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Abstract Off-grid systems based on PV and batteries are becoming a solution of great interest for rural electrification. Nevertheless, sizing these systems is not straightforward since it means matching unpredictable energy sources with uncertain demands while providing the best reliability and costs. In our opinion, the effect of users’ energy consumptions uncertainty on the sizing of these systems has not been appropriately investigated. This paper addresses this issue and analyzes the effect of load profile uncertainty on the off-grid PV systems optimum design. Specifically a novel sizing methodology has been introduced based on: (i) an effective approach of modelling rural energy needs; (ii) an innovative stochastic method which formulates different possible realistic daily load profiles for un-electrified rural areas; (iii) a PV-battery techno-economic analysis via steady-state simulation; (iv) the evaluation of the optimum system sizing via a numerical method based on net present cost and loss of load probability. Finally, the proposed methodology has been applied to find the optimum size of an off-grid PV system for a peri-urban area of Uganda. The results show that the optimum system configurations are significantly affected by load profiles; consequently an approach to identify the robust solution with regards the assumed uncertainty is proposed.

Commercial buildings are responsible for a significant share of the energy requirements of European Union countries. Related consumptions due to heating, cooling, and lighting appear, in most cases, very high and expensive. Since the real estate is renewed with a very small percentage each year and current trends suggest reusing the old structures, strategies for improving energy efficiency and sustainability should focus not only on new buildings, but also and especially on existing ones. Architectural renovation of existing buildings could provide an opportunity to enhance their energy efficiency, by working on the improvement of envelopes and energy supply systems. It has also to be noted that the measures aimed to improve the energy performance of buildings should pay particular attention to the cost-effectiveness of the interventions. In general, there is a lack of well-established methods for retrofitting, but if a case study achieves effective results, the adopted strategies and methodologies can be successfully replicated for similar kinds of buildings. In this paper, an iterative methodology for energy retrofit of commercial buildings is presented, together with a specific application on an existing office building. The case study is particularly significant as it is placed in an urban climatic context characterized by cold winters and hot summers; consequently, HVAC energy consumption is considerable throughout the year. The analysis and simulations of energy performance before and after the intervention, along with measured data on real energy performance, demonstrate the validity of the applied approach. The specifically developed design and refurbishment methodology, presented in this work, could be also assumed as a reference in similar operations.

One of the limits of railway freight market, if compared to road one, is its lower flexibility in the first and in the last connection of the overall delivery chain. In this paper, a battery module is proposed and sized as an innovative solution to replace the diesel engine in the freight locomotives equipped with diesel last mile module currently available on the market. Indeed, nowadays there are well-developed technologies of lithium-ion batteries, coming from road transportation system and some of them are being already tested in railway systems, with promising results. An estimation of the total energy necessary to compose the train and to move it under an energized catenary is carry out using traction diagrams: the sizing of the traction battery, of automotive type made by Lithium Werks, will be based on this estimated value. The economic feasibility of the proposed system is then presented using break-even analysis by presenting three different scenarios: two at the current conditions and one at 2030. Finally, the proposed electrical circuit is simulated in Matlab Simulink environment, to verify its actual stability and to show a start-up cycle.

AMEBA is an Italian acronym which stands for “alta moderazione e basso arricchimento” (high moderation and low enrichment). The AMEBA reactor is nothing more than a PWR which possesses very unusual values of both volumetric ratio moderator/fuel and U-235 enrichment of UO2. The possibility is shown of the technical realisation of a nuclear power plant equipped with an AMEBA PWR reactor. Among the most enticing properties of AMEBA are the following: self-shut-down in any abnormal condition, elimination of all need for control rods and boric acid dissolution in the water, absolute impossibility of reaching values of reactivity greater than a fraction of a dollar, intrinsic subcriticality, attaining to several dollars, in non-operative condition when the water is at ambient temperature, normal operation with a very small-sized pressurizer, self-start-up.

Abstract The sustainable indicators are characterized by a low degree of aggregation and a high amount of information. An indicator must show a synthetic representation of a real environmental, by using a value or a parameter, so that they can be easily used by policy makers. It is necessary to connect, therefore, the various systems in an appropriately integrated sustainable system. The indicators need to be aggregated based on the structure of the data. Each indicator must to be defined through a weight with reference to another weighted indicator. In this paper is illustrated the calculation of the assigned weights that uses a procedure based on fuzzy logic and to define a model that allows us to estimate the sustainability of a city. The final result is, therefore, a combination of values assigned by expert opinion for the various criteria, processed using fuzzy logic to obtain a weight with significant objectivity and as it is possible to estimate the sustainability of the city through the weights.

Abstract Reliability analysis of IESs (Integrated Energy System) is complicated because of the complexity of system topology and dynamics and different kinds of uncertainties. Reliability is often calculated based on statistic methods, which always focus on historical performances and neglect the importance of their dynamics and structure. To overcome this problem, in this paper, a systematic framework for dynamically analysing the real-time reliability of IESs is proposed by integrating different machine learning methods and statistics. Firstly, the bootstrap-based Extreme Learning Machine is developed to forecast the conditional probability distributions of the productions of renewable energies and the energy consumptions. Then, the dynamic behaviour of IESs is simulated based on a stacked auto-encoder model, instead of using traditional mechanism-based simulation models, for improving computational efficiency. Besides, the variables representing the transient properties of natural gas pipeline networks, such as delivery pressures and flow rates, are taken as the indicators for quantifying the energy supply security in natural gas pipeline networks. The time-dependent relationships among these indicators and their statistic correlations are modelled for improving the effectiveness of the analysis results. Finally, the reliability assessment is performed by estimating the probability distribution of each functional state of the target IES. A case study of a realistic bi-directional IES is carried out to demonstrate the effectiveness of the proposed method. The results show that the method is able to effectively evaluate the reliability of IESs, which can provide useful information for system operation and management.

Abstract With environmental concerns and limited natural resources, there is a need for cleaner sources of energy in the transportation sector. Renewable natural gas (RNG) is being considered as a potential fuel for heavy-duty applications due to its comparable usage to diesel and gasoline in vehicles. The idea of compressed RNG vehicles is being proposed especially because it will significantly reduce harmful emissions into the environment. This study examines the feasibility of implementing a nationwide network of compressed RNG refueling infrastructure in order to accommodate a conversion of long-haul, heavy-duty (LHHD) truck fleet from diesel fuel to RNG. Two methods, Constant Traffic and Variable Traffic, along with data about compressed RNG infrastructure and vehicles, were developed and used to predict fuelling requirements for LHHD truck fleet. Then, a detailed economic analysis was conducted on various test cases to estimate how different variables impact the final selling price of RNG. This provided insight with the understanding of what factors go into pricing RNG and if it can compete against diesel in the trucking market. Results disclosed that the cost to purchase RNG is the greatest factor in the final selling price of compressed RNG. Due to the variability in RNG production, however, there is no precise cost, which makes predictions challenging. However, results revealed that it is possible for compressed RNG to be competitive with diesel, with the mean compressed RNG price being 16.5% cheaper than diesel, before being taxed. Future studies should focus on the feasibility of the production of RNG and the associated costs. An in-depth analysis on operational and maintenance costs for compressed RNG refueling stations may also provide predictions that are more accurate. The methodology developed in this feasibility analysis may serve as a useful tool for future techno-economics of RNG refueling stations for other types of internal combustion engine (ICE) fleets or those powered with alternative green fuels.

Abstract The pressure waves generated by a train entering and running through a tunnel are studied experimentally and numerically with the aim of gaining a solid understanding of the flow in the standard tunnel geometry and in the configuration with airshafts along the tunnel surface. Laboratory experiments were conducted in a scaled facility where train models travelled at a maximum velocity of about 150 km/h through a 6-m-long tunnel. The flow was simulated by a one-dimensional numerical code modified to include the effect of the separation bubble forming near the train head. The numerical simulations reproduced well the experimental results. We tested the influence of the train cross-sectional shape and length on the compression wave produced by the vehicle entering the confined area. The cross-sectional shape was not found to be influential as long as the blockage ratio, namely the ratio between the train and tunnel cross-sectional areas, is constant. The pressure waves are one-dimensional sufficiently downwind of the tunnel mouth, which validates the comparison between the experimental and computational results. It is further shown that the numerical code can satisfactorily reproduce the pressure variations for the case with airshaft apertures along the tunnel surface.