Renewable Scenario Generation Using Quantum Generative Adversarial Networks

With the increasing penetration of renewable resources in modern power systems, scenario generation for renewable energy has emerged as a critical tool for system operation and planning. Among the widely studied methods, Generative Adversarial Networks (GANs) are popular. Despite its model-free nature, it suffers from high computational demands due to its extensive trainable parameters and often struggles to capture intricate data patterns. To address these challenges, we employ quantum computing techniques that harness quantum entanglement principles. We propose, for the first time, a method of generating a quantum-enhanced renewable energy scenario based on a hybrid quantum GANs (QGANs) architecture. Specifically, given historical data of renewable resources, we design a quantum generator using parameterized quantum circuits. This quantum neural network achieves superior expressivity compared to its classical counterparts while requiring significantly fewer trainable parameters. Then, the resulting QGANs architecture combines this quantum generator with a classical discriminator for the effective generation of renewable energy scenarios. Quantum measurements also inherently introduce randomness into the generated renewable scenarios. The simulation results verify the rationale of the proposed method and demonstrate its effectiveness in capturing the spatio-temporal characteristics of renewable resources.