Two-tier coordinated optimal scheduling of wind/PV/hydropower and storage systems based on generative adversarial network scene generation

Abstract

In order to achieve the economic consumption of renewable energy in a multi-energy power system including wind/PV/hydropower and energy storage, a two-tier coordinated optimal scheduling method based on generative adversarial network (GAN) scenario generation is proposed in this paper. First, an upper-tier optimization model for the operation of the load and storage system is established to achieve the objective of minimizing the load fluctuation and the cost of energy storage plants. Furthermore, a lower-tier optimization model to minimize the system operation cost and tide risk is established for the optimization operation of renewable energy generation. Second, an improved generative adversarial network is proposed to generate the operation scenes for evaluating the uncertainty characteristics of the wind and photovoltaic (PV) generation. Then, an improved coati optimization algorithm (COA) is used to solve the proposed optimization problem. Finally, the IEEE 30-bus system is selected as the example system for verifying the proposed method. The simulation results corroborate the validity and feasibility of the proposed method.

Opinion on enhancing diversity in photovoltaic scenario generation using weather data simulating by style-based generative adversarial networks

“…Among the various factors influencing PV output, weather conditions play a significant role in causing fluctuations and uncertainties in PV generation. However, the vast majority of the current PV scenario generation literature generates PV scenarios directly, which can overlook the important impact of weather on PV (Cai et al, 2023). To account for weather-related uncertainties and impose stricter physical constraints on PV power generation models, the PV scenario is modeled by simulating weather scenarios, enabling both specificity and generality in the models.…”