Junxiang Li

With the rapid proliferation of prosumers, establishing a rational trading mechanism that simultaneously optimizes power dispatch and mitigates photovoltaic (PV) output uncertainty has become a critical challenge. To address this, this paper proposes an MGO-prosumer trading model that integrates a Stackelberg game with Information Gap Decision Theory (IGDT) while explicitly incorporating user satisfaction. First, a deterministic pricing model is formulated within the Stackelberg game framework to maximize both the Microgrid Operator's (MGO) profit and the prosumers' utility. A satisfaction constraint is introduced to investigate the impact of prosumer comfort on the game equilibrium, as well as the trade-off between economic and environmental benefits. Subsequently, based on IGDT, a Risk-Seeking Strategy (RSS) model and a Risk-Averse Strategy (RAS) model are constructed. Notably, the RAS model features a robust boundary driven by dual baselines that incorporate the satisfaction constraint. These models are employed to analyze MGO pricing and prosumer consumption strategies under different decision-making modes. Finally, a hybrid Alternating Direction Method of Multipliers (ADMM) and bisection algorithm is utilized to solve the proposed models. Simulation results demonstrate that the proposed scheme effectively enhances MGO revenue and prosumer utility while reducing total system load and carbon emissions. Furthermore, the uncertainty model based on dual robust boundaries aligns better with practical electricity trading markets, providing a more scientific response strategy for systems confronting risks.

In response to global climate change, virtual power plants (VPPs) have emerged as critical entities for integrating distributed energy resources and enabling demand response. However, the design of multi-energy collaborative pricing mechanisms for VPPs remains a significant challenge, particularly under carbon trading regulation. This paper addresses this gap by proposing a bi-level optimization model that captures the real-time interactions between users and energy suppliers. The model is designed to simultaneously maximize user utility and minimize supplier costs, explicitly accounting for energy costs, equipment operation and maintenance (O&M) costs, carbon emission costs, and power generation structure constraints. A particle swarm optimization (PSO) algorithm is employed to solve the formulated problem. The results of a case study demonstrate that the proposed mechanism effectively guides users toward peak shaving and valley filling, achieving a real-time balance between supply and demand. Furthermore, the simulation results indicate that the model significantly enhances power system operational efficiency and economic benefits while reducing carbon emissions. This work offers a practical approach for improving renewable energy integration and overall system performance within a carbon-constrained environment.

Persistent energy system inertia continues to hinder China’s carbon reduction progress despite global decarbonization trends. This study develops an explainable machine learning framework to dissect energy-related emission drivers through 14 secondary indicators spanning energy structure, industrial dynamics, social factors, and economic factors. Leveraging panel data from 260 Chinese cities (2000–2023), we conduct comparative analysis of six ML models and identify XGBoost as optimal for capturing nonlinear emission patterns. SHAP value decomposition and feature importance reveals that total energy consumption and energy consumption intensity remain the dominant contributors to carbon and pollution emissions, while the secondary industry still emerges as a critical driver. Our research establishes an actionable framework to identify drivers of carbon mitigation and pollution reduction, analyze their mechanisms, and support policymakers in optimizing policy implementation amid energy transition.

Blockchain technology (BCT) is dramatically altering industries by providing a decentralized, secure, and transparent system to trade without any intermediaries-promoting trust and automation in public and private networks. Managers and decision-makers need to apply multi-attribute group decision-making (MAGDM) processes under uncertainty in order to identify the factors that influence BCT adoption and its possible uses. This study presents a systematic approach to the identification and addressing of barriers preventing the implementation of BCT. First, we introduce a number of intricate aggregation processors based on complex q-rung orthopair fuzzy settings (Cq-ROFSs) for synthesizing the views of specialists. Moreover, we propose a set of interesting complex q-rung orthopair fuzzy Einstein geometric operators such as Cq-ROFEWG, Cq-ROFEOWG, and Cq-ROFEHG operators to enhance aggregation under uncertainty. Then, we describe an advanced EDAS-based decision-making process that meets MAGDM requirements, complete with an explanatory flowchart step-by-step. The proposed technique is used to review the BCT implementation over fourteen critical aspects, thus validating its practical application. Finally, both conventional models and the proposed framework performance comparison reveal the proposed framework can offer enhanced reliability and realistic insights for decision-makers.