Renewable Energy Generation Research Articles

A multiscale approach to optimize off-grid hybrid renewable energy systems for sustainable rural electrification: Economic evaluation and design

This article thoroughly investigates the design and optimization of an off-grid hybrid renewable energy system for a remote town in the province of Ankara, whose traditional power infrastructure is lacking. The goal is to provide an efficient and cost-effective energy system that can supply the village's power needs indefinitely. The optimization process entails selecting the best mix of renewable energy sources and sizing components to obtain the best economic and technical performance. To handle the complexity of the optimization problem, the Nelder-Mead simplex search method is used, taking into account the stochastic nature of renewable energy generation and the nonlinear features of RES-based power plants. The simulation results show that the suggested hybrid system outperforms traditional diesel power generators in terms of economic viability and environmental sustainability. The system assures consistent power supply by using reserve energy devices such as batteries, thereby minimizing the intermittent nature of renewable sources. With a competitive energy cost of €0.63/kWh, the optimized hybrid system ensures a dependable and continuous power supply, fulfilling the village's electrical requirement for an amazing 16 years.

A Multi-scale Framework for Advancing Battery Safety Through Early Calorimetric Analysis of Materials and Components

Batteries are the cornerstone of the global shift toward electrification, powering applications from passenger transportation, like electric vehicles (EVs), to load shifting of renewable energy generation at the grid level. The demand for batteries is accelerating, and is projected to nearly quadruple by 2030. In response to increasing battery demands, in particular demand for longer range EVs, higher energy density and specific energy alternatives to conventional Li-ion battery technologies are being researched. However, higher energy density directly correlates with an increased potential for higher peak temperatures on failure. In addition, safety evaluation of conventional and new battery technologies generally occurs late in development, often at the commercialization stage when mandated by stakeholders or industry standards. This approach can be costly and challenging as it may require a change to manufacturing processes, a re-design of the battery, and even a change in chemistry to meet safety requirements. This is critical; scale-up efforts alone can take tens to hundreds of millions of dollars and >5 years to achieve. Safety risks discovered after commercialization can significantly exacerbate those figures. Furthermore, safety is still a major risk with already commercialized Li-ion batteries. These risks hinder adoption, slow commercialization, and when they become issues, the outcome may result in recalls, fires, injuries, and even fatalities. Providing additional levers to proactively mitigate safety risk at the materials and components scale (e.g., composite cathode) will lead to considerable time and money savings in the long run.

An integrated expert recommender system approach to environmental service priorities in renewable energy

The purpose of this study is to analyze the investment success of renewable energy generation projects design. A novel model has been constructed for this purpose. At the first stage, collaborative filtering methodology is taken into consideration to complete missing evaluations. After that, M-SWARA based on QUSFSs with golden cut is used to compute the weights of these factors. Finally, the components of the service design are ranked by TOPSIS approach. The main contribution of the paper is that a new methodology (M-SWARA) has been created in this study by making improvements to SWARA. With the help of this new model, causal directions between the indicators can also be examined. Similarly, collaborative filtering methodology is taken into consideration to complete missing evaluations. In this process, the decision makers are allowed to leave the questions they wanted blank. This situation is considered as the superiority of the proposed model compared to many previous models in the literature. The findings indicate that cost is the most significant factor for the success of renewable energy investments because it gets the highest weight (.261). The ranking results also demonstrate that product is the most essential component of the service design of renewable energy investments. Therefore, solving the high-cost problem is of vital importance to increase these investments. First, renewable energy companies can reduce costs with more effective financial management. To carry out this process effectively, a finance department consisting of qualified personnel is needed. Thanks to this team, current situations in the financial markets will be better followed and this will play an important role in reducing costs.

The dark side of climate policy uncertainty: Hindering energy transition by shaping environmental taxes effectiveness

Climate policy uncertainty (CPU) may have an adverse impact on the environment by interfering with the effectiveness of environmental policies, but there is currently little evidence to support this indirect effect. By incorporating CPU into the transition function, this paper utilizes the panel smooth transition regression (PSTR) to dynamically analyze how CPU affects the relationship between environmental taxes (ETR) and energy transition. When CPU exceeds the threshold, the promoting effect of ETR on energy transition weakens or reverses. The robustness of the main conclusions is demonstrated by establishing a PSTR estimator with the instrumental variable. This paper also constructs a counterfactual scenario, showing that CPU reduces the positive impact of ETR on renewable energy consumption and generation by 7.6% and 3.5%, respectively. Further analysis indicates that this negative effect arises because CPU likely increases investment risk, particularly for long-term green projects, thereby inhibiting the clean energy market and energy-related green technological innovation. Heterogeneity analysis find that the weakening effect of CPU on the effectiveness of ETR is stronger in countries with low energy resource endowment, high energy intensity, and lower economic development levels, underscoring the need for tailored policy approaches. This research emphasizes that for countries with ambitious energy transition goals, climate policy stability is crucial for ensuring the healthy development of environmental taxes policy and renewable energy markets.

Renewable Energy and Sustainability Convergence

The change to renewable energy in the development of senior citizens homes is crucial as professionals in the built environment and organizations are getting more conscious of environmental conservation. The need to cut energy billing costs, raising energy efficiency, and emit little or no greenhouse gases known to be dangerous to human living makes this very imperative. Solar, wind, and geothermal energy sources are gaining ground. This study seeks to examine the benefits of incorporating renewable energy in housing facilities that accommodate senior citizens. The survey research design, employing qualitative and quantitative data from respondents, was analyzed using descriptive statistics and inferential analysis. The study revealed that structures integrated with solar photovoltaic (PV) systems, solar panels and wind power can reduce dependence on conventional power systems, having a notable positive influence on the greenhouse effect through a reduction in the emission of greenhouse gases, providing satisfactory indoor conditions to have a positive impact on elderly health. Favourable government policies and regulations in terms of subsidies, grants and regulation enforcement of regulations would help greatly. The research concluded that in addition to improving the quality of care for elderly residents, the generation of renewable energy promotes the concept of sustainability.

FINANCIAL AND ECONOMIC PROSPECTS FOR CREATING GREEN ENERGY PROJECTS IN THE UKRAINE-POLAND COLLABORATION

This paper examines the economic and financial advantages of improving international collaboration between Ukraine and Poland in the development of alternative energy projects. It conducts a thorough evaluation of existing and forthcoming renewable energy generation technologies, using expert, Delphi, and methods based on statistics to identify the most promising and universally applicable solutions. The calculation of the payback period for small-scale green energy projects pays particular attention to the actual duration of the period in the case of small business ventures. The findings are summarized with a vision for the future Ukrainian-Polish cooperation in the scope of green energy cooperation and specific instruments and processes that could contribute to the success of the collaboration.The article outlines the potential approach that involves the isolation of the cooperation between Ukraine and Poland in green energy relying on the creation of investment and venture funds, public support for the manufacturing of a renewable energy component, and the establishment of non-volatile industrial complexes. Additionally, measures such as independent small alternative power plants and the initiatives for the development of the Ukrainian-Polish joint network of alternative energy are mentioned.The current war between Ukraine and Russia has drawn attention to the crucial need for energy security and diversification, making Ukraine-Poland collaboration on green energy projects all the more important. Ukraine may minimize its reliance on the Russian energy supply by focusing on renewable energy programs, while Poland can invest in sustainable energy to boost regional stability and economic progress. The article points out that the continuation and advancement of green energy initiatives require international cooperation. The paper offers detailed guidance on how the financial, economic, and technological opportunities and relations between Ukraine and Poland may be enhanced.

Co-digestion of Sewage Sludge and Biowaste for Biogas Production, GHG Avoided Emissions, and Profitable Carbon Credit Development

In Estonia, where biowaste comprises a significant portion of municipal waste, efficient waste management strategies are essential. Co-digestion presents a promising avenue to enhance biogas yield and reduce greenhouse gas emissions. This study explores the potential of co-digesting sewage sludge and biowaste for biogas production, emphasizing carbon credit calculation and offset project development. Sewage sludge was effectively utilized as a co-feedstock for the co-digestion process, contributing to increased biogas production. In Narva City, 20,401.08037 m3 /year of biogas was produced in 2012, facilitating the generation of electricity from renewable sources and thus reducing GHG emissions, which facilitates the calculation of carbon credits. We developed a robust carbon offset project based on the biogas volume, meeting additionality criteria and demonstrating long-term benefits. The revenue potential from carbon credits ranged from 118 EUR to 41300 EUR, depending on market prices and project attributes. Moreover, the offset project and calculated carbon credits offer tangible benefits to sustainable waste management and the implementation of the circular economy. By valorizing sewage sludge and biowaste through anaerobic digestion, the project contributes to waste diversion from landfills, reduces methane emissions, and promotes renewable energy generation. This integrated approach aligns with principles of sustainable waste management and supports the transition towards a circular economy model. Our findings provide valuable insights for policymakers and stakeholders interested in leveraging anaerobic digestion for renewable energy production and carbon mitigation.

Improving the forecast accuracy of wind power by leveraging multiple hierarchical structure

Renewable energy generation is of utmost importance for global decarbonization. Forecasting renewable energies, particularly wind energy, is challenging due to the inherent uncertainty in wind energy generation, which depends on weather conditions. Recent advances in hierarchical forecasting through reconciliation have demonstrated a significant increase in the quality of wind energy forecasts for short-term periods. We leverage the cross-sectional and temporal hierarchical structure of turbines in wind farms and build cross-temporal hierarchies to further investigate how integrated cross-sectional and temporal dimensions can add value to forecast accuracy in wind farms. We found that cross-temporal reconciliation was superior to individual cross-sectional reconciliation at multiple temporal aggregations. Additionally, machine learning based forecasts that were cross-temporally reconciled demonstrated high accuracy at coarser temporal granularities, which may encourage adoption for short-term wind forecasts. Empirically, we provide insights for decision-makers on the best methods for forecasting high-frequency wind data across different forecasting horizons and levels.

Thermo-fluid dynamic numerical analysis of vestibule functions in a solar updraft tower

Solar updraft towers (SUTs) represent a promising avenue for renewable energy generation, leveraging solar energy to drive their operation. Although the collector of SUTs is generally vacant, ongoing research have focused on auxiliary structures inside the collector aimed at enhancing the overall efficiency. This study delved into the computational fluid dynamics (CFD) analysis of a SUT model featuring a 5 m radius collector and a 12 m height chimney. Specifically, we investigated the vestibule functions which created by implementing baffle inside the SUT collector with various radial locations. Through rigorous examination, the thermo-fluid dynamic effects of both roof-mounted and bottom-mounted baffles on SUT performance were explored, aiming to ascertain the optimal vestibule parameters. The vestibule was found to mitigate the backward flow leakage, isolate the inner room from the outside air, and enhance the heat exchange efficiency of the main flow. Notably, when the roof-mounted baffle was positioned at a radial location of 4.5 m, a remarkable 11.4 % and 28.1 % increase in the mass flow rate at the chimney outlet and kinetic power were achieved, respectively. These findings highlight the significant performance improvements achievable through the incorporation of a vestibule within SUTs of a given scale.

Optimizing and Exploring Untapped Micro-Hydro Hybrid Systems: a Multi-Objective Approach for Crystal Lake as a Large-Scale Energy Storage Solution

Increasing electricity demand and concerns about climate change and fossil fuel consumption have highlighted the importance of renewable energy resources and storage systems. This paper proposes a method for exploring untapped pumped hydro storage potentials to accommodate intermittent renewable energy generation profiles. Hourly measured data from 2022 in Benzie County, Michigan, United States, were gathered for system sizing and a thorough, realistic analysis. By employing the multi-objective grey wolf optimization algorithm, we formulated optimal sizing and energy-management strategies for three different scenarios. Unlike similar studies, the 3rd with triple objective functions (OFs) scenario aims to maximize both reliability and ecological OFs while minimizing the cost OF. It has shown promising results with multiple solutions, considering economic, environmental, and reliability factors. A case study conducted in Crystal Lake, Michigan, revealed that although Crystal Lake would function only as a micro-hydro power facility, it is a promising and huge storage unit with a substantial storage capacity of around 14.9734GWh. The system investigated is significant in the USA due to its rapid deployment capabilities, minimal construction requirements, and ease of integration with the distribution grid. The fuzzy logic method was employed to identify the best non-dominant solution among the other solutions. These outcomes include a notably low levelized cost of energy at 0.046147$/kWh, a robust index of reliability of 99.705%, and a significant reduction in CO2 emissions amounting to 7.9142×103 tons/year, when considering the triple OFs. The paper’s methodology provides valuable insights for regions aiming to utilize renewable energy from untapped storage sources.

Cross-domain fault diagnosis for multimode green ammonia synthesis process based on DA-CycleGAN

Green ammonia is a crucial strategy for reducing carbon emissions and promoting sustainable development. However, in industrial applications, the production load of the ammonia synthesis section must be adjusted to accommodate fluctuations in renewable energy generation and hydrogen production. Consequently, the green ammonia synthesis process operates under multiple conditions with varying production loads. The operations of this multimode process introduce new challenges for process safety. Traditional fault diagnosis methods experience significant performance degradation when production conditions change. In new conditions, only a small number of normal samples can be obtained, and no fault samples are available. To address this issue, a novel transfer learning method named DA-CycleGAN is proposed for the multimode green ammonia synthesis process. This method combines a two-dimensional generation model based on CycleGAN (Cycle-Consistent Generative Adversarial Networks) with domain adaptation to enhance model performance in cross-domain tasks. The feasibility of the proposed method was initially validated using the benchmark Tennessee-Eastman process for fault diagnosis. Subsequently, a case study of the green ammonia synthesis process demonstrated that it significantly enhances performance in multimode processes, ensuring process safety and reducing losses for industrial applications.

Collaborative forecasting management model for multi‐energy microgrid considering load response characterization

AbstractMulti‐energy microgrids (MEMG) have become an effective means of integrated energy management due to their unique advantages, including area independence, diverse supply, flexibility, and efficiency. However, the uncertain deviation of the renewable energy generators (REGs) output and the uncertain deviation of the multiple energy load response cumulatively lead to the deterioration of the MEMG model performance. To address these issues, this article proposes a cooperative forecasting management model for MEMG that considers multiple uncertainties and load response knowledge characterization. The model combines a multi‐energy load prediction model with a management model based on deep reinforcement learning. It proposes multiple iterations of data, fits the dynamic environment of MEMG by continuously improving the long short‐term memory (LSTM) neural network based on knowledge distillation (KD) architecture, and then optimizes the MEMG state space by considering the knowledge of load response characteristics, Furthermore, it combines multi‐agent deep deterministic policy gradient (MADDPG) with horizontal federated (hF) learning to co‐train multi‐MEMG, addressing the issues of training efficiency during co‐training. Finally, the validity of the proposed model is demonstrated by an arithmetic example.

IoT-based real-time analysis of battery management system with long range communication and FLoRa

In the current scenario, the world is focused on renewable energy generation to achieve sustainability by 2030 regarding clean and affordable energy. Lithium-ion (Li-ion)-based Battery Energy storage (BES) is a prominent approach that is widely adopted for managing large-scale renewable energy generation. Battery Management Systems (BMS) play a critical role in optimizing battery performance of BES by monitoring parameters such as overcharging, the state of health (SoH), cell protection, real-time data, and fault detection to ensure reliability. Previous studies have concluded that the implementation of Internet of Things (IoT) with LoRa ensures effective real-time monitoring of the BMS of Li-ion batteries. This study proposed and implemented a customized LoRa and IoT-based hardware system with a gateway to acquire parameters such as terminal voltage, current, charge voltage, charge current, cycle, temperature, state of charge (SoC), and SoH, and log them into the cloud server. An OMnet++-based Framework for LoRa (FLoRa) simulation was implemented to analyze the power consumption and residual energy of the customized LoRa nodes. The simulation was configured with a spread factor of 7, a carrier frequency of 433 MHz, a bandwidth of 125 KHz, and a transmission power of 2 dBm. The simulation results indicated that Node 3 had the highest mean power consumption (0.028233) and total energy consumption (0.146592), whereas Node 0 exhibited the lowest mean power consumption (0.023413) and total energy consumption (0.070204). Additionally, a comprehensive dataset encompassing voltage, current, and time was created and utilized for precise calculations of the battery's capacity and state of health, with potential use in future predictions.

Modeling the hydrodynamic wake of an offshore solar array in OpenFOAM

Offshore solar is seen as a promising technology for renewable energy generation. It can be particularly valuable when co-located within offshore wind farms, as these forms of energy generation are complementary. However, the environmental impact of offshore solar is not fully understood yet, and obtaining a better understanding of the possible impact is essential before this technology is applied at a large scale. An important aspect which is still unclear is how offshore solar affects the local hydrodynamics in the marine environment. This article describes the hydrodynamic wake generated by an offshore solar array, arising from the interaction between the array and a tidal current. A computational fluid dynamic (CFD) modeling approach was used, which applies numerical large eddy simulations (LES) in OpenFOAM. The simulations are verified using the numerical model TUDFLOW3D. The study quantifies the wake dimensions and puts them in perspective with the array size, orientation, and tidal current magnitude. The investigation reveals that wake width depends on array size and array orientation. When the array is aligned with the current, wake width is relatively confined and does not depend on the array size. When the array is rotated, the wake width experiences exponential growth, becoming approximately 30% wider than the array width. Wake length is influenced by factors such as horizontal array dimensions and current magnitude. The gaps in between the floaters decrease this dependency. Similarly, the wake depth showed similar dependencies, except for the current magnitude, and only affected the upper meters of the water column. Beneath the array, flow shedding effects occur, affecting a larger part of the water column than the wake. Flow shedding depends on floater size, gaps, and orientation.

Predictive Models for Aggregate Available Capacity Prediction in Vehicle-to-Grid Applications

The integration of vehicle-to-grid (V2G) technology into smart energy management systems represents a significant advancement in the field of energy suppliers for Industry 4.0. V2G systems enable a bidirectional flow of energy between electric vehicles and the power grid and can provide ancillary services to the grid, such as peak shaving, load balancing, and emergency power supply during power outages, grid faults, or periods of high demand. In this context, reliable prediction of the availability of V2G as an energy source in the grid is fundamental in order to optimize both grid stability and economic returns. This requires both an accurate modeling framework that includes the integration and pre-processing of readily accessible data and a prediction phase over different time horizons for the provision of different time-scale ancillary services. In this research, we propose and compare two data-driven predictive modeling approaches to demonstrate their suitability for dealing with quasi-periodic time series, including those dealing with mobility data, meteorological and calendrical information, and renewable energy generation. These approaches utilize publicly available vehicle tracking data within the floating car data paradigm, information about meteorological conditions, and fuzzy weekend and holiday information to predict the available aggregate capacity with high precision over different time horizons. Two data-driven predictive modeling approaches are then applied to the selected data, and the performance is compared. The first approach is Hankel dynamic mode decomposition with control (HDMDc), a linear state-space representation technique, and the second is long short-term memory (LSTM), a deep learning method based on recurrent nonlinear neural networks. In particular, HDMDc performs well on predictions up to a time horizon of 4 h, demonstrating its effectiveness in capturing global dynamics over an entire year of data, including weekends, holidays, and different meteorological conditions. This capability, along with its state-space representation, enables the extraction of relationships among exogenous inputs and target variables. Consequently, HDMDc is applicable to V2G integration in complex environments such as smart grids, which include various energy suppliers, renewable energy sources, buildings, and mobility data.

Research on the Design of Multi-Rope Friction Hoisting System of Vertical Shaft Gravity Energy Storage System

Renewable energy generation methods such as wind power and photovoltaic power have problems of randomness, intermittency, and volatility. Gravity energy storage technology can realize the stable and controllable conversion of gravity potential energy and electric energy by lifting and lowering heavy loads. The hoisting system is an important component of a gravity energy storage system, and its lifting capacity and speed seriously restrict its energy storage capacity, energy conversion efficiency, and operational safety and reliability. In this paper, a design method for a multi-rope friction hoisting system of a vertical shaft gravity energy storage system is proposed. The parameter design and calculation of the hoisting rope, balance rope, and friction wheel of the friction hoisting system under typical conditions were carried out. The static and dynamic anti-slip capabilities of the friction hoisting system under the typical condition were explored. The results show that the maximum acceleration and deceleration speed of the compacted strand wire rope scheme is the largest, and the lifting and lowering time is the shortest. The maximum acceleration and deceleration speed of the triangular strand wire rope scheme is the lowest, and the lifting and lowering time is the longest. The dynamic tension of the hoisting rope at the heavy-load end is positively correlated with the acceleration, and the maximum value occurs in the accelerated lifting stage and decelerated lowering stage of the heavy load. The static anti-slip safety factor between the hoisting rope and the friction lining and the specific pressure between the hoisting rope and the friction lining comply with the requirements of China’s Safety Regulations for Coal Mines. The dynamic anti-slip safety factor of the hoisting system under different rope selection schemes is greater than the minimum value of 1.25 stipulated in the Safety Regulations for Metal and Nonmetal Mines. The research results are of great significance for the safety, reliability, and stable and efficient energy storage of a gravity energy storage system.

PERFORMANCE ASSESSMENT OF A SINGLE CHAMBER MICROBIAL FUEL CELL (MFC)

Microbial fuel cells (MFCs) represent renewable energy technology with potential applications in electricity generation. This study aimed to construct and evaluate the performance of a single-chamber MFC using soil samples. Two MFCs were built for this purpose: one to assess performance by monitoring the variation of voltage and current over time, and the other to examine the effect of cathode surface area on MFC performance. Microbial fuel cells are important due to their potential to generate renewable energy, treat wastewater, remediate contaminated environments, serve as biosensors, and be scalable and integrated with other technologies, making them a promising solution for addressing various environmental and energy challenges. Notable results included recording maximum currents and voltages of 2.2 mA and 0.6 V, respectively, which elucidated the non-linear relationship between current and voltage. Additionally, it was found that the cathode surface area has a direct impact on the current produced. The polarization curve, illustrating current density as a function of voltage, was also analyzed. Another significant finding was a coulombic efficiency of 92.6%. Furthermore, connecting the MFCs in series achieved a voltage of 1.363 V. These results indicate substantial progress in the field. This study contributed to the advancement of MFC technology and its potential for practical applications in renewable energy generation, wastewater treatment, and environmental sustainability.

A comprehensive study on energy management, sensitivity analysis, and inertia compliance of feed-in tariff in IEEE bus systems with grid-connected renewable energy sources

The need to incorporate renewable energy generators (REGs) into the electrical grid has become increasingly crucial due to the push for a more sustainable environment. This study advocates an innovative strategy for optimizing inertia-integrated generation and transmission expansion planning (GTEP) to implement feed-in tariffs (FiT). The application of the GAMS CPLEX solver to the model, which tested on an IEEE 6/IEEE 16 system, reveals that using FiT results in a 12.1 % drop in system cost ($599 million to $526 million) and a 7.91 % rise in total system inertia. Sensitivity analysis highlights the correlation between increased REG integration and FiT payment reduction at 50 % penetration. The model outperforms soft computing optimization techniques, showcasing rapid convergence and computational efficiency. The proposed model's validated superiority in rapid convergence and computational efficiency is demonstrated by comparing its results with those obtained from other soft computing optimization techniques.

Performance analysis of IoT-enabled hydro-photovoltaic power systems considering electrical power mission chains

Synergistically and complementarily managing the operation of hydropower plants and other weather-dependent renewable energy generation plants (e.g., solar energy plants) provides an effective measure to improve the performance efficiency (PE) of renewable energy. However, environmental uncertainty and physical system unavailability pose challenges in assessing the PE of the power generation. This paper proposes an approach to synergistically and complementarily managing the operation of hydro-photovoltaic (HPV) power systems in IoT under uncertain environments and develops a mission chain-based PE model for quantifying the capacity of the fulfilment of a power supply mission to satisfy users’ demand for power. An importance measure-based restoration strategy is then proposed to enhance the PE of an HPV power system (PEPS). The approach is examined by taking a large-scale HPV in China in the case study. The results show that, in winters and in summers, the PEPS is higher than PE of a photovoltaic power system and a hydropower system, respectively, and the PEPS with the importance measure-based restoration strategy is higher than that under the random restoration strategy, respectively. The findings not only lay the foundation for the complementary operation of hybrid renewable energy power systems but also provide a reference for the restoration in case of power cuts.

Novel Ni-doped dual MOF-derived urchin-like Co-Fe layered double hydroxides for oxygen evolution reaction

The oxygen evolution reaction (OER) plays a vital role in water splitting for renewable energy generation, making the development of cost-effective electrocatalysts essential in tackling global energy challenges. In this study, we synthesized ZIF-67@MIL-88 using a self-assembly method, with ZIF-67 serving as the precursor, followed by an aging process. The incorporation of iron-based MIL-88 significantly enhanced the surface morphology and porous structure of the catalytic material. Additionally, we prepared urchin-like CoNi@FeNi layered double hydroxides (LDHs) by etching ZIF-67@MIL-88. LDHs are known for their unique two-dimensional sheet structure and corrosion resistance. We achieved efficient OER catalytic activity through interlayer anion exchange by substituting metal cations at high temperatures. This study highlights recent advancements in the rational design of structurally unique and highly effective CoNi@FeNi-LDH composite materials using a synergistic strategy.