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Abstract The use of electric bus fleets has become a topical issue in recent years. Several companies and municipalities, either voluntarily or to comply with legal requirements, will transition to greener bus fleets in the next decades. Such transitions are often established by fleet electrification targets, which dictate the number of electric buses that should be in the fleet by a given time period. In this paper we introduce a comprehensive optimization-based decision making tool to support such transitions. More precisely, we present a fleet replacement problem which allows organizations to determine bus replacement plans that will meet their fleet electrification targets in a cost-effective way, namely considering purchase costs, salvage revenues, operating costs, charging infrastructure investments, and demand charges. We account for several charging infrastructure options, such as slow and fast plug-in stations, overhead pantograph chargers, and inductive (wireless) chargers. We refer to this problem as the electric bus fleet transition problem, and we model it as an integer linear program. We apply our model to conduct computational experiments based on several scenarios. We use real data provided by a public transit agency in order to draw insights into optimal transition plans.

This brief analyzes the linear profiles presented by certain classes of bifurcation indices with respect to the variation of the loading parameter in power systems. The bifurcation indices discussed here are used in practice to predict proximity to saddle-node and some types of limit-induced bifurcations, given their special profiles. Thus, the linearity of these indices is studied with the help of a simple generic test system. Local analyses and observations are also presented and discussed for realistic power-system models.

Sustainability is characterized by a growing trend in the number of papers published in the last years, for an increasing impact factor and because today a large number of experts and researchers dealing with this issue have published in this journal [...]

This paper presents an ensemble-based scheme for nuclear transient identification. The approach adopted to construct the ensemble of classifiers is bagging; the novelty consists in using supervised fuzzy C-means (FCM) classifiers as base classifiers of the ensemble. The performance of the proposed classification scheme has been verified by comparison with a single supervised, evolutionary-optimized FCM classifier with respect of the task of classifying artificial datasets. The results obtained indicate that in the cases of datasets of large or very small sizes and/or complex decision boundaries, the bagging ensembles can improve classification accuracy. Then, the approach has been applied to the identification of simulated transients in the feedwater system of a boiling water reactor (BWR).

Abstract The effect of flexible hydropower management on prices is a topic widely studied in research and economic analysis. However, the influence of transmission constraints and zonal prices on optimal hydro dispatching has not been highly considered in the literature. In the present study, an iterative algorithm for calculating the optimal bids of hydro plants in a basin is proposed, considering the fundamental influence of these plants in regions when transmission lines are congested and can affect zonal prices. The results show the efficiency of the algorithm and modifications in positioning of hydro plants in the market.

The effects of lightning and impulse currents on the perturbations of the state of polarization (SOP) of optical beams transmitted on aerial cables are emulated in a laboratory environment. Submicrosecond-resolved measurements enable us to distinguish and quantify the magnetooptic contributions due to the discharge current. The maximum of Faraday-induced circular birefringence associated to natural strokes is estimated in /spl Delta/n/sub lr//spl ap/10/sup -7/ and may lead in a few microseconds to iterate swapping between orthogonal SOPs. The corresponding peak in relative phase delay, cumulated in transmission along a span, is evaluated as /spl Delta//spl tau//sub lr//spl ap/0.3 ps.

In this study, an innovative Low Temperature Combustion (LTC) system named Temperature Controlled Reactivity Compression Ignition (TCRCI) is presented, and a numerical optimization of the hardware and the operating parameters is proposed. The studied combustion system aims to reduce the complexity of the Reaction Controlled Compression Ignition engine (RCCI), replacing the direct injection of high reactivity fuel with a heated injection of low reactivity fuel. The combustion system at the actual state of development is presented, and its characteristics are discussed. Hence, it is clear that the performances are highly limited by the actual diesel-derived hardware, and a dedicated model must be designed to progress in the development of this technology. A Computational Fluid Dynamics (CFD) model suitable for the simulation of this type of combustion is proposed, and it is validated with the available experimental operating conditions. The Particle Swarm Optimization (PSO) algorithm was integrated with the Computational Fluid Dynamic (CFD) software to optimize the engine combustion system by means of computational simulation. The operating condition considered has a relatively high load with a fixed fuel mass and compression ratio. The parameters to optimize are the piston bowl geometry, injection parameters and the boosting pressure. The achieved system configuration is characterized by a wider piston bowl and injection angle, and it is able to increase the net efficiency of 3% and to significantly reduce CO emissions from 0.407 to 0.136 mg.