In response to the problem of global warming, the factories are actively adjusting their energy use structure and significantly introducing zero-carbon energy sources such as wind and solar energy to reduce carbon dioxide emissions. The integration of diverse energy sources into a cohesive system presents significant challenges in terms of design complexity and cost. Currently, many researchers have designed some simulation software for optimization of integrated energy systems in industrial factories. However, these approaches are specific to single sites (i.e., not generalizable) and are typically not designed to anticipate capacity expansion of facilities. Herein, an optimization modeling of Multi-energy Expansion Supply system has been developed based on the Genetic Algorithm (GA) to optimize the cost of energy supply systems. This model has been used for optimization of multi-energy system in the new energy supply systems. The proposed method was verified against commercial software results, showing a higher total cost saving (23.19%) and faster payback time (5 years comparing to 9 years). Additional case was studied by comparing the dynamic installation and fixed installation, demonstrating 8.4% more total cost saving and faster payback time (2 years and 4 years). Furthermore, the same demand was fulfilled by different amount of CHP units, achieving 40% initial investment and 36% higher utilization rate. This model will promote the green transformation of the energy structure of traditional industrial factories and the optimization of multi-energy supply systems in new factories.

Under China’s “dual-carbon” strategic goals and the advancement of smart city development, the rapid adoption of electric vehicles (EVs) has deepened the spatiotemporal coupling between transportation networks and distribution grids, posing new challenges for integrated energy systems. To address this, we propose a collaborative optimization framework for power–transportation coupled networks that integrates multi-modal data with physical priors. The framework constructs a joint feature space from traffic flow, pedestrian density, charging behavior, and grid operating states, and employs hypergraph modeling—guided by power flow balance and traffic flow conservation principles—to capture high-order cross-domain coupling. For prediction, spatiotemporal graph convolution combined with physics-informed attention significantly improves the accuracy of EV charging load forecasting. For optimization, a hierarchical multi-agent strategy integrating federated learning and the Alternating Direction Method of Multipliers (ADMM) enables privacy-preserving, distributed charging load scheduling. Case studies conducted on a 69-node distribution network using real traffic and charging data demonstrate that the proposed method reduces the grid’s peak–valley difference by 20.16%, reduces system operating costs by approximately 25%, and outperforms mainstream baseline models in prediction accuracy, algorithm convergence speed, and long-term operational stability. This work provides a practical and scalable technical pathway for the deep integration of energy and transportation systems in future smart cities.

Addressing uncertainties on the demand side caused by electricity price fluctuations during integrated energy system (IES) dispatch, modeling biases resulting from static assumptions about equipment energy efficiency, and cost redundancy issues stemming from unreasonable seasonal allocation of carbon quotas, this study constructs an electricity PDR economic dispatch optimization model incorporating dynamic energy efficiency and dynamic carbon trading. It proposes a “distributed robust optimization (DRO)-model predictive control (MPC)” collaborative framework and a tiered dynamic carbon quota allocation strategy accounting for seasonal output and efficiency variations of equipment, tailored to match carbon emission characteristics across different seasons. At the demand response level, an electricity price elasticity coefficient matrix is introduced to quantify the impact of real-time price fluctuations on load, integrating it into the MPC model to resolve the time-scale mismatch between day-ahead and intraday scheduling. Simulation results demonstrate: The coupled dynamic energy efficiency and carbon trading model reduces total system costs by 13.07% and carbon trading costs by 11.57% compared to the conventional approach. Regarding tracking error, the combination of rolling optimization and feedback correction improves tracking accuracy by 14.66% and 6.13% compared to cases without feedback correction and rolling optimization, respectively, while reducing total costs by 4.36% compared to the case without rolling optimization. This study provides a scientifically feasible optimization solution for low-carbon economic dispatch of IES under uncertainty.

Many environmental and energy concerns exist, including air pollution and shortages of fossil fuels, and these concerns have motivated much research. Communities are seeking alternative fuels and nations are trying to implement them appropriately. The main alternative is renewable energy, including solar, geothermal, and wind. This systematic review analyzed 39 studies (2010-2024) on renewable energy applications in sports facilities, revealing distinct adoption patterns: solar energy dominated the research (64% of studies), followed by hybrid systems (23%), geothermal (8%), and wind (5%). Our PRISMA-guided analysis shows these renewable sources can be effectively used in sports facilities, especially new ones. Key findings indicate that solar applications achieve average energy savings of 39.1% (34.6-43.6% CI) in studied facilities, while geothermal systems show higher savings at 51.3% (45.8-56.8% CI). The primary emphasis in implementation is placed on solar and hybrid applications at sports stadiums (72% of cases), but geothermal and wind power are rarely employed (15% combined), which can be explained through geographical factors and higher initial costs (average $2.8M vs $1.2M for solar installations). Since these technologies have been advancing over the last few years, their application in sports stadiums and high-energy sports arenas will likely increase. Our review found that facilities adopting renewable energy reduced operational costs by 28-47% annually. Equipping new sport facilities with renewable energies typically makes them more environmentally benign, with demonstrated CO₂ reductions averaging 1,287 tons/year per facility. An important aspect is increased energy efficiency, with hybrid systems showing 47.2% (42.1-52.3% CI) improvement over conventional systems. The integration of renewable energy systems into sports facilities leads to considerable cost savings in the long term (average payback period 6.2 years), demonstrates commitment to environmental stewardship, and aligns facilities with sustainable development principles.

Existing design methodologies for off-grid wind–solar–hydrogen integrated energy systems (WSH-IES) are typically case-specific and lack portability. This study aims to establish a unified design framework to enhance cross-scenario applicability while retaining case-specific adaptability. The proposed framework employs the superstructure concept, dividing the off-grid WSH-IES into three subsystems: energy production, conversion, and storage subsystems. The framework integrates equipment selection and capacity sizing into a unified optimization process described by a mixed-integer programming model. Additionally, the modular constraint template ensures generalizability across scenarios by linking the local resource protocol to the techno-economic parameters of the equipment, allowing the model to be adapted to various situations. The model was applied to two case studies. Economic analysis indicates that the pure electricity architecture is dominated by energy storage (battery costs account for 96.8%), while the hybrid architecture redistributes expenditures between batteries (67.8%) and electrolyzers (28.4%). It utilizes hydrogen as a complementary medium for long-duration energy storage, achieving cost risk diversification and enhanced resilience. Under current techno-economic conditions, real-time bidirectional electricity–hydrogen conversion offers no economic benefits. This framework quantifies cost drivers and design trade-offs for off-grid WSH-IES, providing an open modeling platform for academic research and planning applications.

A hybrid data-model-driven framework for price-responsive energy storage dispatch in integrated energy systems

Paving the way for sustainable energy solutions: A real-time pricing mechanism in integrated energy systems with electric vehicles

The future of hydrogen as a strategic enabler in integrated energy systems: Technological developments, barriers, and policy implications

Hybrid Quantum-Transformer Network-Based Probabilistic Multi-Energy Flow Calculation in Integrated Energy System

Multi-objective configuration optimization of zero-carbon park electric-thermal-hydrogen integrated energy system considering degradation of multi-energy-flow equipment

The increasing integration of renewable energy systems (RESs) such as solar and wind into modern power grids using converter‐based, inertia‐less interfaces poses serious frequency stability challenges. This is due to reduced system inertia conventionally provided by synchronous generators. This paper proposes an innovative solution using an adaptive grid‐forming controller (AGFC), designed to emulate and adapt virtual inertia and damping in real time. The AGFC uniquely combines grid‐forming virtual inertia damping control with an adaptive DC link voltage regulation loop, coordinated by a genetic algorithm–optimized proportional integral (GA‐PI) controller. Key innovation lies in employing the GA‐PI control to dynamically manage DC link energy exchange during grid disturbances. Through optimized GA‐PI tuning, rapid adjustment of control parameters is achieved to maintain system stability during sudden load changes. MATLAB/Simulink simulations demonstrate that the proposed AGFC substantially mitigates frequency deviation, rate of change of frequency, and frequency settling time under transient conditions. Compared with conventional grid‐forming control without adaptive DC link management, the designed AGFC depicts superior resilience and adaptability to load disturbances. It enables inverters to autonomously establish voltage and frequency references while providing synchronous generator‐like stabilization. The results demonstrate that the proposed framework enhances the grid’s inertial response and dynamic stability for converter‐interfaced RES. Future research will explore scaling the AGFC for multiarea grids and applying advanced parameter optimization to larger systems. This is expected to further improve system‐wide frequency regulation and stability.

Avoidable exergy performance analysis in the integration of an electrothermal energy system based on transcritical CO2 cycles

Stable grid integration of renewable energy and storage power systems based on the configuration optimization and power management

Random forest-based speed forecasting and carbon capture optimization for shipboard integrated energy systems

Optimized scheduling strategies for integrated energy systems in agricultural parks with near-zero carbon emissions

A bi-level electricity-carbon-hydrogen coupled capacity configuration model of zero-carbon park integrated energy system under robust operation

Pore-scale numerical modeling of transport kinetics for CCUS-EOR and carbon cycling in offshore–onshore integrated energy system

Integrated energy system modeling and optimal scheduling strategy for supply and demand side resource flexibility

Virtual energy flow-based carbon emission optimization and hybrid game model for multi-park integrated energy systems

Optimal dispatch of electricity-hydrogen blended natural gas integrated energy systems incorporating refined gas transmission network modeling