Control Of Energy Storage System Research Articles

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.

Closed loop model predictive control of a hybrid battery-hydrogen energy storage system using mixed-integer linear programming

The derivation of an efficient operational strategy for storing intermittent renewable energies using a hybrid battery-hydrogen energy storage system is a difficult task. One approach for deriving an efficient operational strategy is using mathematical optimization in the context of model predictive control. However, mathematical optimization derives an operational strategy based on a non-exact mathematical system representation for a specified prediction horizon to optimize a specified target. Thus, the resulting operational strategies can vary depending on the optimization settings.This work focuses on evaluating potential improvements in the operational strategy for a hybrid battery-hydrogen energy storage system using mathematical optimization. To investigate the operation, a simulation model of a hybrid energy storage system and a tailor-made mixed integer linear programming optimization model of this specific system are utilized in the context of a model predictive control framework. The resulting operational strategies for different settings of the model predictive control framework are compared to a rule-based controller to show the potential benefits of model predictive control compared to a conventional approach. Furthermore, an in-depth analysis of different factors that impact the effectiveness of the model predictive controller is done. Therefore, a sensitivity analysis of the effect of different electricity demands and resource sizes on the performance relative to a rule-based controller is conducted. The model predictive controller reduced the energy consumption by at least 3.9 % and up to 17.9% compared to a rule-based controller. Finally, Pareto fronts for multi-objective optimizations with different prediction and control horizons are derived and compared to the results of a rule-based controller. A cost reduction of up to 47 % is achieved by a model predictive controller with a prediction horizon of 7 days and perfect foresight.

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.

Research on Power Balance Control Method of Electronic Transformer

Aiming at the problem of poor output stability and power gain of the power electronic transformer of the microgrid energy storage system, a power balance control method for the power electronic transformer of the microgrid energy storage system based on parallel modular multi-level commutation control is proposed. The reactive power outer loop parameter fusion method is used to sample the power dynamic load parameters and information fusion processing for the transformers in the system. According to the static limit active power adjustment of the fuzzy parameter factors on the output voltage side of the DC port, the steady-state gain of the power electronic transformer of the microgrid energy storage system is calculated. The static current limiting adjustment method is used to construct the feedback compensation model of the power electronic transformer of the microgrid energy storage system. In the active power control loop, the optimal design of the power electronic transformer power balance control of the microgrid energy storage system is realized through transient stable power adjustment. The test results show that using this method to perform power balance control on the power electronic transformer of the microgrid energy storage system has high output stability and good active power balance, which improves the robustness and adaptability of the transformer balance control.

Simulation-Based Evaluation of Sliding Mode Control with Washout Filter for Power Balancing in Battery Energy Storage Systems

This research paper presents a simulation-based evaluation of a novel sliding mode control (SMC) approach with a washout filter for power balancing in battery energy storage systems (BESS). Unlike traditional proportional-plus integral control methods, the proposed SMC technique is specifically tailored for charging and discharging BESS units. The primary objective of this study is to investigate the efficacy of the proposed SMC-based scheme in achieving optimal power balancing at the DC bus of the BESS. A comparative analysis is conducted to assess the performance of the developed SMC strategy against the conventional proportional-plus integral controller. MATLAB simulations utilizing Simulink and the Sim Power System toolbox are employed to obtain comprehensive results. The findings of this study provide valuable insights into the effectiveness of the SMC with washout filter for enhancing power balancing in BESS applications, thereby contributing to advancements in energy storage system control methodologies. Keywords: Sliding Mode Control (SMC), Washout Filter, Power Balancing, Battery Energy Storage Systems (BESS), Simulation-based Evaluation, MATLAB Simulation

Research on two-stage optimization control method for energy storage systems based on multi service attribute utility evaluation

ABSTRACT With the high proportion of new energy access and the increasing demand for load electricity, efficient and reasonable control of battery energy storage systems (BESS) in the power grid is the key to promoting new energy consumption, improving the quality and economy of power supply in the power grid. To solve the optimization control problem of BESS under multiple power service objectives, a two-stage optimization control method for BESS based on multi-service attribute utility evaluation has been proposed in this paper. Firstly, based on various power service demands and grid operation constraints, a multi-objective optimized scheduling model for BESS has been established; Then, to solve the complex coupling problem between multiple optimization objectives of BESS, a two-stage optimization structure has been designed that the changes of various service indicators under different optimization objectives have been recognized in the first stage, and the entropy weight method was used to weight each optimization objective in the second stage objectively; Finally, in the IEEE 33-bus simulation example, compared with the suboptimal result under each single-objective method, the relative error of the power line losses, the maximum voltage deviation, the curtailment of new energy and the economics of power grid of this algorithm has been reduced at least 2.31, 1.11, 2.90, and 1.05 times, respectively. Meanwhile, the performances of active power reserve and reactive power reserve are better than most single-objective optimizations.

An adaptive virtual inertia control design for energy storage devices using interval type-2 fuzzy logic and fractional order PI controller

This research paper introduces a novel methodology, referred to as the Optimal Self- Tuning Interval Type-2 Fuzzy-Fractional Order Proportional Integral (OSTIT2F-FOPI) controller for inverter-based energy storage system (ESS) to regulate the input and output power of ESSs, aimed at enhancing the frequency control of microgrids (MGs) with varying levels of renewable energy sources (RESs) integration. The proposed method entails the integration of a self-tuner interval type-2 fuzzy logic controller (IT2FLC) and FOPI controller within a virtual inertia (VI) control loop. Within the OSTIT2F-FOPI control framework, the IT2FLC block tunes the parameters of the FOPI controller in an online method to deal with problems associated with fixed gains of the FOPI controller. Consequently, the VI control unit can emulate varying degrees of inertia and damping based on the integrated levels of RESs or load variations. The self-tuning capability of the proposed method enables real-time performance improvement of the system, resulting in enhanced frequency stabilization and improved control efficiency, particularly in low-inertia MGs. Moreover, a different control methodology, referred to as EO-FOPI-based VI, is developed to compare and assess the results achieved by the suggested OSTIT2F-FOPI control framework. This alternative method incorporates Equilibrium Optimization (EO) to adjust VI and FOPI parameters and provide a comparative analysis of the obtained outcomes.

Distributed fixed-time cooperative control for flywheel energy storage systems with state-of-energy constraints

This paper studies the cooperative control problem of flywheel energy storage matrix systems (FESMS). The aim of the cooperative control is to achieve two objectives: the output power of the flywheel energy storage systems (FESSs) should meet the reference power requirement, and the state of FESSs must meet the relative state-of-energy (SOE) variation rate constraints. When the above objectives are reached, the FESSs are able to track reference power command while ensuring that all flywheels are fully charged or discharged at the same time. To achieve these two objectives, a distributed power allocation strategy is proposed. A distributed estimator is designed to estimate the global state information required for each FESS in the allocation strategy. In addition, a fixed-time estimator based on a dynamic average consensus algorithm is designed, which is capable of quickly estimating the global average information. The convergence of the fixed-time estimator under the boundedness assumption of the time derivative of input signal is analyzed by the Lyapunov stability theory. The simulation results show that the FESSs interact with neighbors through local information such that the evolution of the SOE among the FESSs satisfies the constraints and the total power tracks the total reference power. Furthermore, compared with the existing methods, the proposed dynamic average consensus estimators achieve an accurate tracking of the global state information in a fixed time, regardless of the initial conditions.

Integrated strategy for real-time wind power fluctuation mitigation and energy storage system control

To address the impact of wind-power fluctuations on the stability of power systems, we propose a comprehensive approach that integrates multiple strategies and methods to enhance the efficiency and reliability of a system. First, we employ a strategy that restricts long- and short-term power output deviations to smoothen wind power fluctuations in real time. Second, we adopt the sliding window instantaneous complete ensemble empirical mode decomposition with adaptive noise (SW-ICEEMDAN) strategy to achieve real-time decomposition of the energy storage power, facilitating internal power distribution within the hybrid energy storage system. Finally, we introduce a rule-based multi-fuzzy control strategy for the secondary adjustment of the initial power allocation commands for different energy storage components. Through simulation validation, we demonstrate that the proposed comprehensive control strategy can smoothen wind power fluctuations in real time and decompose energy storage power. Compared with traditional empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) decomposition strategies, the configuration of the energy storage system under the SW-ICEEMDAN control strategy is more optimal. Additionally, the state-of-charge of energy storage components fluctuates within a reasonable range, enhancing the stability of the power system and ensuring the secure operation of the energy storage system.