Sustainable Load Research Articles

Energy Storage Improves Power Plant Flexibility and Economic Performance

Most existing coal-fired power plants were designed for sustained operation at full load to maximize efficiency, reliability, and revenue, as well as to operate air pollution control devices at design conditions. Depending on plant type and design, these plants can adjust output within a fixed range in response to plant operating or market conditions. The need for flexibility driven by increased penetration of variable and non-dispatchable power generation, such as wind and solar, is shifting the traditional mission profile of thermoelectric power plants in three ways: more frequent shutdowns when market or grid conditions warrant, more aggressive load ramp rates (rate of output change), and a lower minimum sustainable load, which provides a wider operating range and helps avoid costly plant shutdowns. Recent studies have shown that the flexibility of a coal-fired power plant can be improved by energy storage. The objective of this work was to analyze a set of energy storage options and determine their impact on the flexibility and economics of a representative coal-fired power plant. The effect of three energy storage systems integrated with a coal power plant on plant flexibility and economics was investigated. The results obtained in this project show that energy storage systems integrated with a thermal power plant improve plant flexibility and participation in the energy and ancillary services markets, which improves plant financial performance. The study was funded by the U.S. Department Office of Fossil Energy FE-1 under award number DE-FE0031903.

Flexible Energy Storage for Sustainable Load Leveling in Low-Voltage Electricity Distribution Grids with Prosumers

The sustainability of the energy sector is linked today with the diminishing of the reliance on fossil fuels and on the large-scale adoption of renewable generation. Medium- and low-voltage electricity distribution grids see the proliferation of microgrids that supply consumers able to generate electricity with local installations of PV panels. These consuming and generating entities, called prosumers, use the local generation for their own consumption needs and are exporting the surplus in the grid, modifying the typical steady state operation conditions. For mitigating this inconvenience, local storage equipment can be used, which also helps the prosumers to reduce their costs and preserve the sustainable operation of the distribution infrastructure. The literature shows that by optimally using storage in microgrids, the deterioration in quality and security of supply can be minimized in the presence of prosumers. This paper presents a study regarding local storage management in prosumer-enabled microgrids, seeking to find the optimal configuration of community (shared) storage systems that charge batteries overnight, during low consumption hours, providing load leveling opportunities and energy loss minimization. A study case performed on a real low-voltage electricity distribution network (LVEDN) shows the performance of the proposed optimization.

Advanced Integration of Forecasting Models for Sustainable Load Prediction in Large-Scale Power Systems

In the burgeoning field of sustainable energy, this research introduces a novel approach to accurate medium- and long-term load forecasting in large-scale power systems, a critical component for optimizing energy distribution and reducing environmental impacts. This study breaks new ground by integrating Causal Convolutional Neural Networks (Causal CNN) and Variational Autoencoders (VAE), among other advanced forecasting models, surpassing conventional methodologies in this domain. Methodologically, the power of these cutting-edge models is harnessed to assimilate and analyze a wide array of influential factors, including economic trends, demographic shifts, and natural phenomena. This approach enables a more nuanced and comprehensive understanding of power load dynamics, essential for accurate forecasting. The results demonstrate a remarkable improvement in forecasting accuracy, with a 15% increase in precision over traditional models. Additionally, the robustness of the forecasting under varying conditions showcases a significant advancement in predicting power loads more reliably. In conclusion, the findings not only contribute substantially to the field of load forecasting but also highlight the pivotal role of innovative methodologies in promoting sustainable energy practices. This work establishes a foundational framework for future research in sustainable energy systems, addressing the immediate challenges and exploring potential future avenues in large-scale power system management.

Efficient Cooling Approach with Integrated PCM Rooftops for Sustainable Load Reduction in Buildings with Natural Ventilation Systems

Efficient Cooling Approach with Integrated PCM Rooftops for Sustainable Load Reduction in Buildings with Natural Ventilation Systems

A Power Aware Long Short-Term Memory with Deep Brief Network Based Microgrid Framework to Maintain Sustainable Energy Management and Load Balancing

Microgrids are seen as the future of reliable, sustainable and green energy source for myriad applications. The increasing dependence on microgrid also adds challenges on reliable management of power supply to vividly variant consumers, the major chunk being households coupled with an unprecedented rise in the demand for EV charging. This study aims at presenting a deep Long Short-Term Memory with Deep Brief Network model to reliably predict the grouped energy load and solar energy outcome in a community microgrid. A cutting-edge hybrid metaheuristic algorithm will be taken into consideration for optimizing the load dispatch of community microgrids that are connected to the grid. Three different scheduling scenarios are evaluated to establish an ideal dispatching design for a grid-linked community microgrid with solar elements and energy storage systems feeding electricity loads and charging electric vehicles. The prediction outcomes are integrated into the model to accommodate the uncertainties associated with solar energy outcome and residential energy load and EV charging to achieve a supply-demand equilibrium. The objective of the proposed model is to obtain an energy-efficient system capable of balancing the load and power of microgrid system which remains unperturbed by the aforesaid oscillations.

Biomechanical evaluation of reconstruction of the posterior complex in restorative laminoplasty with miniplates

ObjectiveTo evaluate the biomechanical effects of different miniplates on restorative laminoplasty.MethodsAssembled restorative laminoplasty models were developed based on 3D printed L4 lamina. Based on different internal fixations, the research was divided into H-shaped miniplates (HSMs) group, two-hole miniplates (THMs) group, and L-shaped miniplates (LSMs) group. The static and dynamic compression tests were analyzed to investigate the biomechanical effects of different internal fixations in restorative laminoplasty, until the failure and fracture of miniplates, or the collapse of miniplates. The static compression tests adopted the speed control mode, and the dynamic fatigue compression tests adopted the load control mode.ResultsThe “door close” and the collapse of lamina occurred in THMs group and LSMs group, and plate break occurred in LSMs group. However, these phenomenon was absent in HSMs group, and only plate crack around a screw and looseness of a screw tail cap were found in HSMs group. The sustainable yield load of HSMs group was greater than that of THMs group and LSMs group (P < 0.05). No significant difference in yielding-displacement was found between HSMs group and LSMs group (P > 0.05), while both were much less than that of THMs (P < 0.05). Moreover, the compressive stiffness and the axial displacement under the same mechanical load were arranged as follows: HSMs group > LSMs group > THMs group (P < 0.05). The results of dynamic compression test revealed that the peak load of HSMs group could reached 873 N and was 95% of the average yield load of the static compression, and was better than that in THMs group and LSMs group (P < 0.05). Besides, according to the fatigue life-peak load diagram, the ultimate load of HSMs group was more than twice that of THMs group or LSMs group.ConclusionsThe mechanical strength of H-shaped miniplates was superior to two-hole miniplates and L-shaped miniplates in maintaining spinal canal enlargement and spinal stability, and was more excellent in fatigue stability and ultimate load.

Optimal Battery Energy Storage System Management with Wind Turbine Generator in Unbalanced Low Power Distribution System

Wind Turbine Generators (WTG) are being integrated into distribution systems on a large scale worldwide as part of a global effort to capture green energy. Wind turbine generator intermittency may be mitigated by Battery Energy Storage Systems (BESS), which have emerged as a viable option in recent years. To find the best position and capacity for wind power generation and BESS charging/discharging dispatches, a Red Fox Optimisation (RFO) algorithm is used while optimising the imbalanced distribution network’s performance under technological restrictions. The charging or discharging criteria for this method is the average feeder load. The charging techniques for BESS using WTG and Sustainable Average Load (SAL) are evaluated in terms of the free-running mode of dispatch cycle. The suggested approach is tested on an IEEE-37 bus Unbalanced Radial Distribution Network (UDN). It has been shown that the suggested method enhances several performance objectives of the distribution system.

Relative Aerobic Load of Daily Activities After Stroke.

Individuals after stroke are less active, experience more fatigue, and perform activities at a slower pace than peers with no impairments. These problems might be caused by an increased aerobic energy expenditure during daily tasks and a decreased aerobic capacity after stroke. The aim of this study was to quantify relative aerobic load (ie, the ratio between aerobic energy expenditure and aerobic capacity) during daily-life activities after stroke. Seventy-nine individuals after stroke (14 in Functional Ambulation Category [FAC] 3, 25 in FAC 4, and 40 in FAC 5) and 22 peers matched for age, sex, and body mass index performed a maximal exercise test and 5 daily-life activities at a preferred pace for 5minutes. Aerobic energy expenditure (mLO2/kg/min) and economy (mLO2/kg/unit of distance) were derived from oxygen uptake ($\dot{\mathrm{V}}{\mathrm{O}}_2$). Relative aerobic load was defined as aerobic energy expenditure divided by peak aerobic capacity (%$\dot{\mathrm{V}}{\mathrm{O}}_2$peak) and by $\dot{\mathrm{V}}{\mathrm{o}}_2$ at the ventilatory threshold (%$\dot{\mathrm{V}}{\mathrm{o}}_2$-VT) and compared in individuals after stroke and individuals with no impairments. Individuals after stroke performed activities at a significantly higher relative aerobic load (39%-82% $\dot{\mathrm{V}}{\mathrm{o}}_2$peak) than peers with no impairments (38%-66% $\dot{\mathrm{V}}{\mathrm{o}}_2$peak), despite moving at a significantly slower pace. Aerobic capacity in individuals after stroke was significantly lower than that in peers with no impairments. Movement was less economical in individuals after stroke than in peers with no impairments. Individuals after stroke experience a high relative aerobic load during cyclic daily-life activities, despite adopting a slower movement pace than peers with no impairments. Perhaps individuals after stroke limit their movement pace to operate at sustainable relative aerobic load levels at the expense of pace and economy. Improving aerobic capacity through structured aerobic training in a rehabilitation program should be further investigated as a potential intervention to improve mobility and functioning after stroke.

A sustainable and secure load management model for green cloud data centres

The massive upsurge in cloud resource demand and inefficient load management stave off the sustainability of Cloud Data Centres (CDCs) resulting in high energy consumption, resource contention, excessive carbon emission, and security threats. In this context, a novel Sustainable and Secure Load Management (SaS-LM) Model is proposed to enhance the security for users with sustainability for CDCs. The model estimates and reserves the required resources viz., compute, network, and storage and dynamically adjust the load subject to maximum security and sustainability. An evolutionary optimization algorithm named Dual-Phase Black Hole Optimization (DPBHO) is proposed for optimizing a multi-layered feed-forward neural network and allowing the model to estimate resource usage and detect probable congestion. Further, DPBHO is extended to a Multi-objective DPBHO algorithm for a secure and sustainable VM allocation and management to minimize the number of active server machines, carbon emission, and resource wastage for greener CDCs. SaS-LM is implemented and evaluated using benchmark real-world Google Cluster VM traces. The proposed model is compared with state-of-the-arts which reveals its efficacy in terms of reduced carbon emission and energy consumption up to 46.9% and 43.9%, respectively with improved resource utilization up to 16.5%.

Open Capacity Model of Medium Voltage Transmission Line in Distribution Network Based on Load Data

Due to the influence of load fluctuation, the power supply stability of distribution network is insufficient. Therefore, the open capacity model of medium voltage lines in the distribution network is constructed by using load data. Based on the introduction of time parameter, the load pressure of the distribution network is calculated by using the peak load of distribution network. According to the relationship between the pressure value of sustainable load of the distribution network and the pressure value under the actual power load, the open capacity model of medium voltage lines is constructed. The test results show that when the design model is used to set the open capacity of medium voltage lines, the fluctuation of the expected outage time and frequency of the distribution network system is small, which indicates that the stable power supply can be realized

Directly air-cooled compact looped heat pipe module for high power servers with extremely low power usage effectiveness

The rapid development of data centers has brought huge energy consumption around the world, and an effective, reliable and energy-saving cooling method is urgently demanded for data centers cooling. This paper proposed air directly cooling looped heat pipe (LHP) modules with excellent thermal performance that can be used for server cooling in data centers based on the unconventional enhancement mechanism for looped heat pipes. The proposed LHP with no compensation chamber has an auxiliary wick which connects the primary wick in the evaporator and extends into the condenser to promote the liquid replenishing. Meanwhile, another approach for reducing the sensible heat leak in the replenishing liquid was introduced with aid of an additional fin and fan assembly installed very close to the evaporator entrance. By means of the above approaches, significant improvement in maximum sustainable heat load of the LHP in horizontal direction was achieved. The improved LHP module has a maximum heat dissipation capacity of 550 W with the minimum total thermal resistance of 0.16 °C/W in horizontal direction, and with a power usage effectiveness as low as 1.02.

Numerical Study on the Long-Term Performance and Load Imbalance Ratio for Medium-Shallow Borehole Heat Exchanger System

To contribute to the goal of carbon neutralization, the closed-loop borehole heat exchanger system is widely applied to use geothermal energy for building cooling and heating. In this work, a new type of medium-shallow borehole heat exchanger (MSBHE) is proposed, which is coaxial type and has a depth range between 200 m to 500 m. To investigate the long-term performance of MSBHE in the area with unbalanced cooling and heating load of buildings and the sustainable load imbalance ratio under different design parameters, a comprehensive numerical model is established. The results show that the drilling depth significantly influences the sustainable load imbalance ratio of MSBHE. As the drilling depth is increased from 200 m to 500 m, the load imbalance ratio of the MSBHE increases from 20.76% to 60.29%. In contrast, the load imbalance ratio is always kept at the same level with different inlet velocities and operation modes. Furthermore, in a 9-MSBHE array system, the heat exchanger located in the middle of the array has the lowest load imbalance ratio of 48.97%, which is 15.98% lower than the borehole in the edge location. This is caused by the significant influence of the shifted-load phenomenon among MSBHEs in an array system. The findings of the work imply that this newly proposed MSBHE can sustain a notable load imbalance ratio, which is particularly applicable to the areas with a strong imbalance of annual building load.

Performance investigation of a double pass PVT assisted heat pump system with latent heat storage unit

The storage of heat and electrical energy plays a crucial role in obtaining heat and electrical energy from solar energy in a sustainable way for low carbon generation. In this study, a hybrid system has been developed and tested that can produce and store both heat and electrical energy from solar energy at the same time and can use the stored energy when necessary in order to provide a sustainable heating load. For this hybrid system, a new type (double pass) of photovoltaic thermal panel and a novel latent heat storage unit integrated with the condenser of the heat pump were designed and manufactured. In addition, numerical analysis was performed using the Ansys-Fluent program to characterize the thermal behavior of the phase change material in the latent heat storage unit. The highest average electrical and thermal efficiency of the photovoltaic thermal panel for the heat pump system were measured as 16.74% and 66.98%, respectively. It was observed that the average coefficient of performance of the heat pump system varied between 2.93 and 3.18. The photovoltaic thermal panel was able to store 1.07 kWh of electrical energy and produced 9.59% more electricity than the photovoltaic panel. The melting time of paraffin was between 183 and 201 min and the solidification time between 348 and 357. Melting and solidification times varied according to the performance of the heat pump was seen.

Adaptive synchronization of complex dynamic networks with switching parameters subject to state constraints in power system

This paper deals with the synchronization control of power complex networks with switching parameters. In the meantime, the node state constraints are considered during the synchronization process. Admittedly, synchronization problem encountered in power complex networks is becoming progressively important due to the increasing connection and disconnection operations resulting from sustainable energy and controllable load. Hereon, the network model considering switching parameters of each node is established to describe the topology variation of power systems that may be confronted in practical terms. Then, by utilizing the adaptive backstepping technique with a barrier Lyapunov function (BLF), a novel synchronization controller is constructed recursively which accomplishes the nodes full states tracking within the predefined transient behavior. Owing to the characteristic of BLF, the designed controller as well as its adaptive law could guarantee both the constrained state of each node restricted by a prescribed range and the synchronization performance. Meanwhile, the bounded output of the system could track the desired trajectory. Finally, scenario simulations are performed to demonstrate the effectiveness and superiority of the proposed method.

Impact of Global Warming on Dissolved Oxygen and BOD Assimilative Capacity of the World’s Rivers: Modeling Analysis

For rivers and streams, the impact of rising water temperature on biochemical oxygen demand (BOD) assimilative capacity depends on the interplay of two independent factors: the waterbody’s dissolved oxygen (DO) saturation and its self-purification rate (i.e., the balance between BOD oxidation and reaeration). Although both processes increase with rising water temperatures, oxygen depletion due to BOD oxidation increases faster than reaeration. The net result is that rising temperatures will decrease the ability of the world’s natural waters to assimilate oxygen-demanding wastes beyond the damage due to reduced saturation alone. This effect should be worse for nitrogenous BOD than for carbonaceous BOD because of the former’s higher sensitivity to rising water temperatures. Focusing on streams and rivers, the classic Streeter–Phelps model was used to determine the magnitude of the maximum or “critical” DO deficit that can be calculated analytically as a function of the mixing-point BOD concentration, DO saturation, and the self-purification rate. The results indicate that high-velocity streams will be the most sensitive to rising temperatures. This is significant because such systems typically occur in mountainous regions where they are also subject to lower oxygen saturation due to decreased oxygen partial pressure. Further, they are dominated by salmonids and other cold-water fish that require higher oxygen levels than warm-water species. Due to their high reaeration rates, such systems typically exhibit high self-purification constants and consequently have higher assimilation capacities than slower moving lowland rivers. For slow-moving rivers, the total sustainable mixing-point concentration for CBOD is primarily dictated by saturation reductions. For faster flowing streams, the sensitivity of the total sustainable load is more equally dependent on temperature-induced reductions in both saturation and self-purification.

Performance analysis of unbalanced radial feeder for integrating energy storage system with wind generator using inherited competitive swarm optimization algorithm

In recent years, the battery energy storage system (BESS) has been considered as a promising solution for mitigating wind turbine generator intermittencies. In this paper, optimal location and capacity of the BESS with wind power penetrations, and charging/discharging dispatches of BESS are determined using inherited competitive swarm optimization (ICSO) algorithm while improving the performance of the unbalanced distribution network subject to technical constraints. The strategy uses the average load of the feeder as the deciding criterion for charging or discharging the battery. The sustainable average load (SAL) charging method is utilized to charge the battery. The free-running and conservative discharging methods of the battery are studied. The proposed methodology is investigated on IEEE 37 bus unbalanced radial distribution system. The application results show that the proposed methodology enhances various performance objectives of distribution system by optimally coordinating the dispatches of BESS.

Assessment of the burning-plasma operational space in ITER by using a control-oriented core-SOL-divertor model

In future tokamaks, the control of burning plasmas will require careful regulation of the plasma density and temperature. Along with the design of effective burn-control systems, understanding how the fusion power varies in the density-temperature space is vital for the operation of fusion power plants. In this work, the steady-state operational space of ITER is studied using a control-oriented core-plasma model coupled to a two-point model of the scrape-off-layer (SOL) and divertor regions. The two models are coupled through the exchange of input-output parameters. The deuterium and tritium recycling from the wall are output parameters of the SOL-divertor model that are used as input parameters in the core-plasma density balance. Furthermore, the separatrix temperature, which is an output parameter of the SOL-divertor model, is incorporated into the radial core-plasma temperature profiles. Therefore, the temperature-dependent power balance of the plasma core is intimately linked to the SOL-divertor model. Both the power entering the SOL from the core, as determined by the core-plasma power balance, and the separatrix density, as dictated by the core-plasma density balance, are input parameters to the SOL-divertor model. They are control knobs in the SOL-divertor model that can be regulated using the core-plasma actuators: auxiliary power and pellet injection. There are various operational limitations, such as the saturation of the aforementioned actuators, that will prevent ITER from accessing certain high-fusion plasma regimes. The achievable tritium concentration in the fueling lines and the maximum sustainable heat load on the divertor will impose further restrictions. By accounting for these limitations, the ITER operational space is computed based on the coupled core-SOL-divertor model and visualized using Plasma Operation Contour (POPCON) plots that map performance metrics, such as the fusion to auxiliary power ratio, over the density-temperature space. Comparisons are drawn between plasmas with different recycling, confinement, and SOL-divertor conditions.

Development of biaxial stretchable nonwoven paddings using novel polymeric fibers

Nonwoven paddings with superior stretch and recovery properties in both directions with good thermal insulation properties were developed for application in winter clothes. Paddings were developed using a self‐crimping elastic fiber composed of both polyethylene terephthalate (PET) and polytrimethylene terephthalate (PTT) polymers, polyester fiber balls, polypropylene fiber balls, along with bicomponent as binder fibers at different blending ratios through carding, postthermal bonding, and gluing process. Developed paddings were characterized and analyzed for their biaxial stretch and recovery behavior, elongation under tensile load, stiffness, thermal insulation, and washing stability. In addition, energy loss was calculated and reported from hysteresis curve obtained during 40% loading (stretch) and unloading (recovery) cycle. The results were statistically evaluated to determine whether fiber types and blend ratios have significant effect on nonwoven paddings properties. This study indicates that nonwoven padding composed of naturally crimped elastic fibers: binder fibers (50:50) has ability to stretch around 120‐140% in both directions, 95% total recovery with minimal energy loss upon 40% stretch and recovery cycle, and it has sustainable tensile breaking load around 11 N along cross direction and 20 N along machine direction. It has stiffness load 5 N which is flexible enough for required applications, possesses good thermal insulation properties, and holds their structural properties after five washing cycles. From statistical analysis, ANOVA results exhibited that the used fiber types and their weight percentages significantly affect stretch and recovery, other properties of nonwoven paddings for required application.

Optimal allocation of energy storage system and its benefit analysis for unbalanced distribution network with wind generation

Battery energy storage system (BESS) can play a major role to overcome the challenges with renewable energy sources, especially in the context of Smart Grid (SG). In this manuscript, BESS optimal location and capacitance of wind power penetrations, and charging/discharging dispatches of BESS are determined using inherited competitive swarm optimization (ICSO) algorithm while improving the performance of the unbalanced distribution network subject to technical constraints. This approach utilizes the average feeder load from decisive criterion for charging or discharging battery. The wind turbine generator (WTG) charging and sustainable average load (SAL) charging methods for BESS are compared considering conservative mode of discharging. The proposed methodology is investigated on IEEE 37 bus unbalanced radial distribution system. The application outcomes displays that proposed methodology enhances various performance objectives of distribution system by optimally coordinating the dispatches of BESS.

Prediction of the Inelastic Behaviour of Radius Segments: Damage-based Nonlinear Micro Finite Element Simulation vs Pistoia Criterion

The Pistoia criterion (PC) is widely used to estimate the failure load of distal radius segments based on linear micro Finite Element (μFE) analyses. The advantage of the PC is that a simple strain-threshold and a tissue volume fraction can be used to predict failure properties. In this study, the PC is compared to materially nonlinear μFE analyses, where the bone tissue is modelled as an elastic, damageable material. The goal was to investigate for which outcomes the PC is sufficient and when a nonlinear (NL) simulation is required. Three types of simulation results were compared: (1) prediction of the failure load, (2) load sharing of cortical and trabecular regions, and (3) distribution of local damaged/overstrained tissue at the maximum sustainable load. The failure load obtained experimentally could be predicted well with both the PC and the NL simulations using linear regression. Although the PC strongly overestimated the failure load, it was sufficient to predict adequately normalized apparent results. An optimised PC (oPC) was proposed which uses experimental data to calibrate the individual volume of overstrained tissue. The main areas of local over-straining predicted by the oPC were the same as estimated by the NL simulation, although the oPC predicted more diffuse regions. However, the oPC relied on an individual calibration requiring the experimental failure load while the NL simulation required no a priori knowledge of the experimental failure load.