Ensure Voltage Stability Research Articles

A stochastic MPC-based energy management system for integrating solar PV, battery storage, and EV charging in residential complexes

This paper presents a Stochastic Model Predictive Control (SMPC)-based energy management system (EMS) for residential complexes with integrated solar photovoltaics (PV), battery energy storage systems (BESS), and electric vehicle (EV) charging infrastructure. The EMS coordinates BESS operations, integrating solar generation, residential load demand, and EV charging. It optimizes BESS charging/discharging based on solar power, load demands, electricity pricing, and feed-in tariffs over a finite horizon, while considering uncertainties through multiple scenarios of load and EV charging demand, as well as solar generation. By accounting for battery degradation, cost savings, and revenue from energy transactions, the proposed EMS enhances BESS longevity and profitability. The EMS also manages reactive power provision from the BESS inverter, ensuring voltage stability in the presence of uncertainties. Extensive case studies on Matlab Simscape Electrical and real-time validation on the OPAL-RT simulator demonstrate the effectiveness of the proposed SMPC-based EMS in optimizing energy use, operational efficiency, and economic returns, contributing significantly to the sustainable energy management of the residential complex.

Recent advances in LDO chip research

Abstract. With the rapid development of integrated circuit technology and the wide application of Internet of things devices, the demand for power management chips is increasing. Low-dropout regulators (LDO) play an important role in modern electronic devices. It provides a stable low output voltage while maintaining low power consumption. Recently some progress has been made in the research of LDO chips, focusing on improving power efficiency, reducing static current, and enhancing transient response. The study shows that the static current has been significantly reduced by using the subthreshold MOS tube and dynamic current bias technology. In terms of power supply rejection ratio (PSRR), by improving the circuit design, the PSRR of LDO can be maintained above -80 dB under various conditions. In addition, improvements to the transient response result in significantly optimized overshoot and recovery times of the output voltage, ensuring voltage stability when the load changes rapidly. These advances enable LDO chips to meet the demanding requirements of high-performance and Internet of things device applications. Future challenges include further reducing static current, improving PSRR performance in high-frequency environments, and effectively addressing thermal management issues.

Coordination of smart inverter-enabled distributed energy resources for optimal PV-BESS integration and voltage stability in modern power distribution networks: A systematic review and bibliometric analysis

Integrating photovoltaic (PV) and battery energy storage systems (BESS) in modern power distribution networks presents opportunities and challenges, particularly in maintaining voltage stability and optimizing energy resources. This systematic review and bibliometric analysis investigates the coordination of smart inverter-enabled distributed energy resources (DERs) for enhancing PV-BESS integration and ensuring voltage stability. The study synthesizes recent advancements in smart inverter technologies, which provide grid support functions such as Volt/VAr control, and their applications in DER coordination. A comprehensive review of the literature is conducted to identify prevailing trends, research gaps, and emerging techniques in the field. Bibliometric analysis is employed to quantify the research landscape, highlighting key publications, citations, publications per country, and collaborative networks. The findings reveal that smart inverters play a crucial role in mitigating voltage violations and improving the hosting capacity of PV systems in distribution networks. Furthermore, optimal inverter settings, strategic placement of PV-BESS, and advanced control algorithms are identified as critical factors for effective DER integration. The study concludes by proposing future research directions, including the exploration of smart inverter interactions with legacy grid management systems and the development of robust algorithms for dynamic and adaptive DER coordination. This review serves as a valuable resource for researchers and practitioners aiming to enhance the stability and efficiency of power distribution networks through advanced DER management strategies.

Estimation of abnormal states in shunt capacitor banks using transient disturbance feature extraction

Shunt capacitor banks are essential for reactive power compensation, ensuring voltage stability, and reducing system losses. These banks consist of multiple units with components in series and parallel. A few component failures do not immediately affect the safe operation of the capacitor bank, but component breakdown can lead to voltage redistribution. Under combined factors such as system overvoltage and equipment aging, and others can trigger an avalanche effect causing capacitor breakdown, resulting in significant safety accident risks. Practical operation experience shows that partial component breakdown generates many transient disturbance signals. Quantitative analysis of these signals can detect capacitor bank anomalies early. This paper proposes the quantitative extraction of transient disturbance characteristics using the Prony algorithm and estimates the phase and number of capacitors that break down to judge capacitor anomalies. The simulation part verifies the theoretical analysis and detection algorithm’s correctness through numerical simulations and PSCAD (Power Systems Computer Aided Design) electromagnetic transient simulations. The numerical simulations consider different signal lengths, noise levels, attenuation coefficients, and oscillation frequencies. In the PSCAD simulation environment, verification models are built under varying sampling frequencies, numbers of breakdown components, signal lengths, and signal-to-noise ratios. These simulation results verify the accuracy of the detection algorithm under different conditions.

A hybrid deep learning approach to solve optimal power flow problem in hybrid renewable energy systems

The reliable operation of power systems while integrating renewable energy systems depends on Optimal Power Flow (OPF). Power systems meet the operational demands by efficiently managing the OPF. Identifying the optimal solution for the OPF problem is essential to ensure voltage stability, and minimize power loss and fuel cost when the power system is integrated with renewable energy resources. The traditional procedure to find the optimal solution utilizes nature-inspired metaheuristic optimization algorithms which exhibit performance drop in terms of high convergence rate and local optimal solution while handling uncertainties and nonlinearities in Hybrid Renewable Energy Systems (HRES). Thus, a novel hybrid model is presented in this research work using Deep Reinforcement Learning (DRL) with Quantum Inspired Genetic Algorithm (DRL-QIGA). The DRL in the proposed model effectively combines the proximal policy network to optimize power generation in real-time. The ability to learn and adapt to the changes in a real-time environment makes DRL to be suitable for the proposed model. Meanwhile, the QIGA enhances the global search process through the quantum computing principle, and this improves the exploitation and exploration features while searching for optimal solutions for the OPF problem. The proposed model experimental evaluation utilizes a modified IEEE 30-bus system to validate the performance. Comparative analysis demonstrates the proposed model’s better performance in terms of reduced fuel cost of $620.45, minimized power loss of 1.85 MW, and voltage deviation of 0.065 compared with traditional optimization algorithms.

CFDI: Coordinated false data injection attack in active distribution network

AbstractThe active distribution network (ADN) can obtain measurement data, estimate system states, and control distributed energy resources (DERs) and flexible loads to ensure voltage stability. However, the ADN is more vulnerable to cyber attacks due to the recent wave of digitization and automation efforts. In this article, false data injection (FDI) attacks are focused on and they are classified into two types, that is, type I attacks on measurement data and type II attacks on control commands. After studying the impact of these two FDI attacks on the ADN, a new threat is revealed called coordinated FDI attack, which can maximize the voltage deviation by coordinating type I and type II FDI attacks. From the attacker's perspective, the scheme of CFDI is proposed and an algorithm is developed to find the optimal attack strategy. The feasibility of CFDI attacks has been validated on a smart distribution testbed. Moreover, simulation results on an ADN benchmark have demonstrated that CFDI attacks could cause remarkable voltage deviation that may deteriorate the stability of the distribution network. Moreover, the impact of CFDI attacks is higher than pure type I or type II attacks. To mitigate the threat, some countermeasures against CFDI attacks are also proposed.

Energy management based on safe multi-agent reinforcement learning for smart buildings in distribution networks

The rapid urbanization and increasing use of distributed renewable energy resources have imposed a significant burden on power networks. Smart buildings equipped with artificial intelligence technology can play a pivotal role in energy management, ultimately enhancing energy efficiency and voltage quality. However, ensuring voltage stability within large-scale smart building systems presents challenges due to the coexistence of diverse energy sources and the fluctuating nature of renewable energy. This paper proposes a safe multi-energy management framework achieved by online decentralized execution and centralized training for large scale smart buildings in distribution networks. The energy management problem is formulated as a safety-augmented Markov decision process, presenting intractability for dynamic programming due to its extensive continuous state space. To solve this issue and improve the convergence speed and training process stability, a safety-augmented constrained multi-agent reinforcement learning algorithm based on reward extrapolation is proposed. In this algorithm, hazard values are introduced to enhance non-safe multi-agent reinforcement learning algorithms and meet safety constraints. A novel reward network is designed by imitating expert underlying intentions to ensure the rationality of the reward function for multi-objective tasks. Additionally, the loss function for estimating the Q-network is redesigned during training process to guarantee effective convergence. Theoretical analysis is conducted to provide the convergence guarantee. Numerical case studies based on actual data are performed to validate the effectiveness and scalability of our approach, showing that smart buildings can achieve superior energy management performance while ensuring voltage safety for distribution networks. The source code of the proposed algorithm is available at https://github.com/SYiyun/CMARL-EX.

Design And Development Of Automated Electrical Circuit Fault Protector With Alarm System

In response to the critical need for fire and accident prevention in homes and establishments, this research presents an automated electrical circuit fault protector with an alarm system. The device automatically interrupts current flow upon detecting short circuits and overloads, safeguarding against potential hazards. Objectives include assessing sensitivity to faults, evaluating wireless remote-control performance, examining sound clarity, and ensuring voltage stability post-fault events. Equipped with a resettable system, the device allows manual or remote control. Using a developmental research method, data collection employed researcher-made observation and evaluation sheets. Thirty expert evaluators assessed the device. Micro testing revealed that the device was sensitive to faults and automatically triggered the alarm. With an overload rating of 1,500-3,000 watts, the device shuts off power automatically after the overload occurs and back to normal with a stable voltage once the problem was fixed, though this device was primarily designed only for micro-testing this device was customizable to user needs. Its alarm system boasts an audible range of up to 20 meters the same is through with remote control operation. Overall, the device effectively detects faults, incorporates an audible alarm, and supports remote operation, serving as a crucial safety measure against electrical mishaps and very acceptable in terms of design, composition, safety, and operating performance.

A comprehensive optimization mathematical model for wind solar energy storage complementary distribution network based on multi-regulatory devices under the background of renewable energy integration

In the context of global energy transformation and sustainable development, integrating and utilizing renewable energy effectively have become the key to the power system advancement. However, the integration of wind and photovoltaic power generation equipment also leads to power fluctuations in the distribution network. The research focuses on the multifaceted challenges of optimizing the operation of distribution networks. It explores the operation and control methods of active distribution networks based on energy storage and reactive power compensation equipment. The stable operation of the distribution network is analyzed under the conditions of wind and photovoltaic integration, with a particular focus on precise regulation to address the limitations of existing methods. Afterwards, the study proposes an improvement plan that combines on load tap changer transformers and reactive power compensation equipment to solve complex power balance problems through second-order cone programming relaxation method. The results of numerical analysis show that the constructed mathematical model maintains a stable voltage of 1 to 1.1 pu at distribution network nodes within 24 h. Especially during peak hours from 15:00 to 24:00, it remains normal without any abnormal fluctuations when the control equipment is not added. These results confirm that precise regulation of multiple devices ensures voltage stability and avoids low or high voltage issues.

Pathway toward Scalable Energy-Efficient Li-Mediated Ammonia Synthesis.

Lithium-mediated ammonia synthesis (LiMAS) is an emerging electrochemical method for NH3 production, featuring a meticulous three-step process involving Li+ electrodeposition, Li nitridation, and Li3N protolysis. The essence lies in the electrodeposition of Li+, a critical phase demanding current oscillations to fortify the solid-electrolyte interface (SEI) and ensure voltage stability. This distinctive operational cadence orchestrates Li nitridation and Li3N protolysis, profoundly influencing the NH3 selectivity. Increasing N2 pressure enhances the NH3 faradaic efficiency (FE) up to 20 bar, beyond which proton availability controls selectivity between Li nitridation and Li3N protolysis. The proton donor, typically alcohols, is a key factor, with 1-butanol observed to yield the highest NH3 FE. Counterion in the Li salt is also observed to be significant, with larger anions (e.g., exemplified by BF4-) improving SEI stability, directly impacting LiMAS efficacy. Notably, we report a peak NH3 FE of ∼70% and an NH3 current density of ∼-100 mA/cm2 via a delicate balance of process conditions, encompassing N2 pressure, proton donor, Li salt, and their respective concentrations. In contrast to the recent literature, we find that the theoretical maximum energy efficiency of LiMAS hinges significantly on the proton source, with LiMAS utilizing H2O calculated to have a maximum achievable energy efficiency of 27.8%. Despite inherent challenges, a technoeconomic analysis suggests high-pressure LiMAS to be more feasible than both ambient LiMAS and a modified green Haber-Bosch process. Our analysis finds that, at a 100 mA/cm2 NH3 current density and a 6 V cell voltage, LiMAS delivers green NH3 at an all-inclusive cost of $456 per ton, significantly lower than conventional cost barriers. Our economic analysis underscores high-pressure LiMAS as a potentially transformative technology that may revolutionize large-scale NH3 production, paving the way for a sustainable future.

Agglomerative Hierarchical Clustering Methodology to Restore Power System considering Reactive Power Balance and Stability Factor Analysis

Despite there are significant advancements in modern power systems, blackouts remain a potential risk, necessitating efficient restoration strategies. This paper introduces an innovative concept for power system restoration, focusing on balancing active and reactive power while ensuring voltage stability. For instance, this paper employs an agglomerative clustering technique, which partitions the power system into segments with balanced reactive power, facilitating swift restoration postblackout. Central to this methodology is the use of the line stability factor, which assesses the voltage stability of individual lines, identifying the system’s stronger and weaker sections based on voltage stability levels. This paper demonstrates the effectiveness of the proposed methodology through case study analysis, comparing voltage stability levels across agglomerative clusters and their geographical locations. The power system is divided into two stable partitions, considering the number of black-start generators, available reactive power, and voltage stability levels. This partitioning reveals that the clusters formed by the agglomerative method are inherently stable, suggesting enhanced system stability, dependability, and availability during the restoration phase following a blackout. In addition, this paper discusses the potential causes of blackouts, offering insights into their prevention, and finishes with a novel clustering methodology for power systems, considering reactive power and voltage stability. This method facilitates the parallel restoration of the system’s independent partitions, significantly reducing restoration time; it addresses critical challenges and outcomes, underscoring the methodology’s potential to revolutionize blackout recovery processes in modern power systems.

Coordinated frequency control study of variable speed variable pitch of DFIG load shedding unit based on VSG

The involvement of wind turbines operating at load shedding levels in primary frequency regulation serves as a crucial technical solution to provide high levels of inertial response capability and frequency regulation capability in power systems with significant proportions of renewable energy. Load-shedding units' frequency regulation capacity is constrained by the optimal operating speed, which prevents the utilization of the rotor's full kinetic energy and consequently limits the system's ability to provide strong inertia support. Additionally, the units are restricted by the maximum speed in the medium wind speed range, resulting in a reduced reserve capacity. The control mechanism of virtual synchronous generator (VSG) can ensure voltage stability at the machine end, and enable real-time tracking of the grid frequency, thereby imparting to wind turbines the on-grid operation characteristics of synchronous generator sets. To achieve this goal, this paper proposes a coordinated frequency control method for variable-speed variable-pitch doubly fed induction generator units based on VSG. Differentiated control strategies are employed for varying wind speed ranges, while an additional frequency control module is utilized to realize frequency regulation functionality. In conjunction with VSG, this approach enhances the inertial response of the units and strengthens their ability to support grid frequency. Simulation results indicate that the proposed control method mitigates the rate of decline in grid frequency and reduces frequency deviation, ultimately promoting grid stability. Additionally, a simulation system was designed based on the conventional four-machine two-area model to verify the effectiveness of this approach.

Supervisory control of reactive power in wind farms with doubly fed induction generator-based wind turbines for voltage regulation and power losses reduction

This paper proposes a strategy based on the secondary voltage control (SVC) and the use of grid codes recommended by the system operator to determine the reactive power reference value between a wind farm (WF) with doubly-fed induction generator (DFIG) and the electrical grid. The proposed strategy seeks to determine the reactive power reference value required of WF during normal operating and grid faults. When the voltage deviation is within the normal range, the proposed strategy maintains a unity power factor (UPF). If the voltage deviation exceeds the normal range, it provides reactive power compensation to ensure voltage stability. To achieve this objective, the proposed strategy is compared with the SVC. This strategy can reduce the size of potential compensation devices, such as static synchronous compensators, to be used to improve the WF response, if needed. The participation of the grid converter side (GSC) in the reactive power control allows the DFIG to reduce power losses and increases the life of the converters, especially the rotor converter side (RSC). The strategy proposed in this work is implemented on Matlab/Simulink using the S-Function builder to evaluate their performance, which facilitates its integration into real control boards and testing in real experiments in the future.

Voltage Stability and Power Sharing Control of Distributed Generation Units in DC Microgrids

Advancements in power conversion efficiency and the growing prevalence of DC loads worldwide have underscored the importance of DC microgrids in modern energy systems. Addressing the challenges of power-sharing and voltage stability in these DC microgrids has been a prominent research focus. Sliding mode control (SMC) has demonstrated remarkable performance in various power electronic converter applications. This paper proposes the integration of universal droop control (UDC) with SMC to facilitate distributed energy resource interfacing and power-sharing control in DC microgrids. Compared to traditional Proportional-Integral (PI) control, the proposed control approach exhibits superior dynamic response characteristics. The UDC is strategically incorporated prior to the SMC and establishes limits on voltage variation and maximum power drawn from the DC–DC converters within the microgrid. A dynamic model of the DC–DC converter is developed as the initial stage, focusing on voltage regulation at the DC link through nonlinear control laws tailored for Distributed Generation (DG)-based converters. The UDC ensures voltage stability in the DC microgrid by imposing predetermined power constraints on the DGs. Comparative evaluations, involving different load scenarios, have been conducted to assess the performance of the proposed UDC-based SMC control in comparison to the PI control-based system. The results demonstrate the superior efficiency of the UDC-based SMC control in handling dynamic load changes. Furthermore, a practical test of the proposed controller has been conducted using a hardware prototype of a DC microgrid.

Design of integral sliding mode control and fuzzy adaptive PI control for voltage stability in DC microgrid

This paper introduces a novel control strategy that merges integral sliding mode control with fuzzy adaptive PI control. This hybrid approach maximizes the benefits of both techniques to ensure voltage stability in DC microgrid. Firstly, a mathematical model characterizes the DC–DC boost converter. Subsequently, a sliding surface, incorporating an integral term, is employed to regulate the converter’s output voltage and current errors. To address uncertainties stemming from factors like input inductance and output capacitance, a dynamic sliding mode controller is formulated. The proposed sliding mode control scheme significantly reduces the time required for voltage stability, curbs system oscillations, and showcases robustness. Furthermore, the inclusion of fuzzy adaptive PI control aids in refining the voltage deviation signal and droop resistance. This enhancement improves the precision of the error tracking system. Finally, the effectiveness of this strategy is demonstrated through MATLAB simulations, supported by experimental validation and analysis. The findings reveal that this control strategy efficiently accelerates the convergence of DC microgrid voltage to a stable state.

Illustration of the voltage stability by using the slope of the tangent vector component

This Paper is dedicated to the analysis of the evolution of the tangent vector during the Continuous Power Flow (CPF) iterations. The flow of the tangent slope (measured in degrees) is shown through the coefficient of lambda tangent vector component and the maximum voltage tangent vector component. A 17 Node Network was used for the purposes of this Paper. The system was modelled in MATLAB software. The admittance matrix of the node voltage equations was formulated and the functions in MATLAB were developed for the systematic formation of the node admittance matrix. Equations for the calculated network were generated in MATLAB. 32 Iterations were performed. Iterations and corrections of iterations were done manually. Firstly, the results for the tangent vectors calculated through the CPF program were compared to the results for the tangents directly calculated with mathematical formula for the tangent, and both results match. The chart, which contains the classical PV curve and the flow of tangent vectors during the CPF iterations, was developed based on the results obtained. The increase in the slope of the tangent in the PV diagram imposes a clear numerical stability limit by specifying an angle limit value, which can be used to trigger an alarm. In addition to the classic Power-Voltage (PV) curve, this serves as an additional indicator for ensuring voltage stability of the examined system.

Optimal load shedding scheme using grasshopper optimization algorithm for islanded power system with distributed energy resources

In this paper, a new optimal load shedding method using a grasshopper optimization algorithm (GOA) is proposed for maintaining the stability of the islanded power system that comprises distributed energy resources (DER). The GOA is used in conjunction with the method of voltage stability margin (VSM) to handle the multi-objective shedding constraints, namely the generation restrictions, allowable load curtailment, and load priority. To assess the effectiveness of the offered method, a comprehensive evaluation study is applied to a system of IEEE 33-bus and four DG units in view of different scenarios. Furthermore, the effectiveness of performance of the GOA-based load shedding is compared in terms of fitness value, voltage stability margin, and percentage of load that should be curtailed with three well-known optimization approaches (i.e., the particle swarm optimization (PSO), grey wolf optimization (GW), and genetic algorithm (GA)) under different islanded scenarios. The obtained results show that the performance of the proposed method is better than other methods with the less value of load curtailed for totally islanded states (i.e., 45.67% for island A, 16.63% for island B, 38.05% for island C, and 27.98% for island D in this study). In addition, the results of the simulation display the efficacy of the proposed scheme to ensure voltage stability with the optimal load quantity to be shed for dissimilar islanding scenarios.

QV Analysis for the Identification of Vulnerable Zones to Voltage Collapse: A Study Case

Radial High-Voltage networks have problems ensuring voltage stability when a fault occurs. Among preventive actions for these events, installing reactive power compensation in the most vulnerable zones to voltage instability is an economical and simple alternative. Previous works have used the QV curves methodology based on contingency power flows to identify such zones. However, this method has been criticized because it does not consider the dynamical effect of some network elements that depend on the voltage levels, especially in contingency scenarios. This article proposes a QV curve analysis based on the operating point resulting from dynamic simulations to amend this critique. To evaluate the proposed procedure, the model of the Patagonian High-Voltage Network in southern Argentina is used through the PSS/E software with the help of the Python programming language. The results detect the most vulnerable areas to voltage instability after a fault occurs and the reactive power necessary to maintain voltage levels in an acceptable operating range. The proposed methodology can be applied to other networks. For example, it can be used in the Latin-American context to assess future network expansions, especially on those linking countries, such as Ecuador with Perú or Colombia, or even the Centro American Interconnected System.

A Joint Optimization Model for Transmission Capacity and Wind Power Investment Considering System Security

Due to continuous growth in electric demand and increasing connection of renewable energy, the power systems are being operated with smaller stability margins. Therefore, a sufficient loading margin is essential to maintain the system secure and ensure voltage stability. To this end, this paper studies the joint optimization of transmission capacity and wind power investment problem, the unique feature of which is incorporating voltage stability margin (VSM) in the planning model. A bi-level model has been formulated whose upper level minimizes the total investment and operation cost minus the weighted VSM. The lower level evaluates the VSM given the optimal expansion plan from the upper level. In addition, the stochastic nature of wind power and load can impact voltage stability. Thus, uncertainties related to intermittent wind generation and demand must be modeled. We use an approximated linear representation to model the AC power system at both levels of the problem. The duality theory (primal-dual formulation) is utilized to transform bi-level programming into single-level mathematical programming. The validity of the constructed methodology is demonstrated on the IEEE 24-bus RTS, which indicates the efficacy and feasibility of the presented model.

A novel distance‐based system reactive power index for system‐level reactive power reserve assessment

AbstractVoltage stability of the power system is of major concern with ever‐increasing stress in the transmission network. The availability of ample reactive power reserve is essential to ensure voltage stability of the power system. Voltage stability and status of reactive power reserves can be monitored in real‐time by using synchrophasor measurements at a very fast reporting rate. Although much attention has been given to online voltage stability monitoring, there has been less focus on real‐time monitoring of reactive power reserve status. In this regard, the major contributions of the study are: (i) development of a novel distance‐based generalised approach for the computation of the system reactive power index, (ii) a proactive methodology for identification and impact estimation of the nearest Q‐limit breach and (iii) formulation of an optimisation problem to identify the limited number of critical generators that need PMUs for accurately predicting the value of the proposed index. The proposed scheme looks at the reactive power reserve of the generators in the network as numerical features. The istance between the base case and nose point numerical features is computed offline. The distance between the current operating point and nose point numerical features can be computed in an online manner. The ratio of these two distance measures is proposed as a system reactive power index. Moreover, a methodology to predict the post‐Q limit breach value of the index is also introduced. The proposed approach can be used with PMUs, linear state estimators or traditional SCADA measurements. Case studies for IEEE 57 and 118 bus test systems are presented to validate the performance of the proposed index.