Zhi Zhang

With the proposal of the "dual carbon" goals and the transformation and upgrading of China’s energy structure, large-scale new energy bases—particularly those in desert-gobi-wasteland areas—are being developed and constructed at an accelerated pace. The installed capacity of wind and solar power has increased rapidly. To enhance the power supply reliability of clean energy bases, improve the utilization rate of new energy, and reduce carbon emissions from fossil fuel power within these bases, this paper conducts planning research on multi-path low-carbon transition of coal power and green electricity-based ammonia production. It proposes a planning method that integrates energy storage, co-firing of coal with green ammonia, low-carbon transformation of coal power, and green ammonia production to improve the utilization rate of new energy. This planning approach achieves a new energy utilization rate of 99.82% in desert-gobi-wasteland areas, realizes a 10% reduction in carbon emissions from fossil fuel power, and enables the stable annual production of 448,000 tons of green ammonia. It can effectively promote interaction between the power system and the energy sector, improve the utilization rate of new energy, reduce carbon emissions, and address the domestic gap in green ammonia supply.

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.

The rapid expansion of renewable energy has posed significant challenges for the power system, including supply adequacy and operational flexibility. Nuclear power, as a stable and clean baseload source, plays a significant role in developing a new-type power system. However, its weak modulation capabilities significantly constrain its peak shaving and frequency regulation performance. The integrated operation of nuclear power and pumped storage, known as “nuclear-pumped storage integration”, can not only compensate for energy shortages but also provide auxiliary services, making it a viable solution for ensuring regional power supply stability. Nevertheless, a significant problem resides in achieving an appropriate benefit allocation between the two entities. To solve this issue, this research presents a mechanism for measuring and allocating the benefits of “nuclear-pumped storage integration” operation in a diverse power market. First, an integrated operating model is built, together with a joint clearing model encompassing the spot energy market and frequency modulation ancillary service market. A full-chain cost–benefit accounting system is designed to evaluate the value of joint operations accurately. Second, by incorporating indicators such as peak-shaving compensation and participation degree, a benefit allocation correction factor is derived, and an improved Shapley value approach is proposed to enhance the rationality and incentive effect of the allocation scheme. Case study results suggest that the “nuclear-pumped storage integration” operation mode can significantly increase overall benefits. Moreover, compared with existing techniques, the suggested improved Shapley value method provides fairness and rationality in benefit allocation, offering a sound decision-making basis for advancing the “nuclear-pumped storage integration” model.

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.

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.

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.