Voltage Control Model Research Articles

Distributed voltage control for multi-feeder distribution networks considering transmission network voltage fluctuation based on robust deep reinforcement learning

In the multi-feeder distribution network, the power balance between photovoltaics generations and load demands across regions is more complex. To solve the above problems, this paper proposes a multi-agent distributed voltage control strategy based on robust deep reinforcement learning to reduce voltage deviation. The whole multi-feeder distribution network is divided into a main agent and several sub-agents, and a multi-agent distributed voltage control model considering the transmission network voltage fluctuations and the corresponding power fluctuations is established. Based on the information uploaded by sub-agents, the main agent models the uncertainty of the transmission network voltage fluctuations and the corresponding power fluctuations as a disturbance to the state, and a RDRL method is employed to determine the tap position of on-load tap changer. Furtherly, each sub-agent uses the second-order cone relaxation technique to adjust the reactive power outputs of the inverters on each feeder. The effectiveness of the proposed method has been verified in two real-world multi-feeder systems. The results show that the proposed method can achieve millisecond-level decision-making, with a voltage deviation only 1.28 % higher than the global optimal results, achieving near-optimal control. The proposed method also demonstrates robustness in handling transmission network uncertainties and partial measurement loss.

Coordinated control strategy of photovoltaic energy storage power station based on adaptive dynamic programming

In order to solve the problem of variable steady-state operation nodes and poor coordination control effect in photovoltaic energy storage plants, the coordination control strategy of photovoltaic energy storage plants based on ADP is studied. Establish the photovoltaic energy storage power station model including photovoltaic system model, super capacitor system model and battery system model; Set the maximum limit of active power change as the power constraint condition for coordinated control of photovoltaic energy storage station; The optimal control problem of multi voltage and reactive power resource coordination is fully considered, the optimal voltage control model is established by using ADP algorithm, and the optimal coordinated control strategy is obtained by online learning the collected dynamic operation information. The experimental results show that this strategy can improve the coordinated control effect of the photovoltaic energy storage station, ensure the photovoltaic energy storage station in a stable operation state, improve the service life of the energy storage device in the photovoltaic energy storage station, and stabilize the steady-state operating node voltage within the ideal fluctuation range.

Multi-time-scale voltage control of the distribution network with energy storage equipped soft open points

The integration of distributed generation (DG) units into distribution networks (DNs) has brought about several operational challenges, including voltage issues and increased power loss. Energy storage equipped soft open points (E-SOPs) can accurately and flexibly control active and reactive power flows to address these problems. Additionally, the photovoltaic (PV) inverter and the network reconfiguration (NR) play a significant role in voltage control by adjusting the reactive power and the topology of the DN, respectively. However, due to differences in response times, there is a lack of systematic coordination between NR and the inverters of the E-SOP and PV. This paper proposes a multi-time-scale voltage control model that includes day-ahead NR scheduling, inter-day droop control optimization of the PV and E-SOP, and real-time local droop control. Considering the uncertainties of renewable DG outputs and loads, a robust optimization method is used in the day-ahead stage to obtain a reliable network structure. Then, with more accurate intra-day predictions, a stochastic optimization method is used to obtain the optimal state-of-charge interval, aiming to provide a flexible regulation range for battery energy storage to cope with the power fluctuations during the real-time stage. In addition, to address the intra-day voltage control model with bilinear constraints of the droop control function, a particle swarm optimization method is used. The results are verified on a 33-bus DN system through comparative analyses, showing effective performance.

Adaptive Time-Delay Attitude Control of Jumping Robots Based on Voltage Control Model

Adaptive Time-Delay Attitude Control of Jumping Robots Based on Voltage Control Model

Single-cell operando SOC and SOH diagnosis in a 24 V lithium iron phosphate battery with a voltage-controlled model

Batteries typically consist of multiple individual cells connected in series. Here we demonstrate single-cell state of charge (SOC) and state of health (SOH) diagnosis in a 24 V class lithium-ion battery. To this goal, we introduce and apply a novel, highly efficient algorithm based on a voltage-controlled model (VCM). The battery, consisting of eight single cells, is cycled over a duration of five months under a simple cycling protocol between 20 % and 100 % SOC. The cell-to-cell standard deviations obtained with the novel algorithm were 1.25 SOC-% and 1.07 SOH-% at beginning of cycling. A cell-averaged capacity loss of 9.9 % after five months cycling was observed. While the accuracy of single-cell SOC estimation was limited (probably owed to the flat voltage characteristics of the lithium iron phosphate, LFP, chemistry investigated here), single-cell SOH estimation showed a high accuracy (2.09 SOH-% mean absolute error compared to laboratory reference tests). Because the algorithm does not require observers, filters, or neural networks, it is computationally very efficient (three seconds analysis time for the complete data set consisting of eight cells with approx. 780.000 measurement points per cell).

Transfer operation ride though control strategy of the aircraft power supply system with lithium battery energy storage

The aircraft power supply system turns towards multi-source, and transfer operation will likely happen during flight missions and fault conditions. The transfer may lead to voltage interruptions and current surges, adversely affecting power quality. This paper proposes incorporating a lithium battery energy storage system (ESS) into the aircraft high-voltage direct current (HVDC) power system. The ESS employs a voltage control model with a lower voltage reference base, facilitating a smooth transfer operation during power interruptions. This approach reduces voltage fluctuations in the power supply system, enhances power quality, and enables seamless power transfer during the transfer operation.

Optimal Voltage Control of Wind Farm with Distributed Energy Storage System

This paper proposes an optimal voltage control method for a wind farm (WF) combined with distributed energy storage systems (DESSs), where the DESSs are connected to the DC sides of wind turbines (WTs). In the proposed control method, the WT units and DESSs can be coordinated to minimize the voltage fluctuation of terminal buses, minimize the distance between the voltage of terminal buses and rated value, and minimize power losses inside the WF collection system. The optimal control (OPC)-based voltage control model is formulated to generate optimal active/reactive power output of WTs and active power output of energy storage units in the WF with DESSs. The DESSs can store active power from WTs to support more capacity of the reactive power output of WTs. The control performance of the proposed voltage control method based on OPC is validated by conducting case studies on the WF with 32*5 MW WTs.

PV Distribution Network Planning Method Considering the Cost and Effectiveness of Voltage Control

The large-scale grid connection of distributed power sources can save energy, reduce emissions, and improve the flexibility of the power supply, but the volatility and uncertainty of its power output lead to voltage fluctuations and power quality problems, among which voltage problems have become an important factor inhibiting the large-scale access of distributed power sources, posing new challenges to the grid planning and operation. To address the grid voltage crossing problem after PV grid connection, firstly, a multi-time scale voltage control model based on voltage control coefficients is established to achieve maximum network safety and economic satisfaction; the problem is then solved using a variety of group traction differential evolutionary algorithms with the objective function being the minimum annual integrated cost and the maximum percentage of clean energy generation. This method offers a strong theoretical and technical guarantee for the planning of distributed power access to the distribution system.

Data-Driven Coordinated Voltage Control Method of Distribution Networks With High DG Penetration

The highly penetrated distributed generators (DGs) aggravate the voltage violations in active distribution networks (ADNs). The coordination of various regulation devices such as on-load tap changers (OLTCs) and DG inverters can effectively address the voltage issues. Considering the problems of inaccurate network parameters and rapid DG fluctuation in practical operation, multi-source data can be utilized to establish the data-driven control model. In this paper, a data-driven voltage control method with the coordination of OLTC and DG inverters on multiple time-scales is proposed without relying on the accurate physical model. First, based on the multi-source data, a data-driven voltage control model is established. Multiple regulation devices such as OLTC and DG are coordinated on multiple time-scales to maintain voltages within the desired range. Then, a critical measurement selection method is proposed to guarantee the voltage control performance under the partial measurements in practical ADNs. Finally, the proposed method is validated on the modified IEEE 33-node and IEEE 123-node test cases. Case studies illustrate the effectiveness of the proposed method, as well as the adaptability to DG uncertainties.

Multi-timescale voltage control for distribution system based on multi-agent deep reinforcement learning

Nowadays a large number of distributed generators and controllable elements connecting to the distribution network have resulted in higher requirements for voltage control. Existing voltage control methods require a specific physical model and are time-consuming with the complexity increasing. To overcome these challenges, this paper proposes a multi-timescale voltage control scheme using multi-agent deep reinforcement learning (MADRL). Firstly, considering the control requirements of different timescale regulators, a multi-timescale voltage control model is formulated. Then the control variables are assigned to multiple agents, and a MADRL-based voltage control scheme is proposed, which is based on the multi-agent deep deterministic policy gradient (MADDPG) algorithm and applies Gumbel-Softmax distribution to solve discrete actions. Through centralized learning and decentralized execution, this scheme can obtain the optimal coordinated control strategy of multiple regulators adaptively and achieve the effect of distributed control. Finally, case studies are conducted on the modified IEEE-123 bus system, and the performance of the proposed method is compared with the conventional model-based optimal voltage control scheme and other DRL-based methods. The results show that the MADRL-based method possesses better performance than other methods.

A new decentralized two-stage multi-objective control of secondary network driven hybrid microgrid under variable generation and load conditions

This paper proposes a new decentralized two-stage multi-objectives voltage control technique to ensure the operational stability of the hybrid MG over the variable renewable generations as well as to confirm its reliable operation against various industrial loads by minimizing the mismatch load terminal voltage. The effective operation of hybrid MG under the running of loads reclines on its dynamics which may become non-linear due to the utilization of additional inverter-oriented secondary network or electronics components in their evolution. Also, the efficacy of the secondary network-driven microgrid (SNM) is ascertained by taking the sustained transient response, however, its supremacy can be influenced by the sporadic features of renewable energy sources (RESs). This work introduces a new decentralized hybrid voltage control (DHVC) model to effectively drive the issues raised from the load’s operation and RESs. The proposed hybrid control technique integrates the linear and non-linear control scheme which works enabling a two-level control framework in MG terminal to confirm its enhanced operational stability and minimized unbalance voltage over the various operating conditions. The non-linear control framework of the proposed DHVC is made by utilizing the partial feedback linearization approach in generalized dynamics deriving from the DC–DC converters used with RESs and battery, while the linear control scheme uses a second-order damping (SOD) control framework to regulate the inverter used with load’s network. The controllability of the proposed DHVC is evaluated by measuring the response of the SNM in terms of operational stability and mismatch voltage minimization over the change of meteorological factors of the RESs and various industrial loads. Also, an external parametric uncertainty with the single-phase secondary network dynamics is studied to guarantee the robustness of the proposed DHVC. Additionally, a comparative study is studied to cover the augmented performance of the hybrid microgrid.

An enhanced master–slave control for accurate load sharing among parallel standalone AC microgrids

SummaryWith the advancement of power electronic converters over the last few decades, the capability to integrate solar photovoltaics, wind, diesel generators, batteries, ultracapacitor, and electric vehicles with standalone networks or utility grids has increased. For the reliable operation of standalone networks, accurate load sharing among these sources is required. As a result, several load sharing schemes with inherent merits and related demerits are presented in the literature. This work intends to address shortcomings of earlier reported control schemes with the inheritance of associated merits using the proposed control scheme. The proposed enhanced Master–Slave control, with advanced droop control scheme, is illustrated for Master voltage source inverter with resistive line impedance and improves the steady‐state response by improving voltage and frequency profile. It reduces the voltage drop caused by load effect and droop effect, allowing the voltage and frequency regulation during load changes and distinct load conditions such as balanced linear load, unbalanced linear load, and balanced nonlinear load in standalone alternating current (AC) microgrids. Furthermore, Slave voltage source inverters independently compensate for local load demand. This paper presents precise current control and voltage control design methodology and a mathematical model for voltage control and their performance analysis in time and frequency domain. Finally, the proposed enhanced Master–Slave control for accurate load sharing among parallel standalone AC microgrids validated using real‐time simulator and experimental results is provided to verify the effectiveness of the proposed control scheme.

Model-Based Design and Experimental Validation of Control System for a Three-Level Inverter

Considering the disadvantages of the traditional development pattern in an embedded control system for an inverter, that is, the single-process thinking and separation of software and hardware, a novel method, which is a model-based design, for developing a double closed-loop control system for the diode-clamped three-level inverter was proposed. System control models, including the PWM control algorithm model, the voltage control model, the neutral-point potential balancing model, and the frequency control model, were built with the MATLAB platform. The code-generation capacity and the operation effect were verified through a series of tests. The inverter with diode clamp and neutral-point potential control was developed. Codes were generated automatically and downloaded to the eZdsp28335 control chip. Experimental waveforms of the phase voltage, line voltage and current were analyzed under regulating the voltage and frequency. The experimental results demonstrate that the models and the generated codes are correct. Further studies have proven the feasibility of the system development model.

Cluster Partition-Based Zonal Voltage Control for Distribution Networks With High Penetrated PVs

With the high penetrated photovoltaics (PVs) accessing distribution networks (DNs) in the future, the overvoltage of distribution network will be more serious, and the voltage control will be more complex. To solve this problem, a cluster partition-based zonal voltage control method for DN is proposed in this study. First, a cluster partition method using a comprehensive performance index is proposed: in terms of DN structure, a global density quality function index is introduced to measure the coupling degree of nodes in clusters. In terms of cluster function, the inconsistency coefficient index is presented to reflect the PV output characteristics, and a node membership function index is presented to limit the scale of the cluster. Then, the DN cluster partition is carried out under the fast Newman (FN) algorithm. Second, a second-order cone programming (SOCP) model of voltage control is established to maximize the PV consumption in each cluster. To achieve the coordinated optimization of clusters, an iterative optimization strategy among clusters is proposed. Finally, the effectiveness and rationality of the proposed method are verified in a 10 kV actual feeder system in Zhejiang Province, China.

Study on the influence of automatic voltage control system with dead zone on the stability of hydropower unit

The existing research on automatic voltage control of hydropower station is mostly simplified as linear control model, ignoring the dead time and delay effect. Based on the existing automatic voltage control model, this paper considers the dead zone of automatic voltage control, establishes the corresponding theoretical equivalent model of dead zone, and analyzes the influence of dead zone on the stability of hydropower grid connection. In this method, the dead zone is simplified as a smooth nonlinear function, which makes the small signal analysis method suitable for the nonlinear system with dead zone. This method has good guidance for the actual operation. The effectiveness of the method is verified by the analysis of an actual hydropower plant.

A Stochastic Economic Dispatch Method Considering a Voltage-Sensitive Load as a Reserve

The volatility and uncertainty of renewable energy seriously affect the stability of power systems. To overcome this challenge and improve the ability of power systems to deal with disturbances, an optimized dispatch method for a high-permeability renewable energy-integrated power system with a voltage-sensitive load (VSL) as the reserve is proposed. To fully utilize the flexibility of the VSL and the reactive power control capability of the transmission grid, this paper first establishes a voltage control model based on coordinated sensitivity-based transmission and distribution and quantifies the active power adjustment boundary considering voltage stability. Then, based on the corresponding risk, a stochastic reserve decision model of a power system considering the regulation capacity of a VSL is established. A case study of a modified IEEE RTS-79 system shows that the method can fully consider the influence of the voltage at the access point on the load power, change the power of the VSL, and provide an active power reserve for the power system. This approach can reduce the risk of wind curtailment and load shedding in the system and improve the economy and flexibility of system operation.

A New Second-Order Cone Programming Model for Voltage Control of Power Distribution System With Inverter-Based Distributed Generation

In this article, a novel second-order cone programming based branch flow model is proposed for optimal power flow (OPF) in radial distribution networks. The main advantage of the proposed architecture is that it can recover the bus voltage angle difference and, thus, control the reactive power flow (PF), leading to a better voltage regulation in the network. Based on the line PF and the line impedance, a convex equation is derived and included with the model to retrieve the bus voltage angle difference. Finally, using the retrieved bus voltage angle difference within the global optimal solution space, an algorithm is developed to control the reactive flow for voltage regulation. The proposed model has been tested on modified IEEE 32 bus (IEEE 33 bus network) and IEEE 123 bus power network systems for different test cases. The simulation results are verified with the OpenDSS PF and the MatPower nonlinear programming (NLP) based OPF solutions. The computational efficiency of the proposed model is superior to the NLP models. Also, the proposed reactive flow control algorithm improves the voltage regulation of a network and promises a globally optimal solution.

An adaptive frequency – Voltage control model for extracorporeal supporting flow regulation in an extracorporeal membrane oxygenation system

Extracorporeal membrane oxygenation (ECMO) is employed to treat critical patients for one to a few days of life support in intensive care units. Venovenous (VV) and venoarterial (VA) ECMO configurations are the most commonly used rescue strategies for temporary cardiac and respiratory function support. However, both ECMO modes sometimes cannot meet a patient’s demands because of (a) less oxygenated blood in either the upper body or lower body, or (b) a deterioration in the patient’s hemodynamic status. Veno-Venoarterial (VVA) ECMO is an upgraded system that provides sufficiently oxygenated blood to the systemic and pulmonary circulation systems. Drainage cannulas and gas flow exchanges are determined to provide the maximum drainage blood flow required by the patient through a servo-regulator that adjusts the motor speed. A generalized regression neural network (GRNN) based estimator is created to automatically estimate the desired pump speed and then provide sufficient drainage flow for temporary life support. To achieve stability flow in an ECMO circuit, a bisection approach algorithm (BAA) is employed to improve the performance of transient responses in step controls and steady state controls. Experimental studies are used to validate the proposed model and it is compared with conventional controllers to indicate good performance in clinical VVA ECMO applications.

Multi-objective optimization for voltage and frequency control of smart grids based on controllable loads

The output uncertainty of high-proportion distributed power generation severely affects the system voltage and frequency. Simultaneously, controllable loads have also annually increased, which markedly improve the capability for nodal-power control. To maintain the system frequency and voltage magnitude around rated values, a new multi-objective optimization model for both voltage and frequency control is proposed. Moreover, a great similarity between the multi- objective optimization and game problems appears. To reduce the strong subjectivity of the traditional methods, the idea and method of the game theory are introduced into the solution. According to the present situational data and analysis of the voltage and frequency sensitivities to nodal-power variations, the design variables involved in the voltage and frequency control are classified into two strategy spaces for players using hierarchical clustering. Finally, the effectiveness and rationality of the proposed control are verified in MATLAB.

A Voltage Control Model of Multi-ports DC Transformer

DC transformer (DCT) is basic and important power conversion equipment in DC distribution network. DC transformer adopts power electronic switches and high-frequency transformer, which can realize power conversion. Multi-ports DCT can connect many different types of power supply or load equipment, and can flexibly control the current, voltage and power flow of multiple ports, which is widely used in DC distribution network. In this paper, the output voltage control model of multi-ports DCT in steady state is established. Combined with the control model, the mechanism of controlling the phase-shift angle to realize the output voltage control is proved mathematically. The correctness of the control model is verified by simulation.