Power System Operation Research Articles

Optimal coordination of directional overcurrent relays in power energy systems with emphasis on the discreteness of variables: Comprehensive comparisons

The challenge related to the coordination of overcurrent relays in a looped network, which is a highly constrained problem, is to find a more optimum solution and at the same time there is no miscoordination. In this problem, the utilized optimization algorithm has a direct effect on finding a more optimal solution. High exploration particle swarm optimization (HEPSO) algorithm, which utilizes multi-crossover mechanism of the genetic algorithm and the bee colony mechanism to increase the exploration capability, has been selected to solve the coordination problem. To validate the HEPSO algorithm, a new algorithm called turbulent flow of water-based optimization (TFWO) is also used, and their results are compared. A new approach to discretization of variables is presented, which prevents miscoordination in the relays coordination. It is a realistic assumption for all relay types, especially digital relays, in preventing miscoordination. Also, the outage of sub-transmission transformers is considered for determining pickup current, which is a practical aspect of power systems operation. The obtained results are compared with the results of other algorithms and methods such as metaheuristic, mathematical and analytical, and hybrid methods. The efficiency of the selected algorithm is proven through comprehensive comparisons. The results show more optimum solutions while there is no miscoordination. Finally, the performance of the selected algorithm and the proposed approaches are tested on IEEE 14 and 30-bus test systems. Since these test systems are large enough, the number of problem variables will increase significantly, effectively validating the power and robustness of the selected algorithm to tackle this complex and constrained problem. The outage of transformers in determining pickup currents had been implemented in this section.

Implicit-integral dynamic optimization based on spatial partitioning and temporal segmentation for the power jumps of renewable energy sources

With the widespread development of renewable energy sources, the increasing short-term power volatility poses a significant challenge to the normal operation of power systems. This paper presents a two-timescale adaptive dynamic optimization (TADO) strategy based on implicit trapezoidal integral to address dynamic state fluctuations due to renewable power jumps. Firstly, a static optimization model is established utilizing the model predictive control (MPC) to obtain the optimal states on a longer temporal scale, considering the uncertain renewable power jumps. Next, provide a partitioning method based on modularity to divide the power system into several independent subsystems with constant tie-line power. Using an implicit trapezoidal integral algorithm, each subsystem's dynamic reactive power optimization model is constructed with an appropriate discrete time granularity, accounting for the electromechanical transient processes due to renewable power jumps. Then, to improve the solving efficiency of the dynamic optimization model, a new solution method is proposed based on temporal segmentation. The whole process's optimal state trajectory is achieved by splicing all sub-optimization results. Finally, case studies conducted on the IEEE 9-bus power system and IEEE 39-bus power system validate the feasibility of the proposed strategy.

Application of a hybrid multi-criteria evolutionary-fireworks algorithm for the optimization of power systems operation

Application of a hybrid multi-criteria evolutionary-fireworks algorithm for the optimization of power systems operation

Model‐free adaptive load frequency control for power systems with wind penetration under deregulation environment

AbstractWith the gradual deregulation of the power system by the power department, the power system has developed into a large‐scale and multiregional control system. Because of the power system internal complexity enhancing, the stable operation of power system becomes increasingly difficult. This paper analyzes the load frequency control problem of multiregional interconnected power system with wind energy. This study designs an improved model‐free adaptive control algorithm based on I/O data. It avoids model establishment of the multiregional power system. It also effectively solves the problem of frequency stability control under the influence of load change, introducing the generation participation matrix to simulate bilateral contracts under the power market. The dynamic evolution relationship of the system with the generation participation matrix is established, taking a three‐regional power system with wind energy as an example. Frequency fluctuations in all three regions are between . Convergence times of frequency deviation are all within 30 s, much less than the response time of load frequency control. The simulation results further demonstrate the effectiveness of the proposed algorithm, comparing the control algorithm proposed in this paper with other algorithms, which proves that the proposed algorithm has good control performance.

A hierarchical framework for integration of smart buildings in fully-renewable multi-microgrids and distribution systems: Towards more sustainable societies

Using smart buildings would be a crucial step towards sustainability. Although in the literature, some research effort has focused on integration of smart buildings into power systems; the integration of fully-renewable smart buildings into fully-renewable microgrids and distribution systems has not been addressed before. In this paper, a tri-level hierarchical stochastic framework is developed for the integration of fully renewable smart buildings in fully renewable multi-microgrids (MGs) and distribution systems. The studied smart buildings have electric water heater, air conditioner, PV and battery. In the first level, the model of each smart building is separately solved to find optimal schedule of its resources and the desired electricity exchange with its corresponding MG. In the second level, each MG solves its operational planning model, considering its transactions with the connected smart buildings and finally, in the third level, distribution system operator solves its operational planning model, accepting the electricity exchange requests taken from all MGs. The proposed methodology is applied to a 69-bus 100 % renewable distribution system with 4 100 % renewable microgrids, integrated with 100 % renewable smart buildings. The results confirm the efficacy of the proposed methodology for the integration of 100 % renewable smart buildings in 100 % renewable multi-MGs. The results testify that that building-to-grid (B2G) capability decreases the operation cost of all smart buildings; the average decrease in operation cost of buildings, caused by B2G, is 38 %. The results on the studied case indicate that that the impact of batteries on operation cost of smart buildings is marginal. The results also show that the higher threshold of comfort index in smart buildings, results in their higher operation cost. The results of this research may be helpful for both smart building operators and power system operators. This research is in line with the global energy transition and decarbonisation targets as the considered smart buildings, microgrids and distribution system are fully renewable.

Development of a coupled agent-based generation expansion planning tool with a power dispatch model

Power companies need to adapt their generation expansion planning in response to changing market, climate and regulatory conditions as global warming, electrification, and technology breakthroughs continue. To fortify energy system resilience, it is critical to understand the collective effects of their autonomous decisions on power systems operations and reliability. To this end, we developed an integrated framework, an agent-based model (ABM) coupled with a power dispatch model (PDM) (referred to as ABM-PDM), tested on the Texas 123-bus transmission system in the Electric Reliability Council of Texas (ERCOT) region. Agents (power generation companies) can invest in natural gas, solar, and wind technologies to maximize profits from 2021 to 2050, using market information from the PDM based on their capital budget and perceived costs, financial incentives for renewable energy, and climate risks. We applied ABM-PDM to assess how power companies respond to future technological advancements and climate change. After demonstrating model credibility, we explored 25 combinations of cost and capacity factors reflecting a variety of technological evolution trajectories. Results indicated that to replace wind over solar for replacing existing fossil-fuel power plants due to lower costs and higher capacity factors. Additionally, as more agents invest, the energy market becomes more competitive, and system-wide electricity prices drop. We also analyzed the impacts of temperature increases on investments using seven projections, from 0 to 6 °C, during the modeling period. The results showed that as temperatures rise, agents invest more to accommodate the increasing loads. ABM-PDM incorporates risk attitude and learning into companies’ decision-making, providing additional information on generation expansion for the non-optimal future of power systems.

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.

Economic Load Forecasting based on IPSO-VMD-DBiLSTM Network

Accurate short-term load forecasting is vital for the stable operation of power systems. Given the highly volatile and nonlinear nature of load sequences, we propose a novel Deep Bidirectional Long Short-Term Memory (DBiLSTM) network incorporating Variational Mode Decomposition (VMD) and an attention mechanism to address the aforementioned challenges. In this study, the model's hyperparameters are optimized using an Improved Particle Swarm Optimization (IPSO) technique. Notably, IPSO employs a nonlinearly decreasing inertia weight to overcome the drawbacks of premature convergence and local optima.

Security-constrained economic dispatch of power systems with line outage using firefly algorithm

The Security-constrained Economic Dispatch (SCED) problem is the problem of finding the optimal generation dispatch for a power system that minimizes the operating cost while ensuring the security of the system. The security of the system is defined as the ability of the system to withstand disturbances without violating any operational constraints. Line outage is a common disturbance that can affect the security of a power system. The Firefly Algorithm, inspired by the flashing behavior of fireflies in nature, is a metaheuristic optimization technique known for its effectiveness in solving complex and dynamic optimization problems. In this study, the FA is adapted to the SCED problem with a specific focus on enhancing grid resilience during line outage scenarios. The proposed method aims to simultaneously optimize the economic cost of power generation and the system's ability to withstand line outages. To demonstrate the effectiveness of the proposed approach, extensive simulations are conducted on standard IEEE test systems with varying degrees of complexity and network sizes. The results showcase the superior performance of the SCEDL-Firefly Algorithm compared to traditional optimization methods, as it provides a resilient dispatch solution that can effectively adapt to unexpected line outages while maintaining economic efficiency. Overall, this research contributes to the advancement of secure economic dispatch techniques and power system resilience by leveraging the Firefly Algorithm's capabilities. The findings offer valuable insights for power system operators and planners seeking to enhance grid reliability in the presence of line outages, ultimately promoting a more sustainable and resilient energy infrastructure.

Sensitivity Analysis and Distribution Factor Calculation under Power Network Branch Power Flow Exceedance

As the scale of power systems continue to expand and their structure becomes increasingly complex, it is likely that branch power flow exceedance may occur during the operation of power systems, posing threats to the safe and stable operation of entire systems. This paper addresses the issue of branch flow exceedance in power networks. To enhance the operational efficiency and optimize the adjustment effects, this paper proposes a method for eliminating branch power flow exceedance by improving the particle swarm optimization (PSO) algorithm through the introduction of sensitivity and distribution factors. Firstly, it introduces the basic theory and calculation methods of sensitivity analysis, focusing on deriving the calculation principles of power flow sensitivity and voltage sensitivity, used to predict the responses of power flow at each branch in the power network to power or voltage changes. Subsequently, the paper provides a detailed derivation of the calculation principles for the line outage distribution factor (LODF), which effectively assesses the changes in branch power flow in the power network under specific conditions. Finally, a method for eliminating branch power flow exceedance based on a combination of sensitivity analysis and PSO algorithm is proposed. Through case analysis, it is demonstrated how to use the sensitivity and distribution factor to predict and control the power flow exceedance issues in power systems, verifying the efficiency and practicality of the proposed method for eliminating branch power flow exceedance. The study shows that this method can rapidly and accurately predict and address branch power flow exceedance in power system, thereby enhancing the operational safety of the power system.

Data Augmentation for Data-Driven Methods in Power System Operation: A Novel Framework Using Improved GAN and Transfer Learning

Data Augmentation for Data-Driven Methods in Power System Operation: A Novel Framework Using Improved GAN and Transfer Learning

Synergies of variable renewable energy and electric vehicle battery swapping stations: Case study for Beijing

Battery swapping technology has emerged as a promising option for simultaneously addressing electric vehicle (EV) range anxiety and uncoordinated charging impacts, thereby enabling a renewable-powered future at the city scale. This study aims to explore the potential synergies between variable renewable energy (VRE), including wind and solar power, and the city-scale operation of battery swapping stations (BSSs) under varying levels of VRE penetration. To this end, an integrated modeling framework that combines multisource traffic data with node-based BSS deployment optimization and hourly power system dispatch simulations was developed. Beijing in 2025 was selected as the case study due to its ambitious EV development goals and the substantial need for VRE integration. The simulation results reveal that system-optimized BSS operations, particularly through bidirectional charging (V2G), can significantly enhance VRE integration, reduce net load fluctuations, and mitigate carbon emissions. Specifically, increasing VRE penetration from 30 % to 70 % reduces VRE curtailment by 1.1 TWh to 6.4 TWh and avoids 3.0 t to 6.3 t of carbon emissions per vehicle annually. The economic analysis further indicates that while current time-of-use electricity pricing leads to higher costs for BSS operations, a real-time pricing mechanism offers a more economically viable solution, benefiting both power system operators and BSS operators. The integrated modeling framework developed in this study not only advances the understanding of city-scale BSS operations but also provides a valuable tool for analyzing the complex interactions between EV infrastructure, VRE integration, and urban power grids.

Medium-term wind power prediction based on LSTM classification aided Pelt-Neuralprophet HHO-SVM

The precision of wind power prediction plays a vital role in ensuring the stable operation of wind power systems. To elevate this accuracy and enhance the real-time performance, this paper proposes a hybrid Support Vector Machine (SVM) method, with using the Long Short-Term Memory Network (LSTM) to categorize the wind data based on the statistical features and feedback to the trained SVM model. The hybrid Harris Hawk Optimization (HHO) SVM method adopts the Neuralprophet algorithm to model the seasonality of wind power data and then uses the Pelt technique to the LSTM aided Pelt-Neuralprophet HHO-SVM (i.e., PN-HHO-SVM) scheme, which can utilize the seasonal fluctuations inherent in wind power data. The Neuralprophet algorithm is employed to formulate the seasonal regression model of wind power data. Then, the Pelt technique is used to process the modeled data to locate the change points, so as to classify the time series with similar statistical properties. Furthermore, to tune the SVM hyperparameters for each identified cluster, the HHO algorithm is adopted. Consequently, the LSTM aided PN-HHO-SVM is achieved. The real data sourced from the National Renewable Energy Laboratory (NREL) are used for validation case studies, demonstrating the superiorities of the prediction performance, especially in distinguishing and discriminating the seasonal dynamics of wind power data characters.

A hybrid SATS algorithm based security constrained optimal power flow using FACTS devices

In the realm of power systems, achieving optimal operation while ensuring security remains a paramount challenge. The security constrained optimal power flow (SCOPF) problem deals with optimizing power system operations while taking into account security limitations. Flexible alternating current transmission system (FACTS) is a system consisting of static equipment used for transmitting electrical energy in the form of AC. The static synchronous series compensator (SSSC) is a specific form of series FACTS device. The unified power flow controller (UPFC) is a FACTS device that is connected in parallel and series with a transmission line. In this research, hybrid simulated annealing and tabu search (hybrid SATS) algorithm is designed to solve SCOPF problems that involve use of FACTS devices. The combination of simulated annealing and tabu search is intended to improve algorithm's pace of convergence and the quality of its solutions. Hybrid SATS with FACTS devices are used to investigate line flow limit violations during single line failures and ensure power flows remain within their security limitations. The efficacy of proposed algorithm is demonstrated through case studies utilizing IEEE 30 bus system. These case studies demonstrate algorithm's capabilities to achieve optimal and secure power system functioning to demonstrate its effectiveness.

On the Solution to Optimal Topology Adjustment in Long-Term Power System Operation with Explicit Short-Circuit Current Limitation Constraints

To restrict the excessive short-circuit current (SCC), this letter proposes an SCC-constrained optimal topology adjustment model considering the N-1 security criterion. Topology adjustment, including transmission switching (TS), bus sectionalization (BS), lines operating out of breaker bay (LOB) and commitment status of units (CU), has nonlinear relationships with SCC and is difficult to express explicitly. In this letter, an explicit formulation of SCC considering topology adjustment is proposed based on the equation of admittance matrix and nodal self-impedance, which is further recast as a mixed-integer linear programming (MILP) model. In addition, the N-1 security criterion is included for avoiding jeopardizing system security by topology adjustment. Numerical case studies validate the effectiveness and advantages of the proposed model in SCC restriction.

Fault diagnosis and intelligent maintenance of industry 4.0 power system based on internet of things technology and thermal energy optimization

The application of Internet of Things technology provides a new opportunity for the fault diagnosis and maintenance of power system. This study aims to explore how to improve the fault diagnosis ability of power system through Internet of Things technology, and realize the efficient utilization of heat energy, so as to achieve the goal of intelligent maintenance. Based on the analysis of current power system operation status, this paper determines the key role of thermal energy management in improving system operation efficiency and reducing energy consumption. It then uses iot sensors and data analytics to monitor heat flow and loss in the power system in real time, identifying potential points of failure through big data. In order to realize intelligent maintenance, this paper designs a fault prediction model based on thermal energy optimization, combined with machine learning algorithm, to further improve the accuracy of fault diagnosis. The experimental results show that the fault diagnosis method combined with the Internet of Things technology can significantly reduce the fault incidence and optimize the efficiency of heat energy use. By applying this model, the overall operating cost of the power system is reduced and the maintenance efficiency is improved.

Определение факта насыщения магнитопровода трансформатора тока на основе непрерывного измерения сопротивления цепи намагничивания

Saturation of electromagnetic current transformers in transient modes of operation of an electric power system can lead to incorrect operation of relay protection devices. One of the most promising directions to improve the stability of the operation of relay protection devices in such modes is digital correction of secondary current. Currently, effective methods have been developed to restore the secondary current based on determination of magnetic circuit saturation. However, the practical application of these methods is impossible due to the imperfection of existing methods of secondary current segmentation. Thus, it is urgent to develop a method to determine saturation for implementation of digital correction algorithms for electromagnetic current transformers. To solve the stated problem, methods of mathematical modeling of electrical circuits containing transformers and the small-disturbance theory have been used. Verification of the research results is carried out in the Matlab software package. A mathematical criterion is formulated to determine the saturation of the magnetic circuit of an electromagnetic current transformer based on the inductance of the magnetization circuit. A method of segmentation of the secondary current has been developed for the implementation of digital correction algorithms based on the proposed criterion. The effectiveness of the developed method to determine the saturation of the magnetic circuit of an electromagnetic current transformer using computational experiments in the Matlab software package is demonstrated. It is planned to verify the developed method using a physical model of an electromagnetic current transformer.

Infrared Image Object Detection Algorithm for Substation Equipment Based on Improved YOLOv8

Substations play a crucial role in the proper operation of power systems. Online fault diagnosis of substation equipment is critical for improving the safety and intelligence of power systems. Detecting the target equipment from an infrared image of substation equipment constitutes a pivotal step in online fault diagnosis. To address the challenges of missed detection, false detection, and low detection accuracy in the infrared image object detection in substation equipment, this paper proposes an infrared image object detection algorithm for substation equipment based on an improved YOLOv8n. Firstly, the DCNC2f module is built by combining deformable convolution with the C2f module, and the C2f module in the backbone is replaced by the DCNC2f module to enhance the ability of the model to extract relevant equipment features. Subsequently, the multi-scale convolutional attention module is introduced to improve the ability of the model to capture multi-scale information and enhance detection accuracy. The experimental results on the infrared image dataset of the substation equipment demonstrate that the improved YOLOv8n model achieves mAP@0.5 and mAP@0.5:0.95 of 92.7% and 68.5%, respectively, representing a 2.6% and 3.9% improvement over the baseline model. The improved model significantly enhances object detection accuracy and exhibits superior performance in infrared image object detection in substation equipment.

Prediction of long-term photovoltaic power generation in the context of climate change

Accurate long-term prediction of power generation in photovoltaic (PV) power stations is crucial for preparing generation plans and future planning. Quantitative prediction of future power generation from PV stations not only contributes to the stable operation of the local power system but also assists managers in formulating regional energy policies to promote renewable energy consumption. We utilized the NEX-GDDP-CMIP6 high-resolution climate dataset and employed the Vine Copula method for post-downscaling. This approach enabled high-resolution forecasts of key meteorological factors under different shared socioeconomic pathways (SSPs) scenarios (SSP245 and SSP585) for a PV power station in Yunnan, China. Additionally, we developed the KM-PSO-SVR power generation prediction model, which enables future accurate long-term PV power generation prediction. The results show that the Vine Copula multi-model ensemble downscaling model can effectively simulate the changes in key meteorological factors in the PV power station area. The KM-PSO-SVR model exhibited good simulation performance, with a mean absolute error of 0.843, root mean square error of 1.136, and correlation coefficient of 0.874 during the validation period. The results indicate that during the decade spanning from January 1, 2025, to December 31, 2034, radiation and wind speed will be decrease, while the temperature is expected to increase. In the SSP245 scenario, there is a 1.585 % increase in the average annual power generation during the future carbon peaking period (2025–2034). However, the SSP585 scenario, representing higher future emissions, shows a lower increase of 1.479 %.

Fault analysis of oil-immersed transformer based on digital twin technology

Oil-immersed transformer is one of the important equipment to support the safe and stable operation of power system. At the same time, in the process of transformer operation, transformer failure is inevitable, this kind of failure belongs to multiple latent faults, which seriously endangers the safe operation of the transformer. Therefore, the maintenance of the transformer has played a crucial role. A fault detection method based on digital twin model is proposed to solve the problem that the internal dynamic characteristics of oil-immersed transformer are difficult to extract, and the fault detection error is large due to noise interference. The dynamic model of oil-immersed transformer is constructed, the main cause of failure is analysed, the failure time of internal insulator is taken as a linear index, and the linear correlation between failure probability and failure time is solved by Weibull distribution function. The fitting function between transformer fault residence time, operating days and fault probability variance is established, and the fitting value obtained is used as the initial data reference of the fault detection model. The digital twin model is used to establish the dynamic fault probability calculation function, and the on-site data is substituted to calculate the fault detection threshold, and then the data is compared to complete the detection.