Reactive Power Optimization Of Distribution Network Research Articles

Reactive Power Optimization in Distribution Networks of New Power Systems Based on Multi-Objective Particle Swarm Optimization

The new power system effectively integrates a large number of distributed renewable energy sources, such as solar photovoltaic, wind energy, small hydropower, and biomass energy. This significantly reduces the reliance on fossil fuels and enhances the sustainability and environmental friendliness of energy supply. Compared to distribution networks in traditional power systems, the new-generation distribution network offers notable advantages in improving energy efficiency, reliability, environmental protection, and system flexibility, but it also faces a series of new challenges. These challenges include potential harmonic issues introduced by the widespread use of power electronic devices (such as inverters for renewable energy generation systems and electric vehicle charging stations) and the voltage fluctuations and flickering caused by the intermittency and uncertainty of renewable energy generation, which may affect the normal operation of electrical equipment. To address these challenges, this study proposes an optimization model aimed at minimizing network losses and voltage deviations, utilizing traditional capacitor adjustments and static var compensators (SVCs) as optimization measures. Furthermore, this study introduces an improved version of the multi-objective particle swarm optimization (MOPSO) algorithm, specifically enhanced to address the unique challenges of reactive power optimization in modern distribution networks. The test results show that this algorithm can effectively generate a large number of Pareto optimal solutions. The application of this algorithm on a 33-node network case study demonstrates its advantages in reactive power optimization. The optimization results highlight the effectiveness and feasibility of the proposed improved algorithm in the application of distribution network reactive power optimization, offering users a uniform and diverse range of reactive compensation solutions.

Active Distribution Network Reactive Power Optimization Based on an Improved Genetic Particle Swarm Optimization Algorithm

As the distributed generation is gradually connected to the distribution network, it will lead to an increase in node voltage deviation and network loss. The active distribution network reactive power optimization approach based on an improved genetic particle swarm optimization algorithm is proposed in this paper to increase voltage quality and system efficiency. First, a reactive power optimization model for active distribution networks that has the least possible active power loss and voltage variation is developed. Then, the genetic idea is applied to the particle swarm optimization algorithm to improve it and address the issues of the long iteration time of the genetic algorithm and the tendency of the particle swarm optimization algorithm to settle into local optimal solutions. Finally, the enhanced genetic particle swarm optimization algorithm described in this paper is validated using the enhanced IEEE-33 node system. The findings indicate that this strategy can significantly shorten computation times while also increasing calculation precision.

Distribution Network Reactive Power Optimization Method Based on Filter Coordinated Quantum State Transfer Algorithm

To solve the problem of mixed integer nonlinear programming in dynamic reactive power optimization of distribution networks, a two-stage dynamic reactive power optimization model is used to reduce the size of the reactive power optimization calculation.The first stage is nonlinear planning, with the objective of minimizing the combined system network loss and node voltage offset. The second stage is a mixed-integer planning, with the objective of minimizing the incremental power system network loss on the basis of satisfying the constraints such as the number of equipment regulation throughout the day. In view of the fact that the conventional penalty function method to deal with the constraints will result in improper selection of penalty factors, an improved quantum state transfer algorithm with mixed filter technique is proposed. The algorithm adopts a two-population parallel search approach, and the individual adaptation degree and constraint violation degree are constituted as filter pairs as evaluation metrics to replace the penalty functions. Finally, the proposed algorithm, the traditional state transfer algorithm and Colony Algorithm are solved separately for the model using the IEEE 14 system as examples, and the correctness of the proposed model and algorithm is verified by comparing and analyzing the simulation results.

Improved SFLA Algorithm with Hybrid SA Strategy for Multi-Objective Reactive Power Planning Optimization of Distribution Network

Aiming at the characteristics of high target dimensionality, strong nonlinearity and complex constraints of distribution network reactive power optimization problems, this paper proposes an improved shuffled frog leaping algorithm based on SA strategy (ISFLASAS) to solve the problem. Firstly, the algorithm uses the global optimization capability of SFLA to detect the optimal solution, and improves the standard SFLA. Incorporating a reciprocal strategy during population initialization to improve the uniform distribution ability of the initial solution and speed up the search for the optimal solution. Secondly, using the local escape ability of the SA strategy, in the later stage of the SFLA calculation, the SA strategy is assisted to jump out of the local optimum with a certain probability to improve the global detection ability. Finally, the IEEE 33-node system is used to simulate and analyze the proposed model algorithm to verify the proposed control strategy.

Research of Distribution Network Reactive Power Optimization Based on Improved Cloud Particle Swarm Optimization BP Neural Network

Research of Distribution Network Reactive Power Optimization Based on Improved Cloud Particle Swarm Optimization BP Neural Network

Reactive Power Optimization in Distribution Network with Wind Farm

The traditional methods to adjust voltage in distribution network reactive power optimization is discretization，and it is difficult to realize the continuous voltage adjustment. A reactive power optimization model and algorithm in distribution network with wind farm is proposed. The network loss，deviation of voltage and stability of voltage are taken into account in the multi-objective reactive power optimization model. The quantum particle swarm optimization（QPSO）algorithm has been used to solve the reactive power optimization problem. The algorithm described particle state by wave function, not only increase the diversity of population，but also avoid premature convergence. The comparison of the simulation result between QPSO and PSO on the modified IEEE 33-bus system demonstrated the effectiveness and advantage of quantum particle swarm optimization.