Load Variations Research Articles

Mountain gazelle optimizer for standalone hybrid power system design incorporating a type of incentive-based strategies

The main objective of this research study is to improve the performance of a standalone hybrid power system (SHPS) that consists of photovoltaic modules (PVMs), wind turbines (WTs), battery system (BS), and diesel engine (DE). The emphasis is on optimizing the system's design by incorporating demand response strategies (DRSs). Incorporating these strategies into the system can enhance system performance, stability, and profitability while also reducing the capacity of SHPS components and, consequently, lowering consumers' bills. To achieve this objective, the sizing model incorporates a novel indicator called the load variation factor (LVF). This paper assesses and contrasts various scenarios, including SHPS without DRS, with DRS, and with DRS but no DE. In this article, interruptible/curtailable (I/C) as one of the DRSs is incorporated into the model used for sizing issues. A newly developed optimization algorithm called the mountain gazelle optimizer (MGO) is utilized for the multi-objective design of the proposed SHPS. The utilization of MGO will facilitate achieving the lowest possible values for each of the following: cost of energy (COE), loss of power supply probability (LPSP), and carbon dioxide (CO2) emissions. This work introduces a mathematical model for the entire system, which is subsequently simulated using MATLAB software. The results reveal that among all the scenarios analysed, scenario iii — which has an LVF of 30% — is the most cost-effective. It has the lowest COE, at 0.2334 $/kWh, hence the lowest net present cost (NPC), at 6,836,445.5 $.

A review of electrolyzer-based systems providing grid ancillary services: current status, market, challenges and future directions

Concerns related to climate change have shifted global attention towards advanced, sustainable, and decarbonized energy systems. While renewable resources such as wind and solar energy offer environmentally friendly alternatives, their inherent variability and intermittency present significant challenges to grid stability and reliability. The integration of renewable energy sources requires innovative solutions to effectively balance supply and demand in the electricity grid. This review explores the critical role of electrolyzer systems in addressing these challenges by providing ancillary services to modern electricity grids. Electrolyzers traditionally used only for hydrogen production have now emerged as versatile tools capable of responding quickly to grid load variations. They can consume electricity during excess periods or when integrated with fuel cells generate electricity during peak demand, contributing to grid stability. Therefore, electrolyzer systems can fulfill the dual function of producing hydrogen for the end-user and offering grid balancing services, ensuring greater economic feasibility. This review paper aims to provide a comprehensive view of the electrolyzer systems’ role in the provision of ancillary services, including frequency control, voltage control, congestion management, and black start. The technical aspects, market, projects, challenges, and future prospects of using electrolyzers to provide ancillary services in modern energy systems are explored.

Evaluation of Power Storage Circuit for Piezoelectric Energy Harvester in Impact Mode

Power storage circuit is crucial to the performance of the vibration energy harvesting system as it will affect the amount of energy that can be harvested. The objective of this paper is to discuss the design and evaluation of power storage circuit for the vibration-based impact mode piezoelectric energy harvester. The proposed power storage circuit consists of rectifier circuit with rectifier capacitor, voltage regulator and the storage device. Evaluation on the rectifier circuit and the power storage circuit is carried out with variation in the resistive load and some other components. Results show that for the multiple impact input, full wave rectifier produces higher output than that of the other circuit. In addition to that, the proposed storage circuit can produce a stable output voltage and output power at about 40 Hz of operating frequency range. This show that piezoelectric energy harvester in impact mode can operate well with the proposed power storage circuit.

Land management policy shift influenced seasonal variation of erosion-induced nitrogen and phosphorus outputs from intensive agricultural catchment

A shift in policy to intensive agricultural production and land management often leads to excessive fertilizer application and accelerated erosion with consequent detrimental effects to water bodies. We investigated the impact of that shift by quantifying the spatial and temporal change in sediment sources and associated total nitrogen (TN) and total phosphorus (TP) pollutants output loads in an intensive agricultural catchment in North China across one year (November 2021–November 2022). We describe the implications of this work for intensive agriculture elsewhere in China and other countries. Seasonal sediment source apportionment was estimated at the catchment outlet using Berillium-7 (7Be) combined with compound-specific stable isotope (CSSI) signatures from sources and sediments. Diagnostic ‘fingerprints’ in MixSIAR were used to discriminate sediment sources between forest and crop farmland converted from forest (F + C(F)), crop farmland (C), and vegetable farmland (V). Our study identified F + C(F) as the dominant sediment source (mean 55.24 ± 2.91 %), intermediate on V (mean 30.06 ± 2.20 %), and least on C (mean 14.70 ± 2.13 %). Sedimentation ranged from 37.98 ± 3.02 to 89.60 ± 12.68 t·ha−1·event−1 and coincided with shifted land use policy and rainfall distribution. The TN and TP in sediment were both mainly derived from F + C(F) (averaged 22.27 ± 4.26 t·event−1 and 11.62 ± 2.28 t·event−1) and least from V (averaged 1.63 ± 0.29 and 2.09 ± 0.33 t·event−1). Despite being a significant sediment source, V contributed little sediment TN and TP input for eutrophication. Our findings imply that F + C(F) are diffuse sources of catchment pollution over the short term. These results describe the successful use of CSSI and 7Be to cost-effectively quantify the seasonal variation of sediment TN and TP loads from land-use-specific sources in the catchment under shifting land management policy in China with potential for use elsewhere. These findings enable soil conservation strategies and land management practices optimized for implementing targeted pollutant abatement initiatives in intensive agriculture in China and elsewhere.

A new-fangled connection of UPQC tailored power device from wind farm to weak-grid

A significant portion of wind power conversion systems worldwide comprise wind farms (WFs) that use Squirrel Cage Induction Generator (SCIG) and are directly linked to the power grid. In facilities that generate electrical energy at a moderate level, WFs are connected by means of distribution systems that use medium voltage (MV). It is not uncommon for such a system to produce a scenario in which the amount of electricity generated corresponds to the grid’s transit volume. When a wind farm’s wind power generation system is connected to a weak grid, the lack of potential control of the Point of Common Coupling (PCC) is a primary issue. This strategy is called a “Wind Farm with Weak Grid Connection.” Therefore, the amalgamation of weak grids, fluctuating electricity from wind, and variations in load on the system cause disruptions in the PCC voltage, further degrading the Power Quality (PQ) and the WF stability. Either the control method at the production level or the compensating strategies at the PCC level can improve this situation. If wind farms are built on SCIG and are directly linked to the grid, it is essential to utilise the last substitute. The technology known as Custom Power Devices (CUPS), proved extremely helpful for this type of application. This study presents a compensation technique based on a specific CUPS device, known as the Unified Power Quality Compensator (UPQC), as a possible solution. The potential terminals of WF needed to be regulated, and the voltage fluctuations on the grid side required to be reduced, so a custom-made control strategy for the UPQC device was designed internally. The control of power, such as active and reactive in the UPQC’s series and shunt converters, as well as the transmission of power via the UPQC DC-Link between converters, are the foundation of the internal control strategy that has been developed. Compared to other bespoke tactics that use reactive power, this strategy increases the UPQC’s capability to provide compensation. The suggested study calculates THD using a FUZZY controller. The results are compared to PI controller results. Simulation findings show how the suggested compensating strategy can minimise THD values and improve wind farm power and stability. The simulations suggest that the proposed compensating strategy enhances WF power and stability.

Optimization of Topological Reconfiguration in Electric Power Systems Using Genetic Algorithm and Nonlinear Programming with Discontinuous Derivatives

This article addresses a comprehensive analysis of power electrical systems, employing a combined approach of genetic algorithms and mathematical optimization through nonlinear programming with discontinuous derivatives (DNLP) in GAMS. The primary objective is to minimize economic losses and associated costs faced by the network operator following disruptive events. The analysis is divided into two fundamental aspects. Firstly, it addresses the topological reconfiguration of the network, involving the addition of lines and distributed energy resources such as distributed generation. To determine the optimal topological reconfiguration, a genetic algorithm was developed and implemented. This approach aims to restore electrical service to the maximum load within the system. Secondly, an optimal energy dispatch was performed for each generator, considering the variation in load throughout the day. The system’s load curve is taken into account to determine the optimal energy distribution. Thus, the problem of economic losses is approached from two perspectives: the minimization of costs due to nonsupplied electrical energy and the determination of efficient energy dispatch for each generator after network reconfiguration. For the analysis and case studies, simulations were conducted on the IEEE 9- and 30-node test systems. The results demonstrate the effectiveness of the proposed solution, evaluated in terms of reduced load shedding and economic losses.

A high-efficiency and wide-load current range LDO with dynamic loop gain control technique

The design proposes a low-dropout regulator incorporating dynamic loop gain control to accommodate a wide load current range. It introduces a multi-phase error detection mechanism, a tri-state damping selector, and a dynamic integration shift register. These elements predict load variations and dynamically increase loop gain when the load variation is significant, all without increasing the input clock. This approach achieves a fast response and low power consumption simultaneously. The chip was implemented in 0.18 μm CMOS, with an input operating voltage of 1.2 V and an output voltage of 1 V. The measured results revealed that it could provide output currents from 0.5 to 150 mA, indicating that it had a 300 times wider current range. The maximum current efficiency was 99.93%, and the core area was 0.198 mm2.

Distributed Secondary Frequency Control for AC Microgrids Using Load Power Forecasting Based on Artificial Neural Network

In islanded ac microgrids with heterogeneous distributed generations (DGs), secondary frequency control (SFC) can restore the frequency that deviates from the nominal level due to the primary P-f droop control. Traditionally, to realize SFC, the instantaneous frequency needs to be directly measured or estimated to produce frequency compensations, which increases system cost and complexity. In this paper, a new spatial load power forecasting for distributed secondary frequency control (SLPF-DSFC) based on artificial neural networks is proposed. Without frequency measurement, it can effectively eliminate frequency deviations and autonomously cope with various load changes by optimizing active power outputs from DGs. The distributed average and pinning protocols based on graph theory are also included. Comprehensive case studies demonstrate that the proposed SLPF-DSFC method shows superior performance over conventional SFC approaches in terms of frequency stabilization and voltage harmonics reduction during load variations, SFC startup/shutdown, as well as communication delays.

Investigating the Roles of the Visual and Parietal Cortex in Representing Content versus Context in Visual Working Memory.

Content-to-context binding is crucial for working memory performance. Using a dual-serial retrocueing (DSR) task on oriented gratings, Yu et al. (2020) found that content (orientation) of both prioritized and unprioritized memory items (PMI; UMI) was represented simultaneously in visual cortex, while their context (location) was represented in intraparietal sulcus (IPS), with a priority-based remapping of the representation of content and context of the UMI in each region, respectively. This registered report acquired fMRI of 24 healthy adults while they performed a DSR task with location as the to-be-reported content and orientation as the task-relevant context. We contrasted three accounts: domain-dependent, the engagement of visual and parietal regions depends on the feature domain (orientation vs location); functional, the engagement of these regions depends on their function (content vs context); and hybrid-a combination of the domain-dependent account and the additional stipulation that IPS encodes context regardless of domain. Delay-period activity in early visual cortex conformed most closely with functional predictions: robust priority-sensitive representation of stimulus location (content), but no evidence for the active representation of stimulus orientation (context). Delay-period activity in IPS, in contrast, conformed most closely to predictions of the hybrid account: active representation of content (location) and of prioritized context (orientation). Exploratory analyses further supported the hybrid account of IPS, revealing univariate sensitivity to variation in both content and context load, the latter in a manner that predicted individual differences in behavior. The representation of visual information in working memory is highly dependent on behavioral context.

Control of three‐level bidirectional buck‐boost converter for battery energy storage system in bi‐polar DC microgrid

AbstractThis paper deals with the model predictive current control of a three‐level bidirectional buck‐boost converter for a battery energy storage system in a bi‐polar direct current (DC) microgrid. It comprises a solar photovoltaic system, a wind energy system, a battery and DC loads. The proposed control scheme provides voltage balance and voltage regulation at the bi‐polar DC link during dynamic operating conditions, such as variations in solar irradiance, wind velocity and load. Predictive current control utilises the converter's discrete behaviour to identify the suitable switching states that minimise the cost function. Furthermore, bidirectional power flow capabilities allow the connection of energy storage devices, such as batteries and ultra‐capacitors, to the bi‐polar DC microgrid. The proposed system is verified through simulations and validated on a lab‐scale prototype. The results have been found satisfactory with respect to steady‐state error, peak overshoot, and settling time.

Fine dust inflow paths and heating load variation based on field measurement of air tightness in two Korean schools

This study aimed to determine the effects of fine dust reduction, as well as the energy load reduction in association with the improvement plans by measuring the airtightness and indoor and outdoor fine dust concentrations in middle and high school classrooms and by identifying the fine dust inflow paths. The air tightness and indoor and outdoor fine dust concentrations were measured at two schools, and the paths of outdoor fine dust inflow were identified for an extensive analysis of the effects of fine dust reduction and the energy load reduction in association with improvement plans. Air tightness in the classroom at school A and B was improved by 32.26% and 38.58% with sealing, respectively. Analyzing the outdoor air inflow paths at school B according to the variation in air tightness, the probability of outdoor air inflow increased in the following order from the highest to the lowest: corridor windows, corridor doors, exterior windows, supply and exhaust units. For the rate of reduction in heating load, the highest rate was shown to be 27% at school A with improved air tightness at the exterior windows and 32% at school B with improved air tightness at the interior doors. This is expected to be reflected in the selection of equipment such as heat pumps and show cost-saving effects such as capacity reduction.

Development of a real-time impedance matching system for ion cyclotron resonance heating in experimental advanced superconducting tokamak

To achieve stable operation of an ion cyclotron resonance heating (ICRH) system in the Experimental Advanced Superconducting Tokamak (EAST), a real-time impedance matching system needs to be established to respond to antenna load variation during long pulse discharges. A new impedance matching method based on capacitors was proposed in this study. By considering the reflected voltage of the transmission line as the feedback parameter, the real-time impedance-matching system can quickly control the motors based on a programmable logic controller to determine the minimum reflection voltage. A real-time impedance matching system was successfully used on the test platform in the laboratory and on the ICRH system in EAST. A significant result is that we can match the variable impedance within 1 s by suitably adjusting the motor controller to ensure high-power and long-pulse operation of the ICRH system to satisfy the requirements of the EAST experiment.

Adaptive control for shape memory alloy actuated systems with applications to human-robot interaction.

Shape memory alloy (SMA) actuators are attractive options for robotic applications due to their salient features. So far, achieving precise control of SMA actuators and applying them to human-robot interaction scenarios remains a challenge. This paper proposes a novel approach to deal with the control problem of a SMA actuator. Departing from conventional mechanism models, we attempt to describe this nonlinear plant using a gray-box model, in which only the input current and the output displacement are measured. The control scheme consists of the model parameters updating and the control law calculation. The adaptation algorithm is founded on the multi-innovation concept and incorporates a dead-zone weighted factor, aiming to concurrently reduce computational complexities and enhance robustness properties. The control law is based on a PI controller, the gains of which are designed by the pole assignment technique. Theoretical analysis proves that the closed-loop performance can be ensured under mild conditions. The experiments are first conducted through the Beckhoff controller. The comparative results suggest that the proposed adaptive PI control strategy exhibits broad applicability, particularly under load variations. Subsequently, the SMA actuator is designed and incorporated into the hand rehabilitation robot. System position tracking experiments and passive rehabilitation training experiments for various gestures are then conducted. The experimental outcomes demonstrate that the hand rehabilitation robot, utilizing the SMA actuator, achieves higher position tracking accuracy and a more stable system under the adaptive control strategy proposed in this paper. Simultaneously, it successfully accommodates hand rehabilitation movements for multiple gestures. The adaptive controller proposed in this paper takes into account both the computational complexity of the model and the accuracy of the control results, Experimental results not only demonstrate the practicality and reliability of the controller but also attest to its potential application in human-machine interaction within the field of neural rehabilitation.

Experimental and numerical study on a SI engine fueled with gasohol and dissociated methanol gas blends at lean conditions

Hydrogen-rich dissociated methanol gas (DMG) and ethanol are both promising alternative fuels for engines. The study investigates the performance of a spark ignition engine using various gasohol-DMG blends under lean conditions, aiming to evaluate the influence of fuel composition, excess air ratio, and operational parameters including engine load and speed. The results demonstrate that the introduction of DMG substantially enhances the engine's fuel economy, combustion efficiency, combustion stability, and the HC and CO emissions. The addition of ethanol has a distinct impact on engine performance, including increased peak cylinder pressure at lower DMG ratios and a subsequent decrease at higher DMG ratios. It also leads to substantial reductions in HC and NOx emissions but shows a non-linear effect on CO emissions with an increasing ethanol blending ratio. An increase in excess air ratio results in simultaneous reductions in peak cylinder pressure, HC, CO, and NOx emissions. Moreover, variations in engine load and speed respectively impact the cylinder's thermal environment and gas flow, consequently influencing combustion and emission characteristics.

Detection and Diagnostics of Bearing and Gear Fault under Variable Speed and Load Conditions Using Heterogeneous Signals

In industrial applications, rotating machines operate under real-time variable speed and load regimes. In the presence of faults, the degradation of critical components is accelerated significantly. Therefore, robust monitoring algorithms able to identify these faults become crucial. In the literature, it is hard to find comprehensive monitoring systems that include variable speed and load regimes with combined gearbox faults using electrical and vibration signals. For this purpose, a novel signal processing methodology including a geometric classification technique is proposed. This methodology is based on using different types of sensors such as current, voltage and vibration sensors with a regime normalization, which allows the grouping of different regimes belonging to the same health state. It consists of reducing dispersion between the class observations and separating other classes representing different health states including the variation in speed and load. Then, a peripheral threshold is proposed in our classifier to diagnose new health states. To verify the effectiveness of the methodology, current, voltage and vibration data from a gearbox system are collected under variable speed and load levels.

Implementation and verification of direct infinite elements in axisymmetric problems

In the field of dynamic soil-structure interaction, the simulation of geotechnical massifs under non-vanishing time-averaged loads is often challenging, depending on factors such as load variations, the 3D model representation and the chosen model boundaries. This study aims to evaluate the effectiveness of the method developed recently by the authors under non-vanishing time-averaged loads. The evaluation involves comparing the results of a homogeneous axisymmetric model at two control points and the simulation of an axisymmetric bilayer. The aim is to demonstrate the robustness of the method in contrast to widely used dissipative devices in scenarios involving sudden loading. Using an implicit integration scheme in the time domain, the method is shown to be effective and correlations with reliable solutions and comparisons with common dissipative devices for sudden-load models are presented. This analysis leads to the conclusion that the proposed procedure adeptly addresses the challenges associated with the simulation of geotechnical masses under non-vanishing time-averaged loads.

HSA-Based Optimal Allocation and Sizing of Shunt Compensators Considering Cable Aging Constraint and Load Variations

Aging of power system equipments can be caused by erosion of metallic structures such as cable sheathing. Higher system risk due to higher failure probability and possible system damage following the end of-life failure, are the direct consequence of cable aging. One of the solutions to solve this problem is the reduction of the cables current flow using different solutions in the power system network. In this paper, an HSA-Based optimal placement and sizing of shunt compensators is proposed to reduce the current flow. The IEEE RTS 24-Bus network is selected to verify the proposed algorithm with and without considering load variations during a year. The obtained results show the possibility of slowing of the cable aging in the studied network using shunt compensators.

Optimizing power consumption and position control in an electro-hydraulic system with cylinder bypass and NN-MPC

This study introduces an innovative approach to enhance the energy efficiency and position control performance of electro-hydraulic systems, employing a comprehensive comparative analysis. It presents and evaluates three control techniques: Proportional-Integral-Derivative (PID) control, Model Predictive Control (MPC), and Neural Network Model Predictive Control (NN-MPC). These methods are systematically assessed across varying load conditions. Notably, our research unequivocally establishes the exceptional performance of the NN-MPC approach, even when confronted with load variations. Furthermore, the study conducts an exhaustive examination of energy consumption by comparing a conventional system, where a flow control valve is not utilized as a hydraulic cylinder bypass, with a proposed system that employs a fully open Flow Control Valve (FCV). The results underscore the remarkable energy savings achieved, reaching up to 9% at high load levels.

The Horizontal Bearing Characteristics and Microscopic Soil Deformation Mechanism of Pile-Bucket Composite Foundation in Sand

The pile-bucket composite foundation represents an innovative foundation form that surpasses the horizontal bearing performance of both single bucket-shaped foundations and pile foundations. The intricate interplay between piles and buckets introduces the complexity of the factors influencing the bearing performance of composite foundations under horizontal loads. In this paper, the indoor model tests were conducted to investigate the effects of relative density and pile-to-barrel diameter ratio on the horizontal bearing capacity and surrounding soil pressure of the pile-bucket composite foundation. A sensitivity analysis on the bearing characteristics of the pile-bucket foundation was performed using ABAQUS/CAE 2020 software. The results reveal a consistent variation in load–displacement curves across diverse diameter ratios of piles to buckets. The pile-bucket diameter ratio significantly impacts the horizontal bearing characteristics of the composite foundation. Reducing the pile-bucket diameter ratio improves the horizontal bearing capacity of the composite foundation. When the diameter ratio of piles to buckets diminishes to ≤0.317, the influence of this ratio on bearing performance becomes markedly pronounced. The displacement range of the surface soil decreases with an increase in relative density, while the influence depth of the surrounding soil of the composite foundation significantly decreases as the pile-to-barrel diameter ratio decreases.

Twin-Delayed Deep Deterministic Policy Gradient Algorithm to Control a Boost Converter in a DC Microgrid

A stable output voltage of a boost converter is vital for the appropriate functioning of connected devices and loads in a DC microgrid. Variations in load demands and source uncertainties can damage equipment and disrupt operations. In this study, a modified twin-delayed deep deterministic policy gradient (TD3) algorithm is proposed to regulate the output voltage of a boost converter in a DC microgrid. TD3 optimizes PI controller gains, which ensure system stability by employing a non-negative, fully connected layer. To achieve optimal gains, multi-deep reinforcement learning agents are trained. The agents utilize the error signal to obtain the desired output voltage. Furthermore, a new reward function used in the TD3 algorithm is introduced. The proposed controller is tested under load variations and input voltage uncertainties. Simulation and experimental results demonstrate that TD3 outperforms PSO, GA, and the conventional PI. TD3 exhibits less steady-state error, reduced overshoots, fast response times, fast recovery times, and a small voltage deviation. These findings confirm TD3’s superiority and its potential application in DC microgrid voltage control. It can be used by engineers and researchers to design DC microgrids.