Energy Output Research Articles

Machine learning-based energy management and power forecasting in grid-connected microgrids with multiple distributed energy sources

The growing integration of renewable energy sources into grid-connected microgrids has created new challenges in power generation forecasting and energy management. This paper explores the use of advanced machine learning algorithms, specifically Support Vector Regression (SVR), to enhance the efficiency and reliability of these systems. The proposed SVR algorithm leverages comprehensive historical energy production data, detailed weather patterns, and dynamic grid conditions to accurately forecast power generation. Our model demonstrated significantly lower error metrics compared to traditional linear regression models, achieving a Mean Squared Error of 2.002 for solar PV and 3.059 for wind power forecasting. The Mean Absolute Error was reduced to 0.547 for solar PV and 0.825 for wind scenarios, and the Root Mean Squared Error (RMSE) was 1.415 for solar PV and 1.749 for wind power, showcasing the model’s superior accuracy. Enhanced predictive accuracy directly contributes to optimized resource allocation, enabling more precise control of energy generation schedules and reducing the reliance on external power sources. The application of our SVR model resulted in an 8.4% reduction in overall operating costs, highlighting its effectiveness in improving energy management efficiency. Furthermore, the system’s ability to predict fluctuations in energy output allowed for adaptive real-time energy management, reducing grid stress and enhancing system stability. This approach led to a 10% improvement in the balance between supply and demand, a 15% reduction in peak load demand, and a 12% increase in the utilization of renewable energy sources. Our approach enhances grid stability by better balancing supply and demand, mitigating the variability and intermittency of renewable energy sources. These advancements promote a more sustainable integration of renewable energy into the microgrid, contributing to a cleaner, more resilient, and efficient energy infrastructure. The findings of this research provide valuable insights into the development of intelligent energy systems capable of adapting to changing conditions, paving the way for future innovations in energy management. Additionally, this work underscores the potential of machine learning to revolutionize energy management practices by providing more accurate, reliable, and cost-effective solutions for integrating renewable energy into existing grid infrastructures.

Laser-induced surface reconstruction to enhance interfacial bonding reliability of CFRP/titanium hybrid joints

Adhesive bonding is widely used in aeronautical carbon fiber-reinforced polymer (CFRP)/titanium (Ti) alloy hybrid structures to satisfy the demands of high-strength and lightweight. The physicochemical properties of adherend surfaces significantly impact the bonding performance. In this study, laser-induced surface reconstruction (LISR) was designed for the reliable joining of Ti alloy and CFRP. A three-level-three-factor central composite design (CCD) approach-based response surface methodology (RSM) analysis was applied to statistically specify parameter optimization of the laser energy output. Surface morphology and wettability of CFRP ablated were studied. Chemical compositions were determined by X-ray photoelectron spectroscopy (XPS). Tensile-shear tests were performed, and fractured surfaces were analyzed. The tensile shear strength with LISR process has been improved by 19.1%, in comparison with that of mechanical grinding treatment. The cross-like texture based on designed geometric configuration was fabricated without damaging the underlying fibers, thus increasing the surface roughness and contact area at interface. The wettability transition mechanism was investigated, demonstrating that the hydrophilicity of reconstructed surfaces primarily attributes to the more polar groups (C–O, CO, etc.) on CF surfaces. This study provides a reliable surface treatment method for fabricating high-performance composite structures by adhesive bonding.

Research on the Configuration of a 100% Green Electricity Supplied Zero-Carbon Integrated Energy Station

In the context of rapid growth in renewable energy installations and increasingly severe consumption issues, this paper designs a 100% green electricity supplied zero-carbon integrated energy station. It aims to analyze its configuration focusing on the following three core features: zero carbon emissions, 100% green electricity supply, and a centralized–distributed system structure. It discusses equipment selection and provides models for configuring upstream green electricity resources, power generation, energy storage, transformer, and energy conversion. The study examines the synergy between lithium-ion battery storage and modular molten salt thermal storage, along with the virtual energy storage characteristics formed by thermal load inertia, supported by mathematical models. Based on the data from a green electricity system in an Eastern Chinese city and typical load profiles, the paper validates a specific configuration for a 100% green electricity supplied zero-carbon integrated energy station, confirming model accuracy and calculating the required scale of upstream green electricity resources. It proves that establishing an electro-thermal storage synergy system is crucial for addressing the significant fluctuations in renewable energy output. It also argues that leveraging thermal load inertia to create virtual storage can reduce the investment in energy storage system construction.

Highly efficient broadband solar thermal absorber for domestic renewable energy solutions

With the invention of the photonics techniques, a new structural design for thermal energy absorber is designed which uses the multilayer structure to absorb the solar energy and this solar energy is converted to thermal energy which is possible to be utilized in industrial heating applications. The use of solar energy makes it a sustainable option to meet the high growing industrial output energy. Materials like Titanium (Ti), Titanium Carbide (TiC), Silicon (Si) are used for this purpose. The use of these materials makes it more cost-friendly compared to the other similar options. The higher absorption rate is achieved by optimizing the design layers. The flowchart for the optimization in materials as well as design is also shown in this research. The optimization makes the design more compact and handy for industrial use. The advantage of these designs over the other designs is the choice of materials as well as their compact size. The results are also analyzed for the wide angle of incidence and polarization. All the results obtained show that the design is highly efficient in the solar spectral range and can be a renewable energy option for industrial and domestic heating applications.

On the utility of partially corrupted flow measurement data arising from adjacent acoustic Doppler current profilers for energy yield assessment

Recommended practice for quantifying the energy resource at a tidal energy site requires the use of multiple instruments deployed across the site. However, the instruments used work by emitting an acoustic pulse and instruments working at the same time have the potential to interfere with each other through a process known as ’cross-talk’. It is important to understand the impact of cross-talk on measurements and how this can be managed and through data processing or suitable positioning of devices. The ReDAPT project conducted a measurement campaign using two Acoustic Doppler Current Profilers (ADCPs) placed upstream of an operational tidal turbine. This aimed to assess the ’in-line’ instrument placement guidelines from IEC 62600-200 for Power Performance Assessment (PPA) in real-world conditions. Consequently, the results within hold potential to support arguments for expanding these zones or adjusting their general dimensions. Despite adhering to industry standards and best practices to eliminate unreliable data in the Quality Control (QC) checks, in both concurrently measuring ADCPs at different time stamps in approximately 15 % of the returned data. This work identified for the first time interference throughout the campaign and quantified subsequent impact on estimates. A method to remove data anomalies caused by interference between closely positioned ADCPs has been developed and demonstrated, resulting in a 7 % variation in estimated Annual Energy Production (AEP). The algorithm effectively removed approximately 90 % of the corrupted measurements. Moving forward, multi-sensor deployments could use the algorithm described to double-check for interference within the data sets, although care should be taken to avoid this by choosing a suitable layout for deployment.

Tube cross-section flatness optimization to enhance the cooling performance of nanofluid-based photovoltaic thermal systems combined with nano-enhanced phase change material

Photovoltaic (PV) systems have emerged as a crucial technology in transitioning to sustainable energy. Despite their growing prominence, PV panels face challenges related to temperature-induced that can lead to reduced energy output. By considering this, the utilization of cooling systems is useful. In the quest to design the active cooling system for PV panels, a key challenge remains in optimizing tube cross-section configurations. In this study, a validated CFD simulation investigates the impact of the tube cross-section flatness (tube flattening) on the cooling performance of the nanofluid-based photovoltaic thermal system combined with nano-enhanced Phase Change Material (PCM). Evaluation parameters for cooling performance are PV surface temperature (T_s), PCM liquid Fraction (LF), tube pressure drop (ΔP), and the PV temperature uniformity (ΔT). Aluminum Oxide (Al2O3) and Silver (Ag) nanoparticles are used in different concentrations (0 %, 1 %, 5 %, 10 %). Also, using the Response Surface Method (RSM), a multi-objective optimization determines the optimal tube flattening percentage for achieving the best cooling performance. The ideal state entails minimizing T_s, maximizing LF, minimizing ΔP, and minimizing ΔT. The findings indicate that increasing tube flattening leads to a decrease in T_s, a decrease in LF, an increase in ΔP, and a reduction in ΔT. Reducing the T_s and ΔT is a favorable effect but increasing the ΔP and reducing the LF is unfavorable to the system's cooling performance. After evaluating different parameters in the optimized scenarios, the most optimal mode is determined when the system uses Ag nanofluid in 10 % concentration, PCM without nanoparticles, and tube with 44 % flattening. In this scenario T_s, LF, ΔP, and ΔT are respectively 54.04 °C, 37.7 %, 117.67 Pa, and 34.74 °C.

The significant enhancement of energy density by redox and molecular crowding synergistic effects for aqueous zinc-ion hybrid capacitors

Aqueous zinc-ion hybrid capacitors (ZHCs) combine the high energy density of metal-ion batteries with the advantages of fast charging and discharging, high power density and long cycle life of capacitors, and are considered to be one of the most promising next-generation energy storage devices. However, the low operating voltage of aqueous electrolyte and the capacity mismatch between the capacitive cathode and the zinc anode lead to the unsatisfactory energy density of ZHCs. To improve the energy output for ZHCs, we designed an active aqueous electrolyte with high working voltage window by utilizing molecular crowding effect of polyethylene glycol (PEG) and the effect between redox-active ions (3I−/I3−). This high-voltage active aqueous electrolyte exhibits a wide voltage window of 2.2 V by inhibiting the hydrogen evolution in HER. The specific capacity of optimal ZHCs is boosted to 469 F g1 by the additional psedocapacitance from ZnI2 redox additive. The energy density of ZHCs reaches 287.3 Wh kg−1 and the capacity retention remains at 91.6 % after 7000 cycles. These results indicate that the proposed high-voltage active aqueous electrolyte is not only facile and effective for ZHCs, but also can be applied to other aqueous device for high energy density and practical application.

Predicting power and solar energy using neural networks and PCA with meteorological parameters from Diass and Taïba Ndiaye

The excessive reliance on conventional fossil fuel-based resources poses a significant threat to our environment. To mitigate this impact, it has become increasingly crucial to increase the integration of intermittent and non-polluting energy sources into our electrical grids. However, while this higher penetration rate brings benefits such as improved producer satisfaction and reduced fossil fuel consumption, it also presents challenges for traditional non-smart electrical networks. To promote intermittent energy sources effectively and maintain a balance between consumption and production, accurate forecasting of these energy outputs plays a vital role. This research paper focuses on studying the application of artificial neural networks for predicting the power and energy output of the Diass solar power plant in the short and medium term. The proposed approach utilizes not only the meteorological data from the city where the power plant is located but also data from a nearby city with a data acquisition station. Principal component analysis (PCA) is employed to select the relevant variables for the prediction model. Furthermore, the results obtained from our approach are compared to existing literature that solely uses meteorological data from the power plant's location. The comparison shows that our method achieves more satisfactory results, with mean absolute errors and root mean square errors of 0.0223 KWh and 0.003 KWh, respectively, and a prediction accuracy of 94.57% in terms of energy and power. It is worth noting that the computational resource requirements for our approach are higher, with simulation times ranging between 1788 seconds and 2201 seconds. By utilizing a broader range of data sources and employing advanced techniques like artificial neural networks, this research contributes to improving the accuracy of solar power generation forecasts. The findings highlight the potential of incorporating additional data inputs and advanced modeling techniques to enhance the performance of renewable energy systems, paving the way for a more sustainable and efficient energy future.

Modelling and optimization of concentrated solar power using response surface methodology: A comparative study of air, water, and hybrid cooling techniques

This research introduces a novel approach specifically designed to improve the design of Concentrated Solar Power plants utilizing the Response Surface Methodology. The objective of the suggested methodology is to enhance energy production efficiency by simultaneously minimizing the levelized cost of electricity and the land footprint associated with the power plant while comparing three different cooling techniques: air, water, and hybrid. Two software tools, System Advisor Model and Design-Expert, are employed to validate the primary model, evaluate the responses, generate the predictive models, and verify the results. The configuration of a Concentrated Solar Power plant is influenced by four main factors: the size of the solar field (solar multiple), row spacing, number of solar assemblies per loop, and size of thermal energy storage. In this study, these factors are varied within the following ranges: solar multiple from 1 to 5, row spacing from 10 to 30 m, number of solar assemblies from 4 to 10 per loop, and thermal energy storage from 5 to 15 h. The generated predictive models demonstrated very high accuracy, particularly for the annual energy production, with an error ranging between 0.2% and 1.5%. The findings showed that the hybrid cooling system is the most cost-effective cooling technique and has the highest energy output compared to the evaporative and air-cooling methods. When optimizing the required area of the hybrid cooled plant with a reduction of 47.44%, the analysis indicated a minimal decrease in energy output of 3.61% and a slight increase in the levelized cost of electricity by 0.95%. According to the results, the effect of area on the annual energy production and levelized cost of electricity is significant below the optimal area, while this effect becomes minor at higher values.

Monitoring solar irradiance and PV module performance in mobile applications

The paper presents a comprehensive evaluation of a PV module attached to an electric vehicle using a custom developed PV monitoring system to estimate the effect of PV energy production on the driving range.A rigid crystalline silicon photovoltaic (PV) module with a nominal peak power of 192.4 W accompanied with four irradiance sensors (positioned horizontally and vertically to the left, right and back) was mounted on an electric car, and various driving scenarios were executed, with data recorded for analysis. The results of a two-month continuous monitoring of the car's usage mainly in the Central Slovenia region in late winter and early springtime, show that the daily energy generation by the installed PV module contributed an additional driving range of up to 5 km per day, totalling 93 km of additional range in the entire monitoring period. Based on irradiance monitoring results applied to a hypothetical case where all sides would be covered with a state-of-the-art PV, the driving range would extent to 471 km in total for the same period. When compared to a fixed, ideally installed PV module at our outdoor PV monitoring site in Ljubljana, the average performance ratio (PR) of the mobile PV module in the two-month period was 87 %, whereas the PR of the fixed installed PV module was 83 %. The energy yield varied significantly under the varying conditions resulting from different car usage scenarios and on average it reached 41 % of the fixed PV module's energy yield. Through real-world experiments and data collection, different car use cases under various driving and parking conditions are presented and evaluated in detail. From these, the base specifications of a dedicated mobile VIPV monitoring system are identified.

Sex Differences in the Energy System Contribution during Sprint Exercise in Speed-Power and Endurance Athletes.

Background/Objectives: A high level of specific metabolic capacity is essential for maximal sprinting in both male and female athletes. Various factors dictate sex differences in maximal power production and energy utilization. This study aims to compare the contribution of energy systems between male and female athletes with similar sport-specific physiological adaptations during a 15-s sprint exercise. Methods: The endurance group consisted of 17 males (23 ± 7 y) and 17 females (20 ± 2 y). The speed-power group included 14 males (21.1 ± 2.6 y) and 14 females (20 ± 3 y). The contribution of phosphagen, glycolytic, and aerobic systems was determined using the three-component PCr-LA-O2 method. Results: Significant differences were observed in the energy expenditure for all systems and total energy expenditure between males and females in both groups (p = 0.001-0.013). The energy expenditure in kJ for individual systems (phosphagen-glycolytic-aerobic) was 35:25:7 vs. 20:16:5 in endurance males vs. female athletes, respectively. In the speed-power group, male athletes expended 33:37:6 kJ and female athletes expended 21:25:4 kJ, respectively. The percentage proportions did not differ between males and females in any system. The contribution of the phosphagen-glycolytic-aerobic systems was 52:37:11 vs. 48:39:13 in endurance male and female athletes, respectively. For speed-power males vs. female athletes, the proportions were 42:50:8 vs. 41:50:9, respectively. Conclusions: Despite the differences in body composition, mechanical output, and absolute energy expenditure, the energy system contribution appears to have a similar metabolic effect between male and female athletes engaged in sprint exercises with similar sport-related adaptations. The magnitude and profile of sex differences are related to sports discipline.

Optimal reactive power dispatch with renewable energy sources using hybrid whale and sine cosine optimization algorithm

The optimization of reactive power dispatch entails the complex challenge of controlling and managing the flow of reactive power in power networks to maintain desired voltage levels across many buses. Nowadays, there is a rising preference for employing renewable energy sources rather than traditional thermal generators. This change presents both challenges and possibilities for power system operators and managers. This paper addresses the Optimal Reactive Power Dispatch (ORPD) problem by presenting a novel approach that incorporates solar and wind power plants into existing power networks using the Hybrid Whale and Sine Cosine Optimisation Algorithm (HWSCOA). Solar and wind power plants are established at bus 5 and bus 8 respectively to replace traditional thermal generators in a specific case study using the IEEE 30-bus system. To handle uncertainties associated with load demand changes and the intermittent nature of renewable energy generation, the study employs probability density functions and a variety of scenarios. The primary goal is to minimize power losses in transmission cables while also lowering voltage changes throughout the network. To address uncertainty in load demands and renewable energy output, a scenario-based methodology is used, generating 30 different scenarios to cover all conceivable outcomes. By presenting the ORPD challenge as an optimization problem, the study hopes to achieve considerable reductions in power losses and voltage variations from nominal levels. The findings of this study reveal encouraging results, including significant reductions in power losses and optimized voltage stability even under shifting conditions.

Energy generation from friction-induced vibration of a piezoelectric beam

The primary challenge in harnessing vibration energy with piezoelectric materials is the discrepancy in frequency between the energy source and the energy generator, which lowers the efficiency of energy harvesting. To address the challenge, a piezoelectric beam under friction-induced vibration (FIV) is designed, modeled, and studied for the first time to realize the pronounced FIV contributing to energy generation by adapting the vibrations of the continuum structure to align close to its resonant frequencies. The Stribeck friction model is applied to characterize the variation of friction based on the relative sliding velocity vr between contacting objects. The analytical solution is derived to solve the dynamic responses, and transient charging simulation validated by the experiment is utilized to assess energy output. Furthermore, parameter studies are conducted based on the validated model with regard to the material properties of the beam and piezoelectric material, electrode connections of piezoelectric patches, and friction model parameters to investigate their influences on energy output. Considering the same dimensional properties, materials with low Young's modulus E and density ρ are desired for the host structure to facilitate large dynamic strain in piezoelectric materials. With an exponential decay factor C = 8, representing optimal material contact interface, pronounced higher FIV mode can be induced leading to higher output power. A root mean square charging power PeRMS of 42.4 mW and a peak instant charging power Pe − peak of 263 mW can be achieved. In the current study, the model is implemented on a beam coupled with one piezoelectric patch, which has potential applicability to non-uniform beams with various layouts of piezoelectric patches. The presented model enables efficient optimization of continuum structural design for higher piezoelectric energy generation under friction.

Enhancing reliability assessment in distributed generation networks: Incorporating dynamic correlation of wind-solar power output uncertainty

Amidst escalating environmental concerns and energy scarcity, the integration of distributed generation (DG) within distribution networks (DN) has emerged as a pivotal developmental trend. The uncertainty inherent in renewable energy output often disrupts DG networks. Notably, the dynamic correlation between key renewable sources, such as wind and solar energy, significantly influences the reliability analysis of these networks.To comprehensively assess the impact of wind-solar power output uncertainty and its dynamic correlation on DN reliability, this study leverages copula theory to express the dynamic correlation coefficient between wind and solar power. This coefficient is formulated as the dynamic correlation of wind-solar power through copula dynamic correlation coefficient. Employing an auto-regressive moving average (ARMA) model with constraints solved using maximum likelihood kernel (MLK), we construct the wind-solar joint output (WSJO) model. Subsequently, utilizing sequential Monte Carlo simulation (MCS) with the WSJO model, we analyze DN reliability. In case of DN failure, the WSJO model generates random samples of the wind-solar joint output sequence. Subsequent power restoration to governed islands enables the calculation of DN reliability indices. The WSJO model constructed in this study accounts for wind resource output uncertainty and dynamic correlation, aligning more closely with actual distributed generation output and enhancing the accuracy of reliability assessment. Finally, we simulate the improved IEEE-RBTS-BUS6-F4 system to underscore the crucial role of considering wind-solar energy's dynamic correlation in DN reliability assessment.

Integrated approach for co-production of bioethanol and light aromatics from lignocellulose through polyethylene glycol-aided acidic glycerol pretreatment

Acid-catalyzed glycerol organosolv (GO) pretreatment is a promising method for lignocellulosic biomass (LCB) fractionation. However, this method often leads to lignin repolymerization, intensifying lignin's inhibitory effects on subsequent enzymatic hydrolysis and limiting the production of highly active lignin, thereby hindering its valorization. This study explored incorporating polyethylene glycol (PEG) into acidic-catalyzed GO to improve bioethanol and bio-oil yields, with a focus on improving the yield of light aromatics, from LCB. Optimized PEG-aided GO pretreatment achieved a significantly higher bioethanol yield (23.7 g/L) compared to GO (17 g/L) and dilute acid (DA: 11.3 g/L) pretreatments. This improvement is attributed to the ability of PEG to mitigate lignin inhibition and modify the physicochemical properties of the pretreated substrate. Furthermore, thermal pyrolysis of PEG-aided GO lignin, obtained after the fermentation process, resulted in a substantially increased bio-oil yield (45.5 %) compared to GO (19 %) and DA (12 %). The enhanced bio-oil yield from PEG-aided GO lignin is ascribed to the promotion of β-O-4 linkages and the formation of β-O-4′ linkages. Characterization of the pyrolysis bio-oil revealed that light aromatic compounds were the dominant fraction, with their relative abundance significantly increasing from DA (5.9 %) to GO (9.7 %) and PEG-aided GO lignin (24.9 %). The PEG-aided GO method achieved an energy output of 8.85 MJ/kg, exceeding that of the GO and DA methods by 31 % and 57 %, respectively. The energy conversion efficiency of the PEG-aided GO method was 70 %, demonstrating a significant improvement compared to GO (57 %) and DA (51 %). This approach promotes the circular economy by upcycling LCB for bioethanol and valuable light aromatic compound production.

Temperature dependence of Fe:ZnSe mid-infrared spectral and laser output properties under ∼4 µm radiation excitation

This paper presents the temperature dependence of the spectroscopic and laser properties of the Fe2+:ZnSe single crystal under excitation by radiation at a wavelength of ∼4.04µm. Excitation was supported by the Er:YAG laser (∼2.94µm) operated in the Q-switched or free-running (FR) mode that pumped the Fe:ZnSe laser cooled by liquid nitrogen to 78 K. This system generated radiation at the wavelength of ∼4.04µm. Temperature dependence of absorption, fluorescence spectra, and fluorescence decay time of Fe2+ ions was measured. The optimal temperature that allows efficient Fe:ZnSe laser system operation with maximum laser output energy was sought in both regimes. It was characterized at a repetition rate of 1 Hz with pulse durations of ∼175ns and ∼155µs in both modes, respectively. The highest laser output energies obtained with the crystal placed in the cryostat in gain-switched (GS) and FR modes were ∼385µJ at 260 K and ∼374µJ at 190 K, respectively. Maximum optical-to-optical efficiencies in the same modes were ∼13% at 260 K and ∼32% at 170 K, respectively. Wide laser radiation oscillation spectra depending on the temperature ranging from ∼4.3µm at 140 K to ∼4.9µm at 340 K obtained with a Gaussian-like beam spatial profile were demonstrated. Under atmospheric conditions at RT, this laser system reached a pulse energy of over 0.5 mJ at ∼4.78µm in ∼135ns pulses, which corresponds to a peak power of ∼3.9kW.

A simultaneous energy self-sufficient desalination and energy output process based on a novel membrane stack design

Electrodialysis (ED) uses electric energy to conduct the desalination, while bipolar membrane reverse electrodialysis (BMRED) converts the chemical energy containing in acids and alkalis into electric energy. Here, a novel membrane stack (BMRED-ED) based on BMRED and ED was designed to realize the simultaneous energy self-sufficient desalination and energy output process. The stack was studied in two different operation modes systematically. Results of the open-loop operation mode demonstrate that the maximum power density (Pd,max) can obtain a highest value of 4.53 W·m−2, and the maximal average apparent permeability (α) between the open circuit voltage (OCV) and theoretical electromotive force (EMF) is 92.23 %. Results of the closed-loop operation mode indicate that the final desalination ratio (ξdel) can attain as high as 89.57 %, and the corresponding energy utilization ratios of the desalination (ηdes) and the energy output (ηout) are 0.90 % and 14.00 %, respectively. Moreover, the effect of desalinated salt solution concentrations and discharging current densities on the closed-loop operation performance are much more remarkable than that of cation species in the alkali and salt solutions. This work provides a feasible and competitive strategy for simultaneous energy self-sufficient desalination and energy output.

Effect of hydrochar from biogas slurry co-hydrothermal carbonization with biomass on anaerobic digestion performance of food waste

Anaerobic digestion is a promising method for converting food waste into renewable methane-rich biogas. However, challenges, such as significant biogas slurry generation, poor stability, and low methane production, limit its widespread adoption. This study produced 15 types of hydrochars by co-hydrothermal carbonization of biogas slurry with sugarcane leaves, cellulosic ethanol residue, or food waste digestate at 180–260 °C to enhance anaerobic digestion performance and methane yield. The methane yield was influenced by both the raw materials and hydrothermal carbonization temperature. The hydrochar derived from sugarcane leaves at 180 °C exhibited the highest methane yield of 461.59 mL/g VS, which is a 13.69 % increase over the food waste group. Additionally, different doses of SL-180 were tested for their effects on the anaerobic digestion performance in food waste. The cumulative methane yield increased from 379.68 mL/g VS to 543.26 mL/g VS with SL-180 dosages ranging from 5 g/L to 40 g/L, enhancing cumulative methane yield by 3.90–48.66 % compared to the food waste group. The primary factor influencing methane yield was hydrochar degradation, whereas secondary factors included maintaining system stability and enriching methane-producing microorganisms, particularly Methanosaeta and Methanomassiliicoccus. Energy assessment and economic analysis indicate that combining hydrothermal carbonization with anaerobic digestion can achieve high energy output and economic benefits. This study offers insights and recommendations for managing biogas slurry, resource recovery, and improving the stability and methane yield in the anaerobic digestion of food waste.

Source-load coordinated dispatching model taking into account the similarity between renewable energy and load power

With the deployment of renewable energy, the load curve is expected to follow the renewable energy output curve to minimize the fluctuation of thermal power output in the source-load coordinated dispatching. The traditional indicators for the load curve are no longer enough to describe the load characteristics. A new load indicator called the source-load similarity distance is proposed by improving the similarity measurement method of the time series and calculating the similarity distance between the renewable energy output curve and the load curve. By combining the Euclidean distance with the improved dynamic time warping, the source-load similarity distance is obtained and the data distribution and morphological fluctuation characteristics can be simultaneously considered. The source-load coordinated dispatching model is also established to minimize the source-load similarity distance. The simulation results show that the source-load similarity distance can effectively describe the similarity characteristics of the renewable energy output curve and the load curve. Increasing the source-load similarity distance can reduce the thermal power operation cost by 56.2 % and the cost of demand response by 25.3 %, and increase the utilization rate of wind power by 4.6 % compared to the dispatching model with the standard deviation indicator.

Quantitative assessment methods and qualitative evaluation system for the complementary characteristics of multi-energy

Assessing and analyzing the complementary characteristics of renewable energy (RE) is crucial for designing, operating, and optimizing multi-energy complementary systems (MECSs). However, unified and precise quantitative descriptions of the complementary and stability characteristics among various energy outputs in MECSs have lacked attention and research. Here, this study innovatively proposed a mathematical model for the multi-energy complementarity index (MECI), which considers the complementarity rates of multiple energy outputs during zero and non-zero output periods, and a mathematical model for the multi-energy volatility index (MEVI), which accounts for fluctuation thresholds and the overall volatility of output processes. An evaluation system for multi-energy complementarity characteristics qualitative analysis has been established. The natural output processes of RE at three MECSs in China were applied in the case calculations and verification. Results show that the hydropower rated discharge (Qrating) has a significant negative correlation with MECI, with the MECI decreasing by an average of 0.0046 for every 5 m³/s increase in Qrating. The relationship between the Qrating and MEVI shows an overall negative correlation with local fluctuations. Notably, The MECI of the BeiPan River MECSs exhibits significant seasonal characteristics, with the MEVI in summer (0.378) and autumn (0.395) higher than those in spring (0.132) and winter (0.160), closely related to the natural seasonal variations of the three energy sources: water, wind, and solar. We believe that the study can assist in evaluating and making decisions on the multi-energy complementarity characteristics of RE bases in the future, making a significant contribution to achieving dual carbon goals.