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There are many promising renewable energy (RE) technologies that could help increase the contribution of RE in energy supply but which are not yet commercially available. The development rate of new RE technologies depends on many factors, such as Research and Development (R&D) efforts and policies. This study focuses on comparing China’s efforts regarding the development of new RE technologies (e.g., wave and tidal, binary geothermal power, floating solar, micro hydro, osmotic energy, floating offshore wind and vertical axis wind turbines) with those of the European Union (EU). For this purpose, we collected data from publications and databases and analysed several indicators: e.g., the development of renewable electricity generation and capacity, demonstration projects, investments in R&D and patent applications. The results show that China has become a big player globally for mainstream renewable electricity (hydropower, wind and solar PV). This development is due to China’s industrial policy and prioritization of effectiveness over cost efficiency. The main developments in China occurred in the 2010s, while the EU was a frontrunner in the 2000s. For the newer or less mainstream technologies, the application in China is still low, compared to the EU, except for floating solar, where China is a lead player. Regarding patent applications, China has shown a higher application amount compared to the EU since 2006. However, only a small share of China’s patents are valid internationally. We conclude that China has emerged as a big player in mainstream renewable energy technologies over the last decade. In regard to new renewable energy technologies, China is predominantly involved in solar energy and, in comparison to the EU, less in other new technologies (e.g., binary geothermal systems and ocean energy).

Data Availability Statement: The data presented in this study are available on request from the corresponding author. Copyright © 2023 by the authors. The future power grid will have more distributed energy sources, and the widespread access of distributed energy sources has the potential to improve the energy efficiency, resilience, and sustainability of the system. However, distributed energy, mainly wind power generation and photovoltaic power generation, has the characteristics of intermittency and strong randomness, which will bring challenges to the safe operation of the power grid. Accurate prediction of solar power generation with high spatial and temporal resolution is very important for the normal operation of the power grid. In order to improve the accuracy of distributed photovoltaic power generation prediction, this paper proposes a new distributed photovoltaic power generation prediction model: ROLL-GNN, which is defined as a prediction model based on rolling prediction of the graph neural network. The ROLL-GNN uses the perspective of graph signal processing to model distributed generation production timeseries data as signals on graphs. In the model, the similarity of data is used to capture their spatio-temporal dependencies to achieve improved prediction accuracy. Guangdong Basic and Applied Basic Research Foundation (2021A1515010742, 2020A1515011160, 2020A1515010801); National Natural Science Foundation of China (52007032); Basic Research Program of Jiangsu Province (BK20200385).

In order to solve the problem of low voltage caused by unbalanced load in the distribution network, a transformer loss intelligent prediction model under unbalanced load is proposed. Firstly, the mathematical model of a transformer with an unbalanced load is established. The zero-sequence impedance and neutral line current of the transformer are calculated by using the Chaos Game Optimization algorithm (CGO), and the correctness of the mathematical model is proved by using actual data. Then, the correlation among network input variables is eliminated by using Principal Component Analysis (PCA), so the number of network input variables is decreased. At the same time, Sparrow Search Algorithm (SSA) is used to optimize the initial weight and threshold of the BP network, and an accurate transformer loss prediction model based on the PCA-SSA-BP is established. Finally, compared with the transformer loss prediction model based on BP network, Genetic Algorithm optimized BP network (GA-BP), Particle Swarm optimized BP network (PSO-BP) and Sparrow Search Algorithm optimized BP network (SSA-BP), the transformer loss prediction model based on PCA-SSA-BP network has been proven to be accurate by using actual data and it is helpful for low-voltage recovery in the distribution network.

Sustainability and resilience are major challenges for the building sector in order to meet energy efficiency and low carbon emissions goals. Based on the defined and quantified targets of the EU climate change policy, Renewable Energy Systems (RESs) are among the top-priority measures for accomplishing the target of decarbonization in buildings. Nevertheless, the choice of the type of RES is not a one-dimensional problem, and the optimal combination may not be unique. The aim of this paper is the energy and environmental evaluation of renewable energy technologies with emphasis on biomass and solar thermal systems for heating applications in residential buildings. More specifically, and aiming at the maximum possible contribution of renewable energy sources in the total final energy consumption for the needs of zero energy buildings, different scenarios are presented based on a Life Cycle Energy Analysis (LCEA) approach. The methodology is based on quantifying the environmental impacts (midpoint analysis), as well as endpoint analysis, in order to define the impact on human health, ecosystem damage, and resource depletion. The LCEA has been conducted, supported by the SimaPro tool, ensuring the environmental impact assessment result. A combination of RES technologies based on solar and biomass are examined and compared to conventional fossil fuel heating systems according to technical, energy, and environmental criteria. Finally, the energy system technologies were compared in correlation to a building’s thermal insulation level. The first set of simulations fulfilled the minimum thermal insulation requirements, according to the national energy performance regulation, whilst the second set of simulations was based on increased levels of insulation. The point of this analysis was to correlate the impact of thermal insulation to RES technologies’ contribution. The results determined that the best available energy solution, focusing on technical and environmental criteria, is the combination of biomass and solar thermal systems for covering the heating processes in residential buildings. More specifically, the combined biomass–solar system has a lower overall environmental impact, due to the reduction in gaseous pollutant emissions, as well as the reduction in the amount of used fuel. The reduction in the total environmental impact amounts to a percentage of approximately 43%.

Unconventional natural gas extraction presents numerous opportunities and risks for communities across the United States. To capture a portion of the revenue generated by the resource states tax unconventional natural gas development. While most states collect revenue via severance taxes, Pennsylvania took a novel approach and established an impact fee on the industry instead. Unlike severance taxes in other states, the fee is collected annually and distributed directly to municipalities. While reports show that municipalities use the funds to pay for critical infrastructure, no best practices on how to allocate the funds exist. Citing the literature on mineral resource extraction and infrastructure-led development in American communities, this study examined impact fee payments made to counties with unconventional natural gas wells. The study evaluated whether counties that used the funds to invest in infrastructure were better off in terms of employment and income than other shale-producing counties that did not. Panel fixed- and random-effects regressions suggested that no statistically significant employment or income effects existed. The results suggest that local infrastructural investments are not a successful way to overcome the resource curse issues identified in the literature.

The uncertainty quantification is an indispensable part for the validation of the nuclear safety best-estimate codes. However, the uncertainty quantification usually requires the combination of statistical analysis software and nuclear reactor professional codes, and it consumes huge computing resources. In this paper, a design method of coupling interface between DAKOTA Version 6.16 statistical software and nuclear reactor professional simulation codes is proposed, and the integrated computing workflow including interface pre-processing, code batching operations, and interface post-processing can be realized. On this basis, an integrated framework of uncertainty quantification is developed, which is characterized by visualization, convenience, and efficient computing. Meanwhile, a typical example of small-break LOCA analysis of the LOBI test facility was used to validate the reliability of the developed integrated framework of uncertainty quantification. This research work can provide valuable guidance for developing an autonomous uncertainty analysis platform in China.

Flexible AC transmission systems (FACTSs) can maximize capacity utilization under time-varying grid usage patterns by actively controlling the power flow of the transmission lines, e.g., with phase-shifting transformers (PST). In this paper, we propose an algorithm to determine the minimum number of PSTs and their location such that the grid can operate robustly for any realization of the (active) power set points from a known, continuous uncertainty set. As we show in our experiments, only considering a few extreme grid scenarios cannot provide this guarantee. The proposed algorithm considers the trade-offs between PST placement and operational decisions, such as PST control and redispatch. By minimizing the worst-case redispatch cost, it yields two affine linear control policies for these as a byproduct. Power flow is modeled as a constrained linear system, and the control design and actuator minimization tasks are formulated as a mixed-integer linear program (MILP). We also design a greedy algorithm, whose optimal value differs less than 20% from the MILP solution while being one to two orders of magnitude faster to compute. The proposed algorithm is evaluated for a small demonstrative 3-bus example and the IEEE 39 bus test system.

Electrical utility substations are wired with intelligent electronic devices (IEDs), such as protective relays, power meters, and communication switches. Substation engineers commission these IEDs to assess the appropriate measurements for monitoring, control, power system protection, and communication applications. Like real electrical utility substations, complex electrical substation grid testbeds (ESGTs) need to be assessed for measuring current and voltage signals in monitoring, power system protection, control (synchro check), and communication applications that are limited by small-measurement percentage errors. In the process of setting an ESGT with real-time simulators and IEDs in the loop, protective relays, power meters, and communication devices must be commissioned before running experiments. In this study, an ESGT with IEDs and distributed ledger technology was developed. The ESGT with a real-time simulator and IEDs in the loop was satisfactorily assessed and commissioned. The commissioning and problem-solving tasks of the testbed are described to define a method with flowcharts to assess possible troubleshooting in ESGTs. This method was based on comparing the simulations versus IED measurements for the phase current and voltage magnitudes, three-phase phasor diagrams, breaker states, protective relay times with selectivity coordination at electrical faults, communication data points, and time-stamp sources.

The numerical investigation of magneto-hydrodynamic (MHD) mixed convection flow and entropy formation of non-Newtonian Bingham fluid in a lid-driven wavy square cavity filled with nanofluid was investigated by the finite volume method (FVM). The numerical data-based temperature and nanoparticle size-dependent correlations for the Al2O3-water nanofluids are used here. The physical model is a two-dimensional wavy square cavity with thermally adiabatic horizontal boundaries, while the right and left vertical walls maintain a temperature of TC and TH, respectively. The top wall has a steady speed of u=u0. Pertinent non-dimensional parameters such as Reynolds number (Re=10,100,200,400), Hartmann number (Ha=0,10,20), Bingham number (Bn=0,2,5,10,50,100,200), nanoparticle volume fraction (ϕ=0,0.02,0.04), and Prandtl number (Pr=6.2) have been simulated numerically. The Richardson number Ri is calculated by combining the values of Re with a fixed value of Gr, which is the governing factor for the mixed convective flow. Using the Response Surface Methodology (RSM) method, the correlation equations are obtained using the input parameters for the average Nusselt number (Nu¯), total entropy generation (Es)t, and Bejan number (Beavg). The interactive effects of the pertinent parameters on the heat transfer rate are presented by plotting the response surfaces and the contours obtained from the RSM. The sensitivity of the output response to the input parameters is also tested. According to the findings, the mean Nusselt numbers (Nu¯) drop when Ha and Bn are increased and grow when Re and ϕ are augmented. It is found that (Es)t is reduced by raising Ha, but (Es)t rises with the augmentation of ϕ and Re. It is also found that the ϕ and Re numbers have a positive sensitivity to the Nu¯, while the sensitivity of the Ha and Bn numbers is negative.

The need to reduce CO2 emissions forces the use of biomass as a fuel in the conventional energy conversion process implemented by combustion. Burning biomass alone can be problematic because of the high potential for slugging and fouling on boiler heating surfaces. Therefore, co-firing of biomass with coal is used. This article presents the results of a study of biomass blends of barley, straw, and hard coal biomass from the Polish Makoszowy mine. The sintering of ash from biomass-coal blends was studied by experimental non-standard methods, such as the fracture stress and the pressure drop test. The results were confirmed with the result of thermodynamic modeling using FactSage 8.0 software. Additionally, ash deposition tests were performed in a 3.5 m boiler. The tests conducted showed a significant effect of the addition of biomass to hard coal on the formation of ash deposits on the heating surfaces of the boiler. In addition, the usefulness of non-standard methods in the assessment of the degree of fouling and slugging hazard was confirmed.

This paper provides an overview of an early attempt at developing a simulation model on a solid-state transformer (SST) based on input-serial and output-parallel (ISOP) topology. The proposed SST is designed as a base for a smart grid (SG). The paper provides a theoretical review of the power converters under consideration, as well as their control techniques. Further, the paper presents a simulation model of the proposed concept with a PLECS circuit simulator. The proposed simulation model examines bidirectional energy flow control between the medium-voltage AC grid and DC smart grid, while evaluating power flow efficiency and qualitative indicators of the AC grid. After the completion of design verification and electrical properties analysis by the PLECS simulation models, the synthesis offers recommendations on the optimal layout of the proposed SST topology for smart grid application.

Short-term load forecasting is critical to ensuring the safe and stable operation of the power system. To this end, this study proposes a load power prediction model that utilizes outlier correction, decomposition, and ensemble reinforcement learning. The novelty of this study is as follows: firstly, the Hampel identifier (HI) is employed to correct outliers in the original data; secondly, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is used to extract the waveform characteristics of the data fully; and, finally, the temporal convolutional network, extreme learning machine, and gate recurrent unit are selected as the basic learners for forecasting load power data. An ensemble reinforcement learning algorithm based on Q-learning was adopted to generate optimal ensemble weights, and the predictive results of the three basic learners are combined. The experimental results of the models for three real load power datasets show that: (a) the utilization of HI improves the model’s forecasting result; (b) CEEMDAN is superior to other decomposition algorithms in forecasting performance; and (c) the proposed ensemble method, based on the Q-learning algorithm, outperforms three single models in accuracy, and achieves smaller prediction errors.

Coal gasification is considered a promising solution for the production of synthetic fuels and eventually as a fuel for combined heat and power systems and heating buildings. There are several factors that affect the gasification efficiency and syngas quality, such as gasification parameters (temperature, pressure, etc.), reactants and their ratio, utilisation of catalysts, and gasifier design. The multi-stage gasifier is known as a promising approach in the enhancement of process efficiency, as well as the syngas quality. In this study, the Hungarian brown coal was gasified in a two-stage gasifier. The pyrolysis stage was kept at 600 °C. The gasification stage was conducted at 700, 800, and 900 °C. The steam per carbon (S/C) ratio was examined at 0.75, 1.00, and 1.25. The positive effects of increasing gasification temperature on char and dry gas yield were obviously shown at all S/C ratios. The increase in the S/C ratio did not show a positive effect at all temperature conditions, especially at 700 and 900 °C. The highest dry syngas yield was 1.14 Nm3/kgcoal obtained at 900 °C and the S/C ratio of 1.25. The increase in the gasification temperature also had a significant impact on the volume fraction of CO and CO2. Meanwhile, the syngas concentration varied slightly when the S/C ratio increased from 0.75 to 1.25. From a chemical utilization point of view, the gasification temperature at 900 °C and the S/C ratio of 1.25 resulted in the most promising H2/CO ratio of 1.99. In addition, the highest carbon conversion and cold gas efficiency were achieved at 900 °C and an S/C ratio of 1.00–1.25, respectively.

The Stellarator Power Plant Studies Prospective R&D Work Package in the Eurofusion Programme was settled to bring the stellarator engineering to maturity, so that stellarators and particularly the HELIAS (HELical-axis Advanced Stellarator) configuration could be a possible alternative to tokamaks. However, its complex geometry makes designing a Breeding Blanket (BB) that fully satisfies the requirements for such a HELIAS configuration, which is a difficult task. Taking advantage of the acquired experience in BB design for DEMO tokamak, CIEMAT is leading the development of a Dual Coolant Lithium Lead (DCLL) BB for a HELIAS configuration. To answer the specific HELIAS challenges, new and advanced solutions have been proposed, such as the use of fully detached First Wall (FW) based on liquid metal Capillary Porous Systems (CPS). The proposed solutions have been studied in a simplified 1D model that can help to estimate the relative variations in Tritium Breeding Ratio (TBR) and displacement per atom (dpa) to verify their effectiveness in simplifying the BB integration and improving the machine availability while keeping the main BB nuclear functions (i.e., tritium breeding, heat extraction and shielding). This preliminary study demonstrates that the use of FW CPS would drastically reduce the radiation damage received by the blanket by 29% in some of the selected configurations along with a small decrease of 4.9% in TBR. This could even be improved to just a 3.8% TBR reduction by using a graphite reflector. Such an impact on the TBR is considered affordable, and the results presented, although preliminary in essence, have shown the existence of margins for further development of the FW CPS concept for HELIAS, as they have been not found, at least to date, to be significant showstoppers for the use of this technological solution.

This article deals with the determination of the impact of selected parameters on energy consumption for heating and cooling purposes and CO2 emissions. Mathematical modelling combined with planning a computational experiment was adopted as the research method. The database for creating the models was developed using building energy simulations performed with DesignBuilder software. A single-family house with an area of 101 m2 was the subject of this study. Four deterministic mathematical models for the estimation of annual energy demand for heating, cooling, total final energy demand, and CO2 emissions were developed. Four parameters affecting the energy balance of the house: the area of the glazing system (three levels), U-value of windows (two-, three- and four-pane), U-value of external walls (0.1, 0.15, 0.2 W/m2K) and location (Warsaw, Berlin, Paris) were considered. The article discusses in detail the influence of individual factors on the energy demand and their common interactions. It was found that the level of thermal insulation of the glazing system plays the most important role in saving energy. This factor was the only one to show a stable and significant reduction in house energy demand, and thus a reduction in CO2 emissions for all four objective functions.

Staged multi-cluster fracturing of horizontal wells is one of the most important tools to achieve efficient development of unconventional oil and gas reservoirs. The multi-stage fracturing technique forms complex fractures with multiple clusters and branches in the formation, causing competing diversions leading to more complex proppant transport patterns, and the proppant placement method determines the flow conductivity of complex fractures, so it is necessary to investigate the proppant transport patterns in complex fractures. To address this issue, a field-scale geometric model is established for numerical simulation, and the multiphase flow diversion pattern in the wellbore, the proppant distribution pattern under different network conditions, and the optimization of different construction parameters are investigated. The results are obtained as follows: the distribution of solid and liquid phases in each cluster of the well conforms to the trend of variable mass flow; the proppant is distributed at the heel end in multiple clusters of fractures, and the sand and liquid are unevenly distributed among clusters of fractures, and the number of branching affects the proppant transport; through sensitivity analysis of the influencing factors, the pumping displacement, fracturing fluid viscosity and proppant particle size are optimized, and the construction parameters of 14 m3/min, 5 mPa·s, 70/140 mesh, 12% sand ratio are determined. This study has a certain guiding significance for the optimization of fracturing parameters in this block.

For the unsteady multi-boundary inverse heat conduction problem, a real-time solution method for boundary heat flux based on dynamic matrix control is proposed in the paper. The method solves the heat flux at the boundary in real-time by measuring the temperature information at the measurement points of the heat transfer system. A two-dimensional direct heat conduction model of the heat transfer system is established in the paper, and is solved by the finite difference method to obtain the temperature information of the measurement points under any heat flux boundary. Then, the correspondence between the heat flux of boundary and the temperature information is presented by means of a step-response model. The regularization parameters are introduced into the method to improve the stability of the inversion process, and the effect of real-time inversion on the heat flux of the boundary is achieved through rolling optimization. The experimental results show that the proposed method can achieve real-time inversion of the heat fluxes of the two-dimensional boundary with good accuracy.

An axial compressor has high efficiency under design conditions, but its stable working range is narrow. Adjusting the rotational speed can effectively expand the stable working range. In this paper, a five-stage axial compressor for a specific compressed air energy storage (CAES) system is taken as the research object, and different rotational speed (DRS) characteristics are studied with NUMECA software. Firstly, the influence of DRS on overall aerodynamic performance is explored, and the working flow range of the compressor is increased from 11.5% to 54.0%. Secondly, the effect of DRS on inlet parameters of the first stage rotor is analyzed, and the reasonable distribution of inlet parameters is obtained. Thirdly, the changing law of the internal flow is investigated at DRS. The corner separation is gradually enhanced when the rotational speed increases, and the leakage flow velocity at the rotor tip gradually improves. Finally, the loss distribution of tip clearance is researched. The result shows that the loss distribution increases significantly in both circumferential and spanwise directions when the speed increases. This work aims to provide a reference for the stable and efficient operation of axial compressors in CAES systems under the wide working range.

A new primary frequency controller in power grids undergoing massive wind power penetration is the focus of this paper. The inescapable problem in a largely wind penetrated power grid is to ensure the maintenance of its frequency in the nominal band prescribed by the power system operator (PSO). However, with the massive arrival of wind farms with conventional control schemes, the operation of maintaining and restoring the frequency to the regulatory regimes remains very complicated. In order to overcome the above problem, this paper proposes a new strategy for primary frequency control in power grids using model predictive control (MPC) for a multi-cluster doubly-fed induction generator (DFIG)-based wind farm (WF), with a main objective of reducing the frequency nadir (FN), eliminating the second frequency dip (SFD), and providing the optimal support during wind speed variations. In this approach, a rolling prediction and optimization control strategy is developed based on the dynamic power system model to ideally predict the additional power to be provided. Moreover, in order to avoid second frequency dips, the wind turbines (WTs) are not allocated to extract additional power from the grid during the frequency event, the rotor speeds are not recovered to the maximum power point tracking (MPPT) operating points during the primary frequency control. The performance of the proposed controller was evaluated using a two-zone electrical system in MATLAB/Simulink®. The obtained results disclose that the frequency nadir is enhanced with more than 6.1% compared to the conventional schemes. In addition, the frequency response settling time has been improved with more than 10.51 s.

The oblique flow compressor is one of the important components in the compressed air energy storage (CAES) system. The structural shape of the radial inlet chamber (RIC) directly affects the compressor performance, and a reasonable RIC design should achieve the smallest total pressure loss and outlet distortion as much as possible to meet the structural design. To study the influence of splitter blades, 4 RICs equipped with different numbers of splitter blades are designed, and the performance of 4 RICs and the overall performance of the compressor is calculated. The results show that with the increase in the number of splitter blades, the stall margin increases from 6.3% to 13.94%. At the design point, the isentropic efficiency is highest for the RIC with 17 splitter blades, and the pressure ratio is highest for the RIC with 11 splitter blades. Compared with the direct axial intake mode, the uniformity of the relative leakage distribution and the attack angle distribution of the impeller leading edge under 4 radial intake modes are poor. However, with an increase in the number of splitter blades, the uniformity of the relative tip leakage and the attack angle distribution gradually increase. The flow loss of RIC will increase simultaneously, though the uniformity of the outlet aerodynamic parameters distribution improves, and the influence on the downstream component performance gradually weakens. There is an optimal number of splitter blades in RIC, which balances the total pressure loss and distortion coefficient.

As the siting of wind turbines increasingly transitions from shallow water to offshore deep-water locations, improving the platform stability of floating offshore wind turbines is becoming a growing concern. By coupling a porous shell commonly used in traditional marine structures, with a FOWT (floating wind turbine platform), a new spar-buoy with a porous shell was designed. A numerical model investigating the coupling effect of the aero-hydro-mooring system is developed, and the results of the motion response are compared with the OC3-Hywind spar. The motion response of the two platforms was simulated in the time-domain with the incident wave period varied in the range of 5~22 s. The exciting wave force with added mass and radiation damping of the spar with the porous shell is compared with the OC3-Hywind spar. The results demonstrate that the motion response amplitude of the spar with the porous shell decreases in all three main motion freedoms (i.e., surge, heave and pitch, etc.), among which the heave motions are most significantly attenuated. The study shows that the coupling of porous shells with a floating platform to achieve the reduced motion responses is feasible and can be an innovative structure for the development of deep-sea offshore floating wind turbines.

Sustainable development in coal mining requires a continuous and efficient method of coal extraction. Research shows that gob-side entries retained through roof cutting retained gob-side (RCGE) are vital for improving mining efficiency, enhancing coal recovery rates, and enabling continuous production. However, the mechanism of surrounding rock deformation during close-distance co-mining of coal seams with this technique is not yet clear. For the Jiaokou coal mine in China, due to an unreasonable stagger distance between upper and lower working faces, the gob-side entries retained at the 9102 tailgate and 10102 headgate experience severe rock pressure, leading to significant prop damage and a sharp reduction in the cross-section of the entry. This greatly hampers the reuse of these entries. To investigate this issue, we established a model to study the stress distribution of surrounding rocks at different stagger distances (20 m, 40 m, 60 m, 80 m, and 120 m) through numerical simulation and optimized the support parameters for the retained entries. Our research found that when the subsidence of the roof in the upper coal seam exceeds 0.74 m but is less than 1.33 m, there is sliding instability in the mining body. When the subsidence exceeds 1.33 m, the mining body will rotate and deform, causing significant mining pressure within the retained entry. A stagger distance of 40 m between the upper and lower working faces can reduce pressure on the face during the mining of the lower coal seam. Extensive field measurements of rock pressure revealed that the damage rate of the single column in the gob-side entries of the upper and lower coal seams does not exceed 5% and 1%, respectively. In summary, this study provides a practical method to reduce damage to entries during the mining process, thereby increasing the continuous production capability of the coal mine. This is critical for the sustainable development of coal mining.

This paper analyses the influence of the type of electrode in a spark plug on exhaust gas emission. The objects of the research were the following vehicles of different years of production: the Volkswagen Beetle 1300, the Honda Nighthawk 650, the BMW e46 318i, the Hyundai i10, and the Audi A4 B6. The vehicles were powered by petrol and LPG. Spark plugs were selected for the vehicles, with different kinds of construction for the main electrodes and different numbers of poles but with similar heat values. A comparative analysis of the composition of the exhaust gas mixture was performed, depending on the set of spark plugs used. The amount of CO, HC, CO2, and O2 emissions was analysed. The results were compared with the applicable exhaust gas emission standards. Both in the case of E5 95 petrol and LPG gas, lower exhaust gas emissions were observed when iridium spark plugs were used.

This review explores the performance and reliability of power semiconductor devices required to enable the electrification of heavy goods vehicles (HGVs). HGV electrification can be implemented using (i) batteries charged with ultra-rapid DC charging (350 kW and above); (ii) road electrification with overhead catenaries supplying power through a pantograph to the HGV powertrain; (iii) hydrogen supplying power to the powertrain through a fuel cell; (iv) any combination of the first three technologies. At the heart of the HGV powertrain is the power converter implemented through power semiconductor devices. Given that the HGV powertrain is rated typically between 500 kW and 1 MW, power devices with voltage ratings between 650 V and 1200 V are required for the off-board/on-board charger’s rectifier and DC-DC converter as well as the powertrain DC-AC traction inverter. The power devices available for HGV electrification at 650 V and 1.2 kV levels are SiC planar MOSFETs, SiC Trench MOSFETs, silicon super-junction MOSFETs, SiC Cascode JFETs, GaN HEMTs, GaN Cascode HEMTs and silicon IGBTs. The MOSFETs can be implemented with anti-parallel SiC Schottky diodes or can rely on their body diodes for third quadrant operation. This review examines the various power semiconductor technologies in terms of losses, electrothermal ruggedness under short circuits, avalanche ruggedness, body diode and conduction performance.

Demand response is an effective way to alleviate the pressure on power systems and improve energy utilisation efficiency. This study constructs a tripartite evolutionary game model on government, power companies and virtual power plants (VPPs), and analyses the dynamic behavioural selection mechanism of the three parties under demand-response mode. The results show that: (1) government guidance and management are effective means to promote the stability and equilibrium of the power system; (2) an increase in government subsidy, a reduction in the demand-response cost and an increase in opportunity cost will increase the enthusiasm for changes in demand-response behaviour in power companies; (3) government subsidies will improve the demand-response behaviour of VPPs. This study effectively provides theoretical support for the demand response of power systems, and realises the goal of power energy saving through the optimal choice of behaviour strategies for all parties in the power system.

No one denies the importance of renewable energy sources in modern power systems in terms of sustainability and environmental conservation. However, due to their reliance on environmental change, they are unreliable systems. This paper uses a Unified Power Flow Controller (UPFC) to enhance the reliability and performance of grid-tied renewable energy systems. This system consists of two renewable sources, namely photovoltaic cells (PV) and wind turbines (WTs). The UPFC was selected for its unique advantage in both active and reactive power control. The UPFC is controlled with an optimized Fractional Order Proportional–Integral–Derivative (FOPID) controller. The parameters of this controller were tuned using an Atomic Search Optimization (ASO) algorithm. Simulation results confirm the efficiency of the suggested controller in supporting the reliability and performance of the hybrid power system during some disturbance events including voltage sag, swell, and unbalanced loading. In addition, power quality can be improved through reducing the total harmonic distortion. It is worth mentioning that two maximum point tracking techniques had been included for the PV and WT systems separately. MATLAB/SIMULINK 2021a software was used to model the system.

Frequency response analysis (FRA) is being employed worldwide as one of the main methods for the internal condition assessment of transformers due to its capability of detecting mechanical changes. Nonetheless, the objective interpretation of FRA measurements is still a challenge for the industry. This is mainly attributable to the lack of complete data from the same or similar units. A large database of FRA measurements can contribute to improving classification algorithms and lead to a more objective interpretation. Due to their destructive nature, mechanical deformations cannot be performed on real transformers to collect data from different scenarios. The use of simulation and laboratory transformer models is necessary. This research contribution is based on a new method using Finite Element Method simulation and a lumped element circuit to obtain FRA traces from a laboratory model at healthy and faulty states, along with an optimization method to improve capacitive parameters from estimated values. The results show that measured and simulated FRA traces are in good agreement. Furthermore, the faulty FRA traces were analyzed to obtain the characterization of faults based on the variation of the lumped element’s parameters. This supports the use of the proposed method in the generation of faulty frequency response traces and its further use in classifying and localizing faults in the transformer windings. The proposed approach is therefore tailored for generating a larger and unique database of FRA traces with industrial importance and academic significance.

Energy demand for active mechanical space cooling is projected to double by 2050. Wider adoption of passive cooling systems can help reduce demand. However, familiarity with these systems remains low, and innovation in the field is constrained due to a lack of cost-effective, accessible performance evaluation methods. This paper reports the design, construction, and commissioning of an affordable, self-contained environmental test chamber. The novel chamber replicates a range of outdoor conditions common in hot, dry regions, making possible year-round testing of reduced-scale prototypes. Data from calibration testing are reported, showing no significant difference in evaporative efficiency when a reduced-scale prototype tested in the chamber is compared with datasets from prior full-scale testing. Analyzing the results using an independent sample two-tailed t-test with a 95% confidence interval found a p-value of 0.75. While measured outlet air velocities for reduced-scale and full-scale prototypes differed to some extent (root mean square error of 0.45 m/s), results were nevertheless deemed comparable due to errors introduced by the rapid change in wind speeds and directions at full scale. Future chamber modifications will correct misalignments between data collected from the two scales and prevent observed increases in the chamber’s relative humidity levels during testing.

The widespread electrification of public transportation is increasing and is a powerful way to reduce greenhouse gas (GHG) emissions. Using real-world driving data is crucial for vehicle design and efficient fleet operation. Although electric powertrains are significantly superior to conventional combustion engines in many aspects, such as efficiency, dynamics, noise or pollution and maintenance, there are several factors that still hinder the widespread penetration of e-mobility. One of the most critical points is the high costs—especially of battery electric buses (BEB) due to expensive energy storage systems. Uncertainty about energy demand in the target scenario leads to conservative design, inefficient operation and high costs. This paper is based on a real case study in the city of Seville and presents a methodology to support the transformation of public transportation systems. We investigate large real-world fleet measurement data and introduce and analyze a second-stage feature space to finally predict the vehicles’ energy demand using statistical algorithms. Achieving a prediction accuracy of more than 85%, this simple approach is a proper tool for manufacturers and fleet operators to provide tailored mobility solutions and thus affordable and sustainable public transportation.

Data Availability Statement: Data available on request due to privacy Copyright © 2023 by the authors. Over the past few years, fuel prices have increased dramatically, and emissions regulations have become stricter in maritime applications. In order to take these factors into consideration, improvements in fuel consumption have become a mandatory factor and a main task of research and development departments in this area. Internal combustion engines (ICEs) can exploit only about 15–40% of chemical energy to produce work effectively, while most of the fuel energy is wasted through exhaust gases and coolant. Although there is a significant amount of wasted energy in thermal processes, the quality of that energy is low owing to its low temperature and provides limited potential for power generation consequently. Waste heat recovery (WHR) systems take advantage of the available waste heat for producing power by utilizing heat energy lost to the surroundings at no additional fuel costs. Among all available waste heat sources in the engine, exhaust gas is the most potent candidate for WHR due to its high level of exergy. Regarding WHR technologies, the well-known Rankine cycles are considered the most promising candidate for improving ICE thermal efficiency. This study is carried out for a six-cylinder marine diesel engine model operating with a WHR organic Rankine cycle (ORC) model that utilizes engine exhaust energy as input. Using expander inlet conditions in the ORC model, preliminary turbine design characteristics are calculated. For this mean-line model, a MATLAB code has been developed. In off-design expander analysis, performance maps are created for different speed and pressure ratios. Results are produced by integrating the polynomial correlations between all of these parameters into the ORC model. ORC efficiency varies in design and off-design conditions which are due to changes in expander input conditions and, consequently, net power output. In this study, ORC efficiency varies from a minimum of 6% to a maximum of 12.7%. ORC efficiency performance is also affected by certain variables such as the coolant flow rate, heat exchanger’s performance etc. It is calculated that with the increase of coolant flow rate, ORC efficiency increases due to the higher turbine work output that is made possible, and the condensing pressure decreases. It is calculated that ORC can improve engine Brake Specific Fuel Consumption (BSFC) from a minimum of 2.9% to a maximum of 5.1%, corresponding to different engine operating points. Thus, decreasing overall fuel consumption shows a positive effect on engine performance. It can also increase engine power output by up to 5.42% if so required for applications where this may be deemed necessary and where an appropriate mechanical connection is made between the engine shaft and the expander shaft. The ORC analysis uses a bespoke expander design methodology and couples it to an ORC design architecture method to provide an important methodology for high-efficiency marine diesel engine systems that can extend well beyond the marine sector and into the broader ORC WHR field and are applicable to many industries (as detailed in the Introduction section of this paper). This research received no external funding.

Increasing levels of distributed generation (DG), as well as changes in electricity consumption behavior, are reshaping power distribution systems. These changes might place particular stress on the secondary low-voltage (LV) distribution systems not originally designed for bi-directional power flows. Voltage violations, reverse power flow, and congestion are the main arising concerns for distribution system operators (DSOs), while observability in these grids is typically nonexistent or very low. The present paper addresses this issue by developing a method for nodal voltage estimation in unbalanced radial LV grids (at 0.4 kV). The workflow of the proposed method combines a data-driven grey-box modeling approach with generalized additive models (GAMs). Furthermore, the proposed method relies on experimental data from a real-world LV grid in Denmark and uses data input from only one measuring device per feeder. Predictions are evaluated by using a test data set of 31 days, which is more than twice the size of the training data set of 13 days. The prediction results show high accuracy at root mean squared errors (RMSEs) of 0.002–0.0004 p.u. The method also requires a short computation time (14 s for the first stage and 2 s for the second stage) that meets requirements for the practical, real-time monitoring of DSO grids.

Silicon solar cells featuring tunnel oxide passivated contacts (TOPCon) benefit from high efficiencies and low production costs and are on the verge of emerging as the new photovoltaic market mainstream technology. Their association with Perovskite cells in 2-terminal tandem devices enables efficiency breakthroughs while maintaining low fabrication costs. However, it requires the design of a highly specific interface to ensure both optical and electrical continuities between subcells. Here, we evaluated the potential of tunnel diodes as an alternative to ITO thin films, the reference for such applications. The PECV deposition of an nc-Si (n+) layer on top of a boron-doped poly-Si/SiOx passivated contact forms a diode with high doping levels (>2 × 1020 carrier·cm−3) and a sharp junction (

An improved fractional-order sliding mode control (FOSMC) for PMSM is presented in this study to set the unavoidable parameters and to improve permanent magnet synchronous motors (PMSMs) drive performance, such as current and speed tracking accuracy. To determine the optimal parameters of the FOSMC for control speed in a PMSM drive, a neural network algorithm with reinforcement learning (RLNNA) is proposed. The FOSMC parameters are set by the ANN algorithm and then adapted through reinforcement learning to enhance the results. The proposed controller using RLNNA based on fractional-order sliding mode control (RLNNA-FOSMC) can drive the motor speed to achieve the referred value in a finite period of time, leading to faster convergence and improved tracking accuracy. For a fair comparison and evaluation, the proposed RLNNA-FOSMC is compared with conventional FOSMC by applying the integral of time multiplied absolute error as an objective function. The most commonly used objective functions in the literature were also compared, including the integral time multiplied square error, integral square error, and integral absolute error. To validate the performance of the RLNNA-FOSMC speed controller, different scenarios with different speeds steps were carried out. The computational results are promising and demonstrate the effectiveness of the proposed controller. Overall, the proposed RLNNA-FOSMC controller for the PMSM speed control system performed better than conventional FOSMC in numerical simulations.

Two prototypes with energy efficiency calculations have been developed to enable real-time efficiency assessment and data collection. The results of the experiment demonstrate that the use of microprocessor technology and the Internet of Things can significantly improve the efficiency and accuracy of energy audits in power transformers. The prototypes developed in this study provide real-time efficiency assessment and data collection, enabling more effective energy management and cost savings for industrial users. During the experiment, it was found that resonance can cause the same losses as a poor power factor of the system, highlighting the importance of addressing energy quality issues in addition to energy efficiency. These findings have important implications for energy efficiency policies and practices in the context of climate change mitigation and rising energy prices.

Sustainability of energy supply has become a prime concern for energy producers and consumers alike. There is heightened awareness in the global community about the decreasing supply of conventional fossil fuels along with increasing fuel and energy demand and the consequent rise in unit energy cost. In addition to the sustainability aspect, the environmental impact of emissions from fossil fuel combustion is the focus of global targets for emissions reduction. In this context, the research and application of sustainable and non-polluting fuels become significant. Internal combustion (IC) engines are part of a significant energy-consuming sector, and the application of sustainable and non-polluting fuels within IC engines would be impactful. Biogas and hydrogen are viewed as sustainable and non-polluting alternatives to conventional fossil fuels. However, either of these used individually offer certain disadvantages. Experimental results and analysis of the performance and emissions characteristics of an IC engine fueled with biogas blended with 5, 10, and 15% hydrogen volume fractions are studied. An increase in hydrogen content increases the engine’s performance and power and reduces carbon monoxide (CO) and total hydrocarbons (THCs). However, nitrogen oxides (NOx) are found to increase due to higher combustion temperatures attributed to hydrogen. A 17.5% increase in brake power is observed for 15% hydrogen-enriched biogas, compared to plain biogas, at an equivalence ratio of 0.6. Similarly, a 17% increase in BTE, a 50% decrease in CO, a 68% decrease in UHC, but a 71% increase in NOx are observed for 15% hydrogen-enriched biogas.

NaOH, dolomite and NiCl2 were used as catalysts to examine their effects on co–pyrolysis with waste bicycle tires (WT) and waste engine oil (WEO). The pyrolysis behaviors with catalysts were investigated by thermogravimetric analysis. The activation energy of the catalytic main reaction stage was derived by the Kissinger–Akahira–Sunose (KAS) method under four different heating rates conditions. The calculations show that all three catalysts can reduce the activation energy of the reaction. Co–pyrolysis of WT and WEO with different catalysts was performed in a self–made lab bench at 600 °C to explore the impact on the distribution of three–phase products. The properties of gas and oil products were characterized by FTIR and Py–GC/MS (Agilent 7890B, Santa Clara, CA, USA). With the mixing of catalysts, activation energy (Eα) decreased by 15–30% in the main reaction process. NaOH and dolomite increased the yield of gas by 7% and 10%. NaOH can significantly improve the yield of CH4. The proportion of limonene in pyrolysis oil increased to 19.65% with 10% NaOH. This article provides a new method for efficiently producing limonene by mixing WT and WEO with NaOH.

This study explored the consequences of allocating commitments to remove CO2 to countries according to their responsibility for human-made climate change based on historical (cumulative) CO2 emissions from fossil fuel use and industry. The ‘carbon debt’ to be restored through CO2 removal was calculated as the remaining carbon budget for warming by 2 °C minus emissions until 2100. The study included the remaining carbon budget from the recent literature and scenarios for greenhouse gas emissions. This experiment showed that industrialized countries would need to take on the biggest share of CO2 removal if the calculation of historical emissions starts with the industrial era. If accounting instead starts with the global negotiations on climate policy in 1990, however, developing countries would have to take on the largest commitment for CO2 removal. Given this scheme and with the aim of settling the carbon debt over two decades with equal annual efforts, the eight countries with the largest shares of historical emissions would have to take on annual CO2 removal efforts from 1 to 12 Gt CO2. These CO2 removal commitments would imply substantial efforts for many countries but nevertheless depend on the choice of a fairness principle and calculation method to render this operational.

The development of efficient and compact reactors is an urgent need in the field of distributed hydrogen production. Steam reforming of methane is the main method to produce hydrogen. Aiming at the problems of high heat and mass transfer resistance of the existing fixed bed reactors, and the difficulty of replacing the wall-coated catalyst in the microreactors, a composite packed bed was proposed to meet the demand of small-scale hydrogen production. The structure consists of a multi-channel framework with high thermal conductivity, which is filled with Ni/Al2O3 catalyst particles in each channel. A three-dimensional numerical model of the steam methane reforming process in the novel reactor was established using ANSYS FLUENT software. The heat transfer and reaction characteristics in the reactor were studied. Firstly, the advantages of the multi-channel skeleton in enhancing the radial heat transfer performance were verified by comparing it with the traditional randomly packed bed without the channel skeleton. Secondly, the influences of inlet velocity, inlet temperature, and heating wall temperature on the heat transfer and reaction performances in the reactor were studied, and a sensitivity factor was adopted to do the sensitivity analysis. The results show that the methane conversion rate is most sensitive to the wall temperature, while the inlet velocity and inlet temperature have less effect. Finally, the effects of two skeleton materials were studied. The results show that when the wall temperature is higher than 1200 K, there is no significant difference between these two reactors, which indicates that the use of cordierite with a lower price, but also with a lower thermal conductivity can significantly reduce the reactor’s cost. The conclusions can be used as a reference for the design of small-scale hydrogen production reactors.

In microgrids (MGs), energy management systems (EMSs) have been using increasingly detailed models of generation units, loads, and networks to make decisions on the power/energy contribution of each available unit to meet the electrical energy demand. This work aims to investigate the use of digital twins (DT) of small productive processes (SPPs) to regulate endogenous process variables to ensure final product quality, while the expected power consumption is estimated and communicated to the EMS so that it can make its decisions on the participation of each power source in meeting the electrical energy demand. The literature review reveals that this is one of the first attempts, in the context of MGs, to generate DT for SPPs that combine not only the electrical energy consumption, but also link it with the energy/mass balances taking place in the SPPs, highlighting the complexity that SPPs have as electrical loads. The results demonstrate that environmental conditions significantly influence the final electrical consumption of the SPPs. Additionally, the MG exhibits better economic performance when the SPP DT supports EMS decision-making, which is of great importance in MGs due to the special conditions they have for electric power generation, being more challenging in isolated MGs.

The mineralization of carbon dioxide offers a way to permanently sequester carbon while producing construction materials, combining the concepts of carbon capture and utilization (CCU) and carbon capture and storage (CSS). However, it is important to evaluate different mineralization processes in terms of their environmental impact. This study provides the first comparative life cycle assessment (LCA) analysis that focuses on the utilization of industrial waste materials. We analyzed the climate and material footprint of six mineralization pathways from cradle to gate using steel slag, concrete waste, municipal solid waste incineration (MSWI) ash, and olivine as feedstock. A sensitivity analysis was used to identify the factors with the greatest impact on environmental performance. Our results show that all processes generate significantly negative values for the global warming impact (GWI) and raw material input (RMI), ranging from −0.6 to −1.3 kgCO2eq.kgfeed−1 and −0.6 to −1.6 kgkgfeed−1, when cement substitute is considered as product. Five out of six processes produce negative values for these factors when sand is considered as a product. When operated as a CCS technology without product use, the processes result in GWI values ranging from −0.13 to 0.01 kgCO2eq.kgfeed−1. Our study confirms that industrial mineralization is a promising technology for reducing carbon dioxide emissions. Future process development should focus on replacing carbon dioxide-intensive products while balancing energy and chemical demand with process efficiency.

Recent years have seen the development of cutting-edge technology, such as offshore wind turbines and wave energy converters. It has previously been investigated whether integrating offshore wind turbines with wave energy converters is feasible. Diversifying the sources of offshore renewable energy also lowers investment costs and power fluctuation. This paper focuses on the development of a hybrid wind–wave energy system as well as the development of a techno-economic model to assess the system performance for a case study. A levelized cost of energy is calculated for the hybrid system by the Norwegian North Sea based on current knowledge about the technology costs. The economic benefits of sharing the common components of a wind-wave hybrid farm are inspected. Combinations of different wind–wave offshore hybrid systems are presented. Three technologies for both offshore wind turbines and wave energy converters are compared to find the most cost-efficient device pairing. The potential benefits of a shared infrastructure and the operational expenses are included in the evaluation. The combination yielding the lowest production cost of the cases studied is a combination of 160 MW of wind power and 40 MW of wave power, with a levelized cost of energy of EUR 107/MWh when the shared costs are 15%. In the study region, the average electricity price in Autumn 2022 was over EUR 300/MWh due to the European energy crisis.

The deformation and failure of surrounding rock mass under different water environments is a basic mechanical problem encountered in the safe operation of ground pumped storage power station and abandoned mine pumped storage power station. According to the influence of different water environments on the failure characteristics of deep surrounding rock mass, it is necessary to summarize the damage evolution law of deep rock mass under different water environments and construct the constitutive model. In this paper, the loading mechanical test is carried out after the natural immersion of the rock in different water environments. The influence of the change of the geological water environment on the damage evolution characteristics of the rock is analyzed from the perspective of the deterioration of the mechanical parameters. On this basis, the damage statistical constitutive model is constructed, and the damage evolution analysis is carried out. The results show that the degradation degree of mechanical parameters such as compressive strength and elastic modulus of sandstone is in the order of distilled water immersion, simulated groundwater immersion and natural state. The damage evolution of sandstone under water–rock interaction is divided into four stages: no damage, rapid damage, deceleration damage and failure. The theoretical curve of the model is in good agreement with the uniaxial test curve of rock under different water environments. The segmented damage constitutive model based on the long compaction stage of sandstone under water–rock interaction reasonably reflects the change of stress–strain relationship of damage failure, and the physical meaning of parameters is clear.

Renewable energy sources play a key role in the transition towards clean and affordable energy. However, grid integration of renewable energy sources faces many challenges due to its intermittent nature. The controllability of aggregated regenerative electric heating load provides a method for the consumption of renewable energy sources. Based on the concept of a virtual power plant (VPP), this paper considers the cooperative energy management of aggregated residential regenerative electric heating systems. First, considering physical constraints, network constraints, and user comfort, comprehensive modeling of a VPP is given to maximize its social benefits. In addition, this VPP is investigated as a participant in day-ahead energy and reserve markets. Then, to solve this problem, a distributed coordination approach based on an alternating direction method of multipliers (ADMM) is proposed, which can respect the independence of users and preserve their privacy. Finally, the simulation results illustrate the effectiveness of our algorithm.

This study aimed to investigate the potential of supercritical carbon dioxide (sCO2), organic Rankine cycle (ORC), and thermoelectric generator (TEG) systems for application in automotive exhaust waste heat recovery (WHR) applications. More specifically, this paper focuses on heavy-duty diesel engines applications such as marine, trucks, and locomotives. The results of the simulations show that sCO2 systems are capable of recovering the highest amount of power from exhaust gases, followed by ORC systems. The sCO2 system recovered 19.5 kW at the point of maximum brake power and 10.1 kW at the point of maximum torque. Similarly, the ORC system recovered 14.7 kW at the point of maximum brake power and 7.9 kW at the point of maximum torque. Furthermore, at a point of low power and torque, the sCO2 system recovered 4.2 kW of power and the ORC system recovered 3.3 kW. The TEG system produced significantly less power (533 W at maximum brake power, 126 W at maximum torque, and 7 W at low power and torque) at all three points of interest due to the low system efficiency in comparison to sCO2 and ORC systems. From the results, it can be concluded that sCO2 and ORC systems have the biggest potential impact in exhaust WHR applications provided the availability of heat and that their level of complexity does not become prohibitive.

Current rice straw disposal practices have serious repercussions on the environment and, in addition, do not consider its energy potential. On the contrary, the anaerobic digestion of rice straw makes it possible to produce renewable energy and to reintroduce into the soil the nutrients present in the digestate, at the same time, reducing greenhouse gas emissions from paddies. For rice straw of different geographical origin, by applying a mass balance method to the digester, the minimum requirements in terms of conditioners (nitrogen, phosphorus and potassium) and water, which allow obtaining the maximum production of methane, were calculated. The results obtained show that after the first 30 days (hydraulic retention time) for each ton of rice straw digested, the daily water consumption varies considerably from one country to another, from a minimum value of 1.5 m3/d to a maximum of 4.3 m3/d. After the same time, the addition of nitrogen and phosphorus is only required for the optimal anaerobic digestion of Indian rice straw. The low presence of these elements in Indian straw requires an addition of 3 kg/d of urea and 1.5 kg/d of superphosphate to compensate for the lack of nitrogen and phosphorus, respectively. In all the examined cases, the concentration of potassium, even if higher than the optimal value, does not reach levels that can significantly affect the methane production.

Livestock in the state of Iowa, United States (US) produce over 50 × 106 Mg of wet-basis manure yearly. Biogas production from manure’s anaerobic digestion (AD) can reduce greenhouse gas emissions, control odors, and provide renewable energy. Despite these benefits, AD is rarely deployed at swine farms in Iowa. In this work, we explore the economics of AD systems in Iowa to evaluate reasons for low deployment and explore the production cost impacts of biogas cleaning and injection into the natural gas grid, amending manure with biomass, and centralizing digesters across multiple farms. This work presents a static, spreadsheet-based technoeconomic model that embodies literature-based estimates of key system technical parameters, costs, and transportation fuel incentives and permits the examination of various scenarios. Key findings include that under the model assumptions, distributed, farm-scale digesters are not competitive with average natural gas prices in Iowa. A centralized production scenario can be competitive, provided that programs such as the low-carbon fuel standard (LCFS) and the renewable fuel standard (RFS) have sufficiently high credit values.

The Silurian strata in the Shunbei No. 5 fault zone have the characteristics of long open holes, easy leakage and complex leakage. In the early stages, plugging technologies and methods such as bridging plugging, cement, chemical consolidation and high-water-loss plugging have poor effects and low plugging efficiency. Plugging slurry directly prepared with drilling fluid has low filtration characteristics, and the main reason is that the plugging material cannot filter quickly after the fluid enters the fracture. Based on the basic principle of fast filtration, the main plugging fluid M-Fluid, the micro-elastic high-strength main plugging agent M-Block and the filling agent Filling-Seal have been developed. In combination with the water-loss and wall-building properties of the circulating drilling fluid after plugging, a fast plugging technology for fractured volcanic rock formation has been established. The laboratory evaluation experiment showed that the filtration rate increased rapidly with the increase of temperature, and the filtration rate was about 0.31~0.79 mL/s, while the filtration rate of the drilling fluid was 0.0067 mL/s under the same conditions. The pressure-bearing capacity of various plugging evaluation methods, such as the simulated fracture of a large-grain sand bed, artificial fracture of small core and full-size core and multi-form fracture of double core, all exceed 5 MPa, and the system has a good plugging effect for complex fractures.

The efficiency of Ni/TiO2 catalysts for renewable diesel production was evaluated in the present study. Two series of catalysts were synthesized and characterized using various physicochemical techniques (N2 physisorption, XRD, SEM, XPS, H2-TPR, and NH3–TPD). In the first series of catalysts, successive dry impregnations (SDI) were used for depositing 10, 20, 30, 50, and 60 wt.% Ni. The yield towards renewable diesel is maximized over the catalyst with 50 wt.% Ni loading. Selecting this optimum loading, a second series of catalysts were synthesized via three additional preparation methods: wet impregnation (WI) and deposition–precipitation using either ammonia (DP-NH3) or urea (DP-Urea) as the precipitation agent. The catalysts’ efficiency in the production of green diesel is influenced by the preparation method following the order: DP-Urea > DP-NH3 > WI ≈ SDI. The metallic surface area and the balanced acidity mainly determine the performance of the catalysts.

Power distribution systems (PDS) are the infrastructure and equipment used to distribute electricity from the transmission system to end-users, such as homes and businesses. PDS are usually designed to operate in a radial mode, where power flows from one substation to the end user through a series of feeders. The extension of distribution lines to attend new customers along with the growing demand for electricity result in increased energy losses and voltage reductions. Various solutions have been proposed to solve these issues, such as selecting the optimal set of conductors, optimizing the placement of voltage regulators, using capacitor banks, reconfiguring the distribution system, and implementing distributed generation. A well-known approach for reducing energy losses and enhancing voltage profile is the optimal conductor selection (OCS). While this can be beneficial, it may not be sufficient to fully reduce technical losses and improve the system voltage profile; therefore, it must be combined with other strategies. This paper presents a new approach that combines the OCS with the optimal placement of capacitor banks (OPCB) to minimize technical losses and improve the voltage profile in PDS. The main contribution of this paper is the integration of these two problems into a single mixed integer linear programming (MILP) model, therefore guaranteeing the achievement of globally optimal solutions. Three test systems of 27, 69, and 85 buses were used to illustrate the effectiveness of the proposed modeling approach. The results indicate that the combination of OCS and OPCB effectively minimizes energy losses and enhances the voltage profile. In all cases, the solutions obtained by the proposed MILP approach were better than those previously reported through metaheuristics for the combined OCS and OPCB problem.