Optimal Voltage Control Research Articles

Distributed Secondary Voltage Optimal Control of Active Distribution Network and the System Stability Investigation

Distributed Secondary Voltage Optimal Control of Active Distribution Network and the System Stability Investigation

Double-time-scale distributed voltage control for unbalanced distribution networks based on MPC and ADMM

With the increasing penetration of distributed generators (DGs) in the distribution system, the distributed coordinated optimal control of DG and voltage control devices has become imperative. This paper proposes a double-time-scale distributed voltage control scheme for unbalanced distribution networks with distributed generators based on model predictive control (MPC) and alternating direction method of multiplier (ADMM) to regulate the voltage profile across a network. To better coordinate the economical operation and voltage regulation, the voltage control devices with different temporal characteristics are separately adjusted. In the fast-time-scale control, the decision variables are the active and reactive power outputs of DGs. In the slow-time-scale control, the on-load tap changer, step voltage regulators and capacitor banks are optimized. A tailored solution method based on McCormick envelopes and branch-and-bound algorithm is developed to efficiently solve the optimization problems by transforming the mixed-integer nonlinear program (MINLP) into a second order cone program (SOCP). The voltage control scheme is a combinatorial optimization problem whose computational complexity grows exponentially with the number of binary variables. To resolve this issue, a distributed optimal voltage control strategy is developed based on the alternating direction method of multipliers (ADMM). Through the ADMM, the optimization problem is distributed among the zones of the distribution networks, without requiring a central controller. The modified IEEE 123-node system is used to verify the effectiveness of the proposed control scheme as a test system.

Optimal Secondary Control to Suppress Voltage Fluctuations in an HVDC-Linked Wind Farm Grid

High-voltage direct-current (HVDC) systems using voltage source converters (VSCs) have been developed to suppress voltage fluctuations in offshore grids due to the intermittent power generation of offshore wind farms (OWFs). This paper proposes a new strategy for the optimal secondary voltage control of HVDC-linked OWFs. In the proposed strategy, optimal coordinated control of an HVDC system and OWFs is achieved to minimize real-time voltage fluctuations throughout an offshore grid. A full-order dynamic model of HVDC-linked OWFs is implemented, and its unobservable and uncontrollable state variables are then eliminated using a model reduction algorithm. Given the dynamic model, a linear quadratic Gaussian regulator is designed for optimal coordinated control, and an eigenvalue analysis is conducted focusing on the effects of communication time delays on the stability of the proposed strategy. Simulation case studies are also carried out to verify that the proposed strategy more effectively improves real-time regulation of offshore grid voltages, compared to conventional strategies, while ensuring voltage stability even for relatively large delays in communications systems.

Distributed Optimal Voltage Control and Berth Allocation of All-Electric Ships in Seaport Microgrids

The uncoordinated berth allocation of all-electric ships (AES) will impose great challenges on voltage profiles in seaport microgrids. This paper proposes a distributed optimal voltage control and berth allocation strategy to mitigate voltage violations while balancing the benefits between microgrids and AES. The voltage control problem is formulated as a second-order cone program (SOCP) to minimize network power loss, while the berth allocation problem is formulated as a mixed-integer linear program (MILP) to minimize total service time. To build the bridge between microgrids and berthed-in AES, the power interaction model at the seaport is proposed. The proposed model introduces a time-cost factor to evaluate the economic cost of service time and also unifies the timescales between voltage control and berth allocation. Considering different system operators and objectives of microgrids and AES, this paper develops an alternating direction of multipliers (ADMM)-based distributed optimization algorithm to solve the problem. The method can help reach the convergence of ADMM in solving models with discrete variables. The proposed approach is tested on the EU 16-bus microgrid embedded with a seaport and simulation results verify the effectiveness both in mitigating voltage violations and maximizing the total benefits of microgrids and AES.

Coordinated Real-Time Voltage Control in Active Distribution Networks: An Incentive-Based Fairness Approach

This paper proposes a three-stage incentive-based fair voltage control strategy to mitigate fast voltage violations in distribution networks while ensuring the benefits of both distribution system operators (DSO) and photovoltaics (PV) customers. In the first stage, optimal power flow (OPF) is formulated as a mixed-integer second-order cone programming (MISOCP) and performed in the central controller (CC) to obtain day-ahead dispatch of on-load tap changers (OLTC) and capacitor banks (CBs) based on forecasting data of PV and load. In the second stage, two voltage control modes namely optimal voltage control mode (OVM) and fair voltage control mode (FVM) are first formulated. The incentive-based mode selection method is then developed to make a decision by comparing the total benefits in each mode. In the third stage, a multi-dimensional adaptive local voltage control strategy is proposed to sequentially regulate reactive power output and active power curtailment of PVs in real time. The control curve is designed with an adaptive voltage dead band to activate voltage regulation in time. The multi-dimensional characteristics also help track the optimal operating points with varying PV and load to further reduce network power losses. Moreover, the fairness is guaranteed by providing economic compensation for each PV from the DSO, which is estimated in the second stage and corrected in the third stage. The proposed approach is tested on the IEEE 33-bus distribution system and simulation results verify the effectiveness both in addressing voltage problems and ensuring the fairness of PVs in economic benefits.

Distributed control strategy of DC microgrid based on consistency theory

In order to solve the cooperative control problem among multiple distributed units in a distributed DC microgrid, a distributed control based on a consensus algorithm is firstly proposed, which can realize power distribution among distributed units. The distributed voltage control of the observer will generate steady-state errors due to problems such as time delay initial value of the observer. A voltage optimization control strategy based on the PI consistency algorithm is further proposed, which converts the voltage control problem into an optimization problem, and the optimization goal is to ensure the deviation sum of each busbar from the rated value is the smallest, and the stability of this method is proved. Finally, the effectiveness of the proposed method is verified by MATLAB/Simulink software.

Distributed Voltage Optimization Control of BESS in AC Distribution Networks with High PV Penetration

Large-scale integration of PV generators in distribution grids will impair the voltage stability due to the stochastic and fluctuated PV power generation. To tame the volatile PV power generation, battery energy storage systems (BESS) are deployed as an effective yet expensive power buffering mechanism. In this paper, a dual ascent-based voltage optimization control is proposed to achieve the concurrent regulation of battery State-of-Charge (SoC), nodal voltages, and distribution loss. This control features the limited dependence on the communication network with information interaction between neighboring nodes. Besides, it can achieve the optimal power flow minimizing the distribution loss while maintaining the BESS SoC within a healthy range. The derivation of the control framework is provided, and comparative simulations in the IEEE 37-node distribution system are performed to validate the effectiveness of the proposed control algorithm.

Reactive Power and Voltage Optimization of New-Energy Grid Based on the Improved Flower Pollination Algorithm

In order to solve the reactive power and voltage control problem caused by the high proportion of new energy connected to the power grid, this paper takes the minimum voltage deviation, minimum network loss and maximum dynamic reactive power margin of the whole system as the comprehensive optimization objectives and establishes a reactive power and voltage optimization model by considering the reactive power regulation ability of SVC (Static Var Compensator) and new energy units. In view of the continuous and discrete variables in the model, the traditional continuous FPA (Flower Pollination Algorithm) is discretized to form an improved continuous-discrete hybrid FPA, and the tournament selection mechanism is adopted to speed up the convergence. Through the example analysis of the IEEE-39 bus system, the feasibility of the proposed reactive power and voltage optimal control method in the new energy grid is verified. Compared with GA (Genetic Algorithm), the results show that the improved FPA has high optimization accuracy, which is suitable for solving the reactive power and voltage optimization problem of the new energy grid.

유도 부하에 적합한 넓은 입력 전압 범위와 높은 역률의 2단 AC-AC 컨버터 설계

In this paper, a two-stage AC-AC power supply is designed to specifically handle the operation and performance improvements of the power system with highly inductive loads. The proposed power supply scheme includes an input interleaved power factor correction (PFC) stage to improve the input power factor (PF) and regulate the DC bus voltage required for the second stage, which is a voltage source inverter (VSI) with an output low pass filter to adjust the load harmonics contents and regulate the load voltage and frequency to the specified AC load values. The small-signal models of the PFC and VSI stages are obtained, and optimal voltage and current control loops for both stages are designed based on the obtained models to improve the required system power performance and stability. PSIM simulation is used to examine the performance of the proposed power supply under various input-output operation conditions. This work achieves a 500VA, (180-260)V RMS Power supply with an input PF of 99%, better output voltage regulation, and a lower THD value for the purpose of supplying industrial inductive loads.

Voltage Optimization Control Strategy for Islanded Microgrid Source-Grid-Load Active-Reactive Power Coordination Based on Collaborative Di-MPC

To cope with the volatility and randomness of wind power, photovoltaic (PV) power, and load demands in the islanded microgrid, and also to ensure the safety and economic operation of the islanded microgrid system. A collaborative Distributed model predictive control (Di-MPC) based voltage optimization control strategy is proposed, which considers the strong coupling characteristic of active and reactive power due to the impedance ratio of islanded microgrid, and the requirements of real-time and robustness in the optimization as well. By coordinating the controllable devices in the source-grid-load side of the islanded microgrid, the proposed strategy aims to make full use of the voltage regulation capability of each controllable device. Firstly, by considering the different operating characteristics of the controllable devices, a multi-time scale distributed voltage optimal control model is established. It divides the optimal control process into long-time scale and short-time scale and optimizes for respective objective functions and control variables in different time scales. Secondly, a collaborative Di-MPC-based voltage optimal control strategy is proposed. With the proposed collaboration mechanism, the power output increments of the distributed generators (DGs) are solved in the short-time scale, and the errors in the long-time scale control are also fixed. Finally, the simulation results show that compared with a traditional optimal control method and a centralized model predictive control (CMPC) method, the proposed voltage optimization control strategy can effectively reduce the voltage deviation and fluctuation at each node while ensuring the economic operation of the islanded microgrid system.

Optimal Voltage and Frequency Control in Solar Integrated Power Network

This study presents the modelling and simulation of a single area powersystem network containing three different generating sources, solar thermalpower plant (STPP), thermal, and diesel power plants. The simultaneousfrequency and voltage control problem has been investigated using LinearQuadratic Regulator (LQR) and State Feedback based Sliding Mode Control(SF-SMC) design. Also, the integration of Particle Swarm Optimization(PSO) has been considered for the selection of LQR cost function weightmatrices and SMC control parameters. Proper selection of LQR and SF-SMCparameters allows the controllers to handle the complexities involved in avery efficient manner and also augments necessary robustness in the con-troller design. The transient performance of the PSO based LQR and SF-SMCcontrolled system has been assessed in terms of time-domain performanceindices, and it has been compared with the conventional PID and 2-DOF PIDcontrollers. Additionally, the robustness of the proposed controllers has beentested and verified after contemplating variation in model parameters

Collaborative Management of Multi-Type Energy for Optimal Control of Voltage and Loss of Distribution Systems with DGs and SVCs

Aiming at the deficiencies and shortcomings of traditional reactive power compensation devices in controlling and improving voltage quality, a bilevel programming model for the coordinated optimal allocation of active and reactive power capacity of photovoltaic power generation systems, wind-driven generators, shunt capacitors and static var compensators is established based on chance-constrained programming. In the optimization model, the randomness and intermittence of the output power of the distributed generation and the reactive power regulation ability of the distributed generation are considered. The correlation of illumination, wind speed and load fluctuation is considered and affected by seasonal variation. Latin hypercube sampling method is used to deal with the correlation of random variables. Monte Carlo probabilistic power flow method is used to calculate the power flow of distribution network with intermittent distributed generation systems. The location and capacity of active and reactive power sources in outer programming are optimized by adaptive artificial fish swarm algorithm, and the allocation scheme of active and reactive power sources determined by outer programming is optimized by inner adaptive artificial fish swarm algorithm. The two-layer nested adaptive artificial fish swarm algorithm is used to solve the optimal allocation problem constructed in this paper, and the optimal allocation scheme of active and reactive power supply capacity is determined. Taking IEEE33 distribution system as an example, the feasibility and applicability of the proposed model and algorithm are verified.

Optimization of the Structure of Power System Multi-Agent Control

The article provides an overview of the application of multi-agent control, provides a classification of multi-agent system architectures, and analyzes existing methods for constructing the structure of multi-agent control in the electric power industry. A method for constructing an optimal structure of multi-agent optimal control is proposed. A field of applications of proposed method for a multi-agent voltage and reactive power optimal control in a power system is discussed. The proposed method of optimal design of multi-agent power system control structure can be applied to various objective function of optimization. The application of multi-agent control for automatic voltage and reactive power control in the power system is currently being researched.

Decentralized Robust LMI-based Voltage Control Strategy for Autonomous Inverter-Interfaced Multi-DG Microgrids

This article's objective is to propose a decentralized one Degree of Freedom (DoF) robust control system with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H_\infty$</tex-math></inline-formula> performance criteria for optimal voltage control of an uncertain inverter-interfaced AC microgrid in the islanding mode of operation. The islanded microgrid system comprises multiple Distributed Generation (DG) units connected with general topology. The proposed one DoF voltage control system provides robust performance alongside robust stability against various sources of uncertainty, such as local load variations, Plug-and-Play (PnP) operation of the DG units, topology variations, and different subsequent changes. To that end, first, each DG unit is modeled as a Linear Time-Invariant (LTI) system with a new polytopic type uncertainty. Next, a Linear Matrix Inequality (LMI) based state-feedback control system with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H_\infty$</tex-math></inline-formula> performance criteria is implemented on the LTI polytopic islanded microgrid system. The controller's decentralized structure excludes the need for communication channels, and the controller's robust performance characteristic eliminates the necessity of pre-filter design. Simulating multiple scenarios in MATLAB/SimPowerSystems Toolbox confirms the developed controller's performance in different scenarios, including voltage tracking, local load changes, PnP operation, and topology changes.

Distributed optimal voltage control strategy for AC grid with DC connection and offshore wind farms based on ADMM

A distributed optimal voltage control (DOVC) strategy based on the alternating direction method of multipliers (ADMM) is proposed for the AC grid with voltage source converter (VSC) DC connection and offshore wind farms (OWFs). The proposed DOVC strategy achieves global optimal power distribution inside the AC grid with DC connection to minimize the power losses while minimizing the bus voltage deviation from the rated value as well as power losses inside several OWFs. The optimal power flow (OPF) of the AC grid with VSC DC connection combined with model predictive control (MPC) is relaxed by using the semidefinite programming (SDP) relaxation technique. The MPC-based voltage optimization problem for the OWF is formulated by considering the dynamics of WTs. The ADMM is used to decompose the large-scale strongly coupled optimization problem, which is calculated in AC-DC grid and OWF controllers in parallel while guaranteeing global optimality. Compared to conventional control methods, the control performance of the system is enhanced. A modified IEEE-30 bus system with DC connection incorporating three OWFs consisting of 288 wind turbines (WTs) is used to evaluate the proposed DOVC strategy.

Optimal Local Volt/Var Control for Photovoltaic Inverters in Active Distribution Networks

The penetration of photovoltaics (PVs) has been increasing in active distribution networks (ADN), which leads to severe voltage violation problems. PV inverters can provide fast and flexible reactive power support and are now allowed to participate in the voltage regulation process. This paper proposes a real-time combined central and local Volt/Var control (VVC) strategy to mitigate voltage violation problems while minimizing the network power loss. Based on the historical PV and load data, the load flow and optimal power flow are performed in the centralized controller (CC) to obtain multiple voltage and optimal power settings for each PV system. The local controller (LC) then generates voltage control curves with these optimal scatters. To improve the voltage control effect, a novel 3-Dimension voltage control curve is proposed considering both the measurements of node voltage and PV generation. Moreover, a data-driven deep convolution neural network is designed and trained to generate optimal local voltage control curves without prior knowledge of specific curve functions. The proposed approach is tested on the IEEE 33-bus distribution system and simulation results verify the effectiveness in voltage control compared with the optimal Q(V) and Q(P) method.

Coordinated voltage and frequency control for HVDC sending end under pole-block fault: Using model predictive control

Once the pole-block fault occurs, serious power unbalance takes place in the high-voltage direct current (HVDC) transmission system, which results in severe voltage and frequency deviation. In this situation, to ensure the reliable operation of HVDC sending end, a large amount of power surplus should be dissipated. Regular dispatch schemes like generation shedding cannot always ensure rapidity and optimality and sometimes even lead to stability problems. To address this issue, this paper presents a model predictive control (MPC) scheme for coordinated operation of power sources, including synchronous condenser, battery energy storage system (BESS), and wind farm. It achieves the purpose of optimal voltage control and frequency regulation under HVDC pole-block fault. This scheme has two layers. Based on model predictive control and moving horizon estimation (MHE), the top-layer controller calculates the overall active and reactive power references, which are further fed to the bottom-layer power source controllers for execution. Simulation results demonstrate the effectiveness of the proposed scheme.

Distributed Imitation-Orientated Deep Reinforcement Learning Method for Optimal PEMFC Output Voltage Control

In order to improve the stability of proton exchange membrane fuel cell (PEMFC) output voltage, a data-driven output voltage control strategy based on regulation of the duty cycle of the DC-DC converter is proposed in this paper. In detail, an imitation-oriented twin delay deep deterministic (IO-TD3) policy gradient algorithm which offers a more robust voltage control strategy is demonstrated. This proposed output voltage control method is a distributed deep reinforcement learning training framework, the design of which is guided by the pedagogic concept of imitation learning. The effectiveness of the proposed control strategy is experimentally demonstrated.

Voltage Constrained Reactive Power Planning Problem for Reactive Loading Variation Using Hybrid Harris Hawk Particle Swarm Optimizer

This study proposes the application of an efficient and hybrid meta-heuristic algorithm of Harris Hawk-Particle Swarm Optimizer (HHOPSO) for solving voltage constrained reactive power planning (VCRPP) problem. The prime objective of present work is diminishing the transmission loss and overall operating cost coupled with retention of voltage consistency and enhanced congestion management in transmission lines. The efficacy of the proposed algorithm is tested on standard IEEE 57 bus test system with optimal transformer tap settings, reactive generation control, generator voltage control and optimal control of Var sources. The best location of Var sources is determined by Voltage Collapse Proximity Indicator (VCPI) method. Simulation results are generated under normal and variable reactive power loading condition which makes the reactive power planning problem more challenging and validates the robustness of the proposed algorithm. The results of proposed algorithm when compared with the other optimization algorithms surfaced in contemporary state of art literature yields superior solution in maintaining diversity and solution optimality.

Distributed optimal voltage control in islanded microgrids

Distributed optimal voltage control in islanded microgrids