The proposal of the “carbon neutrality” target promotes the rapid development of variable renewable energy, which leads to the reduction of inertia and the deficiency of flexible reserve in power systems. Wind turbines (WTs) now are asked for providing frequency support and reserve through proper control strategy. This proposes a new issue on how to coordinate the operation of conventional units and WTs. This paper proposes a frequency security constrained scheduling approach considering wind farms providing frequency support and reserve. Two dynamic frequency indices: RoCoF and nadir, are derived and introduced into the scheduling model. The frequency support capability of wind farm is accurately quantified by considering the real grid-connected WT capacity and wake effect. A wind power reserve model considering uncertainty is established by linking the reserve under the forecasted scenario and uncertain scenarios through reserve auxiliary variables. The contribution of wind farms to power system frequency support and reserve is quantified in the robust scheduling model. Case studies show that the frequency support and reserve from wind farms can effectively reduce the whole operation cost and VRE curtailment. Moreover, the value of wind farms in CO ₂ emission reduction is properly quantified.

The stability issue of high proportion variable renewable energy (VRE) when connected to the grid has become an important factor limiting the consumption of VRE and seriously threatening the stable operation of the power systems. However, there is still a lack of targeted discrimination theory and compensation mechanisms in areas where the stability margin undergoes drastic changes due to changes in the static operating point of the systems. To this end, based on the small signal stability analysis method, this paper proposes a vulnerable nodes localization method for the multiple grid-connected converter systems (MGCCS) that considers the dynamic response characteristics and static operating point offset of converters. Firstly, a frequency domain negative feedback model is established for MGCCS with passive busbars. Then the sensitivity function for the control parameters of active nodes and the quantitative indicators for identifying vulnerable nodes are derived. Finally, a comprehensive compensation scheme for both active nodes and passive busbars is proposed. Case analysis demonstrates that the proposed vulnerable nodes identification method and comprehensive compensation scheme offer substantial benefits in the realm of stability design and operation planning of MGCCS.

High renewable energy penetration poses significant challenges to supply‐demand balancing in transmission networks, making transmission‐storage cooperative planning (TSCP) a crucial strategy for mitigating renewable variability. Meanwhile, the carbon reduction potential on the demand side remains insufficiently explored. Coordinating grid‐side resource planning with demand response can enhance system flexibility and support deep decarbonization. To address this, this study develops a TSCP model integrated with carbon‐aware demand response under a bi‐level robust optimization framework. The upper level performs robust TSCP under source–load uncertainty, leveraging battery and hydrogen storage to smooth renewable output fluctuations, while the lower level adjusts user‐side electricity consumption in response to price signals to reduce emissions. A dynamic carbon pricing model is proposed based on carbon emission flow analysis, integrated with locational marginal pricing to form a unified electricity‐carbon price signal that stimulates low‐carbon responsiveness on the demand side. A nested alternating optimization procedure with column‐and‐constraint generation (NAOP‐C&CG) algorithm is developed for an efficient solution. Case studies on the Northwest China HRP‐38 system show that the proposed method reduces total system cost by 48.47%, decreases the load shedding rate by 0.92%, and improves renewable energy utilization by 6.6%. Furthermore, it achieves CO ₂ emission reductions of 10.82 Mt and 9.4 Mt compared to fixed and conventional ladder‐type carbon pricing schemes, respectively.

Developing large‐scale renewable energy source (RES) bases in desert, Gobi and barren regions is important for low‐carbon power systems. However, their integration into receiving‐end grids through DC tie‐lines displaces conventional units and brings challenges to power balance and frequency security. To address this issue, this paper proposes a frequency‐security‐constrained co‐scheduling framework for an RES base and a receiving‐end grid under RES uncertainty. First, a stepwise flexible operation model of the DC tie‐line is established to improve the matching between RES base output and DC transmission. Second, an inter‐regional frequency support mechanism is developed by coordinating DC tie‐line virtual inertia and energy storage fast frequency response. Based on this mechanism, a dynamic frequency response model of the receiving‐end grid is derived and reformulated into tractable second‐order cone constraints. On this basis, generative adversarial network (GAN)‐based scenario generation and a probability‐box ambiguity set are combined to model RES output uncertainty and embedded in a distributionally robust co‐scheduling model, minimizing the worst‐case expected cost under frequency‐security constraints. Case studies on a modified IEEE RTS‐79 system and the Ningxia–Hunan inter‐regional power system show that the proposed method reduces online conventional unit capacity by 26.33% and improves RES utilization by 4.52% while ensuring frequency security.