Control Of Energy Storage System Research Articles

Knowledge mapping and evolutionary analysis of energy storage resource management under renewable energy uncertainty: a bibliometric analysis

The research on energy storage resource management is an important measure to cope with the present problem of uncertainty in the use of renewable energy, in order to explore the evolution of the research focus and future trend of energy storage resource management under the uncertainty of renewable energy, this paper adopts the method of bibliometric analysis, and conducts a visual analysis of the relevant 800 documents in the core collection of the Web of Science by using the CiteSpace to draw co-occurrence maps and analyze the collaborative networks of different countries/regions, institutions and authors in this field, and further conducted journal co-citation analysis, literature co-citation analysis and author co-citation analysis. The keyword co-occurrence, emergent analysis, and cluster co-occurrence analysis reveal the current research focus and trend in this field, and summarize and propose four future key focus directions: energy storage technology improvement, energy storage system integration, expansion of business models for energy storage resource management, and intelligent control of energy storage system, which provide new research paths for solving the problem of renewable energy uncertainty in the future.

Experimental investigation of a distributed photovoltaic heating system based on building envelope thermal storage

Low-carbon heating in farmhouses can be achieved using photovoltaic (PV) heating. However, the severe mismatch between the PV energy supply and the building thermal load requires expensive energy storage devices and control systems. In this study, a controller-less PV heating system utilizing the building envelope for thermal storage was evaluated on a farmhouse in northern China. The building envelope simultaneously functions as the heating terminal and is composed of insulation boards, electric heating wires, and the thermal storage wall. PV electricity is fed to be absorbed by the electric heating wire to produce heat. The thermal storage wall absorbs the heat generated by the heating wire before releasing it into the room through convection and radiation. Long-term monitoring reveals that the black bulb temperature of the farmhouse was above 16 °C for more than 60 % of the time throughout the heating season. The building envelope acting as a thermal reserve improved system supply and demand matching by 77 % and the indoor temperature fluctuation was reduced by about 47 %. The thermal storage wall can provide 58 % of the heating energy when there is no PV power supply. The payback period of the heating system is only 6.5 years, verifying the good rate of return of the system. This study proposes a lower cost energy storage solution for PV heating than previous studies, and engages the building envelope into the building energy system.

A quasi-harmonic voltage compensation control of current-controlled battery energy storage systems for suppressing mid-frequency oscillations and harmonics

In grid-connected mode, current-controlled battery energy storage systems (BESS) face the issues of harmonic caused by nonlinear loads and interactive instability under weak grids. Firstly, the mechanisms of mid-frequency oscillations (MFO) and mid-frequency harmonics (MFH) are revealed by the impedance network theory and the circuit principle. Because harmonic currents of nonlinear loads tend to interact with the mid-frequency impedance of BESS to produce harmonic voltage drops, the grid is susceptible to MFH. Worse still, the grid-connected system based on BESS tends to exhibit negative damping characteristics in the mid-frequency band as the grid stiffness decreases, which triggers MFO. Aiming at the above problems, this paper proposes a quasi-harmonic voltage compensation control strategy without any harmonic extractor and provides a detailed parameter design rule. The proposed control strategy can effectively suppress MFO by enhancing the damping between BESS and weak grids. Meanwhile, the disturbance resistance of BESS to nonlinear loads is significantly improved because the impedance magnitude of BESS in the mid-frequency band is obviously reduced. Finally, the effectiveness of the proposed strategy is verified by simulation and experiment.

Design of an adaptive frequency control for flywheel energy storage system based on model predictive control to suppress frequency fluctuations in microgrids

Frequency fluctuations are brought on by power imbalances between sources and loads in microgrid systems. The flywheel energy storage system (FESS) can mitigate the power imbalance and suppress frequency fluctuations. In this paper, an adaptive frequency control scheme for FESS based on model predictive control (MPC) is proposed to suppress the frequency fluctuation in microgrids. Firstly, a linear-fuzzy control is proposed and employed in a frequency regulation controller (FRC) to carry out adaptive frequency control according to the frequency fluctuation and the energy of the FESS, which improves the utilization rate of the FESS, and at the same time avoid the power oscillation caused by overcharging and over-discharging situations. Secondly, a voltage regulation controller (VRC) for dynamic power balance is designed to improve the speed of FESS power balance and suppress the large fluctuation of DC bus voltage. In addition, a MPC based on quadratic programming method is proposed to make the actual current quickly follow the reference current, speed up the power response, and respond to system changes in time through real-time feedback state variables to improve system stability. Finally, the stability of the proposed control system is verified by simulation and experiment.

Forecast Uncertainties Real-Time Data-Driven Compensation Scheme for Optimal Storage Control

This study introduces a real-time data-driven battery management scheme designed to address uncertainties in load and generation forecasts, which are integral to an optimal energy storage control system. By expanding on an existing algorithm, this study resolves issues discovered during implementation and addresses previously overlooked concerns, resulting in significant enhancements in both performance and reliability. The refined real-time control scheme is integrated with a day-ahead optimization engine and forecast model, which is utilized for illustrative simulations to highlight its potential efficacy on a real site. Furthermore, a comprehensive comparison with the original formulation was conducted to cover all possible scenarios. This analysis validated the operational effectiveness of the scheme and provided a detailed evaluation of the improvements and expected behavior of the control system. Incorrect or improper adjustments to mitigate forecast uncertainties can result in suboptimal energy management, significant financial losses and penalties, and potential contract violations. The revised algorithm optimizes the operation of the battery system in real time and safeguards its state of health by limiting the charging/discharging cycles and enforcing adherence to contractual agreements. These advancements yield a reliable and efficient real-time correction algorithm for optimal site management, designed as an independent white box that can be integrated with any day-ahead optimization control system.

Coordinated Frequency Regulation between DFIG-VSWTs and BESS Hybrid Systems

As the utilization of wind power systems continues to increase, reducing overall system inertia, there is a consequential negative impact on the power system’s ability to regulate frequency. Consequently, this study focuses on examining the fast-frequency regulation in high penetration of wind power, especially doubly fed induction generators—the most commonly installed wind turbine type, and an energy storage system installed in the wind farm. This study proposes a coordinated control of wind turbine generators and battery energy storage systems that provides fast-frequency regulation to the system while simultaneously ensuring the safety of the battery. Firstly, the fast-frequency regulation capability of the wind turbine will be studied. Secondly, primary frequency control of the battery energy storage system considering adaptive droop control based on state of charge is proposed to prevent both over-charging and over-discharging of the battery. Finally, this study proposed a coordinated fast-frequency regulation approach for the hybrid wind-storage system, which is evaluated under various wind speed scenarios. This approach involves a detailed analysis of the operational characteristics of the wind turbine generators to ensure optimal performance. The proposed method is validated through simulation using a MATLAB model of the wind-storage system, and comparative results with alternative control methods confirm the effectiveness of the proposed approach in raising the frequency nadir and avoiding the secondary frequency dip.

Optimization of battery/ultra‐capacitor hybrid energy storage system for frequency response support in low‐inertia microgrid

AbstractModern power system networks are under statutory obligations to integrate renewable energy sources (RES). The primary reason is to meet ever‐increasing energy demand and also to curtail environmental pollution by greenhouse gases. However, the higher penetration of RES has the tendency of reducing inertia of overall power system network. Consequently, frequency stability is affected and deviates beyond allowable permissible limits leading to power blackouts, load shedding, and even total system failure. To address the issues associated with reduced inertia, an optimal control of hybrid energy storage system (HESS) has been proposed. HESS is basically a combination of battery and ultracapacitor, where ultracapacitor addresses rapidly varying power component by mimicking inertia while the battery compensates long‐term power variations. Thus, the HESS is effectively controlled to compensate the loss of inertia by regulating its energy flow. For the purpose of improved efficiency and better power management of the HESS, an improvised particle swarm optimization (MPSO)‐based virtual inertia control design has been proposed. The proposed MPSO is utilized to tune the gains of bidirectional dc–dc converter in such a way that improves frequency nadir with faster response to transient disturbances. This proposed method is simulated in MATLAB and its merits are validated in real time using hardware in loop. On analysing of the results, it can be observed that frequency nadir is improved by 48.96% with significant reduction in rate of change of frequency in comparison to conventional particle swarm optimization.

Application of virtual synchronous generator technology in power systems and its modeling in the Matlab software package

Currently, the commissioning of power plants based on renewable energy sources (RES) using inverters is becoming widespread. However, these power plants do not participate in maintaining the stability of the power system during emergency disturbances, since they are switched off by technological protections when a disturbance occurs. In the future, when power plants based on renewable energy sources become widespread, ensuring the possibility of their participation in maintaining sustainability will significantly increase the reliability of the energy system. A preliminary assessment of the effectiveness of participation in maintaining sustainability should be carried out through computer modeling.THE PURPOSE: To realize the possibility of participation of RES-based power plants in ensuring reliable and sustainable operation of the power system, as well as to realize the possibility of performing a preliminary assessment of the effectiveness of the proposed method.METHODS. In this paper, the application of virtual synchronous generator technology is proposed to realize the possibility of participation of RES-based power plants in ensuring the reliability and sustainability of the power system. Also, a method of modeling the virtual synchronous generator in Matlab software package is proposed.RESULTS. The technology of virtual synchronous generator is proposed, which with the use of inverter, electric energy storage and appropriate control system can provide the increase of dynamic stability of power system. A method of modeling the virtual synchronous generator in Matlab software package is proposed. The results obtained allow us to conclude that the constructed model of a virtual synchronous generator is efficient, maintains the stability of the system and ensures correct control of the inverter, as a result of which the parameters of the electrical network are maintained within acceptable limits. The resulting computer model can be used in further research.CONCLUSION. The technology of virtual synchronous generator allows to imitate in the inverter the inertial and damping characteristics of the traditional synchronous generator, and the proposed computer model will help to evaluate the effectiveness of this technology when implemented in the power system.

Super-twisting sliding mode controller for energy storage system of a novel multisource hybrid electric vehicle: Simulation and hardware validation

The rapid depletion of fossil fuels, oil, and natural gas due to their excessive use is a source of motivation for finding alternative solutions. Global warming is also an important issue caused by the emission of harmful gases. Consequently, extensive research has been conducted on Fuel Cell-based Hybrid Electric Vehicles (FCEVs) to address the twin challenges of resource depletion and harmful emissions. This study presents a novel model comprising of four sources i.e. Fuel Cell (FC), Photo Voltaic panel (PV), battery, and Supercapacitor (SC). The Proton exchange membrane fuel cell (PEMFC) in which hydrogen is used as fuel is the primary source whereas the other three sources act as secondary sources. These sources are interconnected via DC converters to a DC bus. To regulate this Hybrid Energy Storage System (HESS), a Super-Twisting Sliding Mode Controller (ST-SMC) has been developed, ensuring global stability through the application of Lyapunov criteria. In addition, the proposed controller is compared with a conventional Sliding Mode Controller (SMC) and Integral Sliding Mode Controller (ISMC). Both simulation (MATLAB/Simulink 2021b) and hardware tests (MicroLabBox dSPACE RTI1202) confirm the system's stability, resilience, and efficient functionality across various dynamic scenarios. Comparative analysis between the three demonstrates the superiority of ST-SMC over the SMC and ISMC, as verified by the results.

Rectification and converter control of the FPSLGs for energy storage applications

AbstractThis article presents two power converters with controllers attached to the Free‐Piston Stirling Linear Generator (FPSLG) and energy storage system (ESS). The rectifier uses hysteresis‐SVPWM current regulation and CB‐SPWM, while the buck‐boost converter utilizes the dual‐loop PI control method. The storage battery receives energy from a linear‐generator with a rectifier and converter. The FPSE and linear motors in the FPSLG convert thermal to electrical energy. A two‐level electrical energy conversion technique using a three‐phase AC signal to DC and DC‐DC converter has been devised to reduce output electrical energy oscillation as well as stabilize the generating system's DC power. Initially, the FPSE's nonlinear model and the three‐phase permanent magnet linear‐motor's linear mathematical model were created and followed by the ESS and control mechanism simulation. The simulation shows that the ESS system and control approach can give stable power to the storage battery using linear‐generator power. The PR controller replaces the PI controller in the position loop to improve frequency and displacement tracing. The control system design was simulated in MATLAB and the topology's feasibility and correctness were confirmed. Comparative analysis shows that, rectifier uses the H‐SVPWM's unity power factor of AC and stability controllability of the DC‐side voltage is better than the CB‐SPWM approach.

Optimal Scheduling Strategy for Distribution Network with Mobile Energy Storage System and Offline Control PVs to Minimize the Solar Energy Curtailment

As offline control photovoltaic (PV) plants are not equipped with online communication and remote control systems, they cannot adjust their power in real-time. Therefore, in a distribution network saturated with offline control PVs, the distribution system operator (DSO) should schedule the distributed energy resources (DERs) considering the uncertainty of renewable energy to prevent curtailment due to overvoltage. This paper presents a day-ahead network operation strategy using a mobile energy storage system (MESS) and offline control PVs to minimize power curtailment. The MESS model efficiently considers the transportation time and power loss of the MESS, and models various operating modes, such as the charging, discharging, idle, and moving modes. The optimization problem is formulated based on mixed-integer linear programming (MILP) considering the spatial and temporal operation constraints of MESSs and is performed using chanced constrained optimal power flow (CC-OPF). The upper limits for offline control PVs are set based on the probabilistic approach, thus mitigating overvoltage due to forecasting errors. The proposed operation strategy was tested in the IEEE 33-node distribution system coupled with a 15-node transportation system. The test results show the effectiveness of the proposed method for minimizing curtailment in offline control PVs.

Enhancement of Power Quality through the Implementation of a Hybrid Artificial Intelligence Model within Solar-Wind Energy Systems

The generation of electricity is significantly assisted by hybrid solar-wind power systems, which also play a crucial role in the advancement of intelligent grids. Furthermore, the integration of wind and solar energy storage control systems, in conjunction with energy markets, has rendered the electricity grid more economically feasible. To meet the growing demand for electricity, hybrid renewable energy systems connected to microgrids consist of substantial identifying elements. The issue of harmonic distortion in microgrids arising from nonlinear loads is a fundamental area of research. Understanding the impact of microgrids on power quality is also of great importance. This study focuses on enhancing power quality in solar-wind hybrid systems by incorporating emerging Artificial Intelligence (AI) methodologies. The proposed approach comprises two main components: (i) detection of distortions caused by voltage fluctuations and (ii) improvement of power quality through an AI-based control system. Initially, power distortions are identified and eliminated using the Enhanced Kalman Filter (EKF) algorithm. A Hybrid Artificial Neural Network with Fuzzy Intelligence (HANFI) model is then developed based on this power data. This model integrates two prominent neural network architectures with fuzzy systems. Experimental validation of the system is conducted using MATLAB, which facilitates the simulation of hybrid energy systems.

A Combined Persistence and Physical Approach for Ultra-Short-Term Photovoltaic Power Forecasting Using Distributed Sensors.

This paper presents a novel method for forecasting the impact of cloud cover on photovoltaic (PV) fields in the nowcasting term, utilizing PV panels as sensors in a combination of physical and persistence models and integrating energy storage system control. The proposed approach entails simulating a power network consisting of a 22 kV renewable energy source and energy storage, enabling the evaluation of network behavior in comparison to the national grid. To optimize computational efficiency, the authors develop an equivalent model of the PV + energy storage module, accurately simulating system behavior while accounting for weather conditions, particularly cloud cover. Moreover, the authors introduce a control system model capable of responding effectively to network dynamics and providing comprehensive control of the energy storage system using PID controllers. Precise power forecasting is essential for maintaining power continuity, managing overall power-system ramp rates, and ensuring grid stability. The adaptability of our method to integrate with solar fencing systems serves as a testament to its innovative nature and its potential to contribute significantly to the renewable energy field. The authors also assess various scenarios against the grid to determine their impact on grid stability. The research findings indicate that the integration of energy storage and the proposed forecasting method, which combines physical and persistence models, offers a promising solution for effectively managing grid stability.

Enhancing DC microgrid performance with fuzzy logic control for hybrid energy storage system

Enhancing DC microgrid performance with fuzzy logic control for hybrid energy storage system

Shape-stable phase change materials based on carboxymethyl chitosan/boron nitride nanosheets high-strength aerogel

The application of phase change materials (PCMs) in energy storage and temperature control systems holds significant promise for enhancing energy efficiency and environmental sustainability. Herein, the development of a boron nitride nano-aerogel-supported PCM, reinforced by carboxymethyl chitosan (CMCS) and oxidized corn starch (DCS) cross-linking was reported. This approach involved the utilization of polydopamine-modified boron nitride nanosheets (PDA@BNNS) for the fabrication of a thermally conductive skeleton, while carboxymethyl chitosan (CMCS) and oxidized corn starch (DCS) were utilized as reinforcing material and cross-linking agent, respectively. The skeleton was prepared via a sol-gel method and freeze-drying technique, while polyethylene glycol (PEG) was subsequently encapsulated using a vacuum impregnation method. Notably, the prepared skeleton exhibits a compressive stress of 12.18 MPa at 80 % strain, much higher than other boron nitride nanosheets aerogels reported in literature. The cross-linked boron nitride skeleton enhances the thermal conductivity of the composites by 137.4 % compared to pure PEG. Furthermore, due to the highly porous structure of the skeleton, the PEG mass fraction reaches 96.0 wt%, resulting in a phase change enthalpy of 181.5 J/g. The results suggest that this composite material is a promising PCM candidate for thermal energy storage applications.

Data-driven Koopman model predictive control for hybrid energy storage system of electric vehicles under vehicle-following scenarios

In the current studies on energy management strategy (EMS) for vehicle-following scenarios, the accuracy of vehicle state predictions based on mechanistic models is influenced by the time-varying conditions, affecting the optimization control performance. To address this issue, a data-driven Koopman model predictive control for hybrid energy storage system (HESS) of electric vehicles (EVs) in vehicle-following scenarios is proposed, combining the safety speed planning and energy management strategy. Firstly, a data-driven Koopman vehicle state prediction model is constructed in the upper layer for estimating parameters, such as road surface smoothness and slope. This model is then integrated into Model Predictive Control (MPC) to optimize the speed of the following vehicle. Subsequently, in the lower layer, utilizing the output from the upper layer and predicting load power, the load power is further allocated within the HESS. Simulation results demonstrate that in scenarios considering factors like slope, the hierarchical MPC with the Koopman model reduces energy consumption by 5.55% compared to the hierarchical MPC with mechanistic model.

LFC performance advancement of two-area RES penetrated multi-source power system utilizing CES and a new CFOTID controller

This study addresses the challenges of load fluctuations and the assimilation of renewable energy sources (RES) causing undesirable frequency deviations. To avoid widespread deviations in frequency, an intelligent, competent and robust load frequency control (LFC) scheme and energy storage system are obligatory for disturbance rejection in RES penetrated realistic interconnected multi-source power system (IMSPS). Hence, a new cascade fractional order tilt integral derivative (CFOTID) controller is suggested with capacitive energy storage (CES) for a pragmatic RES based thermal-hydro-gas IMSPS having governor dead-band and generation rate constraint nonlinearities. The salp swarm algorithm (SSA) is utilized successfully for fine-tuning of the controller parameters. Scrutiny of the outcome results reveals the sovereignty of the CFOTID controller over several existing well proven controllers and SSA-based FOTID. It is observed that power generation due to CES further advance the system results in terms of minimized overshoot, undershoot, and settling time. Lastly, a sensitivity analysis is conducted to confirm the robustness of the controller.

Call for Papers: Special Section on Advanced Modeling, Control, Applications and Safety of Energy Storage Systems

Call for Papers: Special Section on Advanced Modeling, Control, Applications and Safety of Energy Storage Systems

A comprehensive investigation of DG integration with DC microgrid

This comprehensive review critically analyses the complex correlation between DC microgrids and the incorporation of Distributed Generation (DG). It offers a full evaluation of fundamental principles, advanced control strategies, technology advancements, and practical implementations in real-world scenarios. This research examines the ability of Direct Current (DC) microgrids to effectively integrate and optimize various DG sources within the context of a paradigm shift towards decentralized energy solutions. The paper critically examines the obstacles and opportunities associated with renewable energy systems and different DG technologies. It provides essential insights for researchers, engineers, and politicians who are involved in the complex process of integrating distributed energy into existing systems. This review serves as a crucial resource that contributes to the understanding of DG in DC microgrids. It plays a significant role in developing conversations surrounding energy solutions that are both resilient and sustainable. This paper not only discusses the difficulties related to the integration of DG, but also emphasizes practical examples that demonstrate the flexibility and effectiveness of DC microgrid designs. This study serves as a crucial resource for stakeholders who aim to use the potential of DC microgrids, since it encompasses a comprehensive analysis of power electronics, energy storage, and sophisticated control systems. By combining critical evaluation with practical implementation, this review offers valuable insights and guidance in the field. The complete investigation plays a significant role in advancing the integration of DG, which is essential for creating a resilient and sustainable energy environment. It provides basic information that is needed for achieving effective integration. This review paper provides a comprehensive examination of the integration of DG within DC microgrids. From laying the groundwork with fundamental principles to scrutinizing challenges, technological advancements, and practical applications, this review aims to offer a holistic understanding of the dynamic landscape where DC microgrids intersect with distributed energy generation.

A deep reinforcement learning approach based energy management strategy for home energy system considering the time-of-use price and real-time control of energy storage system

To enhance the flexibility of the home load optimization dispatching strategy and ensure the safe operation of the energy storage system, an optimization dispatching strategy for home energy management system (HEMS) based on time-of-use pricing and real-time energy storage system control is proposed. Firstly, a HEMS dispatching model is established with the constraints of dispatchable load and energy storage system working status, aiming to minimize the total cost for home users. This dispatching strategy controls the energy storage system charging and discharging behavior based on time-of-use pricing and real-time battery state of charge, which helps to reduce the power cost for home users and ensure the safe operation of the battery. Then, the optimal dispatching problem of HEMS is modeled as a Markov decision process (MDP) and solved by a deep reinforcement learning algorithm called soft actor-critic (SAC). The example results verify the effectiveness and superiority of the proposed method compared with other benchmark methods, which the system cost can be reduced by 15.87% at least.