Node Voltage Research Articles

Evolution of topological extended state in multidimensional non-Hermitian topolectrical circuits

The extended state pertains to the dispersion of the system's eigenfunctions across the whole lattice. Recent studies have shown that the non-Hermitian skin effect (NHSE) can reshape the wavefunction of topological modes. The localized states of topological modes within the bandgap gradually delocalized into extended states through the manipulation of NHSE. Here, we clarify the NHSE direction using the Bloch spectral winding numbers and reestablish the bulk-boundary correspondence through the non-Bloch winding numbers in the generalized Brillouin zone. We elucidate the formation of extended state by employing the localized decay length. Then, we have designed non-Hermitian topological circuits for experimental verification based on the voltage follower. The corner states, edge states, and extended states in 1D, 2D, and 3D circuits were observed through the measurement of node voltage. Our work can achieve the sustainable extended mode and provides significant cases for the analysis of topolectrical circuits.

An optimization dispatching strategy for integrated electricity and natural gas systems with fast charging stations

With the increasing number of electric vehicles (EVs), a large amount of charging load will affect the safety and economic operation of the distribution network (DN). In this paper, an optimization dispatching strategy for integrated electricity and natural gas systems (IEGS) with fast charging stations (FCSs) is proposed to address the impact of fast charging loads. Firstly, the FCS load model is established based on the simulation of vehicle traveling and vehicle charging. Secondly, a natural gas supply model is established, which includes constraints such as the adjustment range, direction, and total number of supply flow adjustments. Then, an optimization dispatching strategy for IEGS based on energy storage and gas storage is proposed. Finally, the effectiveness of the proposed strategy is verified. The simulation results show that when the proposed strategy is applied, the voltage of FCS node will be stabilized within a good range. And the energy purchase cost will be reduced by more than 50%. The proposed method improves the carrying capacity of DN for FCSs.

Computing the Network’s Equilibrium Point at the Fault Clearing Instant in Transient Stability Studies

This paper proposes an approach for computing the network’s equilibrium point related to the fault clearing time in transient stability studies. The computation of this point is not a trivial task, particularly when the algebraic network’s equations are expressed in the power balance form. A natural attempt to solve this problem is using Newton’s method. However, convergence issues are found because of the lack of a general strategy for initializing nodal voltages at the clearing time. This problem has not been widely discussed in the existing literature and, therefore, is comprehensively analyzed in this paper. Furthermore, the paper proposes the use of a network’s model based on current injections and an extended admittance matrix to overcome the problem. This model is efficiently solved via the fixed-point iteration method, which involves factorization of the extended admittance matrix into the product of a lower triangular matrix [L] and an upper triangular matrix [U]. This solution executes a just once and only forward–backward substitution during the iterative solution process. Case studies clearly demonstrate the proposal’s effectiveness in computing the equilibrium point in operating conditions where Newton’s method fails to converge.

Research on multi-time scale Volt/VAR optimization in active distribution networks based on NSDBO and MPC approach

To explore the potential of all-round and multiangle voltage and reactive power control to reduce losses in a new power system with a high proportion of distributed resources connected to a distribution network, this study proposes a Volt/VAR optimization method for distribution networks using a nondominated sorting dung beetle optimizer (NSDBO) and model predictive control (MPC). According to the characteristics of the various adjustable resources in the network, they are divided into day-ahead and intraday optimizations. The optimization process in the day-ahead stage uses the NSDBO to create a discrete equipment scheduling plan. The optimization process in the intraday stage combines the discrete equipment scheduling plan with the MPC to create the scheduling plan for photovoltaics, energy storage, and vehicle charging stations. Two-stage optimization scheduling is achieved with the lowest discrete equipment regulation cost and minimization of network loss and node voltage deviation penalty cost as the optimization goal. The feasibility of this method for coordinating various resources in the network, processing discrete and continuous variables, and coping with the volatility and uncertainty of high-proportion distributed resources is verified through case analysis. The effectiveness of this method in improving the security and economy of distribution networks is demonstrated. The superiority of the solution speed and quality is also confirmed.

Energy storage configuration method for distribution networks based on moment difference analysis

The integration of a high proportion of distributed PV into distribution networks can cause power backfeeding and voltage limit violations. This paper introduces moment difference analysis theory for distribution networks, reframing PV consumption as the balancing moment difference equations. When maximum node voltage approaches the upper limit, the moment difference between PV and load stabilizes to an approximately constant, termed the standard moment difference. The standard moment difference represents the limit of the network's capacity to consume distributed PV. Essentially, the PV moment is the target for integration, while the load moment serves as the resource to consume the PV moment. Based on this theory, a method for energy storage configuration is proposed. Simplifying a complex multi-branch distribution network into single-branch lines and solving linear equations determines the optimal storage configuration. This method was tested on the Jibei Power Grid and the IEEE 33 and 69 bus systems, increasing computational efficiency by 1611.47–4973.82 times compared to the PSO algorithm, while maintaining an error margin of less than 2.6 %. Furthermore, the discharging strategy reduces the ESS capacity to between 12.39 % and 31.69 % of the original estimate, thereby enhancing both the economic and practical appeal of the approach.

Integration of Photovoltaic and DSTATCOM In the Distribution Network Using Rat Swarm Optimization Technique

Power loss minimization and improved voltage profile have been major challenges faced by the electrical distribution network (DN) mainly because of the long length of the feeders and the high resistance to reactance (R/X) ratio of the DN. A lot of techniques have been investigated to solve these problems. One of the most prominent is the optimal integration of distributed generation (DG) such as photovoltaic (PV) as well as the integration of Distribution Static Synchronous Compensator (DSTATCOM) into the network. The main challenge with this solution has been the determination of the optimal sizes and sites of the DG and/or DSTATCOM. This paper seeks to optimize the simultaneous allocation of multiple DSTATCOMs and PVs in the DN for power loss reduction and voltage profile improvement using the rat swarm optimization (RSO) technique, which is a simple, yet robust optimization technique. The optimization problem is formulated to minimize power loss, voltage deviation index, and maximize the voltage stability index. The IEEE 33 node DN is used as a test network and the simulation results show the effectiveness of the RSO technique in finding the best sizes and locations of the PVs and the DSTATCOMs. The power losses of the network are reduced from 210.996 kW, and 143.129 kVAr when there is no DSTATCOM nor PVs in the network to 26.155 kW, and 19.128 kVAr when DSTATCOM and PVs are simultaneously allocated into the network. A remarkable improvement in the voltage profile of the network is also observed with the minimum node voltage being 0.98 p.u. compared to 0.9038 p.u. when there are no DSTATCOMs or PVs. The RSO results were compared with other techniques from the literature, and it proved its superiority.

Distributed PV carrying capacity prediction and assessment for differentiated scenarios based on CNN-GRU deep learning

The increasing penetration of distributed photovoltaic (PV) brings challenges to the safe and reliable operation of distribution networks, distributed PV access to the grid changes the characteristics of the traditional distribution grid. Therefore, the assessment of distributed PV carrying capacity is of great significance for distribution network planning. To this end, a differentiated scenario-based distributed PV carrying capacity assessment method based on a combination of Convolutional Neural Networks (CNN) and Gated Recurrent Unit (GRU) is proposed. Firstly, the meteorological characteristics affecting PV power are quantitatively analyzed using Pearson’s correlation coefficient, and the influence of external factors on PV power characteristics is assessed by integrating the measured data. Then, for the problem of high blindness of clustering parameters and initial clustering centers in the K-means clustering algorithm, the optimal number of clusters is determined by combining the cluster Density Based Index (DBI) and hierarchical clustering. The improved K-means clustering method reduces the complexity of massive scenarios to obtain distributed PV power under differentiated scenarios. On this basis, a distributed PV power prediction method based on the CNN-GRU model is proposed, which employs the CNN model for feature extraction of high-dimensional data, and then the temporal feature data are optimally trained by the GRU model. Based on the clustering results, the solution efficiency is effectively improved and the accurate prediction of distributed PV power is realized. Finally, taking into account the PV access demand of the distribution network, combined with the power flow calculation of distribution network, the bearing capacity of distribution network considering node voltage in differentiated scenarios is evaluated. In addition, verified by source-grid-load measured data in IEEE 33-bus distribution system. The simulation results show that the proposed CNN-GRU fusion deep learning model can accurately and efficiently assess the distributed PV carrying capacity of the distribution network and provide theoretical guidance for realizing distributed PV access on a large scale.

Collaborative Optimization for Active Distribution Network with Soft Open Point and Energy Storage

Background: The increasing use of high-penetration distributed clean energy has led to voltage fluctuations in the distribution network that surpass safety limits and pose challenges to economic and low-carbon operation. Traditional voltage regulation devices are unable to meet the real-time high-precision voltage control needs when encountering frequent fluctuations due to physical limitations. The Soft Open Point (SOP) can provide continuous reactive power to achieve rapid voltage regulation. Objective: We propose a collaborative optimization approach that fully considers the regulatory capabilities of SOP and energy storage to eliminate voltage violations and reduce network losses. Methods: This study introduces an active distribution network reactive power voltage optimization approach that incorporates intelligent SOP and energy storage, combining conventional devices (such as on-load tap changers and capacitor banks) with contemporary devices (such as SOP, distributed generators, and energy storage) to establish synergy. A coordinated optimization model was developed, considering various operational constraints to minimize network losses and regulate the voltage of distribution network nodes. By using linearization and quadratic relaxation, the original non-convex mixed-integer non-linear optimization model was transformed into a Mixed-Integer Second-Order Cone Programming (MISOCP) model to meet the requirements of rapid voltage regulation. Results: A case study was conducted on the IEEE 33-node test system, showing that the proposed approach effectively reduces network losses and eliminates voltage violations. Conclusion: The feasibility and effectiveness of the proposed methodology have been validated, confirming its ability to ensure the safe and economic operation of active distribution networks.

A Review of Current Differencing Buffered Amplifiers: Performance Metrics and Technological Advances

Current Differencing Buffered Amplifiers (CDBAs) are a critical class of analog circuit components capable of handling both current and voltage signals with minimal power consumption. Due to their low impedance voltage output, they play a significant role in modern electronics for developing high-performance, high-precision analog and mixed-signal circuits. But, designing and characterizing CDBAs pose several challenges, such as ensuring stability at high frequencies, minimizing noise impact for high-precision applications, and enhancing adaptability. Integrating CDBAs with other analog components to create multifunctional integrated circuits opens many opportunities in the analog signal-processing domain. This paper reviews the evolution and applications of CDBAs in analog signal processing. Various implementation schemes, including those using commercial Current Feedback Amplifiers (CFAs) and novel CMOS configurations, are analyzed for their performance metrics such as supply voltage, power dissipation, input/output impedances, and technology node. Future trends and challenges in advancing CDBA technology towards higher integration and lower-voltage operation are discussed, highlighting potential applications in next-generation electronics.

Evaluation Method for Voltage Regulation Range of Medium-Voltage Substations Based on OLTC Pre-Dispatch

A new energy industry represented by photovoltaic and wind power has been developing rapidly in recent years, and its randomness and volatility will impact the stable operation of the power system. At present, it is proposed to enrich the regulation of the power grid by tapping the regulation potential of load-side resources. This paper evaluates the overall voltage regulation capability of substations under the premise of considering the impact on network voltage security and providing a theoretical basis for the participation of load-side resources of distribution networks in the regulation of the power grid. This paper proposes a Zbus linear power flow model based on Fixed-Point Power Iteration (FFPI) to enhance power flow analysis efficiency and resolve voltage sensitivity expression. Establishing the linear relationship between the voltages of PQ nodes, the voltage of the reference node, and the load power, this paper clarifies the impact of reactive power compensation devices and OLTC (on-load tap changer) tap changes on the voltages of various nodes along the feeder. It provides theoretical support for evaluating the voltage regulation range for substations. The day-ahead focus is on minimizing network losses by pre-optimizing OLTC tap positions, calculating the substation voltage regulation boundaries within the day, and simultaneously optimizing the total reactive power compensation across the entire network. By analyzing the calculated examples, it was found that a pre-scheduled OLTC (on-load tap changer) can effectively reduce network losses in the distribution grid. Compared with traditional methods, the voltage regulation range assessment method proposed in this paper can optimize the adjustment of reactive power compensation devices while ensuring the voltage safety of all nodes in the network.

Research on reactive power voltage control strategy of distribution networks containing distributed PV

Abstract With the large-scale increase in photovoltaic power generation, its integration into the grid will affect the system voltage of the distribution network. This paper presents a reactive voltage control strategy for the distribution network based on deep reinforcement learning. Firstly, the objective function model is established based on the node voltage and network loss of the distribution network. Secondly, each photovoltaic inverter is modeled as an agent of the enhanced learning environment. The collaborative control problem of the photovoltaic inverter is transformed into an observable Markov decision-making process through the distribution network partition. The paper uses a double delay depth deterministic gradient algorithm to solve the problem. Finally, combined with the improvement of 33 nodes for verification, the proposed strategy has the stability of improving voltage, presenting an ability to effectively reduce network loss and verify the effectiveness of the proposed strategy.

Optimal Reconfiguration of Bipolar DC Networks Using Differential Evolution

The search for more efficient power grids has led to the concept of microgrids, based on the integration of new-generation technologies and energy storage systems. These devices inherently operate in DC, making DC microgrids a potential solution for improving power system operation. In particular, bipolar DC microgrids offer more flexibility due to their two voltage levels. However, more complex tools, such as optimal power flow (OPF) analysis, are required to analyze these systems. In line with these requirements, this paper proposes an OPF for bipolar DC microgrid reconfiguration aimed at minimizing power losses, considering dispersed generation (DG) and asymmetrical loads. This is a mixed-integer nonlinear optimization problem in which integer variables are associated with the switch statuses, and continuous variables are associated with the nodal voltages in each pole. The problem is formulated based on current injections and is solved by a hybridization of the differential evolution algorithm (to handle the integer variables) and the interior point method-based OPF (to minimize power losses). The results show a reduction in power losses of approximately 48.22% (33-bus microgrid without DG), 2.87% (33-bus microgrid with DG), 50.90% (69-bus microgrid without DG), and 50.50% (69-bus microgrid with DG) compared to the base case.

Multi-Stage Optimal Power Control Method for Distribution Network with Photovoltaic and Energy Storage Considering Grouping Cooperation

In view of the current problem of insufficient consideration being taken of the effect of voltage control and the adjustment cost in the voltage control strategy of distribution networks containing photovoltaic (PV) and energy storage (ES), a multi-stage optimization control method considering grouping collaboration is proposed. Firstly, the mechanism by which the access of the PV and ES to the distribution network impacts the node voltage is explored. Then, the unit regulation cost of a photovoltaic inverter and energy storage power is studied. On this basis, the voltage–cost sensitivity is proposed based on the traditional node power–node voltage sensitivity. According to the differences between each group of regulation resources, a multi-stage voltage over-run control strategy based on grouping cooperation is proposed. Finally, the improved 33-node model is taken as an example to verify the correctness and effectiveness of the proposed method.

Distributed control of virtual energy storage systems for voltage regulation in low voltage distribution networks subjects to varying time delays

Distributed communication-based strategies are popular for regulating nodal voltages in distribution networks with high penetration of Photovoltaic (PV) sources. Time delays inevitably pose challenges to efficient voltage regulation and power sharing. In response, this paper presents a distributed, event-triggered voltage regulation approach that enables power sharing across virtual energy storage systems (VESS) with different parameters while accommodating diverse time delays. The process begins by determining the delay margin for the primary Volt/Watt controller in a low-voltage distribution network (LVDN), laying the foundation for stable feedback control gain design. To accommodate any arbitrary large and time-varying delays, a distributed resilient event-triggered secondary controller for VESS is designed to share voltage regulation tasks according to their capacities and state of energy (SoE) values. A mathematical analysis, grounded in the Lyapunov–Krasovskii approach, evaluates the stability and convergence of the proposed controller amidst time delays thoroughly. To validate the proposed method, simulation studies are conducted, which demonstrate the effectiveness in mitigating voltage limit violations, even with variable communication delays, and highlight balanced power sharing and SoE among VESS.

Distributed optimization control strategy for distribution network based on the cooperation of distributed generations

AbstractAiming to improve the voltage distribution and realize the proportional sharing of active and reactive power in the distribution network (DN), this article proposes a distributed optimal control strategy based on the grouping cooperation mechanism of the distributed generation (DG). The proposed strategy integrates the local information of the DG and the global information of the DN. Considering the high resistance/reactance ratio of DN, distributed optimization control strategies for node voltage control and active power management are developed with the consensus variable of active utilization rate. And distributed strategy for reactive power management is proposed with a consensus variable of reactive utilization rate. The convergence of the distributed control system for each group is proved. The validity and robustness of the proposed strategy are verified by several simulations in the IEEE 33‐bus system.

Optimized Genetic Algorithms: Study of the Impact on Load Distribution in the Power System

In order to balance the power flow, voltage and current between renewable energy power generation and thermal power generation, this paper takes renewable energy as the research object, analyzes the power flow and voltage after grid connection, and proposes an improved genetic algorithm for research. Firstly, the log function is used to analyze the power flow and voltage data before and after grid connection, reduce the complexity of the data, and calculate the proportion of photovoltaic energy. Then, the load analysis of the power system was carried out by genetic iteration to identify the overloaded nodes, and the identification standard was greater than 80% of the rated load. Finally, a load set is formed, and information such as changes, power flow, and voltage of different power generation nodes is recorded. The results show that the genetic algorithm can maximize the proportion of renewable energy to 50% and reduce the power flow change rate of the power system to 10%. At the same time, improve the load efficiency of the power system, so that the node load is between 75~80%, average the load of each node, especially the photovoltaic matrix and fan, and shorten the load deployment time, so that it is 10s smaller. Compared with the previous algorithms, the genetic algorithm proposed in this paper is better, which can meet the load distribution requirements of the power system and expand the energy proportion of renewable energy.

On the Internationalization of English Teaching System in the Planning of Sustainable Development of Future Renewable Energy

Energy is an essential element of human production and business activities. At present, China's energy development and utilization is facing the contradiction between increasing environmental pressure and high emission energy consumption structure. Based on the optimal power flow algorithm, this paper optimizes the scheduling and international teaching of the power system with a high proportion of renewable energy. The probability density function of fan output and photovoltaic output is obtained by wind speed and light intensity model, and the probability density function of load is also proposed. Then, interruptible load, microgrid and energy storage are used as flexible resources to improve the system’s flexibility to meet the random fluctuations caused by a high proportion of wind and light, so as to maintain the stable operation of the power grid. The mathematical model of process optimization power flow satisfying process constraints is a nonlinear time-varying system. The key to process optimization power flow algorithm is to find a simple and effective linearization and discretization method that is in line with the actual power network. On the basis of ensuring the accuracy of the model, the theory, model and algorithm of the process optimization power flow are studied on the basis of the proposed linear time period, the approximate linear time variability of the node voltage, and the goal of greatly simplifying the problem. Finally, through simulation analysis, comparing the results of model 1 and model 2, we can see that from the point of view of the power abandonment cost, the power abandonment cost of model 1 is 2.29 million yuan, which is less than the 8 million yuan of model 2. From the perspective of depth peaking and start-stop times, the total depth peaking of model 1 is 331 times, which is greater than that of model 2, 258 times. The total start-stop of model 1 is 15 times, which is more than 9 times of model 2, so the total cost of optimized electricity of wind and light in model 2 is less than that in model 1. According to the optimal scheduling model of wind and light renewable energy, the effective utilization rate of renewable energy in China is low, and the teaching system should pay attention to this problem. Therefore, this paper puts forward an international English teaching strategy for the utilization rate of renewable energy.

Optimal placement of wind turbine in distribution grid to minimize energy loss considering power generation probability

This research proposes an algorithm to determine the optimal position of wind turbines in a distribution grid for minimizing its annual energy loss. In this paper, the probability distribution of power generation at each wind turbine node is considered and the nonsimultaneous occurrence of the power generation at wind turbine nodes is also interested. Moreover, we also consider the wind energy exploitability at each node in the grid. This algorithm is coded in MATLAB environment and it is verified via a sample IEEE 33 bus distribution grid with a sample data of power generation probability. The verifying results indicated that the optimal number of wind turbines at each node to obtain a minimal annual energy loss. Results also indicated that the node voltage is varied because of the change of power generation; however, the voltage at all nodes is in normal operation range and its median value is between about 1.01 pu to 1.05 pu. The power factor of wind turbines has an impact on the optimal number of wind turbines at nodes and the efficiency of wind turbine installation.

Multi-objective Reactive Power Optimization of a Distribution Network based on Improved Quantum-behaved Particle Swarm Optimization

Background: Reactive power optimization (RPO) is crucial for distribution networks in the context of large-scale renewable distributed generation (RDG) access. Objective: To address the problems caused by the connection of RDG, an RPO model and an improved quantum-behaved particle swarm optimization (IQPSO) algorithm are proposed. Method: In this study, a dynamic S-type function is proposed as the objective function of the minimum active power loss, whereas an exponential function is proposed as the objective function of the minimum voltage deviation to establish an RPO objective function. The operating cost of distribution is considered as the third objective function. To address the RPO problem, a QPSO algorithm based on the ε-greedy strategy is proposed in this paper. ModifiedIEEE33 bus and IEEE69 bus systems were used to evaluate the proposed RPO method in simulations. Results: The simulation results reveal that the IQPSO algorithm obtains a better solution, and the proposed RPO model can considerably reduce active power loss, node voltage deviation, and distribution network operating costs. Conclusion: The RPO model and IQPSO algorithm proposed in this study provide a highperformance method to analyze and optimize reactive power management in distribution network.

Design of a Radiation-Hardened SRAM Cell using 16nm Technology Node

Electronic circuits are exposed to very high energy radiation in the harsh conditions of outer space. This leads to soft errors such as single-event upsets (SEU), double-event upsets (DEU) and single-event transients (SET). The memory circuits are the most susceptible to these soft errors resulting in severe data loss. This paper proposes the design for an SRAM cell that is radiation hardened by design (RHBD). A comparative study of the standard SRAM cell and the RHBD SRAM cell indicates that the proposed design is resilient to SEUs and DEUs. The proposed design is an improvement on the 8T SRAM cell design and includes a 20T triple interlocked cell (TICE) design. The error correction capabilities of both designs are compared by manually injecting bit upsets at crucial nodes in the circuit. It is observed that the TICE design provides a 96.75% and 98.5% improvement over the standard 8T cell for 1-0 and 0-1 bit upsets respectively. The proposed design has been implemented using the 16nm model from the Arizona State University’s Predictive Technology Model (ASU-PTM), and the LTSpice software was used to carry out the simulations. Tools used: LTSpice v17 is the tool used to design the schematic circuits and to obtain the simulation results. The library file used to design the schematic is the 16nm technology node from the library of the Arizona State University’s Predictive Technology Model (ASU-PTM). Methods: To perform a comparative study of the 8T SRAM and 20T TICE, the schematic circuits were designed using the 16nm technology node in LTSpice. A baseline for the expected voltages of logic 0 and logic 1 is established before injecting errors at the sensitive voltage nodes of the circuit. The next step is to manually inject bit upsets at the crucial nodes of the memory cell to induce either a 0-1 upset or a 1-0 upset. The read/write operation is carried out along with the error injection to compare the error mitigation capacity of the circuits. Finally, the results are tabulated to prove that the 20T TICE is resilient to soft errors arising from exposure to high radiation. Results: During the normal operation of the 8T SRAM cell, the observed output voltage corresponding to logic 0 and logic 1 are 25mV and 780mV respectively. The voltage levels increased to 400mV during a 0-1 upset and dropped to 25mV during a 0-1 bit upset. This indicates that the cell is not reliable. In case of the 20T RHBD cell, the voltage levels for the normal operation were 5mV (logic 0) and 780mV (logic 1). The major improvement is shown in the case where the 20T TICE is injected with bit upsets, since it delivers a voltage of just 6mV during a 0-1 bit upset and maintains a voltage of 770mV during a 1-0 bit upset. Conclusion: The work performs the schematic designs for the comparison of the error mitigation capabilities of a 8T SRAM cell and a 20T TICE which is radiation hardened by design. The study proves that the 20T cell is capable of successfully mitigating both a 1-0 bit upset and a 0-1 bit upset