Storage Configuration Research Articles

Comparison Performance Analysis of HDD, SSD and NVME for OLTP Database Server

Databases are an essential component of life in modern society. The database has become an online Transaction Processing (OLTP) operational support company. This study discusses the storage configuration in a virtual server database for OLTP Database with a benchmark against three storage configurations for four databases: SQL Server, MySQL, PostgreSQL, and MariaDB for Windows Server virtualization. Benchmarks using TPC-C to get the best performance from all three configurations have been tested. The results of this study show that the storage configuration of HDD as a virtual disk drive OS and NVME as a virtual disk drive database get better performance as online transaction processing (OLTP) compared to HDD as a virtual disk drive OS and HDD or SSD as a virtual disk drive database. Using the TPC-C benchmark, the study tested the performance of four databases (SQL Server, MySQL, PostgreSQL, and MariaDB) in various virtual storage configurations. The results of 36 experiments involving different numbers of virtual users (10, 50, and 100) showed that a hybrid configuration that used HDDs for the operating system and NVME for database storage provided the best performance. On SQL Server, SSD configurations show better results for smaller user loads. The results suggest that the combination of HDD and NVME is the optimal configuration for most databases in OLTP tasks, and it provides useful guidance in efficient storage investment decision-making. Further research is expected to be able to conduct tests.

Research on optimal configuration of mobile energy storage in distribution networks considering various energy utilization efficiencies

The increasing integration of renewable energy sources such as wind and solar into the distribution grid introduces new complexities and instabilities to traditional electrical grids. This study tackles these challenges by optimizing the configurations of Modular Mobile Battery Energy Storage (MMBES) in urban distribution grids, particularly focusing on capacity-limited areas. Our method investigates five core attributes of energy storage configurations and develops a model capable of adapting to the uncertainties presented by extreme scenarios. This approach not only enhances the adaptability of energy storage systems but also equips decision-makers with proactive and flexible tools for decision-making in complex environments. Empirical evidence from the study shows that modular mobile energy storage significantly improves distribution grid performance by effectively managing the challenges posed by renewable integration. Furthermore, the research confirms that optimizing decision-makers’ cognitive parameters to align with subjective preferences ensures economic viability and enhances grid resilience. This study offers a new perspective and methodology for configuring energy storage, contributing to more flexible and reliable grid operations amidst widespread renewable integration.

Energy storage configuration method for distribution networks based on moment difference analysis

The integration of a high proportion of distributed PV into distribution networks can cause power backfeeding and voltage limit violations. This paper introduces moment difference analysis theory for distribution networks, reframing PV consumption as the balancing moment difference equations. When maximum node voltage approaches the upper limit, the moment difference between PV and load stabilizes to an approximately constant, termed the standard moment difference. The standard moment difference represents the limit of the network's capacity to consume distributed PV. Essentially, the PV moment is the target for integration, while the load moment serves as the resource to consume the PV moment. Based on this theory, a method for energy storage configuration is proposed. Simplifying a complex multi-branch distribution network into single-branch lines and solving linear equations determines the optimal storage configuration. This method was tested on the Jibei Power Grid and the IEEE 33 and 69 bus systems, increasing computational efficiency by 1611.47–4973.82 times compared to the PSO algorithm, while maintaining an error margin of less than 2.6 %. Furthermore, the discharging strategy reduces the ESS capacity to between 12.39 % and 31.69 % of the original estimate, thereby enhancing both the economic and practical appeal of the approach.

Optimization of shared energy storage configuration for village-level photovoltaic systems considering vehicle charging management

Distributed renewable energy is more abundant in rural areas, and a large amount of distributed photovoltaic grid-connected power brings challenges to the stable of the power grid. How to promote the self-generation and self-consumption of distributed renewable energy has become an urgent problem. In this paper, a village-level distributed photovoltaic power generation system including energy storage and electric vehicles is constructed. With the goal of minimizing the photovoltaic grid-connected power and maximizing the annual comprehensive revenue, the planning model of energy storage capacity allocation for village-level distributed power generation system is constructed. Considering the charging management for different numbers of electric vehicles, the optimal energy storage capacity allocation strategy is solved using the improved particle swarm algorithm.Five scenarios are set up as examples to be analyzed.The conclusions are:(1)After the configuration of a reasonable energy storage,the grid-connected generation of the system is significantly reduced,and the annual comprehensive revenue of the system is significantly increased.(2)After considering charging management for different numbers of electric vehicles, the capacity and power of the energy storage configuration can be significantly reduced, thus reducing the energy storage investment cost and operation and maintenance cost, and further significantly improving the annual comprehensive revenue of this system.

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.

Optimal economic configuration by sharing hydrogen storage while considering distributed demand response in hydrogen-based renewable microgrid

With the issues of the greenhouse effect and environmental pollution globally, a high proportion of renewable energy has been integrated into microgrids. Its interconnection brings many challenges to the electric power industry. This paper proposes a new distributed response strategy through sharing hydrogen storage resources, aiming to solve the supply-demand imbalance in microgrids. First, the uneven power distribution from shared energy storage stations necessitates ensuring a fair power dispatch among users. Next, a microgrid demand response model with multi-constraint conditions and high penetration of wind and solar power is established, incorporating constraints like user power consumption, energy storage capacity, and charging/discharging power while considering differences in user electricity behaviors. Finally, the optimal economic configuration uses Lagrange duality theory to process the integrated models, including the demand response, hydrogen sharing, and power generation models, to make the optimization goals fairer. Results demonstrate that our strategy can adjust user electricity usage according to real-time supply-demand conditions, reducing users' total costs by 39.4 %. Compared with the existing configurations of sharing hydrogen storage configurations, our model uses more accurate utility functions, demonstrating better economic advantages, with users' total costs reduced by 4.89 % and revenue of the shared storage station increased by 4.02 %.

A method for selecting the type of energy storage for power systems with high penetration of renewable energy with multi-application scenarios

Energy storage (ES) configurations effectively relieve regulatory pressure on power systems with a high penetration of renewable energy. However, it is difficult for a single ES type to satisfy the complex regulatory demands of a power system. There has been little research on the selection methods for multiple types of ES that meet the demands of multiple application scenarios of power systems. This study introduces a method for the selection of ES types for power systems with a high penetration of renewable energy to determine the optimal ES types for multi-application scenarios. First, a generation method for ES schemes is proposed based on K-means clustering and combination theory. Then, considering the demands of peak shaving, frequency regulation and reserve of power systems with high penetration of renewable energy, an optimisation model of the expected indices of the ES is constructed to meet the demands of multi-application scenarios. Finally, the comprehensive evaluation method for ES schemes based on the error mean and the selection method for optimal ES types based on projection technology are proposed. Numerical studies show that for the power system in the example whose demand coefficients for peak shaving, frequency regulation and reserve are 30.59 %, 26.29 % and 43.11 %, respectively, a combination of pumped storage and colloidal battery, lithium iron phosphate battery, or lithium titanate battery is the most suitable ES type for the power system. This study offers a reference for the selection of ES types when an ES is configured.

Optimizing supply and production management through energy storage strategies: A solar cold production approach using artificial neural networks

The reliability of clean renewable energy hinges on robust energy systems, with storage serving a critical function. This paper investigates the influence of various storage types and configurations on thermal performance, with a focus on optimal sizing for economic and environmental cost reduction. To achieve this objective, we simulate a solar cooling facility with varied configurations of hot/cold storage installations. This study employs an ANN methodology with a multi-layer perceptron approach to forecast unit performance for each configuration based on data generated during the simulation process. In the pursuit of the most efficient and high-performance network, a comprehensive investigation is conducted on the number of neurons, activation functions, and training algorithms. Subsequently, the optimization process, conducted through a genetic algorithm, determines the Pareto fronts representing the best solution sets. The comparison shows that a system design with double hot and cold storage tanks shows superior techno-economic-environmental performance. Among possible optimum solution sets, a point with this specification is selected; flow rate ratio, minimum flow ratio, cooling capacity ratio, cold storage ratio, and hot storage ratio of 1.2, 0.4, 0.91, 3.4, and 3.8, respectively. This configuration anticipates a levelized cost of cooling at 341 USD/MWhr, representing a 13 % reduction compared to the benchmark.

Optimal Battery Storage Configuration for High-Proportion Renewable Power Systems Considering Minimum Inertia Requirements

With the continuous development of renewable energy worldwide, the issue of frequency stability in power systems has become increasingly serious. Enhancing the inertia level of power systems by configuring battery storage to provide virtual inertia has garnered significant research attention in academia. However, addressing the non-linear characteristics of frequency stability constraints, which complicate model solving, and managing the uncertainties associated with renewable energy and load, are the main challenges in planning energy storage for high-proportion renewable power systems. In this context, this paper proposes a battery storage configuration model for high-proportion renewable power systems that considers minimum inertia requirements and the uncertainties of wind and solar power. First, frequency stability constraints are transformed into minimum inertia constraints, primarily considering the rate of change of frequency (ROCOF) and nadir frequency (NF) indicators during the transformation process. Second, using historical wind and solar data, a time-series probability scenario set is constructed through clustering methods to model the uncertainties of wind and solar power. A stochastic optimization method is then adopted to establish a mixed-integer linear programming (MILP) model for the battery storage configuration of high-proportion renewable power systems, considering minimum inertia requirements and wind-solar uncertainties. Finally, through a modified IEEE-39 bus system, it was verified that the proposed method is more economical in addressing frequency stability issues in power systems with a high proportion of renewable energy compared to traditional scheduling methods.

Hybrid-Energy Storage Optimization Based on Successive Variational Mode Decomposition and Wind Power Frequency Modulation Power Fluctuation

In order to solve the problem of frequency modulation power deviation caused by the randomness and fluctuation of wind power outputs, a method of auxiliary wind power frequency modulation capacity allocation based on the data decomposition of a “flywheel + lithium battery” hybrid-energy storage system was proposed. Firstly, the frequency modulation power deviation caused by the uncertainty of wind power is decomposed by the successive variational mode decomposition (SVMD) method, and the mode function is segmented and reconstructed by high and low frequencies. Secondly, a mathematical model is established to maximize the economic benefit of energy storage considering the frequency modulation mileage, and quantum particle swarm optimization is used to solve the target model considering the charging and discharging power of energy storage and the charging state constraints to obtain the optimal hybrid-energy storage configuration. Finally, the simulation results show that, in the step disturbance, the Δfmax of the hybrid-energy storage mode is reduced by 37.9% and 15.3%, respectively, compared with single-energy storage. Under continuous disturbance conditions, compared with the single-energy storage mode, the Δfp_v is reduced by 52.73%, 43.72%, 60.71%, and 47.62%, respectively. The frequency fluctuation range is obviously reduced, and the frequency stability is greatly improved.

Evaluating the feasibility and economics of hydrogen storage in large-scale renewable deployment for decarbonization

Renewable energy (RE) is pivotal for achieving a net-zero future, with energy storage systems essential for maximizing its utility. This study introduces a modeling framework that simulates large-scale RE deployment in Taiwan, emphasizing hydrogen as a primary storage medium. Utilizing hourly time steps, the model assesses various energy generation and storage configurations to demonstrate the technical feasibility of meeting Taiwan's 2050 net-zero target, which calls for a 60 % RE share primarily through solar and wind resources. However, achieving this target and the ambitious goal of 100 % RE penetration requires substantial enhancements in both generation capacity and storage solutions. The research evaluates the economic impacts by analyzing the levelized cost of energy (LCOE), revealing that optimal configurations, such as a wind capacity of 30 GW with a 5 % minimum capacity factor constraint on electrolyzers, significantly reduce LCOE to $0.176/kWh, underscoring the cost-effectiveness of hydrogen storage compared to battery alternatives in large-scale settings. The study underscores the necessity for ongoing investigations into replacing thermal power plants and maintaining grid stability amidst high RE penetration. To fully harness Taiwan's RE potential and contribute to global sustainability, effective deployment of hydrogen storage will require robust stakeholder support, continual technological advancements, and enabling policies. This framework, while tailored to Taiwan's power system, offers insights applicable to various global contexts.

Optimizing energy storage configuration scheme considering optimization of electricity prices, carbon emissions, and flexible load

Optimizing energy storage configuration scheme considering optimization of electricity prices, carbon emissions, and flexible load

Distributed photovoltaic supportability consumption method considering energy storage configuration mode and random events

In order to improve the control capability of distributed photovoltaic support, a distributed photovoltaic support consumption method based on energy storage configuration mode and random events is proposed. A networked and constrained parameter analysis model for distributed photovoltaic power supply control was constructed. Based on the direct flexible mode of optical storage, an AC/DC voltage level control model for distributed solar power supply control was constructed. In the operation mode of DC hybrid distribution network, the demand response tracking identification method was used to analyze the uncertain characteristic parameters of distributed solar power supply load, and combined with the planned energy storage capacity parameters, the distributed solar power supply load and photovoltaic output were estimated. By configuring the optimal energy storage capacity, adjusting the power distribution of the microgrid, and integrating the analysis of uncertain factors and random events in the energy storage configuration mode, the design of distributed photovoltaic support consumption has been achieved. The experimental results show that the distributed photovoltaic absorption control using this method has lower load requirements, can effectively reduce the exchange power of the interconnection line, and improve the configuration scale, system reliability, and economy of the photovoltaic energy storage system.

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.

Economic Study of Wind and Solar Power Generation with Energy Storage Configuration

Economic Study of Wind and Solar Power Generation with Energy Storage Configuration

Scenario-Driven Optimization Strategy for Energy Storage Configuration in High-Proportion Renewable Energy Power Systems

Scenario-Driven Optimization Strategy for Energy Storage Configuration in High-Proportion Renewable Energy Power Systems

Optimal configuration method of photovoltaic energy storage in distribution network based on source-load coordination

Abstract To enhance the configurability of photovoltaic energy storage within distribution network systems and foster synchronized development of power sources and loads, a source-load coordinated approach for optimal photovoltaic energy storage configuration in distribution networks is introduced. An alternative multi-objective framework for optimal allocation of photovoltaic energy storage capacity in distribution networks is formulated, which is the optimal goal of maximum economic benefit of photovoltaic energy storage, the optimal goal of minimum network loss and the optimal goal of source-network load coordination. The constraints of the system’s power flow, energy storage charging and discharging capabilities, and an optimized allocation strategy for energy storage are established, and the objective function is solved with full consideration of source-network load coordination factors. The energy storage optimization is updated in the iterative process. Based on the update results, the process for optimal allocation of photovoltaic energy storage in the distribution network has been devised to attain the most efficient allocation. Experimental results indicate a minimal discrepancy between the actual and specified energy storage output, along with a reduced average output power resulting from the optimized photovoltaic energy storage configuration, which shows excellent performance in energy storage optimization configuration.

Economic evaluation of the second-use batteries energy storage system considering the quantification of environmental benefits

The number of retired batteries from electric vehicles continues to grow, which not only puts pressure on battery recycling but also leads to environmental pollution. By replacing the conventional batteries with the second-use batteries in configuring the energy storage system, the investment cost can be effectively reduced, and the issue of retired batteries disposal can be alleviated. In view of this, the paper investigates the quantification of the environmental benefits of second-use batteries, and comprehensively evaluates the second-use batteries energy storage system (SUBESS) in terms of environmental and economic benefits. The energy storage configuration model aimed at maximizing annual net income is developed, and the improved particle swarm optimization algorithm is applied to determine the optimal capacity and power in a photovoltaic (PV) pilot area. Under the same capacity condition, several evaluation indexes are used to compare the economics of the SUBESS with the conventional batteries energy storage system (CBESS). The results show that: (1) Compared to end-of-life disposal of batteries, secondary utilization will yield greater environmental benefits. (2) When the cost per unit capacity of the SUBESS is lower than 63.99 % of the CBESS, the economy of the SUBESS is better than that of the CBESS. Finally, targeted suggestions are made to further promote the scale development of the retired batteries secondary utilization industry. The research results not only validate the economic feasibility of the SUBESS but also have significant environmental benefits, which can provide a reference for potential investors.

Cascade Latent Heat Storage for Solar Cooking Applications

The present investigation explores the potential of multiple phase change material (PCM) based storage for real-time solar thermal applications. There are two types of storage configurations tested in this work for the simultaneous charging and discharging operation, i.e., single PCM-based NaNO3 storage and 2-stage cascade latent heat storage having NaNO3 and NaNO2 as the storage mediums in the consecutive stages. The storage is designed to achieve the end-use temperature condition of 513 K (the highest temperature required for cooking) with the latent thermal energy accumulation capacity of 1 MJ. The study envisages that instead of using a single PCM in the storage unit, 2-stage cascading of the PCMs can improve energy accumulation and retrieval in storage. After 8 hours of operation, none of the storage achieves the complete charging state. The energy accumulated in the NaNO3/NaNO2 cascade storage is found to be 2.6 times higher than the energy accumulated in the NaNO3 storage. NaNO3/NaNO2 storage achieves the desired constant outlet temperature of cold heat transfer fluid for chapati making (531 K) after 165 minutes of operation. At this moment, the latent energy accumulated in the NaNO3 and NaNO3/NaNO2 storage is observed as 35% and 14%, respectively

Coordinated Control Strategy of Source-Grid-Load-Storage in Distribution Network Considering Demand Response

This study aims to minimize the overall cost of wind power, photovoltaic power, energy storage, and demand response in the distribution network. It aims to solve the source-grid-load-storage coordination planning problem by considering demand response. Additionally, the study includes a deep analysis of the relationship between demand response, energy storage configuration, and system cost. A two-level planning model is established for wind power and photovoltaic power grid connection, including demand response, wind power, photovoltaic power, and energy storage. The model minimizes the sum of the differences between the total load and the total new energy generation after demand response in each time period as the bottom-level objective and minimizes the overall cost of the distribution network as the top-level objective, achieving the coordinated configuration of wind power, photovoltaic power, and energy storage. The simplex method is used to solve the model, and the improved IEEE33 node system is used as an example for verification. The simulation results fully prove the model’s correctness and the algorithm’s effectiveness, supporting the coordinated planning of distribution networks.