GA Optimization Method for a Multi-Vector Energy System Incorporating Wind, Hydrogen, and Fuel Cells for Rural Village Applications

Utilization of renewable energy (e.g., wind, solar, bio-energy) is high on international and governmental agendas. In order to address energy poverty and increase energy efficiency for rural villages, a hybrid distribution generation (DG) system including wind, hydrogen and fuel cells is proposed to supplement to the main grid. Wind energy is first converted into electrical energy while part of the generated electricity is used for water electrolysis to generate hydrogen for energy storage. Hydrogen is used by fuel cells to convert back to electricity when electrical energy demand peaks. An analytical model has been developed to coordinate the operation of the system involving energy conversion between mechanical, electrical and chemical forms. The proposed system is primarily designed to meet the electrical demand of a rural village in the UK where the energy storage system can balance out the discrepancy between intermittent renewable energy supplies and fluctuating energy demands so as to improve the system efficiency. Genetic Algorithm (GA) is used as an optimization strategy to determine the operational scheme for the multi-vector energy system. In the work, four case studies are carried out based on real-world measurement data. The novelty of this study lies in the GA-based optimization and operational methods for maximized wind energy utilization. This provides an alternative to battery energy storage and can be widely applied to wind-rich rural areas

Active disturbance rejection control of a longitudinal tunnel ventilation system

This paper proposes an innovative approach for controlling pollutant release in a long-distance tunnel via longitudinal ventilation. Enhanced by an active disturbance rejection control (ADRC) method, a ventilation controller is developed to regulate the forced air ventilation in a road tunnel. As a result, the pollutants (particulate matter and carbon monoxide) are reduced by actively regulating the air flow rate through the tunnel. The key contribution of this study lies in the development of an extended state observer that can track the system disturbance and provide the system with compensation via a nonlinear state feedback controller equipped by the ADRC. The proposed method enhances the disturbance attenuation capability in the ventilation system and keeps the pollutant concentration within the legitimate limit in the tunnel. In addition to providing a safe and clean environment for passengers, the improved tunnel ventilation can also achieve better energy saving as the air flow rate is optimized

Are your comments outdated? Towards automatically detecting code-comment consistency

In software development and maintenance, code comments can help developers understand source code, and improve communication among developers. However, developers sometimes neglect to update the corresponding comment when changing the code, resulting in outdated comments (i.e., inconsistent codes and comments). Outdated comments are dangerous and harmful and may mislead subsequent developers. More seriously, the outdated comments may lead to a fatal flaw sometime in the future. To automatically identify the outdated comments in source code, we proposed a learning-based method, called CoCC, to detect the consistency between code and comment. To efficiently identify outdated comments, we extract multiple features from both codes and comments before and after they change. Besides, we also consider the relation between code and comment in our model. Experiment results show that CoCC can effectively detect outdated comments with precision over 90%. In addition, we have identified the 15 most important factors that cause outdated comments, and verified the applicability of CoCC in different programming languages. We also used CoCC to find outdated comments in the latest commits of open source projects, which further proves the effectiveness of the proposed method

Integration and optimisation of bio-fuel micro-tri-generation with energy storage

PhD thesisThis study addresses the global technical challenges of resource depletion and climate change by developing the first demonstration of incorporating smart energy storage (super-capacitors and batteries) with bio-fuel micro-tri-generation (BMT-HEES) for domestic applications. The developed system is capable of producing required heat, electricity and refrigeration from renewable bio-fuels for an average British household usage, and dynamically regulating the energy distribution within the system by using a novel energy storage system and a following electric load (FEL) energy management method. In this study, an extensive literature review has been carried out to investigate previous trigeneration and hybrid energy storage systems with a particular focus on their features, advantages and challenges which provide a basis for further improvements. The research work started with a preliminary investigation to fully understand the dynamic characteristics of lead acid batteries and super-capacitors used in combination to provide the desirable electrical output. The test results suggested that the super capacitors performed better than batteries in meeting transient electrical demands. In order to develop a complete BMT-HEES system, computational modeling and simulation was then conducted in the Dymola simulation environment, where the complete BMT-HEES system with advanced operational strategies has been implemented followed by case studies. System performance was assessed by evaluating key performance indicators including fuel consumption, dynamic response of each power sources, operational durations and energy efficiencies. A full experimental setup of the proposed system was also developed. Experimental tests on individual components and the BMT-HEES system as a whole have validated the effectiveness of the developed methodologies and techniques. Specific case studies have proved that the system can improve over the existing ones in terms of energy efficiency (with 47.86% improvement compared to one tri-generation system without HEES) and dynamic response for selected days as reported in the case studies. Test results from both simulation and physical experiments show that BMT-HEES can satisfy the fluctuating energy demands faithfully and instantly with high system efficiency for domestic applications. In addition, the predicted performance based on the developed methodologies has a good agreement with actual measurements. The low error of each assessment indicator provides iii the confidence that the system models can predict the system performance with good accuracy (all of the errors were within 3%). The developed technologies in this study can help cut down the carbon footprint in domestic environments, facilitate a shift towards an environment-friendly lifestyle, and in the long run, improve the quality of human life. Moreover, the established system is flexible, scalable and inter-connectable. That is, the system can incorporate other types of bio-fuels or other sources of new and renewable energy (wind, solar, geothermal, biomass etc.), depending on the availability of the energy and location of the system used. In addition to the small-scale domestic environment, the physical system can be scaled up to be used in larger commercial and industrial environments. It may be used as a stand-alone energy system or it can be interconnected with neighboring energy systems or connected with the power grid as a distributed generation set if there is a need (or a surplus) of generated electricity. Without doubt, this will require further work on this inter-disciplinary topic as well as new innovations in the fields of energy networks and smart grids

Smart energy management for unlocking demand response in the residential sector

This paper presents a smart energy management system for unlocking demand response in the UK residential sector. The approach comprises the estimation of one-hour energy demand and PV generation (supply) for scheduling the 24-h ahead demand profiles by shifting potential flexible loads. Real-time electrical demand is met by combining power supplies from PV, grid and batteries while minimizing consumer’s cost of energy. The results show that the peak-to-average ratio is reduced by 22.9% with the cost saving of 34.6% for the selected day

Forecasting algorithms and optimization strategies for building energy management & demand response

In this paper, we look at the key forecasting algorithms and optimization strategies for the building energy management and demand response management. By conducting a combined and critical review of forecast learning algorithms and optimization models/algorithms, current research gaps and future research directions and potential technical routes are identified. To be more specific, ensemble/hybrid machine learning algorithms and deep machine learning algorithms are promising in solving challenging energy forecasting problems while large-scale and distributed optimization algorithms are the future research directions for energy optimization in the context of smart buildings and smart grids

Confounder Balancing in Adversarial Domain Adaptation for Pre-Trained Large Models Fine-Tuning

The excellent generalization, contextual learning, and emergence abilities in the pre-trained large models (PLMs) handle specific tasks without direct training data, making them the better foundation models in the adversarial domain adaptation (ADA) methods to transfer knowledge learned from the source domain to target domains. However, existing ADA methods fail to account for the confounder properly, which is the root cause of the source data distribution that differs from the target domains. This study proposes an adversarial domain adaptation with confounder balancing for PLMs fine-tuning (ADA-CBF). The ADA-CBF includes a PLM as the foundation model for a feature extractor, a domain classifier and a confounder classifier, and they are jointly trained with an adversarial loss. This loss is designed to improve the domain-invariant representation learning by diluting the discrimination in the domain classifier. At the same time, the adversarial loss also balances the confounder distribution among source and unmeasured domains in training. Compared to existing ADA methods, ADA-CBF can correctly identify confounders in domain-invariant features, thereby eliminating the confounder biases in the extracted features from PLMs. The confounder classifier in ADA-CBF is designed as a plug-and-play and can be applied in the confounder measurable, unmeasurable, or partially measurable environments. Empirical results on natural language processing and computer vision downstream tasks show that ADA-CBF outperforms the newest GPT-4, LLaMA2, ViT and ADA methods

Anti-electrostatic hydrogen bonding between anions of ionic liquids: A density functional theory study

Hydrogen bonds (HBs) play a crucial role in the physicochemical properties of ionic liquids (ILs). At present, HBs between cations and anions (Ca-An) or between cations (Ca-Ca) in ILs have been reported extensively. Here, we provided DFT evidences for the exists of HBs between anions (An-An) in the IL 1-(2-hydroxyethyl)-3-methylimidazolium 4-(2-hydroxyethyl)imidazolide [HEMIm][HEIm]. The thermodynamics stabilities of anionic, cationic, and H2O dimers together with ionic pairs were studied by potential energy scans. The results show that the cation-anion pair is the most stable one, while the HB in anionic dimer possesses similar thermodynamics stability to the water dimer. The further geometric, spectral and electronic structure analyses demonstrate that the inter-anionic HB meets the general theoretical criteria of traditional HBs. The strength order of four HBs in complexes is cation-anion pair > H2O dimer = cationic dimer > anionic dimer. The energy decomposition analysis indicates that induction and dispersion interactions are the crucial factors to overcome strong Coulomb repulsions, forming inter-anionic HBs. Lastly, the presence of inter-anionic HBs in ionic cluster has been confirmed by a global minimum search for a system containing two ionic pairs. Even though hydroxyl-functionalized cations are more likely to form HBs with anions, there still have inter-anionic HBs between hydroxyl groups in the low-lying structures. Our studies broaden the understanding of HBs in ionic liquids and support the recently proposed concept of anti-electrostatic HBs

Energetic macroscopic representation control method for a hybrid-source energy system including wind, hydrogen, and fuel cell

This paper proposes a new control method for a hybrid energy system. A wind turbine, a hydrogen energy storage system, and a proton exchange membrane fuel cell are utilized in the system to balance the load and supply. The system is modeled in MATLAB/Simulink and is controlled by an improved energetic macroscopic representation (EMR) method in order to match the load profile with wind power. The simulation and test results have proved that (1) the proposed system is effective to meet the varying load demand with fluctuating wind power inputs, (2) the hybrid energy storage system can improve the stability and fault-ride-through performance of the system, and (3) the dynamic response of the proposed system is satisfactory to operate with wind turbines, energy storage, and fuel cells under EMR control