Optimal Voltage Regulation Research Articles

Adaptive Reactive Power Management with Thyristor-Controlled Transformer and Fixed Capacitor

The objective of this article is to develop and analyse a thyristor-controlled transformer with a fixed capacitor for reactive power compensation in power systems. Reactive power compensation is crucial for enhancing the efficiency and stability of power systems by reducing power losses, improving voltage profiles, and minimizing equipment stress. Traditional compensation methods often rely on fixed capacitors, reactors, or static VAR compensators, but these systems lack the flexibility required for dynamic control of reactive power under varying load conditions. The proposed approach integrates a thyristor-controlled transformer with fixed capacitors, allowing for precise, real-time adjustment of reactive power flow. The novelty of this article lies in the hybrid configuration of the thyristor-controlled transformer and fixed capacitor, which provides a cost-effective and robust solution compared to conventional systems. Unlike traditional methods that depend solely on switching capacitors or reactors, the use of thyristors allows for fine-tuning of reactive power, offering improved performance under variable loading conditions without the need for complex control algorithms. This setup enhances the adaptability of reactive power management, thus maintaining optimal power factor and voltage regulation. The findings from the simulation and experimental results demonstrate significant improvements in power factor correction, voltage stabilization, and reduction in harmonic distortion. The proposed system exhibits a faster response time and greater control accuracy compared to existing compensation techniques. These advantages make the thyristor-controlled transformer with a fixed capacitor a promising alternative for power utilities seeking to enhance the operational efficiency and reliability of their networks. This article contributes to the advancement of reactive power compensation technologies, providing a scalable solution suitable for modern power system.

Distributed Photovoltaic Generation Aggregation Approach Considering Distribution Network Topology

Distributed photovoltaics (DPVs) are widely distributed and the output is random, which brings challenges to the safe operation of the distribution network, so the construction of photovoltaic aggregations can effectively participate in the flexible regulation of the power system. At present, the extraction of DPV clustering features is not sufficient, only considering the output characteristics of PVs. Certain PVs under some nodes may have a more pronounced regulation effect, but they may be ignored in the clustering process. To address the above problems, this paper proposes a DPV aggregation approach considering the distribution network topology. It combines the voltage sensitivity and the power curve and regards them as clustering features to form the DPV aggregation with the highest average voltage sensitivity participating in voltage regulation. The simulation on the IEEE 33-node system verifies that the proposed aggregation approach can select DPV aggregation more suitable for voltage regulation, and make full use of the aggregation to realize the optimal voltage regulation effect.

Multi-mode monitoring and energy management for photovoltaic-storage systems

The integration of photovoltaic generation systems and variable demand can cause instability in the distribution network, due to power fluctuations and the increase in reactants, particularly in the industrial sector. In response, photovoltaic units have been equipped with local storage systems, which eventually absorb power fluctuations and improve installation performance. However, during this procedure other functionalities that energy storage could provide are neglected. Consequently, this study provides a multi-mode energy monitoring and management model that enables voltage regulation, frequency regulation and reactive power compensation through the optimal operation of energy storage systems. With this objective, a smoothing control algorithm is developed that interacts with parameters of the electrical grid at the common connection point and also allows the compensation of reactive power based on an industrial demand profile. This strategy uses the Long short-term memory neural network of historical demand data prior to energy consumption with a relatively low RMSE of 1.2e-09. The results are previously validated in a development environment using a real-time OPAL-RT simulator and tests in the electrical Microgrid laboratory at the University of Cuenca. This configuration allows establishing a demand forecasting model that improves the supervision, automation and analysis of daily energy production. A series of results are provided and analyzed that demonstrate that the new tool allows taking advantage of the provision of multimode functionalities, achieving optimal voltage regulation and improving power quality by reducing the total harmonic distortion THD (V) and THD (I) indices by 0.5. % and 2 % respectively.

Modular Circuit Synthesis Oriented Modelling Approach for Non-Isolated DC-DC Converters in DCM

The continued and envisioned large-scale integration of renewable energy sources as a reaction to rising global temperatures and climate change will need a readily available DC grid to increase commissioning and operating efficiency. The effective operation of these grids is predicated on the correct control of its main control points. A plethora of DC-DC converters that find use in DC microgrids act as the main control points. DC-DC converters are non-linear and can operate in different modes with completely unique characteristics. To utilise classical control techniques, laborious equivalent linear models are derived for DC-DC converters using averaging modelling schemes. The application and limitations of these modelling techniques are well captured in the available literature. The most common limitation of the available modelling schemes is that more focus is dedicated to converter attributes like order, functionality and operating mode, even when optimal power flow and voltage regulation within the DC network are of more interest. Structure-based modelling techniques like the use of basic building blocks nullify converter attributes in the modelling process which translates to modelling efficiency. In light of the merits seen with the use of basic building blocks when modelling converters in CCM, the current study extends these merits to converters operation in DCM. Similar to modelling converters in CCM, modelling techniques that are available in the literature continue to consider converter attributes in the modelling process for DCM operation. Moreover, the two modes of operation are treated as unique entities and often modelled in a non-unified manner, which compromise modelling efficiency since the same converter can operate in a different state solely based on loading. The aim is to increase modelling efficiency but also nullify operating mode in the modelling process. The same basic building blocks are now modelled as two-port networks for DCM operation and adopted based on the exact configuration of a specified converter to compute its steady-state and dynamic models. All the advantages seen when modelling converters in CCM using basic building blocks are retained and augmented when considering DCM operation. Thus, any converter with well-defined basic building blocks can be easily modelled solely based on the connection of constituent basic building blocks.

RED FOX-BASED FRACTIONAL ORDER FUZZY PID CONTROLLER FOR SMART LED DRIVER CIRCUIT

Recently, traditional lighting has been replaced by high-power LED (HPLED) due to its significant developments, prolonged lifetime, high brightness, high reliability, and high energy efficiency. Even though conventional converters can precisely match each LED's current, they have some limitations when driving a long string of LEDs. To solve this issue, this paper presents a novel red fox optimized fractional order fuzzy PID Controller (RF-FOFPID) based high gain improved transformerless dc-dc (ITDC) boost converter for a smart LED driver circuit with optimal voltage regulation capability. The error bias is increased to zero by applying FOPID to the fuzzy logic-based compensation steps. The Red Fox optimization algorithm is used to adjust the control parameters of the fractional-level fuzzy PID controller with higher precision to solve limited problems with diverse search spaces. The Simulink model of the proposed converter was created using the MATLAB software tool Simulink and compared with controllers using fractional order PID, fuzzy PID, and conventional proportional integral derivative (PID). The results demonstrated that in terms of minimum overshoot, settling time, rise time, and steady-state error, the proposed converter based on RF-FOFPID outperforms current converters in voltage regulation.

A Modular Algorithm Based on the Minimum-Cost-Path Problem for Optimizing LTC Operations in Photovoltaic Integrated Distribution Systems

This paper presents a novel modular voltage control algorithm for optimal scheduling of a distribution system’s load tap changers to minimize the number of tap changes while maintaining a voltage deviation (VD) around a desired target. To this end, a bi-objective optimal voltage regulation (OVR) problem is addressed in two distinct stages. First, the operational constraint on the load tap changer is removed to form a single-objective OVR problem relating to the voltage. The solution obtained in this stage is ultimately utilized to determine the penalty value assigned to the distance from the optimal (solely in terms of voltage) control value. In the second stage, the optimal scheduling problem is formulated as a minimum-cost-path problem, which can be efficiently solved via dynamic programming. This approach allows the identification of optimal scheduling that considers both the voltage-related objective as well as the number of load tap changer switching operations with no added computational burden beyond that of a simple voltage optimization problem. The method imposes no restriction on the load tap changer’s operation and is tested under two different target functions on the standard IEEE-123 test case. The first attains a nominal voltage with a 0.056 p.u. voltage deviation and the second is the well-known conservation voltage reduction (CVR) case with a 0.17 p.u. voltage deviation. The method is compared to an evolutionary-based algorithm and shows significant improvement in the voltage deviation by a factor of 3.5 as well as a computation time acceleration of two orders of magnitude. The paper demonstrates the effectiveness and potential of the proposed method as a key feature in future cutting-edge OVR methods.

Adaptive Fuzzy Logic-Based Control and Management of Photovoltaic Systems with Battery Storage

Renewable energy sources (RESs) such as solar photovoltaic (PV) systems are increasingly used as distributed generation for replacing the conventional energy. At the same time, energy storage systems like battery (BAT) must be applied for maintaining the balance between fluctuating energy production and load consumption. BAT’s state of charge (SOC) should be maintained within their design limits unaffected by RES intermittency and/or load power variations. This necessitates advanced power control and management methodologies for overcoming challenging conditions. This paper discusses and evaluates an optimal DC bus voltage regulation approach: an intelligent controller using an adaptive fuzzy logic controller (FLC) and a novel supervisory power management strategy for PV systems with BAT. The objectives are to keep a stable power flow in the system and guarantee the continuity of service by ensuring that the system components do not exceed their limits. In this manner, the DC bus voltage regulation of the PV/BAT system can be improved in comparison with conventional regulation. Therefore, the most important contributions of this work are as follows. (1) Development of comprehensive and modular novel energy management system (EMS): its originality is related to the inclusion of the control system limits with faster SOC balancing and smaller DC bus voltage fluctuation. (2) Providing a simple power flow management implementation that considers the optimal energy flow between PV system, BAT system, and load: a balance between minimal energy flow in the connecting line and the least requirements of BAT capacity is kept, reducing component constraints with a very straightforward structure. (3) Furthermore, FLC offers high robustness and smooth performances. FLC is added to the control strategy design requirements to reduce DC bus voltage deviation. (4) Real-time simulation/experimentation-based complete cases utilizing Matlab/Simulink and DSpace are illustrated to testify the effectiveness of the proposed FLC and EMS.

Design of a wireless charging system in DC microgrids with accurate output regulation and optimal efficiency

This paper presents a general circuit and control design method for wireless power transfer (WPT) systems in DC microgrids to achieve optimal power transfer efficiency, while maintain accurate output voltage regulation. An auxiliary inductor is added at the transmitter resonator to form a current sink to ensure zero voltage switching (ZVS) of the primary-side full-bridge inverter with even extreme-light load conditions. Besides, an adaptive proportional-integral (PI) controller is adopted to track the output voltage references by regulating the phase shift angle of the phase shift control for the full-bridge inverter. The coefficients of the adaptive proportional-integral controller are determined by the inductor of the auxiliary inductor. Both simulation and experimental results have validated the effectiveness of the proposed circuit and control design in achieving optimal efficiency and output voltage regulation for wireless power transfer systems in DC microgrids with source and load variations.

An Auxiliary Passive Circuit and Control Design for Wireless Power Transfer Systems in DC Microgrids with Zero Voltage Switching and Accurate Output Regulations

This paper presents an auxiliary passive circuit and control design for wireless power transfer (WPT) systems in DC microgrids to achieve optimal power transfer efficiency while maintain accurate output voltage regulation. An auxiliary inductance is added at the transmitter resonator to form a current sink to ensure zero voltage switching (ZVS) of the primary-side full-bridge inverter with even extremely light load conditions. Moreover, an adaptive proportional–integral (PI) controller is adopted to track the output voltage references by regulating the phase shift angle of the phase shift control for the full-bridge inverter. The coefficients of the adaptive PI controller are determined by the inductance of the auxiliary inductance. Both simulation and experimental results validated the effectiveness of the proposed circuit and control design in achieving optimal efficiency and output voltage regulation for WPT systems in DC microgrids with source and load variations.

Increasing Unbalanced Distribution Network’s Hosting Capacity for Distributed Energy Resources by Voltage Regulators

Renewable energy sources (RESs) are becoming promising nowadays. Deploying distributed energy resources (DERs) is thus increasing accordingly, but the intermittent nature associated with RES remains a significant challenge. Also, the distribution grid faces technological difficulties with the large-scale integration of PV generations and EV charging loads. This paper suggests a strategy for a novel optimal voltage regulator (VR) placement model associated with the optimal placement of electric vehicle chargers (EVCs) and photovoltaic (PV) cells. Also, VRs’ reference voltages are determined optimally to enhance the hosting capacity (HC) of three-phase unbalanced distribution feeders for large-scale residential/public EVCs and PVs. Two scenarios are defined to study the impact of VRs on the network’s HC for DERs, namely with and without VRs in the real three-phase unbalanced feeder. The developed model determines the optimal placement of VRs and their reference voltages linked with EVCs and PVs penetration. Time-Varying Acceleration Coefficients Iteration Particle Swarm Optimization (TVAC-IPSO) algorithm has been implemented to improve the real three-phase unbalanced distribution feeder’s HC for DERs. The efficiency of the proposed model and the solution method is validated on the modified IEEE 37-node medium voltage (MV) radial distribution network connected to 35 low voltage (LV) distribution feeders. The obtained results represent that installing a proper number of VRs will increase the network’s HC for DERs.

Deep reinforcement learning based topology-aware voltage regulation of distribution networks with distributed energy storage

Both the high penetration of clean energy with strong fluctuation and the complicated variable operation condition bring great challenges to the voltage regulation of the distribution network. To deal with the problem, a topology-aware voltage regulation multi-agent deep reinforcement learning (MADRL) algorithm is proposed. The distributed energy storages (DESs) are modeled as agents to regulate voltage autonomously in real-time, which could fast adapt to dynamic topological scenarios. Firstly, taking the minimization of voltage fluctuation and maximization of reserve capacity as the target, the optimal voltage regulation model is established. Secondly, a topology extraction method considering voltage sensitivity is proposed for dynamic topology clustering, and the obtained typical topology is added to the observation set of agents. Then, the optimal voltage regulation model is formulated to the decentralized partially observable Markov decision process (Dec-POMDP) framework, in which only local information is required for the agent during the test process to decision-making to realize the hierarchical and partitioned control of voltage. Finally, the multi-agent deep deterministic policy gradient (MADDPG) algorithm is used to solve the Dec-POMDP model. The feasibility and superiority of the proposed algorithm are verified and analyzed in the simulation under different scenarios.

Frequency stability enhancement of an islanded microgrid: A fractional-order virtual synchronous generator

Virtual synchronous generator (VSG) could provide virtual damping and inertia for an islanded microgrid, which enhances the system frequency and voltage supporting capability. However, the additional introduced virtual inertia in VSG increases the system order, which aggravates the oscillation possibility of the output active power during the transient states and even may endanger the system stability. In this study, a novel fractional-order model predictive controller (FOMPC) is proposed for a fractional-order virtual synchronous generator controller (FOVSG) to alleviate the output power oscillation and achieve an optimal frequency and voltage regulation for an islanded microgrid. First, the FOVSG model is established by changing the integer-order virtual inertia into the fractional-order. Second, based on the definition of the Grünwald-Letnikov (GL) fractional calculus, the integral-order model predictive control (MPC) is extended to the FOMPC. Then a cost function is designed for frequency deviation and rated power variation of FOMPC-FOVSG. Finally, the optimal control law is obtained by solving a quadratic programming problem. Hardware-in-the-loop (HIL) experiments were carried out to demonstrate the superiority of FOMPC-FOVSG over existing VSG techniques under several scenarios.

Optimal Solution for Frequency and Voltage Control of an Islanded Microgrid Using Square Root Gray Wolf Optimization

Voltage and frequency deviation in the islanded operation of a microgrid (MG), due to the uncertainty and lack of inertia in the selection of optimal proportional integral (PI) controller gain, is a challenging task. Although various optimization algorithms have been proposed to achieve this task, most of them require a large number of iterations and are time intensive, making them inefficient for real-time applications. Gray wolf optimization (GWO), a new meta-heuristic algorithm, addresses these issues and has many advantages, including simplicity due to fewer control parameters, flexibility, and globalism. This paper proposes a simple and efficient modified algorithm, called square root gray wolf optimization (SRGWO) algorithm, to realize superior hunting performance. SRGWO is verified using twenty-three benchmark test functions. The algorithm is applied for optimal voltage and frequency regulation of a photovoltaic-based microgrid system operating in the islanded mode during distributed generation insertion and load change conditions. The voltage and frequency gain parameters of the PI controller are optimized. A comparison of the simulation results of the SRGWO algorithm with those of the original gray wolf algorithm (GWO), particle swarm optimization (PSO), augmented gray wolf optimization (AGWO), enhanced gray wolf optimization (EGWO), and gravitational search algorithm (GSA) reveal that the proposed SRGWO algorithm significantly improves system performance while maintaining its simplicity and easy implementation. Furthermore, the SRGWO algorithm obtains the minimum fitness function value in fewer iterations than other algorithms. Moreover, it improves the power quality of the system with regard to minimum total harmonic distortion.

Max-consensus protocol to determine the regulated node in distributed voltage regulation

Literature that deals with optimal voltage regulation in the power distribution grid often neglects the impact of communication on their control strategies or makes hard assumptions about the communication network. This paper extends a nearly optimal reactive power allocation mechanisms for distributed renewable energy sources with communication considerations and proposes a max-consensus-based communication schema to determine the regulated node in a distributed manner. The proposed consensus mechanisms is validated by co-simulation of a multi-agent system, the IEEE 906 low voltage test feeder and a simple communication simulator to evaluate the impact of message delay and communication network topology.

Optimal Individual Phase Voltage Regulation Strategies in Active Distribution Networks with High PV Penetration Using the Sparrow Search Algorithm

This study aimed to propose individual phase voltage regulation strategies using the sparrow search algorithm (SSA) in the IEEE 8500-node large-scale unbalanced distribution network with high photovoltaic (PV) penetration. The proposed approach is capable of individual phase regulation, which coordinates the on-load tap changer (OLTC), voltage regulator (VR), switched capacitor bank (SCB), and volt–var setting controlled by a smart inverter to improve voltage variation and unbalance. Consequently, the change time of VRs, the switched times of SCBs, and the individual phase voltage magnitude and unbalance ratio are considered in the fitness function for the SSA. The simulation scenarios fully consider the unbalanced load conditions and PV power output patterns, and the numerical results demonstrate that the voltage variation and unbalance are clearly improved, by 15% and 26%, respectively. The fitness values, operation times of OLTC, VR, and SCB, and the settings of the volt–var controlled smart inverter are also optimized by the SSA. The outcomes of this study are helpful for distribution system operators in formulating voltage control strategies corresponding to different system conditions.

Voltage regulation in LV distribution networks with PV generation and battery storage

Abstract High penetration of photovoltaic (PV) generation in low voltage (LV) distribution networks can leads some power quality problems. One of the most important issues in this regard is the impermissible voltage deviation in periods with a large imbalance between PV generation and local load consumption. Accordingly, many authors deal with this issue. This work investigates voltage regulation for LV distribution networks equipped with the hybrid distribution transformer (HDT), and with high penetration of PV units. A two-stage algorithm for voltage regulation is proposed. In the first stage, a local (distributed) voltage control is performed by minimizing the injection power of the PV-battery storage system (BS)-local load entity at the common bus. In the second stage, optimal coordination is performed between the HDT and the local voltage control. In fact, the second stage is an optimal voltage regulation problem. The aim is to minimize the voltage deviations at load buses by optimal settings the voltage support of the HDT. A PSO algorithm is used to solve this optimization problem. the proposed approach is implemented in MATLAB software and evaluated on the IEEE european LV test feeder.

Integrated resource planning in sustainable energy-based distributed microgrids

This work is devoted to assess the optimal allocation of suitable integrated resources planning for ecofriendly sustainable energy-based hybrid microgrids with distributed generation. The issues due to intermittency of renewable resources and low inertia of these microgrids are alleviated by coordination of suitable demand-response support (DRS) and virtual-inertia support (VIS) systems. Primarily, 3-distributed unequal microgrids are planned with locally available Solar/Wind/Bioenergy resources including combined storage-based VIS and Electric-Vehicle charging station-based DRS units for supply and demand-side management respectively. The planned system is modelled in MATLAB considering real-time recorded Solar/Wind data with 12-months loading and simulated in different scenarios of source and load variations. The controllers are tuned using a new Quasi-oppositional chaotic Selfish-herd optimization (QCSHO) algorithm, for optimal frequency and voltage regulation in the distributed microgrids, after confirming it’s superiority over other contemporary algorithms by comparing the system responses. Then the coordination of frequency, voltage and tie-line loadings are investigated for the projected microgrids without retuning the controllers in 5-extreme scenarios of source and load variations. Lastly, the responses are evaluated for 22-diverse possible allocations of DRS and VIS units in different microgrids. Also the most suitable allocation of integrated resources are recorded after comparing their overall responses.

Optimal Voltage Regulation Scheme for PV-Rich Distribution Systems Interconnected with D-STATCOM

This paper proposes an optimal voltage regulation scheme (OVRS) for distribution systems with rich photovoltaic (PV). Various regulation devices are optimally controlled in a coordinated manner: PV inverter, D-STATCOM, step voltage regulator (SVR), and on-load-tap-changer (OLTC). A data structure algorithm is proposed to split the distribution system into layered zones considering the radial structure of the system. The solution process of the proposed scheme is accomplished by a meta-heuristic optimizer. OVRS addresses the voltage violations while yielding a coordinated operation of the various control devices. The proposed OVRS involves three control levels to completely prevent voltage violations. In the first control level, the PV inverter and D-STATCOM mitigate rapidly the local voltage deviation through injecting/absorbing optimized reactive power. The second control level is a decentralized-based control scheme that utilizes the voltage control devices in each zone to handle the voltage violations if any. For each zone, the control devices in the upper-stream zones (parent zones) are managed by the third control level to ensure cooperative control actions. The simulation results on the 119-bus distribution system, with clear, low fluctuation, and high fluctuation of solar radiation profiles, demonstrate the effectiveness of the proposed OVRS.

Optimal voltage regulator of power supply system for submersible processing equipment

The article considers the synthesis of the optimal voltage regulator for the electric power supply system of the electromechanical complex of the submersible apparatus with the power transmission through the cable line. Based on the reduced linearized model of the multidimensional nonlinear control object the regulator with adjustable parameters is synthesized, providing the DC voltage stabilization at the power supply system load. The variant of building proportional feedback on output state variables is used for the regulator in the system with parametric and external disturbances. Due to the technical impossibility of measuring the voltage on the load, a combined optimal regulator with negative feedback is organized, which makes it possible to stabilize the voltage at the payload within the specified limits when receiving data from the output filter of voltage source inverter.

A Fault Level-Based System to Control Voltage and Enhance Power Factor Through an On-Load Tap Changer and Distributed Generators

The on-load tap changer is critical for voltage regulation, however, its voltage regulation philosophy is suitable for one-directional power flow power systems. Since distributed generators introduce bidirectional power flow, the conventional operation of the on-load tap changer will be highly impacted, resulting in high voltage magnitudes that exceed acceptable limits, frequent voltage magnitude fluctuations, and the increase in overall reactive power supply when distributed generators start absorbing/injecting reactive power. Therefore, an adaptive control system is proposed on this paper that alters the existing voltage regulation philosophy of the on-load tap changer and also coordinate the voltage regulation capabilities of distributed generators with those of the on-load tap changer for optimal overall voltage regulation and the reduction of reactive power that flows through the power system, enhancing the power factor thereof. The proposed control system will change the centralized, and single-variable busbar voltage monitoring technique of the conventional philosophy with a decentralized, and multi-variable strategy that controls voltage based on the average voltage of the entire distribution network. The control system will calculate the on-load tap changer and distributed generators setpoints based on the overall average network voltage, average voltage deviation from nominal voltage, generators points of connection voltage and the total reactive power that flows through the power transformer. The control system was tested on a 22kV network modelled in MATLAB SIMULINK and the results attained revealed that the proposed multi-stage control system can successfully control voltage and simultaneously improve power factor.