Integrated energy systems (IESs) are essential for enabling the energy transition in communities and reducing CO₂ emissions. This paper proposes a novel IES that combines photovoltaic (PV) and solar thermal energy with coordinated electrical and thermal energy storage to meet the energy demands of residential communities. The system also incorporates hydrogen production for fuel cell vehicles. A dual-objective optimization model was developed, minimizing both economic costs and CO₂ emissions. The system’s performance was evaluated using data from a case study in Dalian, which showed that the IES successfully reduced the annual total cost and CO₂ emissions compared to conventional systems. The key findings showed that PV electrolysis for hydrogen production provides both economic and environmental advantages. The system’s integration of solar thermal energy offers higher economic efficiency, while PV energy supplies enhance coordination. Additionally, carbon trading prices effectively reduce emissions, but excessively high prices do not always lead to better emission outcomes. This study introduces a comprehensive, multi-energy approach for optimizing the energy supply, contributing novel insights to the field of sustainable energy systems.

To address the challenge of the synergistic optimization of carbon reduction and economic operation in the integrated energy systems (IES) of industrial parks, this paper proposes an optimization scheduling model that incorporates carbon trading and supply–demand response (SDR) coordination mechanisms. This model is based on an IES coupling power-to-gas (P2G) and carbon capture and storage (CCS) technologies. First, the K-means clustering algorithm identifies three typical daily scenarios—transitional season, summer, and winter—from annual operation data. Then, we construct a synergistic optimization model that integrates a carbon trading mechanism, tiered carbon quota allocation, and SDR coordination. The model is solved via mixed-integer linear programming (MILP) to minimize total system operating costs. Systematic comparative analysis across six scenarios quantifies the incremental benefits: P2G–CCS coupling achieves a 15.2% cost reduction and 49.3% emission reduction during transitional seasons; supply–demand response contributes 3.5% cost and 5.6% emission reductions; technology synergies yield an additional 21.6 percentage points of emission reduction beyond individual contributions. The integrated system achieves 100% renewable energy utilization and optimizes peak-to-valley differences across electricity, heating, and cooling loads. Carbon price sensitivity analysis reveals three response stages—low sensitivity, rapid reduction, and saturation—with the saturation point at 200 CNY/t (28.6 USD/t), providing quantitative guidance for tiered carbon pricing design. This research provides theoretical support and practical guidance for achieving low-carbon economic operations in industrial parks.

The need for the statistical stability of data is increasing nowadays as the data resource has become a more and more important production factor. In this study, a set of general identification and correction models are established for data outlier modification. The research object we chose is the data of per capita energy consumption. Based on the joint diagnosis method of outliers and the regional convergence theory, the abrupt outliers are identified and corrected. The study finds that there is an outlier in the data of the Ningxia Hui Autonomous Region. According to the club grouping method, 30 provinces in China are divided into two clubs and the Ningxia Hui Autonomous Region is determined to be in the first club. We calculate the convergence rate and obtain the correction results combining the half-life cycle model.