Reactive Power Control Problem Research Articles

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.

The Influence of Reactive Power Control on the Turbine Generator Operational Reliability

The need to operate turbine generators in the reactive power absorption mode is stemming from a shortage of compensation devices in the grid. The operation of a conventional turbine generator as a reactive power compensator sometimes entails undesirable consequences for it. Recent years have seen a growing number of generator failures related to their prolonged operation at low excitation levels, which is the necessary condition for the generator to absorb reactive power. The article briefly describes the main factors that determine the relevance of the problem of reactive power control in the Moscow Region power system and the need to shift turbine generators for operation in the reactive power absorption mode. The article also provides information on the influence of operating modes in terms of reactive power control on the technical condition of the power plant main electrical equipment. The study is aimed at assessing the influence of reactive power control on the reliability of turbine generators. Based on a retrospective analysis of information on the technical condition of turbine generators and information about their damageability, recommendations on the possibility of using turbine generators for operation in reactive power absorption modes are drawn up. The operation of turbine generators in under-excitation modes eventually entails, regardless of their type and capacity, deterioration of the technical condition of the teeth of stator core end packages. In the absence of proper monitoring, fragments of crumbled sheets of active steel can cut through the stator winding case insulation, and the risk of its breakdown during operation or testing will increase. Under these circumstances, it is not possible to guarantee their reliable long-term operation.

Coordinated Electric Vehicle Active and Reactive Power Control for Active Distribution Networks

The deployment of renewable energy in power systems may raise serious voltage instabilities. Electric vehicles (EVs), owing to their mobility and flexibility characteristics, can provide various ancillary services including active and reactive power. However, the distributed control of EVs under such scenarios is a complex decision-making problem with enormous dynamics and uncertainties. Most existing literature employs model-based approaches to formulate active and reactive power control problems, which require full models and are time-consuming. This article proposes a multiagent reinforcement learning algorithm featuring a deep deterministic policy gradient (DDPG) method and a parameter sharing framework to solve the EVs’ coordinated active and reactive power control problem toward both demand-side response and voltage regulations. The proposed algorithm can further enhance the learning stability and scalability with privacy perseverance via the location marginal prices. Simulation results based on a modified IEEE 15-bus network are developed to validate its effectiveness in providing system charging and voltage regulation services. The proposed location marginal price (LMP) PSDDPG algorithm is evaluated to achieve 38%, 16%, and 25% speedup, and 1.58, 0.69, and 0.27 times higher reward over the benchmarks DDPG, TD3, and LMP-DDPG, respectively.

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.

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.

Effective Volt/var Control for Low Voltage Grids with Bulk Loads

This paper investigates the voltage and reactive power control problem in low voltage grids with connected prosumers and bulk loads. The X(U) local control, which maintains the voltage at the feeders’ ends within a predefined band, and its combination with Q-Autarkic customer plants are the most effective and reliable strategies in grids with high prosumer share. However, these strategies may need adaptations to guarantee voltage limit compliance when bulk loads, such as electric vehicle parking garages and community-owned photovoltaic systems, are connected to the low voltage feeders. This paper extends the X(U) local control concept to involve bulk loads in Volt/var control and investigates the resulting load flows in different real low voltage grids. The results show that the extended control arrangement reliably removes all voltage limit violations by deteriorating the effectiveness of the original X(U) local control arrangement: reactive power flows and equipment loading within the low voltage grids are increased.

Reactive power control

The article investigates the methods of control of reactive power modes. It is shown that ensuring the efficiency of electricity transmission and distribution is inseparable from setting and solving problems related to reducing electricity losses in networks. Moreover, one of the most effective ways to reduce electricity losses, as well as improve its quality at the terminals of electrical receivers is to compensate for reactive power, which is carried out using various compensating devices. It is shown that the control of the reactive power mode is carried out in accordance with the Methodology for calculating the fee for the flow of reactive energy between the power transmission organization and its consumers. It is shown that the indicator of economically advantageous value of the level of reactive energy consumption can be cos φз, the value of which is predetermined. The procedure for controlling the reactive power mode contains two main stages: the stage of determining the magnitude of the possible reduction of the current cos φ above the set and the stage of determining and implementing control effects aimed at eliminating possible deviations. Preferably, it is preferable to focus on those methods that are based on the study of forecast estimates, which constitute the source information for management decisions. It is expedient to use adaptive methods of exponential smoothing as a basis for operative forecasting of electric loading. Reactive power mode is controlled by compensating units. It is shown that the control of voltage modes in the power supply system significantly affects the modes of reactive power consumption. In this regard, it is advisable to comprehensively solve the problem of reactive power control both by controlling the compensating units and the impact on the voltage regimes of the power supply system. In the calculation model, the reactive load of the distribution network is given by its static characteristics, which can be the basis for regulating the reactive load. To implement regulation in the power supply centers of electrical networks, technical means are provided on the basis of changing the transformation coefficient or generating reactive power by counter-voltage regulation.

Distributed Resilient Voltage and Reactive Power Control for Islanded Microgrids under False Data Injection Attacks

This paper addresses the problem of voltage and reactive power control of inverter-based distributed generations (DGs) in an islanded microgrid subject to False Data Injection (FDI) attacks. To implement average voltage restoration and reactive power sharing, a two-layer distributed secondary control framework employing a multiagent system (MAS)-based dynamic consensus protocol is proposed. While communication network facilitates distributed control scheme, it leads to vulnerability of microgrids to malicious cyber-attacks. The adverse effects of FDI attack on the secondary controller are analyzed, and the necessary and sufficient conditions to model stealthy attack and probing attack are discussed in detail. A trust-based resilient control strategy is developed to resist the impacts of FDI attack. Based on the forward-backward consistency criterion, the self-monitoring and neighbor-monitoring mechanisms are developed to detect the misbehaving DGs. A group decision-making mechanism is also introduced to settle conflicts arising from the dishonest trust index caused by colluding attacks. A novel mitigation countermeasure is designed to eliminate the adversarial effects of attack: the discarding information mechanism is used to prevent the propagation of false data in the cooperative network while the recovery actions are designed to correct the deviations of collective estimation error in both transient disturbance and continuous FDI attack scenarios. Through a theoretical analysis, it is proved that the proposed mitigation and recovery mechanism can maintain the correct average estimates of voltage and reactive power, which ensures the secondary control objectives of microgrids under FDI attack. Simulation results on an islanded microgrid show the effectiveness and resilience of the proposed control scheme.

A Projection-Based Decomposition Algorithm for Distributed Fast Computation of Control in Microgrids

We present a novel algorithm for the computation of optimal predictive storage and reactive power control in microgrids operating in grid-tied mode. This algorithm is based on the dual decomposition method, but local constraints are handled by means of primal projections. The use of projections significantly increases the speed of convergence of the approach with respect to the dual decomposition algorithm, which uses dual variables for the local constraints of the problem. Convergence of the algorithm to an optimizer is shown for a general class of quadratic programs, which includes a storage and reactive power control problem. In addition, a distributed implementation of the algorithm which is based on the Jacobi overrelaxation is presented. Simulations compare the algorithm performance with that of a purely dual decomposition approach over a set of standard distribution feeder test cases acting as grid-connected microgrid proxies.

Optimal reactive power control of DGs for voltage regulation of MV distribution systems using sensitivity analysis method and PSO algorithm

This paper addresses the problem of reactive power control of distributed generation (DG) units in the medium voltage (MV) distribution systems to maintain the system voltages within the predefined limits. An efficient approach for the load flow calculation is used here which is based on the topological structure of the network. It has been formulated for the radial distribution systems. A direct voltage sensitivity analysis method is developed in this paper which is also based on the topological structure of the network and independent of the network operating points. Thus, the sensitivity matrix is calculated once with the load flow program and it is used in all the system working conditions. The problem of DGs reactive power control is formulated as an optimization problem which uses the sensitivity analysis for linearizing the system around its operating points. The objective of the optimization problem is to return the system voltages inside the permitted range by using the reactive power of DGs in an optimal way. The optimal solutions are obtained by implementing particle swarm optimization (PSO) algorithm. Then, the results are verified by running a load flow considering new values of DGs reactive power. The procedure is repeated as long as a voltage violation is observed. Simulation results reveal that the proposed algorithm is capable of keeping the system voltages within the permitted limits.

Hybrid intelligent algorithm to solve multi objective reactive power control problem

Reactive power and voltage control is a minimization problem. In this paper the multi objective function consist of minimization of real power losses, minimization of voltage deviation and minimization of voltage stability index subjected to equality and inequality constraints are considered. This can be achieved by proper adjustment of control variables such as the generator voltages, the transformer tap settings, and the switchable VAR sources that would minimize the real power loss, the voltage deviation and the voltage stability index. In this work, the hybrid intelligent algorithm Genetic Algorithm - Artificial Bee Colony (GA- ABC) are used. The hybrid algorithms used are the combination of best features of two intelligent algorithms. GA has good crossover operator and hence inspires hybridization with ABC to identify the source effectively. The population is evolving iteration by iteration to find global optimal solution. To evaluate the performance of the developed algorithm standard IEEE 30 bus system is used. This hybrid algorithm gives better optimal solution as compared to individual intelligent algorithm.

An Efficient Approach for Voltage Stability Improvement Based on Artificial Bee Colony Algorithm

Voltage stability assessment plays an important role in planning and operation of secured power system economically. In this paper an effective algorithm based on Artificial Bee Colony approach is developed to solve reactive power control problem for the improvement of voltage in all load buses. This algorithm employs optimal settings of control variables to achieve best solution for the objective functions. The proposed formulation is examined in standard IEEE 30 bus test system with the objective function of real power loss minimization as well as minimization of voltage stability index for the improvement of voltage stability. The test results are compared with the existing approach explained in the literature. From the test results it is absorbed that the voltage stability is improved satisfactorily.

Multi-objective probabilistic reactive power and voltage control with wind site correlations

This paper proposes a multi-objective probabilistic reactive power and voltage control in distribution networks using wind turbines, hydro turbines, fuel cells, static compensators and load tap changing transforms. The objective functions are total electrical energy costs, the electrical energy losses, total emissions produced, and voltage deviations during the next day. Since the wind sources and load demand have intermittent characteristics, a probabilistic load flow based on 2m + 1 point estimated method is used to investigate the objective functions. The correlation in wind speed is considered as the distances between WTs are not large in distribution systems. Furthermore, a multi-objective modified bee swarm optimization is proposed to solve the optimization problem by defining a set of non-dominated points as the solutions. A fuzzy based clustering is used to control the size of the repository and a niching method is utilized to choose the best solution during the optimization process. Performance of the proposed algorithm is tested on a 69-bus distribution feeder. The results confirm the necessity of modeling the reactive power and voltage control problem in a stochastic framework. Also, the effects of wind site correlations on different objective functions are discussed completely.

Robust Adaptive Reactive Power Control for Doubly Fed Induction Generator

The problem of reactive power control for mains-side inverter (MSI) in doubly fed induction generator (DFIG) is studied in this paper. To accommodate the modelling nonlinearities and inherent uncertainties, a novel robust adaptive control algorithm for MSI is proposed by utilizing Lyapunov theory that ensures asymptotic stability of the system under unpredictable external disturbances and significant parametric uncertainties. The distinguishing benefit of the aforementioned scheme consists in its capabilities to maintain satisfactory performance under varying operation conditions without the need for manually redesigning or reprogramming the control gains in contrast to the commonly used PI/PID control. Simulations are also built to confirm the correctness and benefits of the control scheme.

Differential Evolution Algorithm Based Optimal Reactive Power Control for Voltage Stability Improvement

Voltage stability assessment plays a major role in planning and operation of power system. This paper presents an efficient approach to solve reactive power control problem for voltage stability improvement. In this approach the voltage stability index is formulated to identify the most vulnerable bus at various operating conditions. The bus with the value of maximum VSI is considered as the most critical bus. To maintain the stability of the system the severity of the load buses has to be minimized. This can be achieved by the optimal settings of control variables using Differential Evolution Algorithm. The effectiveness of the proposed approach has been examined on the standard IEEE 30 bus test system under stressed and contingency condition.

以量子启发式引力搜寻法作配电系统的虚功率及电压控制

本文是藉由调度配电系统之主变压器有载分接头和电容器来控制虚功率与电压。其主要目的是降低配电系统中的馈电线损失及改善汇流排的电压偏移量，以达到节约能源、并使系统电压能够操作在稳定的范围内。利用虚功率与电压控制设备，加以适当的规划调度，来控制电压及提供虚功率，同时也必须满足限制条件，其限制条件包含主变压器有载分接头及所有电容器一天之中的最大总切换次数需在限制的次数内，及各汇流排的电压需维持在其限定的范围内，以及电力守恒与线路输电容量限制。在满足限制式的条件下，配电网路能供给用户稳定的电压并提升供电品质。本文采用量子启发式引力搜寻法(Quantum-Inspired Gravitational Search Algorithm, QGSA)来作配电系统的虚功率及电压控制问题。量子启发式引力搜寻法是一种以引力搜寻法为基础，并结合量子计算观念与原理，更新解的位置，使其可以有效的搜寻到全域最佳解的算法。为了证实本文所使用的方法有效性，文中以一30个汇流排的配电系统作测试。由结果得知，本文所采用的方法确实能有效的得到令人满意的结果。 This paper investigates reactive power and voltage control in a distribution system by dispatching the main transformer ULTC and capacitors. The main purpose is to reduce the feeder loss and improve the bus voltage profile of the distribution system. The devices of reactive power and voltage control are properly dispatched to control the voltage and to provide reactive power. And the constraints that must be considered include the maximum allowable number of switching operation in a day for ULTC and each capacitor, the bus voltage limit on each bus, the line flow limit on each feeder, and the power conservation limit on each bus. Under satisfying these constraints, the distribution network can provide stable voltage to the consumers, and the quality of power supply can be enhanced. The quantum-inspired gravitational search algorithm (QGSA) is proposed for solving the reactive power and voltage control problem in a distribution system. The QGSA approach is based on the gravitational search algorithm. And the concept and principles of the quantum computing are added to update the solution, so that the global optimal solution can be efficiently found. To confirm the usefulness of the proposed method, a 30-bus distribution system is performed. It is found from the results that the proposed method can effectively get a satisfying solution.

A hybrid PSO technique for procuring VAR ancillary service in the deregulated electricity markets

This paper develops a new market-based technique for acquiring VAR ancillary service in the electricity market. The main objective of the developed market is to enable transmission operator “TO” to procure VAR service in a long term contract from the critical VAR providers that satisfy minimum VAR service payment while maintaining system security. Reactive power control problem for voltage stability is introduced into the VAR market problem in an explicit manner for normal and emergency states. An integration of particle swarm optimization “PSO” is presented with successive linear programming “SLP” for dealing with the VAR ancillary service problem. The problem is formulated as a large-scale nonlinear constrained optimization problem with a non-differentiable objective function representing VAR payment and operational costs. This type of problem is hard to be treated straightforwardly by the classical optimization methods. Therefore, we propose here a two-layer hybrid PSO/SLP approach, which is suited for carrying out the difficulties associated with non-differentiable and discontinuous objective functions. The proposed method has been examined on the standard IEEE 57 bus-system and compared with GA/SLP method to demonstrate its capability.

An approximate solution to lower tap changing or capacitors regulator times in reactive power control in distribution systems

In the operation of distribution system, if the tap of transformer and capacitors regulators were frequently changed, these devices would be easily damaged. So it is necessary to lower the tap-changing and capacitors regulators times. Due to the fact that capacitors exert smaller influence on the voltage level than transformers do, it is appropriate to first adjust the capacitors settings with tap position fixed so as to reduce substantially the probability of voltage limits violations while minimizing losses, and then to adjust transformer taps to maintain voltage within the specified range. The problem of reactive power and voltage control is well decoupled. The objective function is not related to the taps changing of transformers, but it is only related to capacitive susceptance; therefore, the capacitors variables can be adjusted alone to reduce the objective function, then the taps changing of transformers to meet the voltage constraints. In this paper, a two-phase based loss optimization algorithm, which is suitable to distribution network, is presented. In this method, losses in the system can be optimized, and the tap-changing or capacitors regulators times can be lowered. IEEE 30-bus system indicates the proposed algorithm’s validity, fast convergence, and computation efficiency.

Ant Colony System-Based Algorithm for Constrained Load Flow Problem

This paper presents the ant colony system (ACS) method for network-constrained optimization problems. The developed ACS algorithm formulates the constrained load flow (CLF) problem as a combinatorial optimization problem. It is a distributed algorithm composed of a set of cooperating artificial agents, called ants, that cooperate among them to find an optimum solution of the CLF problem. A pheromone matrix that plays the role of global memory provides the cooperation between ants. The study consists of mapping the solution space, expressed by an objective function of the CLF on the space of control variables [ant system (AS)-graph], that ants walk. The ACS algorithm is applied to the IEEE 14-bus system and the IEEE 136-bus system. The results are compared with those given by the probabilistic CLF and the reinforcement learning (RL) methods, demonstrating the superiority and flexibility of the ACS algorithm. Moreover, the ACS algorithm is applied to the reactive power control problem for the IEEE 14-bus system in order to minimize real power losses subject to operating constraints over the whole planning period.

Optimum Firing of SVC's for Normal and Emergency Reactive Power Control

An approach for optimal on-line control strategy of system voltage and reactive power using static VAR compensators (SVC) has been developed. The approach is based on sensitivity relations and linear programming. The simulation of SVC's parameters and firing angle has been presented in the problem of reactive power control in the power system. The article investigates the effect of SVC's on the power system during normal and emergency conditions and determines the different operating modes of SVC's. The choice of SVC's firing angles as control variables in the developed algorithm can be practically implemented for smooth on-line control. Also, the multiple linear objective functions have been considered to be optimized simultaneously and consequently the inherent conflict between them have been overcome. The developed approach is tested on a Word & Hale system to reveal its advantages.