Management Of Energy Storage System Research Articles

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.

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.

Health-aware Energy Storage System Management Considering State Coupling Characteristics for Navigation in Restricted Waters

Health-aware Energy Storage System Management Considering State Coupling Characteristics for Navigation in Restricted Waters

Optimal operation and maintenance of energy storage systems in grid-connected microgrids by deep reinforcement learning

The operation of microgrids, i.e., energy systems composed of distributed energy generation, local loads and energy storage capacity, is challenged by the variability of intermittent energy sources and demands, the stochastic occurrence of unexpected outages of the conventional grid and the degradation of the Energy Storage System (ESS), which is strongly influenced by its operating conditions. To effectively address these challenges, a novel method for combined operation and maintenance management of ESS has been developed. Unlike the currently available solutions, which typically address the one-day-ahead scheduling problem, the present work considers, for the first time, the realistic case of a microgrid in which the ESS degrades and unexpected outages of the conventional grid can occur along the long-time horizons of the entire microgrid lifetimes. The proposed method, which is based on deep reinforcement learning, is tested on a simulated grid-connected microgrid of a residential building equipped with photovoltaic modules and an ESS. The method outperforms other state-of-the-art approaches based on heuristics and metaheuristics by increasing the profit by 15% and reducing the average number of ESS replacements during its lifetime. Therefore, it can be concluded that the proposed DRL-based framework allows achieving prescriptive maintenance since the suggested actions are optimal from the point of view of effectively maximizing the profit and minimizing the maintenance interventions over the entire lifetime of the microgrid.

Adaptive filter based method for hybrid energy storage system management in DC microgrid

Based on previous energy storage technology research, no single energy storage device combines the high energy density and high power density. Hybrid energy storage system combines multiple energy storage technologies to achieve enhanced performance and efficiency in energy storage applications. This paper proposes a hybrid energy storage system that consists of batteries and supercapacitors for maintaining the stable functioning of DC microgrids. However, effective control and management systems are necessary to maximize their performance. This paper proposes a novel adaptive Frequency-Based Control with State of Charge management as a solution for achieving smooth operation of DC microgrids. By utilizing the state of charge of high power density and high energy density energy storage systems as control inputs, the proposed method adjusts the current flow into the storage devices, resulting in improved power management, accurate voltage regulation, enhanced SOC control, and increased system stability. The proposed adaptive control method is compared to the conventional Frequency-Based Control through MATLAB/Simulink simulations using the same configuration, operation mode and parameter value. In the case studies, the proposed method demonstrates a reduction of 6.7% in peak voltage and 30% in dip voltage compared to the conventional method. Moreover, the settling time of the proposed method is improved by 0.9 and 0.03 s, respectively, compared to the conventional method. The results demonstrate that the proposed method outperforms the conventional method, providing better power management, faster and more precise voltage regulation, and enhanced SOC control.

Effect of ternary hybrid nanoparticles (GO-MgO-TiO2) and radiative heat transfer on the solidification process of PCM inside a triplex tube with hollow fins

Nowadays, due to the features such as high latent heat and constant phase transition temperature, the utilization of phase change materials (PCMs) in energy storage and thermal management systems has attracted scholars' attention. However, the low thermal conductivity and solidification process of these materials has always been a challenge for widespread implementation of them in disparate industries. The current research investigates the characteristics of the PCM solidification process within a triplex tube Latent Heat Thermal Energy Storage System (LHTESS) with innovative hollow fins in the presence of ternary hybrid nanoparticles (THNPs) and radiative heat transfer. The governing equations are solved using Galerkin Finite Element Method (GFEM) with various boundary conditions in the outer wall, such as constant temperature, adiabatic, and coolant fluid temperature (triplex), through FlexPDE software. Furthermore, the effect of key parameters, including shape factor and volume fraction of THNPs, and radiative heat transfer on thermal storage performance is scrutinized. Ultimately, the Taguchi method is adopted to determine the optimal point at which the full solidification time (FST) is lowest. Results show that at 400000 s, the triplex case improved total discharging energy by 23% and 4% in comparison to the constant temperature and the adiabatic case, respectively. Additionally, by including THNPs with a volume percentage of 0.1 and a shape factor of 16.1 instead of 3, the required time for FTS reduces by approximately 13.1% and 21.9%, respectively. Also, the overall solidification times of the PCMs decrease to 36% by enhancing the radiation parameter from 0 to 1. These outcomes illustrate THNPs and considered fins are effective for reducing the FST and the shape factor of THNPs and radiation are key parameters that should be considered by designers.

Peer-to-Peer Energy Trading Case Study Using an AI-Powered Community Energy Management System

The Internet of Energy (IoE) is a topic that industry and academics find intriguing and promising, since it can aid in developing technology for smart cities. This study suggests an innovative energy system with peer-to-peer trading and more sophisticated residential energy storage system management. It proposes a smart residential community strategy that includes household customers and nearby energy storage installations. Without constructing new energy-producing facilities, users can consume affordable renewable energy by exchanging energy with the community energy pool. The community energy pool can purchase any excess energy from consumers and renewable energy sources and sell it for a price higher than the feed-in tariff but lower than the going rate. The energy pricing of the power pool is based on a real-time link between supply and demand to stimulate local energy trade. Under this pricing structure, the cost of electricity may vary depending on the retail price, the number of consumers, and the amount of renewable energy. This maximizes the advantages for customers and the utilization of renewable energy. A Markov decision process (MDP) depicts the recommended power to maximize consumer advantages, increase renewable energy utilization, and provide the optimum option for the energy trading process. The reinforcement learning technique determined the best option in the renewable energy MDP and the energy exchange process. The fuzzy inference system, which takes into account infinite opportunities for the energy exchange process, enables Q-learning to be used in continuous state space problems (fuzzy Q-learning). The analysis of the suggested demand-side management system is successful. The efficacy of the advanced demand-side management system is assessed quantitatively by comparing the cost of power before and after the deployment of the proposed energy management system.

Emerging urchin-like core-shell mineral microspheres with efficient photothermal conversion and solar energy storage

Enormous challenges still seriously restrict the application of phase change materials (PCMs) in thermal energy storage and heat management systems, such as their leakage, low thermal conductivity, and low photothermal conversion efficiency. We reported an effective strategy for the morphology-controlled synthesis of the composite microsphere with an urchin-like core-shell structure for encapsulated paraffin, which synergistically enhanced efficient solar thermal conversion and heat energy storage. The SCGT was prepared by coating a layer of urchin-like TiO2 on the core composed of sepiolite, cellulose nanocrystals, and graphite (SCG). The synergistic effect of the core-shell structure makes paraffin-SCGT (P-SCGT) composites possess a high photothermal conversion efficiency of 94 % and outstanding heat energy storage capacity, with the melting and crystallization enthalpy of 137.4 J/g and 127.5 J/g. Furthermore, the mortar cylinder developed with the P-SCGT and mortar (P-SCGT-M) indicated exceptional thermal management abilities in attenuating the temperature fluctuations between diurnal and nocturnal periods, even multiple heating and cooling cycles. Therefore, the prepared materials with comprehensive properties would be perspective applied in solar collector systems and energy-saving buildings.

Resilience of active networks with optimal mobile energy storage systems management

Resilience of active networks with optimal mobile energy storage systems management

Game theoretical approach for critical sizing of energy storage systems for residential prosumers

The integration of Energy Storage Systems (ESS) into the distribution system has resulted in changes in the behavior of energy management systems. The primary objective of ESS management on the consumer side is to minimize operational costs reflected in energy bills, while the main goal for the Distribution System Operator (DSO) is to improve and smooth the aggregated load profile. Utilizing ESSs in distribution systems requires thorough research, as incorrect implementation can reduce controllability of distribution networks. This paper investigates the relationship between energy cost and the load responsiveness index of PV-ESS equipped residential loads using game theoretic approach. Sensitivity analysis is conducted to examine the relationship between load responsiveness and factors such as load profile, PV size, and energy cost. The goal of the paper is to propose a new approach for optimal sizing of ESS.

Phase Change Composite Microcapsules with Low-Dimensional Thermally Conductive Nanofillers: Preparation, Performance, and Applications

Phase change materials (PCMs) have been extensively utilized in latent thermal energy storage (TES) and thermal management systems to bridge the gap between thermal energy supply and demand in time and space, which have received unprecedented attention in the past few years. To effectively address the undesirable inherent defects of pristine PCMs such as leakage, low thermal conductivity, supercooling, and corrosion, enormous efforts have been dedicated to developing various advanced microencapsulated PCMs (MEPCMs). In particular, the low-dimensional thermally conductive nanofillers with tailorable properties promise numerous opportunities for the preparation of high-performance MEPCMs. In this review, recent advances in this field are systematically summarized to deliver the readers a comprehensive understanding of the significant influence of low-dimensional nanofillers on the properties of various MEPCMs and thus provide meaningful enlightenment for the rational design and multifunction of advanced MEPCMs. The composition and preparation strategies of MEPCMs as well as their thermal management applications are also discussed. Finally, the future perspectives and challenges of low-dimensional thermally conductive nanofillers for constructing high performance MEPCMs are outlined.

Coordinated emergency control strategy of high‐voltage direct current transmission and energy storage system based on Pontryagin minimum principle for enhancing power system frequency stability

AbstractWith the increase of the proportion of renewable energy sources, the rotational inertia of the power system decreases, which results in the risk of frequency instability increasing. Based on Pontryagin minimum principle, this paper presents a systematic emergency control strategy by coordinating the active power of voltage source converter based high‐voltage direct current transmission (VSC‐HVDC) and energy storage system (ESS) to improve the system frequency stability, reduce the operation cost of VSC‐HVDC and ESS and implement energy management of ESS. In the stage of frequency fall, a hierarchical control strategy is proposed to prevent the frequency from dropping below the preset threshold value and achieve the optimal distribution of support power between VSC‐HVDC and ESS. For the system level, the total optimal power support trajectory of VSC‐HVDC and ESS is optimized by the Gaussian pseudospectral method with the minimum total control energy, considering the constraints of frequency. For the station level, the power references of VSC‐HVDC/ESS are determined by the model predictive controller (MPC), with the minimum frequency deviation of the non‐disturbed system connected to VSC‐HVDC, control cost of VSC‐HVDC and loss cost of ESS. In the stage of frequency recovery, an energy recovery control strategy is designed to aim at quickly recovering energy of ESS while considering the minimum frequency limit. Finally, the correctness and effectiveness of the proposed strategy are verified in the improved IEEE 39 bus system and a real‐power system.

Optimal Battery Energy Storage System Management with Wind Turbine Generator in Unbalanced Low Power Distribution System

Wind Turbine Generators (WTG) are being integrated into distribution systems on a large scale worldwide as part of a global effort to capture green energy. Wind turbine generator intermittency may be mitigated by Battery Energy Storage Systems (BESS), which have emerged as a viable option in recent years. To find the best position and capacity for wind power generation and BESS charging/discharging dispatches, a Red Fox Optimisation (RFO) algorithm is used while optimising the imbalanced distribution network’s performance under technological restrictions. The charging or discharging criteria for this method is the average feeder load. The charging techniques for BESS using WTG and Sustainable Average Load (SAL) are evaluated in terms of the free-running mode of dispatch cycle. The suggested approach is tested on an IEEE-37 bus Unbalanced Radial Distribution Network (UDN). It has been shown that the suggested method enhances several performance objectives of the distribution system.

Battery Energy Storage Systems in Ships’ Hybrid/Electric Propulsion Systems

The shipping industry is going through a period of technology transition that aims to increase the use of carbon-neutral fuels. There is a significant trend of vessels being ordered with alternative fuel propulsion. Shipping’s future fuel market will be more diverse, reliant on multiple energy sources. One of very promising means to meet the decarbonisation requirements is to operate ships with sustainable electrical energy by integrating local renewables, shore connection systems and battery energy storage systems (BESS). With the increasing number of battery/hybrid propulsion vessels in operation and on order, this kind of vessel propulsion is becoming more common, especially in the segment of short range vessels. This paper presents review of recent studies of electrification or hybridisation, different aspects of using the marine BESS and classes of hybrid propulsion vessels. It also reviews several types of energy storage and battery management systems used for ships’ hybrid propulsion. The article describes different marine applications of BESS systems in relation to peak shaving, load levelling, spinning reserve and load response. The study also presents the very latest developments of hybrid/electric propulsion systems offered by leading maritime market manufacturers.