Network Of Transmission System Operators Research Articles

Hybrid strategy of flexibility regulation and economic energy management in the power system including renewable energy hubs based on coordination of transmission and distribution system operators

Energy management of energy hubs in the power system impacts both transmission and distribution networks and their operations. Hence, to enhance efficiency, there should be bilateral coordination between distribution and transmission networks. Moreover, thanks to including power sources, storage, and responsive loads, hubs can earn financial benefits from the energy and ancillary services market. Therefore, this paper presents the operation of distribution and transmission networks in the presence of flexible-renewable energy hubs based on transmission system operator (TSO)-distribution system operator (DSO) coordination according to the market clearing price modeling in energy and flexibility markets. The proposed scheme minimizes the sum of the expected operating costs of the networks and hubs minus the expected flexibility profit of the hubs. The scheme is constrained to optimal power flow equations of the networks, flexibility limit of the networks, and flexibility-operation model of renewable energy hubs located in transmission and distribution networks. The unscented transformation method is used to model the uncertainties of load, renewable sources, and stationary storage (aggregation of electric vehicles). Energy price and flexibility are among the dual variables of the problem. To simultaneously calculate these variables and the main variables of the problem, an integrated model of the suggested scheme based on the penalty function technique is extracted. Eventually, numerical results validate the capability of the scheme in enhancing the operation, economic, and flexibility status of the proposed scheme, in which renewable sources along with storage and responsive load in the energy hub can obtain 100 % flexibility conditions for the networks. They help enhance the economic and operation indices of distribution and transmission networks by about 26 %-44 % and 22 %-33 % compared to the case without hubs and renewable sources.

Alignment of aFRR and mFRR prequalification process in Croatia with the target market design

Commission Regulation (EU) 2017/1485 of 2 August 2017 establishing a guideline on electricity transmission system operation defines a prequalification process as a process to verify the compliance of a reserve providing unit or a reserve providing group with the requirements set by the transmission system operator. Croatian Transmission System Operator Plc. (HOPS) carries out a prequalification process in order for (potential) ancillary service providers to prove their capability of providing ancillary services, which means that the prequalification process is a prerequisite for the provision of ancillary services. As part of the European Network of Transmission System Operators for Electricity, HOPS participates in the European implementation projects PICASSO and MARI, which aim is to establish a single European platform for aFRR and mFRR balancing services. Within the PICASSO and MARI projects, the technical characteristics of the products traded on unique European platforms were defined. This paper analyzes the difference between the requirements from the existing prequalification process rules and the technical requirements for products within the PICASSO and MARI projects, explores practice in Slovenia and Germany and suggests the required adoptions of aFRR and mFRR prequalification processes in Croatia.

CLASSIFICATION AND METHODS OF DETECTION OF PHISHING ATTACKS

The reliable operation of data transmission networks, computer systems and mobile devices is a mandatory condition for the effective functioning of the state and society, and the life of an individual. The reliability of key information systems for public use depends on many factors: cyber attacks, hardware and software failures, and various types of errors. Rapid expansion of threats to national security in the 21st century. entrusts state authorities with the tasks of their prevention, detection and neutralization. Cyber security is increasingly viewed as a fundamental problem of the state, which comprehensively affects its security, defense, economy, and almost all spheres of public life. The Internet gave a powerful impetus to the development of mass communication, trade and information exchange. However, today it is the area where many crimes are committed. The impersonal nature of digital infrastructure has made identity theft a natural and highly attractive project. Cybercriminals actively use various means of stealing information, including phishing. At the current stage and in the future, the development of both individual societies and states, as well as the world in general, will be carried out in accordance with the concept of the information society, which is connected with the use of information and telecommunication technologies in the acquisition, storage and processing of information in everyday life Phishing is a serious online security problem that involves spoofing genuine websites to trick online users and steal their confidential information. Analyzing these definitions, we can conclude that “phishing” can be considered in different ways, but the main purpose of its conduct remains unchanged—stealing data. The practical value of the results lies in the possibility of using the obtained classifier for further creation of software solutions for recognizing phishing sites. It, as well as a set of characteristics, can be implemented in anti-phishing browser extensions or other anti-phishing tools.

Natural gas consumption estimation in the European Union

On 5 August 2022, the European Commission adopted the Regulation (EU) 2022/1369 on coordinated demand reduction measures for gas to decrease, on a voluntary basis, 15 % the gas consumption in the European Union (EU) over the period 1 August 2022 – 31 March 2023 compared to the previous five years. In 2023, the prolongation of this regulation was proposed until 31 March 2024 in order to save gas, facilitate the refilling of storages during the summer, and avoid exhaustion at the end of next winter. Thus, there is a need to monitor as closely as possible the natural gas consumption in the EU and keep track of the accumulated demand reduction for natural gas. However, the most up-to-date figures from the statistical office of the EU (Eurostat) are provided with a delay of one to two months and with coarse granularity (monthly). This paper (i) proposes a methodology to estimate the EU natural gas consumption with daily granularity, and (ii) presents two applications for monitoring the gas demand reduction with coarser granularity (weekly and monthly). The methodology, benchmark, and applications exclusively rely on publicly available sources such as Eurostat, the transparency platforms from the European Network of Transmission System Operators for Gas (ENTSOG), Gas Infrastructure Europe (GIE), as well as ENaGaD. ENaGaD is the in-house Joint Research Centre’s database of time series of natural gas consumption of the EU Member States.

Extreme Gradient Boosting Model for Day-Ahead STLF in National Level Power System: Estonia Case Study

Short-term power load forecasting refers to the use of load and weather information to forecast the Day-ahead load, which is very important for power dispatch and the establishment of the power spot market. In this manuscript, a comprehensive study on the frame of input data for electricity load forecasting is proposed based on the extreme gradient boosting algorithm. Periodicity was the first of the historical load data to be analyzed using discrete Fourier transform, autocorrelation function, and partial autocorrelation function to determine the key width of a sliding window for an optimization load feature. The mean absolute error (MAE) of the frame reached 52.04 using a boosting model with a 7-day width in the validation dataset. Second, the fusing of datetime variables and meteorological information factors was discussed in detail and determined how to best improve performance. The datetime variables were determined as a form of integer, sine–cosine pairs, and Boolean-type combinations, and the meteorological features were determined as a combination with 540 features from 15 sampled sites, which further decreased MAE to 44.32 in the validation dataset. Last, a training method for day-ahead forecasting was proposed to combine the Minkowski distance to determine the historical span. Under this framework, the performance has been significantly improved without any tuning for the boosting algorithm. The proposed method further decreased MAE to 37.84. Finally, the effectiveness of the proposed method is evaluated using a 200-day load dataset from the Estonian grid. The achieved MAE of 41.69 outperforms other baseline models, with MAE ranging from 65.03 to 104.05. This represents a significant improvement of 35.89% over the method currently employed by the European Network of Transmission System Operators for Electricity (ENTSO-E). The robustness of the proposal method can be also guaranteed with excellent performance in extreme weather and on special days.

Methods to Minimize the Effects of Geometric Asymmetry of Multi-Circuit, Multi-Voltage Overhead Lines on Transmission Network Operation

Multi-circuit, multi-voltage overhead lines (MMOLs) are becoming a favorable solution in the perspective of ensuring the efficient transmission of electrical energy and limitations relating to the expansion of the transmission network. The main part of this manuscript contains a method that minimizes the effects of risks on the operation of the power system due to asymmetrical MMOLs, with particular emphasis on the circuit with the lowest-rated voltage. The selected set of criteria includes elements such as the determination of the neutral point displacement voltage in the network with the lowest-rated voltage (zero voltage, U0), selected voltage quality factors, and short-circuit analyses to examine the impact of differences between the use of accurate and simplified mathematical models on the calculation of short-circuit currents. Methods to minimize the effects of MMOL geometric asymmetry have been proposed using simple technical steps, such as selecting the appropriate pole silhouette (indication of the methodology for identifying such supporting structures), changing the way of supplying individual circuits, or using partial transposition in the circuit with the lowest-rated voltage. The proposed methods are verified by analyzing a case study of the development of the transmission network in Poland.

The role of smart communities integrated with renewable energy resources, smart homes and electric vehicles in providing ancillary services: A tri-stage optimization mechanism

Increasing the connection of renewable energy sources and electric vehicles (EVs) to modern cities with smart homes (SHs) makes the need for new mechanisms to coordinate them with the operation of transmission (TN) and distribution networks (DNs) inevitable since the uncertain behavior of the above-mentioned resources can create various technical and economic challenges for the operators. Hence, this article introduces a tri-stage mechanism for the joint management of the energy and ancillary services markets in coordinated TN and DN, taking into account wind and solar renewable resources, SHs and EVs. In the first stage of this mechanism, SHs do their daily planning with the possibility of participating in the regulation market and send it to the DN operator. In the second stage, DN operator determines its strategy for participation in the energy and ancillary services markets according to the programs received from SHs and sends it to the TN operator. Finally, in the third stage, the TN operator settles the markets according to the plans received from the DN operators. This model is designed in the mixed-integer linear programming (MILP) format and simulated using CPLEX solver in GAMS. The simulation outputs illustrate that SHs by controlling their internet-of-things (IoT)-based appliances and EVs have provided cheap services in the regulation market and subsequently reduced the costs of this market by 10.4%. Also, the results reveal that the participation of large-scale industrial units in the demand response program (DRP) not only reduces the costs of the energy and ancillary services markets but also improves the voltage profile during the peak period.

Applying Fuzzy Time Series for Developing Forecasting Electricity Demand Models

Managing the energy produced to support industries and various human activities is highly relevant nowadays. Companies in the electricity markets of each country analyze the generation, transmission, and distribution of energy to meet the energy needs of various sectors and industries. Electrical markets emerge to economically analyze everything related to energy generation, transmission, and distribution. The demand for electric energy is crucial in determining the amount of energy needed to meet the requirements of an individual or a group of consumers. But energy consumption often exhibits random behavior, making it challenging to develop accurate prediction models. The analysis and understanding of energy consumption are essential for energy generation. Developing models to forecast energy demand is necessary for improving generation and consumption management. Given the energy variable’s stochastic nature, this work’s main objective is to explore different configurations and parameters using specialized libraries in Python and Google Collaboratory. The aim is to develop a model for forecasting electric power demand using fuzzy logic. This study compares the proposed solution with previously developed machine learning systems to create a highly accurate forecast model for demand values. The data used in this work was collected by the European Network of Transmission System Operators of Electricity (ENTSO-E) from 2015 to 2019. As a significant outcome, this research presents a model surpassing previous solutions’ predictive performance. Using Mean Absolute Percentage Error (MAPE), the results demonstrate the significance of set weighting for achieving excellent performance in fuzzy models. This is because having more relevant fuzzy sets allows for inference rules and, subsequently, more accurate demand forecasts. The results also allow applying the solution model to other forecast scenarios with similar contexts.

One Network for Europe – Scalability and Replicability in View of Harmonised Electricity Markets

Renewable energy production is mentioned by many as one of the key factors in mitigating the effects of climate change. However, there may be several downsides to the spread of renewables, such as their impact on the balance of the electricity system and on receiving networks. The project OneNet (One Network for Europe) is funded through the EU’s eighth Framework Programme Horizon 2020. It is titled “TSO – DSO (TransmissionDistribution System Operator) Consumer: Large-scale demonstrations of innovative grid services through demand response, storage and small-scale (Renewable Energy Source) generation” and responds to the call “Building a low-carbon, climate resilient future”. While the electrical grid is moving from being a fully centralized to a highly decentralized system, grid operators must adapt to this changing environment and adjust their current business model to accommodate faster reactions and adaptive flexibility. This is an unprecedented challenge requiring an unprecedented solution. Thus, the two major associations of grid operators in Europe, the European Network of Transmission System Operators for Electricity (ENTSO-E) and European Distribution System Operators (EDSO), have activated their members to establish a unique consortium.

Pengukuran Tahanan Isolasi Terminal Incoming Gardu Kubikel 20KV di PT Haleyora Power Region 3 Jawa Timur Area Mojokerto

PT Haleyora Power is a subsidiary of PT PLN which is engaged in the operation and maintenance of electricity transmission and distribution networks. One of them is routine maintenance of the 20kv cubicle substation with the aim of maintaining the reliability of distribution in the electricity system. For Region 3 East Java, the Mojokerto area itself has 3 working areas, namely, Mojokerto, Jombang, and the Nganjuk area. In the process the cubicle is divided into 3 points, namely, incoming as input from the 20kv transformer output owned by PLN, metering is the part that functions to monitor (current, voltage, and electricity demand), the last is outgoing which is the 20kv output which will enter the transformer input customer/industry property. In this study, what was measured was insulation resistance using an insulation tester / megger and focused on the incoming terminal, the terminal points measured were between terminals r-s, r-t, s-t, r-n, s-n, and t-n. The minimum standard for measuring insulation resistance in a 20kv cubicle is 20MOhm. With an insulation resistance of more than 20MOhm, the insulation resistance obtained is good enough.

Sizing of Hybrid Power Systems for Off-Grid Applications Using Airborne Wind Energy

The majority of remote locations not connected to the main electricity grid rely on diesel generators to provide electrical power. High fuel transportation costs and significant carbon emissions have motivated the development and installation of hybrid power systems using renewable energy such these locations. Because wind and solar energy is intermittent, such sources are usually combined with energy storage for a more stable power supply. This paper presents a modelling and sizing framework for off-grid hybrid power systems using airborne wind energy, solar PV, batteries and diesel generators. The framework is based on hourly time-series data of wind resources from the ERA5 reanalysis dataset and solar resources from the National Solar Radiation Database maintained by NREL. The load data also include hourly time series generated using a combination of modelled and real-life data from the ENTSO-E platform maintained by the European Network of Transmission System Operators for Electricity. The backbone of the framework is a strategy for the sizing of hybrid power system components, which aims to minimise the levelised cost of electricity. A soft-wing ground-generation-based AWE system was modelled based on the specifications provided by Kitepower B.V. The power curve was computed by optimising the operation of the system using a quasi-steady model. The solar PV modules, battery systems and diesel generator models were based on the specifications from publicly available off-the-shelf solutions. The source code of the framework in the MATLAB environment was made available through a GitHub repository. For the representation of results, a hypothetical case study of an off-grid military training camp located in Marseille, France, was described. The results show that significant reductions in the cost of electricity were possible by shifting from purely diesel-based electricity generation to an hybrid power system comprising airborne wind energy, solar PV, batteries and diesel.

ANALYSIS OF REGULATORY DOCUMENTATION AND TECHNICAL REQUIREMENTS REGARDING THE FUNCTIONING OF THE NETWORK IN "ISLAND MODE"

The article examines main issues regarding the admissibility and conditions of operation of microgrids in the so-called " islanding mode" (IM), namely: identification of the appearance of an IM, regulatory and legal aspects of admissibility, and technical requirements for its implementation. Main methods and approaches to the issue of identification of IM, concepts of "planned" and "unwanted" IM are analyzed, possible advantages and consequences of the operation of a part of the distribution network in the IM are considered. An analysis and comparison of the legal framework regarding the possibility of IM usage in the countries of the European Union and Ukraine was carried out. Changes to the national legislation regarding the harmonization of regulatory documentation of Ukraine and the European Union, which were caused by Ukraine's desire to integrate its energy system into the European Network of Transmission System Operators for Electricity (ENTSO-E), are presented. The normative and legal documentation of both national and international use, regarding the norms of integration into the distribution networks of sources of distributed generation and their functioning in the IM, is presented and reviewed. Issues related to the technical support of the operation of the IM in the distribution network, subject to compliance with the relevant requirements for power supply quality indicators, are considered. Additionally, analysis of the advantages that distribution networks and consumers have with the possibility of using IM was carried out and the problems that may arise in the event of incorrect planning, identification and technical implementation of the isolated operation of a part of the network with local energy sources were identified. Information has been collected and presented regarding the maximum limits to the frequency and voltage in the distribution network, which are permissible for short-term deviations of these indicators depending on the standard that regulates the operation of the corresponding distribution networks. The importance of taking into account all the previously mentioned aspects of the operation of distribution networks in the IM is summarized, and conclusions are drawn regarding the feasibility of ensuring the possibility of the operation of networks in the specified mode in Ukraine

Exploration and Practice of Ideological and Political Teaching of Fluid Transmission and Distribution Pipe Network

In the new era of socialism with Chinese characteristics, professional courses and ideological and political courses should develop in the same direction and synergy.How to effectively integrate ideological and political elements into professional courses and build a large pattern of all-round education is a new topic for professional teachers to cultivate people by virtue. In teaching design of "fluid transmission and distribution network" course, based on the Guiding Outline of Ideological and Political Construction of Higher Education Courses issued by the Ministry of Education, the curriculum teaching content was redesigned, which is around the "engineering fluid distribution network, fluid distribution network function and composition, fluid distribution network of hydraulic calculation and case analysis, fluid transmission and distribution network operation and adjust" four modules of professional knowledge and curriculum ideological content fusion design. Put the "salt" of ideological and political education into the teaching process of professional courses, and realize the unity of knowledge teaching, ability training and value guidance, and truly achieve the goal of curriculum education.

Effective RNN-Based Forecasting Methodology Design for Improving Short-Term Power Load Forecasts: Application to Large-Scale Power-Grid Time Series

This article introduces a carefully-engineered forecasting methodology for day-ahead electric power load forecasts evaluated using the European Network of Transmission System Operators for Electricity (ENTSO-E). Two steps were employed to configure the desired forecasting methodology: First, a straightforward processing pipeline is proposed to enable systematic preprocessing of raw multivariate time-discrete power data extracted from the ENTSO-E repository, including a stride-based sliding window approach to generate time series-based batches ready for the supervised learning procedure. Second, the lightweight type of recurrent neural network method, namely gated recurrent units (GRU), is selected and carefully calibrated to yield accurate multi-step forecasts, which was trained using the preprocessed multivariate time series data to render day-ahead power load forecasts. The forecasting estimates generated by the proposed GRU model are evaluated using a set of regression-based metrics to assess the models’ precisions. The empirical results show that the proposed forecasting methodology yields outstanding day-ahead power load forecasting performance regarding the enterprise-class measured data compared to a statistical model, namely autoregressive integrated moving average with exogenous variables (ARIMAX), as well as the actual day-ahead forecasts generated by the ENTSO-E platform.

Natural gas supply from Russia derived from daily pipeline flow data and potential solutions for filling a shortage of Russian supply in the European Union (EU)

Abstract. Russia is the largest natural gas supplier to the European Union (EU). The invasion of Ukraine was followed by a cutoff of gas supplies from Russia to many EU countries, and the EU is planning to ban or drastically reduce its dependence on Russia. We provide a dataset of daily gas consumption in five sectors (household and public building heating, power, industry, and other sectors) with supply source shares in the EU27 (27 EU member countries) and UK from 2016 to 2022. The datasets are available at Zenodo platform: https://doi.org/10.5281/zenodo.7549233 (Zhou et al., 2022). The dataset separates the contributions of Russian imports, liquefied natural gas (LNG) imports, and other supply sources to both direct supply and storage supply for gas consumption estimations. The dataset was developed with a gas network flow simulation model based on mass flow balance by combining data from multiple datasets including daily ENTSOG (European Network of Transmission System Operators for Gas) pipeline gas transport and storage, ENTSOE (European Network of Transmission System Operators for Electricity) daily power production from gas, and Eurostat monthly gas consumption statistics per sector. The annual consumption data were validated against the BP Statistical Review of World Energy and Eurostat datasets. We secondly analyzed the share of gas supplied by Russia in each country to quantify the “gap” that would result from a cessation of all Russian exports to Europe. Thirdly, we collected multiple data sources to assess how national gaps could be alleviated by (1) reducing the demand for heating in a plausible way using the lower envelope of gas empirical consumption – temperature functions, (2) increasing power generation from sources other than gas, (3) transferring gas savings from countries with surplus to those with deficits, and (4) increasing imports from other countries like Norway, the USA, Australia, and northern African countries from either pipelines or LNG imports, accounting for existing capacities. Our results indicate that it should be theoretically possible for the EU to collectively make up for a sudden shortfall of Russian gas by combining the four solutions together, provided a perfect collaboration between EU countries and the UK to redistribute gas from countries with surplus to those with deficits. Further analyses are required to investigate the implications with respect to the costs, including social, economic, and institutional dimensions; political barriers; and negative impacts on climate policies, with inevitable increases in CO2 emissions if the use of coal is ramped up in the power sector.

Multivariate time series imputation for energy data using neural networks

Multivariate time series with missing values are common in a wide range of applications, including energy data. Existing imputation methods often fail to focus on the temporal dynamics and the cross-dimensional correlation simultaneously. In this paper we propose a two-step method based on an attention model to impute missing values in multivariate energy time series. First, the underlying distribution of the missing values in the data is learned. This information is then further used to train an attention based imputation model. By learning the distribution prior to the imputation process, the model can respond flexibly to the specific characteristics of the underlying data. The developed model is applied to European energy data, obtained from the European Network of Transmission System Operators for Electricity. Using different evaluation metrics and benchmarks, the conducted experiments show that the proposed model is preferable to the benchmarks and is able to accurately impute missing values.

Simulation Research on Trajectory Planning of Double-arm Cooperative Live Working Robot for Transmission Lines

High-voltage transmission lines are the main artery for power transmission, dampers, insulators and drainage plates are typical hardware on transmission lines. The harsh service environment will damage transmission line hardware and seriously affect the safety and stable operation of transmission lines. The electrical power robots are used to carry out transmission line maintenance operations. It is of great significance to improve work safety and work efficiency. Through the analysis of typical hardware maintenance tasks, it can be seen that most of the operations require the coordinated movement of the double arms to complete. Based on this, this paper proposes a basic configuration of a double-arm collaborative robot. At the same time, based on the polynomial interpolation theory, the robot arm motion trajectory planning is carried out, and then the trajectory planning simulation model of the robot manipulator in the MATLAB environment has been established and the trajectory simulation research has been carried out on the robot manipulator to reconstruct the end tool and the manipulator to carry the end tool to the work point. The simulation results show that compared with the single-arm operation mode, the cooperative operation of the double arms can not only improve the operation efficiency, but also complete some tasks that cannot be completed by the single-arm operation, the research in this paper has important theoretical significance and practical application value for the intelligent operation and maintenance management of transmission network.

SECURITY OF OPERATION AND IDENTIFICATION OF POSSIBLE BOTTLENECKS IN SOUTHEAST EUROPE TRANSMISSION NETWORK UNDER MARKET CONDITIONS

Based on the results of calculations of power flows and on the analysis of the operation of the HV transmission network in Southeast Europe, with the market focus on generation facilities, the basic characteristics of the network are described, and possible bottlenecks identified. The calculations were done within the EC-funded REBIS project (Regional Balkans Infrastructure Study) and the Generation Investment Study (GIS) of new power generation capacities in the region. The market offering of generation facilities in Southeast Europe at forecast system peak loads in 2010 and 2015, for different hydrological conditions, was determined by the WASP (IAEA) and GTMax (Argonne NL) software. Calculations of power flows were carried out by using the PSS/E software (Siemens PTI). The article particularly discuses simulated power plant output and the operation of the transmission network in the Republic of Croatia.

Wind Power Generation Scheduling Accuracy in Europe: An Overview of ENTSO-E Countries

Despite the rapid spread of the use of wind energy to generate electricity, harnessing this energy source remains a great challenge due to its stochastic nature. One way of dealing with this is to prepare accurate wind power forecasts. This paper explored the accuracy of day-ahead and intraday scheduling of energy generation of the onshore and offshore wind farms of the member countries of the European Network of Transmission System Operators (ENTSO-E) in the period from 2013 to 2021. The precision of the scheduling activities showed a varying picture: the onshore wind farms of Germany, Spain, France, and Sweden produced more precise forecasts than others, with annual downward and upward regulatory needs between 0.8% and 14.4%, and from 0.8% to 6.5%, of the yearly energy generation, respectively. In certain countries, however, the forecasts were less accurate, with discrepancies exceeding 41% for downward and 132% for upward regulation. As for offshore wind farms, the annual downward and upward regulatory needs ranged between 0.9% and 61.7%, and from 1.3% to 44.1%, respectively, with Germany and Denmark producing the most accurate schedules. The innovative novelty and practical contributions of this study are that it determines and presents information related to the accuracy of the day-ahead and intraday wind power generation forecasting of the ENTSO-E countries, which is of practical relevance to the transmission system operators (TSOs), the main actors in the energy market and the decision-makers, too. This information may also help investors who invest in onshore and offshore wind farms with the economic aspects, and it may also greatly contribute to the market-related development of the management systems of energy storage solutions related to these technologies.

Optimal Transmission Switching for Short-Circuit Current Limitation Based on Deep Reinforcement Learning

The gradual expansion of power transmission networks leads to an increase in short-circuit current (SCC), which has an impact on the secure operation of transmission networks when the SCC exceeds the interrupting capacity of the circuit breakers. In this regard, optimal transmission switching (OTS) is proposed to reduce the short-circuit current while maximizing the loadability with respect to voltage stability. However, the OTS model is a complex combinatorial optimization problem with binary decision variables. To address this problem, this paper employs the deep Q-network (DQN)-based RL algorithm to solve the OTS problem. Case studies on the IEEE 30-bus system and 118-bus system are presented to demonstrate the effectiveness of the proposed method. The numerical results show that the DQN-based agent can select the effective branches at each step and reduce the SCC after implementing the OTS strategies.