Storage Management System Research Articles

Integrated optimization for sizing, placement, and energy management of hybrid energy storage systems in renewable power systems

Power systems reliant on renewable energy sources (RES) encounter supply-demand imbalances and stability challenges due to their inherent uncertainties. Hybrid energy storage systems (HESS) have emerged as a flexible and cost-effective solution to address these issues. This paper proposes an integrated optimization method for the capacity, location, and energy management of a HESS in RES-based power systems. The method optimizes battery energy storage system (BESS), electrolyzer (EL), fuel cell (FC), and hydrogen storage tank (HST) to minimize total costs, including power purchase, curtailment compensation, operation and maintenance (O&M), and investment costs, alongside voltage profile deviation index (VPDI) and active power loss index (APLI). A novel hybrid framework is proposed, utilizing mixed-integer linear programming (MILP) optimization at the master level and non-dominated sorting genetic algorithm II (NSGA-II) optimization at the slave level. This framework ensures that MILP provides robust solutions, while NSGA-II derives balanced optimal solutions from the optimal Pareto front. The proposed method was validated using real seasonal data from Korea and tested in IEEE 33 and IEEE 69 bus systems against various benchmark cases. Results indicate significant improvements, with VPDI enhanced by 16.00 % and 10.11 %, APLI by 15.64 % and 12.15 %, and total costs reduced by 58.67 % and 50.56 %, respectively. The sensitivity analysis demonstrated the robustness of the proposed method, showing zero sensitivity to iterations and positive correlations with increased levels of RES. Further verification against other optimization algorithms showed that the proposed method excels in cost efficiency, voltage stability, and line loss reduction, providing highly reliable results.

Synergistic Effects of Energy Storage Systems and Demand-Side Management in Optimizing Zero-Carbon Smart Grid Systems

The urgent need to mitigate climate change and reduce reliance on fossil fuels has driven the global shift towards renewable energy sources (RESs). However, the intermittent nature of RESs poses significant challenges to the widespread adoption of Zero-Carbon Smart Grids (ZCSGs). This study proposes a synergistic framework to address this hurdle. It utilizes energy storage systems (ESSs) by comparing Vanadium redox flow batteries (VRFBs) and Lithium ion batteries (LIBs) to identify the most suitable option for ZCSGs, with precise models enabling robust performance evaluation. Moreover, an accurate demand-side management (DSM) strategy considering power elasticity to manage discrepancies between electricity load, RES generation, and ESS availability is introduced for estimating fair, dynamic tariffs. An advanced load and weather-forecasting strategy is introduced for improving grid planning and management. An advanced optimization algorithm enhances grid stability and efficiency. Simulations demonstrate significant reductions in carbon footprint, peak power demand, and reliance on fossil fuels. The study finds that VRFBs outperform LIBs in cost and security, and dynamic tariffs based on accurate DSM significantly reduce energy costs. This work explores the challenges and opportunities of this integrated approach, offering policy recommendations and future research directions for truly optimized ZCSG implementation.

Energy Management System for Polygeneration Microgrids, Including Battery Degradation and Curtailment Costs.

Recent advancements in sensor technologies have significantly improved the monitoring and control of various energy parameters, enabling more precise and adaptive management strategies for smart microgrids. This work presents a novel model of an energy management system (EMS) for grid-connected polygeneration microgrids that allows optimizing the management of electrical storage systems, electric vehicles, and other deferrable loads such as heat pumps. The main novelty of this model is that it incorporates both climate comfort variables and the consideration of the degradation of the energy storage capacity in the control strategy, as well as a penalty for the dumping of surpluses. The model has been applied to a smart, sustainable building as a case study. The results show that the proposed model is highly adaptable to diverse weather conditions, minimizing renewable energy losses while satisfying the energy demand and providing comfort to the building's users. The study shows (i) that EVs' dynamic charging schedules play a crucial role, (ii) that it is possible to minimize a battery's degradation by optimizing its cycling, averaging one cycle per day, and (iii) the critical impact of seasonal weather patterns on microgrid energy management and the strategic role of EVs and storage systems in maintaining energy balance and efficiency.

State-of-charge Estimation of All-vanadium Flow Battery Based on AH-UKF-KF

Abstract As an essential parameter for the management and control of energy storage systems, accurately estimating the state of charge (SOC) of vanadium redox flow batteries (VRBs) is of great significance. Nevertheless, substantial errors still exist in SOC estimation. In this paper, the working principle of VRB is thoroughly investigated, the equivalent loss circuit of VRB is established, the pros and cons of various SOC estimation algorithms for VRB are comprehensively compared, and the AH-UKF-KF algorithm is proposed by fusing the Kalman correlation derivation algorithm with the ampere-hour integration (AH) method. Firstly, the algorithm employs the AH and UKF to estimate the SOC, and then uses the two outcomes as the input of KF to obtain the final SOC results. Finally, the 5kW/30kWh all-vanadium flow battery platform is utilized to verify the accuracy and reliability of the algorithm.

Cutting-Edge Energy Storage System and Battery Management Technology

Cutting-Edge Energy Storage System and Battery Management Technology

Empowering energy management in smart buildings: A comprehensive study on distributed energy storage systems for Sustainable consumption

The increment of photovoltaic generation in smart buildings and energy communities makes the use of energy storage systems desired to increase the self-consumption efficiency. This paper proposes and explores a model for energy storage systems management that considers local renewable generation, local demand, and retailer energy prices. The proposed model was tested at both energy community-level and the smart building level, demonstrating their capabilities of deployment. To validate the proposed model, a case study with two scenarios, including a 251 members energy community, was executed. The results demonstrate significant cost reductions for community members when adopting energy storage systems and the proposed management model. Regarding the smart building application four scenarios were tested, it is demonstrated that the demand for energy from the retailer could be set to zero during periods of time to enable its participation in demand response events. Overall, this paper contributes to the state-of-the-art by identifying and evaluating a model that manages the energy storage systems charge and discharge operation to actively reduce energy costs at the community-level (19.26%) and building level (11.75%) and to demonstrate that part of the loads can be optimized to understand if the building can be energy net-zero.

MAXIMIZING SELF-CONSUMPTION FROM PHOTOVOLTAICS

The present study refers to the maximization of self-consumption through the automation of a residential consumer that is or not fed from the low-voltage electrical network and from its own photovoltaic system. The level of self-consumption from photovoltaic sources in the residential sector is strictly dependent on the power installed in the own sources, as well as the load profile of the respective consumer. Two major directions are relevant regarding the possibility of increasing and optimizing self-consumption: the management of electrical energy storage systems (especially through the use of battery systems), respectively the implementation of load management systems, through the partial or total redistribution of their operation over time, but also through prioritization of critical consumers.

Optimal Management of Energy Storage Systems in Hospitals with Quantum Spherical Fuzzy Decision-Making Modelling

The purpose of this study is to identify prioritized strategies to increase the effectiveness of energy storage investments in hospitals. For this purpose, 5 literature-based criteria affecting energy storage investments in hospitals are identified. These criteria are weighted by the quantum spherical fuzzy DEMATEL method. On the other side, 4 different renewable energy alternatives are identified. The performance of these alternatives are ranked with quantum spherical fuzzy TOPSIS approach. It is determined that storage capacity is the most critical factor to increase the effectiveness of the energy storage systems in the hospital. Similarly, technological infrastructure is another key issue for the development of this process. However, it is also seen that security issues, legal effectiveness and financial situations have the lower weights. In addition to them, the ranking results demonstrate that wind energy is the most appropriate renewable energy type with respect to the energy storage performance of the hospitals. Geothermal energy can also be considered for this situation. On the other hand, solar and hydropower energy types have lower performance in this framework.

A comparative study of strategic petroleum reserve policies of major countries in the world

Petroleum security is the guarantee of national security that plays an important role in national energy security, while petroleum strategic reserve is the most basic guarantee of national petroleum security. At present, the number of selected countries in the study of petroleum strategic reserves is insufficient, as there are too much vertical comparison and the horizontal comparison analysis is not enough. Based on this, on the basis of comparing the strategic petroleum reserves of major countries in the world in terms of storage methods, address selection, policy formulation, management system and utilization mechanism, this paper conducts longitudinal comparison and horizontal comparative analysis and research, trying to find out their common policy characteristics and advantages from different strategic petroleum reserve policies, modes and practices of various countries, and carries out eliminating the rough and learning from the strong. The experience gained can be used for reference by other countries.

A predictive fuzzy logic and rule-based control approach for practical real-time operation of urban stormwater storage system

Predictive real-time control (RTC) strategies are usually more effective than reactive strategies for the intelligent management of urban stormwater storage systems. However, it remains a challenge to ensure the practicality of RTC strategies that use accessible, non-idealized predictive information while improving their efficiency for successive rainfall events instead of specific phases. This study developed a predictive fuzzy logic and rule-based control (PFL-RBC) approach to address the continuous control of individual storage systems. This approach incorporates total rainfall depth forecast information with an intra-storm fuzzy logic system to optimize peak flow control and several rule-based strategies for pre-storm water detention, reuse, and release control. Computational experiments were conducted using a storage tank case study to test the proposed approach under various rainfall conditions and storage sizes. The results showed that PFL-RBC outperformed static rule-based control in infrequent design storms and realistic continuous rainfall events, reducing flood peaks and volumes by 55 %∼87 % and 7 %∼20 %, respectively, and significantly increasing water detention time and reuse volume. Meanwhile, PFL-RBC required less predictive information to achieve a 6 %∼15 % advantage in peak flow control compared to optimized model predictive control. More importantly, PFL-RBC was reliable in the face of input uncertainty, with <25 % performance loss for water quantity control when the realistic forecast error ranged from -50 % to +50 %. These findings suggest that the proposed approach has great potential to enhance the efficiency and practicality of stormwater storage operations.

Stochastic Convex Cone Programming for Joint Optimal BESS Operation and Q-Placement in Net-Zero Microgrids

Microgrids have emerged as a pivotal solution in the quest for efficient, resilient, and sustainable energy systems. Comprising diverse distributed energy resources, microgrids present a compelling opportunity to revolutionize how we generate, store, and distribute electricity, while simultaneously reducing carbon footprints. This paper proposes an optimal battery energy storage system (BESS) management scheme, along with capacitor placement for reactive power (Q)-compensation, and scheduling for the purpose of a renewable-based microgrid’s loss minimization. The proposed model evaluates the impact of BESS management on energy efficiency and analyzes how optimal scheduling of BESS influences system losses. Furthermore, it investigates the coordinated planning and operation of active assets within the microgrid, such as controllable capacitor banks, in enhancing overall efficiency. The model is formulated as a mixed-integer second-order cone programming (MISOCP) problem which is solved for both deterministic and stochastic generation and consumption data. The proposed model is tested on a 21-bus microgrid comprising photovoltaic and hydropower energy resources, and the efficacy of the model is approved by several case studies. The simulation results show that the proposed method can reduce microgrid energy losses by approximately 12 percent using the deterministic approach and around 14 percent with the stochastic approach.

A novel approach for solidification enhancement of phase change materials through direct contact with a boiling fluid: An experimental investigation and visualization

Due to their remarkable properties, such as high latent heat and stable temperatures during phase transitions, phase change materials (PCMs) are widely used in thermal energy storage (TES) and thermal management systems (TMSs). However, their low thermal conductivity has limited their broader application across various industries. This study experimentally explores, for the first time, the effects of injecting boiling fluid (BF) into the PCM container as a new method to enhance heat transfer during the solidification process of PCM. To this end, Paraffin wax and acetone with saturation temperature lower than paraffin’s freezing point are selected as PCM and BF, and the influence of four various parameters, namely the initial PCM temperature (75, 85, and 95 °C), the height of the PCM inside the container (15 cm, 25 cm, and 35 cm), the flow rate of the BF (0.32, 0.46, and 0.57 L/min), and the nozzle diameter (2 mm, 3 mm, and 4 mm), on the solidification behavior of PCM, temperature distribution inside the container, and heat transfer enhancement (released energy and Nusselt number) is thoroughly scrutinized. In order to visualize and further evaluate the two simultaneous phase transitions (vaporization of the BF and solidification of the PCM), a Hele-Shaw cell with a height, width, and thickness of 50, 20, and 1 cm is considered as the PCM storage system. The findings indicate that when the BF is injected into the paraffin storage, the high heat transfer coefficient of boiling superbly improves the thermal behavior of PCM, and the solidification process occurs in a much shorter time. Also, boiling of the acetone inside the container and its direct contact with paraffin leads to a significant mixing inside the container and the creation of vortices and turbulent flow in the system, which further improves the freezing phenomenon. Furthermore, it was observed that regardless of other parameters, the flow rate of BF is the most crucial parameter, and after that, the height of the paraffin and the initial temperature have the greatest impact. By employing the highest flow rate, the proposed technique is capable of freezing 0.7, 0.5, and 0.3 L of paraffin in 48, 33, and 18 s, respectively, indicating multiple times of improvement in the full solidification time compared to previous studies.

Research Progress and Suggestions on Natural Gas Industry Technology Revolution in the Context of Energy Revolution

The energy revolution is an important driving force for the progress of productivity and civilization in human society. As a big country in global energy consumption and carbon emission, promoting the energy revolution is a strategic choice to ensure the sustainable development of China's energy industry, which is in line with China's national conditions of the construction of modern energy system. Based on the strategic background of the energy revolution, this paper combines China's natural gas production capacity and consumption demand in the context of national conditions, and from the perspective of technological revolution, we have sorted out the research results of relevant scholars on the natural gas industry and the technological development of the whole industry chain of “production, supply, storage and marketing”, and discussed the technological development of China's natural gas industry in the context of the energy revolution. The results of the study show that: (1) the innovative technology of natural gas industry is diversified, low-carbon and green development, but from the dimension of safety, stability and economy, the performance is relatively weak. (2) The overall synergistic development of the whole industrial chain technology is uneven, the gap between supply and demand is increasing, and the degree of information and intelligence is low. And then for the development of China's natural gas industry technology puts forward proposals: (1) to promote innovative natural gas technology breakthroughs, integration of new areas of technology, to achieve the dual objectives of low-carbon economic development and environmental protection; (2) to increase natural gas exploration and development efforts, breakthroughs in the integration of the digital transformation of traditional exploration technology, and continue to support the increase in storage and production; (3) to accelerate the upgrading of natural gas supply and demand, and the establishment of a synergistic, intelligent, diversified pipeline network system. (4) Boost the construction of gas storage system, pay attention to the standardized gas storage management system, and play the role of valley cutting and peak leveling; (5) Build a new comprehensive energy service system, and build a multi-species, multi-link intelligent technology integration to create a new service system.

Based on Internet + Warehousing Logistics Management Exploration

The deep integration of the Internet and warehousing logistics not only promotes the transformation and upgrading of the industry, but also brings the innovation of management mode and service concept for enterprises. This paper analyzes the development status of ware-housing logistics industry, analyzes the problems faced by warehousing logistics management in the Internet environment, and puts forward a series of optimization countermeasures and suggestions. Through the introduction of cloud computing, big data, Internet of Things and other advanced technologies, the establishment of intelligent storage management system, optimize the business process, improve the service level, promote warehousing and logistics enterprises to achieve cost reduction and efficiency, and enhance the core competitiveness. At the same time, it is also necessary to improve industry standards and norms, strengthen the construction of talent teams, build an Internet + warehousing and logistics ecosystem, and help build a strong logistics country.

Ag nanowires modified expanded graphite based composite phase change materials with enhanced thermal conductivity, light/electro-to-thermal performances

Phase change materials with excellent thermal energy storage property displayed great potential application in many fields, such as lithium battery thermal management, energy saving building and waste heat utilization. In this paper, a novel composite phase change material of Ag nanowires modified expanded graphite/lauric-myristic-palmitic acid (AgNWs@EG/LMP) with the function of photo/electro-thermal conversion property was prepared which the AgNWs@EG was acted as the matrix material and LMP was the phase change material. The composite exhibited high latent heat of 118.42 J/g at the phase change temperature of 33.62 °C with the LMP supporting ratio of 76.18%. The thermal conductivity of AgNWs@EG/LMP reached 4.23 W/(m∙K) which enhanced 1914% than that of LMP. The photo-thermal conversion efficiency reached 88.01% and the electro-thermal conversion efficiency was 65.4% with an input voltage of 1.5 V. Further results showed the composite AgNWs@EG/LMP possessed good chemical compatibility, good thermal stability and reliability. Therefore, the novel composite of AgNWs@EG/LMP with good thermal property and multi-energy conversion functions will process potential application in energy storage system and thermal management fields.

Optimizing voltage references in a 3-leg power VSC under single-phase- to-ground faults

Distribution grid deals with several challenges such as the integration of the electrical vehicle, connection of renewable energy resources, management of storage systems, etc. These new factors can often produce some non-desirable effects in the grid such as electrical faults, which can damage the electricity supply quality. In order to improve the supply quality during electrical faults, power converters with specific control algorithms can be connected that modify the voltage on the loads connected to the grid during the duration of the fault. An optimal control can be ensured if a proper estimation of the voltage in the load is carried on. This paper calculates the voltage drop between the output of the power converter and the fault point so as to optimize voltage references in the power converter during single- phase-to-ground faults in islanded mode. Simulation results guarantee the successful performance of the proposal.

Multi‐objective optimisation framework for standalone DC‐microgrids with direct load control in demand‐side management

AbstractRenewable energy‐powered DC microgrids have emerged as a sustainable alternative for standalone power systems in remote locations, which were traditionally reliant on diesel generators (DIG) only. To ensure power quality and reliability, energy storage systems (ESS) and demand‐side management (DSM) techniques are employed, addressing the intermittent nature of renewable energy sources (RES). This manuscript presents a novel multi‐objective optimisation framework to determine the equipment sizing, depth of discharge (DoD) of ESS, and share of controllable loads contributing to DSM in a standalone DC microgrid incorporated with RES as a primary energy source and a backup DIG. The proposed optimisation strategy utilises genetic algorithm with the objectives of minimizing lifecycle cost and carbon footprint. A novel battery energy storage system (BESS) management criterion is introduced, which accounts for battery degradation in the lifecycle cost calculation. The minimum allowable DoD of the BESS is considered a decision variable in the optimisation problem to assess the impact of higher DoD on lifecycle cost improvement. MATLAB simulation results demonstrate that the proposed optimisation model significantly reduces the levelized cost of electricity and per unit carbon footprint compared to previous models. Additionally, it identifies an optimal range of DoD for the BESS to enhance the lifecycle cost of a standalone DC microgrid.

Comparative Review of Thermal Management Systems for BESS

The integration of renewable energy sources necessitates effective thermal management of Battery Energy Storage Systems (BESS) to maintain grid stability. This study aims to address this need by examining various thermal management approaches for BESS, specifically within the context of Virtual Power Plants (VPP). It evaluates the effectiveness, safety features, reliability, cost-efficiency, and appropriateness of these systems for VPP applications. Among the various hybrid cooling options, two notably promising combinations are highlighted. First, the integration of heat pipes with phase change materials, which effectively conduct heat away from sources with minimal temperature differences, enabling swift heat transfer. Second, the combination of heat pipes with liquid passive cooling, which utilizes the efficient heat transfer properties of heat pipes and the steady cooling offered by liquid systems. This study offers recommendations for choosing the best thermal management system based on climate conditions and geographic location, thereby enhancing BESS performance and sustainability within VPPs.

A Framework to Identify Non-Achievers in eLearning Business Informatics Lab Courses

Learning Management Systems store valuable data in their repositories. Analyzing such data could contribute to identifying non-achievers in eLearning courses. This study presents an integrated framework (encompassing stages from a risk management process) to predict non-achievers in eLearning Business Informatics Lab Courses through a discriminant function analysis of student engagement data. In detail, data regarding with student interaction with the learning activities were elicited from the Moodle LMS log files. The paper also presents a specific eLearning Business Informatics Lab Course, designed upon Business Informatics competencies, tailored to a Business Informatics Curriculum for undergraduate Accounting Students. A discriminant function analysis was used to develop a competent prediction model. Linear discriminant functions were generated for achievers and non-achievers respectively. Students were classified into non-achievers or achievers according to the maximum score of the discriminant functions. The high classification percentage of our model indicates that our framework could be used to identify non-achievers in any eLearning Business Informatics Lab course sharing the same structure. A linear discriminant analysis (LDA) was also employed to indicate the training potential of our model. The evaluation metrics of our trained model indicate that our model could potentially be used to develop an alert system.

A matheuristic for active flexibility management of battery energy storage systems in the context of local flexibility markets

Flexibility has gained notoriety with the advancement of topics such as microgrids, renewable generation, distributed energy resources, and energy storage. Proposals like local flexibility markets (LFMs) demonstrate the growing interest in existing demand-side flexibility and the development of methods to access this resource. This work presents a matheuristic to managing battery energy storage systems (BESS) by controlling flexibility reserves of power and energy independently and parameterized through virtual control parameters. The system executes the processes of reserving flexibility, unlocking it, and utilizing it during day-ahead planning and in real-time operation to meet flexibility actions for increasing and reducing demand within LFMs. The management system's behavior is presented in an application case that demonstrates the system’'s ability to manage different flexibility levels. We present the impact of flexibility management on operating profitability, observing a lower loss of profitability in the allocation of power (kW) capacity and a higher loss of profitability in the energy (kWh) flexibility reserve. The method's effectiveness is presented through a set of proposed flexibility metrics, which allow for representing the available flexibility based on power, energy, and time duration of the parameterized demand response action.