Study of aging mechanisms in LiFePO4 batteries with various SOC levels using the zero-sum pulse method.

Investigating the correlation between aging mechanisms and state of charge (SOC) can optimize cycling conditions and prolong the life cycle of lithium-ion batteries (LIBs). A long-term cycle between a certain SOC range is usually employed to study this correlation. However, this method necessitates a lengthy period, running from months to years, prolonging the research duration significantly. The aging mechanisms obtained through this method are a result of the coupling of various SOC levels; the aging mechanisms at a specific SOC level are not accurately decoupled and analyzable. The proposed Zero-sum pulse method, using symmetrical pulses with small SOC amplitude variations on SOC, can explore aging mechanisms of LIBs at a specific SOC level and reduce the time to less than a week, which significantly expedite the research process. The aging mechanisms at 30%, 50%, 70%, and 90% SOC levels are explored to verify the accuracy and timeliness of this method.

Data-driven characterization of cooling needs in a portfolio of co-located commercial buildings.

The increasing cooling needs in commercial buildings, exacerbated by climate change, warrant immediate attention. These buildings, characterized by their long lifespans and slow stock turnover, change consumption over time. This study develops simple, interpretable data-driven models using weather- and occupancy-related features to analyze the cooling in different types of co-located buildings. Over five years, our models effectively predict the cooling load across buildings with R-squared values of 81%-87%. Factoring out geography-driven differences, we identify strong heterogeneity within and across different buildings. The average estimated base load cooling varies between 0.50 and 4.4 MJ/m2/day across buildings, with healthcare facilities exhibiting the highest loads and residences the lowest. Consumption increases by 7.6%-9.8% for every 1°C increase in mean daily outside temperature, with up to 27% reductions in offices on weekends. These insights enable diagnoses of inefficiencies, post-retrofitting performance tracking, and proactive planning for climate-related impacts.

Accelerating urban energy transitions: Harnessing EEET framework dynamics for sustainable development in resource-based cities of Sichuan Basin, China.

This study examines managing urban energy systems in the Sichuan Basin, considering natural gas production. An integrated approach assessed the economy, energy, ecology, and technology (EEET) connections. The study identifies systemic issues and significant differences across locations and emphasizes the interdependent nature of economic expansion, energy innovations, and environmental commitment. Findings offer valuable insights for policymakers, providing strategic directions for overcoming barriers to sustainable city and energy advancements, and nurturing resilient urban environments. The EEET framework sheds light on the complex interactions of urban development, enabling officials to focus on areas for improvement. Addressing conflicts can create more cohesive and sustainable urban landscapes, uncovering serious environmental and energy-use issues in cities with dense industrial zones. Decision-makers could prioritize solutions like enforcing pollution controls and encouraging green energy use. Addressing disputes among urban development participants can craft strategies to encourage cooperation, finding win-win outcomes for sustainable city growth.

A high-efficiency poly-input boost DC-DC converter for energy storage and electric vehicle applications.

This research paper introduces an avant-garde poly-input DC-DC converter (PIDC) meticulously engineered for cutting-edge energy storage and electric vehicle (EV) applications. The pioneering converter synergizes two primary power sources-solar energy and fuel cells-with an auxiliary backup source, an energy storage device battery (ESDB). The PIDC showcases a remarkable enhancement in conversion efficiency, achieving up to 96% compared to the conventional 85-90% efficiency of traditional converters. This substantial improvement is attained through an advanced control strategy, rigorously validated via MATLAB/Simulink simulations and real-time experimentation on a 100 W test bench model. Simulation results reveal that the PIDC sustains stable operation and superior efficiency across diverse load conditions, with a peak efficiency of 96% when the ESDB is disengaged and an efficiency spectrum of 91-95% during battery charging and discharging phases. Additionally, the integration of solar power curtails dependence on fuel cells by up to 40%, thereby augmenting overall system efficiency and sustainability. The PIDC's adaptability and enhanced performance render it highly suitable for a wide array of applications, including poly-input DC-DC conversion, energy storage management, and EV power systems. This innovative paradigm in power conversion and management is poised to significantly elevate the efficiency and reliability of energy storage and utilization in contemporary electric vehicles and renewable energy infrastructures.

A lightweight method of integrated local load forecasting and control of edge computing in active distribution networks.

The strong resource constraints of edge-computing devices and the dynamic evolution of load characteristics put forward higher requirements for forecasting methods of active distribution networks. This paper proposes a lightweight adaptive ensemble learning method for local load forecasting and predictive control of active distribution networks based on edge computing in resource constrained scenarios. First, the adaptive sparse integration method is proposed to reduce the model scale. Then, the auto-encoder is introduced to downscale the model variables to further reduce computation time and storage overhead. An adaptive correction method is proposed to maintain the adaptability. Finally, a multi-timescale predictive control method for the edge side is established, which realizes the collaboration of local load forecasting and control. All cases can be deployed on an actual edge-computing device. Compared to other benchmark methods and the existing researches, the proposed method can minimize the model complexity without reducing the forecasting accuracy.

Knacks of marine predator heuristics for distributed energy source-based power systems harmonics estimation.

The power system incorporates renewable energy resources into the main utility grid, which possesses low or no inertia, and these systems generate harmonics due to the utilization of power electronic equipment. The precise and effective assessment of harmonic characteristics is necessary for maintaining power quality in distributed power systems. In this paper, the Marine Predator Algorithm (MPA) that mimics the hunting behavior of predators is exploited for harmonics estimation. The MPA utilizes the concepts of Levy and Brownian motions to replicate the movement of predators as they search for prey. The identification model for parameter estimation of harmonics is presented, and an objective function is developed that minimizes the difference between the real and predicted harmonic signals. The efficacy of the MPA is assessed for different levels of noise, population sizes, and iterations. Further, the comparison of the MPA is conducted with a recent metaheuristic of the Reptile Search Algorithm (RSA). The statistical analyses through sufficient autonomous executions established the accurate, stable, reliable and robust behavior of MPA for all variations. The substantial enhancement in estimation accuracy indicates that MPA holds great potential as a strategy for estimating harmonic parameters in distributed power systems.

Strategic management of CO2: A scalable model for CCS in decarbonised societies.

In future decarbonised energy systems, residual carbon emissions require strategic planning and management. In environmental management, an evaluation of carbon removal considering local geographic frameworks is needed. This paper introduces a scalable and adaptable model for evaluating the economics and geography of future carbon capture and storage (CCS) configurations across geographical scales, covering capture, transport, and storage of carbon. The model is applied to the North Denmark Region, showing that future energy production carbon sources will be concentrated in Thisted and Jammerbugt, while industrial sources remain in Aalborg and Rebild municipalities. Carbon transport configurations, including truck, pipeline, and shipping are assessed, for the carbon to be stored in onshore and offshore geological storages. The regional scale findings suggest that pipelines and onshore storage provide the most economical configuration. However, a sensitivity study using a smaller geographical scope indicates potential for optimising carbon transport by evaluating both carbon volume and distance. The paper discusses how the model's flexibility and scalability enable the integration of alternate cost components, thereby supporting the calculation of the carbon repurposing potentials, including carbon capture, utilisation, and storage (CCUS) configurations.

Restructuring effects of industrial and energy structures on sectoral CO2 emission peak trajectories in China.

Economic sectors are pivotal in achieving China's dual carbon goals; nevertheless, the combined impact of industrial and energy consumption structures on sectors' peak pathways remains unresolved. We extend the optimization of separate industrial and energy structures to a multi-objective dynamic input-output optimization model. Findings indicate the following. (1) China's energy-related CO2 emissions are projected to peak in 2028, reaching a volume of 10.06-10.25 Gt. The contribution of industrial structure upgrading to this peak is three times greater than that of energy structure transformation. (2) Approximately 40% of sectors can delay their peaks until after 2030 without impeding China's overall peaking before 2030. (3) Compared with the single objective of minimizing CO2 emissions, China can not only achieve its carbon peak earlier but also enhance its average annual gross domestic product (GDP) growth rates by more than 0.26 percentage points and increase the non-fossil energy use's share by at least 2.78%.

Assessment of frequency response techniques in diagnosing polymer electrolyte membrane fuel cells.

In this study, we evaluated the effectiveness of various frequency response analysis (FRA) techniques for identifying fault states in the diagnosis of polymer electrolyte membrane fuel cells (PEMFCs). To this end, an identifiability analysis was conducted to determine the reliability of parameters obtained by fitting a previously validated PEMFC model to the spectra from different methods. Specifically, we focused on electrochemical impedance spectroscopy (EIS) and the newly introduced concentration frequency response analysis (CFRA). The identifiability analysis revealed that CFRA, when applied with water pressure as the input and voltage as the output, provides the most accurate parameters estimates related to mass transport in the cathode electrode and the Nafion electrolyte, yielding physically meaningful insights. Consequently, employing this input for PEMFC diagnosis emerges as a promising approach. Furthermore, our findings underscore the importance of meticulously evaluating the quality of parameter estimation, even when utilizing well-established techniques such as EIS.

Economic and technical analysis of an HRES (Hybrid Renewabl e Energy System) comprising wind, PV, and fuel cells using an improved subtraction-average-based optimizer.

HRES (Hybrid Renewable Energy Systems) has been designed because of the increasing demand for environmentally friendly and sustainable energy. In this study, an Improved Subtraction-Average-Based Optimizer (ISABO) is presented for optimizing the HRES system by wind power, fuel cells, and solar energy. The suggested approach, by introducing adaptive mechanisms and enhancing processes, improves the performance of the traditional subtraction-average-based optimization. Optimization aims to provide reliable and efficient energy while lowering system expenses. The efficacy of ISABO is evaluated for this goal and compared with other optimization techniques. According to the findings, The ISABO algorithm, when equipped with adaptive mechanisms, surpasses conventional optimization techniques by achieving a 12 % decrease in Net Present Cost (NPC) and Levelized Cost of Electricity (LCOE) along with a 45 % cost reduction in electrolyzers. Through simulations, it has been shown that the ISABO algorithm ensures the lowest average NPC at $1,357,018.15 while also upholding system reliability with just a 0.8 % decline in Load Point Supply Probability (LPSP) in the event of a PV unit failure. This research validates that hybrid PV/wind/fuel cell systems present superior cost-effectiveness and reliability, thereby opening doors for more economical renewable energy solutions. The study reveals hybrid PV/wind/fuel cell systems are more cost-effective than purely wind, PV, or fuel cell systems. This advancement in HRES design and optimization techniques will enable more cost-effective renewable energy options.

Advanced Computational Methods for Modeling, Prediction and Optimization-A Review.

This paper provides a comprehensive review of recent advancements in computational methods for modeling, simulation, and optimization of complex systems in materials engineering, mechanical engineering, and energy systems. We identified key trends and highlighted the integration of artificial intelligence (AI) with traditional computational methods. Some of the cited works were previously published within the topic: "Computational Methods: Modeling, Simulations, and Optimization of Complex Systems"; thus, this article compiles the latest reports from this field. The work presents various contemporary applications of advanced computational algorithms, including AI methods. It also introduces proposals for novel strategies in materials production and optimization methods within the energy systems domain. It is essential to optimize the properties of materials used in energy. Our findings demonstrate significant improvements in accuracy and efficiency, offering valuable insights for researchers and practitioners. This review contributes to the field by synthesizing state-of-the-art developments and suggesting directions for future research, underscoring the critical role of these methods in advancing engineering and technological solutions.

Leveraging automotive fuel cells can supply zero-emission peak power in the near-term.

An increasingly decarbonized yet resilient power grid requires the corresponding build-out of dispatchable zero-emission resources to supply peak power. However, there is a recognized dearth of solutions which can serve multi-day peak demand events both cost-effectively and with near-term deployability. Here, we find that pairing low-cost automotive fuel cells with hydrogen storage in salt caverns can serve as a peaker plant at less than 500 US$/kW at present, a fraction of the cost of conventional fossil fuel-fired peakers. We demonstrate the peaker's value for long duration storage by comparing it with pumped hydro and assessing its profitability within Texas' energy-only market region. Although deployment of these peakers is constrained by the presence of salt caverns, we show that a number of sites in the United States and Europe are endowed with suitable salt formations, while utilizing hydrogen storage in pressurized containers could form a location-agnostic peak power solution.

Overcoming the central planner approach - Bilevel optimization of the European energy transition.

The energy transition is a multinational challenge to mitigate climate change, with a joint reduction target for greenhouse gas emissions. Simultaneously, each country is interested in minimizing its own energy supply cost. Still, most energy system models neglect national interests when identifying cost-optimal transition pathways. We design the European energy system transition until 2050, considering competition between countries in a shared electricity and carbon market using bilevel optimization. We find that national objectives substantially impact the transition pathway: Compared to the model solved using the common centralized optimization, the overall installed capacity increases by just 3% when including national interests. However, the distribution of the installed capacity changes dramatically by more than 40% in most countries. Our results underline the risk of miscalculating the need for national capacity expansion when neglecting stakeholder representation in energy system models and demonstrate the need for cooperation for an efficient energy transition.

Interdependent water and power infrastructure model (IWPIM): A modeling approach for water and energy resource management in rural communities.

The importance of the dependencies between water and power systems is more acutely perceived when challenges emerge. As both energy and water supply are limited, efficient use is a must for any sustainable future, especially in rural areas. Although important, a modeling tool that can analyze water-energy systems interdependencies in rural systems, at the architectural level highlighting the physical interconnections and synergies of these systems, is still lacking. We present a multi-agent system model that captures the features of both systems, at the same levels of fidelity and resolution, with coordinated operations and contingency components represented. Unlike other models, ours captures architectural features of both systems and technical constraints of the systems' components, which is critical to capture physical intricacies of the interplay between systems components and shed light on the impacts of disruptions of either system on the other. This model, which includes multiple infrastructure components, shows the importance of a holistic understanding of the systems, for cooperation across systems physical boundaries and enhanced benefits at larger scales. This study looks to investigate water-power resource management in an irrigation system via the analysis of physical links and highlight strengths and vulnerabilities. The effects of water shortage, water re-allocation and load shedding are analyzed through scenarios designed to illustrate the utility of such a model. Results highlights the importance of inter-reservoir relationships for alleviating effects of disruption and unforeseen rise in energy demand. Water storage is also critical, helping to mitigate the impacts of water scarcity, and by extension, to keep the energy system unaffected. It can be a viable part of the solution to compensate for the negative impact of shortage for both resources.

Lithium-ion battery state of health and failure analysis with mixture weibull and equivalent circuit model.

Existing methods for interpreting Electrochemical Impedance Spectroscopy data involve various models, which face significant challenges in parameterization and physical interpretation and fail to comprehensively reflect the electrochemical behavior within batteries. To address these issues, this study proposes a Temperature-Controlled Second-Order R-CPE Equivalent Circuit Model to capture the non-ideal capacitive characteristics of electrode surfaces. Additionally, the study employs a Copula based Joint Mixture Weibull Model and multi-output Gaussian Process Regression to enhance the precision in capturing the distribution of battery electrochemical parameters and predict SoH curves. Experimental validation shows that the model used in this article has an average RMSE error of 8.5%, and the prediction of the SoH curve after the 100th cycle can achieve an average RMSE error of 9.2%. These findings provide insightful implications for understanding the electrochemical complexities and parameter interdependencies in the battery aging process, offering a robust framework for future research in battery diagnostics.

Enhanced control strategy for photovoltaic emulator operating in continuously changing environmental conditions based on shift methodology.

This article investigates an inventive methodology for precisely and efficiently controlling photovoltaic emulating (PVE) prototypes, which are employed in the assessment of solar systems. A modification to the Shift controller (SC), which is regarded as a leading PVE controller, is proposed. In addition to efficiency and accuracy, the novel controller places a high emphasis on improving transient performance. The novel piecewise linear-logarithmic adaptation utilized by the Modified-Shift controller (M-SC) enables the controller to linearly adapt to the load burden within a specified operating range. At reduced load resistances, the transient sped of the PVE can be increased through the implementation of this scheme. An exceedingly short settling time of the PVE is ensured by a logarithmic modification of the control action beyond the critical point. In order to analyze the M-SC in the context of PVE control, numerical investigations implemented in MATLAB/Simulink (Version: Simulink 10.4, URL: https://in.mathworks.com/products/simulink.html ) were utilized. To assess the effectiveness of the suggested PVE, three benchmarking profiles are presented: eight scenarios involving irradiance/PVE load, continuously varying irradiance/temperature, and rapidly changing loads. These profiles include metrics such as settling time, efficiency, Integral of Absolute Error (IAE), and percentage error (epve). As suggested, the M-SC attains an approximate twofold increase in speed over the conventional SC, according to the findings. This is substantiated by an efficiency increase of 2.2%, an expeditiousness enhancement of 5.65%, and an IAE rise of 5.65%. Based on the results of this research, the new M-SC enables the PVE to experience perpetual dynamic operation enhancement, making it highly suitable for evaluating solar systems in ever-changing environments.

Towards CO2 valorization in a multi remote renewable energy hub framework with uncertainty quantification.

In this paper, we propose a multi-RREH (Remote Renewable Energy Hub) based optimization framework. This framework allows a valorization of CO2 using carbon capture technologies. This valorization is grounded on the idea that CO2 gathered from the atmosphere or post combustion can be combined with hydrogen to produce synthetic methane. The hydrogen is obtained from water electrolysis using renewable energy. Such renewable energy is generated in RREH, which are locations where RE is cheap and abundant (e.g., solar PV in the Sahara Desert, or wind in Greenland). We instantiate our framework on a case study focusing on Belgium and 2 RREH, and we conduct a techno-economic analysis under uncertainty. This analysis highlights, among others, the interest in capturing CO2 via Post Combustion Carbon Capture (PCCC) rather than only through Direct Air Capture (DAC) for methane synthesis in RREH. By doing so, a notable reduction of 10% is observed in the total cost of the system under our reference scenario. In addition, we use our framework to derive a carbon price threshold above which carbon capture technologies may start playing a pivotal role in the decarbonation process of our industries.

A Meta-Analysis Review: Nanoparticles as a Gateway to Optimized Boiling Surfaces.

Pool boiling is essential in many industrial manufacturing applications. In addition, it can become critical in the journey towards improving energy generation efficiency and accomplishing the goal of net-zero carbon emissions by 2050 via new or traditional power generation applications. The effectiveness of boiling is governed by the bubble cycle. The chemistry and topographical features of the surface being heated have been found to highly impact the boiling performance, such as in the case of pool boiling enhancement when employing hydrophilic and hydrophobic surfaces via nano/micro heater surface modification. Nevertheless, it is questionable how feasible it is to create these surfaces for large-scale applications due to their manufacturing and maintenance cost and complexity. The current work assesses whether the use of nanoparticles in traditional coolants could potentially unlock the mass production of optimised heating surface modification through a metadata literature review analysis. It was discovered that self-assembled layers created as a result of the deposition of nanoparticles in coolants undergoing pool boiling seem to behave most similarly to manufactured hydrophilic surfaces. The creation of enhanced patterned-heat transfer surfaces is shown to be possible via the use of a combination of different nanoparticle suspensions in coolants.

Investigating the activity of indigenous microbial communities from Italian depleted gas reservoirs and their possible impact on underground hydrogen storage.

H2 produced from renewable energies will play a central role in both greenhouse gas reduction and decarbonization by 2050. Nonetheless, to improve H2 diffusion and utilization as a fuel, large storage capacity systems are needed. Underground storage of natural gas in depleted reservoirs, aquifers and salt caverns is a well-established technology. However, new challenges arise when it comes to storing hydrogen due to the occurrence and activity of indigenous microbial populations in deep geological formations. In a previous study, four Italian natural gas reservoirs were characterized both from a hydro-chemical and microbiological point of view, and predictive functional analyses were carried out with the perspective of underground hydrogen storage (UHS). In the present work, formation waters from the same reservoirs were used as inoculant during batch cultivation tests to characterize microbial activity and its effects on different gas mixtures. Results evidence a predominant acidogenic/acetogenic activity, whilst methanogenic and sulfate reducing activity were only marginal for all tested inoculants. Furthermore, the microbial activation of tested samples is strongly influenced by nutrient availability. Obtained results were fitted and screened in a computational model which would allow deep insights in the study of microbial activity in the context of UHS.

Optimization of off-grid hybrid renewable energy systems for cost-effective and reliable power supply in Gaita Selassie Ethiopia.

This paper explores scenarios for powering rural areas in Gaita Selassie with renewable energy plants, aiming to reduce system costs by optimizing component numbers to meet energy demands. Various scenarios, such as combining solar photovoltaic (PV) with pumped hydro-energy storage (PHES), utilizing wind energy with PHES, and integrating a hybrid system of PV, wind, and PHES, have been evaluated based on diverse criteria, encompassing financial aspects and reliability. To achieve the results, meta-heuristics such as the Multiobjective Gray wolf optimization algorithm (MOGWO) and Multiobjective Grasshopper optimization algorithm (MOGOA) were applied using MATLAB software. Moreover, optimal component sizing has been investigated utilizing real-time assessment data and meteorological data from Gaita Sillasie, Ethiopia. Metaheuristic optimization techniques were employed to pinpoint the most favorable loss of power supply probability (LPSP) with the least cost of energy (COE) and total life cycle cost (TLCC) for the hybrid system, all while meeting operational requirements in various scenarios. The Multi-Objective Grey Wolf Optimization (MOGWO) technique outperformed the Multi-Objective Grasshopper Optimization Algorithm (MOGOA) in optimizing the problem, as suggested by the results. Furthermore, based on MOGWO findings, the hybrid solar PV-Wind-PHES system demonstrated the lowest COE (0.126€/kWh) and TLCC (€6,897,300), along with optimal satisfaction of the village's energy demand and LPSP value. In the PV-Wind-PHSS scenario, the TLCC and COE are 38%, 18%, 2%, and 1.5% lower than those for the Wind-PHS and PV-PHSS scenarios at LPSP 0%, according to MOGWO results. Overall, this research contributes valuable insights into the design and implementation of sustainable energy solutions for remote communities, paving the way for enhanced energy access and environmental sustainability.