Control Of Storage System Research Articles

A Coordinated Control Strategy of Multi-Type Flexible Resources and Under-Frequency Load Shedding for Active Power Balance

With the increasing expansion of power systems, there is a growing trend towards active distribution networks for decentralized power generation and energy management. However, the instability of distributed renewable energy introduces complexity to power system operation. The active symmetry and balance of power systems are becoming increasingly important. This paper focuses on the characteristics of distributed resources and under-frequency load shedding, and a coordinated operation and control strategy based on the rapid adjustment of energy storage power is proposed. The characteristics of various controllable resources are analyzed to explore the rapid response capabilities of energy storage. The energy storage types are categorized based on the support time, and the final decision is achieved with power allocation and adjustment control of the energy storage system. Additionally, a comprehensive control strategy for under-frequency load shedding and hierarchical systems is provided for scenarios with insufficient active support. The feasibility of the proposed model and methods is verified via a multi-energy system case.

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.

Demand flexibility and cost-saving potentials via smart building energy management: Opportunities in residential space heating across the US

Leveraging demand-side flexibility resources (e.g., buildings) is a crucial and cost-effective strategy for addressing the operational and infrastructure-related challenges in power grids pursuing deep decarbonization with high renewable energy penetration. However, the demand flexibility opportunities and financial benefits for residential space heating, which are sizeable demand-side flexibility resources, through emerging building energy management solutions (i.e., smart control and phased change material (PCM) thermal storage) across the US are not fully understood. In this paper, we systematically assess the demand flexibility and cost-saving/revenue potentials in residential space heating through detailed building-level simulations for five consecutive years at a 5-min temporal resolution in 20 metro areas across the high-heating-demand regions of the US. The results show a high degree of synergy between PCM thermal storage and smart control, which enables substantial demand flexibility potential, reaching 98.5% of peak load shifting, and electricity cost-saving/revenue potential, reaching 338.3% of electricity cost reductions, for residential space heating in the US. By achieving such performance, adopting smart control and PCM thermal storage is financially viable in 50% of the tested metro areas. The results reveal that the demand flexibility and cost-saving/revenue potentials of residential space heating in the US are further enhanced by higher volatilities in electricity prices. Active PCM thermal storage has lower energy efficiency but much higher energy flexibility than passive PCM thermal storage. Based on the findings, recommendations for integrating PCM thermal storage and smart control systems within residential space heating are provided.

Low-carbon environmental control system for storage of earthen relics in exhibition hall: From the perspectives of nature based solution

For the purpose of effectively providing protection and utilization of historical earthen sites, the fully-enclosed exhibition halls with electricity driven air-conditioning have been successfully adopted to provide a suitable preservation air environment. However, its electricity consumption and carbon emissions are considerable due to the continuous operation of environmental control, the energy consumption of air cooling in exhibition halls can reach more than 10 times that of office buildings. To further reduce energy consumption, this current study drew on the concept of NBS (Nature Based Solution) and proposed a low-carbon environmental control system for an enclosed exhibition hall for storage of earthen relics by applying natural energy (solar photovoltaic and earth-air-cooling) and space optimization. Firstly, a numerical model was established to simulate the preservation environment in exhibition halls and the electricity consumption of each energy subsystem. Then the model was applied to optimize the design parameters (e.g. earth-air tunnel and airflow pattern) for the environmental control system. Numerical results showed that the electricity consumption was reduced by 58% by applying earth-air-cooling and solar photovoltaic. After optimization of the exhibition hall height, electricity consumption was approximately reduced by 65%. This research is of great value to mitigate energy consumption problems encountered in earthen relics museums and other similar environments.

Stochastic Control of Battery Energy Storage System with Hybrid Dynamics

This paper addresses the control of load demand and power in a battery energy storage system (BESS) with Boolean-type constraints. It employs model predictive control (MPC) tailored for such systems. However, conventional MPC encounters computational challenges in practical applications, including battery storage control, and requires dedicated, mostly licensed solvers. To mitigate this, a solver-free yet efficient, suboptimal method is proposed. This approach involves generating randomized control sequences and assessing their feasibility to ensure adherence to constraints. The sequence with the best performance index is then selected, prioritizing feasibility and safety over optimality. Although this chosen sequence may not match the exact MPC solution in terms of optimality, it guarantees safe operation. The optimal control problem for the BESS is outlined, encompassing constraints on the state of charge, power limits, and charge/discharge modes. Three distinct scenarios evaluate the proposed method. The first prioritizes minimizing computational time, yielding a feasible solution significantly faster than the optimal approach. The second scenario strikes a balance between computational efficiency and suboptimality. The third scenario aims to minimize suboptimality while accepting longer computation times. This method’s adaptability to the user’s requirements in various scenarios and solver-free evaluation underscores its potential advantages in environments marked by stringent computational demands, a characteristic often found in BESS control applications.

Bridging the Cybersecurity Gap: A Comprehensive Analysis of Threats to Power Systems, Water Storage, and Gas Network Industrial Control and Automation Systems

This research addresses the dearth of real-world data required for effective neural network model building, delving into the crucial field of industrial control and automation system (ICS) cybersecurity. Cyberattacks against ICS are first identified and then generated in an effort to raise awareness of vulnerabilities and improve security. This research aims to fill a need in the existing literature by examining the effectiveness of a novel approach to ICS cybersecurity that draws on data from real industrial settings. Real-world data from a variety of commercial sectors is used in this study to produce a complete dataset. These sectors include power systems, freshwater tanks, and gas pipelines, which together provide a wide range of commercial scenarios where anomaly detection and attack classification approaches are critical. The generated data are shown to considerably improve the models’ precision. An amazing 71% accuracy rate is achieved in power system models, and incorporating generated data reliably increases network speed. Using generated data, the machine learning system achieves an impressive 99% accuracy in a number of trials. In addition, the system shows about 90% accuracy in most studies when applied to the setting of gas pipelines. In conclusion, this article stresses the need to improve cybersecurity in vital industrial sectors by addressing the dearth of real-world ICS data. To better understand and defend against cyberattacks on industrial machinery and automation systems, it demonstrates how generative data can improve the precision and dependability of neural network models.

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

An Internal Voltage Robust Control of Battery Energy Storage System for Suppressing Wideband Harmonics in VF Control-Based Islanded Microgrids

An Internal Voltage Robust Control of Battery Energy Storage System for Suppressing Wideband Harmonics in VF Control-Based Islanded Microgrids

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.

Hybrid energy storage system control strategy to smooth power fluctuations in microgrids containing photovoltaics

Abstract The use of a hybrid energy storage system (HESS) consisting of lithium-ion batteries and supercapacitors (SCs) to smooth the power imbalance between the photovoltaics and the load is a widespread solution, and a reasonable probabilistic allocation of the batteries and SCs affects the performance of the HESS. This paper focuses on developing a simple and effective power allocation strategy for the HESS. The moving average filtering (MAF) is considered to effectively decompose the low-frequency components as well as the high-frequency components in the signal and is simple to implement, and we spoil the low-frequency components decomposed by MAF several times to obtain more excellent low-frequency components; moreover, during HESS charging, when the state of charge (SOC) of the SC is high, we reduce the SC’s charging power by decreasing the number of times of MAF usage while ensuring the performance. During the HESS discharging, when the SOC of the SC is low, this strategy is also used to reduce the SC’s discharging power to ensure the SC’s safe operation. The proposed approach is fully validated, and it has excellent performance.

Control of Battery Energy Storage System for Wind Turbine based on DFIG during Symmetrical Grid Fault

This paper presents a specific control strategy of Doubly Fed Induction Generator (DFIG) conceived for a wind turbine system operating under electrical grid disturbance. Two mains aspects related to DFIG behavior are studied: fluctuation of its output power and Low-Voltage Ride Trough capability (LVRT) of wind turbine generator system. In order to smooth the output power injected in the electrical grid and to improve dynamic behavior of a variable speed wind turbine generator system under symmetrical voltage dip, a control strategy of the Battery Energy Storage System (BESS) is proposed. Simulations have been carried out in Matlab environment, and the results validate the effectiveness of our control.

Analysis of thermal storage behavior of composite phase change materials embedded with gradient-designed TPMS thermal conductivity enhancers: A numerical and experimental study

Phase change materials (PCMs) exhibit considerable potential for utilization in energy storage and temperature regulation applications, primarily attributed to their notable latent heat capacity. Nevertheless, the intrinsically limited thermal conductivity of PCMs necessitates the use of thermal conductivity enhancers (TCEs) that possess adjustable features to compensate for greater heat storage efficiency. Porous structures with flexible design freedom have garnered growing interest in contrast to traditional random foams, owing to their significantly larger surface areas and fully interconnected pore networks. This study fabricated three polar form-designed triply periodic minimal surfaces (TPMS) porous structures using selective laser melting (SLM) based on various radial density gradients, namely the uniform density, the linear gradient, and the Boltzmann gradient. The TPMS porous structures were incorporated as TCEs within a paraffin matrix to form composite TPMS-PCMs. The melting behavior of composite TPMS-PCMs during the charging process was investigated by employing both visual experiments and numerical methods. A thorough analysis was undertaken regarding the progression of solid-liquid phase interfaces, temperature distribution, convection distribution, and heat storage rate to elucidate the mechanisms that contribute to enhanced heat transfer. The findings indicate that the configuration of the density gradient has a notable impact on the melting behavior of composite TPMS-PCMs by tuning heat transfer paths. The heat storage rate of the linear gradient case is the highest among the three, reaching 12.1 W, 1.6 times that of the uniform density case, and twice that of the Boltzmann gradient case. Although the Boltzmann gradient case has the lowest heat storage rate, it demonstrates exceptional performance in terms of temperature uniformity. The average temperature gradient along the radius during melting is 348.6 °C/m, which is only 59% observed in the linear gradient case and 56% in the uniform density case. These findings substantiate the effectiveness of the radial gradient density design in the control of the thermal storage process of composite TPMS-PCMs. They serve as a reference for optimizing thermal storage and temperature control systems that rely on latent heat storage in the future.

Internet of things temperature control of indirect dual tank heat storage system in solar photo-thermal power plant

In order to study the temperature control of the IoT for indirect dual tank heat storage systems in solar thermal power plants, the author proposes a refined design method for heat storage systems. Through CFD software FLUENT analysis, the author proposes a temperature control scheme for storage tanks in solar thermal power plants and applies this method to the temperature control and salt injection processes of actual commercial power plants. The refined design of a systematic heat storage system mainly involves precise calculation of molten salt content and refined analysis of overall and local stresses in the storage tank. For molten salt storage tanks with a diameter of about 30 m, every 1 cm of liquid level design error will result in economic losses of tens of thousands of Yuan. In addition, the design process of storage tanks should fully consider temperature control, salt injection, and some special operating conditions during operation. By numerically simulating the flow process of flue gas and molten salt in the storage tank and the real-time wall temperature of the storage tank, a temperature control scheme that does not exceed the maximum allowable wall temperature difference of the storage tank is obtained to reduce thermal stress during the temperature control process of the storage tank, reduce the risk and failure rate of the storage tank. The temperature control scheme has achieved good results in practical projects.

Research on glass intelligent storage control system

Research on glass intelligent storage control system

Suppressing solar PV output fluctuations by designing an efficient hybrid energy storage system controller

The stochastic nature of solar photovoltaics (PV), marked by high-frequency voltage fluctuations due to dynamic climatic conditions such as cloud cover and temperature variations, presents a significant challenge to power quality stability, especially in microgrids. This variability poses a threat to the stability of power electronic devices responsible for power control and monitoring, potentially compromising the power grid's stability. To address this challenge, energy storage systems (ESS) are commonly employed. In this study, we develop a hybrid energy storage system (HESS) incorporating a battery, supercapacitor, and fuel cell. The primary aim is to adjust the inverter voltage for the photovoltaic system using newly developed proportional-integral (PI) and model predictive control (MPC) controllers within the HESS framework. Importantly, this controller eliminates the need for precise knowledge of system parameters and offers robustness, insensitivity to parameter changes, and resilience to time-varying external disturbances, ensuring satisfactory performance. By mitigating power fluctuations, the generated power can be seamlessly integrated into the grid, significantly reducing costs associated with device damage in the power path.Notably, we integrate the proposed photovoltaic system with an RLC series load using an IGBT inverter. To assess the performance of the HESS in the proposed photovoltaic system, four distinct scenarios are examined. These scenarios involve altering the PV system's location and testing two energy storage systems, namely the battery and fuel cell, which are separately designed components of the HESS for a 14-bus microgrid.

Hybrid energy storage system control and capacity allocation considering battery state of charge self-recovery and capacity attenuation in wind farm

Hybrid energy storage system (HESS) can cope with the complexity of wind power. But frequent charging and discharging will accelerate its life loss, and affect the long-term wind power smoothing effect and economy of HESS. Firstly, for the operational control of HESS, a bi-objective model predictive control (MPC) -weighted moving average (WMA) strategy for energy storage target power controlling is proposed, considering both energy storage state of charge (SOC) self-recovery and grid-connected power stabilization. The strategy can quickly adjust the SOC of HESS in the wind power smoothing process and reduce the battery's life loss. Then, since the energy storage capacity determines its power smoothing ability, this paper proposes a battery life model considering the effective capacity attenuation caused by calendar aging, and introduces it into the HESS cost calculation model to optimize the capacity allocation. Finally, combined with the wind power data, the simulation verifies that the proposed strategy can effectively balance the contradiction between energy storage lifetime and wind power smoothing effect. Simultaneously, the HESS optimized capacity allocation results considering battery's effective capacity attenuation can ensure the long-term wind power smoothing effect and better HESS operational states, contributing to the long-term and stable operation of wind-storage combined system.

Optimal control of a solar-driven seasonal sorption storage system through deep reinforcement learning

Deep reinforcement learning (DRL) has demonstrated its effectiveness in the control of energy systems, although it has not yet been applied to sorption thermal energy storage (TES) systems. The operation of sorption TES systems is notably more complicated compared to other TES variants. The discharge of a sorption TES occurs at a particular desorption and evaporation temperature. Achieving a continuous and efficient discharge of a sorption TES is a challenging control task if heat required at the evaporator is obtained from the sun or the environment. Its operation is especially complicated during winter, because of the limited availability of solar irradiation and low ambient temperatures. Thus, this study analyzes for first time in the literature the competitiveness of deep reinforcement learning to control a solar-driven seasonal sorption TES system and compares it against traditional optimized rule-based control strategy. The system, located in Central Europe, supplied domestic hot water and space heating to a single-family house. Two DRL models were developed and trained to operate the system under two different sets of data: 120 winter consecutive days and 60 winter non-consecutive days. The results showed that the DRL control strategy reduced the system operational costs by 28% in a 60 winter days scenario. For a 120 winter days scenario, the operational cost savings decreased to 13% because the smart control performed worst once the sorption TES was fully discharged. These results were derived from a four-year validation data set, bolstering their robustness. The study demonstrates the successful application of DRL in controlling a solar-driven seasonal sorption TES system, yielding considerable economic savings compared to an RBC strategy. Subsequent work will consist of implementing the smart control strategy at prototype level to assess its performance.

Research on control strategy of flywheel energy storage system based on active disturbance rejection control

The flywheel energy storage system (FESS) has been attracting the attention of national and international academicians gradually with its benefits such as high energy power density, high conversion productivity, and inexpensive pollution. For the mutual limitation problem of reaction speed and overshoot of the conventional PI controller, it is hard to satisfy the demand of high efficiency control. In this study, the Active Disturbance Rejection Controller (ADRC) is adopted to substitute the classical PI controller in the flywheel energy storage control system. The control system of an external loop of speed and an internal loop of current is adopted at the motor side. The standard ADRC is adopted by increasing the new nonlinear control function. The control system adopted the control strategy of the DC bus voltage external loop and the current internal loop on the grid side, as well as the second-order Linear Active Disturbance Rejection Controller (LADRC), to enhance the control ability of the DC bus voltage. In the charge/discharge control of the FESS, both the velocity external loop and current internal loop control strategies are utilized to simplify the system structure. The performance shows that the improved control system could effectively enhance the charging and discharging speed of the FESS and effectively suppress the DC bus voltage rise caused by flywheel state switching so that the system has better robustness.

Energy Balance Control of Energy Storage System Based on Improved Virtual DC Motor

Energy storage units have a big role in microgrids. To enhance the inertia of the DC microgrid while achieving energy balancing of each energy storage system, an energy balancing control of the energy storage system with virtual DC motor characteristics is proposed. By adding the VDCM technique to the traditional constant voltage control and adding the SoC information of the respective energy storage system to the virtual armature induction potential of the armature loop equation in the VDCM control, compared to existing controls, this control method can enhance DC microgrid inertia while balancing the SoC of the energy storage system, as well as charging and discharging mode switching without changing the control strategy. Finally, a multi-storage DC microgrid model is built in Matlab/Simulink, and the feasibility of the method is verified by simulation.