Optimal Reactive Power Control Research Articles

Adaptive Reactive Power Optimization in Offshore Wind Farms Based on an Improved Particle Swarm Algorithm

To address the reactive power optimization control problem in offshore wind farms (OWFs), this paper proposes an adaptive reactive power optimization control strategy based on an improved Particle Swarm Optimization (PSO) algorithm. Firstly, an OWF multi-objective optimization control model is established, with the total sum of voltage deviations at wind turbine (WT) terminals, active power network losses, and reactive power margin of WTs as comprehensive optimization objectives. Innovatively, adaptive weighting coefficients are introduced for the three sub-objectives, enabling the weights of each optimization objective to be adaptively adjusted based on real-time operating conditions, thus enhancing the adaptability of the reactive power optimization model to changes in operating conditions. Secondly, a Uniform Adaptive Particle Swarm Optimization (UAPSO) algorithm is proposed. On one hand, the algorithm initializes the particle swarm using a uniform initialization method; on the other hand, it improves the particle velocity update formula, allowing the inertia coefficient to adaptively adjust based on the number of iterations and the fitness ranking of particles. Simulation results demonstrate the following: (1) Under various operating conditions, the proposed adaptive multi-objective reactive power optimization strategy can ensure the stability of node voltages in offshore wind farms, reduce active power losses, and simultaneously improve reactive power margins. (2) Compared with the traditional PSO algorithm, UAPSO exhibits an approximately 10% improvement in solution speed and enhanced solution accuracy.

Network partition and distributed voltage coordination control strategy of active distribution network system considering photovoltaic uncertainty

Aiming at the voltage exceeding problem caused by the introduction of Photovoltaic (PV) in Active distribution network (ADN), a comprehensive electrical distance index was established considering active and reactive voltage sensitivity, and the uncertainty of traditional PV probability density function (PDF) was optimized by random simulation method. The electrical distance expectation matrix between nodes was established by discretization of PV output probability distribution characteristics, and the ADN system was partitioned by affinity propagation (AP) algorithm. Taking active power loss as the objective function, the non-convex reactive power optimization control problem is transformed into a convex quadratic programming problem by LinDistFlow equation. An improved alternating direction method of multipliers (ADMM) which adaptively adjusts the penalty parameters is used to convert the finite boundaries of adjacent regions into data. By coordinating on-load tap-changer (OLTC), capacitor banks (CBs), and PV inverters on different time scales, the fast optimal control of global voltage in ADN is realized. The proposed method is tested on IEEE 33-bus and IEEE 123-bus distribution systems. After the optimization control algorithm described in this paper, the system network loss was reduced by 3.88%. Compared with the semi-definite programming relaxation, the calculation speed of LinDistFlow equation is increased by 72.2%. The results fully verify the feasibility and high efficiency of the control strategy described in this paper.

Multi‐layer and timescale optimal power flow control in capacitive coupling inverter‐based microgrids

AbstractCompared with the traditional solution, the microgrid (MG) employed capacitive coupling inverters (CCIs) with higher reactive power capacity can effectively reduce the probability of low voltage. However, because of the special coupling structure, different power flow control methods will greatly affect the cost and operation efficiency of the CCI. A multi‐layer and timescale optimal power flow control considering inverter‐layer is therefore proposed in this paper. Firstly, the power output characteristics of CCI are analyzed, and the power flow control model of CCI with an energy storage unit is established. Secondly, an optimal control method on different timescales was proposed. By coordinating the active power output and the reserved charging state margin of the energy storage unit, the CCI‐MG reactive power regulation with the most economical and minimum loss was realized, so as to achieve long‐term voltage stability and dynamic satisfaction of load demands. Furthermore, the optimal power flow control of CCI is proposed. The simulation results show that the proposed method can effectively realize the optimal control of active and reactive power of MG, and also improve the voltage stability of AC bus under the economical operation of CCI.

Source Network Load Storage Access to Power Wireless Private Network Technology Based on 5G Ultra Dense Networking

We make the source network load storage access power wireless private network, this paper proposes a source network load storage access power wireless private network technology based on 5G ultra dense network. The multiple rotation scheduling and self-organizing learning methods are used to establish the deployment model of the source network load storage access node of the power wireless private network under the 5G communication mode. According to the routing control algorithm design of the 5G ultra dense networking node, combined with the integration analysis of the access load parameters, the source network load storage access model of the 5G ultra dense networking under the dynamic load distributed control mode is established. Through the method of optimal control of reactive power and voltage of distribution network, the transmission link equilibrium structure model of 5G source network load storage access to power wireless private network is constructed. Combined with the coverage analysis of link topology structure and the benefit maximization constraint analysis of production and consumption users, the active and reactive capacity analysis of transaction between production and consumption user groups and multiple production and consumption users is adopted. Combined with the energy storage characteristics analysis and power flow parameter calculation of the source network load storage access power, the 5G ultra dense networking and private network access to the source network load storage access power are realized. The test shows that this method has better power balance dispatching ability and larger output power gain when it is applied to the design of source network load storage access power wireless private network.

Reactive power optimization control for multi-energy system considering source-load uncertainty

Reactive power control in power systems is an important way to ensure stable system operation. However, as the coupling between multi-energy systems increases, the uncertainty of supply and demand in the power system poses new challenges for reactive power control. Therefore, the main objective of this study is to perform reactive power balance control of the grid in a multi-energy system. Firstly, we studied the characteristics of reactive power demand during the energy transport of a multi-energy system. In addition, we studied the reactive power output characteristics of various power sources and their uncertainties. The reactive power balance of the multi-energy system and its uncertainty model are established. Then, we studied the adjustable pressure limits of the energy medium transmission pipelines of the heat supply network and the gas pipeline network. The reactive power demand capacity of the system is determined according to the system's energy condition and safe operation boundary. Finally, we established a reactive power optimization simulation model of the multi-energy system, considering the heat-gas network constraints. The study proposes that the optimal reactive power control method can improve the voltage stability of the multi-energy system.

Co-simulation-based optimal reactive power control in smart distribution network

The increasing integration of distributed energy resources such as photovoltaic (PV) systems into distribution networks introduces intermittent and variable power, leading to high voltage fluctuations. High PV integration can also result in increased terminal voltage of the network during periods of high PV generation and low load consumption. These problems can be solved by optimal utilization of the reactive power capability of a smart inverter. However, solving the optimization problem using a detailed mathematical model of the distribution network may be time-consuming. Due to this, the optimization process may not be fast enough to incorporate this rapid fluctuation when implemented in real-time optimization. To address these issues, this paper proposes a co-simulation-based optimization approach for optimal reactive power control in smart inverters. By utilizing co-simulation, the need for detailed mathematical modeling of the power flow equation of the distribution network in the optimization model is eliminated, thereby enabling faster optimization. This paper compares three optimization algorithms (improved harmony search, simplicial homology global optimization, and differential evolution) using models developed in OpenDSS and DigSilent PowerFactory. The results demonstrate the suitability of the proposed co-simulation-based optimization for obtaining optimal setpoints for reactive power control, minimizing total power loss in distribution networks with high PV integration. This research paper contributes to efficient and practical solutions for modeling optimal control problems in future distribution networks.

An enhanced arithmetic optimization algorithm for optimal control of reactive power

AbstractIn this article, an enhanced arithmetic optimization algorithm (EAOA) is utilized to resolve the optimal reactive power dispatch problem (ORPD) of power plants, which is a non‐linear, non‐smooth, complex optimization problem. Typically, it is formulated as a constrained optimal power flow problem. This paper utilizes the gamma distribution to generate initial random solutions, which improves the accuracy of the arithmetic optimization algorithm (AOA). The original mode of deviation of math optimizer accelerated (MOA) is replaced with the improved pattern of change in the prey's energy in Harris Hawk Optimization (HHO), which creates a superior balance between the exploration and exploitation of AOA. This paper implements logarithmic and exponential operators in place of division and multiplication operators, which improves the exploration ability of AOA and also utilizes the gamma distribution in the generation of random numbers, which are essential for the selection of operators in both search phase which enlarges the search area and improves the global optimum solution. A parametric analysis is performed on 10 benchmark functions for nine possible values before selecting the algorithm's initial parameters. A comparative statistical analysis along with Friedman's test is carried out on 23 benchmark functions to test the overall performance as compared to Sine‐Cosine Algorithm (SCA), Swarm‐Salp Algorithm, Grey Wolf Optimization (GWO), and basic Arithmetic Optimization Algorithm (AOA). EAOA is applied to the IEEE 57 bus system for optimal reactive power control. EAOA reports a significant reduction in a power loss of 23.2 MW, which is 18.596% smaller than the system's base case power loss of 28.5 MW as compared to Artificial Bee Colony (ABC), Firefly Algorithm (FA), Bat‐inspired Algorithm (BAT), Cuckoo Search Algorithm (CSA), and Modified Ant Lion Algorithm (MALO) which reports a reduction in a power loss of 14.73%, 15.43%, 15.43%, 16.14%, and 16.14% respectively. EAOA reduces the TVD to 0.76 pu, compared to ABC, FA, BAT, CSA, and MALO, which minimizes the TVD to 0.84, 0.82, 0.88, 0.82, and 0.79, respectively. This proves that EAOA performs better in resolving ORCP problems regarding the quality of solutions and convergence. A detailed comparative statistical analysis of EAOA with CSA, BAT, and WOA (Whale Optimization Algorithm) is conducted in terms of Probability Distribution Functions (PDF) and Cumulative Distribution Functions (CDF), along with the Wilcoxson sign rank test, which proves that EAOA gives the solution with a higher degree of precision.

Voltage Management of Distributed Photovoltaic Cluster Connected to a Distribution Network

The proportion of distributed photovoltaic grid connection in the distribution network is gradually increasing, and its characteristics of high volatility and poor stability have brought new challenges to the overvoltage problem faced by the distribution network structure and safe and stable optimization operation. This paper mainly studies clustering and reactive power optimization control of distributed Photovoltaic. The system architecture of the cloud edge is constructed, and the voltage autonomy strategy within the region after partition is proposed. The model is established with the total voltage offset and the minimum line loss as targets. Adopting adaptive PSO-BP algorithm model for optimization solution. Finally, conduct simulation analysis on the MATLAB platform. We can see that the proposed method can not only reduce the uneven voltage distribution, network loss, but also reduce the calculation burden of the controller has a positive effect.

Optimal Power Control in Distributed Generation Units in an Islanded Microgrid

This article studies the effect of new optimization techniques with the aim of finding the best solution using a fuzzy approach for operating an independent microgrid. The power flow technique is used to model and solve the problem of optimal active and reactive power control of distributed generation units (DGs) in an island microgrid (MG), while taking into account the improvement of the voltage profile and the voltage stability index by regulating the droop characteristics of DG sources. The suggested algorithms, Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Harmony Search (HS), Hybrid HS-GA, and Teaching-Learning Based Optimization, solve the optimization issue (TLBO). The numerical results of optimization algorithms are compared in terms of best solution, number of iterations, and convergence rate. Regarding multi-objective nature of the problem, the non-dominated sorting method is used to obtain the Pareto front for all algorithms. Finally, the best solution is selected from Pareto solutions using proposed fuzzy approach. Simulation results on the standard IEEE 33-bus network in MATLAB show that the TLBO algorithm has higher efficiency, and faster convergence rate compared to other methods in this study.

Research on Reactive Power Optimization Control Method for Distribution Network with DGs Based on Improved Second-Order Oscillating PSO Algorithm

With the increasing penetration of distributed generation (DG) in the distribution network, the original network structure of the distribution network has been changed. In addition, the randomness and intermittency of renewable power generation will also have an impact on the voltage and power flow of the distribution network. To solve this problem, this paper proposes a reactive power optimization control method for distribution network with DGs based on second-order oscillating particle swarm optimization (PSO) algorithm with a constriction factor. Considering the economic operation of the distribution network, the proposed control method realizes the coordinated operation of the DGs and battery group with the conventional static reactive power compensation device, so as to improve the voltage quality of the distribution network and reduce the system network loss. At the same time, an improved second-order oscillating PSO algorithm is proposed to improve the speed and convergence of the multiobjective algorithm. Finally, the effectiveness of the proposed control method is verified by using MATLAB/Simulink on IEEE 33 bus distribution network with DGs in both static and dynamic situations.

Coordinated active and reactive power management for enhancing PV hosting capacity in distribution networks

AbstractThis paper proposes an operational planning model based on optimal active and reactive power control strategies to enhance solar photovoltaics (PV)’ hosting capacity in distribution networks. Reactive power control is carried out through optimum static VAr compensators (SVCs) placement, while active power control is performed through flexible loads, particularly shiftable and interruptible loads. The first stage of the proposed two‐stage stochastic model assigns decision‐making regarding calculating PV hosting capacity at different nodes, in addition to the allocation and capacity of SVCs. In the second stage, the first stage decisions are assessed to ensure the power flow constraints under various uncertainties such as daily load and stochastic PV generation. The presented model is investigated through numerical analyses on modified IEEE 15‐bus and IEEE 33‐bus distribution systems considering different active‐reactive strategy cases. While most previous works only rely on one type of active or reactive power control strategy, this study investigates the challenges of the respective application of active and reactive power control in various modes of fundamental practices. The obtained results prove the superiority of the proposed hybrid active‐reactive control strategy for enhancing PV hosting capacity compared to respective active or reactive power controls.

Solar-PV inverter for the overall stability of power systems with intelligent MPPT control of DC-link capacitor voltage

This paper demonstrates the controlling abilities of a large PV-farm as a Solar-PV inverter for mitigating the chaotic electrical, electromechanical, and torsional oscillations including Subsynchronous resonance in a turbogenerator-based power system. The oscillations include deviations in the machine speed, rotor angle, voltage fluctuations (leading to voltage collapse), and torsional modes. During the night with no solar power generation, the PV-plant switches to PV-STATCOM mode and works as a Solar-PV inverter at its full capacity to attenuate the oscillations. During full sun in the daytime, on any fault detection, the PV-plant responds instantly and stops generating power to work as a Solar-PV inverter. The PV-farm operates in the same mode until the oscillations are fully alleviated. This paper manifests the control of the DC-link capacitor voltage of the Solar-PV inverter with a bacterial foraging optimization-based intelligent maximum power point tracking controller for the optimal control of active and reactive power. Kundur’s multi-machine model aggregated with PV-plant is modeled in the Matlab/Simulink environment to examine the rotor swing deviations with associated shaft segments. The results for different test cases of interest demonstrate the positive outcomes of deploying large PV-farms as a smart PV-STATCOM for controlling power system oscillations.

A novel meta-heuristic approach for optimal RPP using series compensated FACTS controller

One of the most important aspects of power system planning is reactive power (VAR- Volt Ampere- Reactive) optimization and voltage control, which has an influence on both economics and stability of the transmission systems. It is perhaps one of the most difficult optimization problem to solve since it is nonlinear and includes both consistent and sporadic choice parameters. The goal is to primarily calculate the cost components, such as operating cost due to energy loss, series compensation (Thyristor-Controlled series Capacitor-TCSC) device operating costs, and actual power loss cost. In the proposed work, to enhance system voltage profile, TCSC is deployed at weak points across several echelon after computing mathematical models for standard benchmark functions. The paper goes into great depth with application of Oppositional based learning on Harris Hawks Optimizer (OHHO). This meta heuristic optimization approach, has been used to solve the VAR optimization issue. Finally, the benchmark functions outputs are thoroughly examined for two test systems like Ward Hale 6 bus system and modified IEEE- 30 bus test system to demonstrate the validity of the proposed hybrid intelligent approach for series compensated FACTS controller.

A split Bregman method solving optimal reactive power dispatch for a doubly-fed induction generator-based wind farm

This paper proposes an optimal reactive power control method to maximize wind farm revenue and minimize the total electrical losses of a doubly-fed induction generator (DFIG)-based wind farm. Specifically, the split Bregman method is used to solve the optimal control problem in a distributed manner. That is, the optimization problem is decomposed into sub-problems by the optimal distributed control strategy, and each sub-problem is solved independently in each local controller through the parallel method, which reduces the calculating burden and improves the information privacy. Thus, when a fault occurs, the proposed distributed control strategy can overcome the system fault and improve the reliability and security of the system. Furthermore, an economic financial model of annual revenue is contributed to examine the income impact with or without certified emission reduction (CER) by the clean development mechanism (CDM). Compared with the dual ascent (DA) method, sequential quadratic programming (SQP) method and the proportional dispatch method (PDM), the annual revenue (AR) of the wind farm using the proposed split Bregman method is the highest. Simulation results demonstrate that this method has promising performance in both optimization quality and computational efficiency.

Optimal reactive power control for dual‐active‐bridge converter using improved dual‐phase‐shift modulation strategy for electric vehicle application

AbstractDual active bridge (DAB) based DC‐DC converters are in high demand for offering quick charging capacity to Electric vehicles (EVs). The conventional technique for DAB is based on single‐phase‐shift (SPS), though it provides simple control but suffers from high peaking in the inductor current under varying charging rates for EV and dc‐link voltage ratio. In this paper, initially the expression for optimal reactive power flow into DAB converter under SPS and dual phase‐shift (DPS) control techniques are thoroughly examined. Based on the analytical analysis, an improved dual‐phase‐shift modulation (IDPSM) method for real and reactive power control through a DAB converter is proposed for varying charging rates of an EV and dc‐link voltage ratio. The proposed IDPSM control technique is implemented using the inverse perturb and observe (IPO) method that provides optimum inner phase‐shift between the legs of the H‐bridge to track minimum reactive power or back power flow into the system. This method automatically adjusts the inner and outer phase shift ratios under different loading conditions. Further, the proposed IDPSM scheme enhances the range of ZVS operation even under light load conditions, which is lacking with the conventional control techniques. The Lyapunov theory used to determine the stability of the DAB DC/DC converter. To validate the proposed control scheme, a detailed simulation is carried out using MATLAB/Simulink with a 2 kW system under varying charging rates of an EV. Further, to support the effectiveness of the proposed scheme, a comparison with different phase shift modulation schemes is also presented. Finally, an experimental prototype for DAB is developed to confirm the feasibility of the control technique.

A New Self-Adaptive Teaching–Learning-Based Optimization with Different Distributions for Optimal Reactive Power Control in Power Networks

Teaching–learning-based optimization has the disadvantages of weak population diversity and the tendency to fall into local optima, especially for multimodal and high-dimensional problems such as the optimal reactive power dispatch problem. To overcome these shortcomings, first, in this study, a new enhanced TLBO is proposed through novel and effective θ-self-adaptive teaching and learning to optimize voltage and active loss management in power networks, which is called the optimal reactive power control problem with continuous and discontinuous control variables. Voltage and active loss management in any energy network can be optimized by finding the optimal control parameters, including generator voltage, shunt power compensators, and the tap positions of tap changers, among others. As a result, an efficient and powerful optimization algorithm is required to handle this challenging situation. The proposed algorithms utilized in this research were improved by introducing new mutation operators for multi-objective optimal reactive power control in popular standard IEEE 30-bus and IEEE 57-bus networks. The numerical simulation data reveal potential high-quality solutions with better performance and accuracy using the proposed optimization algorithms in comparison with the basic teaching–learning-based optimization algorithm and previously reported results.

Volt/VAR Optimization: A Survey of Classical and Heuristic Optimization Methods

Reactive power optimization and voltage control is one of the most critical components of power system operation, impacting both the economy and security of system operation. It is also one of the most complex optimization problems, being highly nonlinear, and comprising both continuous and discrete decision variables. This paper presents the problem formulation, and a thorough literature review and detailed discussion of the various solution methods that have been applied to the Volt/VAR optimization problem. Each optimization method is described in detail, and its strengths and shortcomings are outlined. The review provides detailed information on classical and heuristic methods that have been applied to the Volt/VAR optimization problem. The classical methods reviewed include (i) first- and second-order gradient-based methods, (ii) Quadratic Programming, (iii) Linear Programming, (iv) Interior-Point Methods, (iv) and mixed-integer programming and decomposition methods. The heuristic methods covered include (i) Genetic Algorithm, (ii) Evolutionary Programming, (iii) Particle Swarm Optimization, (iv) Fuzzy Set Theory, and (v) Expert Systems. A comparative analysis of the key characteristics of the classical and heuristic optimization methods is also presented along with the review.

Reactive Power Optimization Control for Multi-Energy System Based on Pressure Limit of Hot and Gas Pipe Network

Reactive Power Optimization Control for Multi-Energy System Based on Pressure Limit of Hot and Gas Pipe Network

Review on Reactive Power and Voltage Optimization of Active Distribution Network with Renewable Distributed Generation and Time-Varying Loads

With a high proportion of renewable distributed generation and time-varying load connected to the distribution network, great challenges have appeared in the reactive power optimization control of the active distribution networks. This paper first introduces the characteristics of active distribution networks, the mechanism and research status of wind power, photovoltaic, and other renewable distributed generators, and time-varying loads participating in reactive power and voltage optimization. Then, the paper summarizes the methods of reactive power optimization and voltage regulation of active distribution network, including multi-timescale voltage optimization, coordinated optimization of network reconfiguration and reactive power optimization, coordinated optimization of active and reactive power optimization based on model predictive control, hierarchical and zoning control of reactive power, and voltage and power electronic switch voltage regulation. The pros and cons of the reactive power optimization algorithms mentioned above are summarized. Finally, combined with the development trend of the energy Internet, the future directions of reactive power and voltage control technology in the active distribution network are discussed.

A Multi-Period Optimal Reactive Power Dispatch Approach Considering Multiple Operative Goals

The optimal reactive power dispatch (ORPD) problem plays a key role in daily power system operations. This paper presents a novel multi-period approach for the ORPD that takes into account three operative goals. These consist of minimizing total voltage deviations from set point values of pilot nodes and maneuvers on transformers taps and reactive power compensators. The ORPD is formulated in GAMS (General Algebraic Modeling System) software as a mixed integer nonlinear programming problem, comprising both continuous and discrete control variables, and is solved using the BONMIN solver. The most outstanding benefit of the proposed ORPD model is the fact that it allows optimal reactive power control throughout a multi-period horizon, guaranteeing compliance with the programmed active power dispatch. Additionally, the minimization of maneuvers on reactors and capacitor banks contributes to preserving the useful life of these devices. Furthermore, the selection of pilot nodes for voltage control reduces the computational burden and allows the algorithm to provide fast solutions. The results of the IEEE 118 bus test system show the applicability and effectiveness of the proposed approach.