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.

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.

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.

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.

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.

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.

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.

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.

Data-driven Reactive Power Optimization of Distribution Networks via Graph Attention Networks

Reactive power optimization of distribution networks is traditionally addressed by physical model based methods,which often lead to locally optimal solutions and require heavy online inference time consumption.To improve the quality of the solution and reduce the inference time burden,this paper proposes a new graph attention networks based method to directly map the complex nonlinear relationship between graphs(topology and power loads)and reactive power scheduling schemes of distribution networks,from a data-driven perspective.The graph attention network is tailored specifically to this problem and incorporates several innovative features such as a self-loop in the adjacency matrix,a customized loss function,and the use of max-pooling layers.Additionally,a rulebased strategy is proposed to adjust infeasible solutions that violate constraints.Simulation results on multiple distribution networks demonstrate that the proposed method outperforms other machine learning based methods in terms of the solution quality and robustness to varying load conditions.Moreover,its online inference time is significantly faster than traditional physical model based methods,particularly for large-scale distribution networks.

Design of Robust Var Reserve Contract for Enhancing Reactive Power Ancillary Service Market Efficiency

In a deregulated Var market, market power issue is more serious than in an energy market since reactive power cannot be transmitted over long distances. This letter designs a multi-timescale Var market framework, where market power that may arise in the hourly-ahead Var support service market due to system configuration deficiency and market structure flaws can be eliminated by day-ahead contract-based Var reserve service market. Settlement of day-ahead Var reserve contract is formulated as a two-stage robust optimization (TSRO) model considering worst case of uncertainty realization and potential market power that may arise in hourly-ahead market. TSRO with integer recourses is then solved by a new column and constraint generation algorithm. Results show a robust Var reserve contract can fully eliminate market power, and prevent suppliers from manipulating market prices.

Multi-timescale Affinely Adjustable Robust Reactive Power Dispatch of Distribution Networks Integrated with High Penetration of PV

Photovoltaic(PV)power generation has highly penetrated in distribution networks,providing clean and sustainable energy.However,its uncertain and intermittent power outputs significantly impair network operation,leading to unexpected power loss and voltage fluctuation.To address the uncertainties,this paper proposes a multi-timescale affinely adjustable robust reactive power dispatch(MTAAR-RPD)method to reduce the network power losses as well as alleviate voltage deviations and fluctuations.The MTAAR-RPD aims to coordinate on-load tap changers(OLTCs),capacitor banks(CBs),and PV inverters through a three-stage structure which covers multiple timescales of“hour-minute-second”.The first stage schedules CBs and OLTCs hourly while the second stage dispatches the base reactive power outputs of PV inverter every 15 min.The third stage affinely adjusts the inverter reactive power output based on an optimized Q-P droop controller in real time.The three stages are coordinately optimized by an affinely adjustable robust optimization method.A solution algorithm based on a cutting plane algorithm is developed to solve the optimization problem effectively.The proposed method is verified through theoretical analysis and numerical simulations.

An Adaptive Many-objective Robust Optimization Model of Dynamic Reactive Power Sources for Voltage Stability Enhancement

The deployment of dynamic reactive power sourcecan effectively improve the voltage performance after a disturbance for a power system with increasing wind power penetration level and ubiquitous induction loads.To improve the voltage stability of the power system,this paper proposes an adaptive many-objective robust optimization model to deal with thedeployment issue of dynamic reactive power sources.Firstly,two metrics are adopted to assess the voltage stability of the system at two different stages,and one metric is proposed to assess the tie-line reactive power flow.Then,a robustness index isdeveloped to assess the sensitivity of a solution when subjectedto operational uncertainties,using the estimation of acceptablesensitivity region(ASR)and D-vine Copula.Five objectives areoptimized simultaneously:①total equipment investment;②adaptive short-term voltage stability evaluation;③tie-line power flow evaluation;④prioritized steady-state voltage stabilityevaluation;and⑤robustness evaluation.Finally,an anglebased adaptive many-objective evolutionary algorithm(MaOEA)is developed with two improvements designed for the application in a practical engineering problem:①adaptive mutationrate;and②elimination procedure without a requirement for athreshold value.The proposed model is verified on a modifiedNordic 74-bus system and a real-world power system.Numerical results demonstrate the effectiveness and efficiency of theproposed model.

Allocation and sizing of reactive power compensators considering PV power and load demand uncertainty using beetle-antenna grey wolf optimization

Distributed photovoltaic(PV)systems play an important role in supplying many recent microgrids.The absence of reactive power support for these small-scale PV plants increases total microgrid losses and voltage-instability threats.Reactive power compensations(RPCs)should be integrated to enhance both microgrid losses and voltage profiles.RPC planning is a non-linear,complicated problem.In this paper,a combined RPC allocation and sizing algorithm is proposed.The RPC-integrating buses are selected using a new adaptive approach of loss sensitivity analysis.In the sizing process,the uncertainties in PV power and load demand are modelled using proper probability density functions.Three simulation techniques for handling uncertainties are compared to define the accurate and fast accurate method as follows:Monte Carlo simulation(MCS),scenario tree construction and reduction method,and point estimation method(PEM).The load flow equations are solved using the forward-backward sweep method.RPCs are optimally sized using the beetle-antenna-based strategy with grey wolf optimization(BGWO)to overcome the local minima problem that appeared in the other pre-proposed methods.Results have been compared using particle swarm optimization and conventional GWO.The proposed model is verified using the IEEE 33 radial bus system.The expected power loss has been reduced by 22% and 31% using compensation of 26% and 44%,respectively.The results obtained prove that the BGWO optimal power flow and PEM to handle the uncertainty can significantly reduce the computation time with sufficient accuracy.Under the study conditions,PEM reduces the computation time to 4 minutes compared with 4 hours for MCS,with only a 3% error compared with MCS as an uncertainty benchmark method.

Complete active-reactive power resource scheduling of smart distribution system with high penetration of distributed energy resources

In traditional power systems,besides the conventional power plants that provide the necessary reactive power in transmission system,the shunt capacitors along with the tap changers of transformers are also employed in distribution networks.In future years,because of the high number of distributed resources integrated into the distribution networks,it will be essential to schedule complete active-reactive power at distribution level.In this research work,an economic framework based on the active-reactive power bids has been developed for complete active-reactive power dispatch scheduling of smart distribution networks.The economical complete active-reactive power scheduling approach suggested in this study motivates distributed energy resources(DERs)to cooperate in both active power markets and the Volt/Var control scheme.To this end,using DER’s reactive power capability,a generic framework of reactive power offers for DERs is extracted.A 22-bus distribution test system is implemented to verify the impressiveness of the suggested active-reactive power scheduling approach.

Response Time of Reactive Power Based on Different Definitions and Algorithms

Dynamic reactive power compensation equipment typically requires a fast response to output the necessary reactive power.The term"dynamic response time of reactive power"is often used but has never been clearly defined.This paper summarizes the reactive power calculations under different definitions and algorithms and considers these calculations in terms of signal processing to simulate and analyze the step response.This paper subsequently focuses on the widely used instantaneous reactive power algorithm and finally concludes that the dynamic reactive power response time closely depends on the reactive power calculation method itself.The single-phase instantaneous reactive power algorithm has the fastest response time.The reactive power response time of dynamic reactive devices in power systems is a minimum of a quarter of one cycle time for the well-known and widely used single-phase reactive power algorithms.

Optimal reactive power dispatch of wind power plant cluster considering static voltage stability for low-carbon power system

The implementation of developing the wind power is an important way to achieve the low-carbon power system.However,the voltage stability issues caused by the random fluctuations of active power output and the irrational regulations of reactive power compensation equipment have become the prominent problems of the regions where large-scale wind power integrated.In view of these problems,this paper proposed an optimal reactive power dispatch(ORPD)strategy of wind power plants cluster(WPPC)considering static voltage stability for lowcarbon power system.The control model of the ORPD strategy was built according to the wind power prediction,the present operation information and the historical operation information.By utilizing the automatic voltage control capability of wind power plants and central substations,the ORPD strategy can achieve differentiated management between the discrete devices and the dynamic devices of the WPPC.Simulation results of an actual WPPC in North China show that the ORPD strategy can improve the voltage control performance of the pilot nodes and coordinate the operation between discrete devices and the dynamic devices,thus maintaining the static voltage stability as well.

Real-time Dispatchable Region of Renewable Generation Constrained by Reactive Power and Voltage Profiles in AC Power Networks

A large amount of renewable energy generation(REG)has been integrated into power systems,challenging the operational security of power networks.In a real-time dispatch,system operators need to estimate the ability of the power network to accommodate REG with a limited reserve capacity.The real-time dispatchable region(RTDR)is defined as the largest range of a power injection that the power network can accommodate in a certain dispatch interval for a given dispatch base point.State-of-the-art research on the RTDR adopts a DC power flow model regardless of the voltage profiles and reactive power,which can overlook potentially insecure operational states of the system.To address this issue,this paper proposes an AC power flow based RTDR model simultaneously considering the reactive power and voltage profiles constraints.The nonlinear constraints in our model are approximated using a linear power flow model together with a polytope approximation technique for quadratic constraints.An adaptive constraint generation algorithm is used to calculate the RTDR.Simulation results using the IEEE 5-bus and 30-bus systems illustrate the advantages of the proposed model.

A Distributed Reactive Power Sharing Approach in Microgrids with Improved Droop Control

Conventional droop control causes frequency and voltage deviations(from rated value)in a inverter-intensive microgrid(MG),and the reactive power sharing cannot be obtained when the communication structure of the MG or load suddenly changes.Compared with a centralized control and droop control scheme,a distributed hierarchical control structure of the MG can overcome the limitation of communication and realize reactive power sharing.In this paper,an improved droop control is adopted,which is based on the hierarchical control structure.The hierarchical control structure consists of zerolevel control,primary control and a proposed secondary control.First,the secondary controller is modeled,and the MG system composed of distributed generators(DGs)is considered as a multi-agent system.The secondary controller can make up for the shortcomings of the droop controller and adjust the frequency and voltage to their rated values.Secondly,the reference voltage and frequency of the zero-level control are calculated,and combined with the primary control.The zero-level control and primary control can make the voltage and frequency of the MG run stably and provide reference voltage for the inverter.Finally,the stability of the system is proved by the theory of multi-agent consistency.A simulation system is established in the Matlab/Simulink environment,and the results show the effectiveness of the proposed controller.

Fixed-time Distributed Voltage and Reactive Power Compensation of Islanded Microgrids Using Sliding-mode and Multi-agent Consensus Design

This paper investigates a fixed-time distributed voltage and reactive power compensation of islanded microgrids using sliding-mode and multi-agent consensus design.A distributed sliding-mode control protocol is proposed to ensure voltage regulation and reference tracking before the desired preset fixed-time despite the unknown disturbances.Accurate reactive power sharings among distributed generators are maintained.The secondary controller is synthesized without the knowledge of any parameter of the microgrid.It is implemented using a sparse one-way communication network modeled as a directed graph.A comparative simulation study is conducted to highlight the performance of the proposed control strategy in comparison with finite-time and asymptotic control systems with load power variations.

Voltage sag compensation strategy for unified power quality conditioner with simultaneous reactive power injection

Unified power quality conditioner(UPQC)holds the capability of solving power quality problems,especially shows good performance in the voltage sag compensation. In this paper, a compensation strategy based on simultaneous reactive power injection for UPQC(namely UPQC-SRI) is proposed to address the issue of voltage sag. The proposed UPQC-SRI determines the injection angle of compensation voltage with consideration of optimal configuration of UPQC current-carrying.Moreover, the compensation strategy also considers the current-carrying limit of UPQC, and then the zero active power injection region of UPQC-SRI(also called UPQCSRI region) is obtained. Under the conditions which exceed the UPQC-SRI region, the limit value of shunt current is determined by this proposed strategy. Finally, the proposed strategy and the corresponding algorithm are verified under the PSCAD/EMTDC platform. The result indicates the proposed UPQC-SRI strategy in this paper can provide more persistent voltage sag compensation than the previous strategies for the sensitive load.