Secure Operation Of System Research Articles

Mitigating Risk and Emissions in Power Systems: A Two-Stage Robust Dispatch Model with Carbon Trading

The large-scale integration of renewable energy sources is crucial for reducing carbon emissions. Integrating carbon trading mechanisms into electricity markets can further maximize this potential. However, the inherent uncertainty in renewable power generation poses significant challenges to effective decarbonization, renewable energy accommodation, and the security and cost efficiency of power system operations. In response to these challenges, this paper proposes a two-stage robust power dispatch model that incorporates carbon trading. This model is designed to minimize system operating costs, risk costs, and carbon trading costs while fully accounting for uncertainties in renewable energy output and the effects of carbon trading mechanisms. This model is solved using the column-and-constraint generation algorithm. Validation of an improved IEEE 39-bus system demonstrates its effectiveness, ensuring that dispatch decisions are both robust and cost-efficient. Compared to traditional dispatch models, the proposed model significantly reduces system risk costs, enhances the utilization of renewable energy, and, through the introduction of a ladder carbon trading mechanism, achieves substantial reductions in carbon emissions during system operation.

Optimal replacement of coal with wind and battery energy storage

Abstract Electric utilities are considering replacing their coal power plants with renewables and energy storage to reduce emissions. However, they have also expressed concerns about operational changes and system reliability brought by these replacements. Utilities in remote rural areas face more challenges as they also face energy insecurity while having limited interconnections to wider systems and reliance on imported fuels. Therefore, it is critical for remote utilities to understand different coal replacement approaches and their impacts on system expansion, operation and energy security. In this paper, we define and investigate three approaches to replace coal using wind and batteries: 1) replacing exact coal generation, 2) replacing at least coal generation, and 3) replacing total energy provided by coal. We develop a case study inspired by the small remote grid in Fairbanks, Alaska, which has a single limited interconnection with the grid south of it. We utilize a power system expansion and economic dispatch model that co-optimizes the capacities of wind and batteries required for each approach and the hourly dispatch of energy and reserves for one year. We further analyze the operational cost variability under fixed and fluctuating fuel prices. We find that replacing the exact coal generation requires minimal operational changes, but also significantly more wind and battery capacities. In contrast, replacing total energy provided by coal induces more cycling in other resources, challenging grids with limited flexibility-providing resources. However, replacing total energy provided by coal allows more generation variability in response to fuel price fluctuations, enhancing energy security.

Minimizing Business Disruption During Enterprise End-Point OS Upgrade while Maintaining a Robust Security Posture

The deployment of a major end-point operating system (OS) upgrade presents significant challenges for enterprise organizations, requiring a comprehensive strategy to minimize business disruption and maintain a robust security posture. Beyond the operational complexities of managing user productivity and application compatibility, organizations must also proactively address the critical security risks associated with such large-scale technology transitions. Cybercriminals often target organizations undergoing major IT changes, seeking to exploit vulnerabilities that may arise due to shifts in the OS architecture, user workflows, and security controls. This paper outlines practical strategies for executing successful OS upgrade projects that balance operational resilience and cyber risk management. Key focus areas include proactive management of end-point security, comprehensive contingency planning and rollback procedures, strategies to sustain productivity and business continuity, effective end-user training and communication, and valuable insights into the next- generation OS deployment. By focusing on these critical aspects, organizations can navigate major OS upgrade initiatives while preserving vigorous business operations, enhancing end-user experience, and safeguarding sensitive data and systems from costly security incidents.

Methodological approach to quantitative assessment of the security of open operating systems AS of the Internal Affairs Bodies

Objective. The article considers the provisions of the fuzzy logic approach in relation to the method of quantifying the security of open operating systems (OS) of automated systems of the internal affairs bodies of the Russian Federation (AS of the Russian Federation), taking into account possible security threats and the requirements of the standard GOST R ISO/IEC 15408 for leveling possible consequences. Information Message № 240/24/4893 dated October 18, 2016 «On Approval of Information Security Requirements for Operating Systems» of the FSTEC of Russia defines 6 OS security classes. Operating systems corresponding to protection classes 1, 2 and 3 are used in information (automated) systems in which information containing information constituting a state secret is processed, operating systems corresponding to protection classes 4, 5 and 6 are not intended for processing such information. In the presented study, the open operating systems of the AS of the Russian Federation are understood as OS AS, in which information containing information constituting a state secret is not processed. Method. The study was conducted based on the method of analyzing possible security threats to open operating systems, as well as the requirements of GOST R ISO/IEC 15408 standard, using the provisions of fuzzy logic. Result. The result of the automated system for calculating the security index of the analyzed open OS is one of the specified criteria for the degree of security of the OS, based on the provisions of fuzzy logic. Conclusion. The authors propose a method for assessing the security of open OS of the AS of the Russian Federation, based on the provisions of fuzzy logic.

Cooperative energy interaction for neighboring multiple distribution substation areas considering demand response

With the growing integration of renewable energy into medium- and low-voltage distribution networks, the distribution substation area (DSA) has emerged, encompassing energy storage and loads. This paper introduces an energy interaction framework for multiple DSAs aimed at enhancing local renewable energy consumption. The energy interaction issue among various DSAs is modeled as a Nash bargaining problem to encourage energy exchanges. However, the variability in pricing and internal demand response may influence scheduling decisions, necessitating further investigation. To address price forecast errors, scenarios are developed using a stochastic programming approach to represent price uncertainties while adjusting the DSA’s load accordingly. Optimal power flow constraints are integrated into the model to bolster power system operation security. Additionally, the transmission capacity can impact scheduling outcomes and operational costs. The influence of transmission limitations on operational strategies is examined within the allowable capacity. To solve this issue, the bargaining model is divided into two subproblems, and an enhanced alternating direction multiplier method (ADMM) is used to maintain the privacy of DSAs. The simulation results obtained using the IEEE-33 bus system indicate that energy interaction among multiple DSAs significantly lowers operating costs and facilitates the integration of renewable energy.

Optimal power flow in hybrid AC-DC systems considering N-k security constraints in the preventive-corrective control stage

The optimal power flow methods for AC-DC systems containing VSC-HVDC generally only consider the economy during normal operation, overlooking the distribution of line transmission power in fault conditions. As a result, lines that continue to operate after a fault may experience overloading or operate at full capacity. Thus, a method for optimal power flow calculation is proposed that incorporates N-k security constraints in the preventive-corrective control stage for secure and economic operation of hybrid AC-DC systems. This method ensures that the line transmission power in the system meets the limits in the normal, short-term fault, and long-term fault states. In addition to the optimal power flow in the normal state, the method incorporates the system's imbalance as an indicator to evaluate system resilience. It combines this indicator with the economic, network loss, and performance metrics of the system, forming a two-stage bi-level multi-objective optimization model. Furthermore, to address the curse of dimensionality in anticipating system fault sets, a method for generating the anticipated fault set using non-sequential Monte Carlo simulation is proposed, along with a fault scenario search approach based on robust thinking to identify the most severe faults. Finally, the traditional IEEE 30-bus system was improved, and simulation verification was conducted using examples of an AC/DC system with a three-terminal DC network and a wind-solar-storage hybrid AC/DC system with a three-terminal DC network. The simulation results indicate that the proposed optimal power flow method considering the preventive-corrective control stage with N-k security constraints can effectively enhance system resilience. Furthermore, it improves the economic efficiency while ensuring the secure operation of the system.

An assessment methodology for the flexibility capacity of new power system based on two-stage robust optimization

The inherent variability in wind and solar power output presents a significant challenge to the flexibility balance of power systems. This paper introduces an innovative method for evaluating the flexibility capacity of a new power system, employing a two-stage robust optimization approach. Firstly, a power system flexibility supply and demand balance mechanism model is constructed and quantitatively characterized for the power system flexibility shortfalls set. Subsequently, taking into account timescale characteristics and directionality, the time series production simulation technique is applied to establish the effective ramping and flexibility supply distribution model of thermal power and energy storage units, enabling an analysis of the power system's supply regulation capabilities. On this basis, a power system flexibility capacity assessment method is proposed, which divides the system regulation resources into demand set and supply set, and constructs a power system flexibility capacity assessment model to ensure that the maximum system flexibility margin and the lowest operating cost are taken as the optimization objectives under the system security operation constraints. The column and constraint generation (C&CG) robust optimization algorithm is used to decompose the master problem and the max-min dual-layer subproblems for iterative solving, and the optimal capacity of each unit of the system in terms of output and flexibility is derived. Finally, the effectiveness and superiority of the proposed method is verified through case analysis, which shows that the method can improve the flexibility capacity by 14.9% and reduce the operating cost by 15.83% compared with the traditional proportional allocation method.

Metaheuristics based dimensionality reduction with deep learning driven false data injection attack detection for enhanced network security

Recent sensor, communication, and computing technological advancements facilitate smart grid use. The heavy reliance on developed data and communication technology increases the exposure of smart grids to cyberattacks. Existing mitigation in the electricity grid focuses on protecting primary or redundant measurements. These approaches make certain assumptions regarding false data injection (FDI) attacks, which are inadequate and restrictive to cope with cyberattacks. The reliance on communication technology has emphasized the exposure of power systems to FDI assaults that can bypass the current bad data detection (BDD) mechanism. The current study on unobservable FDI attacks (FDIA) reveals the severe threat of secured system operation because these attacks can avoid the BDD method. Thus, a Data-driven learning-based approach helps detect unobservable FDIAs in distribution systems to mitigate these risks. This study presents a new Hybrid Metaheuristics-based Dimensionality Reduction with Deep Learning for FDIA (HMDR-DLFDIA) Detection technique for Enhanced Network Security. The primary objective of the HMDR-DLFDIA technique is to recognize and classify FDIA attacks in the distribution systems. In the HMDR-DLFDIA technique, the min–max scalar is primarily used for the data normalization process. Besides, a hybrid Harris Hawks optimizer with a sine cosine algorithm (hybrid HHO‐SCA) is applied for feature selection. For FDIA detection, the HMDR-DLFDIA technique utilizes the stacked autoencoder (SAE) method. To improve the detection outcomes of the SAE model, the gazelle optimization algorithm (GOA) is exploited. A complete set of experiments was organized to highlight the supremacy of the HMDR-DLFDIA method. The comprehensive result analysis stated that the HMDR-DLFDIA technique performed better than existing DL models.

Design of a cybersecurity defense system based on big data and artificial intelligence

With the development of big data and artificial intelligence technologies, hacker attack techniques and capabilities have continuously improved, and the methods of network attacks have diversified. Without cybersecurity, there is no national security. Therefore, cybersecurity has become a focal point of attention. To comprehensively enhance the network security defense capabilities of computer operating systems, aligning with the rapid development trends of big data and artificial intelligence technologies, this study focuses on constructing an efficient, stable, and practical cybersecurity defense system. This system deeply integrates advanced technologies of big data analysis and artificial intelligence, thoroughly analyzing the current status of network information security in computer operating systems and closely aligning with the practical needs of design and production. The aim is to provide highly valuable reference solutions for the field of cybersecurity defense.

Interpretable feature-temporal transformer for short-term wind power forecasting with multivariate time series

The inherent randomness and volatility of wind power generation present significant challenges to the reliable and secure operation of the power system. Therefore, it is crucial to have interpretable wind power forecasting (WPF) to ensure seamless grid integration and effective risk assessment. Existing forecasting models often focus on improving WPF performance and ignore the interpretability of the model, resulting in ambiguous forecasting. In this paper, the interpretable feature-temporal transformer (IFTT) for short-term wind power forecasting with multivariate time series is presented. The model uses an encoder-decoder architecture to effectively integrate historical information and future prior information from multiple variables. The designed decoupled feature-temporal self-attention (DFTA) module and variable attention network (VAN) effectively realize the interpretability of temporal information and multi-variable inputs while extracting important features. The Auxiliary Forecasting Network (AFN) plays a key role in providing pseudo-future wind speed predictions, which serve as an essential input for the model's decoder, and enhancing forecasting accuracy through multi-task learning. Experimental results on multiple datasets in different geographical locations show that the proposed algorithm is superior to various advanced methods. Besides, the interpretability of the IFTT model offers valuable insights for ensuring the safety of wind power utilization and supporting informed risk decision-making.

Efficient Malware Detection in Android Devices to Improve Cyber Security

The project introduces a novel framework for detecting Android malware based on permissions, utilizing multiple linear regression methods. Permissions play a crucial role in the security of the Android operating system, serving as fundamental indicators of an application's behavior. Through static analysis, the framework extracts application permissions and employs machine learning techniques to conduct security analyses. Specifically, the framework employs multiple linear regression techniques to develop two classifiers for permission-based Android malware detection. These classifiers leverage the relationships between various permission attributes to accurately identify potentially malicious applications. Notably, the framework achieves notable performance levels using classification algorithms without the need for overly complex models. In the project, the existing system utilizes the Random Forest (RF) algorithm, while the proposed system adopts the Support Vector Machine (SVM) algorithm. Both algorithms are evaluated in terms of accuracy, with the results demonstrating that the proposed SVM approach outperforms the existing RF method. This highlights the effectiveness of SVM in accurately detecting Android malware based on permission analysis.

Anomaly‐detection‐based learning for real‐time data processing in non‐intrusive load monitoring

AbstractA power system can be regarded as a cyber‐physical system with physical power networks and a cyber system based on increasing engagement with information communication technologies for smart grid functionalities for more efficient operations and control. Non‐intrusive load monitoring (NILM), an emerging smart‐grid technology, can be used to better understand the electricity usage profile and composition of smart meters using advanced data analysis algorithms. Although NILM enables various smart grid services, wider applications of NILM require addressing the challenges regarding cyber security and data privacy risks. Anomaly detection in appliance data is one of the most effective measures against potential cyber intrusions from a data perspective. This study proposes a framework of anomaly detection‐based learning algorithms to identify the anomalous periods of electricity loading data, which may be a subject for potential cyber‐attacks. Comparison studies with the hidden Markov model are performed to validate the proposed approaches. The simulation results show that these anomaly detection‐based learning algorithms work well and can precisely determine anomalous loading periods. Moreover, these trained models perform well on the testing dataset without prior knowledge of the data, providing the possibility of the real‐time assessment of power‐ loading states. The proposed framework can also be used to develop protective measures to ensure secure system operation and user data privacy.

Enhancing Software Supply Chain Resilience: Strategy for Mitigating Software Supply Chain Security Risks and Ensuring Security Continuity in Development Lifecycle

This article delves into the strategic approaches and preventive measures necessary to safeguard the software supply chain against evolving threats. It aims to foster an understanding of the challenges and vulnerabilities inherent in software supply chain resilience and to promote transparency and trust in the digital infrastructure that underpins contemporary society. By examining the concept of software supply chain resilience and assessing the current state of supply chain security, the article provides a foundation for discussing strategies and practices that can mitigate security risks and ensure security continuity throughout the development lifecycle. Through this comprehensive analysis, the article contributes to the ongoing effort to strengthen the security posture of software supply chains, thereby ensuring the reliable and secure operation of digital systems in a connected world.

Ancillary services from wind and solar energy in modern power grids: A comprehensive review and simulation study

Renewable energy sources like wind and solar have increased demand for surplus power capacity. The demand is primarily fueled by the growing impact of forecasting errors associated with these intermittent energy sources. Implementing advanced control methods for automatic generation control (AGC) is essential to integrate wind and solar power with conventional generation sources to balance the power system and reduce reliance on traditional reserves. Therefore, this paper comprehensively overviews solar and wind energy integration in the AGC framework to provide optimal grid ancillary services. Initially, the paper presents an overview of the basic equations used to integrate reserve power from the photovoltaic (PV) system by employing the de-loading strategy. Subsequently, a comprehensive review is conducted on integrating the PV system in AGC strategies to provide grid ancillary services. The study also analyzes the contribution of wind power in AGC services using relevant equations and past practices. The paper presents a real-time dynamic control strategy to optimize the dispatch of the AGC unit by integrating the operating reserves from wind energy systems in conjunction with thermal power systems. The study simulates an 8-bus, 5-machine model using the Dig-SILENT Power Factory. The findings reveal that utilizing operating reserves from wind power can significantly reduce large-scale forecasting errors in massively renewable energy resources (RES) integrated power systems, thereby ensuring the necessary system operational security and reducing the reliance on traditional generating units.

Intrusion detection and prevention systems (IDS/IPS) for OS protection

As cyber threats are increasing so it becomes important to secure operating systems. Traditional methods to secure OS is inadequate against sophisticated attacks so there is the necessity of intrusion detection and prevention systems(IDS/IPS). This research will help to protect OS from various cyber threats by making OS stronger with (IDS/IPS). This study investigates operational mechanisms and integration strategies of IDS/IPS within various different OS environments. The review of literature shows the evolution of IDS/IPS technologies, from signature-based to anomaly- based systems, which play a crucial role in preventing unauthorized access and reducing risks. Additionally, integrating IDS/IPS with OS represents a shift towards proactive security measures. Case studies highlight successful integration scenarios, demonstrating the practical benefits and challenges. In terms of methodology, this research uses a comprehensive evaluation framework, including metrics like detection rate and false positives. Real-world data, simulations, and comparative analysis are used to assess how effectively IDS/IPS enhance OS security. The results show a significant improvement in the ability to detect and prevent cyber threats when IDS/IPS are properly integrated with OS. This research is highly significant, showing the importance of adopting IDS/IPS for modern OS security. It emphasizes the need for organizations to shift to proactive defense strategies and strengthen their OS environments by leveraging IDS/IPS technologies. Future research directions include exploring AI- driven advancements in IDS/IPS and addressing emerging challenges in the ever-changing cyber threat landscape. This study provides a clear understanding of how IDS/IPS can be a crucial component in the arsenal of OS security. By shedding light on effective integration strategies and performance evaluation, it empowers stakeholders with insights on how to make their OS more resilient against evolving cyber threats. Key Words :- Intrusion Detection, Intrusion Prevention, IDS, IPS, Operating System Security, Cybersecurity.

A multi-timescale optimization method for integrated energy systems with carbon capture and accounting

As the goal of “double carbon”, integrated energy systems aiming at the development of low-carbon economy are developing rapidly, and carbon capture and other emission reduction technologies are gradually gaining more extensive development space. For controlling carbon emissions and enhance the consumption of renewable energy. This work proposes to introduce carbon capture technology in the framework of integrated energy system and optimize the energy dispatching of integrated energy system in multiple time scales, and design a multi-time scale optimization model of integrated energy system with carbon capture. Based on the basic architecture of a low-carbon integrated energy system, this study analyzes the power characteristics of each unit of the integrated energy system, which consists of thermal power units, gas turbines, electric boilers, batteries, gas storage, heat storage, etc. By studying the energy conversion and storage processes of each unit, a power model of each unit of the integrated energy system is established. On this basis, the relationship between carbon emissions and unit output of thermal power units and gas turbines was studied, and a carbon emission model for the energy supply unit in the comprehensive energy system was established. At the same time, in order to solve the problem of carbon emission reduction under the day ahead scheduling plan of the integrated energy system, considering the emission reduction goals and system operation security factors, the study analyzed the economic model and carbon emission model of the integrated energy system, established the day ahead low-carbon scheduling model of the integrated energy system, and reasonably planned the output of each unit that can achieve the carbon emission reduction goals on the premise of meeting the balance of supply and demand. The innovation of the research method of this paper is that this paper establishes a multi time scale rolling optimization model under the emission reduction goal of the integrated energy system. Based on the day ahead scheduling scheme obtained in the day ahead low-carbon scheduling phase, the day ahead plan is first revised through 4 h rolling scheduling in the day; Then, with the goal of minimizing the adjustment amount, fine tune the unit output within 15 minutes to provide a daily output plan for subsequent low-carbon emission reduction targets. The outcomes indicate that in the practical application, the carbon emission of the optimized model in the peak hour 11:00 to 12:00 phase is 118 tons, which is 7 tons less than the 125 tons of the traditional model. In summary, it demonstrates that the studied multi-timescale optimization model of integrated energy system with carbon capture has good application. We have studied and analyzed the low-carbon implementation mechanism of coordinated cooperation in multiple time scales, and constructed a multi time scale rolling optimization model, laying a theoretical foundation for subsequent low-carbon scheduling research. This enables the system to formulate more accurate and reasonable scheduling plans, while improving the low-carbon performance and economic benefits of the system, providing reference for the low-carbon development of the power system.

Research on Fast Frequency Response Control Strategy of Hydrogen Production Systems

With the large-scale integration of intermittent renewable energy generation presented by wind and photovoltaic power, the security and stability of power system operations have been challenged. Therefore, this article proposes a control strategy of a hydrogen production system based on renewable energy power generation to enable the fast frequency response of a grid. Firstly, based on the idea of virtual synchronous control, a fast frequency response control transformation strategy for the grid-connected interface of hydrogen production systems for renewable energy power generation is proposed to provide active power support when the grid frequency is disturbed. Secondly, based on the influence of VSG’s inertia and damping coefficient on the dynamic characteristics of the system, a VSG adaptive control model based on particle swarm optimization is designed. Finally, based on the Matlab/Simulink platform, a grid-connected simulation model of hydrogen production systems for renewable energy power generation is established. The results show that the interface-transformed electrolytic hydrogen production device can actively respond to the frequency disturbances of the power system and participate in primary frequency control, providing active support for the frequency stability of the power system under high-percentage renewable energy generation integration. Moreover, the system with parameter optimization has better fast frequency response control characteristics.

A joint planning strategy for EV charging system towards net-zero transportation electrification

Electric vehicles (EVs) are pivotal in the progression toward sustainable transportation. However, a growing body of recent studies has challenged the often-assumed notion that electric vehicles are often considered to be net-zero emissions. These studies suggest that electric vehicles could potentially generate substantial emissions due to their reliance on fossil fuels to generate electricity, especially when receiving fast charging services. This phenomenon has been mitigated with the use of renewable energy sources in the power system. However, due to the intermittent nature of renewable energy sources, additional requirements have arisen. To address this situation, this paper presents an interdisciplinary study of collaborative fast-charging stations (FCS) and distributed renewable energy planning based on the interactions between traffic and power networks in order to increase the adoption rate of electric vehicles and reduce emissions caused by traffic and power systems. The locations and sizing of fast-charging stations, distributed photovoltaic generation, and renewable energy power supply for electric vehicles are determined in the proposed strategy through multi-objective integer programming. The proposed planning strategy considers heterogeneous electric vehicle driving range constraints, the operation security of the power distribution system, the reliability and expansion of the current power distribution, as well as different types of air pollutant classifications resulting from the charging behaviors. To guarantee the feasibility and accuracy of the planning results, an interactive algorithm is applied to solve the multi-objective integer model. Several cases are conducted to validate the proposed approach. The results demonstrate that the proposed planning strategy has good performance in the planning of fast-charging stations and distributed renewable energy integration.

Artificial Neural Network based FACTS in a Contingency Situation

The two biggest issues facing today's energy management systems are the ongoing monitoring of online voltage stability and the improved loadability of the transmission lines for the current electrical power system. As a result, it is highly difficult and time-consuming to assess online voltage stability under diverse loading conditions. This study describes a practical voltage stability monitoring system that automates online voltage monitoring and alerts the operator before voltage drops by computing line voltage stability indices using an ANN. This study compares evaluations of system voltage stability and loadability at the load with FACTS devices. The results suggest increase in system loadability while ensuring the security of power system operation.

Securing Operating Systems (OS): A Comprehensive Approach to Security with Best Practices and Techniques

Abstract Operating system (OS) security is paramount in ensuring the integrity, confidentiality, and availability of computer systems and data. This research manuscript presents a comprehensive investigation into the multifaceted domain of OS security, aiming to enhance understanding, identify challenges, and propose effective solutions. The research methodology integrates diverse approaches, including an extensive exploration for available knowledge process mechanics, empirical data collection, case studies investigations, experimental analysis, comparative studies, qualitative analysis, synthesis, and interpretation. Through various experimental perspectives, theoretical foundations, historical developments, and current trends in OS security are also explored. Empirical data collection involves gathering insights from publicly available reports, security advisories, case studies, and expert interviews to capture real-world perspectives and experiences. Case studies illustrate practical implications of security strategies, while experimental analysis evaluates the efficacy of security measures in controlled environments. Comparative studies and qualitative analysis provide insights into strengths, limitations, and emerging trends in OS security. The synthesis and interpretation of the findings offer actionable insights for improving OS security practices, policy recommendations, and providing towards future research directions. This research contributes to advancing knowledge in OS security and informs the development of effective strategies to safeguard computer systems against evolving threats and vulnerabilities.