System Voltage Stability Research Articles

A novel multi-objective optimization based multi-agent deep reinforcement learning approach for microgrid resources planning

Multi-agent deep reinforcement learning (MADRL) approaches are at the forefront of contemporary research in optimum electric vehicle (EV) charging scheduling challenges. These techniques involve multiple agents that respond to a dynamic simulation environment to strategically integrate EV charging stations (EVCSs) on microgrids by incorporating the constraints posed by stochastic trip durations. In addition, recent research works have demonstrated that planning frameworks based on multi-objective optimization (MOO) techniques are suitable for the efficient functioning of microgrids comprising renewable energy sources (RESs) and battery energy storage systems (BESSs). Even though MADRL techniques have been used to solve the optimum EV charging scheduling challenges and MOO frameworks have been developed to determine the optimal RES-BESS allocation, the potential of merging MADRL and MOO is yet to be explored. Therefore, this research provides an opportunity to determine the effectiveness of combined MOO-MADRL dynamics and their computational efficacy. In this context, this work presents a novel Multi-objective Artificial Vultures Optimization Algorithm based on Multi-agent Deep Deterministic Policy Gradient (MOAVOA-MADDPG) planning framework for allocating RESs, BESSs, and EVCSs on microgrids. The objective function is formulated to optimize the network power losses, total installation and operational costs, greenhouse gas emissions, and system voltage stability. Moreover, the proposed framework incorporates the sporadic nature of RES systems and intends to improve the state of charge (SOC) of the EVs present in the network. The presented approach is validated using practical weather data and EV commuting behavior on the modified IEEE 33 bus network, two practical distribution feeders in Bangladesh, and the Turkish 141 bus network. According to the findings, the MOAVOA-MADDPG framework effectively accommodated the financial, technical, and environmental considerations with improved average SOC of the vehicles. Furthermore, statistical analysis, spacing, convergence, and hyper-volume metrics are employed to compare the suggested MOAVOA-MADDPG framework with five contemporary techniques. The findings indicate that, in every metric considered, the MOAVOA-MADDPG Pareto fronts provide superior solutions.

Improvement of voltage stability and loadability of power system employing the placement of unified power flow controller using artificial neural network

<span lang="EN-US">This paper proposes a voltage stability and loadability improvement model of power systems by incorporating the optimal placement of flexible alternating current transmission systems (FACTS) using an artificial neural network (ANN) called OPFANN. The key aspect of this model is to identify the weakest lines which having the most probability of voltage collapse utilized for placing FACTS devices. As installing a new power system network with rapidly increasing power demand cannot be possible, the operator usually operates the power system close to the stability limit. In this regard, continuous monitoring and improvement of system voltage stability and loadability of the existing system are vital issues for energy management systems nowadays. However, the proposed OPFANN introduces a more straightforward and faster scheme for voltage stability monitoring systems using ANN. Intelligent and reliable data samples have been designed to train the ANN based on two-line voltage stability indices (LVSI) techniques. Compared with other works, OPFANN effectively improves voltage stability and loadability at the load point by installing the unified power flow controller (UPFC) FACTS devices to the weakest lines. OPFANN can provide information on voltage collapse points using ANN and reduce the further computational cost of LVSI. Finally, OPFANN ensures faster and more secure operation of the power system.</span>

A Comprehensive Review on the Modelling and Significance of Stability Indices in Power System Instability Problems

Many technological advancements in the modern era have made actual use of electrical power and the constrained operating of power systems within stability limits. Some expeditious load variations and rising power demands initiate complications in voltage stability and can put stress on performance, leading to voltage instability. Voltage Stability Indices can be used to perform voltage stability assessment. This review evaluates various VSIs based on mathematical derivations, assumptions, critical values, and methodology. VSIs determine the maximum loadability, voltage collapse proximity, stability margin, weak areas, and contingency ranking. Stability indices can also specify the optimal placing and sizing of Distributed Generators. Thus, VSIs play a vital role in power system voltage stability. This review is a comprehensive survey of various indices and analyses their accuracy in determining the instability of power systems. Voltage stability is a crucial concern in operating a reliable power system, and the systematic evaluation of voltage stability is essential in a power system. This review considered and analyzed 34 indices from 138 articles from the literature for their significant performance in various power system stability problems. Of 33 indices, were 22 derived from transmission line parameters, referred to as line indices, and 12 from bus and line parameters, referred to as bus indices.

Simulation of Voltage Stability Enhancement at Primary Substation Using Static VAR Compensator

For the voltage stability enhancement of power system, the reactive power compensation is vital important. With the advancement in technology, the Flexible AC Transmission System Devices (FACTS) are widely used for stability improvement. In the primary substation, the stability is poor due to load conditions and system faults. In this research, the voltage stability enhancement for a Primary Substation using Static VAR (volt-amp reactive) compensator (SVC) is presented. SVC can be used to improve system voltage stability and addition of damping control. With the application of SVC, the voltage stability can be improved under normal condition as well as under contingency conditions. Shunt reactive power sources, including capacitive and inductive, are controlled by high power electronic switching devices in an SVC to enhance voltage stability. The goal of this attempt is to use SVC to improve the voltage stability of the 230/33 kV system. To evaluate the voltage stability improvement by SVC, a simulation model is implemented in Matlab/Simulink and performance analysis are carried out based on the simulation results.

Voltage Profile Improvement Using Static Synchronous Compensator STATCOM

Static synchronous compensator (STATCOM) is a regulating device used in AC transmission systems as a source or a sink of reactive power. The most common use of the STATCOM is in improving the voltage stability of the transmission system. A voltage regulator is a custom power device used to correct the voltage disturbance by injecting controllable voltage as well power into the system. This paper implement Nruro-Fuzzy controller to control the STATCOM to improve the voltage profile of the power system The strategy of STATCOM controller is to keep the voltage in the nominal range. The controller has been simulated after some kinds of disturbances and the results show improvements in voltage profile of the system.

Clustering Combined Multi-Objective Optimal Operation of Transmission Systems Considering Voltage Stability

In recent years, power systems have undergone major changes called energy transitions during which synchronous generators have been replaced with power electronics-based generation. Therefore, the voltage stability of power systems has become a major concern owing to the absence of synchronous generators. This study proposes multi-objective optimization using the non-dominated sorting genetic algorithm III to achieve optimal reactive power reserve procurement and improve the voltage stability of the overall system. These systematic approaches require high computational power and are unsuitable for the operational frameworks currently used for large-scale power systems. Previous works have rarely considered the local characteristics of reactive power or generation de-commitment with sufficient re-dispatch owing to greater renewable energy integration. We propose a framework for achieving systematic optimization by considering various objective functions while utilizing the regional aspect of reactive power via spectral clustering-based voltage control area (VCA) identification. The proposed method comprises systematic and regional approaches to optimizing systems for voltage stability improvement based on VCAs. The results demonstrate that the proposed method shows satisfactory performance. These results will be helpful for decision making for power system operations in harsher environments with more renewable energy.

Enhancing System Reliability and Transient Voltage Stability through Optimized Power Sources and Network Planning

Renewable energy is an important means of addressing climate change and achieving carbon peaking and carbon neutrality goals. However, the uncertainty and randomness of renewable energy also have a certain impact on the flexibility, reliability, and transient voltage stability of the power system. These effects also pose great challenges to power system planning. In order to address the impact of renewable energy on power system planning, this paper proposes a two-layer optimization model for power sources and network planning which takes into account both reliability and transient voltage stability requirements. The upper-layer grid planning problem is formulated with consideration of the system reliability index, and the transient stability requirements and construction and operation costs are included in the lower-layer problem to determine a construction scheme for power generation and energy storage units. To solve the complex nonlinear problem efficiently, a two-layer iterative algorithm utilizing the adaptive particle swarm optimization (PSO) technique is proposed. The effectiveness of the proposed method is demonstrated via its application to the IEEE 33 test system. The results show that the proposed optimization approach effectively addresses the power system transmission and generation planning problem while improving the efficiency and reliability of the system’s operation. The findings can guide the design and implementation of future power system planning and operation strategies.

Overcoming the Fluctuating Curve from PV Power Generation by Modelling the Integration of Hydrogen Energy Storage Into Electricity Microgrid

Java Madura Bali (Jamali) system is the Indonesian largest grid, which contributes approximately 61%, with the Coal Thermal dominates by 59% and followed by the New Renewable Energy (NRE) of 9%. Solar Photovoltaic Power Plants (SPP) contribute only 1% from the NRE itself. Low voltage customers (TR) are approximately 61% with the residential contribute approximately 42%. The rates of the national SAIDI is 12.72 hours/subscriber/year and the national SAIFI is 9.25 times per subscriber/year. There is some challenge in regulating voltage in area with high penetration of solar PV, case of voltage fluctuation in response to the fluctuating of solar irradiance, as well as other factors such as load transient. This voltage fluctuation is proportional to the amount of current generated by the PV system and also proportional to the solar irradiance.Hydrogen energy storage (HES) is an alternative to NRE-based energy storage technology. Integrating solar PV with hydrogen energy storage and optimisation device may mitigate PV-driven voltage fluctuation, preventing excessive reserve power flow on the grid. The hydrogen energy storage system can cover variation of the loads at the nearby solar PV system in the microgrid and the ability to manage those loads, as mentioned in the energy management system. Integrating the HES within Solar PV system can improve the curve shape, improve system reliability and voltage stability, especially in the Residential Microgrid. Also may build the opportunities to earn new revenues from wholesale electricity markets, enhancing the reliability rate and competitive monetary value.

A short-term voltage stability online assessment based on multi-layer perceptron learning

In this paper, a deep learning-based approach is used to analyze short-term voltage stability of power systems in order to completely learn the hidden timely dependencies from post fault behavior of the system. The direct measurements from the PMU, which did not require any significant amount of computation, have been used, and the results will be accurate. The proposed approach makes considerable use of a Multi-Layer Perceptron-based deep learning model in order to make the most accurate prediction feasible regarding the short-term voltage stability status. Rapid prediction of voltage stability allowed system operators sufficient time to take effective corrective action against large disturbances. In-depth offline simulations will be executed against various fault types, with the results being fed into the classifier as inputs. After the classifier has been trained using the results of the offline simulations, the online application will be applied to the test samples. The effectiveness of the suggested method will be evaluated based on the results of tests conducted on both small and large-scale test systems.

Voltage Stability Improvement Using Intelligence Technique

Numerous Artificial Intelligence procedures have been anticipated for reactive power dispatch to maintain voltage stability of the power system. This paper presents particle swarm optimization (PSO) approach for framework boundaries improvement and ideal reactive power dispatch. The transformers tap transformers, Generator exciters, switchable VAR sources/Static VAR compensators (SVC) are utilized as control factors for progression of framework boundaries and voltage stability. The recommended procedure depends on the minimization of total of the squares of L-index value at all load bus. The PSO Algorithm is tested on an IEEE 30 bus test system. The presentation of PSO is compared with conventional technique and results are introduced for illustration purpose. The margin of voltage stability is estimated in terms of system parameters.

Multi-task transient stability assessment of power system based on graph neural network with interpretable attribution analysis

Transient power angle stability analysis and voltage stability analysis are the key basis of power system security and stability control. The diversified operation modes of power system put forward higher requirements for real-time and topology generalization of transient stability assessment. Based on Convolution Neural Network (CNN) and Graph Attention Networks (GAT), this paper uses the wide-area transient response features to evaluate the transient angle stability and voltage stability of power system. Firstly, CNN is used to encode the temporal variation characteristics of transient features of electrical components in hidden layer space. Secondly, the encoded hidden layer features are mapped into graph data samples according to the spatial topological correlation of electrical components, so as to train and build a multi-task transient stability assessment model based on GAT. The model can adaptively capture the interaction relationship between encoded hidden layer features of topologically correlated components, and realizes real-time evaluation of transient angle stability and transient voltage stability by perceiving the temporal–spatial correlation of transient features. Finally, Shapley additive explanation (SHAP) method is used to explain the decision-making mechanism of the model and extract the key features that affect the transient stability, so as to enhance the ability of power grid operators to perceive the transient stability situation. The results of IEEE-39 system show that the transient stability assessment model has good accuracy and topology generalization, and the results of interpretable analysis are in line with the conclusions of traditional mechanism cognition, which helps to improve the credibility of the model results.

Multi-objective reactive power optimization strategy of power system considering large-scale renewable integration

To fully tap the abilities of renewables in reactive power optimization, this paper develops a detailed model for the power regulation capabilities of wind turbines and photovoltaic units and studies their impact on the power system’s operation. First, the power system model with renewables integration is established using AC power flow. The wind turbines and photovoltaic units are modeled in detail according to their topologies and operating characteristics, and then further simplified according to the feasible region. An improved DC power flow model is adopted to handle the non-linear characteristics of the power system. On this basis, a multi-objective reactive power optimization model is constructed to minimize the power generation cost, wind and solar power curtailment, and voltage offset. Finally, comparisons between two types of models in different scenarios are designed. Numerical simulations demonstrate that the participation of renewables in reactive power regulation can improve the operational economy and voltage stability of power systems.

Hierarchical Control for Microgrids: A Survey on Classical and Machine Learning-Based Methods

Microgrids create conditions for efficient use of integrated energy systems containing renewable energy sources. One of the major challenges in the control and operation of microgrids is managing the fluctuating renewable energy generation, as well as sudden load changes that can affect system frequency and voltage stability. To solve the above problems, hierarchical control techniques have received wide attention. At present, although some progress has been made in hierarchical control systems using classical control, machine learning-based approaches have shown promising features and performance in the control and operation management of microgrids. This paper reviews not only the application of classical control in hierarchical control systems in the last five years of references, but also the application of machine learning techniques. The survey also provides a comprehensive description of the use of different machine learning algorithms at different control levels, with a comparative analysis for their control methods, advantages and disadvantages, and implementation methods from multiple perspectives. The paper also presents the structure of primary and secondary control applications utilizing machine learning technology. In conclusion, it is highlighted that machine learning in microgrid hierarchical control can enhance control accuracy and address system optimization concerns. However, challenges, such as computational intensity, the need for stability analysis, and experimental validation, remain to be addressed.

Voltage stability assessment for solar photovoltaic penetration using Reactive Power–Voltage and Active Power–Voltage modal analysis

The assessment of voltage stability in a power system is crucial to ensure all buses in the network can maintain the desired voltage or avoid maximum loadability when subjected to load disturbance. Current research has tended to concentrate on developing new voltage stability assessment tools rather than identifying the best location for the penetration of solar photovoltaic (SP) generation in the network. This paper examines and evaluate the power systems voltage stability with increasing SP penetration levels by employing both the Active Power–Voltage (PV) and Reactive Power–Voltage (QV) modal analysis. The effect of load flow study by penetrating SP at the weakest and the strongest load bus has been investigated in this work. Furthermore, the analysis in this work was carried out in the modified Brunei Darussalam Department of Electrical Services (DES) power system. The main contribution of this paper is to assess and identify the best and worst locations for SP penetration in the simplified DES power system network in Brunei Darussalam using both the QV and PV modal analysis. The load flow results show that the penetration of SP generation is much better at the strongest bus due to it does not influence other load buses as compared to the least reliable bus in the network.

Optimal Allocation of Hybrid Renewable Distributed Generation with Battery Energy Storage System Using MOEA/D-DRA Algorithm

The need for energy and environmental sustainability drives the adoption of renewable energy systems to reduce greenhouse gas emissions. Efficient planning of energy sources is crucial for a reliable distribution network. This article solves the optimal allocation of hybrid energy sources (i.e., Renewable Distribution Generators (RDGs) and Battery Energy Storage System (BESS)) problem, aiming to minimize the annual energy loss and investment costs simultaneously. The proposed formulation includes the uncertainties such as forced outages of RDG’s, intermittence in RDG power generation, Expected Energy Not Served (EENS) by DG units, and time-varying demand altogether in the existing formulation. To evaluate the effectiveness of the proposed formulation, a Multi-Objective Evolutionary Algorithm based on Decomposition with Dynamic Resource Allocation (MOEA/D-DRA) is used. Simulation studies are conducted on IEEE 33-node and TNEB 84-node Radial Distribution Systems (RDSs), comparing results with the Rider Optimization Algorithm (ROA) and Hybrid Nelder Mead-Particle Swarm Optimization (HNMPSO) respectively. The proposed formulation for a Multi-objective Optimization of Hybrid Energy Sources allocation problem solved by the MOEA/D-DRA algorithm provides improved benefits like minimum annual energy loss, investment cost, CO2 emission, EENS by the DG units, and enhanced system voltage stability and voltage profile.

POWER QUALITY ENHANCEMENT IN A SOLAR-WIND HYBRID ENERGY GENERATION SYSTEM CONNECTED TO A STANDALONE MICRO-GRID

The present paper highlights several concerns with solar-wind hybrid energy systems that are associated with microgrids as well as potential improvements. In recent years, there have been more renewable energy options available. Alternatives to the present energy sources, such as renewable energy, are very good. The utilization of renewable energy sources like solar and wind power as a consistent energy source is widespread. The focus of this study is on microgrid connected solar-wind hybrid energy system performance analysis and control. In this paper, the harmonics of the system are controlled by passive filters. Additionally, several control topologies based on power electronics control are needed for the micro grid’s integration of various energy sources. A power electronics application is used in microgrids that use a variety of renewable energy sources, however some power quality problems frequently arise. The whole microgrid system's voltage management and voltage stability must be maintained, therefore these power quality issues must be fixed or at the very least reduced. An example of a solar-wind hybrid power system simulation using MATLAB is provided in this study. For micro-grid parameter adjustments, PI-PWM control is included into the MATLAB microgrid simulation. The outcomes of the simulation show that the present THD levels in the grid are less than 5%. The energy storage system also serves as a backup power source in this simulation for power variations brought on by irregular solar and wind power generation in the microgrid.

Research on Distributed Photovoltaic Grid-connected Mode Based on PSASP

With the continuous implementation of the carbon peak and carbon neutrality, a large number of distributed solar power stations have been integrated into the grid, which has caused a strong impact on the safe and stable operation of the traditional power system. Based on this, the grid-connected mode of distributed solar power stations is studied, and three types of grid connection are proposed. Mode A is merged into the 66 kV bus side of the 220 kV substation through the 66 kV dedicated line; Mode B is merged into the 66 kV bus side of the 66 kV substation through the 66 kV dedicated line; Mode C is T-connected to the 66 kV transmission line through 66 kV dedicated line. By using a power system analysis software package (PSASP), we can build a model of a certain regional power grid and study the effects of the above three grid-connected modes on system voltage stability, short-circuit current, and network loss. The study shows that the three modes are all feasible. Mode A has the smallest change in voltage quality and short-circuit current of the grid-connected substation and has the smallest network loss of the three modes.

A Power System Reserve Configuration Solution That Satisfies the Frequency Stability Constraint Under the Given New Energy Index

Aiming at the problem of frequency stability and voltage stability of a new power system, this paper provides a new energy index method based on frequency stability constraints in the standby category of the new power system. Several concepts, such as maximum disturbance constraint and minimum reserve capacity, are proposed. Two constraints, power balance, and frequency constraint are set, and the factors to be considered, such as load frequency effect, security and control measure, demand-side response, and primary frequency change amplitude of each type of unit, are specified. Through analysis and study of regional data, the peak load of power grid new energy is a given index in a given scenario, according to the research of regional power grid in each category of a frequency modulation amplitude, the installed capacity is set to build a solution, to the problem of frequency stability caused by clean energy consumption, thus determine the ratio of each category of power output and heat capacity. Finally, according to the actual data of one regional power grid, the PSD-BPA power system simulation software is used to verify the effectiveness of the method.

The Impact of Serial Controllable FACTS Devices on Voltage Stability

As the world's population continues to grow, the demand for electricity is also increasing rapidly. Ensuring the quality of power supply, voltage stability, unity power factor, and minimizing power losses is essential for delivering power to every user end. In order to achieve this, compensation techniques are needed. This project focuses on Serial controllable Flexible AC Transmission Systems (FACTS) devices, such as Controllable Series Compensator (CSC) and Static Synchronous Series Compensator (SSSC), which have a significant impact on the voltage and power stability of an electric power system (EPS). The mathematical derivation of the voltage dependency of CSC and SSSC is extracted for the single-load infinitive-bus model (SLIB). New analytical equations are developed to compare the impact of CSC and SSSC on voltage stability in a five-bus system. The results of the analysis are expected to reveal that CSC has a crucial role to play in enhancing voltage stability, and its impact is greater than that of SSSC when considering equal CSC and SSSC MVA ratings. When it comes to voltage controllability, SSSC (Static synchronous series compensator) is superior to CSC (Controllable Series Compensator), especially in situations where there are having low voltages or having low loads.

Estimation of multi-infeed HVDC inter converter direct current interaction in close electric proximity

This paper aims at investigating the direct current interaction between various converters of High Voltage Direct Current (HVDC) system, which lie in close electric proximity within the multi-infeed configuration. The interaction between converters in multi-infeed DC system are highly possible events which need to be evaluated in order to avoid the adverse operational results. The various indices such as multi-infeed interaction factor (MIIF) considering AC busbar voltages, local and concurrent commutation failures (CF) in converter stations under different AC disturbances, influence of AC system’s strength on fault recovery time (FRT), transient over-voltage (TOV) under permanent DC faults, voltage and power stability of multi-infeed DC system are comprehensively illustrated in literatures. However, there is lack of literature regarding the investigation of current interaction in multi-infeed DC system. Therefore, in this paper, a power flow approach is adopted to estimate the inter converter direct current interaction. An index, multi-infeed current interaction factor (MICIF) is proposed that can effectively depicts the interaction between converters. The MICIF index is verified through electromagnetic transient simulations of dual infeed HVDC system developed in PSCAD/EMTDC. The results show that MICIF index can accurately assess the interaction between different converter stations of HVDC system under steady and transient states.