This study explores a submerged architectural strategy for data center deployment in coal mining subsidence water bodies, aiming to simultaneously address the underutilization of post-mining landscapes, the high-carbon operation of data centers, and the implementation challenges of China’s dual carbon goals. The proposed structure integrates wall-mounted plate heat exchangers into the façades of underwater data halls, using the natural convection of surrounding water as a low-grade heat sink to replace conventional cooling towers and achieve passive, low-carbon cooling. A thermal exchange model was developed based on heat transfer principles and validated by comparing outputs from TRNSYS simulations and MATLAB-based parameterized calculations, showing a deviation of less than 3% under all test conditions. The model was then used to estimate energy consumption, PUE, and carbon emissions under typical IT load scenarios. Results indicate a 42.5–64.3% reduction in cooling energy use and a 37.7–75.1% reduction in carbon emissions compared to conventional solutions, while a PUE range of 1.06–1.15 is maintained. The system also offers strong spatial adaptability and scalability, presenting a sustainable solution for redeveloping subsidence zones that supports ecological restoration and digital transformation in resource-depleted urban regions.

Wind farms (WFs) in multi-settlement markets confront nested uncertainties and a rigorous two-tiered assessment mechanism: grid compliance metrics enforced by the system operator (SO) and medium-long-term (MLT) contract deviation settlements imposed by the market operator (MO). Existing decision models frequently neglect this dual regulation and the progressive reduction of uncertainties, relying on rigid preliminary baselines that severely squeeze subsequent feasible regions. To overcome this, a multi-stage heterogeneous sequential decision-making framework for wind-storage systems (WSSs), decoupled backward along the time axis, is proposed. Initially, a D-2 robust profiling model via intelligent weighting establishes the physical contract baseline, eliminating cross-stage constraint squeezing on subsequent decisions. Subsequently, a D-1 two-stage robust declaration model with feasibility cuts reformulates non-convex assessment metrics, empowering WFs with a proactive defensive capability to adjust day-ahead (DA) declarations under extreme conditions. Ultimately, a D-day coordinated trading strategy driven by feasible region pre-judgment and price guidance comprehensively co-optimizes operational compliance and real-time (RT) arbitrage. Numerical results demonstrate that the framework successfully alleviates physical and economic cross-stage conflicts and quantifies the impact of the two-tiered assessment boundaries on cross-market decisions. Furthermore, under extreme conditions, trading feasible space for security effectively mitigates left-tail revenue risks, maximizing storage RT arbitrage while evading severe assessment penalties.

China’s new energy sector has witnessed rapid scale expansion with costs significantly reduced. However, issues such as curtailment of wind and solar power have become increasingly severe. To achieve the high-quality development of the new energy, China has issued a series of relevant policies represented by the Circular on Deepening the Market-oriented Reform of New Energy Grid-connected Tariffs and Promoting the High-quality Development of New Energy (NDRC Price 〔2025〕 No. 136), which promote new energy’s full participation in market-oriented transactions, traditional investment decision-making evaluation methods are no longer applicable. In this paper, the full life cycle of new energy is adopted as the temporal scale, regional future power generation capacity growth, load changes and grid constraints are incorporated, electricity and power balance analysis across multiple typical scenarios is conducted, and a regional mechanism electricity price and mechanism electricity quantity calculation model under the Document No. 136 is constructed.. Furthermore, based on the correlation between renewable energy output and load curves, and incorporating multiple critical factors such as medium and long-term, spot, and mechanism electricity volumes, this paper proposes a multi-scenario market-based investment decision model for renewable energy with optimal economic efficiency. It also introduces a market-based storage allocation model for renewable energy. Finally, through case study simulations, the power and electricity balance, mechanism electricity volume, and electricity prices for the next 20 years are analyzed. Taking a specific wind and solar power station as an example, the optimal decomposition method for its renewable energy electricity volume is determined, and an energy storage configuration strategy for the renewable energy station is provided. The economic benefits have been significantly improved, which provides a strong support for the new energy investment decision-making under the Document No. 136.