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In recent years, prices for photovoltaics have fallen steadily and the demand for sustainable energy has increased. Consequentially, the assessment of roof surfaces in terms of their suitability for PV (Photovoltaic) installations has continuously gained in importance. Several types of assessment approaches have been established, ranging from sampling to complete census or aerial image analysis methodologies. Assessments of rooftop photovoltaic potential are multi-stage processes. The sub-task of examining the photovoltaic potential of individual rooftops is crucial for exact case study results. However, this step is often time-consuming and requires lots of computational effort especially when some form of intelligent classification algorithm needs to be trained. This often leads to the use of sampled rooftop utilization factors when investigating large-scale areas of interest, as data-driven approaches usually are not well-scalable. In this paper, a novel neighbourhood-based filtering approach is introduced that can analyse large amounts of irradiation data in a vectorised manner. It is tested in an application to the city of Giessen, Germany, and its surrounding area. The results show that it outperforms state-of-the-art image filtering techniques. The algorithm is able to process high-resolution data covering 1 km2 within roughly 2.5 s. It successfully classifies rooftop segments which are feasible for PV installations while omitting small, obstructed or insufficiently exposed segments. Apart from minor shortcomings, the approach presented in this work is capable of generating per-rooftop PV potential assessments at low computational cost and is well scalable to large scale areas.

In recent years, deregulation in the power generation market worldwide combined with significant variation in fuel prices and a need for flexibility in terms of power augmentation specially during periods of high electricity demand (summer months or noon to 6 PM) has forced electric utilities, cogenerators and independent power producers to explore new power generation enhancement technologies. In the last 5–10 years, inlet fogging approach has shown more promising results to recover lost power output due to increased ambient temperature compared to the other available power enhancement techniques. This paper presents the first systematic study on the effects of both inlet evaporative and overspray fogging on a wide range of combined cycle power plants utilizing gas turbines available from the major gas turbine manufacturers worldwide. A brief discussion on the thermodynamic considerations of inlet and overspray fogging including the effect of droplet dimension is also presented. Based on the analyzed systems, the results show that high pressure inlet fogging influences performance of a combined cycle power plant using an aero-derivative gas turbine differently than with an advanced technology or a traditional gas turbine. Possible reasons for the observed differences are discussed.

Abstract Solutions containing hydrofluoric acid (HF), hydrochloric acid (HCl), and hydrogen peroxide (H2O2) were investigated as novel acidic, NOx-free etching mixtures for texturing of monocrystalline silicon wafers. High etch rates of up to 13.3 nm s−1 were observed at room temperature, which are comparable to the etch rates of KOH-IPA solutions. The silicon surface was investigated by scanning electron microscopy (SEM) and confocal laser scanning microscopy (CLSM), indicating pyramidal textures for diamond wire and SiC-slurry sawn as well as saw-damage etched (polished) wafers. Non-stirred baths generate random pyramidal structures while constantly stirred solutions generate novel random inverted pyramidal surface structures. The light trapping efficiency of wafers etched by the HF-HCl-H2O2 solutions was compared by UV/vis-reflectivity measurements to KOH/i-propanol specimens indicating lower reflectivities for the HF-HCl-H2O2-treated samples. Using the ‘wafer ray tracer’ (pvlighthouse.com) the light absorption properties of monomodal and random inverted pyramid structures were simulated and compared to well-known random and monomodal textures for PERC solar cells, clearly indicating the best performance for random inverted pyramids. Besides, simulation of a PERC solar cell on a roof top at our university was performed, indicating improved performance, especially for random inverted pyramid textures.

Wind turbine generators (WTGs) in a wind power plant (WPP) contain different levels of releasable kinetic energy (KE) because of the wake effects. This paper proposes a releasable KE-based inertial control scheme for a doubly fed induction generator (DFIG) WPP that differentiates the contributions of the WTGs depending on their stored KE. The proposed KE-based gain scheme aims to make use of the releasable KE in a WPP to raise the frequency nadir. To achieve this, two additional loops for the inertial control are implemented in each DFIG controller: the rate of change of frequency and droop loops. The proposed scheme adjusts the two loop gains in a DFIG controller depending on its rotor speed so that a DFIG operating at a higher rotor speed releases more KE. The performance of the proposed scheme was investigated under various wind conditions. The results clearly indicate that the proposed scheme successfully improves the frequency nadir more than the conventional same gain scheme by releasing more KE stored in a WPP, and it helps all WTGs to ensure stable operation during inertial control by avoiding the rotor speed reaching the minimum speed limit.

Smart charging of Electric Vehicles (EVs) reduces operating cost, allows more sustainable battery usage, and promotes the rise of electric mobility. In addition, bidirectional charging and improved connectivity enable efficient power grid support. Today, however, uncoordinated charging, e.g., governed by users’ habits, is still the norm. Thus, the impact of upcoming smart charging applications is mostly unexplored. We aim to estimate the expenses inherent with smart charging, e.g., battery aging costs, and give suggestions for further research. Using typical onboard sensor data we concisely model and validate an EV battery. We then integrate the battery model into a realistic smart charging use case and compare it with measurements of real EV charging. The results show that i) the temperature dependence of battery aging calls for precise thermal models for charging power greater than 7 kW, ii) disregarding battery aging underestimates EVs’ operating cost by approx. 30%, and iii) the profitability of Vehicle-to-Grid (V2G) services based on bidirectional power flow, e.g., energy arbitrage , depends on battery aging costs and the electricity price spread.

This study investigates the feasibility of a device to improve summer comfort in wood-frame houses using a Ventilated Internal Double Wall (VIDW). The idea is to increase the house's thermal inertia and to evacuate accumulated heat during the night with a mechanical ventilation system. The VIDW is a cooling wall. Numerous studies on night ventilation have been conducted, but the active ventilation inside the air gap of a double wall with high thermal inertia has not been studied. The first part of this study examines the impact of VIDWs on the thermal comfort in a timber-frame house. The VIDW is modeled in transient mode based on an electrical analogy with the assumption that the exchange coefficients are constant for a given air velocity. In addition, modeling a stationary CFD in forced convection of the VIDW air gap allowed us to study the cooling potential and the benefit of installing obstacles.

Abstract The impact of a twisted turbulator in a parabolic solar collector on the improvement of thermal–hydraulic performance (THP), as well as energy and exergy Efficiency of MgO-Cu/water hybrid nanofluid (HNF) is numerically evaluated. The main goal of this study is to use a twisted turbulator with pitch ratios (γ) of 1, 1.5, 2, and 2.5 for Reynolds numbers (Re) of 8000 to 32,000 and volume fractions (ϕ) of 1, 2, and 3% of MgO and Cu nanoparticles. Numerical simulation findings are reported in the form of graphs of average Nusselt number (Nuave), pressure drop (Δp), THP, energy Efficiency ( η c ) , and exergy Efficiency ( η ex ) . It can be concluded that Nuave and Δp depends on ϕ and Re and augments linearly with their values. Moreover, Nuave and Δp increases significantly by increasing the pitch ratio of the turbulator. The results revealed that the maximum values of augmentation of η c and η ex are respectively 23.79% and 21.15% by raising Re from 8000 to 32000. In a SC with a turbulator with γ = 2 and ϕ = 3%, η c is raised by 24.16% by increasing Re from 8000 to 32000; while, in the case of γ = 2.5 and ϕ = 3%, η c is enhanced by 18.2%.

Given the significant amount of installed generation-capacity based on wind power, and also due to current economic downturn, the subsidies and incentives that have been widely used by wind-power producers to recover their investment costs have decreased and are even expected to disappear in the near future. In these conditions, wind-power producers need to develop offering strategies to make their investments profitable counting solely on the market. This paper proposes a multi-stage risk-constrained stochastic complementarity model to derive the optimal offering strategy of a wind-power producer that participates in both the day-ahead and the balancing markets. Uncertainties concerning wind-power productions, market prices, demands' bids, and rivals' offers are efficiently modeled using a set of scenarios. The conditional-value-at-risk metric is used to model the profit risk associated with the offering decisions. The proposed model is recast as a tractable mixed-integer linear programming program solvable using available branch-and-cut algorithms. Results of a case study are reported and discussed to show the effectiveness and applicability of the proposed approach.