Storage Optimization Research Articles

Optimization of hydrogen storage at ambient temperature via central composite design using hybrid nanocomposites of zeolitic imidazolate frameworks-8 incorporated rice husk graphene-like as adsorbent

Optimization of hydrogen storage at ambient temperature via central composite design using hybrid nanocomposites of zeolitic imidazolate frameworks-8 incorporated rice husk graphene-like as adsorbent

Experimental Study on Seasonal Ground-Coupled Heat Storage and Harvesting in Cold Regions

In recent years, global efforts toward sustainable energy have intensified, aiming to reduce carbon emissions and boost energy efficiency. Heating in winter and hot water for hygiene are essential, especially in cold climates where heating demands significantly impact household energy consumption. This study examines a city in western Inner Mongolia, characterized by a severe cold climate and unique geology. A test system with dual U-shaped buried pipes for ground source heat storage and extraction was constructed, utilizing two layout schemes. Drilling tests measured formation parameters, highlighting how pipe layout and soil characteristics influence heat storage. Short-term 5-day cycles and cross-seasonal 60-day cycles were tested. Results showed upper soil suited for frequent short-term storage, while lower soil favored long-term energy retention. Increasing buried pipes raised soil temperature and storage capacity, enhancing thermal stability in short-term cycles. However, while the long-term storage capacity improved, heat loss also rose. Effective ground source heat pump design in cold regions should consider environmental temperatures, pipe optimization, soil characteristics, and heat storage duration to achieve stable, efficient operation.

Capacity optimization of PV and battery storage for EVCS with multi-venues charging behavior difference towards economic targets

Capacity optimization of PV and battery storage for EVCS with multi-venues charging behavior difference towards economic targets

Dynamic Modeling and Optimization of Energy Storage in Peer-to-Peer Energy Trading Systems

The power system landscape has evolved from isolated end-users to interactive communities due to advances in information and communication technologies. This paper explores peer-to-peer energy (P2PE) trading and sharing within a community, where customer incentives for energy exchange enhance collective profits. A two-stage optimization (TSO) framework is proposed: the first stage determines customer participation in P2PE, balancing individual and collective benefits, while the second stage optimizes economic aspects of P2P trading using a payment bargaining model. A case study demonstrates significant cost reductions and improved renewable energy utilization, with notable profit increments for participants. The study highlights the effectiveness of Nash bargaining theory and privacy-preserving algorithms in optimizing social welfare and economic interactions. Limitations include a focus on wind energy and simplified assumptions about energy storage. Future research should incorporate diverse renewable sources, dynamic modeling, and multi-community interactions.

Multi-Time Scale Energy Storage Optimization of DC Microgrid Source-Load Storage Based on Virtual Bus Voltage Control

The energy storage adjustment strategy of source and load storage in a DC microgrid is very important to the economic benefits of a power grid. Therefore, a multi-timescale energy storage optimization method for direct current (DC) microgrid source-load storage based on a virtual bus voltage control is studied. It uses a virtual damping compensation strategy to control the stability of virtual bus voltage and establishes a virtual energy storage model by combining different types of distributed capability units. The design of an optimization process for upper-level daily energy storage has the objective function of maximizing the economic benefits of microgrids to cope with unplanned fluctuations in power. A real-time energy-adjustment scheme for the lower level is introduced, and a real-time energy-adjustment scheme based on virtual energy storage for the short-term partition of the source-load storage is designed to improve the reliability of microgrid operations. The experiment shows that, in response to the constant amplitude oscillation of the power grid after a sudden increase in power, this method introduces a virtual damping compensation strategy at 20 s, which can stabilize the virtual bus voltage. From 00:00 to 09:00, the battery power remains at around 4 MW, and from 12:00 to 21:00, the battery exits the discharge state. The economic benefits from applying this method are significantly higher than before. This method can effectively adjust the source-load energy storage in real time. During peak electricity price periods, the SOC value of supercapacitors is below 0.4, and during normal electricity price periods, the SOC value of supercapacitors can reach up to 1.0, which can make the state of the charge value of supercapacitors meet economic requirements.

Evaluation of household electricity consumption in multi-apartment buildings for optimization of rooftop PV systems

Empowering citizen energy communities (CECs) to produce, self-consume and share renewable energy can enhance household energy efficiency, support the adoption of renewable energy sources, reduce energy consumption and supply tariffs, and increase independence from fossil fuels. This study analyzes actual electricity consumption data from 31 dwellings in typical five-story multi-apartment buildings in Riga, Latvia, considering the potential for rooftop solar energy systems within CECs. The novelty of this study lies in the detailed analysis of household electricity consumption data and characteristics (e.g., household size, population, etc.) to predict household load which is essential for the optimal design of a multi-apartment building scale PV system. We evaluate the suitability of typical multi story buildings for rooftop photovoltaic (PV) systems, aiming to determine the potential PV performance relative to final electricity consumption. The study utilizes PolySun computer simulation software. Our findings indicate that a rooftop PV system on a typical 5 story multi-apartment building (60 apartments) can cover up to 77% of annual electricity consumption. This analysis is based on hourly real electricity consumption data. The research outcomes can inform optimization of on-site energy storage to mitigate the impact of PV systems on the grid. The proposed solution is applicable in many Eastern European countries and in some Western European countries that were influenced by the Soviet Union in the latter half of the 20th century.These regions have a similar stock of panel-constructed multi-apartment residential buildings and share comparable climatic conditions.

Optimization of energy storage based on floating charge lithium iron phosphate battery

Abstract Lithium iron phosphate batteries are often used as power supplies, power batteries and energy storage batteries for electronic equipment, and their charge and discharge cycle characteristics have been studied extensively. However, when it is used as a new battery power source in a substation, the battery pack must be hung on the charger in a fully charged state for a long time and run for float charging. In this operating mode, the choice of the float billing voltage of the lithium iron phosphate battery, the size of the float charging existing, and features of unexpected discharge and battery life under lasting float charging still need to be studied. For that reason, a simulation model of the lithium battery power storage space system was developed, and the control criteria were adapted to make the particular curve constant with the examination information. Based on the real control logic of the energy storage inverter, a simulation model of the power storage inverter is built. With simulation screening and comparison with the actual outside characteristics of the inverter, the control parameters of the simulation model are continuously optimized to make them regular with the outside attributes of the actual inverter. The actual battery access point substation operating mode data is collected and sorted out, an appropriate equivalence on the battery system is performed, and an equivalence model in MATLAB/SIMULINK and PSCAD/EMTDC is built, which forms a complete simulation model together with the battery system model[1].

Optimisation of photovoltaic storage and charging system operation based on photovoltaic power generation prediction analysis

Abstract The power generation of photovoltaic power generation systems has more influencing factors. Accurate PV power prediction data is needed in order to maintain the stable operation of the power system. In this paper, a photovoltaic power generation prediction scheme combining the integrated weather-similar day clustering method is designed with reference to the weather characteristics in the northeast region. The training samples are classified into three generalized weather small sample datasets. The radial basis (RBF) neural network prediction optimized with the Fruit Fly Algorithm (FOA) is chosen as the main body of the prediction system model. The prediction model is established. The power prediction is compared with the power prediction using the BP neural network model. The simulated prediction results are analyzed. The results indicate that the FOA-RBF neural network model exhibits superior performance in approximating nonlinear functions compared to the BP neural network. Ultimately, the forecasted data is utilized for operational enhancement, aiming for peak economic efficiency as the goal of optimization. This process considers limitations like energy equilibrium to enhance the financial viability of the optical storage charging facility, which improves the economic efficiency of the photovoltaic storage charging station, enhances the reliability of the system, and verifies the usability of the small-sample PV power prediction strategy based on FOA-RBF neural network designed in this paper for the northeastern region.

Energy storage optimization strategy for photovoltaic-storage-charging microgrid at highway service areas

Abstract To enhance the utilization rate of photovoltaic (PV) systems in highway service areas and reduce energy costs, this paper proposes an optimization model for the configuration and scheduling of energy storage capacity in a PV-storage-charging microgrid for highway service areas. A novel solving algorithm is employed for the simulation analysis. First, a mathematical model of the highway service area microgrid is established, and the electric vehicle charging load in the service area is analyzed using Monte Carlo simulations. Based on this analysis, a microgrid load model for the service area is developed. Secondly, from the economic perspective of the highway microgrid, a bilevel optimization model is proposed to achieve comprehensive optimization of both the configuration and scheduling of the energy storage system. The model is solved using the EDO-MILP (Exponential Distribution Optimizer-Mixed-Integer Linear Programming) algorithm. Finally, the Pan-Da Highway (within Sichuan) distributed PV-storage demonstration project is taken as a case study for simulation and optimization. The simulation results underscore the significance of deploying 2856 kWh/714 kW of energy storage equipment and optimizing its scheduling. Compared to the no-storage scenario, this configuration leads to a substantial reduction of 36.25% in annual costs. Furthermore, when compared to an empirical scenario, the cost savings reach 30.74%. These findings serve as invaluable references for configuring photovoltaic storage systems in newly developed highway service areas, guiding the design and execution of strategies aimed at enhancing the self-sufficiency of green energy. This research not only establishes a theoretical foundation but also presents a practical solution framework for promoting broader energy integration across the entire highway network.

Analysis of energy storage optimization configuration based on the new rural energy system

Abstract Due to the influence of weather factors, distributed photovoltaics exhibit a characteristic of high output in the afternoon and low output in the evening, resulting in an imbalanced supply and demand of rural electricity, a low utilization rate of distributed photovoltaics, and slow progress in poverty alleviation in rural areas. This paper proposes a distributed energy storage optimization configuration model that takes into account various factors such as rural household electricity consumption, aquaculture load power supply characteristics, distributed photovoltaic output characteristics, and the lease period of distributed energy storage box battery modules. The optimization objective is to maximize the benefits of distributed photovoltaic systems in rural areas and minimize the investment costs of farmers, with distributed photovoltaic capacity and the lease period of distributed energy storage as decision variables. Further, considering the problem of the Slap Swarm Algorithm being prone to falling into local optima when solving the model, a set of optimal points is introduced to initialize the population and improve the algorithm’s global search ability. Finally, the effectiveness of the proposed model was verified through a comprehensive example of rural distributed photovoltaics.

Land acquisition and storage strategy and optimization of urban spatial layout

This article explores the relationship between land acquisition and storage strategies and optimization of urban spatial layout. By analyzing the current situation, challenges, and strategies for high-quality development requirements of land reserves, a logical approach for the preparation of land reserve planning has been proposed. Based on the actual case of Jinan City, Shandong Province, this paper summarizes the implementation effect of land reserve planning and looks forward to the future development direction.

AI for Climate Action: Leveraging Artificial Intelligence to Address Climate Change Challenges

This comprehensive article explores the transformative role of Artificial Intelligence (AI) in addressing the global climate crisis, focusing on its applications in climate modeling, renewable energy optimization, and disaster response. Through an extensive literature review, case studies, and expert interviews, we examine how AI technologies are revolutionizing climate prediction accuracy, enhancing data processing capabilities, and enabling sophisticated climate scenario simulations. The article delves into AI's contributions to renewable energy forecasting, smart grid management, and energy storage optimization, highlighting its potential to accelerate the transition to sustainable energy systems. We also investigate AI-driven approaches to disaster response and resilience, including early warning systems, resource allocation during climate-related disasters, and post-disaster recovery planning. While acknowledging the significant advancements AI brings to climate action, this study also addresses the challenges and limitations, such as data quality issues, ethical considerations, and technical barriers. Looking ahead, we discuss emerging AI technologies, their integration with other solutions, and the policy implications for effective climate change mitigation and adaptation. This research underscores the critical role of AI in combating climate change, while emphasizing the need for responsible development and deployment of these technologies to ensure a sustainable and resilient future.

Machine Learning for Optimising Renewable Energy and Grid Efficiency

This research investigates the application of machine learning models to optimise renewable energy systems and contribute to achieving Net Zero emissions targets. The primary objective is to evaluate how machine learning can improve energy forecasting, grid management, and storage optimisation, thereby enhancing the reliability and efficiency of renewable energy sources. The methodology involved the application of various machine learning models, including Long Short-Term Memory (LSTM), Random Forest, Support Vector Machines (SVMs), and ARIMA, to predict energy generation and demand patterns. These models were evaluated using metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). Key findings include a 15% improvement in grid efficiency after optimisation and a 10–20% increase in battery storage efficiency. Random Forest achieved the lowest MAE, reducing prediction error by approximately 8.5%. The study quantified CO2 emission reductions by energy source, with wind power accounting for a 15,000-ton annual reduction, followed by hydropower and solar reducing emissions by 10,000 and 7500 tons, respectively. The research concludes that machine learning can significantly enhance renewable energy system performance, with measurable reductions in errors and emissions. These improvements could help close the “ambition gap” by 20%, supporting global efforts to meet the 1.5 °C Paris Agreement targets.

Design and economic analysis of energy storage optimization allocation for campus microgrid

In the twenty-first century, the "double carbon" plan is advancing steadily. Energy saving and emission reduction is one of the key points of the plan. A power grid independent park, if energy storage equipment can be configured, it can greatly improve the park microgrid power utilization, in order to achieve the purpose of energy saving. Therefore, research on the optimization of the proportion of power grid and microgrid in the park and the optimization of the configuration of energy storage devices can greatly improve the energy utilization rate, so as to achieve the significance of environmental protection. After collecting and sorting the daily load, daily wind power generation and 12-month daily wind power generation data of the three parks A, B and C, and processing the data, a linear function model is established. After using linear programming, operations research and genetic algorithm, the research results are obtained when the three parks are combined and equipped with 50kW/100kWh energy storage devices.

Investigation and Optimization of Electrochemical Lithium Storage in Natural Stibnite

Investigation and Optimization of Electrochemical Lithium Storage in Natural Stibnite

Optimization of Offshore Saline Aquifer CO2 Storage in Smeaheia Using Surrogate Reservoir Models

Machine learning-based Surrogate Reservoir Models (SRMs) can replace/augment multi-physics numerical simulations by replicating the reservoir simulation results with reduced computational effort while maintaining accuracy compared with numerical simulations. This research will demonstrate SRMs’ potential in long-term simulations and optimization of geological carbon storage in a real-world geological setting and address challenges in big data curation and model training. The present study focuses on CO2 storage in the Smeaheia saline aquifer. Two SRMs were created using Deep Neural Networks (DNNs) to predict CO2 saturation and pressure over all grid blocks for 50 years. 18 million samples and 31 features, including reservoir static and dynamic properties, build the input data. Models comprise 3–5 hidden layers with 128–512 units apiece. SRMs showed a runtime improvement of 300 times and an accuracy of 99% compared to the 3D numerical simulator. The genetic algorithm was then employed to determine the optimal rate and duration of CO2 injection, which maximizes the volume of injected CO2 while ensuring storage operations’ safety through constraints. The optimization continued for the reproduction of 100 generations, each containing 100 individuals, without any hyperparameter tuning. Finally, the optimization results confirm the significant potential of Smeaheia for storing 170 Mt CO2.

Computational study of transition metal coordinated polyaniline: A first principle investigation into tuning the electronic properties of the resulting hybrid material

Tuning the electronic properties of polyaniline remains one of the most important features for the development of advanced materials in electronics. In this contribution, we use density functional theory to investigate the electronic properties of polyaniline when coordinating transition metals (Mn, Fe, Co, Cu, Zn) are embedded in the polymer structure.Importantly, the results reveal that in the presence of transition metal with high electronegativity, the coordinated polyaniline winds up with a decreased gap. Indeed, the band gap for H-PANI decreases from 0.911 eV to 0.513 eV for H-PANI-Mn (lower electronegativity) and to 0.201 eV for H-PANI-Zn (higher electronegativity). This reduction in the energy gap is attributed to enhanced electron delocalization due to increased overlap of electron wavefunctions in the hybrid structure. The results also reveal that the presence of transition metals lead to lower the chemical hardness from 3.252 eV in the case of H-PANI into 0.256 eV for H-PANI-Mn and 0.100 eV for H-PANI-Zn. Additionally, the results from molecular electrostatic potential highlight that PANI-Transition metal sustains more delocalization of charge density distribution compared to H-PANI, leading to molecule polarization which does play a crucial role in various chemical phenomena. These later reveal that the electron density polarization in polyaniline can interestingly be controlled through doping and coordinating the polymer structure with additional transition metals. Therefore, the obtained results might be used in the optimization of electrochemical charge storage in supercapacitors.

Optimization of DC Energy Storage in Tokamak Poloidal Coils

Tokamaks are a very promising option to exploit nuclear fusion as a programmable and safe energy source. A very critical issue for the practical use of tokamaks consists of the power flow required to initiate and sustain the fusion process, in particular in the poloidal field coils. This flow can be managed by introducing a DC energy storage based on supercapacitors. Because such storage may be the most expensive and largest part of the poloidal power supply system, an excessive size would cancel its potential advantages. This paper presents innovative strategies to optimize the DC storage in poloidal power supply systems. The proposed solution involves the sharing of the DC storage between different coil circuits. The study is supported by novel analytical formulas and by a circuital model developed for this application. The obtained results show that this method and the related algorithms can noticeably reduce the overall size of the storage and the power exchange with the grid, providing a practical contribution toward the feasibility and the effectiveness of nuclear fusion systems.

Research on the collaborative operation strategy of shared energy storage and virtual power plant based on double layer optimization

Large-scale access to distributed energy resources leads to new energy consumption problems and safe operation risks in the power system. Virtual power plants and shared energy storage are effective ways to promote the flexible consumption of distributed energy resources and improve the reliability and economy of power system operation. Based on the concept of sharing economy and considering the complementary characteristics of source and load resources between different virtual power plants, this paper focuses on the optimisation of shared energy storage and multi-virtual power plant operation. Firstly, distributed wind power, distributed photovoltaic and flexible load resources are aggregated into virtual power plants to analyze the cooperative operation mode of shared energy storage and multi-virtual power plant systems. Secondly, a two-layer decision model for shared energy storage configuration and multi-VPP system operation optimisation is constructed, with the upper model solving the optimal energy storage configuration scheme by maximising the revenue of the shared energy storage operator, and the lower model optimising the multi-VPP system operation strategy by minimising the total operation cost, the maximum amount of new energy consumed, and the minimum amount of carbon emission as the multi-objective optimisation strategy. Finally, a simulation analysis is carried out, and the results show that compared with the independent operation mode of each virtual power plant, the model proposed in this paper increases the annual profit of the shared energy storage operator by 7180¥, reduces the operating cost of the VPP system by 7.08 %, improves the rate of renewable energy consumption by 0.82 %, and reduces the annual carbon emission by 54.91 t, which realizes the mutual benefits of the virtual power plant and the shared energy storage.

Optimization of thermal energy storage in phase change Material/Nano additive heat exchangers utilizing elliptical and circular tubes with and without fins

This study numerically investigates the enhancement of thermal energy storage systems using phase change materials (PCMs) combined with nano additives and finned tubes. The analysis compares elliptical and circular tubes with configurations of two or four fins to improve heat transfer efficiency. Finned elliptical tubes demonstrate a reduction in PCM melting time by up to 19 % at 50 °C, achieving faster melting compared to unfinned designs. Circular tubes with fins also exhibit a 12.6 % reduction in melting time, showing slightly higher thermal performance than elliptical tubes in certain conditions. The study considers different nano additives—graphene nanoplatelets (GnP), copper (Cu), copper oxide (CuO), and aluminum oxide (Al2O3)—to evaluate their impact on PCM heat transfer rates. Among these, GnP achieves the most effective thermal enhancement. The findings indicate that fin configuration, tube geometry, and nano additives significantly affect PCM melting and heat transfer dynamics. This research optimizes the design of PCM-based heat exchangers, contributing to more efficient and sustainable thermal energy storage solutions for various applications.