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.

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.

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.

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.

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.

A pooled-neighbor swarm intelligence approach to optimal reactive power dispatch

This paper presents a pooled-neighbor swarm intelligence approach (PNSIA) to optimal reactive power dispatch and voltage control of power systems. The proposed approach uses more particles’ information to control the mutation operation. The proposed PNSIA algorithm is also extended to handle mixed variables, such as transformer taps and reactive power source in- stallation, using a simple scheme. PNSIA applied for optimal power system reactive power dispatch is evaluated on an IEEE 30-bus power system and a practical 118-bus power system in which the control of bus voltages, tap position of transformers and reactive power sources are involved to minimize the transmission loss of the power system. Simulation results showed that the proposed approach is superior to current methods for finding the optimal solution, in terms of both solution quality and algorithm robustness.

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.

A New Model for Transmission Network Expansion and Reactive Power Planning in a Deregulated Environment

Transmission network expansion planning (TNEP) is a challenging issue especially in new restructured electricity mar-kets environment. TNEP can be incorporated with reactive power planning in which the operating conditions will be satisfied. In this paper a combinatorial mathematical model has been presented to solve transmission expansion and reactive power planning problem (TEPRPP) simultaneously. The proposed model is a non-convex problem having a mixed integer nonlinear nature where the number of candidate solutions to be evaluated increases exponentially according to the system size. The objective function of TEPRPP comprises the new circuits’ investment and production costs as well as load curtailment penalty payments. A real genetic algorithm (RGA) aimed to obtaining a significant quality solution to handle such a complicated problem has been employed. An interior point method (IPM) is applied to solve the proposed concurrent optimization problem in the solution steps of TEPRPP model. This paper proposes a new methodology for the best location as well as the capacity of VAr sources;it is tested on two well-known systems;the Garver and IEEE 24-bus systems. The obtained results show the capability and the viability of the proposed TEPRPP model incorporating operating conditions.

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.

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.

Reactive Compensator Synthesis in Time-Domain as an Alternative to Harmonic Method

The source reactive-current compensation is crucial in energy transmission efficiency. The compensator design in frequency-domain was already widely discussed and examined. This paper presents results of a study on how to design reactive compensators in time-domain. It’s the first time the reactive compensator have been designed in time domain. The example of compensator design was presented.

Optimal Sizing of Capacitor Bank for Increasing Substation Capacity of Mamou

In order to increase the available power of the electrical energy distribution station and improve the voltage profile of the distribution lines, the use of shunt capacitor banks is indicated. The main results obtained during this study are: a reduction in subscribed power from 14913.978 kVA to 14010.100 kVA, a reduction in the transformer load rate from 99.4% to 93.4%, a reduction in reactive power called from 5481.729 kVAr to 481.729 kVAr, an increase in the active power transported by the substation from 8505.062 kW to 8962.323 kW, a reduction in the voltage drop from 4.8% to 3.9%, an increase in the power available at the secondary of the transformer station at full load from 13950 kW to 14700 kW and an annual electrical energy saving of 339943.48 kWh of electrical energy, therefore fuel savings and a reduction in CO2 and SO2 emissions due to this energy saving will be achieved. The installation of capacitor banks for optimization of reactive energy allowed a reduction in the current called therefore a reduction in the absorbed power: 14153.061 kVA, i.e. a reduction of 903.876 kVA. It is therefore essential that energy players are convinced of the need to install capacitors to reduce or even eliminate their reactive energy bill. This is necessarily accompanied by an investment by Electricité De Guinée by setting up active and reactive energy meters but also by implementing pricing in line with the reduction in the transfer of reactive energy in the network.

光伏多功能并网逆变器不同出力状态下无功和谐波优化方法

利用光伏多功能并网逆变器所具有的电能质量治理、谐波谐振抑制的主动优势,结合逆变器在不同出力状态下光伏发电系统的输出特性,构建光伏多功能并网逆变器不同出力状态下的无功和谐波优化方法。在所提3层优化策略中,上层优化策略以光伏并网有功功率最大化为目标函数,中层优化策略以电压谐波总畸变率和电压偏差达到相应电能质量规定指标为目标函数,下层优化策略以电压谐波总畸变率和电压偏差最小为目标函数,并采用自适应蚁群算法进行求解。基于IEEE 33节点系统进行算例分析,结果表明:基于逆变器不同出力状态的优化方法可以有效改善电网的电能质量,较好地提升电网运行的经济性;在系统稳定运行的基础上,光伏多功能并网逆变器在不同出力状态下分阶段、分主次地治理谐波和调整电压偏差,治理效果更佳。

考虑大规模风光分层接入的配电网多层协调无功优化方法

大量风光新能源分层、多点接入给配电网运行带来了电压越限等负面影响,增加了不同电压等级无功协同优化的难度。兼顾不同电压等级调节需求,从模型降维等值的角度,提出了基于等值模型的配电网多层协调无功优化方法。该方法分别针对含有无功调节设备及没有无功调节设备的两种台区系统,利用神经网络对大量台区相关运行数据进行训练得到台区拟合模型,形成不可控和可控两种类型的台区等值模型,并用这两类拟合模型分别代替两类台区系统的物理模型,使台区以数据驱动的方式参与配电网无功优化,形成馈线物理模型和台区拟合模型组成的单一电压等级物数混合优化调度模型。将原多层无功优化问题转换为单层系统优化问题,再对该单层系统无功优化调度问题进行求解。该方法可降低配电网系统优化计算规模,从而减小计算量,以无功优化数学模型与数据驱动方法相结合的方式,实现配电网馈线-台区多层协调无功优化。通过算例验证了所提方法对于解决分布式风光分层接入所导致电压越限问题的可行性、有效性和优越性,能够保障配电网的经济安全稳定运行。

考虑风电不确定性和储能灵活性的有源配电网有功无功协同鲁棒优化

提出考虑风电不确定性和储能灵活性的有源配电网有功无功协同鲁棒优化方法。首先建立计及储能四象限功率可调特性的有源配电网有功无功协同优化模型;其次给出储能约束条件和交流潮流方程的线性化表征方式;然后利用盒式集描述风电出力的不确定性,建立有源配电网有功无功协同鲁棒优化模型,并通过对偶变换得到混合整数线性规划形式的两阶段鲁棒主、子问题,采用列约束生成算法交互迭代求解;最后以IEEE33节点系统为例进行仿真验证。仿真结果表明,所提方法能够克服模型高度非凸非线性对其收敛精度和求解效率的影响,充分利用储能的无功调节能力,实现有源配电网有功无功协同鲁棒优化运行。