Power Grid Structure Research Articles

Intelligent distribution network fault monitoring integrating differential evolution and chaos whale optimization algorithm

Faced with rapid development and increasingly complex power grid structure structure of the power grid, it is necessary to accurately locate faults in modern intelligent distribution networks in order to ensure power supply reliability and improve power supply service quality. Aiming at the low positioning accuracy, long time consumption, and limited coverage types in existing positioning algorithms, a new intelligent distribution network fault positioning algorithm is designed by combining two algorithms to complete the monitoring task. Firstly, intelligent distribution network fault location methods under different distributed power grid connection methods are analyzed. Then, considering the distributed power grid connection, a fault location algorithm is designed by combining differential evolution algorithm, Sine chaotic mapping, and whale algorithm. The research results indicated that the designed method had good benchmark performance, with accuracy and recall values as high as 0.98 and 0.97, respectively. During the training process, it only required 175 iterations to reach a stable state. In practical applications, the accuracy of this algorithm in testing three types of faulty power grids was 98.90%, 98.41%, and 98.25%, respectively. The method can effectively improve the fault location accuracy, providing better positioning technology for fault monitoring problems in power systems.

Integrated Structural Hole and K-shell Algorithm for Tsallis Entropy-based Identification of Key Nodes in Power Grids

Abstract Considering the low-carbon development goals of “peak carbon emissions and carbon neutrality,” traditional energy enterprises, including oil fields, have accelerated the incorporation of new energy into their power grids. However, incorporating new energy generation into traditional oilfield power grids yields a series of safety hazards, making the stability of the oilfield power grid structure increasingly important. In this paper, a redefined theory of structural holes and the K-shell algorithm are utilized to identify both local and global key nodes in the oilfield power grid. The improved Tsallis entropy is employed to recognize these key nodes, accounting for their local influence within the oilfield power grid as well as their global status. Additionally, considering the electrical characteristics of the nodes, a set of measurement metrics suitable for oilfield power grid research is constructed. Finally, the IEEE-39 feeder system is simulated and compared with other key node identification methods. By analyzing the robustness of the topological structure and the loss load value of the power system after removing key nodes, the reliability and superiority of the proposed method are verified.

Coupling methanol oxidation with CO2 reduction: A feasible pathway to achieve carbon neutralization

The energy consumption of up to 90 % of the total power input in the anodic oxygen evolution reaction (OER) slows down the implementation of electrochemical CO2 reduction reaction (CO2RR) to generate valuable chemicals. Herein, we present an alternative strategy that utilizes methanol oxidation reaction (MOR) to replace OER. The iron single atom anchored on nitrogen-doped carbon support (Fe-N-C) use as the cathode catalyst (CO2RR), low-loading platinum supported on the composites of tungsten phosphide and multiwalled carbon nanotube (Pt-WP/MWCNT) use as the anode catalyst (MOR). Our results show that the Fe-N-C exhibits a Faradaic selectivity as high as 94.93 % towards CO2RR to CO, and Pt-WP/MWCNT exhibits a peak mass activity of 544.24 mA mg−1Pt, which is 5.58 times greater than that of PtC (97.50 mA mg−1Pt). The well-established MOR||CO2RR reduces the electricity consumption up to 52.4 % compared to conventional OER||CO2RR. Moreover, a CO2 emission analysis shows that this strategy not only saves energy but also achieves carbon neutrality without changing the existing power grid structure. Our findings have crucial implications for advancing CO2 utilization and lay the foundation for developing more efficient and sustainable technologies to address the rising atmospheric CO2 levels.

Optimization of Frequency Modulation Energy Storage Configuration in Power Grid Based on Equivalent Full Cycle Model

This paper aims to meet the challenges of large-scale access to renewable energy and increasingly complex power grid structure, and deeply discusses the application value of energy storage configuration optimization scheme in power grid frequency modulation. Based on the equivalent full cycle model and a large number of actual operation data, various energy storage technologies are technically analyzed, and the economic and environmental performance of different energy storage configuration schemes are comprehensively evaluated. On this basis, this paper puts forward a set of efficient and economical energy storage configuration optimization strategies to meet the demand of power grid frequency modulation and promote the wide application of energy storage technology. After an in-depth analysis, it is found that the optimized energy storage configuration scheme is excellent in technology, economy, and environmental protection. Specifically, in terms of technical performance, the optimization scheme has significantly improved key indicators such as energy storage efficiency, capacity and power, and response speed, which can better meet the requirements of power grid frequency modulation. Through the verification of actual operation data, it is found that the overall efficiency of the optimized energy storage configuration scheme is above 55%, which is helpful to the stability and efficiency of power grid frequency modulation. In terms of economic performance, although the initial investment cost of the optimization scheme may be high, it is found that it has good economy through the evaluation of long-term operation benefits. Considering that the energy storage system can reduce the operating cost of the power grid, improve the energy utilization rate, and achieve the optimization of cost-effectiveness in the long run, this scheme is economically feasible and attractive. In terms of environmental performance, the optimization scheme effectively reduces the negative impact on the environment by improving energy storage efficiency, reducing emissions, and optimizing resource utilization. This is not only conducive to the sustainable development of the power grid but also in line with the current global trend of promoting green and low-carbon transformation. To sum up, this paper not only provides an efficient and economical energy storage allocation optimization strategy for power grid frequency modulation but also provides a scientific basis for relevant decision-making departments. By promoting the practical application and development of energy storage technology, this paper is helpful to improve the frequency modulation ability of power grid, optimize energy structure, and reduce environmental pollution, and thus achieve the goal of sustainable energy development. The data results and in-depth analysis of this paper provide strong support for the practical application of energy storage configuration optimization scheme and also provide important reference for the further innovation and development of energy storage technology.

Boosting efficiency in state estimation of power systems by leveraging attention mechanism

Ensuring stability and reliability in power systems requires accurate state estimation, which is challenging due to the growing network size, noisy measurements, and nonlinear power-flow equations. In this paper, we introduce the Graph Attention Estimation Network (GAEN) model to tackle power system state estimation (PSSE) by capitalizing on the inherent graph structure of power grids. This approach facilitates efficient information exchange among interconnected buses, yielding a distributed, computationally efficient architecture that is also resilient to cyber-attacks. We develop a thorough approach by utilizing Graph Convolutional Neural Networks (GCNNs) and attention mechanism in PSSE based on Supervisory Control and Data Acquisition (SCADA) and Phasor Measurement Unit (PMU) measurements, addressing the limitations of previous learning architectures. In accordance with the empirical results obtained from the experiments, the proposed method demonstrates superior performance and scalability compared to existing techniques. Furthermore, the amalgamation of local topological configurations with nodal-level data yields a heightened efficacy in the domain of state estimation. This work marks a significant achievement in the design of advanced learning architectures in PSSE, contributing and fostering the development of more reliable and secure power system operations.

Power Grid Anomaly Detection via Hybrid LSTM-GIN Model (Student Abstract)

Cyberattacks on power grids pose significant risks to national security. Power grid attacks typically lead to abnormal readings in power output, frequency, current, and voltage. Due to the interconnected structure of power grids, abnormalities can spread throughout the system and cause widespread power outages if not detected and dealt with promptly. Our research proposes a novel anomaly detection system for power grids that prevents overfitting. We created a network graph to represent the structure of the power grid, where nodes represent power grid components like generators and edges represent connections between nodes such as overhead power lines. We combine the capabilities of Long Short-Term Memory (LSTM) models with a Graph Isomorphism Network (GIN) in a hybrid model to pinpoint anomalies in the grid. We train our model on each category of nodes that serves a similar structural purpose to prevent overfitting of the model. We then assign each node in the graph a unique signature using a GIN. Our model achieved a 99.92% accuracy rate, which is significantly higher than a version of our model without structural encoding, which had an accuracy level of 97.30%. Our model allows us to capture structural and temporal components of power grids and develop an attack detection system with high accuracy without overfitting.

Vulnerability Analysis of Power Transmission Grids Subject to Cascading Failures

Cascading failures are a major threat to interconnected systems, such as electrical power transmission networks. Typically, approaches proposed for devising optimized control strategies are demonstrated with reference to a few test systems of reference (IEEE systems). However, this limits the robustness of the proposed strategies with respect to different power grid structures. Recently, this issue has been addressed by considering synthetic networks randomly generated for mimicking power transmission grids’ characteristics. These networks can be used for investigating the vulnerability of power networks to cascading failures. In this work, we propose to apply a recent algorithm for sampling random power grid topologies with realistic electrical parameters and further extend it to the random allocation of generation and load. Integration with a realistic cascade simulation tool, then, allows us to perform thorough statistical analyses of power grids with respect to their cascading failure behavior, thus offering a powerful tool for identifying the strengths and weaknesses of different grid classes. New metrics for ranking the control and mitigation effort requirements of individual cascade scenarios and/or of grid configurations are defined and computed. Finally, genetic algorithms are used to identify strategies to improve the robustness of existing power networks.

Proximal Policy Optimization-Based Power Grid Structure Optimization for Reliable Splitting

When systems experience a severe fault, splitting, as the final line of defense to ensure the stability of the power system, holds immense significance. The precise selection of splitting sections has become the current focal point of research. Addressing the challenges of a large search space and unclear splitting sections, this paper introduces a grid structure optimization algorithm based on electrical coupling degree. Firstly, employing the theory of slow coherency, a generalized characteristic analysis of the system is conducted, leading to an initial division of coherency groups. Subsequently, an electrical coupling degree index, taking into account the inertia of generators, is proposed. This index can reflect the clarity of grid splitting. Furthermore, a two-layer optimization model for grid structure is constructed, utilizing the Proximal Policy Optimization (PPO) algorithm to optimize the grid structure. This process reduces the size of the splitting space and mitigates the difficulty of acquiring splitting sections. Finally, simulation validation is performed using the IEEE-118-bus system to demonstrate the effectiveness of the proposed optimization algorithm.

The Capacity-to-load ratio optimization method considering collaborative planning of multi-voltage level power grids

The capacity-to-load ratio is an important indicator to guide power grid planning and has a decisive influence on the selection of substation capacities and the construction of power lines. A capacity-to-load ratio optimization method concerning collaborative planning of multi-voltage level power grids is proposed in this paper. This method aims to minimize the construction cost and takes into account the collaborative planning of substations and power lines of multiple voltage levels. By combining the Bayesian optimization algorithm (BOA) and genetic algorithms (GA), a BOA-GA solving algorithm for the planning model is proposed. This algorithm automatically generates planning schemes for multi-voltage level power grids, and obtains the optimal capacity-to-load ratio, as well as the optimal investment of the power grid construction. Case studies demonstrate that the proposed model can optimize the capacity-to-load ratio and automatically plan the network structure of multi-voltage level power grids. The construction cost is significantly reduced while various technical indicators are satisfied. Additionally, the study reveals that the relationship between the capacity-to-load ratio and the overall investment of multi-voltage level power grid construction is a quadratic function over a long-time scale. And the curve is a parabola going upwards.

Evaluation and prediction of the life of vulnerable parts and lithium-ion batteries in electrochemical energy storage power station

The widespread application of renewable energy technology and changes in energy structure has led to changes in the structure and operation of traditional power grids. Electrochemical energy storage systems have gradually achieved commercial operation due to their high energy density, efficient energy conversion, and renewability. This article proposes a life assessment plan for vulnerable parts, conducts statistical analysis on the life data of vulnerable parts, and provides calculation methods for average life, reliability, and reliable life. Then, the residual capacity of lithium-ion is estimated by using electron dispersion spectroscopy, and a dual exponential capacity decay model is established. Finally, the residual life of lithium-ion is estimated through an extended Kalman filter, and the capacity attenuation trend is better captured by combining with Monte Carlo sampling to improve the accuracy of the remaining useful life of lithium-ion.

Research on control strategy of distributed photovoltaic cluster based on improved particle swarm-gray wolf coupling algorithm

Conducting research on cluster control strategies for distributed photovoltaic systems to address voltage fluctuations and reverse power flow caused by large-scale distributed photovoltaic integration is crucial foundational work in establishing a new power system and ensuring its safe and stable operation. Based on the division of distributed photovoltaic cluster, this paper takes distributed photovoltaic cluster as the intermediate layer of control, and researches the two-layer control strategy of inter-cluster coordination and intra-cluster autonomy. The inter-cluster coordination strategy is located at the upper layer, and this strategy based on the power grid structure of the controlled area, comprehensively considering the observability, controllability, degree and betweenness centrality of each node, the latter two characterize the spatial location of the nodes. The dominant nodes of each cluster are selected by the above multiple indicators. The multi-objective inter-cluster coordination control model respectively focusing on the minimum voltage deviation of the dominant node or the minimum network loss of the system is constructed according to whether the voltage of the dominant node crosses the limit or not, and the improved particle swarm-gray wolf coupling algorithm (PSO-GWO) is used to generate the output indicators of each cluster. The intra-cluster autonomy strategy is at the lower layer. In the shortage of adjustable PV resources connected to the dominant node, the intra-cluster autonomy strategy adopting the control sequence of dominant node-downstream node-upstream node to distribute the output indicators of inter-cluster coordination within the cluster; In addition, in view of the situation that the cluster cannot receive the output indicators due to communication failure, The intra-cluster autonomy control model is constructed with the goal of minimum voltage deviation and minimum network loss of the dominant node in the cluster. The distributed photovoltaic inter-cluster coordination + intra-cluster autonomy two-layer regulation strategy is used to simulate the improved IEEE33-nodes system under multiple scenarios. The results show that the proposed control strategy can effectively solve the voltage overlimit, power flow back and other problems, and play an important role in ensuring the safe and economic operation of the system.

Study on the Maximum Outgoing Line Count of 750 kV Substations and Exploration of a Bus Short-circuit Current Suppression Method

With the development and construction of new power systems, the power grid structure is becoming increasingly interconnected, and the issue of short-circuiting currents in UHV systems will gradually manifest over the next five years. Existing measures for short-circuit current control typically involve using standby units, lines, and main transformers. However, conventional measures are gradually unable to meet the control requirements, necessitating strict control of the network topology during the planning phase and the exploration of new technologies and methods to keep pace with the evolving era. This paper studies the maximum outgoing line count of 750 kV substations, providing theoretical references applicable to practical operations for planning departments. A conceptual method based on fast-switching technology to suppress the current during a short circuit at the 750 kV voltage level is proposed, establishing the groundwork for the practical implementation of high-speed switching technology in the 750 kV voltage-level power grid.

Research on frequency modulation capacity configuration and control strategy of multiple energy storage auxiliary thermal power unit

The rapid development of new energy sources has had an enormous impact on the existing power grid structure to support the “dual carbon” goal and the construction of a new type of power system, make thermal power units better cope with the impact on the original grid structure under the background of the rapid development of new energy sources, promote the consumption of new energy sources such as solar and wind energy, and further expand the channels for upgrading and flexibility transformation of thermal power energy storage, improve the stability, safety and economy of thermal power unit operation. Therefore, based on the research status at home and abroad, a hybrid energy storage system is added to solve the above problems. The lithium battery-flywheel control strategy and the regional dynamic primary frequency modulation model of thermal power units are proposed, and study the capacity configuration scheme of flywheel‑lithium battery hybrid energy storage system under a certain energy storage capacity, the frequency modulation performance is evaluated by the system frequency fluctuation degree, fluctuation peak range and other indicators. Study under a certain energy storage capacity thermal power unit coupling hybrid energy storage system to participate in a frequency modulation of the optimal capacity configuration scheme, and perform simulation verification using MATLAB/Simulink. When the thermal power unit is coupled with a 10.8612 MW/2.7151 MWh flywheel energy storage system and a 4.1378 MW/16.5491 MWh lithium battery energy storage system, while adaptive variable coefficient droop control is adopted, the system frequency range is 0.00328 p.u.Hz, and the fluctuation degree of the output power of the thermal power units is 0.00498 p.u.MW, compared to the individual frequency regulation of thermal power units, it reduces 52.07 % and. The results show that when the thermal power unit is disturbed by external load, hybrid energy storage assisted thermal power unit frequency modulation reduces the mechanical loss of thermal power unit to a certain extent and extends the service life of the unit, effectively improve the operation stability and economy of thermal power units, further reduce the phenomenon of abandoning wind and light, and better promote the utilization of new energy.

Engineering fault intelligent monitoring system based on Internet of Things and GIS

Abstract The power grid (referred to as PG for convenience) structure is becoming increasingly complex. Aiming at the problem that it is difficult for traditional PG monitoring methods to accurately detect PG faults, an intelligent PG fault monitoring system is constructed using Internet of Things (IoT) and geographic information system (GIS) to improve the effectiveness of fault monitoring. The sensor equipment is used to collect the current information in the circuit, and the change of induced current is used to judge the cause of the fault, and the fault information is transmitted to the monitoring center through communication technology. The staff can directly locate the geographical location of the fault in the visual interface. One hundred overhead lines of Xianyang Power Supply Company are selected for analysis, and the performance of the traditional PG monitoring method and intelligent PG fault monitoring system is compared. The average fault detection accuracy of the traditional PG monitoring method and the system proposed in this article is 72.0 and 94.8%, respectively. The average fault location accuracy of the traditional PG monitoring method and this system is 80.8 and 96.5%, respectively. The intelligent monitoring system of PG fault based on IoT and GIS has high accuracy in PG fault detection and fault location, which can improve the effectiveness of fault monitoring.

Joint Planning Method of Virtual Power Plant for Distributed Generation and Grid in AC-DC Medium and Low Voltage Distribution Network

The high proportion of virtual power plants (VPPs) used for distributed generation may cause problems such as power fluctuations and voltage distribution. In contrast, AC/DC hybrid power grid has strong transmission capacity due to its power information transmission. However, Its power grid structure and the location and capacity of VPP have an impact on the consumption capacity of the system, and the relationship between them are closely coupled. In addition, Under different operation modes, power grids of different voltage levels also have great differences. Therefore, the cooperative planning of medium and low voltage AC and DC distribution networks can improve the transmission capacity of the power grid Further improve the ability of distribution network to consume distributed power, A method is proposed in this paper about siting and capacity determination and AC-DC distribution grid frame transformation planning for medium and low voltage systems. The upper-layer model mainly focuses on AC-DC transformation scheduling in the power grid, distributed node location in the access grid and transmission line transmission capacity, while the lower-layer model effectively communicates with the low-voltage system through information flow and changes the operation of the power grid to a certain extent.The results show that the distributed generation system can reduce the operation cost and improve the overall operation efficiency of the power grid.

Power Electronic Technology in Smart Grid Prospect

bstract: Electric energy is an extremely complex energy source that has become increasingly important in industry, agriculture, the economy, and daily life. The annual increase in electricity consumption symbolizes more stable economic growth in China, but there are still some problems. Power outages, low power quality, and complex power grid structures are issues that the power grid is facing. The development of power systems, the emergence of power electronics technology, and continuous research on power electronics technology can provide a feasible solution for alleviating and overcoming power problems. This article first summarizes the basic principles of power electronics technology in the construction of intelligent fields and then analyses the current examples of smart grid applications, including high-voltage direct current transmission, high-frequency technology, and intelligent switch technology. Finally, the possibility and potential for the development of power electronics technology were pointed out.

Structure and stability of the Indian power transmission network

We present the study on the Indian power transmission network using the framework of a complex network and quantify its structural properties. For this, we build the network structure underlying the Indian power grid, using two of its most prevalent power lines. We construct an equivalent model of an exponential network and study its structural changes with changes in two parameters related to redundancy and dead-ends. Then we analyze its stability against cascading failures by varying these two parameters using the link failure model. This helps to gain insight into the relation of network topology to its stability, and indicates how the optimum choice of these parameters can result in a power grid structure with minimum failed links. We apply the same model to study the robustness of the Indian power grid against such failures. In this case, we find that when a link connected to a generator fails, it results in a cascade that spreads in the grid until it is split into two separate stable clusters of generators and consumers, with over one-third of its nodes nonfunctional.

Fault Seamless Self-Healing Method of Regional Distributed Network Based on the Cooperation of Source-Load-Storage

The large-scale development of distributed generators poses challenges to the operation of distribution networks in remote areas. The power grid structure of the regional distributed networks in remote areas is relatively weak. When a fault occurs in the connecting channel, the regional distributed network may be separated from the main grid and become an island. Those distributed generators not only cannot maintain stable operation of the island but may lead to reclosing failure. This may lead to power interruption of important loads. Aiming at the demand for continuous power supply of important loads in regional distributed networks, a self-healing control method based on the cooperation of source-load-storage is proposed in this paper. The characteristics of the regional distributed network are analyzed first. A real-time island stability control method based on the operating conditions combined with regulation with tripping is proposed. This method comprehensively evaluates the self-healing ability of the regional distributed network after the island. The regulation strategy is adopted preferentially to realize the stable operation of the island without losing any load. When the regulation ability is insufficient, the strategy combines regulation and tripping. Considering the importance of load and source-load ratio, and according to the real-time power of each feeder, the optimal feeders are cut off. The purpose of island stability control of the regional distributed network was achieved. In this way, the load loss is minimized, and the speed of island re-connection to the grid is also accelerated. The seamless power supply of important loads in a regional distributed network is guaranteed. A complex Hardware-in-Loop simulation test platform of RTDS is built to verify the correctness of the proposed method.

Life cycle assessment of three typical solar energy utilization systems in different regions of China

In the future, solar energy has a major impact on the transformation of energy supply, becoming an attractive option among renewable energy sources. This study compares three typical systems that use solar energy, namely solar water heater (SWH) systems, solar photovoltaic (PV) systems, and photovoltaic/thermal (PVT) systems, under comparable conditions in different regions of China. The comparison is based on a life cycle assessment (LCA), taking into consideration the climate characteristics and energy supply in different regions. The corresponding environmental impact assessment results were obtained using the endpoint method and sensitivity analysis was performed to identify the factors that had a greater impact on the results. The findings indicate that SWH systems are more suitable for the southwest of China, where the power grid structure is dominated by hydropower. PV systems are recommended for northern and northeastern China, where coal power is the main source. In other parts of China, both PVT and PV systems are good options. Taking Shanghai as an example, applying PVT systems in Shanghai can reduce carbon emissions by 11.0% and 55.9% respectively, as well as decrease the environmental impact by 11.1% and 38.9% throughout whole life cycle, compared to PV systems and SWH.

AWL-GCN: Branch Parameter Identification Considering Grid Spatial Structure Constraints

Parameter identification plays an important role in power system. The existing parameter identification methods usually have two limitations: (1) The existing methods only consider the information of single branch and ignore the influence of adjacent branch information, and do not effectively use the topological structure constraints of the power grid. (2) The measurement data has the problems of poor numerical stability, numerical divergence and noise interference. Data contamination has become an important factor restricting the prediction accuracy of branch parameters. In order to solve these problems, this work proposes an Automatic Weighted Loss-Graph Convolution Networks model combined with the spatial structure of power grid. Based on the graphical modeling, the correlation between power grid branches is effectively used, and the adjacent branches are used to provide more effective information for the parameter identification of a branch, which improves the identification accuracy. In addition, the model enhances the local characteristics through the power grid structure information constraints, realizes the local optimal fitting, and constructs a self tradeoff loss function to weaken the impact of pollution data (data noise and data loss) on the model. The test results show that compared with the traditional method, this method has higher accuracy and stronger robustness.