Accurate distribution system state estimation (DSSE) under limited measurement scenarios remains a critical challenge due to the inherent high-dimensional nonlinearity and data scarcity in power distribution networks. Leveraging the exceptional few-shot pattern recognition capabilities of pre-trained large language models (LLMs), this paper proposes a novel DSSE framework that synergizes LLMs with multimodal data integration, enabling effective voltage magnitude and phase angle estimation. Moreover, this study introduces a prompt engineering approach that transforms statistical features extracted from historical data into contextual prompts. By employing natural language representations, this method enhances temporal feature extraction and uncovers latent patterns in scenarios with limited measurements. Furthermore, a channel-independent multimodal fusion architecture that structurally aligns time-series measurements, real-time sensor data, and textual prompts while preserving modality-specific characteristics. Extensive validation on IEEE 33-bus and Simbench 144-bus systems demonstrates the effectiveness of the proposed method, which reduces MAE by 17.7%–25.1% and RMSE by 17.3%–18.2% compared to the second best data-driven baselines under limited measurement scenarios. These results highlight the framework’s strong generalizability across diverse network topologies and its potential for practical deployment in poorly instrumented distribution networks. Our codes are available at https://github.com/gmy1997ee/LLM-MultiModal-for-DSSE.

Hydrogen-enriched compressed natural gas (HCNG) has great potential for renewable energy and hydrogen utilization. However, injecting hydrogen into the natural gas network will change original fluid dynamics and complicate compressed gas’s physical properties, threatening operational safety of the electricity-HCNG-integrated energy system (E-HCNG-IES). To resolve such problem, this paper investigates effect of HCNG on gas network dynamics and presents an improved HCNG network model, which embodies the influence of blending hydrogen on the pressure drop equation and line pack equation. In addition, an optimal dispatch model for the E-HCNG-IES, considering the “production-storage-blending-transportation-utilization” link of the HCNG supply chain, is also proposed. The dispatch model is converted into a mixed-integer second-order conic programming (MISOCP) problem using the second-order cone (SOC) relaxation and piecewise linearization techniques. An iterative algorithm is proposed based on the convex-concave procedure and bound-tightening method to obtain a tight solution. Finally, the proposed methodology is evaluated through two E-HCNG-IES numerical testbeds with different hydrogen volume fractions. Detailed operation analysis reveals that E-HCNG-IES can benefit from economic and environmental improvement with increased hydrogen volume fraction, despite declining energy delivery capacity and line pack flexibility.

The Chinese government is deepening reformation of electricity prices during the 14th Five Year Plan period and has set a carbon emission reduction target of reaching carbon peak before 2030. In this context, will the carbon emission target influence electricity pricing and will electricity price influence competitiveness of Chinese main industries are two questions needing to be answered. This paper compares China’s electricity price level with the selected major countries in the world, and four typical industries are selected to evaluate their electricity burden respectively. Then, the correlation between residential electricity price and industrial electricity price and the influencing factors is analyzed, from the perspectives of scale, structure and technology. According to the model obtained by regression analysis, the electricity price level and corresponding residential and industrial electricity burden in 2025 and 2030 are forecasted.

Following the unprecedented generation of renewable energy, Energy Storage Systems (ESSs) have become essential for facilitating renewable consumption and maintaining reliability in energy networks. However, providing an individual ESS to a single customer is still a luxury. Thus, this paper aims to investigate whether the Shared-ESS can assist energy savings for multiple users through Peer-to-Peer (P2P) trading. Moreover, with the increasing number of market participants in the integrated energy system (IES), a benefit allocation scheme is necessary, ensuring reasonable benefits for every user in the network. Using the multiplayer cooperative game model, the nucleolus and the Shapley value methods are adopted to evaluate the benefit allocation between multiple users. Numerical analyses based on multiple micro-energy grids are performed, so as to assess the performance of the Shared-ESS and the proposed benefit allocation scheme. The results show that the micro-energy grid cluster can save as much as 38.15% of the total energy cost with Shared-ESS being equipped. The following conclusions can be drawn: the Shared-ESS can significantly reduce the operating costs of the micro-energy grid operator, promote the consumption of renewable energy, and play the role of peak-shaving and valley-filling during different energy usage periods. In addition, it is reflected that the multiplayer cooperative game model is effective in revealing the interaction between the micro-energy grids, which makes the distribution results more reasonable.