With the transformation of the global energy structure and the rapid development of new power generation technologies, new power system planning faces the challenge of multi-source–storage coordinated deployment. This paper proposes a new power system planning method, the collaborative planning of source–grid–load–storage, considering wind and photovoltaic power generation systems. First, taking into account the access of renewable energy such as wind and solar power, a renewable energy output model is constructed. Secondly, a typical intraday dispatch model of the new power system is constructed; on this basis, by adding constraints of different types of power sources, a multi-source and storage coordinated deployment planning model is constructed. Finally, through actual case studies in three provinces of Northeast China, the effectiveness of the proposed method is analyzed and verified. The results show that compared with traditional planning methods, the method proposed in this paper can significantly improve the economy and environmental friendliness of the system while meeting the requirements of power supply security.

In light of global climate change, China has set strategic goals for carbon peaking by 2030 and carbon neutrality by 2060, emphasizing the necessity of constructing a new power system with a high proportion of renewable energy sources. As coal-fired power plants are the main carbon emissions source in the power system, their low-carbon transition and morphology structure optimization is crucial. This paper explores the critical role of dynamic carbon emission factors within source–network–storage power system planning and proposes an innovative inverse dynamic carbon emission factor that effectively captures the nonlinear relationship between load rates and emissions. Comparative analyses using the HRP-38 test case demonstrate that the inverse model enhances computational efficiency, reduces solution times, and more accurately reflects the emissions characteristics of coal-fired units across varying operational conditions. Furthermore, the inverse model offers improved economic performance and broader flexibility in unit selection, highlighting its potential to balance carbon emissions control and economic optimization in future power system planning.

China’s energy system is transitioning from a state of coal-dependent, low-electrification to a low-carbon, high-electrification paradigm. Carbon emissions have become a central constraint that directly influences generation expansion and transmission investment decisions. This study develops a bottom-up optimization framework integrating dynamic carbon trajectories into a coupled generation–transmission–storage expansion model. Distinct carbon emission trajectories are established on the basis of Guangdong’s allocated carbon budget, and the analysis evaluates the resulting power system structures and transition pathways under each scenario. Results show that Guangdong’s clean energy transition relies on external power imports, nuclear power, and variable renewable energy (VRE), collectively accounting for 87% of generation by 2060. Flexibility requirements expand substantially, with storage capacity rising from 10% of installed VRE in 2030 to 26% in 2060. Critically, under identical cumulative carbon budgets, an accelerated decarbonization pathway achieving earlier peak emissions demonstrates a pivotal economic trade-off: it imposes modestly higher near-term operation costs but delivers significant long-term savings by avoiding prohibitively expensive end-of-period abatement measures. Specifically, advancing the emissions peak from 2030 to 2025 reduces cumulative system costs over the planning horizon by CNY 53.7 billion and lowers the 2060 levelized cost of electricity by 5.2%.

Under high renewable energy penetration, power system operation modes exhibit significant diversity and structural complexity, posing challenges to traditional scenario‐based analysis methods in capturing their formation mechanisms. This paper proposes an interpretable analytical framework for operation mode identification and driving mechanism analysis. Based on year‐round operational data, clustering is employed to label similar operation states, while the core analysis focuses on a decision tree model combined with the SHapley Additive Explanations (SHAP) method to automatically identify key driving factors and quantify their contributions. A robustness verification strategy is further developed through clustering parameter perturbation and model simplification comparison, ensuring the stability and explanatory adequacy of the identified factors. Case studies on both the HRP‐38 benchmark test system and a real‐world 154 node power system demonstrate that, as renewable energy penetration increases, operation modes become more numerous and structurally complex, with their formation mechanism shifting from load‐ and thermal‐dominated patterns to renewable–storage coordinated structures. The proposed framework provides accurate mode identification while enabling clear and physically interpretable insights into their formation mechanisms, offering practical support for operation analysis and planning‐oriented decision‐making in high‐renewable power systems.