Management Of Energy Storage System Research Articles

Recent Developments of Solar Charge Controllers Technologies: A Bibliometric Study

A solar photovoltaic system is a renewable energy that depends on solar irradiance and ambient temperature. A solar charge controller is required to ensure the energy received by the photovoltaic cell or module is maximized at the output. This study presents the first bibliometric analysis based on a thorough evaluation of the most frequently cited articles on the solar charge controller to forecast future trends and applications. This paper performs a statistical analysis using the Scopus database and extracts the 100 most cited papers. It has been illustrated that the solar charge controller literature expanded swiftly from 2012 to 2023, with 2019 receiving the most publications and papers published in 2018 receiving more citations compared to works published in other years. In recent years, battery storage, electric vehicles, energy storage, controllers, photovoltaic systems, and power management systems have garnered great interest. The solar charge controller's functional evaluation and determination of the optimal renewable energy system might boost its potential. Our analysis of highly cited articles on solar charge controllers emphasizes a selection of features, including control methods and systems, issues, challenges to establishing current constraints, research gaps, and the need to find solutions and resolve problems. Every aspect of the features of this overview is anticipated to contribute to an upsurge through the invention of advanced solar charge controllers and control methods for future photovoltaic systems.

Research on cross-building energy storage management system based on reinforcement learning

Research on cross-building energy storage management system based on reinforcement learning

A Comprehensive Study of Electric Vehicle Charging and Energy Storage System Management

A Comprehensive Study of Electric Vehicle Charging and Energy Storage System Management

Optimal carbon reduction and energy storage management method for new power systems considering carbon emissions

Abstract With the advancement of the national “dual carbon” strategy goals and the planning of new energy systems, the proportion of new energy connected to the power grid is rapidly increasing. Therefore, it is urgent to accelerate the planning and construction of energy storage. In this context, from the perspective of energy storage as a regulatory resource, this paper aims to build a set of energy storage state estimation and residual life prediction methods with high estimation accuracy and low computational complexity and achieve energy storage management on the energy storage management system of edge computing architecture, so as to meet the safe, reliable, lasting and efficient operation needs of the energy storage management system, while promoting wind and solar energy consumption as a means. Taking carbon emission reduction as the optimal goal, energy storage management and utilization methods are built. Through simulation experiments, it is verified that this optimal carbon reduction and energy storage management method has good experimental results.

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.

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.

Optimal control strategies for energy storage systems for HUB substation considering multiple distribution networks

With the global consensus to achieve carbon neutral goals, power systems are experiencing a rapid increase in renewable energy sources and energy storage systems (ESS). Especially, recent development of hub substations (HS/S) equipped with ESS, applicable for resolving site constraints if implemented as mobile transformers, is expanding the development of ESS-equipped facilities. However, these units require centralized control strategies considering variability within integrated networks. While studies on electric vehicle charging considering the variability of renewable energy or load are widely studied, ESS management scheme for individual substations requires further optimization, especially considering the state of distributed sources at lower levels and transmission system operators. Thus, in this study, an optimal control approach for ESS located at the connection point of transmission and distribution systems, including further consideration of the loss in distribution lines and the constraints of renewable energy sources is presented. This study attempts to derive proactive control strategies for ESS in HS/S to operate with various distribution networks. By establishing control priorities for each source through optimal operation strategy, a suitable capacity of ESS and its economic benefits for distribution network management can be examined. Validation of the current analysis results is performed by utilizing MATPOWER. By adapting the operational range of design scenarios, diverse distribution systems can be tested against multiple configurations of connected devices.

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.

COMPREHENSIVE ANALYSES OF METHODS OF ENERGY STORAGE SYSTEMS USAGE FOR MICROGRID

Background. Energy storage systems (ESS) provide uninterrupted power access to users and aim to minimize energy losses. These systems are extensively utilized in microgrids, facilitating smooth transitions between grid-connected and isolated microgrid operations. ESS devices with high power density must manage fluctuating loads, such as substations and distributed power sources like wind turbines. Despite existing standards for ESS management, these regulations are often limited to specific areas, leading to interoperability challenges due to the absence of unified modeling approaches and data models. Objective. The purpose of the paper is to explore methods for controlling energy storage systems and enhancing their interoperability. The study focuses on integrating ESS without necessitating changes in consumer control schemes and utilizing interoperable data models for rapid identification and configuration. Methods. The research involves analysing various methods used in electricity management systems, identifying interaction conflicts, and emphasizing the development of an interoperable network. The study also considers the potential of an ontology-based control system to achieve the desired level of interoperability. Results. The analysis indicates that most existing electricity management systems experience interaction conflicts, highlighting the need for an interoperable network. The proposed systems offer significant advantages in fault tolerance, reliability, and scalability. These systems ensure a smooth transition between networked and isolated operations in microgrids, allowing for instant reconnection to the grid when normal conditions are restored. Conclusions. The findings suggest that developing an interoperable ESS is crucial for improving system efficiency and reliability. Future research directions include forming an ontology-based control system to enhance interoperability. This approach is expected to address current interaction conflicts and pave the way for more resilient and scalable energy storage solutions.

LoadNet: enhancing energy storage system integration in power system operation using temporal convolutional and recurrent models with self-attention

Introduction: In the context of the evolving energy landscape, the efficient integration of energy storage systems (ESS) has become essential for optimizing power system operation and accommodating renewable energy sources.Methods: This study introduces LoadNet, an innovative approach that combines the fusion of Temporal Convolutional Network (TCN) and Gated Recurrent Unit (GRU) models, along with a self-attention mechanism, to address the challenges associated with ESS integration in power system operation. LoadNet aims to enhance the management and utilization of ESS by effectively capturing the complex temporal dependencies present in time-series data. The fusion architecture of TCN-GRU in LoadNet enables the modeling of both short-term and long-term dependencies, allowing for accurate representation of dynamic power system behaviors. Additionally, the incorporation of a self-attention mechanism enables LoadNet to focus on relevant information, facilitating informed decision-making for optimal ESS operation. To assess the efficacy of LoadNet, comprehensive experiments were conducted using real-world power system datasets.Results and Discussion: The results demonstrate that LoadNet significantly improves the efficiency and reliability of power system operation with ESS. By effectively managing the integration of ESS, LoadNet enhances grid stability and reliability, while promoting the seamless integration of renewable energy sources. This contributes to the development of a more sustainable and resilient power system. The proposed LoadNet model represents a significant advancement in power system management. Its ability to optimize power system operation by integrating ESS using the TCN-GRU fusion and self-attention mechanism holds great promise for future power system planning and operation. Ultimately, LoadNet can pave the way for a more sustainable and efficient power grid, supporting the transition to a clean and renewable energy future.

The implementation of an optimized neural network in a hybrid system for energy management

In the face of increasing global energy demand and volatile energy prices, many countries are searching for solutions to ensure their energy independence. One of the most popular solutions is to incorporate renewable energy sources in their energy systems. While there are many advantages to integrating renewable energy sources, it is important to note that their intermittent operation can present challenges. Energy storage and smart grid management systems are key solutions to overcome these challenges and ensure sustainable, reliable use of renewable energy sources. In this article, we present an intelligent electrical energy management system for hybrid energy systems. This management system is based on a multi-layer neural network that has undergone an architecture optimization phase to improve the accuracy of real-time energy management and simplify its implementation. The management model that was built demonstrated highly good performance across a range of test circumstances.

Designing of self balancing amplitude modulated five level inverter for reducing voltage stress gradients on converter switches for electric vehicles

The research article endeavors to explore the efficient multi-quadrant operations in Electric Vehicles (EV), which emphasizing among the renewable sources and Energy Storage Management Systems (ESMS). This coordination is facilitated through the latest advancements in converter circuits, aiming to enhance efficiency and performance in EV operations. The primary converting circuit, or inverter, is crucial in optimizing the utilization of energy sourced from renewable sources. The coordination among these systems along with the converter, produces harmonics, due to the shifting of operating modes in EV motors in quick sessions because of dynamic real-time road conditions. This research paper presents the design and implementation of Self Balancing Modulated Five-Level Inverter along with a powerful modulation technique and coordinated with the ESMS of electric car application. The proposed 5-Level inverter shows the reduction in torque distortions in an electric vehicle drives over conventional two-stage boost inverter (2SBI) circuits. The designed Five-Level Inverter (FLI) circuit characterized by reduced switching operations, shows a significant reduction in voltage stress gradients on switches and achieves self-balancing of voltages with reduced operating components. The developed three-phase boost single circuit named as SBAM-FLI is connected to the grid integrated renewable energy application and the system is tested at constant and variable torque conditions. A comparative analysis is conducted between the operation of self-balancing modulated FLI with 2SBI based on the obtained results and power loss analysis is performed for the performance evolution in EV applications. The proposed topology is adaptable to both the Trapezoidal and Sinusoidal Back EMF drive mechanisms, which shows significant improvement over conventional inverters. The experimental validation of proposed self-balancing modulated FLI design is presented in this research. The experimental setup was realized using Xilinx block sets and seamlessly integrated with the Basys-3 Artix-FPGA board, consequently, both the experimental and simulation outcomes of the proposed converter topology were validated and provides substantial impact on EV applications.

A Case Study of a Tiny Machine Learning Application for Battery State-of-Charge Estimation

Growing battery use in energy storage and automotive industries demands advanced Battery Management Systems (BMSs) to estimate key parameters like the State of Charge (SoC) which are not directly measurable using standard sensors. Consequently, various model-based and data-driven approaches have been developed for their estimation. Among these, the latter are often favored due to their high accuracy, low energy consumption, and ease of implementation on the cloud or Internet of Things (IoT) devices. This research focuses on creating small, efficient data-driven SoC estimation models for integration into IoT devices, specifically the Infineon Cypress CY8CPROTO-062S3-4343W. The development process involved training a compact Convolutional Neural Network (CNN) and an Artificial Neural Network (ANN) offline using a comprehensive dataset obtained from five different batteries. Before deployment on the target device, model quantization was performed using Infineon’s ModusToolBox Machine Learning (MTB-ML) configurator 2.0 software. The tests show satisfactory results for both chosen models with a good accuracy achieved, especially in the early stages of the battery lifecycle. In terms of the computational burden, the ANN has a clear advantage over the more complex CNN model.

An optimal expansion planning of power systems considering cycle-based AC optimal power flow

This paper presents a novel mixed-integer linear optimization formulation of the AC network-constrained, cost-based, integrated expansion planning problem. The formulation is used to determine the investment needs per technology including the location and sizing of new generation, energy storage, and transmission network assets in a future low-carbon power system. To reduce the size of the resulting problem, the AC optimal power flow (AC-OPF) model is represented in a compact way using cycle constraints. A bound tightening procedure is also considered to reduce the search space and improve the solver performance by adjusting the voltage bounds within the AC-OPF. Contrary to typically used formulations of the integrated expansion planning problem, the constraints considered here include all main aspects of system operation, namely unit commitment, energy storage system management, AC-OPF, and reactive power compensation. Thus, in this paper, we examine how both the proposed transmission expansion modeling developments and the interrelation of the integrated planning constraints affect the computation of the solution to the expansion planning problem. The performance of this formulation is assessed on the RTS-GMLC test system by computing the expansion plan and comparing it with the results of three other expansion planning formulations most frequently employed in the recent literature to address the integrated expansion planning problem for medium to large-scale systems. Expansion plans are computed and compared for different case studies and multiple scenarios. According to the comparative analysis, neglecting the AC-OPF or the unit commitment constraints can increase the total system costs by 7.10%–9.57% or 6.29%–8.39%, respectively. Unlike other modeling approaches, the proposed approach does not rely on simplifications that impact the quality of the solution. Thanks to the incorporated cycle-based AC-OPF constraints and the consideration of a bound tightening procedure, the computation time is reduced by 17.67%–27.21%.

An optimal solutions-guided deep reinforcement learning approach for online energy storage control

As renewable energy becomes more prevalent in the power grid, energy storage systems (ESSs) are playing an ever-increasingly crucial role in mitigating short-term supply–demand imbalances. However, the operation and control of ESS are not straightforward, given the ever-changing electricity prices in the market environment and the stochastic and intermittent nature of renewable energy generations, which respond to real-time load variations. In this paper, we propose a deep reinforcement learning (DRL) approach to address the electricity arbitrage problem associated with optimal ESS management. First, we analyze the structure of the optimal offline ESS control problem using the mixed-integer linear programming (MILP) formulation. This formulation identifies optimal control actions to absorb excess renewable energy and perform price arbitrage strategies. To tackle the uncertainties inherent in the prediction data, we then recast the online ESS control problem into a Markov Decision Process (MDP) framework and develop the DRL approach, which involves integrating the optimal offline control solution obtained from the training data into the training process and introducing noise to the state transitions. Unlike typical offline DRL training over a long time interval, we employ the Deep Deterministic Policy Gradient (DDPG) and Proximal Policy Optimization (PPO) algorithms with smaller neural networks training over a short time interval. Numerical studies demonstrate the promising potential of the proposed DRL-enabled approach for achieving better online control performance than the model predictive control (MPC) method under different price errors. This highlights the sample efficiency and robustness of our DRL approaches in managing ESS for electricity arbitrage.

Reviewing the impact of AI on renewable energy efficiency and management

In recent years, the intersection of artificial intelligence (AI) and renewable energy has emerged as a pivotal domain with transformative potential. This review delves into a comprehensive review of the impact that AI technologies have had on enhancing efficiency and management in the renewable energy sector. The integration of AI into renewable energy systems has ushered in a new era of optimization, addressing challenges and unlocking opportunities for sustainable energy production. AI's role in renewable energy begins with its ability to analyze vast datasets generated by energy systems, weather patterns, and consumption trends. Machine learning algorithms have been employed to predict energy demand, optimize grid operations, and enhance forecasting accuracy, thereby contributing to the increased efficiency of renewable energy sources. This application is crucial for overcoming the intermittent nature of renewable sources such as solar and wind power. Moreover, AI-driven technologies facilitate the intelligent management of energy storage systems, enabling better utilization of excess energy during peak production periods. Advanced control mechanisms, empowered by AI, have significantly improved the coordination of diverse energy sources, ensuring a seamless integration of renewable energy into existing power grids. Additionally, AI-driven predictive maintenance has proven instrumental in reducing downtime and optimizing the performance of renewable energy infrastructure. The review also explores the potential environmental and economic benefits of AI in renewable energy, emphasizing the role of smart grids, demand response systems, and decentralized energy production. As the world strives towards a sustainable energy future, this review offers valuable insights into the ongoing evolution of AI technologies and their impact on revolutionizing the efficiency and management of renewable energy systems. The findings presented here contribute to the growing body of knowledge aimed at accelerating the global transition to cleaner and more sustainable energy solutions.

A Coordinated Optimal Operation of a Grid-Connected Wind-Solar Microgrid Incorporating Hybrid Energy Storage Management Systems

The hybrid-energy storage systems (ESSs) are promising eco-friendly power converter devices used in a wide range of applications. However, their insufficient lifespan is one of the key issues by hindering their large-scale commercial application. In order to extend the lifespan of the hybrid-ESSs, the cost functions proposed in this paper include the degradation of the hydrogen devices and the battery. Indeed, this paper aims to develop a sophisticated model predictive control strategy for a grid-connected wind and solar microgrid, which includes a hydrogen-ESS, a battery-ESS, and the interaction with external consumers, e.g., battery/fuel cell electric vehicles. The integrated system requires the management of its energy production in different forms, i.e., the electric and the hydrogen ones. The proposed strategy consists of the economical and operating costs of the hybrid-ESSs, the degradation issues, and the physical and dynamic constraints of the system. The mixed-logic dynamic framework is required to model the operating modes of the hybrid-ESSs and the switches between them. The effectiveness of the controller is analyzed by numerical simulations which are conducted using solar and wind generation profiles of solar panels and wind farms located in Abu Dhabi, United Arab Emirates. Such simulations, indeed, show that the proposed strategy appropriately manages the plant by fulfilling constraints and energy requests while reducing device costs and increasing battery life.

Fuzzy Logic-Based Energy Storage Management for Grid Resilience

In this study, we present and examine the implementation of a fuzzy logic-driven energy storage management system devised to enhance the efficiency of charging and discharging activities in modern power grids. By using real data derived from the installation of the system, our research aims to assess its effectiveness in bolstering grid resilience and stability. The system integrates linguistic factors and employs rule-based decision-making, enabling it to adapt charging and discharging techniques in real-time to the prevailing grid circumstances. The results unequivocally indicate a noteworthy enhancement in the efficiency of the system, exhibiting a percentage alteration spanning from 8% to 12%, hence confirming the system’s inherent ability to mitigate energy losses throughout its operating cycles. Moreover, the use of the fuzzy logic controller significantly enhances the Grid Reliability Index, exhibiting a percentage variation ranging from 10% to 15%, so demonstrating a higher level of stability and responsiveness to grid demand. The controller’s flexibility is highlighted by doing sensitivity analysis, which demonstrates its strong decision-making skills across many contexts. The charging and discharging techniques are meticulously tuned, demonstrating a percentage fluctuation of 15% to 20% in accordance with diverse grid situations, effectively optimizing the exploitation of renewable energy sources. The practical usefulness of the proposed system is confirmed by the validation of simulation results against historical data. The research incorporates ethical issues, such as clear communication, user permission, and socio-economic implications, to underscore the appropriate application of modern energy management technology. The results of this study significantly enhance the overall comprehension of fuzzy logic-driven systems, presenting a very promising resolution for the enhancement of energy storage operations and the progression of robust and environmentallyfriendly energy infrastructures.

СИСТЕМА УПРАВЛЕНИЯ НАКОПИТЕЛЯМИ ЭЛЕКТРИЧЕСКОЙ ЭНЕРГИИ НА ПРИНЦИПАХ НЕЧЕТКОЙ ЛОГИКИ

In rapidly developing world, the complexity of electric energy storage management systems and a significant data flow affect the development of technologies and contribute to the transition from classical to more efficient control methods based on fuzzy logic, which are able to function with complex structures and incomplete information. (Research Purpose) The research purpose is developing the optimal structure of the control system according to the criterion of minimizing the number of rules of fuzzy logic and increasing the energy efficiency of the electric power storage and justifying further research and implementation of this system in the software package. (Materials and methods) Used the methods of analysis, synthesis and modeling used to create the structure of the control system being developed based on the fuzzy logic method. (Results and discussion) The final structure of the power storage management system was determined on the basis of the fuzzy logic method, which is presented as a list of rules. The main variables on which the work of the control system being developed is based have been formulated. (Conclusions) The results obtained are the basis for further research and development of a control system based on a software package capable of influencing the controls of an electric power storage device.

Adaptive Energy Storage System Management Considering State Coupling Characteristics for Dynamic Positioning

Adaptive Energy Storage System Management Considering State Coupling Characteristics for Dynamic Positioning