Explore the vast range of titles available.

As high amounts of new energy and electric vehicle (EV) charging stations are connected to the distribution network, the voltage deviations are likely to occur, which will further affect the power quality. It is challenging to manage high quality voltage control of a distribution network only relying on the traditional reactive power control mode. If the reactive power regulation potentials of new energy and EVs can be tapped, it will greatly reduce the reactive power optimization pressure on the network. Keeping this in mind, our reasearch first adds EVs to the traditional distribution network model with new forms of energy, and then a multi-objective optimization model, with achieving the lowest line loss, voltage deviation, and the highest static voltage stability margin as its objectives, is constructed. Meanwihile, the corresponding model parameters are set under different climate and equipment conditions. Ultimately, the optimization model under specific scenarios is obtained. Furthermore, considering the supply and demand relationship of the network, an improved technique for order preference by similarity to an ideal solution decision method is proposed, which aims to judge the adaptability of different algorithms to the optimized model, so as to select a most suitable algorithm for the problem. Finally, a comparison is made between the constructed model and a model without new energy. The results reveal that the constructed model can provide a high quality reactive power regulation strategy.

This paper presents an improved osprey optimization algorithm (IOOA) to solve the problems of slow convergence and local optimality. First, the osprey population is initialized based on the Sobol sequence to increase the initial population’s diversity. Second, the step factor, based on Weibull distribution, is introduced in the osprey position updating process to balance the explorative and developmental ability of the algorithm. Lastly, a disturbance based on the Firefly Algorithm is introduced to adjust the position of the osprey to enhance its ability to jump out of the local optimal. By mixing three improvement strategies, the performance of the original algorithm has been comprehensively improved. We compared multiple algorithms on a suite of CEC2017 test functions and performed Wilcoxon statistical tests to verify the validity of the proposed IOOA method. The experimental results show that the proposed IOOA has a faster convergence speed, a more robust ability to jump out of the local optimal, and higher robustness. In addition, we also applied IOOA to the reactive power optimization problem of IEEE33 and IEEE69 node, and the active power network loss was reduced by 48.7% and 42.1%, after IOOA optimization, respectively, which verifies the feasibility and effectiveness of IOOA in solving practical problems.

Large-scale and high-proportion access of distributed generation to distribution network breaks the original power flow structure of distribution network, which affects the power quality and safe operation of distribution network system. Voltage stability can be maintained through reactive power optimization, and the system operation economy can be improved. In this paper, the influence of distributed generation access on the stability of distribution network is analyzed. On this basis, the mathematical model of reactive power optimization is established with the comprehensive consideration of active power loss and node voltage deviation of distribution network with distributed generation access, and the improved ant lion algorithm with dynamic weight coefficient is used to solve the model. Finally, the simulation analysis is carried out in IEEE33-bus system, and the improved ant lion algorithm and standard ant lion algorithm are used to solve the reactive power optimization of distribution network. The comparison of the optimization results of the two algorithms proves the feasibility and superiority of the improved ant lion algorithm in solving the reactive power optimization problem.

With the expansion of the scale of electric power, high-quality electrical energy remains a crucial aspect of power system management and operation. The generation of reactive power is the primary cause of the decline in electrical energy quality. Therefore, optimization of reactive power in the power system becomes particularly important. The primary objective of this article is to create a multi-objective reactive power optimization (MORPO) model for distribution networks. The model aims to minimize reactive power loss, reduce the overall compensation required for reactive power devices, and minimize the total sum of node voltage deviations. To tackle the MORPO problems for distribution networks, the improved sparrow search algorithm–particle swarm optimization (ISSA-PSO) algorithm is proposed. Specifically, two improvements are proposed in this paper. The first is to introduce a chaotic mapping mechanism to enhance the diversity of the population during initialization. The second is to introduce a three-stage differential evolution mechanism to improve the global exploration capability of the algorithm. The proposed algorithm is tested on the IEEE 33-node system and the practical 22-node system. The results indicate a reduction of 32.71% in network losses for the IEEE 33-node system after optimization, and the average voltage of the circuit increases from 0.9485 p.u. to 0.9748 p.u. At the same time, optimization results in a reduction of 44.07% in network losses for the practical 22-node system, and the average voltage of the circuit increases from 0.9838 p.u. to 0.9921 p.u. Therefore, the proposed method exhibits better performance for reducing network losses and enhancing voltage levels.

A novel approach of swarm intelligence(SI) optimization, namely Competitive Search Algorithm(CSA), is proposed in this paper based on some social activities in human life, such as all-around sports competitions and talent variety shows. Firstly, the mathematical model and the algorithm framework are introduced and the working principle is explained in detail. Then the computational complexity and the parameter sensitivity in the proposed algorithm are analyzed. Moreover, it is compared and tested with the eleven algorithms commonly used such as the algorithms of Archimedes optimization, the particle swarm in 15 test functions and CEC’14 test functions. The results show that the proposed algorithm has obvious advantages in the search accuracy, the convergence speed and the stability. Finally, the algorithm CSA is applied to the maximum power point tracking(MPPT) in the photovoltaic system and the reactive power optimization of active distribution network. Therefore, the effectiveness of the proposed algorithm is verified.

With access to new energy sources, the problem of reactive power optimization and dispatching has become increasingly important for research. However, the reactive power optimization problem is a mixed integer nonlinear optimization problem. In order to solve the integer variables and nonlinear conditions existing therein, a method for coordinated reactive power optimization and dispatching based on semidefinite programming is proposed. Firstly, a reactive power optimization model considering discrete variables and continuous variables is established with the minimization of total operating cost as the objective function; secondly, the discrete variables are transformed into equality constraints by quadratic equations, and then a solvable semi-definite programming problem is obtained; thirdly, the rank-one constraint is restored by the Iterative Optimization based Gaussian Randomization Method (IOGRM), and the optimal solution equivalent to the original problem is obtained. Finally, the correctness and effectiveness of the proposed model and solution method are verified by analyzing and comparing with the second-order cone programming (SOCP) through the modified IEEE standard example.

To address the enhanced coupling characteristics in reactive power partitioning of power grids with high-penetration renewable energy integration, this paper proposes an optimized reactive power partitioning method that integrates dynamic source–load correlation characteristics and regional coupling degree evaluation. Conventional static electrical distance-based partitioning methods struggle to adapt to dynamic coupling effects caused by renewable energy output fluctuations, leading to degraded partition decoupling performance. This study innovatively constructs a Copula function-based joint probability distribution model for source–load correlation. By employing non-parametric estimation and undetermined coefficient methods to solve marginal distribution parameters, and utilizing the K-means clustering algorithm to generate typical scenario sets, a comprehensive source–load coupling evaluation framework is established, incorporating the renewable energy output proportion and time-varying correlation index. For electrical distance calculation, a generalized construction method for extended sensitivity matrices is proposed, featuring dynamic weight adjustment through regional coupling degree correction factors. Simulation results demonstrate that in practical case studies, compared with traditional partitioning schemes, the proposed method reduces the regional coupling degree metric by 4.216% and enhances the regional reactive power imbalance index suppression by 11.082%, validating its effectiveness in achieving reactive power local balance and reactive power partitioning. This research breaks through the theoretical limitations of static partitioning and provides theoretical support for dynamic zonal control in modern power systems with high renewable penetration.

With the high penetration of photovoltaic (PV) and electric vehicle (EV) charging and replacement power stations connected to the distribution network, problems such as the increase of line loss and voltage deviation of the distribution network are becoming increasingly prominent. The application of traditional reactive power compensation devices and the change of transformer taps has struggled to meet the needs of reactive power optimization of the distribution network. It is urgent to present new reactive power regulation methods which have a vital impact on the safe operation and cost control of the power grid. Hence, the idea that applying the reactive power regulation potential of PV and EV is proposed to reduce the pressure of reactive power optimization in the distribution network. This paper establishes the reactive power regulation models of PV and EV, and their own dynamic evaluation methods of reactive power adjustable capacity are put forward. The model proposed above is optimized via five different algorithms and approximated through the deep learning when the optimization objective is only set as line loss and voltage deviation. Simulation results show that the prediction of deep learning has an incredible ability to fit the Pareto front that the intelligent algorithms obtain in practical application.

In order to avoid frequent actions of transformer taps and capacitor banks caused by reactive power optimization, this paper proposes a reactive power optimization based on AVC time division control strategy. The time division control strategy is used to segment the load curve of the next day, and the reactive power optimization process of each period is calculated by genetic algorithm. The strategy and algorithm are applied to the reactive power and voltage optimization of IEEE 30 bus system. The simulation results show that the method can realize reactive power optimization more efficiently.

As the proportion of renewable energy continues to increase, the large-scale grid integration of photovoltaic (PV) generation presents new technical challenges for reactive power balance in power systems. This paper proposes a coordinated reactive power and voltage optimization method based on Filtered Multiband Decomposition (FMD). First, to address the stochastic fluctuations of PV power, an improved FMD-based prediction model is developed. The model employs an adaptive finite impulse response (FIR) filter to decompose signals and captures periodicity and uncertainty through kurtosis-based feature extraction. By utilizing adaptive function windows for multiband signal decomposition, combined with kernel principal component analysis (KPCA) for dimensionality reduction and a long short-term memory (LSTM) network for prediction, the model significantly enhances forecasting accuracy. Second, to tackle the challenges of integrating high-penetration distributed PV while maintaining reactive power balance, a multi-head attention-based velocity update strategy is introduced within a multi-objective particle swarm optimization (MOPSO) framework. This strategy quantifies the spatial distance and fitness differences of historical best solutions, constructing a dynamic weight allocation mechanism to adaptively adjust particle search direction and step size. Finally, the effectiveness of the proposed method is validated through an improved IEEE 33-bus test case.