Operation Of Electric System Research Articles

Transient behavior of pumped storage-wind hybrid power system under grid-connected operation condition

The large-scale grid connection of renewable energy represented by wind power threatens the safe and stable operation of electric system. The vigorous development pumped storage power station (PSPS) is a global consensus to support the grid-connected of renewable energy. This paper investigates the transient behavior of pumped storage-wind (PSW) hybrid power system under grid-connected operation condition (GCOC). Firstly, a model of PSW hybrid power system under GCOC is constructed. The stable domain is determined according to Hopf bifurcation theory. The influence of system parameters on the stability of PSW hybrid power system under GCOC is analyzed. A synergetic control strategy is designed to improve the dynamic performance of the system. Then, the mechanism of multi-time-scale dynamic response is revealed. Finally, the transient behavior of PSPS under different application scenarios and the interaction between PSPS and wind power station (WPS) are studied. The results show that the stability of PSW hybrid power system under GCOC can be judged by stable domain. The designed synergetic control strategy can improve the dynamic performance of the system. The dynamic response is composed of three oscillations with different frequencies, namely, mass oscillation at low frequency, water hammer oscillation at medium frequency, and electrical oscillation at high frequency. The principal oscillation is caused by water hammer. The interaction between PSPS and WPS has a significant effect on the transient behavior. By adjusting the interaction between PSPS and WPS, the system can absorb more wind power while ensuring safe and stable operation, which can increase profits and avoid unnecessary operation oscillations. The analytical methods and results presented in this paper are of great significance for improving the ability of power grid to accept renewable energy.

Enhanced thermal modeling of power transformers: A comparison of bond graph and IEEE methods considering external environmental factors

The optimal and secure operation of electrical power system is largely affected by the reliability of power transformers in operational and economic terms. The continuous monitoring of power transformer is crucial for ensuring the quality of electrical power supply. Hence, the thermal modeling of power transformer is very essential to prevent their failure. This paper aims to design thermal models of operating power transformer by considering the two external influencing parameters of solar irradiation and wind flow. This paper presents a Bond Graph (BG) method-based design of thermal models for power transformer under operating condition by comparison with the results obtained from IEEE Std. based thermal models and also with the practical readings. The models have been implemented on a 5MVA, 33/11 kV power transformer running at 33 kV Substation, Sardarpura, Jodhpur, by initially considering the input hourly dataset of load and ambient temperature. The designed thermal models were subsequently updated by integrating the impact of solar radiation and wind incident on the surface of power transformer. Comparative study of all the designed thermal models has been done that indicates improved accuracy of the model if the impact of solar radiation and wind flow are included. Also, the effectiveness of the BG based thermal models in accurately predicting the system behaviour validates their applicability for real world engineering applications.

Significantly Enhanced Corona Resistance of Epoxy Composite by Incorporation with Functionalized Graphene Oxide

Enhancing the corona resistance of epoxy resin (EP) is crucial for ensuring the reliable operation of electrical equipment and power systems, and the incorporation of inorganic nanofillers into epoxy resin has shown significant potential in achieving this. In this study, functionalized graphene oxide (KHGO) was synthesized via a sol-gel method to enhance the corona resistance of EP with electrochemical impedance spectroscopy (EIS) used to assess the properties of KHGO/EP composites. Fourier transform infrared spectroscopy (FTIR) and X-ray photoelectron spectroscopy (XPS) verified the successful grafting of epoxy groups onto the GO surface. The thermal conductivity and stability of the KHGO/EP composite initially increased with KHGO content but declined when the content exceeded 1.2 wt.%. Positron annihilation lifetime spectroscopy (PALS) indicated that KHGO improved interfacial compatibility with EP compared to GO, with agglomeration occurring when KHGO content exceeded a threshold value (1.2 wt.%). EIS analysis revealed that the corona resistance of the KHGO/EP composite was optimal at a filler content of 0.9 wt.%. After corona treatment, the saturation water uptake of the 0.9 wt.% KHGO/EP composite decreased by 15% compared to pure EP with its porosity reduced to just 1/40th of that of pure EP. This study underscores that well-dispersed KHGO/EP composite exhibits excellent corona resistance property suggesting the potential for industrial applications in high-voltage equipment insulation.

A Study the optimisation of smart grid power flow

Optimal Power Flow (OPF) is a fundamental concept in the management and operation of modern electrical power systems, particularly within the context of smart grids. As the demand for electricity continues to grow and the energy landscape evolves towards greater sustainability, the need for efficient, reliable, and adaptive power systems has become more critical than ever. The primary objective of OPF is to determine the most efficient way to operate the power system while satisfying various technical and operational constraints. This involves the optimization of several variables, including generator outputs, voltage levels, and power flows, to minimize costs, reduce losses, and enhance system reliability. Smart grids can enhance their resilience to disturbances, support the efficient use of renewable energy, and provide reliable electricity to consumers.

Novel glassbox based explainable boosting machine for fault detection in electrical power transmission system.

The reliable operation of electrical power transmission systems is crucial for ensuring consumer's stable and uninterrupted electricity supply. Faults in electrical power transmission systems can lead to significant disruptions, economic losses, and potential safety hazards. A protective approach is essential for transmission lines to guard against faults caused by natural disturbances, short circuits, and open circuit issues. This study employs an advanced artificial neural network methodology for fault detection and classification, specifically distinguishing between single-phase fault and fault between all three phases and three-phase symmetrical fault. For fault data creation and analysis, we utilized a collection of line currents and voltages for different fault conditions, modelled in the MATLAB environment. Different fault scenarios with varied parameters are simulated to assess the applied method's detection ability. We analyzed the signal data time series analysis based on phase line current and phase line voltage. We employed SMOTE-based data oversampling to balance the dataset. Subsequently, we developed four advanced machine-learning models and one deep-learning model using signal data from line currents and voltage faults. We have proposed an optimized novel glassbox Explainable Boosting (EB) approach for fault detection. The proposed EB method incorporates the strengths of boosting and interpretable tree models. Simulation results affirm the high-efficiency scores of 99% in detecting and categorizing faults on transmission lines compared to traditional fault detection state-of-the-art methods. We conducted hyperparameter optimization and k-fold validations to enhance fault detection performance and validate our approach. We evaluated the computational complexity of fault detection models and augmented it with eXplainable Artificial Intelligence (XAI) analysis to illuminate the decision-making process of the proposed model for fault detection. Our proposed research presents a scalable and adaptable method for advancing smart grid technology, paving the way for more secure and efficient electrical power transmission systems.

A three-level model for integration of hydrogen refuelling stations in interconnected power-gas networks considering vehicle-to-infrastructure (V2I) technology

The coupling with natural gas networks creates an excellent opportunity for renewable-rich power systems to facilitate the utilisation of renewable energy resources; on the other hand, with the increase in employment of fuel cell vehicles (FCVs), the number of hydrogen refuelling stations (HRSs) is increasing. The integration of HRSs in electric distribution systems may impact the operation of electric distribution systems. Through vehicle-to-infrastructure (V2I) technology, vehicles may exchange information with HRSs and know hydrogen prices. The operation of the coupled power and natural gas networks with integration of green HRSs, considering the model of FCVs has not been investigated in the literature, so, it has been set as the goal of this paper. To avoid the challenges of nonlinear gas flow model, they are linearized through piecewise linearisation. The case study is a 33-bus electric distribution system, coupled with a 14-node gas distribution network; each HRS includes a battery, a hydrogen tank, an electrolyzer, a PV and a wind turbine. MILP models have been used for FCVs, HRSs and power-gas networks. CPLEX solver is used for solving the developed MILP models. The results show that the total cost of the coupled electricity-gas network is $760.37 and the refuelling cost of each FCV is $17.30. The results indicate that both electric and hydrogen storage systems enhance the flexibility of HRSs, enabling efficient utilisation of PV modules and wind turbines, so, only in few hours, HRSs need to purchase electricity from electric distribution system; that is why each HRS makes a considerable profit of $519 per day. The achieved results also indicate that the pressure of all gas nodes and gas flow of all pipelines fall within their prespecified range.

Performance evaluation of multivariate deep-time convolution neural architectures for short-term electricity forecasting: Findings and failures

Deep learning (DL) models hold great promise in enhancing the decision-making abilities of electricity market participants and system operators in the short term, as they excel at capturing the intricate interdependencies and uncertainties in electricity consumption. Nevertheless, currently, the adoption of DL models for electricity prediction is not keeping up with the advancements in DL research. Within this framework, the aims of this study are to examine the probabilistic, multivariate, and multihorizon time series prediction of deep time convolution neural (DTCN) models. The effectiveness of DTCN in forecasting electricity consumption over daily, weekly and monthly time horizons is emperically assessed. We analyze both probabilistic and deterministic predictions. The evaluation data comprises real residential electricity consumption, average temperature and humidity with hourly granularity, collected over one year from a residential building. For evaluation, we considered accuracy and uncertainties, sensitivity to hyperparameters, the impact of exogeneous variables, as well as computational efficiency. Results show that the probabilistic models offer greater advantages when it comes to forecasting on multihorizons, especially for diverse datasets with non-uniform time series models. Unlike traditional models, the DTCN models demonstrated superior performance in capturing complex patterns and reducing forecast errors in short-term scenarios. However, their performance can be influenced by the sensitivity of hyperparameters. These findings can act as a benchmark for quantitatively assessing the performance of novel multivariate DL models in predicting electricity demand.

Solar Wind‐Magnetosphere Coupling Efficiency and Its Dependence on Solar Activity During Geomagnetic Storms of 23–24 Solar Cycles

AbstractSpace weather forecasts are of utmost importance in safeguarding navigation, communication, and electric power system operations, satellites from orbital drag, and the astronauts in the International Space Station from hazardous space radiation during extreme space weather conditions. The finest space weather prediction requires a clear understanding of solar wind‐magnetosphere coupling. The in‐situ measurements of the solar wind properties give unique information about the Sun and its activity on smaller to longer timescales. The present work investigates the influence of solar activity on the coupling of solar wind and Earth's magnetosphere during 23–24 solar cycles. The geomagnetic storms with Symmetric H‐component (SYMH) ≤ −85 nT during the 23–24 solar cycles are considered. We present the results of statistical analysis and relationships between the various solar wind parameters such as the total strength of interplanetary magnetic field (B) and its three‐axis components (Bx, By, and Bz), solar wind proton density (Nsw), solar wind speed (Vsw), SYMH indices, the amplitude, duration, and profile of the geomagnetic storms. The integrated electric field and integrated SYMH index during storms show the highest correlation of 0.92, implying that integrated SYMH is a better proxy of the injected solar wind energy in the magnetosphere in the form of the ring current. Moreover, we do see the difference in the solar wind‐magnetosphere coupling efficiency during the phases of 23–24 solar cycles which is intriguing.

Efficiency-oriented vehicle relocation of shared autonomous electric fleet in station-based car-sharing system

Long waiting delays for users and significant imbalances in vehicle distribution are bothering traditional station-based one-way electric car-sharing system operators. To address the problems above, a “demand forecast-station status judgement-vehicle relocation” multistage dynamic relocation algorithm based on the automatic formation cruising technology was proposed in this study. In stage one, a novel trip demand forecast model based on the long short-term memory network was established to predict users' car-pickup and car-return order volumes at each station. In stage two, a dynamic threshold interval was determined by combining the forecast results with the actual vehicle distribution among stations to evaluate the status of each station. Then vehicle-surplus, vehicle-insufficient, vehicle-normal stations, and the number of surplus or insufficient vehicles for each station were counted. In stage three, setting driving mileage and carbon emission as the optimization objectives, an integer linear programming mathematical model was constructed and the optimal vehicle relocation scheme was obtained by the commercial solver Gurobi. Setting 43 stations and 187 vehicles in Jiading District, Shanghai, China, as a case study, results showed that rapid vehicle rebalancing among stations with minimum carbon emissions could be realized within 15 min and the users’ car-pickup and car-return demands could be fully satisfied without any refusal.

Evaluate Prototype Performance of Battery Pack Monitoring for PT XYZ E-Bus Maintenance

One of the primary issues with electric vehicles is battery longevity. This study uses Kodular as an application developer for mobile app monitoring. Furthermore, using the monitoring data, PT XYZ may increase the operational efficiency of electric buses, lengthen battery life, and lower the risk of damage and maintenance costs. The following parameters are used: power voltage, SoC, SoH, system insulation resistance, positive insulation, negative insulation, pack voltage, and current. Inspectors can perform maintenance, diagnose faults, and improve overall system performance by keeping track of these parameters. It also contributes to the prevention of equipment failure, the avoidance of safety hazards, and the reliable operation of electrical and electronic systems. The goal of this research is to examine the performance of prototype monitoring with battery pack measurement parameters on a mobile app using prototype monitoring of battery packs on electric bus maintenance at PT XYZ. In this trial, interval changes happened every 30 minutes from 20:10 to 21:40. This research was carried out four times. The output parameters are pack voltage (545.9 V), current (-67.7 A), power voltage (26676), SoC (81.4%), SoH (100%), system insulation resistance (1233 KΩ), positive insulation (2952 KΩ), and negative insulation (1233 KΩ).

DYNAMIC MODELLING OF THREE-PHASE MICROGRIDS INCLUDING DQ0 CURRENT CONTROLLED PHOTOVOLTAIC GENERATION UNDER TRANSIENT CONDITIONS

ABSTRACT: The photovoltaic generation can be interconnected to power systems or to insulated grids to satisfy the growing electrical demand as an alternative renewable energy source. The photovoltaic generator dynamics affects the control, protection, stability, and power quality of the electrical grid, this network dynamics requires to be examined and conveniently regulated and adjusted. The dynamic averaged model for the dq0 current controlled DC/AC inverter is applied to obtain the time-domain response to electromagnetic transients under special conditions such as electrical load fluctuations, unbalanced faults, voltage and current transients to verify e.g., the ride through capability or to adjust the protection and control devices, these transients can be present during the operation of electrical microgrids and power systems interconnected with photovoltaic generation. The system modelling includes the photovoltaic plant interconnected with the microgrid using the dq0 current controlled DC/AC inverter. The case studies are applied to analyse three-phase electrical microgrids, their time-domain response, and the obtained voltage and current waveforms can be used to verify the dynamic behaviour and stability of the electrical system interconnected with photovoltaic generation and similarly to verify the operation of the dq0 current controller for the inverter. The results can be used to control the microgrid dynamic behaviour. Keywords: current control, dq0 transform, inverter, microgrids, photovoltaic generation, power quality, protection, time-domain, transients, unbalanced faults, unbalanced voltage sags, voltage source converter

Ultra-low concentration detection of dissolved C2H2 in oil using fixed-frequency RF-assisted calibration-free wavelength modulation spectroscopy

The monitoring sensitivity of dissolved C2H2 in transformer oil is crucial to ensuring the safe operation of electric power systems. A highly sensitive sensor system based on fixed-frequency radio frequency assisted calibration-free wavelength modulation spectroscopy (FF-RF-WMS-2f/1f) for dissolved C2H2 in transformer oil was developed and evaluated. Lowering the headspace sampling pressure to 0.1 atm effectively increased the outgassing ratio and detection signal of dissolved C2H2, while also reducing background interference. By employing a novel fixed-frequency RF-assisted modulation technique, we effectively suppressed the amplitude and influence of optical fringes and improved the system’s linear fitting R-squared value for gas-phase concentration from 0.9997 to 0.9999. Furthermore, the minimum detection limit (MDL), as calculated by Allan deviation, was reduced from 8.58 ppb to 4.36 ppb, while the optimal integration time increased slightly from 62 s to 79 s. The equivalent MDL for dissolved C2H2 reached 0.9 ppb. The comparative experiments showed that the error was less than 0.2 ppm or 8 %, the relative standard deviations (RSDs) of continuous measurement were less than 3 %, and the RSD at ultra-low concentration was less than 8 %. The experiments verified the high sensitivity and accuracy of the sensor system, making it suitable for ultra-low concentration dissolved C2H2 detection in transformer insulating oil.

Dynamic assessment approach for intelligent power distribution systems based on runtime verification with requirements updates

The study aims to address the challenge of dynamic assessment in power systems by proposing a design scheme for an intelligent adaptive power distribution system based on runtime verification. The system architecture is built upon cloud–edge-end collaboration, enabling comprehensive monitoring and precise management of the power grid through coordinated efforts across different levels. Specifically, the study employs the adaptive observer approach, allowing dynamic adjustments to observers to reflect updates in requirements and ensure system reliability. This method covers both structural and parametric adjustments to specifications, including updating time protection conditions, updating events, and adding or removing responses. The results demonstrate that with the implementation of adaptive observers, the system becomes more flexible in responding to changes, significantly enhancing its level of efficiency. By employing dynamically changing verification specifications, the system achieves real-time and flexible verification. This research provides technical support for the safe, efficient, and reliable operation of electrical power distribution systems.

Comparative study of dynamic response by three types of energy storage systems for grid studies: A Microgrid Laboratory experimental-based study

Implementing Energy Storage Systems (ESS) is increasingly significant in power electrical systems. This is attributed to their ability to store surplus electricity generated by renewable energy sources such as wind and solar, contributing to the balance between generation and demand. Literature studies indicate that this practice enhances network stability and diminishes the need for expensive redesigns in infrastructure. This paper presents an exhaustive experimentation-based study of the dynamic response provided by three energy storage system technologies: supercapacitors, Lithium-Ion batteries, and Vanadium redox flow batteries, technologies with significant academic, research, and industrial interests nowadays. The objective of this research is the experimental evaluation of the performance of such technologies in real electrical system operations, focusing on determining the efficiency of charge and discharge, as well as the tracking of active and reactive power achieved through the associated grid interface. The experimental tests performed in a microgrid laboratory show these technologies’ advantages and limitations in different grid-integration applications, with the Lithium-Ion battery-based ESS demonstrating the highest efficiency and faster power response. The results achieved and reported in the article can serve as an essential input for researchers and technology developers to feed their models with more precise parameters and data that might provide results closer to the actual behaviour of the studied prototypes. The methodological framework used in this research is descriptive, experimental, and quantitative.

A Soft Computing Approach to Assessing the Financial Sustainability of the Company (Following the Example of the Electricity System Operator EAD)

A Soft Computing Approach to Assessing the Financial Sustainability of the Company (Following the Example of the Electricity System Operator EAD)

Balancing the great British electricity system — Bulk dispatch optimisation

The Balancing Mechanism, managed by the British electricity system operator, National Grid ESO, is designed to maintain a balance between electricity generation and demand. It achieves this by purchasing extra generation (or demand reduction) through accepted offers and extra demand (or generation reduction) through accepted bids from Balancing Mechanism Units (BMUs) in real-time. The current manual approach for instructing BMUs becomes increasingly challenging as access to the market widens and the number of BMUs grows. This paper introduces a proof-of-concept optimisation model to assist Control Room engineers in making optimal decisions for a large number of BMUs. We outline the requirements for instructing BMUs, provide their mathematical formulation, and illustrate this using simple examples. Computational performance results based on test cases with up to 500 units are presented.

Optimizing the Electric Power System Operation Mode under the Conditions of Emergency Power Line Outages and at Peak Loads Using FACTS and Demand Response Programs

Optimizing the Electric Power System Operation Mode under the Conditions of Emergency Power Line Outages and at Peak Loads Using FACTS and Demand Response Programs

Comparison of Artificial Intelligence and Machine Learning Methods Used in Electric Power System Operation

Comparison of Artificial Intelligence and Machine Learning Methods Used in Electric Power System Operation

Research on vibration test method of electric actuator

Abstract The stable, safe, and reliable operation of electric actuator valve systems is of great significance in industrial production. This paper proposes a vibration test method for electric actuators, employing the test to meet the test severity level as the first priority, with random frequency vibration within 10 Hz to 1000 Hz. The vibration acceleration is loaded with a Gaussian distribution, which can be used to simulate the irregular vibration of electric actuators in the actual working conditions of the working environment. The test is divided into up and down vibration (z-axis), left and right vibration (x-axis), and front and rear vibration (y-axis) in three directions independently to verify the electric actuator from the mechanical structure and the circuit structure in order to meet the reliability requirements of the actual production.

Bi-Level Continuous-Time Model for the Coordinated Operation of Coupled Electric Power Transmission and Energy Storage Transportation Systems

Bi-Level Continuous-Time Model for the Coordinated Operation of Coupled Electric Power Transmission and Energy Storage Transportation Systems