Interconnected Power Grid Research Articles

Intelligent Control Algorithms for Enhanced Frequency Stability in Single and Interconnected Power Systems

Ensuring stable power system performance is crucial for reliable grid operation. This study assesses various Load Frequency Control (LFC) strategies, including conventional PID, pole placement, Genetic Algorithm (GA)-optimized PID, Particle Swarm Optimization (PSO)-optimized PID, and an Artificial Neural Network (ANN)-based controller, in single and interconnected power grids. The results reveal that GA- and PSO-optimized PID outperform conventional methods, offering minimal overshoot and fast settling times. Pole placement strikes a balance between response time and stability, while the ANN controller demonstrates adaptability and quick rise times but exhibits higher overshoot and longer settling times compared to the optimization techniques. Tie-line bias control aids in frequency stabilization but presents challenges with overshoot and prolonged settling times. Notably, PSO-optimized PID emerges as a promising solution, effectively mitigating overshoot and achieving rapid frequency recovery. This study underscores the importance of tailored control strategies for optimal LFC, which are essential for enhancing power system stability and efficiency. Future research should explore the potential of advanced techniques, such as deep learning and reinforcement learning, to further improve control performance.

Integrated resource strategic planning considering inter-regional flexibility supply–demand balance: A case study for the Northwest and Central Grid in China

China’s rapid advancement in renewable energy and regional power grid interconnection has highlighted the need for enhanced system flexibility to ensure the security and stability of regional power systems. To address this issue, this study presents a new framework for integrated resource strategic planning for inter-regional flexibility (IRSP-IF). This framework evaluates inter-regional flexibility demand and optimizes power resource allocation across regions. The northwest and central grids in China are used as case studies to evaluate inter-regional power resource allocation from 2022 to 2035. This analysis considers key factors including flexibility supply–demand balance constraints, regional power grid interconnections, and time scales for assessing flexibility demand. The results show that incorporating these constraints and interconnections into the strategic power plan enables greater integration of renewable energy while reducing both installed capacity and the overall cost of regional power resources. In the S3 scenario, which includes inter-regional flexibility supply–demand balance, the total integration of wind and solar power increases by 1.54%. Additionally, the combined average annual fixed and operating costs decrease by 1.44 billion yuan compared to the S2 scenario, which only considers single-region flexibility supply–demand balance. Furthermore, evaluating flexibility demand at a shorter time scale results in a higher proportion of flexible resources, with an average annual growth of 24.43%.

Decarbonizing the electricity sector using terawatt-scale interconnected photovoltaic power grids to meet the climate goals: A comprehensive review and a strategic roadmap

High scalability and quick deployability of solar photovoltaic (PV) make it an ideal candidate for rapid decarbonization of electricity. The typical SPV generation profile and power grids designed for conventional power plants (PP) are the major obstacles to maximizing SPV utilization. While energy storage systems (ESS) are often deemed critical, scalable ESS are site-limited, highly dependent on rare-earth elements, and either have higher embodied energy and emissions or low round-trip efficiencies. This manuscript demonstrates that by strategically interconnecting SPV power plants longitudinally, PV can meet base load demands and extend availability beyond peak-solar hours, thereby reducing the need for ESS and replacing existing carbon-intensive electricity infrastructure. It is demonstrated by modelling two 12 GW longitudinally separated transmission lines interconnecting SPVPPs situated 40° (case-1) and 90° (case-2) apart can provide PV electricity beyond solar hours for 4.69 and 7.33 equivalent hours (daily average), respectively. For cases 1 and 2, the lithium battery-ESS route can result in 4.76 and 3.35 times more carbon emissions and cost 4.23 and 2.98 times more than the transmission route, respectively, for providing the same energy over the transmission line's 40-year lifespan. Technologies such as multi-terminal ultra-high-voltage-DC grids, hybrid superconductive cables, new semiconductor materials for PV and energy systems, etc. are explored for the globally interconnected solar grid. Findings suggest 90 TWp of PV capacity can supply a significant portion of world's energy demand by 2050. This study outlines a comprehensive approach to build a sustainable and interconnected global solar energy infrastructure that aligns with climate objectives.

A Low-Frequency Oscillation Suppression Method for Regional Interconnected Power Systems with High-Permeability Wind Power.

With the integration of large-scale wind power into the power grid, the impact on system stability, especially the issue of low-frequency oscillations caused by small disturbances, is becoming increasingly prominent. Therefore, this paper proposes a damping quantitative analysis method for regional interconnected power systems incorporating large-scale wind power. Using the cross-entropy particle swarm optimization (CE-PSO) algorithm, the control parameters of wind turbines are optimized to suppress low-frequency oscillations in interconnected systems. The method begins with the state equation of the interconnected power system in two regions; it deduces the characteristic polynomial of the interconnected system, including wind farms, and takes into account the influence of wind power integration on the electrical connectivity of the system. Subsequently, the influence of wind turbine control parameters on the system is quantified, and a quantitative analysis model of the impact of wind power integration on system damping characteristics is constructed. Based on this, an optimization model for wind turbine control parameters is established, and the CE-PSO algorithm is utilized to achieve suppression of low-frequency oscillations in interconnected power grids with wind power integration. Finally, the accuracy and effectiveness of the proposed method are verified through a typical electromagnetic transient simulation model of the two-region interconnected power system.

Droop Frequency Limit Control and Its Parameter Optimization in VSC-HVDC Interconnected Power Grids

With the gradual emergence of trends such as the asynchronous interconnection of power grids and the increasing penetration of renewable energy, the issues of ultra-low-frequency oscillations and low-frequency stability in power grids have become more prominent, posing serious challenges to the safety and stability of systems. The voltage-source converter-based HVDC (VSC-HVDC) interconnection is an effective solution to the frequency stability problems faced by regional power grids. VSC-HVDC can participate in system frequency stability control through a frequency limit controller (FLC). This paper first analyses the basic principles of how VSC-HVDC participates in system frequency stability control. Then, in response to the frequency stability control requirements of the sending and receiving power systems, a droop FLC strategy is designed. Furthermore, a multi-objective optimization method for the parameters of the droop FLC is proposed. Finally, a large-scale electromagnetic transient simulation model of the VSC-HVDC interconnected power system is constructed to verify the effectiveness of the proposed droop FLC method.

Development of a DSP based collaborative computing platform for power system

Abstract With the advancement of information technology, there has been tremendous progress in data production, processing, and storage technology, especially in data processing technology. With the rapid development of the power grid, its operation has become more intelligent and efficient. Traditional power system simulation and analysis tools have disadvantages, such as slow message transmission and the inability to share data, which cannot meet the requirements of modern power system simulation calculations. DSP was introduced and developed by the Southern Power Grid Research Institute and can perform various simulation calculations such as transient stability calculation, power flow calculation, and short circuit calculation. It is widely used in departments such as power planning and design. However, the data calculated by DSP is input through a data card, which is cumbersome and difficult to manage. In order to meet the new requirements of power system simulation analysis, a power system collaborative computing platform has been developed based on DSP, which makes DSP data maintenance and management more efficient and intuitive. It comprehensively improves the efficiency of power grid planning work. This article provides a detailed analysis of the architecture design and application problems of the power system collaborative computing platform, clarifies the requirements of large-scale interconnected power grids for simulation computing data management, and proposes solutions that can meet the needs of simulation computing data management in China’s power grid from two aspects: key data architecture issues and software architecture technology.

Impact of ABT based control strategies on ALFC and AVR in DFIG-integrated interconnected power systems

To maintain a stable electricity supply in interconnected power grids, effective frequency and voltage regulation are required. Interconnected power systems use Automatic Load Frequency Control (ALFC) and Automatic Voltage Regulator (AVR) for this purpose. The integration of renewable sources, such as Doubly Fed Induction Generators (DFIGs), creates challenges for such control loops. This study investigates the impact of using various Frequency-Linked Pricing (FLP)-based operating strategies under Availability-Based Tariff (ABT) on combined ALFC and AVR in a two-area interconnected power system with DFIGs. FLP-based operating strategies under ABT react to frequency changes through price signals, which can act faster compared to traditional AGC methods that rely on communication between control centers. This quicker response helps maintain frequency and voltage during load disturbances. Most of the earlier research focused mostly on using only a single type of FLP-based operating strategy under ABT within the ALFC loop, neglecting the combined ALFC-AVR interaction. This work addresses this gap by evaluating three FLP-based operating strategies under ABT along with the traditional approach for their impact on frequency and voltage under step load disturbance. Simulation studies conducted in MATLAB 2022a demonstrate the effectiveness of the proposed strategies, providing valuable insights for improving frequency and voltage control in modern power systems. The investigation reveals that the strategy that considers the difference between the actual and reference values of the unscheduled interchange (UI) charge and the marginal cost of generation to produce the control command outperforms the other strategies and restores the frequency and voltage to their nominal values.

Economic and environmental impacts from the China-Korea power grid interconnection

China and Korea have planned a substantial power grid interconnection project, driven by a shared commitment to achieve their respective policy goals. This paper analyzes the economic and environmental impacts of this cross-border power grid interconnection and presents a comprehensive financial analysis of the project. We formulate a power system model that includes a planned submarine HVDC transmission line between two countries and study various scenarios by using hourly data. Our results demonstrate that the interconnection line operates with a remarkably high utilization rate, ranging from 98.90 % to 99.95 % across all analyzed scenarios. This high utilization rate suggests that the project holds significant economic potential, with estimated net present values ranging from $1,786 million to $3,019 million at a discount rate of 7 %. Furthermore, from the sensitivity analysis on carbon price, discount rate, and transmission capacity, we show how the financial outcome of the project is affected by the change in these parameters. Finally, we discuss the consequence of imposing an additional constraint on the carbon emission to our model and then, minimizing the overall carbon emissions subject to keeping the overall cost without the interconnection. In both variations, we show that the two countries can achieve their policy goals without increasing carbon emission.

A Novel Automatic Generation Control Method Based on the Large-Scale Electric Vehicles and Wind Power Integration Into the Grid.

In order to solve the problem of frequency instability of power system due to strong random disturbance caused by large-scale electric vehicles and wind power grid connection, an improved reinforcement learning algorithm, namely, optimistic initialized double Q, is proposed in this article from the perspective of automatic generation control. The proposed algorithm uses the optimistic initialization principle to expand the agent action exploration space, so as to prevent Q-learning from falling into local optimum by greedy strategy; meanwhile, it integrates double Q-learning to solve the problem of overestimation of action value in traditional reinforcement learning based on Q-learning. In the algorithm, the hyperparameter ατ is introduced to improve the learning efficiency, and the reward bτ based on exploration times is introduced to increase the Q value estimation to drive the exploration of the algorithm, so as to obtain the optimal solution. By simulating the two-area load frequency control model integrated with large-scale electric vehicles and the four-area interconnected power grid model integrated with large-scale wind power generation, it is verified that the proposed algorithm can obtain the global optimal solution, thus effectively solvinng the frequency instability caused by strong random disturbance in the grid-connected mode of large-scale wind power generation, and compared with many reinforcement learning algorithms, the proposed algorithm has better control performance.

Study on the evaluation model of the contribution of power industry expansion considering the influencing factors of new energy consumption

In the application of new energy consumption system engineering, in order to evaluate the contribution of electric power industry expansion, an evaluation model of electric power industry expansion contribution considering the influencing factors of new energy consumption is constructed. In the process of power industry expansion, the growth of new energy installed capacity, power system regulation ability, power grid interconnection and electricity demand are the core factors that affect the change of power contribution to power industry expansion. Using the characteristic extraction method of power consumption behavior of users with industrial expansion, after extracting two characteristics, namely, the utilization hours of user’s industrial expansion capacity and the proportion of new energy load put into operation under the change of four major factors, the monthly industrial expansion power consumption of typical users is predicted by the monthly industrial expansion power consumption forecasting method of users considering industrial expansion capacity, and then the growth curve of user’s industrial expansion power consumption is drawn. Based on the forecast method of monthly industry expansion electricity generated by industry expansion quantity, the industry expansion quantity of typical individual users is calculated, and the industry expansion quantity is input into RBF network model trained by particle swarm optimization algorithm to complete the forecast of monthly industry expansion electricity; Finally, the contribution ratio of each influencing factor is calculated, and the evaluation of power industry expansion contribution considering the influencing factors of new energy consumption is completed. After testing, this model can be used as an available model for evaluating the contribution of electric power industry under the condition of considering the influencing factors of new energy consumption.

A Systematic Approach of Global Sensitivity Analysis and Its Application to a Model for the Quantification of Resilience of Interconnected Critical Infrastructures

We consider a model for the resilience analysis of interconnected critical infrastructures (ICIs) that describes the dependencies among the subsystems within the ICIs and their time-varying behavior. The model response is a function of uncertain inputs comprising ICIs design parameters and failure magnitudes of vulnerable elements in the system, etc. In this methodological paper, we present a systematic approach based on an innovative blend of methods to perform a sensitivity analysis for identifying the most relevant variables affecting the system resilience at different stages, during a disruptive event. The methods considered include the following: the use of the graphical representation of Cusunoro curves for a visualization of the impact of an input on the resilience metric and an understanding of whether the associated dependence is monotonic, increasing, or decreasing; the introduction of an ensemble of indicators related to different properties of the resilience metric to allow the prioritization of variable importance and avoid false negatives, meaning to regard a variable as non-influential when, instead, it plays a relevant role in the determination of the model response; the calculation of first-order variance-based sensitivity indices to have an appreciation on the relevance of interactions when inputs are independent; and a data approach to visually identify relevant second-order interactions. All the sensitivity methods considered are performed on a provided sample, and do not require additional model evaluations. They allow the analyst to post-process the data to extract, simultaneously, several desirable insights. The systematic approach proposed to apply these methods allows us to identify the model input variables and parameters that are not very relevant, while it enables the identification of the relevant ones which allows prioritizing interventions on the vulnerable elements of the system for its resilience at different stages during a disruptive event. Given the methodological nature of the work, a simplified infrastructure model describing an interconnected gas network and electric power grid is taken as case study: this allows us to show that the approach is straightforward to understand and implement, and the results obtained show the usefulness of the approach in providing meaningful insights that can be used by stakeholders and decision makers to inform strategies for the improvement of system resilience. By the application of the simplified ICIs model to the case study, it is shown that the approach can be straightforwardly implemented to identify the most relevant variables on system resilience and obtain the most important subsystems. The key factors which affect system resilience in multiple initial failures scenarios are found; this allows us to identify the key resilience improvement measurements, and their priorities.

Influence of the DC frequency limit controller on the frequency characteristics of the multi-area asynchronous interconnected power grid with renewable energy integration

To systematically analyze the impact of the DC frequency limit controller (FLC) configuration scheme on the frequency characteristics of asynchronous interconnected power grids with renewable energy integration, a comprehensive frequency analysis model for three-area asynchronous interconnected power grids including the FLC is proposed. The model is based on the SFR model considering renewable energy integration and includes a simplified model of the DC FLC. A rigorous validation of the rationality and stability of the model is achieved through detailed mathematical calculations and pole-zero analysis. Taking the local power grids as an example, the established model is used to study the difference between single- and double-sided configuration of the DC FLC, the relationship between the increase in the renewable energy penetration rate and the frequency characteristics of each regional power grid, and the influence of DC FLC configuration on the maximum acceptance ratio of renewable energy. Through data comparison and theoretical analysis, the influence of FLC configuration on the frequency characteristics of each region of the asynchronous power grid under renewable energy integration is obtained. The results show that the configuration of a bilateral FLC on the DC line has more advantages in alleviating the complementary power disturbance at both ends and obvious limitations on the non-complementary power disturbance at both ends. Compared with the configuration of the bilateral FLC, the configuration of a unilateral FLC on the weak grid has a larger proportion of renewable energy acceptance. When the DC FLC power transmission capacity is sufficient, the weak grid and the adjacent two asynchronous grid DC tie lines are configured with a single-sided FLC, and the weak grid is theoretically connected to the renewable energy penetration rate of up to 100%.

Available Transfer Capability Assessment of Multiarea Power Systems with Conditional Generative Adversarial Network

Available transfer capability (ATC) is an important measurement index to evaluate the security margin of interconnected power grids and serve as a reference for the transmission right transaction. In modern power systems, ATC is affected by the transmission network topology, renewable power output uncertainty, and load demand uncertainty. Traditional works usually model the power source-load uncertainty by using robust optimization, interval optimization, or chance-constraint optimization, which cannot fully reflect the probabilistic distribution of the daily source-load uncertainty. This paper proposes an ATC assessment methodology based on the typical stochastic scenarios of renewable output and load demand of multiarea power systems. Furthermore, the conditional generative adversarial network (CGAN) algorithm is adopted to generate and select representative scenario sets based on historical raw data, which can fully reflect the usual operating condition of a system with high renewable energy penetration. The scenario set that is fed into the ATC assessment model can fully characterize the impact of source-load uncertainty on daily ATC. Finally, the proposed method is verified by a modified three-area IEEE 9-bus system and a real-world provincial power system.

Fault Detection and VSC-HVDC Network Dynamics Analysis for the Faults in Its Host AC Networks

High-voltage direct current (HVDC) transmission is preferred over high-voltage alternating current (HVAC) for long power lines for asynchronous power grid interconnection and high-level renewable energy integration. The control and protection functions associated with HVDC systems help with fast and secure clearance of faults. The control and protection challenges in the embedded HVDC network are of great concern for the stable and secure operation of an HVDC network. The DC fault current may reach an extremely high level in a rather short period because of the low impedance in a DC system, which is dangerous for converters, and disturbances in the AC network directly influence the performance of the HVDC system. Sometimes, faults on the AC side may lead to disconnection or failure of the DC link, causing reliability problems as well as huge economic losses. AC and DC protection solutions are being developed for HVDC systems to enhance their sustainability and reliability. As such, AC and DC faults should be detected and cleared at a faster rate. Therefore, in this article, the feasibility of the synchro-squeezed transform (SST) is analyzed for detection purposes. For more accurate and faster detection, the signal is first decomposed using the empirical mode decomposition (EMD) technique, and then the SST is applied. A discrete Teager energy (DTE) spectrum is obtained with the processed signal, which works as the detection index. The algorithm shows low sampling frequency requirements, with higher efficiency and reliability for the purpose. PSCAD/EMTDC version 4.6 software and MATLAB 2022a software is used for the modeling and simulation.

FLC-Based Ultra-Low-Frequency Oscillation Suppression Scheme for Interconnected Power Grids

The appearance of ultra-low-frequency oscillations in the grid at the sending end, after asynchronous grid interconnection, poses a significant threat to the stable operation of the system. For post-asynchronous interconnection in a multi-DC transmission system, an investigation is conducted to analyze the causes of ultra-low-frequency oscillations and the utilization of a Frequency Limit Controller (FLC) which aims to suppress these oscillations. Furthermore, a method is developed to rank DC sensitivity, considering the hydroelectric distribution in the sending-end grid, by combining the DC FLC impact factor and DC control sensitivity. Subsequently, a novel approach for ultra-low-frequency oscillation suppression is proposed. This approach employs the stochastic subspace method for parameter estimation and the NSGA-II optimization algorithm to convert the multi-DC optimization challenge into multiple sequential cyclic optimization problems, each focusing on a single DC, ensuring a more effective suppression of ultra-low-frequency oscillations. The proposed scheme’s effectiveness is validated through simulations using a specific locations’ interconnected power grid.

Cloud Computing for Renewable Power Systems

Renewable energy systems are playing a main role in the future power systems. Therefore, the future electricity infrastructure must support the integration of the existing conventional power system with varies renewable energy resources, which are widely dispersed power sources. This interconnected power pools or grid needs a real-time monitoring, controlling and high computational power. There are many devices connected to the grid enable us to exchange information with power systems. This paper discussed the computation power and the performance of the cloud computing platform by implementing a lab demonstrator which is a load forecasting application in energy management field. Moreover, it announces an insight to the application of cloud computing in the next generation power systems.

Edge-based detection and localization of adversarial oscillatory load attacks orchestrated by compromised EV charging stations

Recent reports indicate that electric vehicle charging stations (EVCSs) are susceptible to remote exploitation through their vulnerable software/cyber components. More importantly, compromised EVCS can be leveraged to perform coordinated oscillatory load attacks against the interconnected power grid, leading to power grid instability, increased operational costs, and power line tripping. In this paper, an edge-based approach for the detection and localization of coordinated oscillatory load attacks initiated by exploited EV charging stations against the power grid is investigated. The edge-based detection relies on the behavioral characteristics of the power grid in the presence of interconnected EVCS while combining cyber and physical layer features to implement deep learning algorithms for the effective detection of oscillatory load attacks at the EVCS. The proposed detection approach was evaluated by building a real-time test bed to synthesize benign and malicious data, which was generated by analyzing real-life EV charging data collected during recent years. The results demonstrate the effectiveness of the implemented approach with the Convolutional Long-Short Term Memory model producing optimal classification accuracy (99.4%). Moreover, this analysis results shed light on the impact of such detection mechanisms towards building resiliency into different levels of the EV charging ecosystem while allowing power grid operators to localize attacks and take further mitigation measures. Specifically, the detection mechanism of oscillatory load attacks is decentralized and created an effective alternative for operator-centric mechanisms to mitigate multi-operator and Man-in-the-Middle (MitM) oscillatory load attacks against the power grid. Additionally, the created test bed is leveraged to evaluate a distributed mitigation technique, which can be deployed on public/private charging stations to average out the impact of oscillatory load attacks while allowing the power system to recover smoothly within 1 second with minimal overhead. Finally, a security metric is createdto evaluate the resiliency of the ecosystem based on its design and architecture.

Dynamic Analysis and Suppression Strategy Research on a Novel Fractional-Order Ferroresonance System

Ferroresonance is characterized by overvoltage and irregular operation in power systems, which can greatly endanger system equipment. Mechanism analysis of the ferroresonance phenomenon depends mainly on model accuracy. Due to the fractional-order characteristics of capacitance and inductance, fractional-order models are more universal and accurate than integer-order models. A typical 110 kV ferroresonance model is first established. The influence of the excitation amplitude on the dynamic behavior is analyzed. The fractional-order ferroresonance model is then introduced, and the effects of the fractional order and flux-chain order on the system’s motion state are studied via bifurcation diagrams and phase portraits. In order to suppress the nonlinear dynamic behavior of fractional-order ferroresonance systems, a novel fractional-order fast terminal sliding mode control method based on finite-time theory and the frequency distributed model is proposed. A new fractional-order sliding mode surface and control law using a saturation function are developed. A robust fractional-order sliding mode controller could achieve finite-time stabilization and tracking despite model uncertainties and external disturbances. Compared with conventional sliding mode methods, the simulation results highlight the effectiveness and superiority. The research provides a theoretical basis for ferroresonant analysis and suppression in large-scale interconnected power grids.

Time-Dependent Unavailability Exploration of Interconnected Urban Power Grid and Communication Network

This paper presents a time-dependent reliability analysis created for a critical energy infrastructure use case, which consists of an interconnected urban power grid and a communication network. By utilizing expert knowledge from the energy and communication sectors and integrating the renewal theory of multi-component systems, a representative reliability model of this interconnected energy infrastructure, based on real network located in the Czech Republic, is established. This model assumes reparable and non-reparable components and captures the topology of the interconnected infrastructure and reliability characteristics of both the power grid and the communication network. Moreover, a time-dependent reliability assessment of the interconnected system is provided. One of the significant outputs of this research is the identification of the critical components of the interconnected network and their interdependencies by the directed acyclic graph. Numerical results indicate that the original design has an unacceptable large unavailability. Thus, to improve the reliability of the interconnected system, a slightly modified design, in which only a limited number of components in the system are modified to keep the additional costs of the improved design limited, is proposed. Consequently, numerical results indicate reducing the unavailability of the improved interconnected system in comparison with the initial reliability design. The proposed unavailability exploration strategy is general and can bring a valuable reliability improvement in the power and communication sectors.

Transient Stability Impact of Short Circuit Fault System Based on SVC And PSS Applications

Researchers are currently focusing on two solutions, namely Static VAR compensator (SVC) and power system stabilizer (PSS), to address different power system stability issues in large area interconnected power grids. To enhance transient stability, researchers have suggested a combined approach involving SVC and PSS. In this method, SVC and PSS coordinate and cooperate with each other to provide better stability assurance. To verify the feasibility of this method, simulation experiments were conducted using MATLAB. Simulated the situation of single-phase ground fault, two-phase ground fault, and three-phase ground fault in the power system, and tested the effectiveness of SVC and PSS respectively. The simulation results show that the combined use of SVC and PSS can significantly improve the transient stability of power systems and effectively reduce the impact of faults on the system. In summary, the combined use of SVC and PSS can effectively solve various power system stability issues that may arise in large-scale interconnected power grids. By conducting simulation experiments in MATLAB, researchers have verified the feasibility of this method under different fault conditions, providing strong support for practical applications.