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India's steadily expanding populace has made it important to create innovative methods of transportation with electric vehicles (EV's) being the most favoured alternative. The major impediment is the negative impact caused by the improper setup of charging stations on the utility distribution system. This paper takes up the issue of optimal planning (siting and sizing) of charging station (CS) infrastructure in the city of Allahabad, India. It has been labelled as a future smart city under a pilot transportation project started by Government of India. A hybrid algorithm comprising of Genetic Algorithm and improved version of conventional Particle Swarm Optimization (GAIPSO) is employed in this study. The aim is to find optimal location of proposed charging stations in the Allahabad distribution system. PSO further re-optimizes the received sub-optimal solution (site and size of station) which leads to an overall improvement in its functionality and solution. Simulation studies on a real time system of Allahabad city clearly exhibits the superior performance of the aforementioned technique with respect to GA and PSO in terms of improvement in voltage profile and quality of solution obtained for the objective function.

A new sizing approach is applied in this paper to stand-alone PV systems design, which is based on systems configurations without shedding load. The investigation is based on a detailed study of the minimum storage requirement and an analysis of the sizing curves. The analysis reveals the importance of using daily series of measured solar radiation data instead of monthly average values. For high-reliability systems, it is important that these data series are as long as possible, while the configuration of large generator and small battery seems to deal better with the unpredictability of solar radiation.

With the continuous development of intelligent transportation technologies, new ways of energy usage in transportation continue to emerge, which puts forward new requirements for the planning and design of energy systems. However, comprehensive analyses on the characteristics of transportation energy systems and the development trend of energy usage patterns brought by intelligent technologies have rarely been carried out so far. This paper explores this subject by reviewing the recent development and utilization of intelligent technologies in the transportation system and its impacts on energy usage. This review is carried out from three aspects, covering the representative intelligent transportation and energy technologies on vehicles, infrastructures and systems. The scope is limited within road, railway and water transport domains, with a focus on the recent developments in China as a representative. In terms of vehicles, the development trend of the power systems for new energy vehicles, the characteristics of energy usages in electric vehicles and the effects on energy saving and emission reduction are summarized. In terms of infrastructures, new technologies on smart road, smart port, intelligent railway energy system and the usage of clear energy on electric grid for transportation are reviewed, with the consideration of their potential influences on energy usages and the energy consumption characteristics of typical facilities also being analyzed. As for the transportation system, this review has focused on intelligent and connected transportation systems, train control and autonomous systems, and intelligent shipping system, with the emphasis on the energy saving and emission reduction effects of applying these intelligent technologies. The overall development trend of the transportation energy system is then analyzed based on the above materials, in particular, the future energy usage patterns in transportation system are given and the major challenges and obstacles approaching the future scenarios are also identified.

Abstract In many visions and roadmaps, there is a broad agreement that fuel cells – both for stationary and mobile applications – are the key technology to allow the development of a hydrogen infrastructure. Furthermore, this development is generally thought to be based on a gradual, decentralised evolution. Nevertheless, in this paper it is argued that, taking into account the entire hydrogen chain (production, transport, storage, distribution and end-use), this decentralised fuel-cell based philosophy shows some serious flaws. Therefore, a new hydrogen-transition approach was pushed forward: mixing in of hydrogen into the natural-gas bulk. Using Flanders – the Northern part of Belgium – as a case study, the development of a transitory hydrogen infrastructure has been studied, taking into account the entire hydrogen chain and its dynamics, from production to end use. In a next step, this transition is being quantified. An optimisation model has been developed using Matlab and the commercial solvers GAMS and CPLEX. Following a mixed-integer linear-programming approach, this model is able to determine the economically optimal hydrogen-production mix and operational behaviour of each hydrogen-production plant separately. The model then allows gaining valuable insights in the importance of storage and the influence of fuel prices and carbon taxes with regard to the development of an early hydrogen economy.

Abstract In this communication, a mathematical model is presented to predict the thermal performance of a spherical solar still. The analysis is based on the heat and mass transfer relationships by Dunkle (1961) which have been modified empirically so as to validate the experimental results of Menguy et al. (1980). Numerical calculations have also been made to study the effect of the absorptivity of the basin liner on the distillate yield of the still

Abstract In the new competitive electricity supply industry, there is a renewed interest in algorithms that can provide savings in operation costs. An optimal scheduling of generators can provide substantial annual savings in fuel costs, but this highly constrained non-linear mixed integer optimisation problem can only be full v solved by complete enumeration, a process which is not computationally feasible for realistic power systems. In the recent past, evolutionary computation techniques have been applied to the solution of the unit commitment problem, but when implemented as stand alone systems, they suff er from computational time limitations, especially when the systems are scaled up. This paper proposes a hybridgenetic algorithm incorporating a priority list unit ordering scheme to solve the generator scheduling problem. Test results on networks with up to 110 generators are presented and the results demonstrate the viability of the hybrid GA method for unit commitment in realistic power systems.

The adjoint method for blade design optimization will be described in this two-part paper. The main objective is to develop the capability of carrying out aerodynamic blading shape design optimization in a multi-stage turbomachinery environment. To this end, an adjoint mixing-plane treatment has been proposed. In the first part, the numerical elements pertinent to the present approach will be described. The gradients of a single objective function of a weighted sum of objectives and constraints with respect to detailed blade shape perturbations are obtained very efficiently by the continuous adjoint method. The steepest descent method is used to drive the design to an optimum. The adjoint mixing-plane treatment enables the adjoint equations to be solved in a multi-stage environment. The adjoint solver is verified by comparing gradient results with a direct finite difference method and through a 2D inverse design. The adjoint mixing-plane treatment is verified by comparing gradient results against those by the finite difference method for a 2D compressor stage. The redesign of the 2D compressor stage further demonstrates the validity of the adjoint mixing-plane treatment and the benefit of using it in a multi-bladerow environment.

The present work aimed at the standardization of transesterification process parameters for the production of methyl ester of filtered neem oil and fuel characterization for engine performance. The effect of process parameters such as molar ratio, preheating temperature, catalyst concentration and reaction time was studied to standardize the transesterification process for estimating the highest recovery of ester with lowest possible viscosity. Based on the observations of the ester recovery and kinematic viscosity, it was found that filtered neem oil at 6:1 M ratio (methanol to oil) preheated at 55 °C temperature and maintaining 60 °C reaction temperature for 60 min in the presence of 2 percent KOH and then allowed to settle for 24 h in order to get lowest kinematic viscosity (2.7 cSt) with ester recovery (83.36%). Different fuel properties of the neem methyl ester and neem oil were also measured. Results show that the methyl ester of neem obtained under the optimum condition is an excellent substitute for fossil fuels.