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In this paper, a new methodology for fault detection and diagnosis in photovoltaic systems is proposed. This method employs a novel Euclidean distance-based tree algorithm to classify various considered faults. Unlike the decision tree, which requires the use of the Gini index to split the data, this algorithm mainly relies on computing distances between an arbitrary point in the space and the entire dataset. Then, the minimum and the maximum distances of each class are extracted and ordered in ascending order. The proposed methodology requires four attributes: Solar irradiance, temperature, and the coordinates of the maximum power point (Impp, Vmpp). The developed procedure for fault detection and diagnosis is implemented and applied to classify a dataset comprising seven distinct classes: normal operation, string disconnection, short circuit of three modules, short circuit of ten modules, and three cases of string disconnection, with 25%, 50%, and 75% of partial shading. The obtained results demonstrate the high efficiency and effectiveness of the proposed methodology, with a classification accuracy reaching 97.33%. A comparison study between the developed fault detection and diagnosis methodology and Support Vector Machine, Decision Tree, Random Forest, and K-Nearest Neighbors algorithms is conducted. The proposed procedure shows high performance against the other algorithms in terms of accuracy, precision, recall, and F1-score.

The following paper presents thermodynamic and optical investigations of hydrogen-enriched methane combustion, showing the potential of a hydrogen admixture as a means to decarbonize stationary power generation. The optical investigations are carried out through a fisheye optical system directly mounted into the combustion chamber, replacing one exhaust valve. All of the tests were carried out with constant fuel energy producing 16 bar indicated mean effective pressure. The engine under investigation is a port-fueled 4.8 L single-cylinder large-bore research engine. The test series compared the differences between a conventional spark plug and an unscavenged pre-chamber spark plug as an ignition system. The fuel blends under investigation are 5 and 10%V hydrogen mixed with methane and pure natural gas acting as a reference fuel. The thermodynamic results show a beneficial influence of the hydrogen admixture on both ignition systems and for all variations concerning the lean running limit, combustion stability and indicated efficiency, with the most significant influence being visible for the tests using conventional spark plugs. With the unscavenged pre-chamber spark plug and the combustion of the 10%V hydrogen admixture, an increase in the indicated efficiency of 0.8% compared to NG is achievable. The natural chemiluminescence intensity traces were observed to be predominantly influenced by the air–fuel equivalence ratio. This results in a 20% higher intensity for the unscavenged pre-chamber spark plug for the combustion of 10%V hydrogen compared to the conventional spark plug. This is also visible in the evaluations of the flame color derived from the dewarped combustion image series. The investigation of the torch flames also shows a difference in the air–fuel equivalence ratio but not between the different fuels. The results encourage the development of hydrogen-based fuels and the potential to store surplus sustainable energy in the form of hydrogen in existing gas grids.

Battery aging is one of the key challenges that electrochemical energy storage faces. Models for both cycling and calendar aging are valuable for quantitatively assessing their contribution to overall capacity loss. Since batteries are stored and employed under varying conditions of temperature and state of charge in their real-life operation, the availability of a suitable model to anticipate the outcome of calendar aging in lithium-ion batteries under dynamic conditions is of great interest. In this article, we extend a novel model to predict the capacity loss due to calendar aging by using variable-order fractional calculus. For this purpose, some theoretical difficulties posed by variable-order definitions are discussed and compared by applying them to fit experimental results with a multi-parameter optimization procedure. We show that employing a variable-order model allows for a significant improvement in accuracy and predictive ability with respect to its constant-order counterpart. We conclude that variable-order models constitute an interesting alternative for reproducing complex behavior in dynamical systems, such as aging in lithium-ion batteries.

The authors compare the energy consumption and CO2 emissions from vehicles using internal combustion engines (ICE), battery electric vehicles (BEV), fuel cell electric vehicles (FCEV), and two types of hybrid vehicles, BEV-ICE hybrid and BEV-FCEV hybrid. This paper considers several scenarios for four countries’ electricity production from primary energy sources to estimate total CO2 release. Energy consumption of the vehicle per 100 km, emissions during manufacturing, battery production, and lifecycle of the vehicle are considered in the total amount evaluation of CO2 released. The results show that with current technologies for battery manufacturing, and a significant proportion of national grid electricity delivered by fossil fuels, BEV is the best choice to reduce carbon emissions for shorter driving ranges. In the case of electricity generation mainly by low-carbon sources, FCEV and BEV-FCEV hybrid vehicles end up with lower carbon dioxide emissions. In contrast, with electricity mainly generated from fossil fuels, electric vehicles do not reduce CO2 emissions compared to combustion cars.

The results in this work show the influence of long-term operation on the decomposition of working fluids in eight different organic rankine cycle (ORC) power plants (both heat-led and electricity-led) in a range of 900 kW el to 2 MW el . All case study plants are using octamethyltrisiloxane (MDM) as a working fluid; the facilities are between six to 12 years old. Detailed analyses, including the fluid distribution throughout the cycle, are conducted on one system. All presented fluid samples are analyzed via head space gas chromatography mass spectrometry (HS-GC-MS). Besides the siloxane composition, the influence of contaminants, such as mineral oil-based lubricants (and their components), is examined. In most cases, the original working fluid degrades to fractions of siloxanes with a lower boiling point (low boilers) and fractions with a higher boiling point (high boilers). As a consequence of the analyses, a new fluid recycling and management system was designed and tested in one case study plant (Case Study #8). Pre-post comparisons of fluid samples prove the effectiveness of the applied methods. The results show that the recovery of used working fluid offers an alternative to the purchase of fresh fluid, since operating costs can be significantly reduced. For large facilities, the prices for new fluid range from € 15 per liter (in 2006) to € 22 per liter (in 2013), which is a large reinvestment, especially in light of filling volumes of 4000 liters to 7000 liters per unit. Using the aforementioned method, a price of € 8 per liter of recovered MDM can be achieved.

Vietnam is widely considered as an energy-intensive economy. Renewable energy integration has been set as an important goal in the country’s revised Power Development Plan 7 (PDP7). This study first conducted a generation adequacy assessment using the basic probabilistic modeling approach to evaluate how the generation fleet, as foreseen in the PDP7, can meet the demand, despite the fast-changing renewable energy sources (RES) generation. The adequacy of the generation was measured using the Loss of Load Expectation (LOLE, expressed in hours) index. The study then conducted in-depth interviews with key stakeholders to identify and propose efficient strategic approach and policy implementations for integration of RES into the current power system in Vietnam. The results suggested that three major pillars should be considered to ensure the success of RES integration: strategic objectives, structural reforms and system transformation.

This paper proposes a new approach to hybrid forecasting methodology, characterized as the statistical recalibration of forecasts from fundamental market price formation models. Such hybrid methods based upon fundamentals are particularly appropriate to medium term forecasting and in this paper the application is to month-ahead, hourly prediction of electricity wholesale prices in Spain. The recalibration methodology is innovative in seeking to perform the recalibration into parametrically defined density functions. The density estimation method selects from a wide diversity of general four-parameter distributions to fit hourly spot prices, in which the first four moments are dynamically estimated as latent functions of the outputs from the fundamental model and several other plausible exogenous drivers. The proposed approach demonstrated its effectiveness against benchmark methods across the full range of percentiles of the price distribution and performed particularly well in the tails.