Electricity‑carbon price dual‑response strategy for power systems considering renewable energy uncertainty

To address the challenges of renewable energy uncertainty to power system security and low-carbon transition, this paper proposes an optimal dispatch strategy with dual response to electricity and carbon pricing. Targeting the temporal and spatial variations in carbon intensity caused by renewable fluctuations and the underutilized carbon reduction potential on the load side, a cross-temporal updating mechanism for nodal dynamic carbon potential is developed. This mechanism enables dynamic and accurate allocation of carbon emission responsibilities between generation and load. To enhance the quality of renewable output scenario generation, an Advanced Temporal-Spatial Generative Adversarial Network (ATS-GAN) is employed, and a data-driven ambiguity uncertainty set for forecast errors is constructed using the Wasserstein distance. Building on this, a distributionally robust optimization model is formulated, incorporating nodal dual response to electricity and carbon signals. Case studies on a modified IEEE 30-node system show that the proposed strategy effectively reduces carbon emissions, facilitates renewable integration, and improves system economic efficiency. Compared to conventional stochastic and robust optimization methods, it achieves a better trade-off between conservatism and computational performance.