Search Results (80)

Tri-reforming of methane (TRM) has emerged as a promising pathway for low-carbon syngas production by integrating steam reforming, dry reforming, and partial oxidation within a single process. This coupling enables simultaneous CH₄ utilization and CO₂ valorization while enabling internal heat generation and flexible adjustment of the H₂/CO ratio for downstream synthesis. However, TRM performance cannot be adequately evaluated using conversion or energy efficiency alone, because the process involves complex interactions among competing reaction pathways, transport phenomena, catalyst stability, and thermodynamic irreversibility. This review provides a multiscale critical assessment of TRM from both first-law energy and second-law exergy perspectives, linking reaction-network fundamentals to reactor-level behavior and system-level performance. The literature evidence shows that although high temperatures and near-autothermal operation can enhance CH₄ conversion and reduce external heat demand, these conditions may simultaneously intensify deep oxidation, hotspot formation, carbon-forming tendencies, and exergy destruction. While equilibrium analyses help define feasible operating windows, they are insufficient without kinetic modeling and reactor-scale studies that capture spatial non-uniformities and pathway competition. Across reported TRM systems, exergy destruction is consistently concentrated within the reformer, identifying the reacting core as the dominant thermodynamic bottleneck. Accordingly, the key challenge in TRM is not simply to maximize conversion but to preserve chemical work potential while maintaining syngas quality and operational stability. Viewed from this perspective, TRM is better understood as an irreversibility-aware multiscale design problem in which optimal performance depends on the integrated optimization of catalyst functionality, reactor architecture, heat management, and system-level operation. Full article

To address the issues of high energy consumption, high carbon emissions, and the waste of associated energy (AE) in coal mine production, which severely hinder global sustainable development goals, this paper proposes a novel low-carbon economic collaborative optimal scheduling model for a coal mine integrated energy system (CMIES) oriented towards sustainable energy transitions. First, a refined utilization model for AE encompassing coal mine gas, ventilation air methane (VAM), and mine groundwater (GW) is constructed, and a tiered carbon emission trading mechanism (TCET) is introduced to constrain carbon emissions and promote ecological sustainability. Second, a concentrated solar power (CSP) plant is integrated to break the rigid “power determined by heat” constraint of a traditional combined heat and power (CHP) unit, thereby enhancing the system’s scheduling flexibility and renewable energy integration. Meanwhile, abandoned mines are retrofitted into solvent storage tanks to construct an integrated flexible carbon capture system (IFCCS), achieving sustainable reuse of mining wastelands. Finally, to tackle the multi-source, heterogeneous uncertainties on both the source and load sides, a hybrid risk assessment method combining information gap decision theory (IGDT) and conditional value at risk (CVaR) is proposed. Case study results demonstrate that, compared to traditional energy supply modes, the proposed model reduces carbon emissions and total costs in the mining area by 66.04% and 15.97%, respectively. This significantly improves resource utilization efficiency and ecological benefits, providing a highly viable pathway for the sustainable development and clean transition of coal mine operations. Furthermore, the proposed hybrid assessment method can effectively assist decision-makers in achieving a refined trade-off between operating costs and system robustness under varying risk preferences. Full article

The rapid growth of renewable energy is driving a profound transformation of electricity grids toward architectures characterized by high shares of inverter-based generation, increased decentralization, and extensive digitalization. While wind and solar technologies have matured at the component level, their large-scale integration introduces technical, operational, and institutional challenges that extend beyond conventional power-system design paradigms. This review provides an integrated synthesis of the technologies, control strategies, and management processes that enable renewable energy integration into emerging electricity grids. Key challenges are analyzed across multiple timescales: fast frequency and voltage dynamics in low-inertia systems (milliseconds to seconds), forecasting, optimization, and automated control (real-time to near-real-time), and long-term planning of transmission, storage, and flexibility resources (years to decades). The synthesis covers grid-forming and grid-following inverter control, with quantitative comparison across short-circuit-ratio regimes; HVDC and HVAC transmission technologies; energy storage systems, including emerging electrochemical and mechanical solutions; smart-grid digitalization through EMS, SCADA, and digital twins; artificial intelligence and machine-learning deployments at major transmission system operators; sector coupling involving hydrogen and carbon capture; and cybersecurity considerations. Real-world case studies are used to illustrate practical lessons, with explicit attention to the brownfield–greenfield distinction between modernization of legacy systems and the design of new networks in developing regions. The review concludes by identifying key research and development priorities for achieving reliable, resilient, and economically efficient high-renewable energy systems. Full article

Hydrogen energy, owing to its advantages of low-carbon cleanliness, long-term storage capacity, and multi-energy coupling potential, has emerged as a crucial medium for enhancing renewable energy accommodation within integrated energy systems. However, the pronounced heterogeneity in hydrogen load behaviors, temporal characteristics, and regulation capabilities poses significant challenges for unified modeling approaches, which struggle to accurately capture the multi-modal regulation potential of hydrogen demand, thereby limiting the precision of system operation optimization. To address this issue, this paper proposes a data-driven hydrogen load flexibility modeling method for integrated energy system (IES) operation optimization. A hybrid LSTM-ISODATA framework is designed to extract deep temporal dependencies and identify six representative hydrogen consumption patterns from typical load sequences. Each hydrogen load category is decomposed into shiftable, transferable, and reducible flexible forms, and a category-specific time-varying flexibility constraint matrix is established to characterize differentiated regulation capabilities. An electricity–heat–hydrogen integrated energy system operation optimization model embedded with classified flexible hydrogen loads is developed and solved via mathematical programming. Simulation results show that the proposed method reduces system operating costs by 10.3% compared with conventional unified modeling, while significantly promoting renewable energy utilization and system operational flexibility. The effectiveness and engineering applicability of the proposed model in IES optimal scheduling are fully validated. Full article

Wind power plays an increasingly important role in the global energy transition. However, its power output exhibits significant uncertainty due to rapid variations in meteorological conditions. Existing forecasting methods still face challenges in large-scale wind farm cluster scenarios. In such cases, spatial heterogeneity and temporal asynchrony among wind farms cannot be fully characterized, which limits the overall prediction accuracy. To address these issues, this study proposes a novel hierarchical and adaptive collaborative forecasting framework for wind farm clusters by integrating meteorology-driven dynamic clustering with deep learning-based prediction. First, a multidimensional feature system is constructed by jointly considering static wind farm attributes and dynamic meteorological variation trends. Based on a sliding time window, real-time meteorological similarity among wind turbines is evaluated, allowing meteorological data to actively drive the formation and continuous evolution of adaptive subcluster structures. Subsequently, a deep learning model is developed to perform short-term power forecasting at the dynamic subcluster level. This approach enables the framework to flexibly capture spatio-temporal heterogeneity while maintaining robust prediction capability under varying cluster structures. Experimental results based on real-world wind farm cluster data demonstrate that the proposed method achieves superior accuracy and robustness compared with conventional whole-farm forecasting and static clustering approaches. The proposed framework enhances forecasting reliability, thereby supporting renewable energy integration and sustainable low-carbon power systems. Full article

This study addresses the critical issues of rigid energy use and insufficient demand-side responsiveness in office buildings’ Variable Refrigerant Flow (VRF) systems under complex summer conditions. Existing research lacks fine-grained characterisation of short-term load fluctuations and often fails to accurately couple energy efficiency with humidity-adapted thermal comfort. To fill this gap, this paper proposes an integrated Model Predictive Control (MPC) framework driven by load characteristic identification and a novel hybrid prediction model. First, based on actual hourly metered data (683,280 records), K-means clustering was employed to identify three typical load patterns, pinpointing short-term peak loads in core office zones as the primary target for flexible regulation. Second, a high-precision GS-DBO-ELM prediction model—integrating Grid Search and Dung Beetle Optimisation—was developed to capture the nonlinear dynamics of VRF energy consumption and Predicted Mean Vote (PMV). The model achieved an R² of 0.99 with relative errors constrained within ±5%. Finally, a multi-objective MPC strategy, solved via an improved Artificial Hummingbird Algorithm (HAGSAHA) and weighted by the Analytic Hierarchy Process (AHP), was implemented to dynamically adjust zone-level temperature setpoints. Results demonstrate that the proposed MPC strategy reduces daily cooling energy consumption by 7.95–10.69% and peak loads by 15.3%, while maintaining strict thermal comfort (PMV within ±0.5). Under a time-of-use pricing mechanism, the flexible scheduling strategy achieved a 12.37% total electricity reduction and a 9.54% reduction in operating costs. This work provides a highly replicable, climate-tailored solution for low-carbon, flexible energy management in public buildings. Full article

This paper develops and applies a techno-economic optimization framework for sizing photovoltaic (PV) and battery energy storage systems (BESSs) in grid-connected energy communities. An in-house developed modeling platform featuring custom MATLAB (R2025a) code implements a mixed-integer linear programming (MILP) model that minimizes differential net present value (NPV) over a 25-year lifetime, integrating capital expenditures, operating cash flows, and carbon taxation. The formulation captures temperature-dependent PV efficiency, battery round-trip efficiency, and time-varying electricity prices, and is validated on a real campus energy community with hourly demand, irradiance, and tariff data. Two design scenarios are examined: the optimal unconstrained case and a budget-constrained configuration (CAPEX ≤ 2.0 M€). Results show the unconstrained system installs 3.19 MW_p PV and 12.3 MWh storage, achieving 78.9% self-sufficiency and a 78.9% emissions reduction. The constrained case installs 0.99 MW_p and 1.68 MWh, achieves 32.0% self-sufficiency, and delivers a 4.46 M€ NPV with payback in 3.9 years. Under current costs and tariffs, PV-dominated configurations provide the highest value, with limited battery benefit except under generous budgets or higher carbon prices. A dedicated CAPEX sensitivity analysis explores PV and battery cost variability and its impact on optimal sizing and economic outcomes. The core methodological contribution is a master-planning formulation that solves design decision variables and optimal dispatch concurrently within a single MILP. The flexible platform enables future reassessment as technology, tariff, and policy landscapes evolve. Full article

In the scenario where renewable energy sources (RESs) are connected to the power system (PS) through a flexible high-voltage direct current (HVDC) transmission system, their output becomes highly intermittent and volatile due to meteorological factors like wind direction and speed. This variability poses significant challenges to the real-time power balance and control of the PS. To address the uncertainties in system operation and the challenges of RES consumption, this paper proposes an artificial intelligence (AI) algorithm-driven collaborative optimization strategy for virtual power plants (VPPs) considering RESs transmitted by flexible HVDC. Firstly, a self-attention mechanism and multiple gated structures are integrated into a long short-term memory (LSTM) deep learning model. This enhancement improves the model’s ability to capture multi-timescale characteristics of RESs, increasing forecasting accuracy and robustness. Based on these forecasts, a total cost optimization model for VPP operation is developed, which includes high penalty costs for wind and solar curtailment. By embedding economic constraints that prioritize RESs usage, the model can reduce waste caused by traditional cost-driven scheduling. Additionally, to solve the high-dimensional nonlinear optimization problem in VPP scheduling, an improved population-based incremental learning (PBIL) algorithm is introduced. It incorporates an elite retention strategy and an adaptive mutation operator to boost global search efficiency and convergence speed. Simulations based on an VPP incorporating typical offshore wind and solar RESs transmitted via flexible HVDC demonstrate that the improved LSTM reduces MAPE by 7.14% for wind and 4.27% for PV compared to classical LSTM, and the proposed method achieves the lowest curtailment rates (wind 10.74%, PV 10.23%) and total cost (43,752 RMB), outperforming GA, PSO, and GW by 10–18% in cost reduction. Simulation results show that the proposed strategy enhances RESs consumption while maintaining system economy under flexible HVDC transmission. This work offers theoretical and practical insights for optimizing PS with high RES penetration and supports the low-carbon transition of new-type PS. Full article

As the global imperative for climate neutrality intensifies, hydrogen (H₂) from fossil fuels remains central to decarbonizing hard-to-abate sectors. Conventional production via steam methane reforming (SMR), however, is carbon-intensive and, even with carbon capture and storage (CCS), incurs energy penalties and long-term storage constraints. This review develops a harmonized well-to-gate, market-oriented framework to evaluate methane pyrolysis (MP) relative to SMR and autothermal reforming (ATR), with or without CCS, moving beyond reactor-focused assessments toward system-level commercialization analysis. MP decomposes methane into hydrogen and solid carbon, avoiding direct CO₂ formation and the need for CCS infrastructure. Integrating with the reverse water–gas shift (RWGS) reaction enables flexible syngas production with adjustable H₂:CO ratios for methanol and chemical synthesis. A central finding is the dominant role of the “carbon lever”: MP generates approximately 3 kg of solid carbon per kg of H₂, making the carbon market’s absorptive capacity the primary scalability constraint. While carbon monetization can reduce levelized hydrogen costs, large-scale deployment would rapidly saturate existing carbon black and specialty carbon markets. Techno-economic evidence indicates that carbon prices above $500/ton are required to achieve parity with gray hydrogen, whereas $150–200/ton enables competitiveness with blue hydrogen. Lifecycle assessments further show that climate superiority over SMR or ATR with CCS requires upstream methane leakage below 0.5% and very low-carbon electricity. Commercial readiness varies, with plasma MP at TRL 8–9 and thermal, catalytic, and molten-media pathways remaining at the pilot or demonstration stage. Parametric decision-space analysis under harmonized boundary assumptions shows that MP is not a universal substitute for reforming but a conditional pathway competitive only under aligned conditions of low-leakage gas supply, low-carbon electricity, credible carbon monetization, and supportive policy incentives. The review concludes with a roadmap that highlights standardized carbon certification, end-of-life accounting, and long-duration operational data as priorities for commercialization. Full article

To address global carbon reduction demands, oxy-fuel combustion in circulating fluidized beds (oxy-CFB) has emerged as a highly promising carbon capture technology, offering extensive fuel flexibility and facilitating bioenergy with carbon capture and storage (BECCS). However, its commercialization is hindered by significant energy penalties and complex scale-up challenges. This review comprehensively analyzes the fundamental multiphase mechanisms, heat transfer behaviors, and multi-pollutant emission characteristics of oxy-CFB systems, drawing upon multiscale modeling advancements and operational data from pilot to 30 MW_th industrial demonstrations. Replacing air with an O₂/CO₂/H₂O mixture fundamentally alters gas–solid hydrodynamics and char conversion pathways, necessitating active fluidization state re-specification. Despite shifting optimal desulfurization temperatures and introducing recarbonation risks, the technology demonstrates inherent advantages in synergistic pollutant control, including the complete elimination of thermal NO_x. While atmospheric oxy-CFB is technically viable, transitioning to pressurized operation is critical to minimizing system efficiency penalties. Furthermore, integrating oxygen carrier-aided combustion (OCAC) and developing advanced predictive control strategies are essential to managing multi-module thermal inertia and enabling rapid dynamic responsiveness for modern power grids. Full article

China’s power sector is undergoing a complicated transformation characterized by intricate dependence on the dominant coal infrastructure and abundant renewable energy resources. This study assesses China’s carbon-neutral transition pathways for the period of 2024–2060 by using the “Establish Before Breaking” principle within a policy-informed, high-resolution energy system modeling framework. To examine the technological, economic, and environmental trade-offs of various carbon-neutral strategies, four scenarios (Reference (REF), Carbon Capture and Storage (CCS), Renewable-Based (REB), and Integrated (ING)) were developed, and their impacts were assessed through the application of the Low Emission Analysis Platform and the Next Energy Modeling (LEAP–NEMO) model. The results reveal that the ING scenario represents the most feasible and policy-consistent pathway, achieving an 88% renewable electricity share and a total installed capacity of approximately 8000 gigawatts (GW) by 2060. This pathway relies on a dual-track strategy that combines accelerated renewable deployment—supported by geographical complementarity and multi-regional Power-to-X (PtX) systems—with the strategic stabilization of conventional generation assets. The findings further demonstrate that retaining a small but critical share of flexible coal-CCS (0.2–0.5%) and nuclear capacity is necessary to address sub-daily variability, mitigate duck-curve effects, and ensure power system reliability under high renewable penetration. This integrated approach offers a systematic pathway for deep decarbonization within China’s unique energy context, ensuring a just, equitable, and sustainable transition. Full article

As renewable energy penetration accelerates, battery energy storage systems have become essential for enhancing flexibility, reliability, and economic efficiency in power system operations. For the daily operations of grids, the unit commitment (UC) problem plays a central role in determining the optimized scheduling of generation resources, but current formulations rarely incorporate battery degradation dynamics. The accurate representation of battery aging is crucial, as degradation costs may influence dispatch. This review provides a synthesis of existing approaches for integrating battery degradation into UC formulations. We survey and compare major classes of degradation models and then examine how these models have been embedded into UC frameworks, highlighting trade-offs between modeling accuracy and tractability. This paper concludes with identified research gaps and recommendations for future UC formulations that more faithfully capture battery degradation while maintaining computational efficiency. This review aims to serve as a foundation for researchers and system operators seeking to incorporate realistic battery aging mechanisms into operational decision-making for the evolving low-carbon grid. Full article

The coupling of electricity, heat, and hydrogen subsystems together with carbon capture technologies introduces complex operational interactions in modern multi-energy systems. Existing game-based scheduling studies mainly focus on electricity–heat or electricity–heat–gas coupling, often neglecting hydrogen blending, carbon capture integration, and strategic coordination among heterogeneous stakeholders. To address these gaps, this study develops a game-theoretic hierarchical optimization framework for electricity–heat–hydrogen integrated energy systems incorporating carbon capture. Compared with conventional multi-energy game models, the proposed framework integrates hydrogen blending and carbon capture into a unified electricity–heat–hydrogen–carbon coupling structure, enabling coordinated low-carbon operation. A Stackelberg leader–follower structure is adopted, where the upper-level operator determines electricity and heat prices, and lower-level participants optimize generation dispatch and demand response accordingly. The bi-level model is transformed into an equivalent single-level formulation using Karush–Kuhn–Tucker conditions and solved through a hybrid particle swarm optimization–mathematical programming approach. Simulation results based on an extended IEEE 30-bus system demonstrate improved coordination, enhanced scheduling flexibility, and reduced operating costs and carbon emissions. Compared with centralized optimization, the proposed framework enables the integrated energy operator and energy supplier to achieve revenues of 3.18 × 10⁵ CNY and 3.95 × 10⁵ CNY, respectively, while reducing the load aggregator’s cost by 41.71%, confirming its effectiveness for coordinated low-carbon IES scheduling. Full article

The accelerating transition toward carbon neutrality necessitates the synergistic integration of power and hydrogen systems to mitigate renewable intermittency; however, coordinating regulatory policies with the operational flexibility of these coupled systems remains a critical challenge under deep uncertainty. Motivated by this gap, this study develops a bilevel carbon price-coupled optimization framework for integrated power–hydrogen systems, aiming to coordinate environmental policy design with operational scheduling under deep uncertainty. The upper-level model represents the decision-making of a market regulator that determines the optimal carbon price and emission allowances to maximize overall social welfare, while the lower-level model captures the coordinated operation of electricity and hydrogen subsystems that minimize total dispatch cost, including renewable utilization, electrolyzer conversion, and fuel-cell recovery.To address stochastic variations in renewable generation and load demand, a Distributionally Robust Optimization (DRO) formulation is introduced using Wasserstein ambiguity sets, ensuring decision feasibility against worst-case probability distributions. The bilevel structure is efficiently solved via a Benders–Column-and-Constraint Generation (CCG) algorithm, which decomposes policy and operation layers into tractable subproblems with provable convergence. Case studies on a 33-bus integrated power–hydrogen network demonstrate that the proposed framework effectively balances economic efficiency and carbon reduction. Results show that the optimal carbon price of approximately 45 $/tCO₂ achieves a 27% emission reduction with only a 9% cost increase, revealing a near-optimal social welfare equilibrium. Hydrogen subsystems operate flexibly, with electrolyzer utilization increasing by 30% and storage cycling deepening by 15%, enabling enhanced renewable absorption. Sensitivity analyses confirm that the DRO layer reduces operational risk by 4% compared with stochastic optimization, validating robustness against distributional shifts. The study provides a rigorous and computationally efficient paradigm for policy-coordinated decarbonization, highlighting the synergistic role of carbon pricing and cross-energy scheduling in the next generation of resilient low-carbon energy systems. Full article

Hydrogen is a promising clean energy vector capable of decarbonizing future energy systems. This study explores blue hydrogen production via a modified autothermal reforming process, integrated with oxy-fuel combustion and carbon capture technologies. The process achieves approximately 99.8% carbon dioxide capture while co-generating electricity, improving both environmental and economic performance. A detailed techno-economic analysis for Alberta, Canada, shows that hydrogen can be produced at a competitive cost of $1.70 per kilogram, depending on natural gas supply pressure, with CO₂ emissions of just 3.82 kg-CO₂/kg-H₂, meeting stringent international low-carbon thresholds. Key parameters like natural gas supply pressure, oxygen-to-methane ratio, and turbine pressure ratio were optimized for flexibility, efficiency, and cost-effectiveness. Sensitivity analysis identified financial, policy, and grid decarbonization factors as key drivers of production costs. Compared to other methods, this process stands out for its superior environmental and economic outcomes, particularly in regions with ample natural gas and carbon capture infrastructure. The study underscores the importance of process innovation in advancing sustainable blue hydrogen. Full article