Nodes Of Power System Research Articles

Analysis of Grid Performance with Diversified Distributed Resources and Storage Integration: A Bilevel Approach with Network-Oriented PSO

The growing deployment of distributed resources significantly affects the distribution grid performance in most countries. The optimal sizing and placement of these resources have become increasingly crucial to mitigating grid issues and reducing costs. Particle Swarm Optimization (PSO) is widely used to address such problems but faces computational inefficiency due to its numerical convergence behavior. This limits its effectiveness, especially for power system problems, because the numerical distance between two nodes in power systems might be different from the actual electrical distance. In this paper, a scalable bilevel optimization problem with two novel algorithms enhances PSO’s computational efficiency. While the resistivity-driven algorithm strategically targets low-resistivity regions and guides PSO toward areas with lower losses, the connectivity-driven algorithm aligns solution spaces with the grid’s physical topology. It prioritizes actual physical neighbors during the search to prevent local optima traps. The tests of the algorithms on the IEEE 33-bus and the 69-bus and Norwegian networks show significant reductions in power losses (up to 74% for PV, wind, and storage) and improved voltage stability (a 21% reduction in mean voltage deviation index) with respect to the results of classical PSO. The proposed network-oriented PSO outperforms classical PSO by achieving a 2.84% reduction in the average fitness value for the IEEE 69-bus case with PV, wind, and storage deployment. The Norwegian case study affirms the effectiveness of the proposed approach in real-world applications through significant improvements in loss reduction and voltage stability.

Application of improved graph convolutional networks in daily-ahead carbon emission prediction

With the increasing complexity of power systems and the proliferation of renewable energy sources, the task of calculating carbon emissions has become increasingly challenging. To address these challenges, we developed a new method for predicting carbon emission factors. Bayesian optimization technique graphical convolutional networks with long- and short-term network (BO-TGNN) is used to predict the carbon emissions of the power system. The method aims to quickly predict the day-ahead carbon emissions of power system nodes with enhanced feature extraction and optimized network training hyperparameters. The effectiveness of the proposed method is demonstrated through simulation tests on three different power systems using four deep learning algorithms. The method provides a tailored solution to the evolving needs of carbon reduction efforts and is a significant step forward in addressing the complexity of carbon emission calculations for modern power systems.

Distributed-Based Rapid Assessment of Carbon Emission Factors for Power Grid

As the size of the grid has gradually increased, there is a decentralization among power systems. Traditional carbon flow tracking models based on the principle of proportional sharing, combined with power system networks, are able to analyse the carbon emissions and the direction of carbon flows at individual nodes. With a multi-node power system network, the data cannot be collected accurately, and the method is calculated over a long period of time, making it hard to address the demands of online analysis. To address the above situation, a rapid assessment of carbon emission factors was presented for power grid for power system nodes based on distributed approach. Unlike solving a system of equations, the carbon emissions of each node in this method are calculated sequentially according to the carbon flow direction of the node. In addition, each node only collects information from nodes connected to itself. In this way the computational efficiency is both improved and the stability of the computation is ensured. Simulation results validate the effectiveness of the method. The experiments also show the computational advantages and evaluation accuracy of the method, which can be used for the carbon flow tracking analysis of power system nodes.

Anomaly attribute mining method for topological nodes of power system based on graph theory

Due to the large scale, high dimension and time series characteristics of power system data, and the normal samples far exceed the abnormal samples, the sample imbalance phenomenon occurs, and it is difficult to mine the abnormal attributes of nodes. Therefore, a method of mining abnormal attributes of topological nodes of power systems based on graph theory is developed. The logical relation between nodes is analyzed by graph theory, and directed graph and undirected graph are obtained. The topology structure of the power system is constructed, the noise in data is removed by the adaptive clustering algorithm, the weight of topological nodes is set, and the abnormal malicious attacks of topological nodes are discovered by the double threshold method. The experimental results show that the proposed method can accurately detect the abnormal topological nodes in the network, perform better in the energy consumption of the neighbor topological nodes, and greatly reduce the energy consumption of the neighbor topological nodes during mining.

Identifying critical nodes of cyber–physical power systems based on improved adaptive differential evolution

The identification and protection of critical nodes have a significant influence on the reliability and robustness of the cyber–physical power system (CPPS), which can effectively reduce the risk of cascading failures. Firstly, this paper establishes a one-to-one coupled cascading fault model, which considers different topological characteristics of power and cyber networks. Based on the interdependence between the two networks, the cascading fault propagation mechanism after node failures in CPPS is analyzed. Then, a critical node identification method based on improved adaptive differential evolution (JADE) is proposed to identify the sets of critical nodes that can maximize the impact on the system load of CPPS. Combined with the cascading failure characteristics of CPPS, in the population generation strategy, the test vectors that do not meet the conditions after the crossover operation are filtered and replaced to improve the search efficiency of the algorithm. Finally, in the case study, an interdependent CPPS is constructed with an IEEE 39-bus system and a scale-free network of the same nodes. The simulation results verify the effectiveness of the proposed node importance assessment strategy. In addition, it is also shown that evaluating the critical nodes in power systems is important for enhancing the robustness and ensuring the security of the power grid operation.

A Rank Analysis and Ensemble Machine Learning Model for Load Forecasting in the Nodes of the Central Mongolian Power System

Forecasting electricity consumption is currently one of the most important scientific and practical tasks in the field of electric power industry. The early retrieval of data on expected load profiles makes it possible to choose the optimal operating mode of the system. The resultant forecast accuracy significantly affects the performance of the entire electrical complex and the operating conditions of the electricity market. This can be achieved through using a model of total electricity consumption designed with an acceptable margin of error. This paper proposes a new method for predicting power consumption in all nodes of the power system through the determination of rank coefficients calculated directly for the corresponding voltage level, including node substations, power supply zones, and other parts of the power system. The forecast of the daily load schedule and the construction of a power consumption model was based on the example of nodes in the central power system in Mongolia. An ensemble of decision trees was applied to construct a daily load schedule and rank coefficients were used to simulate consumption in the nodes. Initial data were obtained from daily load schedules, meteorological factors, and calendar features of the central power system, which accounts for the majority of energy consumption and generation in Mongolia. The study period was 2019–2021. The daily load schedules of the power system were constructed using machine learning with a probability of 1.25%. The proposed rank analysis for power system zones increases the forecasting accuracy for each zone and can improve the quality of management and create more favorable conditions for the development of distributed generation.

Research on improving robustness strategy of grid cyber physical system considering spatial information

Adding additional links can be recognized as an effective way to improve the robustness of grid cyber physical system (GCPS) against cascading failures. However, for a typical large-scale and across-regional GCPS, previous studies mainly focus on the network topology without considering the components/nodes spatial information. Therefore, to improve the robustness of GCPS without changing the existing connections, five different link addition strategies, considering spatial information and construction cost constraints, are proposed. Firstly, a spatial GCPS model is established, which consists of the spatial power system and the spatial information system with hierarchical structures. Among them, the spatial information system consists of the access layer (AL), the backbone layer (BL) and the core layer (CL) and its network nodes are divided into different layers according to their functions. Secondly, based on the spatial information system layer nodes functions and the spatial power system nodes attributions, five link addition strategies, AL-AL, AL-BL, AL-CL, Generators-CL (G-CL) and Loads-CL (L-CL), are investigated under two constraint scenarios. Thirdly, to verify our proposed five link addition strategies have better improvement effects, we compare them with the two existing excellent strategies, including low degree link addition strategy (LD) and random addition link addition strategy (Random). Finally, the statistical test results on the real 39-node power system and the IEEE 118 bus system show that our proposed link addition strategies not only effectively improve the robustness of GCPS, but also have better effects than LD and Random. More specifically, no matter whether GCPS suffered severe or slight attacks, the AL-BL always performs better when either fixed number constraint scenario or fixed length constraint scenario, and the improvement effects of AL-AL, AL-CL, G-CL and L-CL are exhibited as descending orders, respectively. These findings can help maintainers to improve the robustness of GCPS before attacks.

Steady-state voltage stability assessment of new energy power systems with multi-quadrant power modes

The “source–load” attributes of the nodes in a new energy power system are complex and diverse due to a large amount of new energy access. The node power (active and reactive power) in the system can offer a changing mode, which causes several serious challenges to the security of steady-state voltage stability. Aiming at the steady-state voltage stability problem in the system with multi-quadrant power modes, this paper reveals the mechanism of voltage evolution by voltage analytical expressions and the geometric characteristics of voltage components. Then, the indexes of voltage rise and drop are put forward by using a normalized voltage sensitivity. The voltage stability index is formulated based on the characteristics of voltage–power change. These indexes are used for steady-state voltage safety and stability assessment. Through the analysis of results that simulated by the IEEE-14 node power system and the coupled system in which the traditional power supply and new energy are connected to the same point of interconnection in Liaoning province, the results have proved that these indicators can be used to evaluate the steady-state voltage stability under multi-quadrant power modes. It is of great significance to guide the operation of voltage security in the power system with new energy.

Analysis of the Influence of Nodal Reactive Powers on Voltages in a Power System

The paper deals with finding power system nodes where reactive powers have the greatest influence on system voltages. The problem to be solved is important in reactive power planning. Its proper solution indicates in which nodes new sources of reactive power should be installed in order to achieve the assumed goals in the aforementioned planning. So far, the problem formulated earlier has not been satisfactorily resolved. The paper presents an original method, which, based on the entire history of the system operation states, allows a solution to the problem mentioned above to be found. The proposed method assumes the use of measurement data of nodal-voltage magnitudes and nodal reactive power. Correlational relationships between the above-mentioned quantities are investigated. The paper shows that the considered correlational relationships are not linear. In this situation, Kendall’s rank correlation coefficient is used to evaluate the strength of these correlational relationships. Analysis of the strongest relationships allows us to identify those nodal reactive powers that have the greatest influence on the voltages in the power system. The results of the analysis are the basis for determining the location of additional reactive power sources in the power system, which is very essential in reactive power planning. The proposed method is relatively easy to implement and does not require complicated calculations. The paper additionally shows that failure to use the entire spectrum of representative system states when solving the problem under consideration can adversely affect the result.

A Data-Driven Method for Power System Emergency Control to Improve Short-Term Voltage Stability

The power system contains a variety of uncertainties of different types of sources and loads, as well as random contingencies. Under these uncertainties and rapidly changing operating conditions, traditional rule-based methods cannot dynamically handle short-term voltage instability. To alleviate this situation, this paper proposes a novel power system emergency control scheme using a data-driven method, which combines edge-conditioned graph convolutional networks and deep reinforcement learning. The edge-conditioned graph convolutional network is utilized to extract the characteristics from not only power system nodes but also transmission lines. Deep reinforcement learning is introduced to perform load shedding actions, so as to guarantee the safety and stability of the electric power system. The IEEE 39-bus network is utilized for simulations to validate the effectiveness of the proposed data-driven method. The outcomes demonstrate the proposed method can generate a superior strategy in a number of the short-term voltage instability(STVI) circumstances.

Інтелектуальна діагностика електричних мереж

As follows from the conducted analysis [1-2], artificial intelligence has a great impact on modern energy as a whole, and it has also reached the electric power sector, especially "intelligent" networks with a high degree of automation, which closely interact with renewable energy sources. Here, artificial intelligence has gained widespread use in order to predict the level of illumination of a photovoltaic panel and estimate the output power of solar power plants. Considering the fact that artificial intelligence is a powerful and popular tool that is widely used in renewable energy (in particular, in photovoltaic), it is important to understand to what extent this tool can be used when creating a forecast of the generation of electrical energy at the output of a photovoltaic plant. It is becoming clear that with the help of artificial intelligence, it is necessary to increase both energy efficiency and accuracy values in the power grid, since electronic computing machines can process more data than an operator can in a given time period. When diagnosing the quality of electrical energy in a photovoltaic plant, it is important to observe certain provisions, namely: - adequate, for a specific task, the time of control. As a rule, this parameter must be installed in the system in advance; – determination of the number of electrical equipment and/or power system nodes for monitoring; – assignment of the limit level of parameters for measurement; – the choice of the method for performing the analysis of the measured data; – choosing the type and location for saving the received data, also here it is worth providing for compatibility with other devices in the electrical network, for example, control or signaling devices. In order to indicate the main tasks for the diagnostics system in the photovoltaic plant, which will include artificial intelligence, a structural diagram was created that indicates what tasks must be done in each link of the electrical network. It is worth noting that the structure of the diagnostic system can be divided into several components according to their physical location in the system under study. So, for example, sensors are responsible for all data collection in the power grid. Of greater interest are the links that perform monitoring in the power grid, as well as develop conclusions based on the conducted monitoring and accumulate databases for decision-making. An artificial neural network is responsible for fulfilling these requirements, and its data set for training and retraining can serve as a database.

Ambient forcing: sampling local perturbations in constrained phase spaces

Ambient forcing is a novel method to sample random states from manifolds of differential-algebraic equations (DAE). These states can represent local perturbations of nodes in power systems with loads, which introduces constraints into the system. These states must be valid initial conditions to the DAE, meaning that they fulfill the algebraic equations. Additionally, these states should represent perturbations of individual variables in the power grid, such as a perturbation of the voltage at a load. These initial states enable the calculation of probabilistic stability measures of power systems with loads, which was not yet possible, but is important as these measures have become a crucial tool in studying power systems. To verify that these perturbations are network local, i.e. that the initial perturbation only targets a single node in the power grid, a new measure, the spreadability, related to the closeness centrality (Freeman 1978 Soc. Netw. 1 215–39), is presented. The spreadability is evaluated for an ensemble of typical power grids. The ensemble depicts a set of future power grids where consumers, as well as producers, are connected to the grid via inverters. For this power grid ensemble, we additionally calculate the basin stability (Menck et al 2013 Nat. Phys. 9 89–92) as well as the survivability (Hellmann et al 2016 Sci. Rep. 6 29654), two probabilistic measures which provide statements about asymptotic and transient stability. We also revisit the topological classes, introduced in (Nitzbon et al 2017 New. J. Phys. 19 033029), that have been shown to predict the basin stability of grids and explore if they still hold for grids with constraints and voltage dynamics. We find that the degree of the nodes is a better predictor than the topological classes for our ensemble. Finally, ambient forcing is applied to calculate probabilistic stability measures of the IEEE 96 test case (Grigg et al 1999 IEEE Trans. Power Syst. 14 1010–20).

Real-Time Simulation Modeling Method of Multiphase Converters Based on High-Order Approximation in Micro-grid

As more and more distributed energy resources accessing in a micro-grid, the electromagnetic transient simulation of the whole system becomes extremely complicated due to the increase of multiphase converters. The traditional real-time simulation models of multiphase converters are low in efficiency for their large number of electronic switches and power system nodes. To solve this problem, an efficient real-time simulation modeling method based on high-order approximation is proposed. Starting with the symmetry of structure, the twelve-phase converter and induction motor are equivalent to a three-phase converter and induction motor and thus reducing the order of the model. The switching function model of the three-phase converter is derived, and the mathematical expressions of switching functions are calculated by high-order approximation expansion of the Fourier series; then, the dc input current of the three-phase converter is formulated theoretically. Counteracting the harmonics by means of phase-shifting, the high-order approximation model of the twelve-phase converter is constructed. Finally, the real-time simulation platform and experimental platform of the twelve-phase frequency converter and induction motor are built; the simulation results verify the high efficiency and accuracy of the proposed real-time simulation model compared with traditional models of the multiphase frequency converter.

Dual-layer optimized grid-connected operation strategy of electro-thermal multi-microgrid system considering the uncertainty of renewable energy sources

Interconnecting multiple combined heat and power (CHP) microgrids with the distribution network to form a CHP multi-microgrid system can promote the complementarity of various energy forms effectively and improve energy utilization efficiency. Therefore, a dual-layer stochastic optimal scheduling strategy of a multi-microgrid system interconnected with multiple CHP microgrids and multi-node distribution networks is proposed in this paper. In this strategy, the whole system is divided into two layers: (1) The lower layer adopts a stochastic simulation method based on versatile distribution to randomly generate a large number of scenarios and then uses a backward scenario reduction method to reduce the generated scenarios to a target number. Furthermore, with the goal of minimizing the sum of the weighted costs of the microgrid in each scenario, an economically optimal mixed integer programming model for a CHP microgrid with multiple energy storage devices is established. The output power of the electro-thermal units is optimized in each microgrid, and then the power to buy or sell power for each microgrid is determined. (2) The upper layer takes the minimum loss of the distribution system connected to multiple CHP microgrids as the goal and considers the power of each microgrid optimized by the lower layer as constraints. The optimal power flow model of the multi-node power distribution system is formulated based on the second-order cone relaxation technology, and the global optimal solution of the power node is obtained. Finally, the daily dispatch in which the IEEE-30 node power system connected to multiple CHP microgrids is used as an example for analysis. The results show that considering the uncertain factors of wind and solar output can effectively improve the reliability and robustness of the system when multiple CHP microgrids are connected to distribution network. Meanwhile, the coordinated use of multiple flexible coupling devices can reduce economic costs and promote energy consumption.

Statistical method for identification of weak nodes in power system based on voltage magnitude deviation

Identification of weak buses in an electric power system is important for operational and planning purposes. In this paper, a framework that uses the variance of the magnitude of voltage at load buses when reactive power of all loads are varied concurrently is used to identify a set of weak buses. In the framework, reactive power for all load buses are generated concurrently and independently based on the probability distribution function of load demands at each node. Three IEEE test systems are used to test the framework in Pypower in conjunction with Python programming language. The result shows that weak buses can be identified with the framework. It was observed that the first three set of most critical nodes in a network can be identified within a variation of 1% of reactive power at load nodes in case of ordinary power flow (pf), while the same set of nodes can be identified with at least 5% of variation of reactive power at nodes based on optimal power flow (opf). One area of application of this work could be in the use of the variance of voltage magnitude measurements to quickly identify a subset of weak nodes in a network.

Reliability Assessment of the External Power Supply in Electrified Railways With Weak Power Sources

Reliable external power supplies guarantee the safe and reliable operation of electrified rail-ways. Once the external power system fails, it disrupts the normal operation of trains and, especially for railways supplied by weak external power sources, may well incur serious accidents. Therefore, this paper proposes a reliability evaluation method of external power supplies of electrified railways with weak external power sources. Firstly, a weak external power system of electrified railways is modelled. Then, system operational states are analyzed by integrating Monte Carlo simulation with the DC optimal power flow (OPF) algorithm considering system node weight coefficients. Finally, reliability evaluation metrics are defined to quantify the reliability of both power system nodes and traction substations. A case study of a typical weak external power system supplying the electrified railways is presented to demonstrate the proposed method. Results show that the method can effectively quantify the reliability of the external power supply of electrified railways. Meanwhile, the DC OPF algorithm considering weight coefficients can significantly improve the reliability of critical loads, and thus improving the reliability of traction substations and electrical trains. The proposed method can provide valuable guidelines for the planning and designing of electrified railways supplied by weak power sources.

Методика построения интегральных показателей комплексной оценки свойств узлов электроэнергетических систем

The problem of selecting the electric power system control nodes is studied. By performing control of these modes, matters concerned with providing reliable power supply of the required quality to consumers can be settled in the most efficient manner. As an example, a fragment of the electric power system mathematical model used in the Finist mode-setting simulator for a power system dispatch control center operator is considered, which represents a highly branched electrical network consisting of eleven 110 kV nodes, three 220 kV nodes connected with the system, and two generator nodes. A new procedure for selecting the control nodes is proposed, which takes into account a combination of different indicators having different measurement units, dimensions and scales is proposed. These indicators characterize the following properties of power system nodes: the reactive power fraction absorbed at a node, the sensitivity of voltage to reactive load variations, the number of connected power lines, and statistical indicators characterizing the change of voltage at the nodes and reactive power flows for different options of installing the reactive power compensation devices. For combined use of these indicators, they were ranked according to the efficiency of installing reactive power compensation devices in the system. For each indicator, a scale of five ranks (intervals) is set, which determine the preferences (qualitative judgments) of the researcher in evaluating the reactive power compensation devices installation efficiency at the system nodes. The highest rank (5) corresponds to the maximum efficiency, and the lowest rank (1) corresponds to the minimum efficiency. To calculate the individual (integral) priority indicator of installing reactive power compensation devices, the ranks of indicators are added together, and their sum is divided by the product of the number of ranks by the number of the used indicators (features). Based on the calculation results, the rating (location) of each node is determined, and the nodes for installing the reactive power compensation devices are selected according to their effect on ensuring the electric power system operation reliability, active power losses in the network, and voltage regulation. Thus, a new procedure is presented for determining the integral indicators for comprehensively estimating the properties of complex electric power system nodes and selecting the controlled nodes using a system of various indicators. These indicators characterize the studied nodes in terms of the efficiency of installing reactive power compensation devices to reduce active power losses in the network, voltage regulation, and ensuring the electric power system operational reliability. The validity of the results obtained in the study is confirmed by their comparison with the indicators of the balance-conductivity method, which has proven itself in solving problems connected with determining the nodes for controlling electric power system operation modes.

Reconfigure the Distribution Network With Photovoltaic Connection to Minimize Energy Loss Based on Average Branch Power and an Advanced Branch Exchange Algorithm

The distribution network reconfiguration (DNR) minimize energy loss is one of the complicated problems and being studied substantially in recent years. Reconfiguration distribution network through the average branch power is a simple and effective method in obtaining fast optimization results even without using the load curve (for 24 hours). However, with high penetration of photovoltaic (PV) in the distribution network, the power flow on the branches at some survey time may change direction which leads to the average power on the branches might become zero when the energy loss is not minimal. Hence, determining accurately the average branch power in this case with PV participating grids is an important aspect in resolving problem of the distribution network reconfiguration with PV connection and minimizing energy loss. In order to solve this problem, an analytical technique based on load factor is presented in this paper for the purpose of determining accurately the average power on the branches via determining the amount of additional power on the branch when PV is installed in the power system. In addition, an advanced branch exchange method to quickly determine the configuration of the distribution network with PV while achieving the smallest energy loss is also proposed in the paper. The proposed method, which is tested on the IEEE 18 node and IEEE 33 node power system, shows the effectiveness of the proposed method in comparison with many other methods.

Geodesic Vulnerability Approach for Identification of Critical Buses in Power Systems

One of the most critical issues in the evaluation of power systems is the identification of critical buses. For this purpose, this paper proposes a new methodology that evaluates the substitution of the power flow technique by the geodesic vulnerability index to identify critical nodes in power systems. Both methods are applied comparatively to demonstrate the scope of the proposed approach. The applicability of the methodology is illustrated using the IEEE 118-bus test system as a case study. To identify the critical components, a node is initially disconnected, and the performance of the resulting topology is evaluated in the face of simulations for multiple cascading faults. Cascading events are simulated by randomly removing assets on a system that continually changes its structure with the elimination of each component. Thus, the classification of the critical nodes is determined by evaluating the resulting performance of 118 different topologies and calculating the damaged area for each of the disintegration curves of cascading failures. In summary, the feasibility and suitability of complex network theory are justified to identify critical nodes in power systems.

Dynamic reputation–based consensus mechanism: Real-time transactions for energy blockchain

The energy blockchain is a distributed Internet protocol for energy transactions between nodes in power systems. The consensus algorithm is the core component of the energy blockchain and has an essential impact on its application. At present, in the implementation of the energy blockchain, there are problems such as low transaction throughput (transactions per second) and high latency, which cannot meet the application requirements of real-time processing transactions in the energy field. To this end, according to the analysis of conventional blockchain consensus algorithm and traditional practical Byzantine fault tolerance algorithm, a dynamic-reputation practical Byzantine fault tolerance algorithm for the energy blockchain is proposed. The dynamic-reputation practical Byzantine fault tolerance algorithm adopts a credit-based consortium node consensus election method. The monitoring node divides the remaining nodes into two types of nodes according to the reputation value: the consensus node and the secondary node, which, respectively, participate in different stages of the block generation process, and dynamically update the consensus nodes with low reputation ratings. By constructing the experimental platform simulation, the test results verify the effectiveness of the dynamic-reputation practical Byzantine fault tolerance algorithm. Compared with the algorithm of the fabric platform, the dynamic-reputation practical Byzantine fault tolerance algorithm improves the transaction processing speed and is suitable for the blockchain application in the energy field.