Intelligent control scheme for participation of aggregated energy storage in grid frequency regulation

Published On 2023/1/1

Battery Energy Storage Systems (BESSs) have proved to be efficient in frequency regulation by providing flexible charging/discharging powers. This paper proposes an artificial neural network (ANN)-based intelligent control scheme to provide the aggregated BESS with control signals to be efficiently involved in the frequency regulation in a power system. The ANN is proposed to provide online correction for the controller's gains embedded in the control loop of aggregated BESS, passing the control system's reliance on operating point conditions. Then, the steady state power distributions are evaluated, showing that BESSs can facilitate a fast contribution to frequency regulation and smooth removal from the regulation process. Eventually, the OPAL-RT real-time digital simulator is used to perform real-time verifications on the simulated power grid to demonstrate the proposed control scheme's effectiveness.

Published On

2023/1/1

Page

58-62

Authors

Remus Teodorescu

Remus Teodorescu

Aalborg Universitet

Position

Professor at

H-Index(all)

104

H-Index(since 2020)

72

I-10 Index(all)

0

I-10 Index(since 2020)

0

Citation(all)

0

Citation(since 2020)

0

Cited By

0

Research Interests

Power Electronics

Smart Batteries

AI

University Profile Page

Arman Oshnoei

Arman Oshnoei

Shahid Beheshti University

Position

Aalborg University

H-Index(all)

20

H-Index(since 2020)

20

I-10 Index(all)

0

I-10 Index(since 2020)

0

Citation(all)

0

Citation(since 2020)

0

Cited By

0

Research Interests

Intelligent control

Grid-Connected Converter

Battery management system

Power electronics

University Profile Page

Other Articles from authors

Arman Oshnoei

Arman Oshnoei

Shahid Beheshti University

IEEE Systems Journal

Learning-based Virtual Inertia Control of an Islanded Microgrid with High Participation of Renewable Energy Resources

Renewable energy sources (RESs) are increasingly used to meet consumer demands in microgrids (MGs). However, high RES integration introduces system frequency stability, inertia, and damping reduction challenges. Virtual inertia (VI) control has been recognized as an effective solution to improve system frequency response in such circumstances. Conventional control techniques for virtual inertia control (VIC), which rely heavily on specific operating conditions, can lead to flawed performance during contingencies due to their lack of adaptivity. To address these challenges, this paper proposes a novel attitude found on brain emotional learning (BEL) to emulate virtual inertia and damping for effective frequency control. The BEL-based controller is capable of quickly learning and handling the complexity, non-linearity, and uncertainty intrinsic to the MGs, and it operates independently of prior knowledge of the system model and parameters. This characteristic enables the controller to adapt to various operating conditions, improving its robustness. The simulation results across three disturbance scenarios show that the proposed BEL-based controller significantly improves the system’s response. The absolute maximum deviation of frequency was reduced to 0.0561 Hz in the final scenario, marking performance enhancements of 46.62% and 49.04% when compared with the artificial neural network (ANN)-based proportional-integral (PI) control and the standard proportional control, respectively. This underlines the controller’s adaptability and superior effectiveness in varying operating conditions.

Arman Oshnoei

Arman Oshnoei

Shahid Beheshti University

Hydrogen-Incorporated Sector-Coupled Smart Grids: A Systematic Review and Future Concepts

The adoption of solar systems has witnessed a remarkable growth rate in recent years, driven by increasing awareness of renewable energy and declining costs of solar technology. Solar systems offer several advantages, including abundant energy source, reduced carbon emissions, and potential cost savings. However, they also face challenges such as intermittency, limited energy storage capacity, and grid integration issues. By incorporating hydrogen in smart grids, these drawbacks can be addressed as hydrogen can serve as a means of energy storage, allowing excess solar energy to be stored as hydrogen and utilized during periods of low solar generation. Hydrogen-incorporated smart grids thus provide a complementary solution to enhance the reliability, stability, and scalability of solar systems, facilitating their integration into the broader energy landscape. Consequently, this chapter aims to provide a …

Arman Oshnoei

Arman Oshnoei

Shahid Beheshti University

Electric Power Systems Research

Discrete-Time distributed secondary control of DC microgrids with communication delays

This paper addresses the challenge of achieving accurate convergence in distributed secondary controls (DSC) under heterogeneous communication delays. Existing control schemes, based on traditional dynamic consensus protocols, often fail to attain precise convergence, leading to undesired system operating points. In response, we propose a discrete-time averaging algorithm that treats clock lags and heterogeneous communication delays as a graph connectivity problem. Under such conditions, we show that the distributed averaging algorithm can achieve convergence to the exact mean value over a directed graph that is not consistently connected. Furthermore, we employ this algorithm to develop a discrete-time DSC (DTDSC) tailored for voltage restoration and state-of-charge (SOC) balancing in energy storage systems (ESSs) within DC microgrids with enhanced precision against time delays. The …

Arman Oshnoei

Arman Oshnoei

Shahid Beheshti University

Unveiling the potential of renewable energy and battery utilization in real-world public lighting systems: A review

Lighting systems, as one of the biggest energy consumers on a global scale, are being upgraded based on innovative energy-saving (hereafter E-saving), energy-efficiency (E-efficiency), and energy-cost (E-cost) reduction schemes. According to research, among lighting systems, public lighting systems (PLSs) have significant potential for such energy projects. It can be realized through smart dimming, installing light-emitting diode (LED) luminaries, using renewable energy, etc. Accordingly, this work reviews the E-saving, E-efficiency, and E-cost reduction schemes for real-world PLSs by giving related techno-economic formulation. In this regard, smart control/dimming approaches via combined Internet of Things and wireless technologies, installing LED luminaires, optimal layout design, reactive power compensation, etc., are discussed by reporting the saving potentials, the payback period of the investment, and …

Arman Oshnoei

Arman Oshnoei

Shahid Beheshti University

Electric Power Systems Research

Adaptive Generalized Predictive Voltage Control of a Boost Converter for Peak Current Reduction in the Presence of Uncertainties

In islanded microgrids, efficiently controlling the output voltage and frequency of voltage source inverters while maintaining stability poses a significant challenge, particularly during grid fault conditions. This paper presents an online adaptive Kalman-based constrained generalized predictive voltage controller (AGPVC) that there are constraints on the inverter control signal and its changes to maintain microgrid s’ voltage and frequency are stayed within the specified limits and restore them to reference values after short circuit faults, after the system s’ dynamic changes. Notably, the proposed controller operates without requiring knowledge of the system's physical parameters, relying solely on local information to regulate the inverter output. The constrained and adaptive model estimation mechanisms increase the stability and scalability of the proposed method for implementation on the different systems. The …

Arman Oshnoei

Arman Oshnoei

Shahid Beheshti University

Grid Impedance Shaping for Grid-Forming Inverters: A Soft Actor-Critic Deep Reinforcement Learning Algorithm

This paper proposed an advanced method for adjusting grid impedance in grid-forming inverters, utilizing the Soft Actor-Critic Deep Reinforcement Learning (SAC-DRL) algorithm. The approach contains a flexible strategy for controlling virtual impedance, supported by an equivalent grid impedance estimator. This facilitates accurate modifications of virtual impedance based on the grid’s X/R ratio and the converter’s power capacity, aiming to optimize power flow and maintain grid stability. A unique feature of this methodology is the division of virtual reactance into two segments: one adhering to standard control protocols and the other designated for precision enhancement via the SAC-DRL method. This strategy introduces a layer of intelligence to the system, strengthening its resilience against fluctuations in grid impedance. Experimental validations, executed on a laboratory setup, verify the robustness of this approach, highlighting its potential to significantly improve intelligent power grid management practices.

Remus Teodorescu

Remus Teodorescu

Aalborg Universitet

IEEE Transactions on Industrial Electronics

Health Prediction for Lithium-Ion Batteries Under Unseen Working Conditions

Battery health prediction is significant while challenging for intelligent battery management. This article proposes a general framework for both short-term and long-term predictions of battery health under unseen dynamic loading and temperature conditions using domain-adaptive multitask learning (MTL) with long-term regularization. First, features extracted from partial charging curves are utilized for short-term state of health predictions. Then, the long-term degradation trajectory is directly predicted by recursively using the predicted features within the multitask framework, enhancing the model integrity and lowering the complexity. Then, domain adaptation (DA) is adopted to reduce the discrepancies between different working conditions. Additionally, a long-term regularization is introduced to address the shortcoming that arises when the model is extrapolated recursively for future health predictions. Thus, the short …

Remus Teodorescu

Remus Teodorescu

Aalborg Universitet

IEEE/ASME Transactions on Mechatronics

Online Sensorless Temperature Estimation of Lithium-Ion Batteries Through Electro-Thermal Coupling

Owing to the nonnegligible impacts of temperature on the safety, performance, and lifespan of lithium-ion batteries, it is essential to regulate battery temperature to an optimal range. Temperature monitoring plays a fundamental role in battery thermal management, yet it is still challenged by limited onboard temperature sensors, particularly in large-scale battery applications. As such, developing sensorless temperature estimation is of paramount importance to acquiring the temperature information of each cell in a battery system. This article proposes an estimation approach to obtain the cell temperature by taking advantage of the electrothermal coupling effect of batteries. An electrothermal coupled model, which captures the interactions between the electrical and the thermal dynamics, is established, parameterized, and experimentally validated. A closed-loop observer is then designed based on this coupled model …

Remus Teodorescu

Remus Teodorescu

Aalborg Universitet

Artificial Intelligence-Based State-of-Health Estimation of Lithium-Ion Batteries

The State of Health (SOH) estimation for automotive batteries is currently assessed with different techniques which may involve long testing procedure or require costly hardware to be implemented. This paper aims at contributing to this domain by exploiting the response of a lead-acid battery with respect to a short-term current profile using an Artificial Neural Network (ANN) classifier for SOH estimation. The method is applicable onboard the vehicle and no additional instrumentation is required on the retained vehicle. The design and validation of a SOH method with a short-term current profile using Artificial Intelligence (AI) in lead-acid batteries, which are commonly used in heavy-duty vehicles for cranking and cabin systems, are presented. The paper validates the considered approach with experimental data, which are representative of actual vehicle operations. In detail, the paper describes the retained …

Remus Teodorescu

Remus Teodorescu

Aalborg Universitet

arXiv preprint arXiv:2402.07777

Novel Low-Complexity Model Development for Li-ion Cells Using Online Impedance Measurement

Modeling of Li-ion cells is used in battery management systems (BMS) to determine key states such as state-of-charge (SoC), state-of-health (SoH), etc. Accurate models are also useful in developing a cell-level digital-twin that can be used for protection and diagnostics in the BMS. In this paper, a low-complexity model development is proposed based on the equivalent circuit model (ECM) of the Li-ion cells. The proposed approach uses online impedance measurement at discrete frequencies to derive the ECM that matches closely with the results from the electro-impedance spectroscopy (EIS). The proposed method is suitable to be implemented in a microcontroller with low-computational power, typically used in BMS. Practical design guidelines are proposed to ensure fast and accurate model development. Using the proposed method to enhance the functions of a typical automotive BMS is described. Experimental validation is performed using large prismatic cells and small-capacity cylindrical cells. Root-mean-square error (RMSE) of less than 3\% is observed for a wide variation of operating conditions.

Remus Teodorescu

Remus Teodorescu

Aalborg Universitet

IEEE Transactions on Industrial Informatics

A Battery Digital Twin From Laboratory Data Using Wavelet Analysis and Neural Networks

Lithium-ion (Li-ion) batteries are the preferred choice for energy storage applications. Li-ion performances degrade with time and usage, leading to a decreased total charge capacity and to an increased internal resistance. In this article, the wavelet analysis is used to filter the voltage and current signals of the battery to estimate the internal complex impedance as a function of state of charge (SoC) and state of health (SoH). The collected data are then used to synthesize a battery digital twin (BDT). This BDT outputs a realistic voltage signal as a function of SoC and SoH inputs. The BDT is based on feedforward neural networks trained to simulate the complex internal impedance and the open-circuit voltage generator. The effectiveness of the proposed method is verified on the dataset from the prognostics data repository of NASA.

Remus Teodorescu

Remus Teodorescu

Aalborg Universitet

IEEE Transactions on Industry Applications

Small-Sample-Learning-Based Lithium-Ion Batteries Health Assessment: An Optimized Ensemble Framework

Machine Learning is widely studied in battery state of health (SOH) estimation due to its advantage in establishing the non-linear mapping between measurements and SOH. However, the requirement of a big dataset and the lack of robustness limit the practical application, especially in small sample learning. To tackle these challenges, an optimal ensemble framework called BaggELM (bagging extreme learning machine) is proposed for battery SOH estimation. Specifically, the required dataset is reduced by optimizing the input voltage and the hyperparameters of the BaggELM algorithm. Moreover, a statistical post-processing method is used to aggregate multiple ELMs, and the final estimate is determined by the maximum probability density value. As a result, the effects of random parameterization of ELM and the training data size on SOH estimation are suppressed, thus improving the robustness and accuracy of …

Remus Teodorescu

Remus Teodorescu

Aalborg Universitet

Intelligent Cell Balancing Control for Lithium-Ion Battery Packs

This study introduces a balancing control strategy that employs an Artificial Neural Network (ANN) to ensure State of Charge (SOC) balance across lithium-ion (Li-ion) battery packs, consistent with the framework of smart battery packs. The model targets a battery pack consisting of cells with diverse characteristics, reflecting real-world heterogeneous conditions. A fundamental aspect of this approach is the ability to bypass individual cells optimally. This key feature stops current flow to and from the cell, allowing it to rest and cool off while avoiding charging or discharging cycles. The implementation of ANN enables adaptive and dynamic management of SOC, which is essential for optimizing performance and extending the lifespan of battery packs. The results demonstrate the effectiveness of the proposed ANN-based balancing strategy in SOC balancing, demonstrating its potential as a critical solution in enhancing battery management systems for electric vehicles.

Remus Teodorescu

Remus Teodorescu

Aalborg Universitet

Grid Impedance Shaping for Grid-Forming Inverters: A Soft Actor-Critic Deep Reinforcement Learning Algorithm

This paper proposed an advanced method for adjusting grid impedance in grid-forming inverters, utilizing the Soft Actor-Critic Deep Reinforcement Learning (SAC-DRL) algorithm. The approach contains a flexible strategy for controlling virtual impedance, supported by an equivalent grid impedance estimator. This facilitates accurate modifications of virtual impedance based on the grid’s X/R ratio and the converter’s power capacity, aiming to optimize power flow and maintain grid stability. A unique feature of this methodology is the division of virtual reactance into two segments: one adhering to standard control protocols and the other designated for precision enhancement via the SAC-DRL method. This strategy introduces a layer of intelligence to the system, strengthening its resilience against fluctuations in grid impedance. Experimental validations, executed on a laboratory setup, verify the robustness of this approach, highlighting its potential to significantly improve intelligent power grid management practices.

Remus Teodorescu

Remus Teodorescu

Aalborg Universitet

Electric vehicle battery charging strategy

As a key enabler for transportation electrification and a contributor toward the net-zero carbon future, battery plays a pivotal role in determining the energy management performance of electric vehicles. Technical challenges facing the development of advanced automotive battery charging arise from various contradictory objectives, immeasurable internal states, and hard constraints. This chapter presents a critical introduction to the state-of-the-art charging strategies for the electric vehicle battery and their key enabling technologies. Specifically, battery charging solutions for electric vehicles are first classified and discussed. Then, the battery models on which these solutions rest are stated, the related charging frameworks are summarized, and the advantages and drawbacks of the adopted technologies are discussed. Suggestions for overcoming the limitations of the discussed charging strategies are proposed …

Remus Teodorescu

Remus Teodorescu

Aalborg Universitet

Thermal state monitoring of lithium-ion batteries: Progress, challenges, and opportunities

Transportation electrification is a promising solution to meet the ever-rising energy demand and realize sustainable development. Lithium-ion batteries, being the most predominant energy storage devices, directly affect the safety, comfort, driving range, and reliability of many electric mobilities. Nevertheless, thermal-related issues of batteries such as potential thermal runaway, performance degradation at low temperatures, and accelerated aging still hinder the wider adoption of electric mobilities. To ensure safe, efficient, and reliable operations of lithium-ion batteries, monitoring their thermal states is critical to safety protection, performance optimization, as well as prognostics, and health management. Given insufficient onboard temperature sensors and their inability to measure battery internal temperature, accurate and timely temperature estimation is of particular importance to thermal state monitoring. Toward …

Remus Teodorescu

Remus Teodorescu

Aalborg Universitet

Reliability Engineering & System Safety

Predictive health assessment for lithium-ion batteries with probabilistic degradation prediction and accelerating aging detection

Predictive health assessment is of vital importance for smarter battery management to ensure optimal and safe operations and thus make the most use of battery life. This paper proposes a general framework for battery aging prognostics in order to provide the predictions of battery knee, lifetime, state of health degradation, and aging rate variations, as well as the assessment of battery health. Early information is used to predict knee slope and other life-related information via deep multi-task learning, where the convolutional-long-short-term memory-bayesian neural network is proposed. The structure is also used for online state of health and degradation rate predictions for the detection of accelerating aging. The two probabilistic predicted boundaries identify the accelerating aging regions for battery health assessment. To avoid wrong and premature alarms, the empirical model is used for data preprocessing and …

Arman Oshnoei

Arman Oshnoei

Shahid Beheshti University

Cyber-Resilient Adaptive Control of Grid-Following Inverter-Based Resources Against Measurement Manipulation

The cyber vulnerability of smart inverters is exacerbated by the widespread adoption of data transfer and communication platforms. This paper proposes a novel resilient adaptive vector current control scheme designed for threephase grid-following (GFL) inverter-based resources (IBRs) at the device level of modernized power grids. While effective upper-layer control strategies exist, malicious attackers can still exploit susceptibilities in the primary control of GFL IBRs. The control objective is to substantially mitigate the destructive impacts of sensor attacks while ensuring that the system’s outputs (or current signals) track the desired references. The proposed adaptive control scheme is structured based on a state estimator and an attack estimator, which rectifies the manipulated measurements, thereby enhancing resilient performance against timeinvariant and uniform-bounded sensor attacks. Lyapunov theory delivers a rigorous theoretical analysis and asymptotic stability. Comparative simulation results further illustrate the resilience and efficiency of the proposed adaptive control methodology.

Arman Oshnoei

Arman Oshnoei

Shahid Beheshti University

Fractional Order Systems with Application to Electrical Power Engineering

This Special Issue of Fractal and Fractional entitled" Fractional Order Systems with Application to Electrical Power Engineering" showcases the latest research in fractional-order system applications for power engineering. Highlighting fractional calculus's role in modeling, design, analysis, and control, this Special Issue focuses on power electronics, electric motor drives, power systems, and more. Fractional calculus, known for modeling complex dynamic behaviors with higher precision than traditional methods, introduces memory properties and historical dependence, enhancing design flexibility. Covering a range of topics from modeling and simulation to robust control strategies and energy efficiency, this Special Issue places a special emphasis on frequency and voltage control, along with stability in fractional-order energy systems. It addresses the integration of power converters, power quality, and reliability …

Remus Teodorescu

Remus Teodorescu

Aalborg Universitet

Fractional-order control techniques for renewable energy and energy-storage-integrated power systems: A review

The worldwide energy revolution has accelerated the utilization of demand-side manageable energy systems such as wind turbines, photovoltaic panels, electric vehicles, and energy storage systems in order to deal with the growing energy crisis and greenhouse emissions. The control system of renewable energy units and energy storage systems has a high effect on their performance and absolutely on the efficiency of the total power network. Classical controllers are based on integer-order differentiation and integration, while the fractional-order controller has tremendous potential to change the order for better modeling and controlling the system. This paper presents a comprehensive review of the energy system of renewable energy units and energy storage devices. Various papers are evaluated, and their methods and results are presented. Moreover, the mathematical fundamentals of the fractional-order method are mentioned, and the various studies are categorized based on different parameters. Various definitions for fractional-order calculus are also explained using their mathematical formula. Different studies and numerical evaluations present appropriate efficiency and accuracy of the fractional-order techniques for estimating, controlling, and improving the performance of energy systems in various operational conditions so that the average error of the fractional-order methods is considerably lower than other ones.