Response Of Power System Research Articles

Online Prediction Method for Power System Frequency Response Analysis Based on Swarm Intelligence Fusion Model

Instability at transient frequency caused by faults in complex power systems is one of the greatest threats to operational safety. By analyzing the frequency response of power system in real-time and adopting control strategies promptly, power system accidents can be efficiently prevented. While existing online analysis methods integrate physical-driven and data-driven methodologies, they do not effectively utilize frequency timing characteristics. Consequently, a swarm intelligence fusion model, which integrates physical-driven and data-driven methods, is proposed as an improved frequency response analysis method. The transient frequency affecting components are separated into primary state variables and system time series data based on the properties of the time sequence. To preserve the actual relationship of the electrical mechanism model, the system frequency response (SFR) model is used as the physical-driven method for the primary state variables of the system. The Long Short Term Memory (LSTM) network was used as the data-driven method to extract timing features and correct the SFR model’s prediction using the system time series data as input. The two methods are combined using the bootstrap mode to form the fusion model, and the structure of the model is optimized using an improved sparrow search algorithm (ISSA), a swarm intelligence optimization algorithm. The model structure is adapted autonomously, implementing a method for online frequency response analysis. The simulation on the New England 39-bus system has verified that the method can quickly and accurately calculate the dynamic process of frequency response after a large-scale disturbance.

A Novel Jaya Based Automatic Generation Control of Two Area Interconnected Power System with nonlinearity

Background: This paper presents a novel approach to design and optimize Proportional-Integral-Derivative (PID) controller for Automatic Generation Control (AGC) of two-area interconnected power system with different energy sources. Methods: The two area interconnected power system consists of thermal, hydro and wind energy sources in the area-1 and area-2 comprises thermal, hydro and diesel energy sources. The recently developed Jaya Optimization technique is explored to optimize PID controller parameters for the interconnected power system by Matlab. Findings: The dynamic responses of power system is studied including the non-linearities like Governor Dead band (GDB), Generation Rate Constraint (GRC) and boiler dynamics under 1% step load perturbation. Novelty and applications The beauty of Jaya algorithm is that it works with only few control parameters as compared with other algorithms which help in improving dynamic responses and performance values of power system with non-linearities. Hence, the frequency stability is achieved at faster convergence at loads. The comparative analysis of proposed technique with other techniques like Differential Evolution (DE) algorithm and Teaching Learning Based Optimization (TLBO) algorithm for the same power system justify the effectiveness of proposed approach. Keywords: Automatic Generation Control; PID Controller; Nonlinearities; Renewable Sources; Jaya Algorithm

Grid Connected Microgrid Optimization and Control for a Coastal Island in the Indian Ocean

For the suggested site in the Maldives, this research paper analyzes the possibility of a hybrid renewable microgrid that is dispatch strategy-governed in both off-grid and on-grid scenarios. The planned microgrid’s techno-environmental-economic-power-system responses have been assessed. Both the power system response study and the techno-environmental-economic study of the modelled microgrid were carried out using the software platforms DIgSILENT PowerFactory and HOMER Pro respectively. Cycle charging (CC) dispatch technique had the lowest performance for both on and off-grid modes, according to the research, with cost of energy (COE) of 0.135 and 0.213 dollars per kWh, and net present costs (NPC) of 132,906 and 147,058 dollars respectively. With an NPC of 113,137 dollars and a COE of 0.166 dollars/kWh, the generator order strategy operates optimally while in on-grid mode. On the other hand, load following operates at its finest in off-grid mode, with a COE of 0.024 dollars/kWh and a NPC of 141,448 dollars. The microgrid’s reactive power, different bus voltages and frequency responses demonstrate how the proposed system, which employs the dispatch approach, voltage Q droop, and input mode PQ controller, operates steadily. For the purpose of illustrating the importance of the research effort, a comparison section between the planned HOMER optimizer and other optimization approaches is also included. The research was done with the Maldives in mind, but it offers a general notion for setting up a microgrid anyplace in the world with comparable weather and load circumstances. The research was done with the Maldives in mind, but it offers a general notion for setting up a microgrid anyplace in the world with comparable weather and load circumstances.

Hybrid Model-Based BESS Sizing and Control for Wind Energy Ramp Rate Control

This paper presents a hybrid model constituting dynamic smoothing technique and particle swarm optimization techniques to optimally size and control battery energy storage systems for wind energy ramp rate control and power system frequency performance enhancement. In today’s modern power system, a high-proportion renewable energy grid is inevitable. This high-proportion renewable energy grid is a power system with abundant integration of renewable energy resources under the presence of energy storage tools. Energy storage tools are integrated into such power systems to balance the fluctuation and intermittence of renewable energy sources. One of the requirements in a high-proportion renewable energy grid is the fractional power balance between generation and load. One of the requirements set by power system regulators is the generation variation between two time points. A power producer is mandated to satisfy the ramp rate requirement set by the grid owner. This paper proposes dynamic smoothing techniques for initial size determination and particle swarm optimization based on optimal sizing and control of battery energy storage systems for ramp rate control and frequency regulation performance of a power system integrated with a large percentage of wind energy systems. Wind energy data taken from Zhangjiakou wind farm in China are used. The results indicate that the battery energy storage system improves the ramp rate characteristics of the wind farm. In addition, the virtual inertia capability of the battery energy storage system enabled the transient and steady-state frequency response of the test power system to improve significantly.

Dynamic Response of Power Systems With Real GICs: Impact on Generator Excitation Control

Geomagneticallyinduced current (GIC) studies usually focus on transformer response to dc but seldom analyze generator excitation systems. A GIC is driven by an electric field which is induced by a very low-frequency geomagnetic field. It is widely acceptable to model GICs with dc, mostly suitable for static voltage stability analysis, but this does not adequately characterize a power system's dynamic response. Considering this, we studied a multi-machine power system on an OPAL-RT digital real-time simulator (DRTS)-MATLAB/Simulink system. We used real GIC measurement data from high and mid-latitude regions. Wavelet analysis related the frequency components of the GIC to the power system's response. A dynamic generator model with excitation voltage control modelled the source. Our main finding is that excitation system control contributes to reducing the voltage drop caused by increased var demand in saturated transformers with GICs. Measured GICs revealed non-negligible short-term dynamics affecting bus voltages that are not captured with dc-modelling assumptions, yet they are important for voltage stability analysis. This study implies that excitation control creates a buffer for high GICs to flow without violating the grid codes for minimum operating voltages and this paper demonstrates the optimization of this novel approach.

Multiarea Inertia Estimation Using Convolutional Neural Networks and Federated Learning

With the increase in penetration of renewable energy sources (RES), traditional inertia estimation techniques based purely on the number of online synchronous generators are increasingly unsuitable, ultimately leading towards suboptimal frequency control in the electric power grid. The stochastic nature of RES additionally makes the system inertia a time-varying quantity. Furthermore, the frequency and inertial response of power systems change drastically in multiarea power systems with interconnected tie-lines. Hence, it is important for state/parameter estimation (e.g., inertia) in multiarea systems, while ensuring communication between each of the areas. In this article, a client–server-based federated learning framework is used to estimate power system inertia in a multiarea system. Federated learning is a machine learning technique where multiple decentralized devices are trained with local data, and a global model is updated and redistributed by a central server by aggregating the trained weights of the decentralized devices, without exchanging the local data. Using local frequency measurements, obtained from the phase-locked loop of an energy storage system, the inertia at each of the areas can be estimated locally via offline training using convolutional neural networks (CNNs), whereas the CNN weights update in an online fashion. The framework, tested on a two-area power system, accurately estimated the inertia constant for both independent and identically distributed (IID) and non-IID data. Furthermore, the CNN-based method outperformed conventional neural network-based estimation techniques in terms of number of communication rounds and estimation accuracy.

Research on Packet Control Strategy of Constant-Frequency Air-Conditioning Demand Response Based on Improved Particle Swarm Optimization Algorithm

To better utilize air-conditioning load in terms of demand side response potential and improve precision and speed, a control strategy of traditional temperature-control air conditioning, determining frequency load as the research object, and air conditioning determined with a frequency theory model and the Monte Carlo method, were used to construct a power aggregation model. This was combined with user feedback to study thermal comfort as a lateral load demand response resource to determine the potential demand response of power systems. Based on the state-queuing model, an air-conditioning load grouping control strategy using an improved particle swarm optimization algorithm is proposed which can accurately control the air-conditioning load following the reference load.

A Machine Learning-Based Method for Identifying Critical Distance Relays for Transient Stability Studies

Protective relays play a crucial role in defining the dynamic responses of power systems during and after faults. Therefore, modeling protective relays in stability studies is crucial for enhancing the accuracy of these studies. Modeling all the relays in a bulk power system is a challenging task due to the limitations of stability software and the difficulties of keeping track of the changes in the setting information of these relays. Distance relays are one of the most important protective relays that are not properly modeled in current practices of stability studies. Hence, using the Random Forest algorithm, a fast machine learning-based method is developed in this paper that identifies the distance relays required to be modeled in stability studies of a contingency, referred to as critical distance relays (CDRs). GE positive sequence load flow analysis (PSLF) software is used to perform stability studies. The method is tested using 2018 summer peak load data of Western Electricity Coordinating Council (WECC) for various system conditions. The results illustrate the great performance of the method in identifying the CDRs. They also show that to conduct accurate stability studies, only modeling the CDRs suffices, and there is no need for modeling all the distance relays.

Operation and Assessment of a Microgrid for Maldives: Islanded and Grid-Tied Mode

This research work examines the prospect of a dispatch strategy governed hybrid renewable energy microgrid for the proposed location in Maldives for both off and on grid conditions. The techno-environmental-economic-power system responses of the proposed microgrid have been evaluated. The techno-environmental-economic analysis of the proposed microgrid has been conducted utilizing HOMER Pro and the power system response analysis has been conducted using DIgSILENT PowerFactory software platforms. The evaluation shows that, for both on and off grid modes, cycle charging strategy has the worst performance having net present costs (NPC) of $132,906 and $147,058 and cost of energy (COE) of 0.135 $/kWh and 0.213 $/kWh respectively. During on grid mode, generator order performs the best having NPC of $113,137, COE of 0.166 $/kWh. In off grid mode, load following strategy performs the best with NPC of $141,448 and COE of 0.024 $/kWh. The active power and voltage responses of the microgrid shows the stable operation of the proposed system by implementing dispatch techniques and voltage Q-droop and input mode P-Q controller. A comparison section is also presented for demonstrating the significance of the research work. The research work has been conducted considering a location in Maldives but provides an overall idea about establishing a microgrid in anywhere in the world having similar meteorological and load conditions.

Improving the Power System Dynamic Response Through a Combined Voltage-Frequency Control of Distributed Energy Resources

The paper proposes a control scheme to improve the dynamic response of power systems through the automatic regulators of converter-based Distributed Energy Resources (DERs). In this scheme, both active and reactive power control of DERs are varied to regulate both frequency and voltage, as opposed to current practice where frequency and voltage controllers are decoupled. To assess the proposed control against the current state-of-art, the paper also defines a metric that captures the combined effect of frequency/voltage response at any given bus of the network. Results indicate that the proposed control strategy leads to a significant improvement in the stability and performance of the overall power system. These results are based on a comprehensive case study carried out by employing a modified version of the IEEE 39-bus benchmark system, where a portion of the synchronous machines is substituted by converter-interfaced DERs. The impact on the proposed control of load models, the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$R/X$</tex-math></inline-formula> ratio of network lines, as well as the level of DER penetration to the grid, are properly evaluated and conclusions are duly drawn.

Effects of ENSO on hydrological process and hydropower across the Lancang‐Mekong River Basin

AbstractRapid growth in energy demand and accompanying hydropower development has been observed in the Lancang‐Mekong River Basin (LMRB) in the last two decades. However, climate‐induced variations in water resources have brought uncertainties and challenges to energy security across the LMRB, especially for the hydropower sector. In this study, we propose a multiscale approach by linking two spatially distributed models to simulate and evaluate the effects of climatic drivers both on the water resources and hydropower generations in the LMRB. Specifically, we evaluate the effects of midterm large‐scale climatic drivers (i.e., ENSO) on the local hydrometeorological process and energy production. During ENSO events, the midstream to downstream LMRB is more impacted than the upstream LMRB, leading to hydroelectricity anomalies in the LMRB varying within a range of ±11,000 GWh. However, basin‐wide drought conditions caused by El Niño events in the LMRB do not always result in a reduction in hydropower generation. The proposed approach provides a methodological basis for characterizing power system responses to climatic drivers, potentially informing the future energy strategy and infrastructure development in the LMRB.

Analytical transfer function to simulate the dynamic response of the finite-length Warburg impedance in the time-domain

Based on fundamental electrode theory, an analytical transfer function to simulate the frequency impedance spectrum of the finite-length Warburg (FLW) impedance and the dynamic potential response of the FLW impedance in the time-domain has been developed in this study. Parameters reported in the literature estimated from experimental measurements carried out in polymer electrolyte fuel cells (PEFCs) have been considered to validate the new analytical transfer function. The analytical transfer function representing the FLW impedance can be considered in different equivalent electrical circuit configurations to simulate a more accurate dynamic output voltage of an electrochemical power system under the effect of diffusion phenomena. A Simulink model based on the Randles circuit and the new transfer function representing the FLW impedance is constructed to simulate the dynamic output voltage of a PEFC during a current-interrupt incident. In addition, a Simulink model based on an electrical circuit configuration and the new transfer function representing the FLW impedance is constructed to simulate the dynamic output voltage of a Li-ion battery. This study establishes a wider scope to relate the electrochemical impedance spectroscopy to the dynamic output voltage response of electrochemical power systems.

Selection of the best dispatch strategy considering techno-economic and system stability analysis with optimal sizing

In this paper, optimized hybrid off-grid renewable energy systems (HRES) have been designed for two divisional locations in Dhaka and Khulna in Bangladesh. An analysis is conducted using five different load dispatch strategies to find the best dispatch strategy for a cost-effective and technically feasible Islanded hybrid microgrid that will support the growing power demand. A comparative analysis among the various dispatch strategies is also presented to find out the best and worst dispatch strategies for the proposed HRESs. The two HRESs consist of solar PV, a backup diesel generator, wind turbine, load demand of 23.31 kW, and a battery storage system. HOMER predictive strategy, Generator Order, Load Following, Combined Dispatch, and Cycle Charging dispatch strategy has been adopted for evaluation. The two HRESs are optimized for CO2 emission, Levelized Cost of Energy, and Net Present Cost minimization along with a feasible and stable voltage-frequency response on basis of the five dispatch techniques. The techno-economic analysis of the two HRESs is conducted using the HOMER Pro software platform. For the power system response analysis, MATLAB/Simulink is used. This study provides a complete guideline for determining the optimum component sizing ensuring optimum operation and possible costing estimation for the optimized performance of the two HRESs for different dispatch techniques. Also, a comparative study among the designed HRESs, other hybrid systems, and conventional power plants is conducted to prove the significance of this research work.

Design and Implementation of a Hybrid Solar-Wind-Biomass Renewable Energy System considering Meteorological Conditions with the Power System Performances

This paper presents a performance evaluation of an off-grid PV-wind-biomass hybrid energy system for a remote area named Kuakata in Bangladesh considering dispatch strategy-based control, power system response, and reliability analysis-based stability and feasibility study. The simulation and optimization of operations of the system have been done by the HOMER software using the real-time field data of solar radiation, wind speed, and biomass of that particular area for ensuring economical and environmental feasibility offering the least net present cost (NPC), cost of energy (COE), and CO2 emission. The result shows that NPC has been reduced by 88 percent, CO2 emissions have been reduced by 99 percent, the operating cost has been lowered by 99 percent, and COE has been reduced by 92 percent than another available work. Besides, in comparison to traditional power sources, COE has been reduced by 40 percent, NPC has been lowered by 90 percent, and CO2 emissions have been reduced by 99 percent. The proposed system has also been analyzed utilizing DIgSILENT PowerFactory software to find the power system responses, i.e., active, reactive powers, voltage, and frequency responses of the proposed microgrid in a per unit fashion on the occurrence of a three-phase fault. To establish the feasibility of the microgrid, a reliability study considering different reliability indices has also been done. The analyzed hybrid energy system might be applicable to other regions of the world where there are similar climatic conditions.

A multi-signal least-squares-based optimization technique for the identification of power system oscillatory modes

In this paper, a multi-signal identification technique is developed to estimate oscillatory modes contained in power system responses. The proposed technique utilizes least-squares optimization to analyze simultaneously several system measurements and determine close-to-real-time the modal parameters of the examined power system. The Monte Carlo method is applied to synthetic signals to thoroughly quantify the impact of several parameters on the accuracy of the proposed technique and comparisons with conventional identification techniques are performed. The accuracy of the proposed method is also validated using simulated responses obtained from a combined transmission–distribution network. Finally, the scalability of the proposed technique is demonstrated using power hardware-in-the-loop experiments.

Robust Control of Frequency Variations for a Multi-Area Power System in Smart Grid Using a Newly Wild Horse Optimized Combination of PIDD2 and PD Controllers

This paper proposes a new combined controller, the proportional integral derivative-second derivative with a proportional derivative (PIDD2-PD), to improve the frequency response of a multi-area interconnected power system with multiple generating units linked to it. The optimum gains of the presented controller are well-tuned using a wild horse optimizer (WHO), a modern metaheuristic optimization approach. The main study is a two-area-linked power system with varied conventional and renewable generating units. The physical constraints of the speed turbines and governors are considered. The WHO optimization algorithm is proven to outperform various other optimization approaches, such as the whale optimization algorithms (WOA) and chimp optimization algorithms (ChOA). The efficacy of the proposed WHO-based PIDD2-PD controller is evaluated by comparing its performance to other controllers in the literature (cascaded proportional integral derivative-tilted integral derivative (PID-TID), integral derivative-tilted (ID-T) controller). Multiple and varied scenarios are applied in this work to test the proposed controller’s sturdiness to various load perturbations (step, random, and multi-step), renewable energy source penetration, and system parameter variations. The results are provided as time-domain simulations run using MATLAB/SIMULINK. The simulation results reveal that the suggested controller outperforms other structural controllers in the dynamic response of the system in terms of settling time, maximum overshoot, and undershoot values, with an improvement percentage of 70%, 73%, and 67%, respectively.

Frequency regulation of hybrid multi-area power system using wild horse optimizer based new combined Fuzzy Fractional-Order PI and TID controllers

The increasing penetration of renewable energy sources (RES) into modern power systems may reflect the problem of frequency stabilization. Therefore, this paper proposes a new combined Fuzzy Fractional-Order Proportional-Integral (FOPI) and Tilt-Integral-Derivative (TID) controller to improve the frequency response of a hybrid power system. The proposed controller combines the advantages of intelligent Fuzzy FOPI and conventional TID controllers, resulting in more effective and robust load frequency control. In this study, the parameters of the proposed Fuzzy FOPI + TID combination are optimized using a novel metaheuristic algorithm, namely Wild Horse Optimizer (WHO). The case study system is a two-area conventional power system integrated with different RES including photovoltaic (PV) and wind generation as well as distributed electric vehicles (EVs) between the two areas. The effectiveness of the proposed controller is tested under various scenarios such as step load perturbation, random load variation, wind speed fluctuation, solar irradiance change and sensitivity analysis. In addition, the disturbance of wave energy oscillation is applied in Area 2 to evaluate the robustness of the proposed controller. For each scenario, the proposed Fuzzy FOPI + TID controller is compared with the conventional PID, single TID, and individual Fuzzy FOPI controllers using the proposed WHO algorithm and other optimization algorithms presented in the previous literature. The results show that the proposed FOPI + TID controller is superior to other controllers in terms of integral square error, peak overshoot, maximum undershoot and settling time for all scenarios. Furthermore, the presence of EVs helps in improving the frequency and tie-line power deviations. Finally, the time domain simulations are implemented using Matlab/Simulink.

A three-machine equivalent system frequency response model and its closed-form solution

The frequency response (FR) of the large-scale power system shows significant frequency spatial distribution characteristics (FSDC), still using uniform frequency models may cause large errors in the FR solution, and the FSDC should be preserved in the modeling. In this paper, a three-machine equivalent system frequency response (TM-SFR) model is proposed to preserve the FSDC by the parameter equivalent aggregation method, including load, network, and generator. The TM-SFR model is further simplified into a solvable forced vibration equation by loop-opening and approximation, and its closed-form solution (CFS) is obtained by the modal superposition method, to analytically solve the FR following a major disturbance. Case studies are introduced to verify the performance of the TM-SFR model and its CFS over the WSCC 9-bus system, the New England 39-bus system, and the IEEE 118-bus system. The results prove that the proposed model can preserve the intermachine frequency oscillation caused by the FSDC in various scenarios, and improve the FR solution accuracy at high solution speed, which can provide a more reliable basis for frequency stability emergency control.

Effective dispatch strategies assortment according to the effect of the operation for an islanded hybrid microgrid

The optimized design of a freestanding hybrid microgrid for various distinct dispatch controls is assessed in this paper, which considers the optimal sizes of individual components, system response, and reliability analysis. The effective design and management of stand-alone islanded hybrid smartgrids are getting increasingly importance and influences as the prevalence of renewable energy in microgrids grows. Melville Island, off the coast of eastern Queensland, Australia, is taken as the test microgrid in this study. For the optimal sizing and techno-economic assessment of the intended hybrid microgrid system consist of of solar diesel generator, PV , battery storage, and wind turbine, four dispatch approaches have been unitized: load following, generator order, combined dispatch, and cycle charging strategy. The proposed off-grid microgrid's CO2 emissions, total net present cost (NPC), and the Levelized cost of energy (LCOE) have all been optimized. In HOMER software, all the possible dispatch algorithms were analyzed, and the power system responses and reliability study were carried out using DIgSILENT PowerFactory. The findings of the study are useful for determining the optimum hybrid combination and available resources for the best performance of an off-grid microgrid employing various dispatch mechanisms. Following the simulation data, load-following is the best dispatch mechanism for stand-alone microgrid architecture since it has the lowest LCOE and NPC.

Pre-and post-disturbance transient stability assessment using intelligent systems via quick estimating of the critical clearing time

Abstract The real time transient stability assessment is the important steps of the dynamic security evaluation of power systems. To do this, intelligent systems (ISs) such as neural networks as an effective method to accurately and quickly estimate the critical clearing time (CCT) has been widely used. But, choosing the proper inputs for these systems remains a major challenge for researchers, still. Variables related to energy functions such as minimum kinetic energy, the slope of the variation of the minimum kinetic energy and maximum potential energy contain useful information in estimating CCT. Accordingly, in this paper, these variables are used as the IS inputs. However, the time-domain simulation of the power system response to obtain these inputs is time consuming. To be able to use the energy function-based inputs for real time stability assessment, in addition to the main IS used to estimate CCT, another ISs are used. By those ISs, a very limited period of system response is simulated to obtain proper inputs of the main IS. The method is simulated on the 10-generator New England test system. Simulation results show, the CCT can be found by simulation just 0.05 s of the considered power grid.