Search Results (1,820)

The increasing integration of distributed electricity–heat adjustable resources into distribution networks poses significant challenges for virtual power plant (VPP) dispatch, as conventional aggregation models often neglect network constraints, leading to infeasible or unsafe operation plans. To address this issue, this paper proposes a source-grid-load-storage aggregation optimization method that explicitly incorporates both distribution network power flow constraints and district heating network hydraulic–thermal coupling constraints. The network constraints are integrated into the optimization objective as penalty terms, and the dispatch problem is formulated as a Markov decision process. A deep reinforcement learning framework, combining twin delayed deep deterministic policy gradient (TD3) and deep deterministic policy gradient (DDPG) algorithms, is employed to solve the sequential decision-making problem. Simulation results demonstrate that the proposed method effectively ensures distribution network security and heating quality while maintaining economic efficiency, providing a feasible and safe dispatch strategy for VPPs in coupled electricity–heat systems. Full article

A short-term performance test of blended biodiesel (FAME), green diesel (HVO), and diesel was experimentally assessed in a 100 kW Cummins 6BTAA5.9-G12 diesel engine under multiple load conditions. The objective was to determine the technical feasibility, operational trade-offs, and optimal blend formulations for renewable energy deployment in diesel power plants. All tested blends operated stably without engine modification, confirming the “drop-in capability” of FAME–HVO mixtures for existing diesel engines. Specific fuel consumption (SFC) increased notably at high loads, with penalties up to 15.15% for B30D20 and B35D15 relative to neat diesel, although overall efficiency improved with load. Among the ternary fuels, B30D10 and B30D20 provided the most balanced compromise between combustion reactivity and flow properties. Exhaust gas temperatures rose with load for all fuels, with FAME-rich blends exhibiting higher temperatures than neat diesel, while coolant-side analysis showed D100 and D50 as thermally favorable and B50–B100 imposing the highest cooling demand. The results emphasize the need for injection system recalibration on an energy basis for HVO-rich fuels, and for strengthened filtration and maintenance practices for FAME-rich blends to avoid filter clogging and injection instability. Considering performance, operability, and system stability up to 100 kW, B30D10 and B35D15 are identified as optimal compromise blends. The study highlights the necessity of future work on long-term durability, fuel system compatibility, supply chain robustness, and techno-economic viability to safely scale green diesel use in Indonesian stationary power generation. Full article

This study investigates the valorization of construction and demolition (C&D) waste streams and an industrial by-product for sustainable concrete production. Recycled concrete aggregates (RCA) and recycled brick aggregates (RBA), derived from C&D wastes, together with pelletized recycled fly ash aggregates (FAA) produced from thermal power plant fly ash, were used as total replacements for natural coarse aggregates. Six concrete mixtures were prepared at a constant water-to-cement ratio of 0.50 using untreated and slag slurry–treated aggregates. A slag slurry-based two-stage mixing approach (TSMA), incorporating ground granulated blast furnace slag (GGBFS), was applied as a practical and potentially scalable treatment method to enhance aggregate quality and interfacial bonding. The results show that complete replacement of natural aggregates reduced fresh concrete unit weight by up to 17%, while meeting the minimum compressive strength requirements for structural applications. Slag slurry treatment led to statistically significant improvements in mechanical properties, reduced variability, and enhanced overall reliability. In addition, widely used code-based prediction models (TS500, ACI, Eurocode-2, NZS 3101-1:2006, and CSA A23.3-04), originally developed for conventional concrete, were evaluated for their applicability in estimating key mechanical properties of recycled and by-product aggregate concretes, and alternative regression-based models were developed to improve prediction accuracy. Overall, the findings demonstrate the potential for effective utilization of C&D wastes and industrial by-products in structural concrete, contributing to resource efficiency and reduced reliance on natural aggregates. Full article

To improve the flexibility and carbon reduction performance of coal-fired power plants, a solar-aided power generation (SAPG) system integrated with parabolic trough collectors and thermal energy storage (TES) was proposed and investigated using a combined Aspen Plus and System Advisor Model (SAM) framework. Two different integration schemes, namely SAPG-1 and SAPG-2, were evaluated under 100%, 75%, and 50% load conditions with a solar multiple of 2 and a TES duration of 6 h. The thermodynamic, economic, and environmental performances of the systems were comprehensively analyzed. The results show that TES significantly improves solar energy utilization, annual solar contribution, and system dispatchability. Compared with SAPG-2, SAPG-1 demonstrates superior thermodynamic and economic performance due to its lower boiler heat demand and more effective feedwater integration. At full load, the solar contribution of SAPG-1 with TES reaches 16.04%, while the annual solar energy production increases to 190.35 GWh with a capacity factor of 21.75%. In addition, TES integration effectively reduces the levelized cost of electricity and shortens the payback period under both CO₂ pricing and non-CO₂ pricing scenarios. The proposed SAPG framework demonstrates considerable potential for enhancing renewable energy utilization, operational flexibility, and economic feasibility in large-scale solar–coal hybrid power generation systems. Full article

Open-cathode Proton Exchange Membrane Fuel Cells (PEMFCs) are a promising technology for increasing the endurance of small Unmanned Aerial Vehicles (UAVs), ground robots, e-bikes, and light electric vehicles. However, their performance under realistic operating conditions is strongly influenced by rapid variations in load, temperature, and ambient pressure, which are often neglected in design-oriented or quasi-steady-state analyses. This study experimentally investigates a 1 kW open-cathode PEMFC system, including its balance of plant and a passive supercapacitor buffer, under a representative UAV flight power profile. Steady-state and dynamic tests were conducted to assess polarization characteristics, thermal behavior, parasitic power consumption, and hydrogen utilization. Results revealed significant thermal inertia and hysteresis effects during load transients, causing voltage deviations from steady-state performance and stabilization times exceeding 90 s. The supercapacitor effectively reduced stack current ramp rates, although some high-frequency oscillations remained. Under flight-representative conditions, the system achieved stable operation with average voltaic efficiency ranging from 55.3% to 60.7% and net efficiency ranging from 50.2% to 54.2%. Auxiliary components had a measurable impact on overall performance: cooling fans accounted for 2–6% of stack power during steady operation and approximately 2.5% of total mission energy, while hydrogen purge losses can significantly reduce vehicle endurance. The findings demonstrate the importance of energy-based performance assessment, including auxiliary loads and purge losses, to obtain realistic estimates of efficiency and endurance in dynamic PEMFC-powered applications. Full article

The energy recovery of biomass is frequently implemented through single-output systems or passive management schemes, resulting in underutilization of its thermodynamic potential and losses in economic value, climate benefits, and useful co-products. This study formalizes the concept of biopolygeneration as a diagnostic principle aimed at maximizing biomass utilization through the simultaneous production of multiple energy services and the valorization of secondary streams. A dimensionless metric, the Biopolygeneration Diagnostic Index (BDI), is proposed to quantify this concept. The index is bounded within $[0,1]$ and integrates five sub-indices: energy efficiency ($I_E$), thermal integration ($I_T$), energy self-sufficiency ($I_A$), exergetic quality of outputs ($I_Q$), and co-product valorization ($I_V$). Weights were determined using the Analytic Hierarchy Process ($w_1=0.40$, $w_2=0.24$, $w_3=w_4=0.14$, $w_5=0.08$; $\mathrm{CR}=0.007$). The BDI was evaluated using six cases, including five operating plants and one validated computational model representing five biomass conversion technologies in four countries. Results ranged from 0.453 for an engine without combined heat and power (CHP) to 0.733 for a cascade trigeneration system. Under identical feed conditions, the incorporation of CHP ($C1\to C2$) increased the BDI from 0.453 to 0.715, representing a 57.7% improvement attributable solely to heat recovery. Current limitations include the small validation sample ($n=6$) and the reconstruction of $I_A$ and $I_V$ from technological characteristics due to the absence of standardized reporting in the literature. Although these sub-indices account for only 22% of the total weighting ($w_{I_A}+w_{I_V}=0.22$), the present results should be considered a proof of concept rather than a fully empirical validation. The BDI provides a thermodynamically consistent framework for comparing bioenergy systems across technologies and supports technical, regulatory, and investment decision making. Broader validation using larger measurement-based datasets is required before claims of universality can be established. Full article

Coal combustion in large-scale power plants is a major source of atmospheric pollution, including SO₂, NO_x, particulate matter, and the halogen acids HCl and HF. Predicting HCl and HF emissions is challenging due to interactions among fuel composition, fly ash chemistry, combustion conditions, and flue gas dynamics. In this study, artificial neural network (ANN) models are developed from field experiments at the lignite-fired TPP “Kostolac B”. The models incorporate operational parameters (flue gas temperature and flow rate) and fuel/ash characteristics (moisture and total sulphur in coal and CaO content in ash) to estimate HCl and HF emissions. SHAP analysis identified key variables affecting halogen acid release. The developed ANN models achieved satisfactory predictive accuracy, with the test-set performances of RMSE = 2.05 mg/Nm³, R² = 0.80, and MAPE = 18.7% for HCl prediction, and RMSE = 3.23 mg/Nm³, R² = 0.83, and MAPE = 18.7% for HF prediction. SHAP analysis indicated that CaO content in fly ash and coal moisture are the primary drivers of HCl and HF emissions, while operating conditions and coal sulphur content influence emissions through non-linear interaction effects. The proposed ANN-SHAP framework provides a data-driven approach for emission prediction and interpretation, supporting decision-making in emission management. Full article

With the increasing integration of grid-friendly concentrated solar power (CSP) plants into high-proportion new energy power systems, the system is confronted with challenges such as insufficient regulation capability and power balance difficulties. To address these issues, this paper proposes a multi-stage optimal regulation strategy for CSP–wind power systems based on adaptive step-size model predictive control (ASMPC), from the perspectives of tapping transmission line current-carrying capacity and coordinating system regulation resources. This strategy first establishes an electro–thermal–mechanical coupling dynamic line rating (DLR) model to characterize line safety margins, then constructs an optimization decision-making model aiming at minimizing the total multi-stage coordinated scheduling cost and adopts ASMPC to dynamically adjust the control step size, effectively improving scheduling accuracy and real-time correction capability. Simulation results based on the modified IEEE 39-bus system show that the proposed method reduces the total system cost by 26.8% (nearly 30%), increases the CSP unit output ratio by 27.9%, and decreases the average grid load rate by 12.6 percentage points. The proposed strategy can effectively mitigate the impact of source-load uncertain fluctuations and significantly improve the economic operation level of the CSP–wind power combined system. Full article

The turbine-driven coaxial boiler feed pump (TD-BFP) speed regulation system is a core auxiliary machine in thermal power generating units. Its complex physical characteristics, including strong square-law nonlinearity, multivariable coupling, and large inertia, pose significant challenges for conventional fixed-parameter PID controllers, which often suffer from severe regulation lag, integral windup, and high-frequency oscillation during wide-range operating condition transitions. To address these issues, an improved adaptive PID control strategy based on a Back Propagation (BP) neural network is proposed in this paper. Specifically, to overcome the negative control gradient loss caused by the square-law resistance in the physical model, a sign-preserving mapping logic ($u\mid u\mid$) is innovatively designed. Furthermore, a dynamic anti-integral windup mechanism with physical boundary constraints and a first-order inertial filtering algorithm is introduced. Comprehensive simulation experiments on the Matlab/Simulink platform under high-load step operating conditions (3683 r/min and 1104 t/h) reveal that the proposed algorithm achieves millisecond-level, zero-overshoot tracking. Quantitative evaluations demonstrate that, compared with the traditional PID controller, the proposed method reduces the Root Mean Square Error (RMSE) by 88.29% and the Integral of Absolute Error (IAE) by 93.75%, achieving a near-perfect goodness of fit ($R^2$) of 0.9998. Additionally, the Total Variation (TV) of the control command is substantially decreased. These results convincingly demonstrate that the proposed controller perfectly balances extremely high dynamic fitting accuracy with reduced mechanical wear, presenting exceptional engineering application value for the localization transformation of power plant control systems. Full article

Wind turbine blade (WTB) end-of-life waste is projected to increase significantly, yet no sustainable recycling solution with a clear energy, economic, and environmental (3E) assessment exists. This paper presents a validated 3E model of a WTB thermal recycling pilot (1 t/day) to benchmark recycled glass fibre (rGF) against virgin glass fibre (vGF) and identifies the throughput at which rGF becomes competitive. This subsequently leads to a projection of 3E performance at 5000 t/y plant capacity, at which rGF achieves approximately 46% lower specific primary thermal energy, 92% of the CO₂ emissions of vGF, and a selling price of 80% of vGF for a financial break-even. Building on this baseline, a novel combined material, heat, and power system is proposed and simulated, integrating the WTB recycling pilot with a 20 kWₑₗ/130 kWₜₕ organic Rankine cycle to serve residential buildings. Results show that coupling the pilot with 3000 m² of apartments yields a near net-zero CO₂ and energy-cost residential complex, with overall CO₂ emissions falling below those of standalone residential buildings combined with vGF production when more than 25 apartments are integrated. Full article

Cable routing is a pivotal design component for electrical systems and safety-critical engineering fields, such as nuclear propulsion systems, nuclear power plants and aircraft. Scientific and optimized routing schemes are essential for efficient and safe power and signal transmission and for mitigating system failure risks. Previous studies have adopted heuristic search and swarm intelligence optimization algorithms for cable path planning; however, these methods tend to converge to local optima under complex constraints and cannot theoretically guarantee global optimality, failing to address multi-constraint, high-dimensional optimization challenges of confined-space cable routing. This paper proposes a mathematical programming-based systematic optimization model: it first discretizes continuous three-dimensional space into a grid coordinate system and constructs a composite cost field integrating geometric distance and thermal interference, then formulates a multi-objective optimization model considering path length, thermal impact and routing feasibility, which is converted into a single-objective problem via normalized weighting coefficients and solved by exact mathematical programming techniques, yielding a best feasible solution together with a provable lower bound and an optimality gap. When the solver converges within the time limit, global optimality for the discretized model can be certified. Simulation results show the proposed method reduces overall path cost by an average of 31.8% compared with classical algorithms like the A* algorithm, Dijkstra’s algorithm, Rapidly-exploring Random Tree (RRT), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA). Furthermore, it cuts decision variables by an average of 70% (up to 82% in complex scenarios) against the 0–1 Integer Linear Programming (ILP) model and the graph-theoretic Multi-Commodity Flow (MCF) model with multi-cost considerations. These results preliminarily validate the favorable solution quality, computational efficiency and engineering applicability of the proposed model for confined-space cable routing optimization. Full article

Cooling water systems of coastal nuclear power plants in China are frequently threatened by blockages caused by marine organisms. However, long-term studies on macrobenthic community dynamics and their associations with environmental factors are scarce, limiting the precise prevention of such blockage risks. This study conducted quantitative monitoring of macrobenthos and synchronous measurement of water environmental factors at 24 sampling stations in three functional areas (water intake, harbor basin, and drainage outlet) adjacent to the Northeast Fujian NPP from 2018 to 2024. Community structure characteristics were analyzed using the Shannon–Wiener and Margalef indices. The Grappler Method Risk Index (GMRI) was employed to screen species at risk of blocking cooling water systems, and the Mantel test and random forest models were applied to explore the associations between the macrobenthic community and environmental factors. A total of 161 macrobenthic species were identified. Polychaetes (71 species, accounting for 44.1%) were the absolute dominant group, followed by crustaceans (35 species) and Mollusks (30 species). The interannual fluctuation range of the polychaete proportion was 41.1–57.8%, reaching a peak in 2023. There were significant differences in community structure among different areas (PERMANOVA, p < 0.05), with the largest inter-regional difference in 2024 (R² = 0.36). The annual average number of species (9 species), density (155.25 ind./m²), and biomass (29.58 g/m²) in the drainage outlet were higher than those in the water intake and harbor basin. The GMRI identified Protankyra bidentata (spiny sea cucumber, GMRI values of 50.67% to 64.98% from 2019 to 2023) and Actiniaria sp. (sea anemone, a GMRI value of 54.63% in 2021) as medium-risk species for cooling water system blockage, while most other organisms were classified as low risk or extremely low risk. The Mantel test and random forest analysis confirmed that nitrogen nutrients (NO₃⁻) and phosphorus (PO₄³⁻) were significantly positively correlated with the polychaete community. Furthermore, NO₃⁻ and NH₄⁺ each explained 13.66% of the variation in the diversity index (H′), serving as key factors driving community structure. This study demonstrates the co-dominance of thermal and nutrient drivers in shaping macrobenthic communities over a multi-year scale, and identifies specific, morphologically suited taxa as potential blockage risks. The findings provide a scientific basis for targeted risk-species monitoring and support the integration of long-term ecological data into NPP cooling water system security management. Full article

The global push for low-carbon electricity generation has made hydrogen-enriched natural gas an attractive near-term decarbonization option. This paper combines experimental and thermodynamic analyses of H₂–CH₄ combustion in gas turbine combustion chambers. Experiments were conducted on a patented two-stage swirl burner across 240 operating conditions. The effects of hydrogen fraction (γ = 0–40%), swirler vane angle (30°, 45°, 60°), equivalence ratio (φ = 0.17–1.00), and fuel injection strategy were measured against NOx and CO emissions and lean blowout stability. Each 10% increase in hydrogen content raised NOx by 23–24% via the Zel’dovich thermal mechanism, while CO fell by up to 28.5% at φ = 0.3 and 60° due to enhanced OH-radical activity. The minimum recorded NOx was 12.08 ppm (Type 2 injection, 30°, γ = 0%, φ = 0.3). Hydrogen addition improved lean blowout stability by 32–46% per 10% H₂. A parallel thermodynamic analysis showed that integrating an organic Rankine cycle (ORC) and supplementary H₂–CH₄ firing in the heat recovery steam generator cuts specific CO₂ emissions by 7.5–10% and raises net efficiency by 0.79–4.0 percentage points. Critical comparison with 28 published studies identified an optimal operating window: γ = 20–30%, φ = 0.5–0.7, 45° vane angle (SW = 0.8). Full article

The transition of thermal power systems toward lower-carbon electricity raises a critical strategic question: whether to rely on cross-border electricity imports or reactivate domestic nuclear capacity under supply constraints. This study examines the trade-offs between these alternatives within a sustainable finance framework. A contingent-claim model is developed in which a life insurer provides long-term financing to a biomass-energy supplier, a thermal power plant, and a nuclear power plant operating under carbon-pricing regulation. The framework links electricity-market decisions with financial risk valuation, allowing the joint effects of biomass utilization, carbon regulation, electricity imports, and nuclear-security risks to be evaluated. The results show that biomass integration and tighter carbon regulation reduce short-term profitability in thermal generation but support long-run decarbonization. Cross-border electricity imports improve system flexibility and reduce operational volatility, strengthening the financial position of thermal producers. In contrast, nuclear-security disruptions significantly increase default risk for nuclear assets, reflecting their exposure to operational and regulatory uncertainty. By integrating energy-transition strategies with contingent-claim valuation, the analysis highlights the role of financial intermediation in shaping investment incentives and risk allocation in the electricity sector. The findings suggest that coordinated policies combining market integration, low-carbon transition strategies, and stable financing mechanisms can enhance system resilience. Full article

The DC/AC ratio is a critical design variable in utility-scale photovoltaic (PV) systems because it governs inverter loading, clipping behavior, energy yield, and long-term economic performance. However, conventional sizing approaches often rely on heuristic rules or deterministic annual yield optimization without explicitly accounting for the thermodynamic, optical, and stochastic mechanisms that reshape the DC power envelope. This study develops a physics-informed and bankability-oriented PVsyst-based framework for optimal DC/AC sizing by integrating irradiance transposition, incidence-angle modifier losses, temperature-dependent semiconductor behavior, inverter clipping dynamics, degradation, and discounted lifetime levelized cost of electricity (LCOE). A 10 MWp fixed-tilt PV plant located in Western Türkiye under Mediterranean climatic conditions is analyzed. The base-case simulation yields 15.20 GWh/year with a specific yield of 1519 kWh/kWp/year and a performance ratio of 87.5%, while temperature losses are identified as the dominant loss mechanism, accounting for 6.21% of the annual energy reduction. A regression-based thermal sensitivity analysis shows that monthly PR decreases by approximately $4.9\times {10}^{-3}$ per °C increase in ambient temperature. The DC/AC sweep identifies an optimum range of 1.35–1.40, where improved inverter utilization balances nonlinear clipping growth. A temporal clipping analysis confirms that clipping is concentrated during summer midday periods and is sensitive to sub-hourly irradiance variability. Correlated Monte Carlo simulations and LCOE cost-sensitivity analyses demonstrate that the optimum remains structurally robust under uncertainty, degradation, and inverter cost assumptions. The results show that DC/AC sizing should be treated as a coupled thermodynamic–optical–electrical–economic optimization problem rather than a simple capacity-matching decision. Full article