A combined approach of Latin hypercube sampling and K-means clustering is proposed in this study to address the uncertainty issue in wind and solar power output. Furthermore, the loads are categorized into three levels: primary load, secondary load, and tertiary load, each with distinct characteristics in terms of demand. Additionally, a load demand response characteristic model is developed by incorporating the dissatisfaction coefficient of electric and thermal loads, which is then integrated into the system’s operational costs. Moreover, an electricity–hydrogen–thermal power system is introduced, and a source-load coordination response mechanism is proposed based on the different levels of demand response characteristics. This mechanism enhances the interaction capability between the power sources and loads, thereby further improving the economic performance of the virtual power plant. Furthermore, the operation economy of the virtual power plant is enhanced by considering the participation of renewable energy sources in carbon capture devices and employing a tiered carbon-trading mechanism. Finally, the CPLEX algorithm is employed to solve the optimization model of the virtual power plant, thereby validating the effectiveness of the proposed models and algorithms.

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.