Distributed Integrated Synthetic Adaptive Multi-Objective Reactive Power Optimization

Song, Jiayin; Lu, Chao; Ma, Qiang; Zhou, Hongwei; Qi, Yue; Zhu, Qi; Zhao, Yue; Fan, Yi‐Ming; Huang, Qiqi

doi:10.3390/sym14061275

Cited by 1 publication

(1 citation statement)

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“…In Equation ( 12), P loss represents the active power network loss of the system; ∑ f QD , ∑ f QG , and ∑ f Cq , respectively, represent the reactive power price of synchronous machines, DFIG units, and the depreciation cost of discrete device operations; U j is the voltage at node. After normalizing the objective function [34], the final objective function becomes…”

Section: Objective Functionmentioning

confidence: 99%

Research on Dynamic Reactive Power Cost Optimization in Power Systems with DFIG Wind Farms

Xu,

Wang,

Chen

et al. 2024

Processes

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As the power market system gradually perfects, the increasingly fierce competition not only drives industry development but also brings new challenges. Reactive power optimization is crucial for maintaining stable power grid operation and improving energy efficiency. However, the implementation of plant–grid separation policies has kept optimization costs high, affecting the profit distribution between power generation companies and grid companies. Therefore, researching how to effectively reduce reactive power optimization costs, both technically and strategically, is not only vital for the economic operation of the power system but also key to balancing interests among all parties and promoting the healthy development of the power market. Initially, the study analyzes and compares the characteristic curves of synchronous generators and DFIGs, establishes a reactive power pricing model for generators, and considering the randomness and volatility of wind energy, establishes a DFIG reactive power pricing model. The objective functions aimed to minimize the cost of reactive power purchased by generators, the price of active power network losses, the total deviation of node voltages, and the depreciation costs of discrete variable actions, thereby establishing a dynamic reactive power optimization model for power systems including doubly-fed wind farms. By introducing Logistic chaotic mapping, the CSA is improved by using the highly stochastic characteristics of chaotic systems, which is known as the Chaotic Cuckooing Algorithm. Meanwhile, the basic cuckoo search algorithm was improved in terms of adaptive adjustment strategies and global convergence guidance strategies, resulting in an enhanced cuckoo search algorithm to solve the established dynamic reactive power optimization model, improving global search capability and convergence speed. Finally, using the IEEE 30-bus system as an example and applying the improved chaotic cuckoo search algorithm for solution, simulation results show that the proposed reactive power optimization model and method can reduce reactive power costs and the number of discrete device actions, demonstrating effectiveness and adaptability. When the improved chaotic cuckoo algorithm is applied to optimize the objective function, the optimization result is better than 7.26% compared to the standard cuckoo search algorithm, and it is also improved compared to both the PSO algorithm and the GWO algorithm.

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Section: Objective Functionmentioning

confidence: 99%