized by less-densely populated areas. We interviewed 200 individuals to test if they would rent a car and 249 car owners to test if they would rent out their car. We found that 10% of the sample would use a P2PCS service if the hourly rental rate was €7/h. The main reason preventing the service use is that the FVG residents are not aware of the platforms allowing the matching of car owners and renters. In the paper, we describe also the policies to be implemented to support both the demand and the supply side of the market and we estimate the contribution that P2PCS could make to the decarbonization of the regional transport system.

Considering the environmental and energy concerns, the need for finding an alternative fuel is increasing greatly. But also, the toil associated with finding the suitable fuel for the engine under study is very high. As a measure to reduce this work, theoretical analysis can be used in the search of suitable fuel blends. The present study deals with the creation of an Artificial Neural network (ANN), training, testing the network and finally comparing the predicted values with the experimental values. The test engine was operated with lemon peel oil, with different ratios of Di tertiary butyl peroxide (DTBP), rice husk nanoparticles and water injection. Six engine output parameters (BTE, BSFC, HC, NOx, CO & Smoke) were predicted for different input values of engine load, DTBP proportions, RH nanoparticle concentration and water injection percentage. A single cylinder constant speed direct injection diesel engine is used for obtaining all the experimental values. From the obtained results a data set of 70 experimental values were used to train and test the neural network formed. The predicted values were correlated with the experimental values and the R2 was found for each correlation. The R2 values, thus obtained is in the range of 0.98209 to 0.99744 (which is very close to unity) thus proving that the network created were able to predict the performance and emissions of engine accurately. This work shows the prediction of multi component fuel mixture using ANN, which is new and also helps to reduce the experimental works needed to find the engine output parameters.

The increasingly urgent need to decarbonize transport is leading to a much greater uptake of electric vehicles (EVs) in countries across the world. Also, the installation and use of urban light rail systems (trams) is seen as a way of breaking the reliance of commuters on the internal combustion engine, and therefore car ownership. Due to the simplicity of design, most conventional tram systems use unidirectional substations to draw power/energy from the utility supply. Due to their very nature, the substations are not able to return excess regenerated energy from the trams back into utility supply, with this energy often being dissipated in dump resistors onboard the trams to prevent over-voltages on the tram system. This paper explores the possibility of using EV’s as temporary trackside energy storage systems on urban light rail systems through the use of bi-directional connection interfaces (chargers), which allow use of the vehicle battery in typical V2X scenarios. The paper uses the city of Sheffield (UK) Supertram network as an example network on which the effect of EV energy storage could be studied.

To mitigate climate change due to international shipping, the International Maritime Organization (IMO) requires shipowners and ship technical managers to improve the energy efficiency of ships’ operations. This paper studies how voyage planning and execution decisions affect energy efficiency and distinguishes between the commercial and nautical components of energy efficiency. Commercial decisions for voyage planning depend on dynamic market conditions and matter more for energy efficiency than nautical decisions do for voyage execution. The paper identifies the people involved in decision-making processes and advances the energy-efficiency literature by revealing the highly networked nature of agency for energy efficiency. The IMO’s current energy efficiency regulations fail to distinguish between the commercial and nautical aspects of energy efficiency, which limits the ability to mitigate climate change through regulatory measures. Policymakers should expand their regulatory focus beyond shipowners and technical managers to cargo owners to improve energy efficiency and reduce maritime transport emissions.