Power System Operation Research Articles

Optimal independent but coordinated operation of interconnected power, natural-gas, and district-heating distribution systems

Traditionally, power, natural-gas, and district-heating distribution systems have been planned and operated independently. However, the increasing presence of distributed natural-gas-fired generating units, heat pumps units, power-to-gas facilities, and combined heat and power units create an interdependency where the operational decisions of any system affect the operation of the other systems. We analyze centralized and decentralized operations of the three distribution systems and discuss under which conditions centralized and decentralized operations are equivalent. We find this equivalence by replacing each system operation problem with its optimality conditions and comparing them with the optimality conditions of a centralized operation. Using a realistic case study, we quantify the gains resulting from different levels of coordination. Equilibria, i.e., joint optimal solutions for the three systems, are found using an iterative convexification technique, which is novel in the context of this paper. An advantage of the proposed model lies in its potential to expand across a range of interconnected energy systems, which in turn, enables the identification of the equivalences between centralized and decentralized operations.

Design and optimization of line CT energy harvesting device

Abstract The aim of this study is to design and optimize an energy harvesting device for line current transformers (CT). The main function of this device is to extract energy from CT on the line to supply power to related electronic devices such as sensors and monitoring equipment. Firstly, an in-depth analysis of the working principles and characteristics of CT was conducted to determine the design requirements and performance indicators of the energy harvesting device. Subsequently, an energy-harvesting device based on the principle of magnetic coupling was designed. Different circuit principles were developed to accommodate low-load and high-load scenarios on the line, and the structure and parameters were optimized to improve energy conversion efficiency and output stability. Through experimental validation, the performance and stability of the designed energy harvesting device under different working conditions were verified. Finally, the design scheme of the energy harvesting device was optimized, and further improvement and application suggestions were proposed. This study provides a theoretical and practical basis for improving the performance and reliability of line CT energy harvesting devices, which is of great significance for the safe operation of power systems and equipment monitoring.

A new and highly precise forecasting method based on multiple input Convolutional Neural Networks and Generative Adversarial Networks for training data set augmentation

Abstract Forecasting is essential when it comes to power system operation. It is always equivalent with higher revenues and, implicitly, lower maintenance costs. That is why, it is the purpose of this work to introduce a new and highly precise wind power forecasting neural model based on a multiple input (multi-channel) Convolutional Neural Network (CNN) and a Generative Adversarial Network (GAN) for training data augmentation. The training of the CNN is conducted simultaneously with historical data related to both power generation and wind speeds.

Extreme weather impact on carbon-neutral power system operation schemes: A case study of 2060 Sichuan Province

The impact of increasingly frequent and intense climate change events on power supply and consumption has become a concern for many countries. This study evaluated the impact of extreme weather on the power system economy and security by conducting an empirical case study of the Sichuan power network to address uncertainty attributed to different extreme weather events under carbon neutrality scenarios. First, our paper proposed a CNPSSO model of four seasonal typical days by jointly considering the regional hourly disparities of load curves and renewable power output curves. Then, the model creatively introduced the effects of extreme weather on the power supply and demand curves based on various scenario analyses. The most cost-effective output strategy using various generation technologies was provided based on optimization models. Finally, Sichuan Province, a hydropower-dominated province was selected as an empirical case study. The results indicate that extreme weather significantly affects the hourly operational and dispatching schemes of Sichuan's power system. Compared to the "Normal weather scenario”, hydropower's share plummets from 83.3 % to 46.1 %, while pumped storage surges 7.1 billion kWh in summer in extremely hot and dry weather. Imported electricity rises by 24.4 %, 30.5 % and 24.8 % in dry scenario, extremely hot and dry scenario, and extremely cold scenario, respectively. Furthermore, extreme weather increases carbon emissions (mainly from thermal power and imported electricity) and costs. Specifically, when juxtaposed against normal weather, extreme drought, extreme heat drought and extremely cold lead to respective increases in carbon emissions by 32.9 %, 33.5 %, and 12.3 %. Regarding the levelized cost of electricity (LCOE), "Extremely dry scenario”, "Extremely hot and dry scenario”, and "Extremely cold scenario” drive up the LCOE by 7.2 %, 9.2 %, and 4.2 %, respectively.

Enhancing the prediction of electric load demand: a comparative analysis of ARIMA and ANN models for the case of a small touristic island

Abstract Accurate prediction of energy demand is necessary for efficient power system operation, particularly in energy systems with high renewable sources integration. This study compares different methods for predicting load demand using statistical regression. In particular, the goal is to provide insights on the differences between simpler AutoRegressive Integrated Moving Average (ARIMA) and more complex Artificial Neural Network (ANN) models. The algorithms are used to predict electric load demand in Tilos, a small Greek island with strong seasonal trends due to summer tourism. The case study is significant as Tilos’ outdated electrical grid must adapt to an increasing share of renewable energy sources, making load forecasting increasingly important. The algorithms were developed in Python using open-source tools (such as StatsModels and TensorFlow). Hyperparameters’ tuning, crucial for enhancing forecasting effectiveness, was performed using stochastic optimization with Differential Evolution to minimize RMSE. The optimal normalized RMSE was reported as 9.72% for ANN and 9.54% for ARIMA, showing the effectiveness of both methods, with a slight edge for the statistical model. This work provides critical information regarding load prediction methodologies, highlighting practical guidance for energy managers, policymakers, and researchers in power system planning and operation.

South Korean Power System Operation and Renewable Integration: Using Artificial Intelligence Applications

South Korean Power System Operation and Renewable Integration: Using Artificial Intelligence Applications

Parameter estimation of photovoltaic power generation system and digital twin model construction

Abstract Photovoltaic power generation system is an essential part of modern power system, but it has intermittency and limitations and will also significantly reduce the stability of power system operation. For this reason, this paper proposes a new method for estimating the parameters of a photovoltaic power generation system. It constructs a digital twin model of photovoltaic panels for accurate real-time system monitoring. In order to construct a more realistic twin model, an improved iterative method is proposed to analyze and calculate the unknown parameters of the relationship between the internal characteristics of the PV panels. A comparative analysis with the adaptive war strategy is carried out, which verifies the effectiveness of the proposed method, reduces the operating errors of the real object and the twin, makes an essential contribution to the application of the digital twin technology in the photovoltaic power generation system, and lays a smart PV power system foundation.

Development of the model and improvement of the method of automated control of steam turbine parameters to minimize the power imbalance in the energy system to increase its efficiency

The object of research is the process of regulating power imbalances due to automated control of steam turbine parameters. The work solves the problem of minimizing power imbalances in the electric power system by developing a model of automated control of steam turbine parameters. This will ensure high-quality regulation of frequency and power, increase the efficiency and stability of the electric power system, and provide a new management method for reliable power supply to consumers. The paper analyzes existing models and methods of power imbalance regulation, develops a dynamic model of automated control of K-300-240 steam turbines, which includes a mechanical-hydraulic system, a steam boiler and a steam superheater. As a result of the study, an improved power regulation method was proposed, which ensures efficient operation of the electric power system. The evaluation of the effectiveness of the regulation of imbalances was carried out on the basis of the proposed criterion of the efficiency of electricity supply and consumption, which is based on the convolution of partial criteria into the general criterion of the efficiency of electricity supply. The following criteria were proposed as partial criteria for electricity supply and consumption: volume criterion, quality criterion, and electricity supply efficiency criterion. The research results indicate the need for a reserve on each steam turbine in the amount of 10 % of its nominal power, which is explained by the assessment of the efficiency of electricity supply among the considered modes of operation of the systems. The presence of a power reserve on each steam turbine in the amount of 10 % of their nominal power ensures the most efficient power supply within the considered modes of operation of the power system, taking into account disturbances and as an imbalance of power generation and power consumption. The obtained research results can be applied in the strategy of primary regulation of power imbalances in the electric power system, thanks to the creation of a power reserve in the amount of 10 % of the nominal power on each steam turbine, and the organization of automated control of steam turbine parameters.

Synchronized States of Power Grids and Oscillator Networks by Convex Optimization

Synchronization is essential for the operation of ac power systems: all generators in the power grid must rotate with fixed relative phases to enable a steady flow of electric power. Understanding the conditions for and the limitations of synchronization is of utmost practical importance. In this article, we propose a novel approach to computing and analyzing the stable stationary states of a power grid or a network of Kuramoto oscillators in terms of a convex optimization problem. This approach allows us to systematically compute stable states where the phase difference across an edge does not exceed π/2. Furthermore, the optimization formulation allows us to rigorously establish certain properties of synchronized states and to bound the error in the widely used linear power flow approximation. Published by the American Physical Society 2024

Cyber Security of Smart-Grid Frequency Control: A Review and Vulnerability Assessment Framework

Smart grid involves application of information and communication technology (ICT) for monitoring, protection, operation and control of interconnected power systems under various scenarios. Smart grid control (SGC) is an important aspect that is constantly subjected to various vulnerabilities, threats and attacks under central and distributed control architectures. Cybersecurity of SGC, especially, load frequency control (LFC), is an important issue that is addressed in this article. The state-of-the-art in cybersecurity and attacks on SGC and intensive literature review is discussed with a comprehensive list of references on LFC. The authors present a part of their own work carried out on a systematic vulnerability assessment (VA) framework that can be used to identify weak points in the LFC system. The proposed methodology is explained for VA of the standard 39-bus New England test system and the 96-bus reliability test system (RTS) to illustrate the concept of cybersecurity and VA of smart grid LFC.

Исследование влияния отклонений параметров моделей синхронных генераторов на результаты расчета динамической устойчивости электроэнергетической системы

One of the important tasks solved in planning the electric power system (EPS) operation is the assessment of its transient stability. Use of incorrect parameters for EPS element models, in particular synchronous generators (SG), contributes to the occurrence of errors when determining the operating conditions of power systems, and, therefore, can lead to a decrease in the reliability of EPS operation. This article is devoted to the study of the impact of errors in SG model parameters on the transient stability assessment results. The analysis presented in this article allows us to substantiate the relevance of the SG parameter estimation task using a “passive experiment”, such as synchronized phasor measurements. This research is based on the theory of electrical circuits, mathematical description of electrical machines and transients in EPS, as well as statistical analysis. Computational experiments have been carried out in the software package MATLAB using the simulation modeling environment “Simulink”. During the research, two different multi-machine EPS models have been used, including the well-known “IEEE 9-bus” system. To quantitatively assess the influence of SG parameter uncertainties on transient stability analysis, the values of the maximum permissible fault clearance time are determined, as well as the values of the maximum power output of generating equipment in prefault conditions. The authors have carried out a comprehensive analysis of the influence of deviations in SG model parameters on the maximum fault clearance time, as well as on the maximum power output of generating equipment in the prefault conditions for a given fault duration. During the study, two approaches have been considered; in each case, the EPS transient stability assessment has been performed almost automatically, and quantitative indicators have been computed for the influence of SG model parameter deviations on the outcome of EPS transient stability analysis. Analysis of the computational experiment results has demonstrated a significant influence of the SG model errors on the errors in calculating certain transient stability-related parameters. For the considered scenarios, the spread of errors in calculating the maximum fault clearance time reached 100 % relative to a base-case (errorless) result, and the error in calculating the maximum power output of generating equipment in prefault conditions for a given fault duration may reach 20 % relative to the errorless result.

Very short-term wind power forecasting considering static data: An improved transformer model

The randomness and fluctuations in wind power generation present significant challenges for grid and wind farm dispatching. Accurate very short-term wind power forecasting (WPF) is therefore essential for the efficient operation of modern power systems. Data-driven models, such as Transformers, have demonstrated their effectiveness in WPF due to their ability to efficiently capture global features in long sequences. However, limited research has examined the impact of incorporating static data into WPF, which may limit forecasting accuracy. This paper proposes a Temporal Fusion Transformer forecasting model to address this challenge. This approach employs static data as the input features for the model. The model includes feature selection through a variable selection network and employs a specialized temporal fusion decoder to learn effectively from these static features. The case results show that the results of the proposed model are more accurate than the state-of-the-art methods, reducing MAPE by at least 1.32%, RMSE by 0.0091, and improving R2 by 0.035 in case studies. Additionally, the model maintains a manageable computational burden, underscoring its practical applicability.

Comparison of Advanced Flexible Alternating Current Transmission System (FACTS) Devices with Conventional Technologies for Power System Stability Enhancement: An Updated Review

The continuously growing penetration of renewable energy sources (RESs) in electrical networks provides increasing challenges and critical situations to be managed by worldwide system operators. Due to their features and variability, non-programmable RES power plants, whose increasing penetration reduces the inertia level of the power system, may determine the instability effects on the grids, especially from the frequency and voltage regulation standpoints. The present study focuses on the support that advanced FACTS (Flexible Alternating Current Transmission System) devices, such as STATCOMs (Static Synchronous Compensators), can provide to the power system operation in terms of system inertia improvement, frequency stability, and voltage stability. In particular, a review of the scientific literature and practice is performed, with the aim of benchmarking the ongoing evolution of these technologies, also comparing them with different options based on synchronous condensers, synchronous condensers integrated with flywheels, and STATCOMs with supercapacitors. The outcome of the analysis consists of an updated evaluation of the state-of-the-art technological development in the field and of a comparison between different FACTSs with the purpose of identifying the most suitable solutions for different practical situations, also taking account of synergies across various options. This study includes an updated overview regarding the status of STATCOM installation in the Italian power grid.

Levy flight elitism-enhanced salp swarm algorithms for solving the ORPD problem

The optimal reactive power dispatch (ORPD) challenge is crucial for ensuring the efficient and reliable operation of power systems, hinging on the effective coordination of multiple devices.As a result, solving the ORPD problem involves tackling a complex nonlinear optimization challenge that demands advanced computational algorithms to determine the optimal solution. This article introduces an enhanced version of the modified algorithm, called L-E-SSA, which is inspired by the coordinated movement of a swarm led by a single member (salp swarm algorithm SSA), incorporating Levy Flight elitism, to effectively solve the ORPD problem. This study aims to reduce power losses (Ploss) and total voltage deviation (TVD) by optimizing the control variables. Numerical simulations are carried out on IEEE (30-bus) and IEEE (118-bus) power systems. To evaluate the performance and effectiveness of the modified algorithm (L-E-SSA). The results indicate that the L-E-SSA algorithm delivered strong performance and secured a competitive ranking through statistical analysis. Simulations demonstrate that the proposed method has the potential to be a highly effective solution for the ORPD problem.

Wind farm cluster power prediction based on graph deviation attention network with learnable graph structure and dynamic error correction during load peak and valley periods

The power prediction accuracy of wind farm cluster (WFC) seriously affects its consumption and the safe and stable operation of power system. The fluctuation of power between wind farms (WFs) significantly affects the wind power ultra-short-term prediction (WPUP) accuracy of WFC. In this regard, this paper proposes a graph deviation attention network (GDAN) considering improved clustering distance and learnable graph structure (LGS) for predicting and correcting the wind power of WFC. And used a weighted distance function combining sequence convergence smoothing effect and correlation to dynamically divide the WFC, and to learn and construct the graph structure. Proposed the GDAN with LGS to mine the convergence correlation of WF sub-clusters and establish power prediction model. Considering the characteristics of load peak and valley periods (LPVP), introduced a power correction coefficient to reduce the error, and used the successive variational mode decomposition (SVMD) to extract its key components to achieve power prediction and correction. The proposed method is applied to the WFC in Western Inner Mongolia, China. Compared with the comparison model before correction, the RMSE, MAE and MAPE are reduced by 4.27 %, 3.55 % and 17.92 % respectively, and the R2 and Pr are increased by 11.87 % and 9.88 % respectively.

Consensus-Based Power System State Estimation Algorithm Under Collaborative Attack.

Due to its vulnerability to a variety of cyber attacks, research on cyber security for power systems has become especially crucial. In order to maintain the safe and stable operation of power systems, it is worthwhile to gain insight into the complex characteristics and behaviors of cyber attacks from the attacker's perspective. The consensus-based distributed state estimation problem is investigated for power systems subject to collaborative attacks. In order to describe such attack behaviors, the denial of service (DoS) attack model for hybrid remote terminal unit (RTU) and phasor measurement unit (PMU) measurements, and the false data injection (FDI) attack model for neighboring estimation information, are constructed. By integrating these two types of attack models, a different consensus-based distributed estimator is designed to accurately estimate the state of the power system under collaborative attacks. Then, through Lyapunov stability analysis theory, a sufficient condition is provided to ensure that the proposed distributed estimator is stable, and a suitable consensus gain matrix is devised. Finally, to confirm the viability and efficacy of the suggested algorithm, a simulation experiment on an IEEE benchmark 14-bus power system is carried out.

Leveraging the Synergy of Digital Twins and Artificial Intelligence for Sustainable Power Grids: A Scoping Review

As outlined by the International Energy Agency, 44% of carbon emissions in 2021 were attributed to electricity and heat generation. Under this critical scenario, the power industry has adopted technologies promoting sustainability in the form of smart grids, microgrids, and renewable energy. To overcome the technical challenges associated with these emerging approaches and to preserve the stability and reliability of the power system, integrating advanced digital technologies such as Digital Twins (DTs) and Artificial Intelligence (AI) is crucial. While existing research has explored DTs and AI in power systems separately, an overarching review of their combined, synergetic application in sustainable power systems is lacking. Hence, in this work, a comprehensive scoping review is conducted under the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). The main results of this review analysed the breadth and relationships among power systems, DTs, and AI dynamics and presented an evolutionary timeline with three distinct periods of maturity. The prominent utilisation of deep learning, supervised learning, reinforcement learning, and swarm intelligence techniques was identified as mainly constrained to power system operations and maintenance functions, along with the potential for more sophisticated AI techniques in computer vision, natural language processing, and smart robotics. This review also discovered sustainability-related objectives addressed by AI-powered DTs in power systems, encompassing renewable energy integration and energy efficiency, while encouraging the investigation of more direct efforts on sustainable power systems.

EMB-YOLO: A Lightweight Object Detection Algorithm for Isolation Switch State Detection

In power inspection, it is crucial to accurately and regularly monitor the status of isolation switches to ensure the stable operation of power systems. However, current methods for detecting the open and closed states of isolation switches based on image recognition still suffer from low accuracy and high edge deployment costs. In this paper, we propose a lightweight object detection model, EMB-YOLO, to address this challenge. Firstly, we propose an efficient mobile inverted bottleneck convolution (EMBC) module for the backbone network. This module is designed with a lightweight structure, aimed at reducing the computational complexity and parameter count, thereby optimizing the model’s computational efficiency. Furthermore, an ELA attention mechanism is used in the EMBC module to enhance the extraction of horizontal and vertical isolation switch features in complex environments. Finally, we proposed an efficient-RepGDFPN fusion network. This network integrates feature maps from different levels to detect isolation switches at multiple scales in monitoring scenarios. An isolation switch dataset was self-built to evaluate the performance of the proposed EMB-YOLO. The experimental results demonstrated that the proposed method achieved superior detection performance on our self-built dataset, with a mean average precision (mAP) of 87.2%, while maintaining a computational cost of only 6.5×109 FLOPs and a parameter size of just 2.8×106 bytes.

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.

A hybrid load forecasting system based on data augmentation and ensemble learning under limited feature availability

Accurate power load forecasting is an important part of power system operation planning, it can ensure the stable operation of power systems and improve the efficiency of energy utilization. The power load is affected by many factors including temperature, season, population density, and so on, however due to privacy protection and other reasons, it is difficult to obtain some characteristic information that affects the load. The lack of characteristic data will reduce the accuracy of load forecasting and the generalization ability. To solve it, a new hybrid load forecasting framework is proposed, which is composed of two subsystems: a data preprocessing system and a high-precision forecasting system. Based on the load sequence itself, subsystem 1 obtains the trend data and denoising data by variational mode decomposition method, obtains the indicator variable for the weekend according to the one-hot encoding, and also introduces the electricity price data, thus obtaining the 4-dimensional extended data. Subsystem 2 constructs a hybrid prediction model by synthesizing various models, including deep learning and machine learning models, to forecast the expanded data. Finally, the multi-objective JAYA algorithm based on tent chaotic mapping and cross-perturbation strategy is used to ensemble the prediction results of the sub-models. To verify the superiority of the proposed forecasting framework, we conducted experiments using four sets of load data from New South Wales, Australia. The experimental results show that the average absolute percentage error of the hybrid framework on the four data sets are MAPEMarch=0.8070, MAPEJune=0.8296, MAPESeptember=0.7238 and MAPEDecember=0.7709, which are significantly lower than other models and provide a basis for power system scheduling management.