Abstract

With the large-scale integration of a high proportion of renewable energy into distribution networks, its inherent volatility and randomness introduce challenges such as voltage limit violations and power flow limit violations to distribution network operation. To address these challenges, topology changes are required to optimize power flow distribution, improve voltage quality, and enhance renewable absorption capacity. However, traditional methods that rely on precise physical models struggle to meet real-time scheduling demands, while conventional data-driven approaches lack generalization capability. This paper, based on transfer reinforcement learning, conducts corresponding research on the optimal scheduling problem under distribution network topology changes. Experiments on a modified IEEE 123-bus system demonstrate that compared to traditional methods, the proposed algorithm significantly improves decision-making efficiency across two typical topological change scenarios: network reconfiguration and change in the number of power sources.

Abstract

The continuous expansion of renewable energy installations has resulted in the emergence of integrated energy bases, which couple renewable power generation with conventional thermal units. These bases have experienced rapid development and have gradually become key participants in electricity and carbon markets. The behavior of these entities in terms of decision-making exerts a simultaneous influence on the clearing results of both markets. In this context, an analysis of actual operating conditions at integrated energy bases reveals that the carbon emission intensity of thermal units exhibits fluctuations tied to unit load factors. The carbon emission fluctuation characteristics of thermal power units exhibit distinct differences across different stages of power generation. This characteristic exerts a significant influence on the decision-making behavior of thermal power units engaged in the electricity and carbon markets. It is evident that a carbon-electricity coordinated decision-making model for integrated energy bases with combined renewable-thermal operation, considering the dynamic carbon emission characteristics of thermal power units, has been established. Utilizing an enhanced IEEE-5 node as a test case, the efficacy of the proposed method is substantiated.

Abstract

Long-term heat extraction from a closed-loop geothermal well results in a temperature decrease of formation around the well, which in turn lowers the outlet temperature of the heat extraction fluid (water). To improve this unfavorable situation, an integrated heat extraction–heat injection model has been developed in this study. The model is used to analyze formation temperature decline and its distribution after the first heating season, fluid temperature distribution in the well during heat injection, and fluid outlet temperature during the second heating season. The results indicate that during the first heat-extraction period, the formation temperature decreases under different flow rates reached a minimum value at depth of about 2860m. After 180 days of heat extraction, a plume-shaped low-temperature zone is developed around the borehole, with the maximum influence radius reaching about 12.6m at a depth of 2860m. During heat injection, the injected fluid in the inner pipe with a lower flow rate shows a U-shaped temperature profile. The upward flow in the outer pipe (annulus) transfers heat to the formation all the way during ascent when the flowrates are higher than 0.5kg/s, showing a monotonic decrease in temperature. A comparison of heat re-extraction shows that the heat-injection reduces the average temperature decline by about 50.8%. This study demonstrates that appropriate heat-injection, along with flow rate control, can significantly improve the temperature stability of formation around the geothermal well.

Abstract

In recent years, CO2 refrigeration systems have attracted significant attention due to their eco-friendly characteristics. However, their substantial throttling losses and significant reduction in cooling efficiency at high ambient temperatures limit their adoption. Although two-phase ejectors have been proven to recover expansion energy and reduce throttling losses, conventional ejectors controlled by needle valves (Needle-EJ) experience a dramatic efficiency decline under low-to-medium load conditions. This phenomenon significantly reduces the energy-saving potential and limits the practical application of ejectors. To address this issue, this research developed a novel pulse width modulation-controlled ejector (PWM-EJ) that integrated a high-speed solenoid valve directly into the motive nozzle. This enabled the effective throat area to be rapidly modulated on and off without altering the downstream geometry. Comparative experiments were conducted under typical air-conditioning conditions (Indoor: 27°C/50% RH; Outdoor: 35 – 42°C) at 2.5 – 5 kW cooling capacity. Results show that both ejector systems achieved a 20% – 28% improvement in coefficient of performance (COP) compared to a baseline expansion valve (EXV) system under maximum load conditions. Furthermore, within the 60% – 80% nozzle opening range, the PWM-EJ exhibited 15% – 18% higher isentropic efficiency and achieved a 3% – 8.5% system COP improvement over the Needle-EJ. These findings validate the PWM regulation as a more practical, robust and efficient ejector flow control technique than needle-valve regulation, representing a critical step towards the real-world deployment of ejector CO2 refrigeration systems.

Abstract

The optimal control of chilled water (CHW) plants is critical for enhancing the energy efficiency of data centers. The effectiveness of traditional rule-based control (RBC) largely relies on the expertise of control engineers, while the application of model-based predictive control (MPC) is constrained by its dependence on extensive historical data and highly accurate models. Recently, reinforcement learning (RL)-based control has attracted increasing research attention due to its characteristics of adaptive control. However, RL-based control studies in HVAC domain are mainly for air-side systems, with relatively limited exploration of CHW plants. To address this gap, this paper evaluated the application of Deep Q-Network (DQN) for the cooling water system optimal control in a CHW plant. A detailed simulation environment model was developed using the real data from a data center CHW plant, and the DQN algorithm was subsequently assessed within this environment. The results showed that, compared with RBC, DQN-based control achieved 15.4% monthly energy savings, closely approaching the 15.9% energy savings attained by MPC. Moreover, the control actions generated by DQN-based control converged toward patterns similar to those of MPC. These findings suggested that DQN-based optimal control holds strong potential to improve energy performance for CHW plants without sufficient historical data.

Abstract

The efficient resource recovery of decommissioned wind turbine blades is a key challenge for the sustainable development of the wind power industry. Pyrolysis technology, as a promising recycling method, relies fundamentally on understanding the pyrolysis mechanism of the epoxy resin matrix. This study aims to reveal the pyrolysis behavior and reaction mechanisms of epoxy resin from decommissioned blades through a multi-scale approach integrating macro- and micro-scale investigations. Thermogravimetry–Fourier transform infrared spectroscopy (TG-FTIR) was employed to experimentally study the powdered blade material, obtaining macroscopic pyrolysis characteristics, kinetic parameters, and the evolution of gaseous products. Meanwhile, ReaxFF reactive force field molecular dynamics simulations were applied to construct an epoxy resin model and simulate its pyrolysis process, tracking bond breaking, generation of small molecular products, and reaction pathways at the atomic scale. The TG-FTIR results showed that the pyrolysis of epoxy resin mainly occurs in two stages, with the maximum mass loss peak at 428°C; the main gaseous products included CO, CO₂, H₂O, phenol, and various hydrocarbons. The molecular simulation results were highly consistent with the macroscopic experiments, not only reproducing the mass loss trend and main product categories but, more importantly, revealing that the initial pyrolysis reactions start with the cleavage of ether (C–O) and C–N bonds, and visually demonstrating the formation pathways of key products such as CO, CO₂, and H₂O. Through mutual validation and complementation between experiments and simulations, this study clarifies, for the first time from a multi-scale perspective, the pyrolysis mechanism of epoxy resin from decommissioned wind turbine blades, identifying initial reaction pathways and key weak bond sites. The research outcomes provide an important theoretical basis and data support for optimizing the pyrolysis process parameters of decommissioned blades and achieving directed conversion and high-value recovery.

Abstract

The government of Pakistan has announced Electric Vehicle (EV) policies to encourage faster adoption. Such policies set certain targets, but not even 5% of these targets were met as announced in the National Electric Vehicle Policy (NEVP) 2019. The recently announced New Energy Vehicle (NEV) Policy 2025 also lacks evidence-based foundations, with targets that appear ambitious but unrealistic as highlighted by Pakistan Automobile Industry. This research employs System Dynamic Modelling (SDM) to forecast impact of various policies and generate scenarios for wider EV adoption in Pakistan. Four scenarios were set in this paper: scenario 1 (Business as Usual), scenario 2 (Government Support and Finance Policy), scenario 3 (Carbon Trading and Emission Trading System) and scenario 4 (a mixture of policies). In 2020, EV sector was still characterized by its early-stage development, reflecting limited technological advancement and infrastructure readiness. The SDM results indicate that, by 2040, the total number of EVs will amount to 120,000, 202,000, 146,000 and 202,000 under scenarios 1-4, respectively. Findings indicate that, the country’s policies, such as financial schemes, play an important role in shaping Pakistan’s EV market penetration. The analysis result demonstrates that subsidies and carbon pricing drive significant adoption gains, though integrated policies yield the most substantial impact.

Abstract

This paper presents a novel data-driven framework that integrates the analysis of household energy poverty vulnerability with the assessment of technological pathways for the net-zero transition, creating an integrated basis for equitable transition planning. Using the Energy Performance Certificates (EPCs) for England and Wales, we apply K-Prototypes clustering at the LSOA scale to jointly analyse numerical and categorical housing features. Clusters are ranked by a composite “energy poverty transition” score, capturing efficiency, demand, emissions, heating costs, and fuel type. The results reveal pronounced spatial variation in energy poverty transition risk across regions and settlement types, highlighting localised concentrations of vulnerability embedded within the housing stock. The method provides local authorities with a scalable and transparent tool for identifying priority areas for targeted intervention, supporting more equitable and context-sensitive net-zero strategies, while underscoring the need for complementary datasets to inform delivery strategies.

Abstract

The iron and steel supply chain is a high energy consuming sector spanning mining, steelmaking, and downstream product use. This study integrates material flow analysis, complex network modeling, and multi-regional LMDI decomposition to assess CO₂ emissions and their drivers across major regions from 2000 to 2020. Results show a structural shift toward a China-centered supply chain, with economic growth contributing +3.15 GtCO₂ as the dominant driver, while cleaner energy (ΔCOE, -0.22 GtCO₂) and efficiency gains (ΔEI, -0.55 GtCO₂) increasingly offset emissions. These findings highlight the redistribution of emissions along the global supply chain and support region-specific strategies for low-carbon transitions in the steel sector.

Abstract

Biosafety laboratories are essential facilities for life science research. To maintain the stability of internal environmental parameters such as temperature and humidity, pressure differentials, and cleanliness, their energy consumption is significantly higher than that of conventional civilian buildings. This paper summarizes biosafety laboratories’ current energy consumption status and analyzes key factors influencing their energy use, including air change rates, pressure control, airflow organization, and air treatment. Furthermore, it proposes general and specific energy-saving measures for different biosafety laboratory levels. Finally, recommendations are provided for the future construction and development of biosafety laboratories.