Control Of Energy Storage System Research Articles

Review on modeling and control of megawatt liquid flow energy storage system

Flow battery has recently drawn great attention due to its unique characteristics, such as safety, long life cycle, independent energy capacity and power output. It is especially suitable for large-scale storage system and has a good application prospect. In this paper, the overall structure of the megawatt-level flow battery energy storage system is introduced, and the topology structure of the bidirectional DC converter and the energy storage converter is analyzed. Secondly, the influence of single battery on energy storage system is analyzed, and a simulation model of flow battery energy storage system suitable for large power grid simulation is summarized. The model of the whole system of flow battery is summarized from the grid-connected side and the energy storage system itself. The model of flow battery energy storage system should not only accurately reflect the operation characteristics of flow battery itself, but also meet the simulation requirements of large power grid in terms of simulation accuracy and speed. Finally, the control technology of the flow battery energy storage system is discussed and analyzed. The multi-DC/DC control strategy on the DC side and the parallel operation control strategy of the energy storage converter on the AC side are compared and analyzed. The advantages and disadvantages of each control method are analyzed accurately, which can provide reference for the modeling and control strategy of the megawatt flow battery energy storage system.

Sliding Mode Control of Energy Storage Systems for Reshaping the Accelerating Power of Synchronous Generators

The rotor angle stability has been a key concern in the stable operation of power systems, especially in the renewable energy age. However, to improve the power system rotor angle stability, none of the existing methods considered achieving it through reshaping the accelerating power of synchronous generators (SGs) and decoupling them after grid disturbances. In this paper, the sliding mode control (SMC) theory is utilized to tackle the coupling among SGs. After careful modeling and controller design, a SMC-based energy storage system (ESS) controller is proposed to temporarily decouple the interacted SGs through reshaping the accelerating power of SGs to the desired value. The proposed controller can: 1) drive the SG’s accelerating power to follow the reference in infinite time; 2) decouple the interacted SG dynamics and stabilize the disturbed SG exponentially. The proposed controller achieves the above two goals without using the communication and hard-to-estimate SG rotor angles. Case studies are carried out on the 2-Area 4-Generator System and the Australian 14-Generator Equivalent System to demonstrate the effectiveness of the proposed controller. The results also show that the proposed controller stabilizes the disturbed power systems with better performance over existing controllers.

Primary Frequency Regulation Strategy of PMSG-Based Wind Turbines Integrated Inertia Control

Direct-drive permanent magnet synchronous wind turbine-generator (PMSG) generally operates in maximum power point tracking (MPPT) mode. PMSG cannot provide additional active power support when the grid frequency fluctuates.The gradual increase of wind power penetration leads to greater frequency regulation pressure, which brings hidden dangers to the power grid. This paper focuses on the grid connection of direct-drive PMSG, proposing a primary frequency regulation strategy integrated with inertia control. The proposed strategy controls the rotor kinetic energy to simulate inertia of synchronous units by adding the support power of the response frequency variation. It concludes with variable power tracking control and energy storage system control. Compared with the traditional strategy retains active power, this proposed strategy increases wind energy utilization. Adopting variable power tracking control combined with energy storage can overcome secondary frequency drop and reduce energy storage configuration capacity.

Inertia Droop Control and Stability Mechanism Analysis of Energy Storage Systems for DC-Busbar Electric Vehicle Charging Station

Although the photovoltaic (PV) integrated dc-busbar electric vehicle charging station (EVCS) is a promising energy supply form for EVs, its inertialess and poor damping always lead to the potential system instability. In this article, inertia droop control (IDC) strategies are, thus, proposed for a bidirectional dc converter (Bi-C) to improve dynamic stability and provide a high-quality power supply for EVs. Compared with the virtual inertia and damping control (VIDC), the proposed IDC can be implemented by easily modifying conventional droop control, avoiding complex control structures. First, virtual inertia is achieved in the proposed IDC strategies in two ways: 1) dynamic virtual impedance and 2) adaptive droop algorithm. Damping support comes from the redesigned damping term featuring secondary voltage regulation to eliminate the voltage deviation. Then, a feedback analytic method is proposed to comprehend stability mechanisms from multiview, and a double-coordinate-based phasor diagram is developed as its analytical tool to observe the interaction among variables intuitively. Accordingly, IDC’s negative feedback property is revealed, and the impactors of system inertia, stiffness, and dissipation properties are explored. Dynamic performance and small-signal stability analysis are also presented to guide parameter selection and optimize the transient response. Experiment results verify that IDC has similar effects to VIDC while simplifying the control structure.

Resilience-Oriented Control for Cyber-Physical Hybrid Energy Storage Systems Using a Semiconsensus Scheme: Design and Practice

Hybrid energy storage systems (HESSs) can simultaneouslyharness the advantages of batteries and supercapacitors (SCs) in various loading situations. Coupled with communication links, cyber-physical HESSs would be threatened by unexpected cyber attacks that may cause damage to electrical devices and even collapse the entire system. To overcome the adverse impacts of attacks, a resilience control scheme is proposed in this article. The proposed scheme provides a unified form integrated with adaptive laws to guarantee multifunctional HESS operations, i.e., dc bus voltage restoration, transient current allocation between batteries and SCs, proportional battery current sharing, and battery state-of-charge balancing, under cyber attacks. The proposed resilience control allows us to establish communication links only among batteries, whereas SCs are free of the data exchange process, which can save system capital costs. The stability of the proposed control is proved by the Lyapunov stability theory. The effectiveness and feasibility of the proposed approach are validated on a hardware-in-the-loop testing platform.

Application of artificial intelligence for prediction, optimization, and control of thermal energy storage systems

Application of artificial intelligence for prediction, optimization, and control of thermal energy storage systems

Prespecified-Time Observer-Based Distributed Control of Battery Energy Storage Systems

The distributed control of battery energy storage systems (BESSs) plays an important role in the practical operation of microgrid. This brief studies the state-of-charge (SoC) balancing and the total charging/discharging power tracking issues for BESSs with multiple distributed heterogeneous battery units under the premise that only partial battery units can access the desired power. First, two distributed prespecified-time observers are proposed to estimate average battery units state and average desired power respectively. Second, a novel distributed BESSs control strategy based on prespecified-time observers is proposed, which realizes SoC balancing and power tracking of BESSs assuming only partial battery units can access the desired power. Two distinctive advantages of this brief are (i) both the average desired power and the average battery state can be accurately estimated within the prespecified time, which can be determined in advance and independent of initial states or control parameters, and (ii) the communication costs are reduced effectively and single-point failures can be avoided. Finally, two simulation examples are given to verify the effectiveness and superiority of the proposed control strategy.

Event-Triggered Active Disturbance Rejection Control for Hybrid Energy Storage System in Electric Vehicle

In this article, an event-triggered active disturbance rejection control (ET-ADRC) method is designed for the battery-supercapacitor hybrid energy storage system (HESS) in electric vehicles (EVs). The proposed method combines the advantages of the ADRC method and the ET mechanism. It inherits the fast response from the ADRC-based control module, which has an inner proportional–integral (PI) current loop and an outer ADRC voltage loop. To improve the computational efficiency, an ET control module is adopted by utilizing the system state information at the last triggering and current sampling time. When the state error exceeds the preset range, the ADRC-based control module is triggered and the control signals are updated; otherwise, the ADRC-based control module is halted with unchanged control signals. A series of simulation cases have been conducted for the verification of the response speed and computational efficiency of the proposed method. A scaled-down hardware platform is set up to carry on the experiments. The experimental results demonstrate that under the similar voltage ripples (VRs), the proposed ET-ADRC method responses faster than the conventional PI method and reduces the computational burden to almost 60% of the original ADRC method.

Optimal control of energy storage system of high-permeability distributed photovoltaic low-voltage distribution network

Abstract Photovoltaic (PV) is one of the very promising renewable energy sources, but its output power is fluctuating. To maintain PV-energy storage system-load power balance in low-voltage distribution networks, we propose a new optimized sag control strategy, which is no longer indexed by the battery voltage but by the battery state of charge (SOC) because the battery SOC can better reflect the remaining battery capacity for better performance. In addition, to further optimize its performance, we try to use the ratio of intercell SOC for optimization. We analysed the deficiencies of sag control in detail, derived a control strategy with cell SOC as an indicator and finally analysed its performance in detail. To verify the performance of the proposed strategy, based on MATLAB, we designed the power variation of 2 lithium-ion battery packs to maintain the bus power stability under two cases of sudden rise as well as sudden fall of PV power and 3 lithium-ion battery packs to maintain during the bus power stabilization with one set of performance after failure, and the simulation results prove the feasibility of the proposed strategy.

A Framework for States Co-Estimation of Hybrid Energy Storage Systems Based on Fractional-Order Theory

Accurate state estimation is crucial to the control and management of the hybrid energy storage system (HESS). Although there have been many studies on state estimation of lithium-ion batteries (LIBs) and ultracapacitors (UCs), state estimation for HESS is scarce and poorly defined. To fill this gap, this article aims to propose a co-estimation framework of the state-of-charge (SoC) and remaining discharging time (RDT) for the HESS based on fractional-order theory. First, considering the impedance characteristics of the LIB and UC, the electrochemical impedance spectroscopy (EIS) experiment is conducted to establish the fractional-order models (FOMs). The grey wolf optimizer (GWO) is used to identify the model parameters. Then the fractional extended Kalman filter (FEKF) is used to obtain the SoC for the LIB and UC. A statistical method with the forgetting factor is used for load current forecasting. Finally, the co-estimation framework of the SoC and RDT for the HESS is presented and verified with experimental data. The root-mean-square error (RMSE) of the SoC estimation for the HESS is less than 0.92% in two cases. The experimental results indicate that the proposed co-estimation method of the HESS has achieved high precision and a fast convergence rate under the typical current working condition. This work can be used for a better energy management strategy development.

The Effect of the Fast Frequency Response of the Solar Energy System on the Power System Dynamic Frequency Evolution

The existence of a large-scale photovoltaic power generation system without any rotational inertia can deteriorate the frequency stability of the power system. The solar energy storage system consisted of photovoltaic power generation and the energy storage device with fast charge and discharge can provide fast frequency responses to the grid through the power electronics controls, and therefore improve the dynamic frequency security of the low-inertia system. This paper discusses the modeling and fast frequency controls, namely virtual inertia and droop control. The modified WSCC 9-bus system with high photovoltaic power penetration and low inertia serves to show the effects of the solar energy storage system’s fast frequency responses on the frequency evolutions of the new power system. The results show that in the low inertia system with large-scale photovoltaic power generation, the active power droop control of the solar energy storage system can decrease the frequency deviations, but may introduce low-frequency oscillations during the frequency evolution following a contingency; the virtual inertia control can depress both the rate of change of frequency as well as the oscillations during the frequency evaluation.

Fuzzy adaptive virtual inertia control of energy storage systems considering SOC constraints

Energy storage systems based on virtual synchronous control provide virtual inertia to the power system to stabilize the frequency of the grid while smoothing out system power fluctuations, and the constraining effect of the energy storage state of charge (SOC) has a significant impact on regulating virtual inertia and damping. For this reason, this paper proposes a method for fuzzy adaptive virtual inertia control of energy storage systems considering SOC to avoid deep over-charging and over-discharging of energy storage units, which can effectively extend the working life of energy storage. Finally, the effectiveness of the proposed control strategy is verified by hardware-in-the-loop experiments.

Data-driven model predictive control of community batteries for voltage regulation in power grids subject to EV charging

In this paper, a Model Predictive Control (MPC) for community Battery Energy Storage Systems (BESS) is proposed to mitigate the Electric Vehicle (EV) charging demand while maintaining voltage regulation in residential power grids. With the increased penetration of renewable-based Distributed Energy Resources (DERs) and the EV uptake, distribution grids are highly subject to voltage fluctuations due to back power feed from DERs or significant EV charging demand. Conventionally, Distribution Network Operators (DNOs) have addressed this issue by adjusting the taps of transformers. However, managing the naturally unpredictable renewable generation and the power consumption behaviour of technology owners requires advanced real-time algorithms. In this paper, we apply a real-time control algorithm, based on Model Predictive Control (MPC), to a community BESS in order to reduce voltage fluctuations. The MPC algorithm performs in real-time based on readings from smart meters of households. It predicts the future values of voltages at households based on the current and past readings of smart meters, and calculates the BESS charging or discharging rate such that future voltage deviations are minimised. Simulations on the real-world power consumption data from Victorian residential consumers, in Australia, show that the proposed controller performs significantly better than the fixed charging/discharging BESS schedule in both voltage regulation and BESS degradation.

A Novelty of Several Optimization Criteria Hybrid Energy Storage System and Power Management in EV Applications

The power management capabilities of hybrid energy storage systems offer several advantages for EVs, including improved performance, longer battery life, and reduced cost. HES technologies which combine Li-ion cells with ultra-capacitors (UC), are developing as just a possible alternative to the constraints of conventional ESS for conserving electricity. Lithium-ion batteries have a limited cycle life, meaning that they gradually degrade with each charge-discharge cycle. This can reduce the overall lifetime of the ESS and require more frequent replacements, adding to the cost and environmental impact of EVs. To circumvent those constraints, mixed battery packs combining battery technology as well as superconductors are being developed. To improve the design and control of hybrid energy storage systems for electric vehicles (EVs), a bi-level multi-objective framework has been proposed. A Bi-level multi-objective design and control framework, incorporating NSGA-II and fuzzy logic control, to obtain an optimal sized hybrid energy storage system and corresponding power management strategy. This dynamic and multi-objective architecture may achieve optimum solution hybrids battery bank as well as a matching significant power scheme one at a moment through integrating non-dominated sorting genetic and fuzzy logic control methods. To evaluate the effectiveness of the proposed bi-level multi-objective framework, Pareto optimal solutions of different hybrid energy storage systems are obtained. This approach can lead to significant improvements in the performance and efficiency of hybrid energy storage systems, making them more suitable for EVs and other applications that require high-performance energy storage solutions. Index Terms—Batteries, ultra capacitors, recursive imprecise input, non – linear and non optimisation, and e – mobility

Coordinated control of wind farm and supercapacitor energy storage system for dynamic performance reinforcement of multi-area power systems

Coordinated control of wind farm and supercapacitor energy storage system for dynamic performance reinforcement of multi-area power systems

Provision of Ramp-rate Limitation as Ancillary Service from Distribution to Transmission System: Definitions and Methodologies for Control and Sizing of Central Battery Energy Storage System

Provision of Ramp-rate Limitation as Ancillary Service from Distribution to Transmission System: Definitions and Methodologies for Control and Sizing of Central Battery Energy Storage System

Coordinated control of wind farm and supercapacitor energy storage system for dynamic performance reinforcement of multi-area power systems

Coordinated control of wind farm and supercapacitor energy storage system for dynamic performance reinforcement of multi-area power systems

Control of Heterogeneous Battery Energy Storage Systems-Based Microgrid Connected via Detail-Balanced Communication Topology

This letter proposes a distributed secondary control for heterogeneous battery energy storage systems (BESSs) to achieve finite-time consensus in frequency and active power while maintaining a balanced energy-level. The proposed scheme incorporates heterogeneity in electrical as well as control aspects and models heterogeneous BESS-based islanded AC microgrid as a multi-agent system with agents interacting according to a detail-balanced topology with heterogeneous edge weights. The finite-time stability of the closed-loop system, under the proposed controllers, is rigorously proved by considering a single composite homogeneous Lyapunov function, thereby calculating an upper limit on convergence time. Efficacy of the proposed controllers is illustrated by simulating a BESS-based microgrid in a Simulink environment.

Modeling and control strategy for hydrogen production systems coupled with PV and battery storage

Environmental conditions can significantly affect the performance of photovoltaic (PV) hydrogen production systems, resulting in fluctuations in PV output and suboptimal hydrogen production. In order to solve these problems, a voltage stabilization control based approach has been implemented for a photovoltaic integrated hydrogen production system, which is based on an existing hydrogen production model coupled to PV storage. The aim of this approach is to enhance system stability, improve the quality of photovoltaic power generation, and optimize hydrogen production. The strategy includes maximum power point tracking (MPPT) control for the PV system, as well as coordinated control of the electrochemical energy storage system to ensure stable bus voltage and efficient use of solar resources. The effectiveness of this control strategy was demonstrated by simulation.

Lyapunov Optimization in Online Battery Energy Storage System Control for Commercial Buildings

The high instantaneous discharging capability of battery energy storage systems (BESSs) make them ideal candidates for reducing peak loads in commercial buildings. An efficient online BESS control algorithm can be beneficial for reducing the monthly electricity bill of individual commercial buildings. Conventional model-based BESS control algorithms rely heavily on accurate long-horizon net load forecast, which is difficult to obtain. To address this problem, we develop a Lyapunov optimization-based online BESS control algorithm and derive its theoretical performance bound. Comprehensive numerical study results using real world commercial building smart meter data in southern California show that our proposed Lyapunov optimization-based online control algorithm with time-varying weighting parameters yields higher savings in electricity cost and requires less computation time in comparison to the state-of-the-art baseline algorithms.