Reactive Power Flow Convergence Adjustment Based on Deep Reinforcement Learning

Power flow calculation is the basis of power grid planning and many system analysis tasks require convergent power flow conditions.To address the unsolvable power flow problem caused by the reactive power imbalance,a method for adjusting reactive power flow convergence based on deep reinforcement learning is proposed.The deep reinforcement learning method takes switching parallel reactive compensation as the action space and sets the reward value based on the power flow convergence and reactive power adjustment.For the non-convergence power flow,the 500 kV nodes with reactive power compensation devices on the low-voltage side are converted into PV nodes by node type switching.And the quantified reactive power non-convergence index is acquired.Then,the action space and reward value of deep reinforcement learning are reasonably designed and the adjustment strategy is obtained by taking the reactive power non-convergence index as the algorithm state space.Finally,the effectiveness of the power flow convergence adjustment algorithm is verified by an actual power grid system in a province.

Research on Reactive Power Optimization of Offshore Wind Farms Based on Improved Particle Swarm Optimization

The lack of reactive power in offshore wind farms will affect the voltage stability and power transmission quality of wind farms.To improve the voltage stability and reactive power economy of wind farms,the improved particle swarmoptimization is used to optimize the reactive power planning in wind farms.First,the power flow of offshore wind farms is modeled,analyzed and calculated.To improve the global search ability and local optimization ability of particle swarm optimization,the improved particle swarm optimization adopts the adaptive inertia weight and asynchronous learning factor.Taking the minimum active power loss of the offshore wind farms as the objective function,the installation location of the reactive power compensation device is compared according to the node voltage amplitude and the actual engineering needs.Finally,a reactive power optimizationmodel based on Static Var Compensator is established inMATLAB to consider the optimal compensation capacity,network loss,convergence speed and voltage amplitude enhancement effect of SVC.Comparing the compensation methods in several different locations,the compensation scheme with the best reactive power optimization effect is determined.Meanwhile,the optimization results of the standard particle swarm optimization and the improved particle swarm optimization are compared to verify the superiority of the proposed improved algorithm.

A Two-Layer Active Power Optimization and Coordinated Control for Regional Power Grid Partitioning to Promote Distributed Renewable Energy Consumption

With the large-scale development and utilization of renewable energy,industrial flexible loads,as a kind of loadside resource with strong regulation ability,provide new opportunities for the research on renewable energy consumption problem in power systems.This paper proposes a two-layer active power optimization model based on industrial flexible loads for power grid partitioning,aiming at improving the line over-limit problem caused by renewable energy consumption in power grids with high proportion of renewable energy,and achieving the safe,stable and economical operation of power grids.Firstly,according to the evaluation index of renewable energy consumption characteristics of line active power,the power grid is divided into several partitions,and the interzone tie lines are taken as the optimization objects.Then,on the basis of partitioning,a two-layer active power optimization model considering the power constraints of industrial flexible loads is established.The upper-layer model optimizes the planned power of the inter-zone tie lines under the constraint of the minimum peak-valley difference within a day;the lower-layer model optimizes the regional source-load dispatching plan of each resource in each partition under the constraint of theminimumoperation cost of the partition,so as to reduce the line overlimit phenomenon caused by renewable energy consumption and save the electricity cost of industrial flexible loads.Finally,through simulation experiments,it is verified that the proposed model can effectively mobilize industrial flexible loads to participate in power grid operation and improve the economic stability of power grid.

Evaluation of Reactive Oxygen Species (ROS) Generated on the Surface of Copper Using Chemiluminesence

The antibacterial activity of copper is well-known from an ancient civilization, however, its biocidal mechanism has not been necessarily elucidated. Notwithstanding up to now, mainly 4 processes have been proposed. Among them, it is cleared that 4 kinds of reactive oxygen species (ROS): hydroxyl radical ·OH, hydrogen per oxide H2O2, superoxide anion ·O-2 and singlet oxygen 1O2, play an important role for contact-killing of bacteria, viruses and fungi. In this paper, generation of ROS on the surfaces of copper plates heated from room temperature to 673 K for 4.2 × 102 s in air, was investigated using the chemiluminescence. ROS have been evaluated by selecting the most suitable scavengers, such as 2-propanol for ·OH, sodium pyruvate for H2O2, nitro blue tetrazolium for ·O-2, and sodium azide NaN3 for 1O2. At the same time the outermost surface of copper, on which thin film of cuprous oxide Cu2O was first formed and then cupric oxide CuO was laminated on Cu2O, was examined by thin-film XRD and TEM analysis to estimate the amounts and kinds of copper oxides. It was found that the most amounts of ROS were obtained for the 573 K-heated Cu plate and they were composed of ·OH, H2O2, and ·O-2..

Probabilistic day-ahead simultaneous active/reactive power management in active distribution systems

Distributed generations(DGs)are main components for active distribution networks(ADNs).Owing to the large number of DGs integrated into distribution levels,it will be essential to schedule active and reactive power resources in ADNs.Generally,energy and reactive power scheduling problems are separately managed in ADNs.However,the separate scheduling cannot attain a global optimum scheme in the operation of ADNs.In this paper,a probabilistic simultaneous active/reactive scheduling framework is presented for ADNs.In order to handle the uncertainties of power generations of renewable-based DGs and upstream grid prices in an efficient framework,a stochastic programming technique is proposed.The stochastic programming can help distribution system operators(DSOs)make operation decisions in front of existing uncertainties.The proposed coordinated model considers the minimization of the energy and reactive power costs of all distributed resources along with the upstream grid.Meanwhile,a new payment index as loss profit value for DG units is introduced and embedded in the model.Numerical results based on the 22-bus and IEEE33-bus ADNs validate the effectiveness of the proposed method.The obtained results verify that through the proposed stochastic-based power management system,the DSO can effectively schedule all DGs along with its economic targets while considering severe uncertainties.

An Improved Catastrophic Genetic Algorithm and Its Application in Reactive Power Optimization

This paper presents an Improved Catastrophic Genetic Algorithm (ICGA) for optimal reactive power optimization. Firstly, a new catastrophic operator to enhance the genetic algorithms’ convergence stability is proposed. Then, a new probability algorithm of crossover depending on the number of generations, and a new probability algorithm of mutation depending on the fitness value are designed to solving the main conflict of the convergent speed with the global astringency. In these ways, the ICGA can prevent premature convergence and instability of genetic-catastrophic algorithms (GCA). Finally, the ICGA is applied for power system reactive power optimization and evaluated on the IEEE 14-bus power system, and the application results show that the proposed method is suitable for reactive power optimization in power system.

Chaos quantum particle swarm optimization for reactive power optimization considering voltage stability

The reactive power optimization considering voltage stability is an effective method to improve voltage stablity margin and decrease network losses,but it is a complex combinatorial optimization problem involving nonlinear functions having multiple local minima and nonlinear and discontinuous constraints. To deal with the problem,quantum particle swarm optimization (QPSO) is firstly introduced in this paper,and according to QPSO,chaotic quantum particle swarm optimization (CQPSO) is presented,which makes use of the randomness,regularity and ergodicity of chaotic variables to improve the quantum particle swarm optimization algorithm. When the swarm is trapped in local minima,a smaller searching space chaos optimization is used to guide the swarm jumping out the local minima. So it can avoid the premature phenomenon and to trap in a local minima of QPSO. The feasibility and efficiency of the proposed algorithm are verified by the results of calculation and simulation for IEEE 14-buses and IEEE 30-buses systems.

Power System Reactive Power Optimization Based on Fuzzy Formulation and Interior Point Filter Algorithm

Considering the soft constraint characteristics of voltage constraints, the Interior-Point Filter Algorithm is applied to solve the formulation of fuzzy model for the power system reactive power optimization with a large number of equality and inequality constraints. Based on the primal-dual interior-point algorithm, the algorithm maintains an updating “filter” at each iteration in order to decide whether to admit correction of iteration point which can avoid effectively oscillation due to the conflict between the decrease of objective function and the satisfaction of constraints and ensure the global convergence. Moreover, the “filter” improves computational efficiency because it filters the unnecessary iteration points. The calculation results of a practical power system indicate that the algorithm can effectively deal with the large number of inequality constraints of the fuzzy model of reactive power optimization and satisfy the requirement of online calculation which realizes to decrease the network loss and maintain specified margins of voltage.

Two-stage ADMM-based distributed optimal reactive power control method for wind farms considering wake effects

Since the connection of small-scale wind farms to distribution networks,power grid voltage stability has been reduced with increasing wind penetration in recent years,owing to the variable reactive power consumption of wind generators.In this study,a two-stage reactive power optimization method based on the alternating direction method of multipliers(ADMM)algorithm is proposed for achieving optimal reactive power dispatch in wind farm-integrated distribution systems.Unlike existing optimal reactive power control methods,the proposed method enables distributed reactive power flow optimization with a two-stage optimization structure.Furthermore,under the partition concept,the consensus protocol is not needed to solve the optimization problems.In this method,the influence of the wake effect of each wind turbine is also considered in the control design.Simulation results for a mid-voltage distribution system based on MATLAB verified the effectiveness of the proposed method.

Electrical Energy Transmission by Several Wires and Reactive Power Problems

The main goal of this article is to compare traditional three phase system with one wire three-phase system. There are several obvious advantages of one wire system, for example cost, reliability and other. But there is a problem as well, connected to reactive power in both systems. This article proposes explanation of reactive power emergence in systems SWER, three-phase and in their one wire versions.

Reactive Power Compensation and Harmonic Suppression for Power Supply System of HT-7U Superconductive Tokamak

In this paper, a strategy for the reactive power compensation and harmonic suppression of the power supply system in HT-7U superconductive Tokamak is proposed. The optimized approach is given in the parameters design for passive filter. Also a controlling method with fast response time and good accuracy is put forward for the compensator, which is more suitable for the dynamic load.

Simulation and reliability analysis of shunt active power filter based on instantaneous reactive power theory

This paper first discusses the operating principle of instantaneous reactive power theory. Then, the theory is introduced into shunt active power filter and its control scheme is studied. Finally, Matlab/Simulink power system toolbox is used to simulate the system. In the simulation model, as the most common harmonic source, 3-phase thyristor bridge rectifier circuit is constructed. The simulation results before and after the shunt active filter was switched to the system corresponding to different firing angles of the thyristors are presented and analyzed, which demonstrate the practicability and reliability of the proposed shunt active filter scheme.

A Genetic Algorithm for Reactive Power Optimizationof Power System

Tis paper presents a genetic algorithm for reactive power optimization of power system in a more effective and rapid manner, and verifies the results with an IEEE 30-bus test system.

Minimization of Reactive Power Fluctuation in JT-60SA Magnet Power Supply

This paper describes an asymmetric control method for the firing angle and a start/stop timing shift control of four thyristor converters called ＂Booster PS＂ to minimize the reactive power fluctuation during plasma initiation in JT-60SA. From the simulation using the ＂PSCAD/EMTDC＂ code, it is found that these control methods can drastically reduce the reac- tive power induced by the four units of the ＂Booster PS＂. In addition, the voltage fluctuation of the motor-generator connected to the ＂Booster PS＂ is expected to be suppressed. This can also contribute to achieve stable control of the JT-60SA magnet power supplies.

Design to conditioning circuits of dynamic compensation of reactive power in the intelligence voltage controller

The paper introduces one design idea that making use of SCM to control Real-timely the dynamic compensation of reactive power.Firstly,design one Circuit to Sample the voltage and current,and by these datas we can easily calculate the power factor,and Voltage controller in the microcontroller to determine whether input the compensation capacitance according to the size of power factor,the paper also analyzes the principle of capacitance compensation and calculation method. Dynamic compensation for the entire process is quick and accurate.

Reactive Power Reserve Improvement Using Power Systems Inherent Structural Characteristics

This paper considers the use of the inherent structural characteristics of power system networks for improving the reactive power reserve margins for both topologically weak and strong networks. The inherent structural characteristics of the network are derived from the Schur complement of the partitioned Y-admittance matrix using circuit theory representations. Results show that topologically strong networks, operating close to the upper voltage limit could be made to increase their loadability margin by locating reactive power compensators close to generator sources, whereas topologically weak (ill conditioned) networks could be made to operate within the feasible operating limits by locating reactive power compensators on buses farther from generator sources.

Research of Rural Power Network Reactive Power Optimization Based on Improved ACOA

In view of the serious reactive power loss in the rural network, improved ant colony optimization algorithm （ACOA） was used to optimize the reactive power compensation for the rural distribution system. In this study, the traditional ACOA was improved in two aspects： one was the local search strategy, and the other was pheromone mutation and re-initialization strategies. The reactive power optimization for a county＇s distribution network showed that the improved ACOA was practicable.

Research on the Method of Calculating Node Injected Reactive Power Based on L Indicator

With the power grid load increasing, the problem of grid voltage stability is increasingly prominent, and the possibility of voltage instability is also growing. In order to improve the voltage stability, this paper analyzed how the voltage stability was influenced by different reactive power injection based on the simplified L-indicator of on-line voltage stability monitoring. According to the basic differential property of the simplified L-indicator, a general and normative analytical algorithm about reactive power optimization was deduced. The analytical algorithm can calculate the load node injected reactive power, and then the network can run in the optimal steady state on the basis of the calculation results. According to the simulation results of IEEE-14, IEEE-30, IEEE-57 and IEEE-118, the feasibility and effectiveness of the proposed algorithm to improve voltage stability and reduce the risk of grid collapse were verified.

Microcontroller Control of Reactive Power Compensation for Growing Industrial Loads

Many industrial installations in developing countries start-up as small factories, without regard for the need of compensation of reactive power, leading to significant financial losses in the long term. By improving the power factor, the customer can reduce its power demand and potentially increase efficiency of their equipment. A PIC microcontroller is used to switch capacitor banks to compensate for the reactive power. In order to determine the size of the capacitor bank needed, the microcontroller calculates the phase difference between the voltage and the current. The results obtained based on the lagging power factor for three test loads show an improvement in the power factor from 0.52 to 0.96 under different test load conditions.