Stability Of Distribution Network Research Articles

Predicting Electric Vehicle Charging Demand in Residential Areas Using POI Data and Decision‐Making Model

Transport electrification is a crucial element of the ongoing energy transition, essential for achieving carbon peaking and carbon neutrality goals. The proliferation of electric vehicles (EVs) introduces significant challenges to power distribution network stability due to their aggregated charging load in residential areas, particularly during peak electricity consumption periods. This paper proposes a method to predict the spatiotemporal distribution of EV charging demand in residential areas using geographic information points of interest (POI) data features and a decision‐making model. Utilizing real historical data, probability distribution models for EV users' arrival times and charging characteristics were constructed using Gaussian Mixture Models (GMM). The spatiotemporal characteristics of EV travel and charging behaviors were analyzed, and a comprehensive charging decision model incorporating both emergency and stochastic scenarios was developed. The model's efficacy in capturing the probability distributions of characteristic variables was validated through a case study. The results demonstrate the model's potential for accurately predicting EV charging demand, providing valuable insights for infrastructure planning and resource allocation. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Improving the Voltage Stability of Distribution Network Using Capacitor Banks and PV Arrays Based on OpenDSS

Improving the Voltage Stability of Distribution Network Using Capacitor Banks and PV Arrays Based on OpenDSS

An Improved Analysis of Distributed Resource Access Location and Capacity on Voltage Stability of Distribution Networks in Smart Grid

Concerns about how Distributed Energy Resources (DERs) may affect distribution network voltage stability have been brought up by the widespread usage of DERs in the elegant Grid. A Smart Grid connects a range of distributed energy resources (DERs) to the distribution network, including electric cars, wind turbines, and solar panels. This connection can alter the working conditions of the network. These changes can lead to voltage stability problems, which can affect the overall efficiency and reliability of the Smart Grid. Thus, there is a need for an improved analysis of the effects of Distributed Resource Access Location and Capacity (DRALC) on voltage strength in distribution networks. Here, we propose an improved analysis of the impact of DRALC on voltage stability in distribution networks in the Smart Grid. Our analysis considers the location and capacity of DERs and their effect on voltage stability using a combination of analytical and simulation methods. We first develop a mathematical model to represent the distribution network, which includes the DERs as voltage sources. Next, we use the voltage stability index and load flow analysis to evaluate the voltage stability of the network under different DRALC scenarios. Finally, we validate our analysis by performing simulations on a real-world distribution network.

Power prediction of regional distributed photovoltaic clusters with incomplete information based on improved weighted fusion and transfer learning

AbstractThe rapid expansion of low‐voltage distributed photovoltaic (PV) systems with decentralized layouts poses significant data collection challenges. The scarcity of data amplifies prediction complexities, affecting the operational security and stability of distribution networks. To enhance predictive accuracy for distributed regional PV power generation, including unmonitored low‐voltage systems, this paper proposes a novel prediction approach that combines weight optimization and transfer learning. Firstly, to more accurately extrapolate from neighbouring monitored PV to estimate unmonitored PV outputs, a novel SSA‐MLP‐XGBoost model is proposed for monitored PV power plants, in which the hyperparameters of multilayer perceptron (MLP) and (extreme gradient boosting) XGBoost models are fine‐tuned using the sparrow search algorithm (SSA), and the optimized models are synergistically integrated to heighten prediction precision for monitored plants. Secondly, based on the predicted power curves of monitored PV power plants, a weight optimization model is presented to optimize the combined weights between unmonitored and monitored power stations. Furthermore, the power generation model from monitored facilities is transplanted to unmonitored plants for power generation estimation. Ultimately, the authors combine the weight optimization and transfer learning model to get more accurate and robust model. Validation across three regional distributed photovoltaic clusters demonstrates a noteworthy improvement in prediction accuracy—20%, 7.5%, and 25% for the respective clusters compared to the existing methods.

Fault recovery strategy for urban distribution networks using soft open points

AbstractIn recent years, the frequent occurrence of natural disasters has seriously threatened the security and stability of distribution networks. Amidst these natural disasters, researchers have increasingly focused on ensuring fault recovery in distribution networks. Soft open points (SOPs) are new types of power electronic devices that can effectively recover faults in distribution networks. When a fault occurs, SOPs can quickly block and isolate fault short‐circuit current, provide power access to non‐fault areas, and optimise the power flow of distribution networks. Based on the critical role of distributed generation (DG) power supply in fault recovery, this study proposes a method that leverages the synergistic capabilities of SOPs and DG to recover faults in distribution networks. By employing a binary particle swarm optimisation algorithm, this method effectively improves load recovery, reduces the number of switching operations, and minimises network loss. The proposed method was simulated and verified using the IEEE 33‐node and 99‐node systems.

Voltage stability improvement in distribution networks by using soft open points

The integration of renewable energies is gaining popularity as a result of their sustainability and green nature. On the other hand, aged power infrastructure not only has various issues in accommodating such resources, but also poses risks to the distribution network operation. To this aim, soft open points (SOPs) can be used for supporting distribution networks. Although SOP has been used for different purposes, the potential of SOP for increasing the voltage stability entails further study. As a result, an efficient strategy for the operation of SOPs in distribution networks is developed in this study, which considers the voltage stability index as an objective function. The developed framework is optimized using the particle swarm optimization method, which takes network, resource, and other technical limitations into account. The uncertainties of loads and renewable energies are also modeled through the scenario-based method. The suggested approach is tested on an IEEE standard distribution network under two conditions, comprising the normal and fault situations. After analyzing the various case studies and experiments, the effectiveness and applicability are verified accordingly. The findings show that SOP has a significant impact on improving voltage stability of distribution networks, throughout the normal and fault conditions.

Emergency Power Supply Restoration Strategy for Distribution Network Considering Support of Microgrids with High-Dimensional Dynamic Correlations

With the rapid development of renewable energy, microgrids are becoming more and more essential in distribution networks. However, uncertainties brought by new energy sources have posed great challenges to the energy safety and stability of distribution networks, especially in the process of fault restoration. To address these issues, this paper investigates an emergency power supply recovery strategy for distribution networks by considering the support capability of microgrids and the high-dimensional dynamic correlations of uncertainty. Firstly, the structure of distribution networks including microgrids is analyzed. Then, the high-dimensional dynamic vine copula model is proposed to model the joint output uncertainty process. Next, island partition and operation models are established, including the objective function and constraint conditions, and the model solving methods are also presented. Finally, an example analysis is conducted to verify the effectiveness of the proposed strategy. The results show that the proposed strategy is able to deal with the uncertainties brought by renewable energy sources in distribution networks with microgrid support, improving energy safety and stability. This research provides valuable insights for the development of emergency power supply strategies in distribution networks.

Research on the Influence of Wind Power Access on Voltage Stability of Distribution Network

This paper proposes a voltage stability index suitable for distributed generation and distribution network which is derived by using the relationship between power balance and voltage balance and the impact of wind power access location, access capacity, and fault on the voltage stability. The results show that the shorter the length and the larger the wind power capacity, the more obvious its role in supporting the distribution network voltage. But the greater the negative impact of wind power’s instability on the distribution network voltage, the above factors must be comprehensively considered in network planning.

Stability of distribution network with large-scale PV penetration under off-grid operation

Large-scale photovoltaic (PV) penetration reduces system damping and causes stability problems on off-grid distribution systems. The single-machine equivalent method is typically used to simplify the full-order model by ignoring the differences in PVs. However, this results in substantial errors. When the differences between the control models of PVs are greater, the calculation results lead to greater error. Then the stability of the PV system cannot be judged. This study validated that the oscillation mode of the PV in the subsynchronous frequency band has a linear correlation with the operating conditions and control parameters. Accordingly, the authors proposed to divide the oscillation mode interval by taking the maximum and minimum PV operating conditions as the boundary to assess the stability of the system. Considering the difference in the operating conditions of the PVs, the average power was used to dynamically aggregate the PV electric farm to determine whether the system was at risk of instability. Finally, the effectiveness of the proposed method was verified through case studies on a 12-PV off-grid distribution system and a large-scale PV farm system. The verification results show that the proposed method can determine the stability of the distribution system accurately. The proposed method is significant for developing strategies to enhance the stability of off-grid distribution systems.

Optimal planning of hybrid electric-hydrogen energy storage systems via multi-objective particle swarm optimization

Abstract: In recent years, hydrogen is rapidly developing because it is environmentally friendly and sustainable. In this case, hydrogen energy storage systems (HESSs) can be widely used in the distribution network. The application of hybrid electric-hydrogen energy storage systems can solve the adverse effects caused by renewable energy access to the distribution network. In order to ensure the rationality and effectiveness of energy storage systems (ESSs) configuration, economic indicators of battery energy storage systems (BESSs) and hydrogen energy storage systems, power loss, and voltage fluctuation are chosen as the fitness function in this paper. Meanwhile, multi-objective particle swarm optimization (MOPSO) is used to solve Pareto non-dominated set of energy storage systems’ optimal configuration scheme, in which the technique for order preference by similarity to ideal solution (TOPSIS) based on information entropy weight (IEW) is used select the optimal solution in Pareto non-dominated solution set. Based on the extended IEEE-33 system and IEEE-69 system, the rationality of energy storage systems configuration scheme under 20% and 35% renewable energy penetration rate is analyzed. The simulation results show that the power loss can be reduced by 7.9%–22.8% and the voltage fluctuation can be reduced by 40.0%–71% when the renewable energy penetration rate is 20% and 35% respectively in IEEE-33 and 69 nodes systems. Therefore, it can be concluded that the locations and capacities of energy storage systems obtained by multi-objective particle swarm optimization can improve the distribution network stability and economy after accessing renewable generation.

Frequency and Voltage Stability of Distribution Network using Photovoltaic Stabilization System and Battery Storage System

In a power system, various factors such as changes in generator output power due to load changes and different types of short circuits may change the network frequency/voltage as a consequence of changes in network condition. These volatilities in frequency/voltage may cause system frequency/voltage instability. Among different devices used for power system stability, stabilizers are the most important equipment for network stabilization and generator synchronization. In this paper, a photovoltaic system accompanied by battery storage system is used to improve the power system stability following voltage/frequency sudden variations as a result of unpredicted events in the distribution network. To do that, an IEEE 14-bus standard test network is simulated in MATLAB/Simulink, and the performance of the proposed system after different common disturbances is investigated. The results verify the performance of the proposed stabilizer so that it maintains the system stability successfully with zero percent deviation from the original state of the network.

Open Capacity Model of Medium Voltage Transmission Line in Distribution Network Based on Load Data

Due to the influence of load fluctuation, the power supply stability of distribution network is insufficient. Therefore, the open capacity model of medium voltage lines in the distribution network is constructed by using load data. Based on the introduction of time parameter, the load pressure of the distribution network is calculated by using the peak load of distribution network. According to the relationship between the pressure value of sustainable load of the distribution network and the pressure value under the actual power load, the open capacity model of medium voltage lines is constructed. The test results show that when the design model is used to set the open capacity of medium voltage lines, the fluctuation of the expected outage time and frequency of the distribution network system is small, which indicates that the stable power supply can be realized

OpenTUMFlex: A flexibility quantification and pricing mechanism for prosumer participation in local flexibility markets

Increasing distributed energy resource penetration requires additional methods to tackle grid congestion and enhance reliability. In addition, advancements in demand-side management can help strengthen distribution network stability. A viable approach is to develop local flexibility markets (LFMs) to aggregate prosumer flexibility potentials and offer grid ancillary services. To facilitate such prosumer–market interactions, an open-source model, named OpenTUMFlex is developed in this study to quantify and price prosumer flexibility. The core of this model is an energy management system that optimizes prosumer data to obtain a cost-optimal operation schedule. Flexibility potential is defined as all possible deviations from this optimal operation without violating device constraints. Flexibility services are characterized as positive and negative flexibility based on the energy exchanged with the grid. Also, a predictive pricing mechanism is introduced to identify the minimum price the prosumer needs to be compensated for their flexibility service. The outcome of this model is a flexibility offer table using which the prosumer can bid in the LFMs. An iterative interaction between the OpenTUMFlex model and LFM is possible as the model integrates the flexibility service requirements from the selected bid into its operational schedule and generates a new optimal operation schedule with renewed flexibility offers. The model is analyzed using two case studies where a sample flexibility service implementation for different prosumer devices is discussed. In addition, a unique flexibility representation technique is introduced to illustrate the flexibility offers graphically. Future research will focus on identifying the impact of forecast uncertainty on flexibility quantification.

Joint expansion planning of distribution network with uncertainty of demand load and renewable energy

Rational planning plays a key role in developing and improving operation security and stability of distribution networks (DNs). This paper proposes a novel method to solve the expansion of transmission lines (TLs) and the allocation of distributed generators (DGs) considering the uncertainty of load demand and renewable energy. The spanning-tree is a theoretical method specifically tailored to guarantee the radiality operation of distribution networks. Also, to guarantee the stability and reliability of the network, the probability function is used to describe the stochastic nature of load behavior and renewable sources fluctuation. Thus, the most proper installation location and time for TL and DG assets can be identified by modeling the joint expansion planning. The objective of the proposed model is minimizing the present value of the total social costs, including the cost of investment, purchasing energy, and environmental benefits. This mixed-integer nonlinear problem (MINLP) optimization model is solved based on GAMS environment. The effectiveness and scalability of this model is validated and illustrated based on the IEEE RTS-24 system.

An improved MOPSO algorithm for optimal sizing & placement of distributed generation: A case study of the Tunisian offshore distribution network (ASHTART)

Investigation of power distribution systems is an attractive field in the power system analysis. The location and size of Distributed Generation (DG) are important and an uncontrollable choice might result in a negative impact on the system’s behavior. This paper presents an improved approach for optimum simultaneous DG placement and sizing to reduce power loss and improve the voltage profile and the stability of distribution networks. Numerous methods have been used for solving this difficult combinatorial problem like Particle Swarm Optimization (PSO) algorithm used in this work. The suggested MOPSO method combined with MATPOWER toolbox was tested on standard IEEE 33-bus and IEEE 69-bus radial distribution networks (RDN) throw different scenarios. The major contribution of this paper was that the procedure was applied to the Tunisian electricity distribution network (ASHTART) of the SEREPT (Society for Research and Exploitation of Petroleum) to prove the efficiency of applying this method on real-world systems. This paper will also compare the proposed method with previous findings methods and the obtained results testify to the robustness and effectiveness of the improved MOPSO algorithm in terms of reduction in total active power losses (TAPL), maximization of a percentage of minimum voltage improvement (%MVI), amelioration in voltage profiles and also maximization in voltage stability index (VSI) while finding the optimal location and size of the DG units (OPSDG).

Influence of electric vehicle distributed energy storage access on safety and stability of distribution network

This paper proposes a distributed energy storage control strategy for electric vehicles to improve the security and stability of distribution network when electric vehicles are connected. Firstly, the load characteristics of electric vehicles are investigated, and the optimal power flow model including energy storage power station, electric vehicle charging station considering V2G and distribution network is established. The objective function is established to minimize network loss, voltage deviation and load peak to peak. The problem is transformed into a mixed integer second-order cone optimization problem for solution, and based on the analysis of distributed energy storage model and constraints, the distributed energy storage control strategy of electric vehicle is established. Through the solution analysis in IEEE33 node system, it can be concluded that using this strategy can improve the power quality of distribution network and effectively improve the security and stability of distribution network.

Optimal reactive power dispatch and distributed generation placement based on a hybrid co‐evolution algorithm and bi‐level programming

To improve the economy and stability of distribution networks with a high penetration of distributed generation (DG), active management strategies should be considered during the planning stages and operating stage. This article presents a bi-level model of DG placement and an optimal reactive power dispatch, where the total investment and operation cost are minimized through an optimal allocation of the DG at the upper planning level, and the active power loss and voltage deviation are optimized based on the optimal reactive power output of the DG, static var compensator (SVC), and shunt capacitor bank (SCB). In this study, a period division strategy based on the Grey relation analysis was determined to reduce the switching time of the SCB. The co-evolution algorithm based on the improved sparrow algorithm was used to solve the adopted bi-level model. The validation results for the IEEE-33 node system show that the adopted bi-level model and strategy applied in this study can reduce the comprehensive cost and effectively improve the voltage profile while reducing the active power loss of the distribution network. In addition, the results obtained by the algorithm adopted in this research are compared to those of other previously published studies. The proposed algorithm yields much better results in terms of the improvement in the voltage profile and active power loss minimization.

Electric Heating Load Forecasting Method Based on Improved Thermal Comfort Model and LSTM

The accuracy of the electric heating load forecast in a new load has a close relationship with the safety and stability of distribution network in normal operation. It also has enormous implications on the architecture of a distribution network. Firstly, the thermal comfort model of the human body was established to analyze the comfortable body temperature of a main crowd under different temperatures and levels of humidity. Secondly, it analyzed the influence factors of electric heating load, and from the perspective of meteorological factors, it selected the difference between human thermal comfort temperature and actual temperature and humidity by gray correlation analysis. Finally, the attention mechanism was utilized to promote the precision of combined adjunction model, and then the data results of the predicted electric heating load were obtained. In the verification, the measured data of electric heating load in a certain area of eastern Inner Mongolia were used. The results showed that after considering the input vector with most relative factors such as temperature and human thermal comfort, the LSTM network can realize the accurate prediction of the electric heating load.

Stability Analysis of CFETR Distribution Network

The safe operation of distribution network is a prerequisite for the stable operation of CFETR. The electricity load is classified succinctly in this article to identify specific properties. By calculating the influence of CFETR load operation on the distribution network stability in Matlab, a comprehensive analysis of the interaction between load and distribution network is carried out. According to load statistics and stability analysis, the general principle and method of parameters determination for CFETR distribution network is obtained. The power flow is calculated by ETAP 12.6.0 simultaneously to verify the feasibility of the CFETR distribution network configuration.

Automatic Identification of Low Voltage Distribution Network Topology Based on HPLC

With the rapid development of China’s national economy and the deepening of reform and opening up, people’s demand for electricity is growing. This paper studies the automation of low-voltage distribution network topology based on HPLC power line carrier technology. It is a key technology to effectively extend the communication distance of power line communication network in smart grid. For the power line communication network, we establish the network model of power line carrier layered forwarding tree. Based on the study of the topology and various operation modes of DC distribution network, a new hierarchical coordinated control method is proposed. By coordinating the working mode of each sub module, the power balance and voltage stability of distribution network are maintained under different conditions.