Modified Test System Research Articles

The performance of quantitative D-dimer assays in Chinese clinical laboratories by analyzing data from National External quality Assessment Scheme

Background and aimsTo investigate performance of D-dimer assays in China and address analytical quality issues. Materials and methodsD-dimer assays data were collected from China National External Quality Assessment Scheme (China NEQAS) from 2014 to 2022. We analyzed reagents, assay results, reporting unit and cutoffs in 2022 China NEQAS. Interlaboratory coefficient variations (CVs) and influence of modified/unmodified test systems on CVs were investigated over 9 years. ResultsThere were 82 reagent brands in China NEQAS, but 55 reagent instructions did not indicate expression unit (DDU or FEU). Up to 7-fold of the ratio of max-to-min mean results was shown among different assays with same unit on the same sample. A prevalence of FEU (63.4%) over DDU (17.1%) was observed. Although 669 laboratories (37.9%) among 1766 laboratories used reagents without VTE exclusion claim, they also reported cutoffs. The CVs of only two assays were decreasing over years. CVs of modified test systems were higher than those of unmodified systems before improvement. ConclusionsExpression unit should be required to label in package inserts by regulatory authority. Laboratory professionals should follow instructions for use and prefer unmodified test systems for clinical safely application. Harmonization of reporting units through collaborative efforts is the promising step.

An Improved Grid-Forming Control Strategy for Microgrid Stability in Modern Power System

Permanent Magnet Synchronous Generator-Wind Turbines (PMSG-WTs) are becoming increasingly popular, which is contributing to significant issues concerning the stability of modern power. One crucial aspect is the Low Voltage Ride Through (LVRT) capability, especially in interconnected networks. To address this issue, this study presents a novel control strategy for Grid-Forming Inverters (GFMIs) using Droop Control in modern power systems. The proposed strategy, termed the De-loaded Technique enhanced by Three-Dimensional Fuzzy Logic Control and Supplementary Control Loop (DT-3D FLC-SCL), aims to enhance the stability and performance of five microgrids (MGs) by controlling both, machine-side and grid-side of the WTs. To evaluate the behavior of PMSG-WTs during low voltage conditions, simulation tests are conducted using the IEEE 14-bus modified test system in MATLAB/Simulink environment.

Application of modified artificial hummingbird algorithm in optimal power flow and generation capacity in power networks considering renewable energy sources

Today's electrical power system is a complicated network that is expanding rapidly. The power transmission lines are more heavily loaded than ever before, which causes a host of problems like increased power losses, unstable voltage, and line overloads. Real and reactive power can be optimized by placing energy resources at appropriate locations. Congested networks benefit from this to reduce losses and enhance voltage profiles. Hence, the optimal power flow problem (OPF) is crucial for power system planning. As a result, electricity system operators can meet electricity demands efficiently and ensure the reliability of the power systems. The classical OPF problem ignores network emissions when dealing with thermal generators with limited fuel. Renewable energy sources are becoming more popular due to their sustainability, abundance, and environmental benefits. This paper examines modified IEEE-30 bus and IEEE-118 bus systems as case studies. Integrating renewable energy sources into the grid can negatively affect its performance without adequate planning. In this study, control variables were optimized to minimize fuel cost, real power losses, emission cost, and voltage deviation. It also met operating constraints, with and without renewable energy. This solution can be further enhanced by the placement of distributed generators (DGs). A modified Artificial Hummingbird Algorithm (mAHA) is presented here as an innovative and improved optimizer. In mAHA, local escape operator (LEO) and opposition-based learning (OBL) are integrated into the basic Artificial Hummingbird Algorithm (AHA). An improved version of AHA, mAHA, seeks to improve search efficiency and overcome limitations. With the CEC'2020 test suite, the mAHA has been compared to several other meta-heuristics for addressing global optimization challenges. To test the algorithm's feasibility, standard and modified test systems were used to solve the OPF problem. To assess the effectiveness of mAHA, the results were compared to those of seven other global optimization algorithms. According to simulation results, the proposed algorithm minimized the cost function and provided convergent solutions.

Improving the economic and environmental benefits of the energy system: A novel hybrid economic emission dispatch considering clean energy power uncertainty

New energy power generation plays an important role in adjusting energy structure, reducing environmental pollution, and promoting sustainable social development. However, new energy power generation represented by solar and wind power has significant volatility, meanwhile, the wind and solar curtailment caused by uncertainty reduce utilization rate. In this context, considering the impact of uncertainty in new energy generation on system scheduling and developing new algorithms that can efficiently solves such complex scheduling problems are necessary. Therefore, this study constructed a hybrid dynamic economic emission model (HDEED) with the new energy generation penalty mechanism, and proposed a novel multi-objective tunicate swarm algorithm (MTSA)-based solution method. First, based on the unit operation characteristic equations, considering wind and solar power uncertainty, the HDEED model with the new energy generation penalty mechanism was constructed. Second, on the basis of the single objective tunicate swarm algorithm, MTSA algorithm was proposed by adding the running operators, and the feasibility of the proposed algorithm was verified. The test results showed that the Pareto fronts obtained by MTSA algorithm presented good distribution and diversity compared with existing algorithms. Finally, the proposed model and algorithm were proved by modified test system, meanwhile, the impacts of clean energy power on system operation stability, economic and environmental benefits were discussed. The results revealed that the penalty costs caused by uncertainty were higher than the operation and maintenance costs of new energy power stations, which accounted for about 2.1% of the total costs. The outcomes obtained in this study contribute to improving the utilization rate of clean energy and promoting the sustainable development of energy system.

Unified AC Transmission Expansion Planning Formulation incorporating VSC-MTDC, FACTS devices, and Reactive Power compensation

The main aim of the static Transmission Network Expansion Planning (TNEP) is to determine which and where new transmission equipment must be installed. The complexity added by the non-linearities leads to simplifications, which include the DC model. However, most non-linearities are solvable nowadays. Thus, the new scenario of large non-dispatchable power sources penetration and the several developments in technologies, e.g, Flexible AC Transmission Systems (FACTS) devices and High Voltage Direct Current (HVDC) interconnections, motivate the use of the AC model with its non-linearities. Some research works address the use of some of those technologies for TNEP in an independent fashion, which can lead to sub-optimal solutions. In this work, Voltage Source Controlled-Multiterminal HVDC (VSC-MTDC) systems, FACTS devices, and Reactive Power Planning (RPP) are integrated into the same planning optimization process, so that a unified AC TNEP formulation is proposed. A non-linear mathematical programming technique and a differential evolution based metaheuristics are chosen to achieve an optimal transmission configuration. To evaluate the benefits of the proposed approach, two IEEE modified test systems (9 and 118 buses) are used. Results suggest that more economical solutions can be obtained if different types of reinforcement strategies are taken into account in a unified approach.

Synchronverter-based frequency control technique applied in wind energy conversion systems based on the doubly-fed induction generator

• A novel synchronverter-based approach for frequency support is proposed. • A comprehensive analysis of three frequency control techniques is presented. • A modified IEEE 14-bus test system is used to assess a wind farm. The increasing penetration of wind energy conversion systems (WECSs) based on the doubly-fed induction generator (DFIG) has raised serious concerns about the stability of modern power systems. One important issue is the frequency control of interconnected networks, which may become more complex owing to the low inertia of wind turbines. In this context, this work presents a novel frequency control approach that associates the grid-side converter (GSC) with a synchronverter. To assess the performance of the proposed solution, a thorough comparison with other well-known frequency control techniques is presented, that is, pitch angle control and switching angle controller (SAC). Simulation tests of the IEEE 14-bus modified test system are carried out in PSCAD/EMTDC® software to assess the behavior of a 165 MW wind farm (WF) when frequency variations occur. The results show that the classical control approaches can restore the system frequency within a time interval up to 63% shorter and a frequency dip lower than 13% when compared with WECSs that cannot provide frequency support. In turn, the synchronverter-based technique presents improved performance while attenuating a frequency dip by 45.7% and 56.9% when compared with the aforementioned strategies and WECSs without frequency support, respectively.

Maximum available capacity evaluation of distribution network considering photovoltaics and electric vehicles

The surge of new loads such as the electric vehicles (EVs) and the large-scale integration of photovoltaics (PVs) pose a significant impact on the safe and economic operation of the distribution network. To evaluate the maximum available capacity of the distribution network, this paper analyzes the charging profile of the EVs, and established the model that predicts the medium- and long-term charging load of the EVs. Then, the maximum available capacity evaluation model that considers the distribution network reconfiguration under the influences of the EVs and PVs is developed. The proposed model is solved using the second-order cone relaxation technique. Besides, to determine the best location of the PVs, the genetic algorithm with the elite retention strategy is adopted to obtain the optimum allocation of the PVs. Simulation results carried out on the modified test system that includes two IEEE 33-bus systems show that the proposed method can accurately evaluate the maximum available capacity of the distribution network, and quantify the effect of the network reconfiguration and PVs on the maximum supply capability of the distribution network. In addition, the proposed method can also provide the theoretical support for the optimum investment in the distribution network.

An Optimal Power Flow Solution of a System Integrated with Renewable Sources Using a Hybrid Optimizer

A solution to reduce the emission and generation cost of conventional fossil-fuel-based power generators is to integrate renewable energy sources into the electrical power system. This paper outlines an efficient hybrid particle swarm gray wolf optimizer (HPS-GWO)-based optimal power flow solution for a system combining solar photovoltaic (SPV) and wind energy (WE) sources with conventional fuel-based thermal generators (TGs). The output power of SPV and WE sources was forecasted using lognormal and Weibull probability density functions (PDFs), respectively. The two conventional fossil-fuel-based TGs are replaced with WE and SPV sources in the existing IEEE-30 bus system, and total generation cost, emission and power losses are considered the three main objective functions for optimization of the optimal power flow problem in each scenario. A carbon tax is imposed on the emission from fossil-fuel-based TGs, which results in a reduction in the emission from TGs. The results were verified on the modified test system that consists of SPV and WE sources. The simulation results confirm the validity and effectiveness of the suggested model and proposed hybrid optimizer. The results confirm the exploitation and exploration capability of the HPS-GWO algorithm. The results achieved from the modified system demonstrate that the use of SPV and WE sources in combination with fossil-fuel-based TGs reduces the total system generation cost and greenhouse emissions of the entire power system.

Comparative techno-economic analysis of power system with and without renewable energy sources

<p>The primary aim of this work is to feature the advantages of integrating natural source of energy from the solar and wind to the prevailing electric power systems. Two types of analysis are carried out in two test systems (standard and modified test systems) and the outcome of the test systems are compared. The two analyses are technical analysis and economic analysis. The stability of the voltage is analyzed under technical analysis and the price of energy consumed from the electric grid is calculated and analyzed under the economic analysis. Dynamic hourly load data, hourly solar radiation, hourly wind velocity, and dynamic electricity prices are considered for the standard IEEE system and modified test system (with the integration of RES). Voltage stability index (L-Index) and price of the electricity consumed from electric grid are found for standard test system and the outcome is compared with the outcome of modified test systems. MATLAB coding is done for techno-economic analysis for both test systems. It is inferred from the outcome that the integration of renewable energy sources fairly contributes to the economic benefit of the system by lowering the power purchased from the grid and enhance the stability of the system.</p>

Optimal scheduling for power system peak load regulation considering short-time startup and shutdown operations of thermal power unit

To balance the peak–valley difference of the system load in electrical power systems, the peak load regulation problem has become a major barrier, resulting in challenges to unit commitment (UC). In addition to the new construction of specific system peak load regulation resources on the generation side and the demand response programs on the demand side, this paper focuses on the capacity of thermal power units in system peak load regulation. Considering the temporal distribution of system load off-peak hours, the potentiality of the deeper peak load regulation mode and the short-time startup and shutdown regulation mode of thermal power units are analysed. This paper presents an optimal scheduling model for power system peak load regulation considering the short-time startup and shutdown operations of a thermal power unit. First, an improved linear UC model considering the thermal units’ startup and shutdown trajectories is established. Next, for different peak load regulation modes of thermal units, the corresponding peak load compensation rules are processed and converted into linear formulations. An integrated optimal scheduling model for power system peak load regulation with a suitable rolling optimization strategy is proposed. A real prefecture-level urban power system in southwest China and its modified test systems are used to test and verify the validity and effectiveness of the proposed methodology.

Development and Validation of Artificial Neural Network-Based Tools for Forecasting of Power System Inertia With Wind Farms Penetration

Increased penetration of power electronics interfaced renewable energy sources-based generation, for instance, wind farms, that displaces much of the conventional synchronous generation, has a profound effect on the inertia of modern/future power system networks. This article presents the development and validation of artificial neural network (ANN)-based tools, utilizing the power system variables measured by phasor measurement units through wide-area measurements systems, for estimation/forecasting of power system inertia with high penetration of wind farms. The development stage involves the correlation analysis to identify the best power system variables that can be nominated as inputs, and the training of the proposed inertia forecasting tools with the best-nominated inputs that are highly correlated with the power system inertia. Whereas, in the validation stage, the functionality of the trained ANN-based inertia forecasting tools have been validated using the hardware-in-the-loop testing facility developed at the University of Manchester. The development and validation procedures of the proposed inertia forecasting tools have been demonstrated on the IEEE 9-bus modified test system. The validation results revealed the effectiveness of the proposed inertia forecasting tools in estimating the inertia of modern/future power system networks with high penetration of wind farms.

Long-term Static and Operational Reserves Assessment Considering Operating and Market Agreements Representation to Multi-Area Systems

This paper exploits different computational modelling to assess long-term static and operating reserves to multi-area systems. To deal with intermittent renewable generation and other types of technologies, this paper is proposing a flexible simulation model able to capture not only technological innovation of power system components and their electric and energetic behaviors, but also operational procedures and market agreements representations to yield planning insights about the performance of the multi-area systems. Results based on two modified test systems with a variate of generation technologies and interconnections are used to show the potential of the simulation model.

Flexible scheduling of reconfigurable microgrid-based distribution networks considering demand response program

In future smart distribution grids, the number of microgrids can be increased within the network. In this paper, the optimal reconfiguration of microgrid-based distribution networks is modeled by considering demand response program to enhance the network scheduling flexibility. According to the proposed method, the optimal configuration of distribution network is determined to meet the best scheduling goals. Furthermore, the optimal state of interconnecting switches between microgrids and the main grid is determined as well. The optimal topology of microgrid-based distribution networks is given for each hour of operation, using optimal power flow. The results are obtained with and without demand response program that are applied to each microgrid. Different load profiles such as residential, industrial, commercial and official are considered and manipulated with suitable demand response programs. For better observation, various cases are investigated separately to meet the different goals considered by the distribution network operator. In the presented paper, the optimization aims to minimize total system operation cost and losses with optimal networked-microgrid reconfiguration. The effects of demand response programs are shown in the results of optimal topology of microgrids. To show the effectiveness of the presented approach, IEEE 85-bus modified test system is employed.

Optimal scheduling of distribution network incorporating topology reconfiguration, BES and load response: A MILP model

Distributed generation (DG) is becoming increasingly important due to the serious environmental pollution caused by conventional fossil-energy-based generation and the depletion of non-renewable energy. As the flexible resources in the active distribution network (ADN), battery energy system (BES) and responsive load (RL) are all able to assist renewable DG integration in day-ahead dispatch. In addition, the security and economic level can be well improved by adjusting network topology. Therefore, in this paper, a coordinated day-ahead scheduling method incorporating topology reconfiguration, BES optimization and load response is presented to minimize the total day-ahead operation cost in the ADN. Linearized current injection models are presented for renewable DG, RL and BES based on the linear power flow model, and an extensible linear switching operations calculation (ELSOC) method is proposed to address the network reconfiguration. Thus, a mixed integer linear programming (MILP) model is proposed to optimal coordinated operation of ADN. The correctness and effectiveness of the proposed method are demonstrated by the simulations on the modified test system. In addition, the combined scenario and Monte-Carlo method is used to handle the uncertainties of loads and DGs, and the results of different uncertainties can further verify the feasibility of the proposed model.

Probabilistic load flow with correlated wind power sources using a frequency and duration method

Probabilistic load flow (PLF) is an important tool in power system planning and operation. One limitation of conventional PLF is that only the probability information of random variables is obtained as a reference for related analyses. Frequency and duration information often plays an important role in power system assessment. In this study, a frequency and duration method for PLF with correlated wind power sources and loads is proposed based on Latin hypercube sampling (LHS), Nataf transformation, and Markov chains. In the proposed method, the spatial and temporal correlations among wind power sources and loads are addressed using Nataf transformation and Markov chains, which are incorporated into the LHS framework to solve the PLF problem with frequency and duration quantities. With the proposed method, not only the probability information but also the frequency and duration information of the random output variables in a PLF problem can be efficiently and accurately obtained. The performance of the proposed PLF method is verified using comparative tests on three modified test systems.

Development of a genetic algorithm for maximizing wind power integration rate into the electric grid

In this paper, a new method was proposed with the objective of maximizing the rate of wind power integration into the electric grid. This method was based on the optimization of the parameters of the turbine governors (TGs) by means of a genetic algorithm. The tuning of TGs' parameters was formulated relying on an objective function aiming at reaching the maximum wind power penetration rate. The dynamic grid modeling consisted of synchronous machines, regulators, and wind turbines. The IEEE 14-bus modified test system was adopted to test the grid using the Power System Analysis Toolbox. The simulation results revealed that, with the optimized TGs' parameters, the rate of wind power integration improved considerably.

Optimization of damping controller for PSS and SSSC to improve stability of interconnected system with DFIG based wind farm

This paper studies the stability issues of the Double Fed Induction Generator (DFIG) based wind farm in the power system with variable wind speed in strong and weak grid. With the intermittent generation of the wind power, it experiences instability and malfunction. The nature of Electromechanical Modes of Oscillations (EMOs) for DFIG incorporated into the multi-machine system under various conditions is explored considering system uncertainties. In this paper, the impact of Power System Stabilizer (PSS) and Static Series Synchronous Compensator (SSSC) on the stability of wind power system is analyzed by utilizing modified IEEE 14-bus test system. The rotor speed deviation is the objective function for particle swarm optimization and is formulated based on the transient and small signal stability. The transient and small signal stability investigations are performed by using time domain simulation and eigenvalue analysis respectively. The sensitivity analysis is carried out with degree of compensation of SSSC and coefficient of damping gain of PSS as sensitivity parameter. The performance of the modified test system is examined for transient and small signal stability for DFIG based wind farm for variable wind speed and grid strength considering different combinations of PSS and SSSC. From the results, it is noticed that oscillations in the system are minimized, damping, transient and small signal stability are enhanced and also the capability of synchronous generator and DFIG under extreme disturbance is intensified by optimization the coordinated controllers’ parameters. The analyses also show that damping ratio is improved for most of the EMOs. Sensitivity analysis identifies EMOs that have detrimental and beneficial impacts on the system.

Coordination of generation, transmission and reactive power sources expansion planning with high penetration of wind power

High penetration of renewable energy is one of the important feature of future power systems. However, the randomness and intermittence of renewable energy have brought new challenges to planners. To accommodate the uncertainty and variability of wind power, an innovative scenario-based stochastic model that incorporates generation, transmission, and reactive power planning is presented, considering spinning reserve, unit ramp rate, and current output of units. To consider reactive power flow and voltage, a second-order cone programming model based on relaxed AC optimal power flow is introduced. Moreover, unit status and optimal transmission switching are implemented to promote wind consumption, reduce congestion, and attain a more flexible topology. The proposed methodology organically combines operational constraints with planning issues. Finally, two modified test systems with high wind power integration are utilized to illustrate the effectiveness of the presented model.

Research on resolution test system of LLL and infrared fusion equipment

Based on the analysis of the resolution test system for low-light level(LLL) vision equipment and the multi-resolution time domain (MRTD) test system for infrared equipment, the conception of resolution test scheme for LLL and infrared image fusion equipment was proposed. The exiting equipment of infrared MRTD test system was utilized, the resolution test system of the LLL and infrared fusion image was established by modifying light source, incident light path and target plate, and the analysis on the scheme was conducted.Results indicate that the modified test system can both realize infrared reflection and low-light projection. The measurement accuracy of infrared temperature difference is close to 0.01 K, and the measurement accuracy of low-light illumination can reach 1 lx. The test of fusion image resolution meets the requirement of multiple decrement(1/62≈0.891).

Reliability assessment method of composite power system with wind farms and its application in capacity credit evaluation of wind farms

This paper presents a non-sequential Monte Carlo Simulation (MCS)-based method for the reliability assessment of composite power system with wind farms (WFs). A multistate probability table and its corresponding Spearman’s rank correlation coefficient (SRCC) are combined to represent the power outputs of WFs, which makes the multistate model of WFs compatible with the non-sequential MCS while considering the dependence among power outputs of WFs. By constructing a system state array with encoding conversion, a state merging technique is proposed, which significantly reduces the number of system states to be evaluated. In addition, the parallel computing technique is employed to accelerate the contingency analysis for the merged system states. Furthermore, the capacity credit (CC) of WFs considering both wind power correlation and transmission network constraints is evaluated based on the proposed reliability assessment method. Finally, the effectiveness of the proposed reliability assessment method and its application in the CC evaluation are demonstrated using extensive numerical studies on several modified test systems.