Energy Output Research Articles

Experimental Implementation of Reinforcement Learning Applied to Maximise Energy from a Wave Energy Converter

Wave energy has the potential to provide a sustainable solution for global energy demands, particularly in coastal regions. This study explores the use of reinforcement learning (RL), specifically the Q-learning algorithm, to optimise the energy extraction capabilities of a wave energy converter (WEC) using a single-body point absorber with resistive control. Experimental validation demonstrated that Q-learning effectively optimises the power take-off (PTO) damping coefficient, leading to an energy output that closely aligns with theoretical predictions. The stability observed after approximately 40 episodes highlights the capability of Q-learning for real-time optimisation, even under irregular wave conditions. The results also showed an improvement in efficiency of 12% for the theoretical case and 11.3% for the experimental case from the initial to the optimised state, underscoring the effectiveness of the RL strategy. The simplicity of the resistive control strategy makes it a viable solution for practical engineering applications, reducing the complexity and cost of deployment. This study provides a significant step towards bridging the gap between the theoretical modelling and experimental implementation of RL-based WEC systems, contributing to the advancement of sustainable ocean energy technologies.

Green Hydrogen Synthesis from Human Urine as Sustainable Bioenergy Resources

With the earths cry for help as pollution rate increases, researchers are faced with a common task of tackling pollution resulting from dependence on fossil fuel as well as delivering sustainable energy. Renewable energy resources such as solar, wind, hydro to mention a few are currently finding applications within the world energy mix but some limitations which range from meteorology of locations to expected maximum energy output attainable. Hydrogen, the most abundant element in the world stand chance of abating this problem. However conventional method of its producing, poses severe treat to the atmospheric environment with the release of oxides of carbon hence, referred as blue hydrogen. Contrarily, green hydrogen from human urine stands a more sustainable and environmentally friendly energy resource. This study was aimed to empirically model the synthesis of green hydrogen from urea in human urine by an electrolytic process. The synthesized hydrogen was characterized on physiochemical properties of conductivity, turbidity, pH, specific gravity and colour, while the precursor urine characterized on gender, exposure duration and storage temperature. The synthesis process was modelled using Microsoft excel solver for the overall cell energy or polarization curve model, the Faraday’s efficiency model and gas purity model at electrolyte concentrations of 25 wt./wt., 30 wt./wt. and 35 wt./wt. of potassium oxide (buffer} to urea over a five-temperature interval range of 45 to 85 oC. Findings revealed that the gas produced was 99.88% hydrogen at the cathode. Also, hydrogen produced increased with increase in electrolyte concentration and moderate temperature with optimal conditions at 35 w/w electrolyte concentration and 65 oC. However, the minimum cell voltage was 2.06 V at 85 oC and 35 w/w electrolyte concentration. With an exception of the Faraday’s efficiency model at 30 wt./wt. electrolyte concentration across the system’s operating temperature range yielding an R2 value of 0.711, all the models yielded coefficient of determination values in the range of 0.96 and 0.99, indicating good fit for the alkaline urine electrolysis for green hydrogen synthesis from human urine.

Impacts of groundwater flow on borehole heat exchangers: Lessons learned from Estonia

The production of geothermal energy relies on subsurface properties. In low-enthalpy geothermal regimes, thermal energy extracted with a borehole heat exchanger (BHE) is the subsurface heat conducted from the rock and grout to the BHE fluid. Estonian geology provides a good opportunity to investigate how groundwater flow impacts BHE heat production. The hundreds of metres thick sedimentary rock sequence holds four main aquifers. We modelled single BHEs with lengths of 400 m, 500 m and 1000 m and a BHE field of ten 400-m wells at three sites across Estonia. Then, we parametrized different hydraulic conditions to compare how the groundwater flow velocity impacts the thermal energy yield of the systems.Our results indicate that if groundwater flow increases above 0.03 m/day, it increases the total energy yield from 3 % to 20 %. Thermogeological parameters, such as subsurface temperature and thermal conductivity, affect the thermal energy yield of the wells more than the modelled groundwater flow. Our study provides an estimate of the thermal energy yield from different BHE types and the detailed sensitivity analysis. We conclude that geothermal energy is a significant renewable energy resource for Estonia and areas with similar geology and climate.

Performance of Zigzag Photovoltaic Noise Barriers near a Belgian Highway

Herein, the energy performance of photovoltaic noise barriers (PVNBs) with cassette and shingles built‐on designs is evaluated using imec's energy yield framework. The simulation is validated through on‐site electrical and thermal measurements, and then the same design is employed for a case study near E19 road in Belgium using different scenarios. To optimize the energy yield, variations in the noise barrier height, orientation, and PV module tilt are introduced. The energy yield is then simulated to identify the optimal combination of parameters to maximize energy production. The results show that the cassette built‐on PVNB with fixed cassette distance provides higher energy yield throughout the year compared to other scenarios, and a low‐rise noise barrier is more energy‐efficient due to reduced shading effects. Sound pressure simulation conducted in COMSOL reveals that the cassette built‐on and shingles built‐on have comparable performance in sound reduction, and high‐rise noise barriers with small tilts (20°–40°) are optimal for sound pressure attenuation.

Energy-economic-environmental analysis of bifacial photovoltaic thermal (BPVT) solar air collector with jet impingement

Jet impingement cooling enhances photovoltaic (PV) system efficiency by using high-speed fluid jets to reduce panel temperatures, improving performance and longevity. The effectiveness depends on factors like fluid flow rate, nozzle placement, and distance from the panel. While it boosts energy output, it may increase energy use for fluid circulation and add complexity to the system. This research explores a groundbreaking approach to enhancing the efficiency of bifacial photovoltaic thermal (BPVT) systems by integrating jet impingement technology. A novel design featuring a jet plate reflector is introduced, offering the dual benefit of cooling the PV panels while simultaneously reflecting light to optimize energy capture. The study comprehensively analyses the system’s performance, including energy output and a detailed techno-economic and environmental-economic evaluation. The modelling in this study was validated and reasonably consistent with experimental results. The system's output air temperature and thermal efficiency are 302.07–318.75 K and 33.83–62.28 %, respectively. The temperature and electrical efficiency range for PV systems are 304.39–339.54 K and 9.39–11.22 %. Reduced mass flow rate and increased solar irradiation are the most economically advantageous operating parameters for the proposed system, resulting in lower annual pumping costs and more significant annual energy gains for the system. CBR variations range from 0.1363 to 9.3445, with an average of 2. Additionally, by using BPVT with jet impingement to generate electricity rather than fossil fuels, it is possible to reduce annual carbon dioxide emissions by approximately 1.61 tons and save RM93.51 annually. In general, the proposed method should be used to minimize environmental pollution.

Energy budgeting, carbon footprint and economics of sunflower and pigeonpea system under moisture conservation practices in rainfed semi-arid tropics

In drylands, soil water availability in the profile during crop period, especially at critical stages is most vital. System which is productive, energy efficient with a minimum carbon (C) footprint under limited water condition is a concern. Therefore we evaluated sunflower and pigeonpea crops over two-years in terms of productivity, energy, C-footprint and profitability under different moisture conservation measures. These were raised bed, ridge and furrow, tied ridges and furrow, conservation tillage, flatbed sowing and opening furrow after every three rows at 30 days, and flatbed sowing under sole crops of sunflower and pigeonpea and intercropping at 1:1 adopted split-plot design. The results showed in-field moisture conservation by ridge and furrow produced considerably greater sunflower (59.6–66%) and pigeonpea (85.2–128.7%) yields and profitable (604.1 US$ ha−1) over traditional flatbed sowing. It also exhibited greater output energy (37.8–83.0%), energy use efficiency(4.2 kg GJ−1),and lower energy intensity (21.57 × 10−3 GJ US$−1). Sunflower and pigeonpea sole crops produced higher seed yields over intercropping. But it has yield advantage in terms of greater pigeonpea equivalent yield (0.53–1.13 Mg ha−1), profitability (691.8 USD ha−1), output energy (42.4 GJ ha−1) and energy ratio. Among the input energy sources, chemical fertilizers accounted 53.6 % of the total input energy. Interestingly, least C-footprints were recorded under ridge and furrow (182 kg CE kg−1grain)and intercropping (0.248 kg CE kg−1 grain). Results confirmed sunflower and pigeonpea intercropping on ridge and furrow was productive, energy saving, C-footprint reduction and economical practice under rainfed condition.

Thermal Properties of Seed Cake Biomasses and Their Valorisation by Torrefaction

Seed cakes, by-products from the cold press extraction of vegetable oils, are valuable animal feed supplements due to their high content of proteins, carbohydrates, and minerals. However, the presence of anti-nutrients, as well as the rancidification and development of aflatoxins, can impede their intended use, requiring alternative treatment and valorisation methods. Thermal treatment as a procedure for the conversion of seed cakes from walnuts, hemp, pumpkin, flax, and sunflower into valuable products or energy has been investigated in this paper. Thermogravimetry shows the particular behaviour of seed cakes, with several degradation stages at around 230–280 and 340–390 °C, before and after the typical degradation of cellulose. These are related to the volatilisation of fatty acids, which are either free or bonded as triglycerides, and with the thermal degradation of proteins. Torrefaction at 250 °C produced ~75–82 wt% solids, with high calorific values of 24–26 kJ/g and an energy yield above 90%. The liquid products have a complex composition, with most parts of the compounds partitioning between the aqueous phase (strongly dominant) and the oily one (present in traces). The structural components of seed cakes (hemicelluloses, cellulose, and lignin) produce acetic acid, hydroxy ketones, furans, and phenols. In addition to these, most compounds are nitrogen-containing aromatic compounds from the degradation of protein components, which are highly present in seed cakes.

Direct Normal Irradiance Prediction based Optimum Interval Tilt angles for Enhancement of Energy Output, Levelized Cost of Energy and CO2 Emission in a Grid-Connected PV System

Abstract The tilt angle of photovoltaic (PV) panels is a crucial determinant of their performance and can be adjusted using different tracking methods. Periodically changing the tilt angle strikes a practical balance between efficiency and cost. This work introduces a Bi-directional Long Short Term Memory (Bi-LSTM)-based Direct Normal Irradiance (DNI) prediction to estimate the time intervals for the tilt angle adjustments. DNI Prediction involves 22-year (2000–2022) historical time series data and the Bi-LSTM deep learning model to predict DNI at different time frames for the location Madurai, India. Using the Predicted DNI, Tilt angle-based DNI is mapped using the tilt angle correlation through a nearest neighborhood interpolation method. DNI potential over a specific period is utilized to find the optimum time intervals for the tilt angle adjustments. The simulation study of this work is implemented with the design of a 5 kW grid-connected solar PV system in PVSYST software. The effectiveness of the proposed methodology is evaluated based on improvements in power output, Levelized Cost of Energy (LCOE) and carbon emission reductions and compared with other existing methods. The results showed that using the proposed optimal tilt angle intervals led to a 10.31% increase in PV output power, the lowest LCOE at 3.61 c/kWh and 8.363 tCO2/year carbon emissions.

Assessing the Techno-enviro-economic viability of wind farms to address electricity shortages and Foster sustainability in Palestine

This study critically examines the feasibility of wind turbines in addressing electricity shortages in the coastal region of Palestine. Assessing technical, economic, and environmental aspects offers vital insights for sustainable energy solutions. Hence, 10 turbines with different rated powers were selected to determine the optimal and economically viable solution. Wind velocities were assessed at altitudes of 10, 65, and 80 m. Various performance indicators such as wind power density, capacity factor (Cf), anticipated energy yield, cost of wind-generated electricity, benefit-cost ratio (BCR), and simple payback period were examined. The findings indicate that the Siemens SWT-2.3-93 turbine yields an annual energy production of 3910.3 MWh. The Lagerwey-LW58/750 turbine boasts the highest capacity factor at 20.5 % making it the most cost-effective option with generation costs of $0.0604/kWh to $0.0825/kWh. It exhibited the highest benefit-cost ratio, ranging from 1.287 to 1.758, and payback period of 6.33 years. Consequently, the Lagerwey-LW58/750 turbine emerges as the optimal choice for implementing the wind power plant project in Palestine's coastal region. The use of wind turbines to generate electricity leads to 3323.75 ton/year of emissions reduction. This research provides crucial insights for decision-makers aiming to tackle electricity shortages and enhance environmental sustainability in Palestine's energy sector.

Simulation Study on Factors Affecting the Output Voltage of Extended-Range Electric Vehicle Power Batteries

The power battery configuration of an extended-range electric vehicle directly affects the overall performance of the vehicle. Optimization of the output voltage of the power battery can improve the overall power and economy of the vehicle to ensure its safe operation. Factors affecting the output voltage of power batteries under different operating conditions, such as nominal voltage and the number of series and parallel connections of the battery cells, have been studied. This study uses AVL Cruise to establish an overall model of an extended-range electric vehicle to simulate the output voltage characteristics under the different operating conditions of the NEDC (New European Driving Cycle), WLTC (World Light Vehicle Test Cycle) and CLTC (China Light Duty Vehicle Test Cycle). The influence of the output voltage of the power battery under different operating conditions is studied to ensure that the power battery can output energy with high efficiency. The operating conditions have an impact on the output voltage with an idle voltage fluctuation of the operating conditions. The nominal voltage variation and the number of series and parallel connections of the battery cells affect the frequency and time of breakdown.

The Impact of Ambient Weather Conditions and Energy Usage Patterns on the Performance of a Domestic Off-Grid Photovoltaic System

Solar photovoltaic (PV) systems are growing rapidly as a renewable energy source. Evaluating the performance of a PV system based on local weather conditions is crucial for its adoption and deployment. However, the current IEC 61724 standard, used for assessing PV system performance, is limited to grid-connected systems. This standard may not accurately reflect the performance of off-grid PV systems. This study aims to evaluate how ambient weather conditions and energy usage patterns affect the performance of an off-grid PV system. This study uses a 3.8 kWp building-integrated photovoltaic (BIPV) system located at SolarWatt Park, University of Fort Hare, Alice, as a case study. Meteorological and electrical data from August and November are analyzed to assess the winter and summer performance of the BIPV system using the IEC 61724 standard. The BIPV system generated 376.29 kWh in winter and 366.38 kWh in summer, with a total energy consumption of 209.50 kWh in winter and 236.65 kWh in summer. Solar irradiation during winter was 130.18 kWh/m2, while it was 210.24 kWh/m2 during summer. The average daily performance ratio (PR) was 44.01% in winter and 28.94% in summer. The observed decrease in PR during the summer month was attributed to the higher levels of solar irradiance experienced during this time, which outweighs the increased AC energy output. The low-performance ratio does not indicate technical issues but rather a mismatch between the load demand and PV generation. The results of this study highlight the need for a separate method to assess the performance of off-grid PV systems.

Bi-Level Operation Optimization and Performance Analysis for a Distributed Energy System with Energy Network

The increasing energy crisis and environmental problems promote the development of distributed energy systems (DESs) that utilize combined heat and power technology, renewable energy technology, and waste heat recovery technology to meet various load requirements. Although there is existing work and research on a variety of DESs, most previous studies tend to focus on energy production with little consideration of a distributed energy network and its energy loss, which results in large errors in energy-efficiency calculations and performance analyses. In this paper, a new DES model is proposed with full consideration of an energy network and the full use of solar energy, terrestrial heat, and exhaust gases. At the same time, an effective bi-level optimization method is also proposed for the daily operation of the DES in order to improve the system performance and benefits in the energy, the economy, and the environment. Specifically, the co-generation energy station and water heating network in the DES are optimized separately with two different optimization models. The first-level optimization model is used to seek the optimal values of water mass flow rate and water temperature of the water heating network, and the second-level optimization model is built to determine the optimal energy purchasing strategy and the optimal energy outputs of the co-generation energy station. In order to verify the advantages and effectiveness of the proposed system and method, a contrastive simulation study is undertaken to provide a comparison of the global optimization method. Simulation results show that energy loss, energy cost, and energy consumption of the DES using the bi-level optimization operation strategy only account for 22.51%, 33.42%, and 51.31% of the quantities of the global optimization method, respectively. The hourly curves of the optimal operating variables also demonstrate that the proposed bi-level optimization method can improve the operating stability of the DES better than the global optimization method.

Investigating the physical characteristics and antibacterial properties of the cork back from Quercus variabilis

This study presents for the first time results on structure feature, basic physical properties, torrefaction treatment, adsorption property, component content and antimicrobial properties of the cork back from Quercus variabilis. The cork back consisted of numerous white stone cell bundles with a dense and firm texture and irregularly shaped cells exhibiting considerable deformation and wrinkles. The cork back had the average density of 0.37 g/cm3, average hardness of 91.60 HA and the average thickness was 31.44 % of the total cork thickness. The average colorimetric parameters values of L*, a*, and b* of the cork back were 21.92, 2.34, and 6.42, respectively. Increasing torrefaction temperature increased the quality loss of the cork back, decreased energy yield and volatile matter content, and increased ash and fixed carbon content. With the elevation of fixed carbon, the higher calorific value of the cork back also increased. Following high-temperature torrefaction at 300 °C, the higher calorific value of cork back was 24.00 MJ/kg which increased by 30.29 % compared to untreated cork back. The cork back powder had significant adsorption performance for methylene blue and the maximum adsorption capacity could reach 511.61 mg/g. Compared with cork，the cork back had a high content of lignin and polysaccharides, with a suberin content of only 4.07 %. The total extractives content in cork back was 7.49 %. The water extractives of the cork back exhibited the most significant inhibitory effect on Escherichia coli, with the lowest inhibitory concentration recorded at 2 mg/mL. This research offers essential data for the processing and utilization of the cork back, which contributes to enhance the utilizing efficiency of cork resources.

A High-Efficiency Piezoelectric Energy Harvesting and Management Circuit Based on Full-Bridge Rectification

This paper presents a high-efficiency piezoelectric energy harvesting and management circuit utilizing a full-bridge rectifier (FBR) designed for powering wireless sensor nodes. The circuit comprises a rectifier bridge, a fully CMOS-based reference source, and an energy management system. The rectifier bridge uses a PMOS cross-coupled structure to greatly reduce the conduction voltage drop. The CMOS reference source provides the necessary reference voltage and current. The energy management system delivers a stable 1.8 V to the load and controls its operation in intermittent bursts. Fabricated with a 110 nm CMOS process, the circuit occupies an area of 0.6 mm2, and is housed in a QFN32 package. Test results indicate that under 40 Hz frequency and 4 g acceleration vibrations, the chip’s energy extraction power reaches 234 μW, with the load operating every 3 s at a supply voltage of 1.8 V. Thus, this FBR interface circuit efficiently harnesses the energy output from the piezoelectric energy harvester.

Predicting solar energy output with machine learning

Using machine learning, satellite-derived data can be integrated with ground-measured data to generate accurate solar radiation estimates, enabling the implementation of solar technology in developing countries.

The Low-Carbon Path of Active Distribution Networks: A Two-Stage Model from Day-Ahead Reconfiguration to Real-Time Optimization

The integration of renewable energy sources and distributed energy storage systems increasingly complicates the operation of distribution networks, while stringent carbon reduction targets demand low-carbon operational strategies. To address these complexities, this paper introduces a two-stage model for reconfiguring distribution networks and ensuring low-carbon dispatch. Initially, second-order cone programming is employed to minimize losses in the network. Subsequently, the outputs of renewable energy and energy storage systems are optimized using the mantis search algorithm (MSA) to achieve low-carbon dispatch, with the network’s carbon potential as the evaluation metric. The proposed model demonstrates a significant reduction in average active power loss by 34.85%, a decrease in daily carbon emissions by 509.97 kg, and a reduction in carbon emission costs by 17.24%, thereby markedly enhancing the economic and social benefits of grid operations.

Analyzing the effects of shading on power output in curved photovoltaic array on airship

This study investigates the use of photovoltaic (PV) arrays on airships, focusing on challenges due to the curved structure that affects solar radiation distribution. A novel Photo-Thermal-Electric Model (PTEM), developed in MATLAB/Simulink, is introduced to simulate PV array behavior under various conditions, including the impact of solar altitude, laying angle, and airship flight attitude on power generation. PTEM's effectiveness is demonstrated through detailed power output graphs, revealing that radiation gradients significantly affect power generation, often leading to overestimations in the Photo-Thermal Model (PTM) model by 3 %–30 %. Comparative analysis shows that PTEM provides more accurate energy output estimates for individual PV modules, especially in areas with pronounced radiation gradients, differing from PTM estimates by hundreds of watts. These findings offer valuable insights for enhancing PV module lifespan and optimizing energy system designs, contributing to more efficient renewable energy solutions for long-endurance aircraft.

Investigating the performance of water-mounted solar photo-voltaic systems using different simulation tools

Water-mounted solar photovoltaics plants, which include canal-top and floating systems, offer several advantages over traditional ground mounted systems. However, studying their feasibility can be challenging due to limitations in commercial simulation tools. These tools often rely on unrealistic parameters, and studies comparing them have concluded that they may not be suitable for simulating these newer systems. This research addresses the above-mentioned gap by developing improved simulation methods for water–mounted solar plants. The study compared the actual performance of a 1 MW canal-top system and a 732 kW floating system in India with simulations from six different commercial tools. The analysis revealed that PVsyst and SolarGIS were the most accurate in predicting performance for the canal-top system, while PVsol and SolarGIS–Prospect performed best for the floating system. By following comprehensive methodology outlined in this study, minimal Root–Mean–Square error values ranging from 0.74 to 3.33 for the canal-top system and 2.13 to 5.20 for the floating system were achieved for different parameters. These results highlight that accurate performance predictions depend on three factors: the precision of meteorological data, the capabilities of the simulation tool for water-mounted systems, and the tool’s ability to be customized for specific system details. This study helps solar developers and researchers choose the most suitable simulation tools and customize them for feasibility studies. It also helps stakeholders predict the energy output of existing water–mounted systems more accurately, reducing the risk of penalties due to deviations from predicted output.

Research on fault diagnosis for three-phase rectifier device in direct current transmission system based on continuous wavelet transform and vision transformer

Abstract As a core component of photovoltaic power generation systems, the three-phase rectifier device plays a crucial role, and its failure can potentially reduce energy conversion efficiency and output quality. Presently, the performance of fault time-domain signal diagnosis methods based on three-phase rectifier circuits is challenging to enhance. This paper proposes a novel fault detection method for three-phase rectifier devices based on Vision Transformer, referred to as CWT-ViT, to address this issue. This method transforms time-domain fault signals into images through Continuous Wavelet Transform (CWT), subsequently inputting these images into a Vision Transformer model. Relying on its powerful self-attention mechanism and fully connected layers, it realizes the extraction and learning of rectifier device image features. A Simulink simulation model of a three-phase bridge controllable rectifier circuit is established for fault injection to collect fault signals. Fault diagnosis experiments demonstrate that the proposed diagnostic method achieves a prediction accuracy of 98.6%, maintaining a relatively high precision level. In comparison to four excellent classification models currently available: AlexNet, RepVGG, ResNet, and GoogLeNet, the proposed method demonstrates superior diagnostic performance. Additionally, this paper conducts ablation experiments to meticulously analyze the impact of each module in the fault diagnosis process. This research achieves more precise and efficient fault diagnosis in photovoltaic power generation systems, thereby reducing downtime and maintenance costs for actual equipment and enhancing the stability of photovoltaic power generation systems. This research provides an innovative, intelligent solution for the intelligent operations and maintenance of photovoltaic power generation.

Feasibility of Photonuclear Transmutation of Long-Lived Fission Products Extracted from Used Nuclear Fuel

To achieve the goal of net-zero carbon emission in energy production, nuclear power capacity and waste generation are expected to expand significantly in the next few decades. In the condition of continuous fuel recycling, long-lived fission products (LLFPs) are dominant contributors to the disposal impacts of nuclear waste. In this study, six LLFPs, including 99Tc, 129I, 135Cs, 126Sn, 93Zr, and 79Se, were identified as the primary contributors to more than 99% of long-term radiotoxicity of disposed nuclear waste across a wide range of fuel cycle scenarios. To reduce the amounts of LLFPs sent to geological repositories, the nuclear transmutation of LLFPs is being pursued. Specifically, this work systematically assessed the feasibility of transmuting LLFPs via photonuclear reactions. Photon transmutation is physically viable for the identified primary LLFPs except for 99Tc. For the five transmutable LLFPs, the achievable photon transmutation performance without isotopic separation was evaluated based on scoping calculations and consideration of nuclear data uncertainties. Using an extremely intense laser Compton photon source of 1019 γ /s, the effective transmutation half-life can be reduced to a few years. However, the absolute transmutation rates of LLFPs remain below 1 kg/yr. The energy required to power the photon source for transmuting all LLFPs produced in a nuclear reactor exceeds the net energy output of the reactor. Several potential strategies for improving photon transmutation performance were analyzed. None can substantially enhance the performance to make it practical for industrial applications.