Flywheel Energy Storage Research Articles

Optimizing superconducting magnetic bearings of HTS flywheel systems based on 3D H-[formula omitted] formulation

The superconducting flywheel system exploiting the magnetic coupling between the bulk high temperature superconductors (HTSs) and permanent magnets (PMs) exhibits excellent performance of self-stable levitation, and is promising for power applications. In this paper, we use the H-ϕ formulation combined with moving mesh to establish a full 3D model for the thrust type and journal type bearings in the HTS flywheel system. We then proposed different Halbach schemes to enhance the magnetic flux density of the rotor and thus the coupling, and investigated the levitation force, relaxation characteristics, electromagnetic transient distribution, and temperature characteristics of the bearings. Results show that, under axial zero field-cooled (ZFC) condition, the optimized PM rotor scheme can significantly improve the maximum levitation force and stiffness by 4.0 and 2.3 times respectively for the thrust type bearing, and the maximum levitation force of journal type bearing can be improved by a factor of 5.5. Under the radial ZFC condition, the maximum levitation force and stiffness of the journal type bearing have been increased by 4.9 times and 2.9 times. For the relaxation of both bearings during operation, the optimized PM rotors lead to relatively greater attenuation of levitation force. The proximity of the optimized PM rotors intensifies the magnetic flux movement of the HTS bulks but only brings about a limited temperature rise, and the superconductors still maintain a good low-temperature working environment. This study provides an effective methodology for analyzing the HTS bearing systems and good references for the optimal design of compact HTS flywheel energy storage systems (FESSs).

Efficiency Analysis of Regenerative Brake System Using Flywheel Energy Storage Technology in Electric Vehicles

Efficiency Analysis of Regenerative Brake System Using Flywheel Energy Storage Technology in Electric Vehicles

Minimum loss optimization of flywheel energy storage systems via distributed adaptive dynamic programming

AbstractIn this article, a distributed controller based on adaptive dynamic programming is proposed to solve the minimum loss problem of flywheel energy storage systems (FESS). We first formulate a performance function aiming to reduce total losses of FESS in power distribution applications. Then we use the Hamilton–Jacobi–Bellman (HJB) equation to solve this optimal control problem. The solution of the HJB equation is approximated by neural networks. To achieve distributed control, we estimate the global variables in the HJB equation by using the dynamic average consensus algorithm. A barrier Lyapunov function and a saturation function are introduced to handle the issue of state and input constraints, respectively. Then the stability of the system is proved through the Lyapunov stability analysis. Finally the effectiveness of the proposed strategy is verified by simulations. Simulation results show that FESS can track the power command while minimizing total power losses by interacting with neighbors. The proposed algorithm leads to a loss reduction of compared to the equal power distribution strategy.

Strategy of Flywheel–Battery Hybrid Energy Storage Based on Optimized Variational Mode Decomposition for Wind Power Suppression

The fluctuation and intermittency of wind power generation seriously affect the stability and security of power grids. Aiming at smoothing wind power fluctuations, this paper proposes a flywheel–battery hybrid energy storage system (HESS) based on optimal variational mode decomposition (VMD). Firstly, the grid-connected power and charging–discharging power of the HESS are determined based on the sliding average algorithm. Secondly, the VMD algorithm, optimized using long short-term memory (LSTM), is used to decompose the hybrid energy storage power (HESP) into a series of sub-modes with frequencies from low to high. Then, the state of charge of the battery energy storage system and the speed of the flywheel energy storage system are monitored in real time, and the primary power of the HESS is modified according to the rules formulated by fuzzy control. Finally, through a simulation example, it is concluded that the method meets the requirements of smoothing wind power fluctuations and gives full play to the characteristics of energy storage battery and flywheel energy storage to ensure the stable operation of the energy storage system. The method presented in this paper can provide a reference for HESP configuration and control operation strategy formulation.

Research on Energy Storage Flywheel Motor Drive Control Technology

Currently there is a way to use for energy storage: batteries for chemical energy storage, super for electric field energy storage, and flywheel batteries for mechanical energy storage. Flywheel energy storage has the advantages of high energy storage density, high efficiency, short charging time, no pollution, wide applicability, no noise, long service life, easy maintenance and continuous operation. Recently the most promising and competitive new energy storage technology - flywheel energy storage technology, relative to other forms of energy storage, by domestic and foreign experts unanimously optimistic. This paper will focus on the composition and operation principle of flywheel energy storage system, the classification of drive control strategy, charging control strategy, discharge control strategy and other aspects.

Review of Mechanical, Electrochemical, Electrical, and Hybrid Energy Storage Systems Used for Electric Vehicles

The population rate in the world is increasing rapidly. Depending on the population, the need for transportation increases at the same rate. Traditional vehicles, which provide great convenience in transportation, bring with them some disadvantages. For example, the fossil fuel used in conventional vehicles creates greenhouse gases such as CO2 and N2O. This has a negative impact on global warming. To eliminate these negativities, interest in electric vehicle (EV) and hybrid electric vehicles (HEV) technology studies has increased recently. Some problems have arisen with these technological studies. The range problem in vehicles is the biggest of these problems. Therefore, various solutions are sought for energy storage problems in vehicles. In this article, studies on HEV and energy storage in EVs are examined. According to the data obtained because of this examination, the performance analysis of the Energy Storage Systems (ESS) was made. The performances of the electrochemical batteries used in HEVs and EVs were compared. In addition to these, flywheel energy storage system was also investigated in HEVs and EVs to recover the energy lost because of braking.

Joint scheduling method of peak shaving and frequency regulation using hybrid energy storage considering degeneration characteristic

AbstractIn this paper, a joint scheduling method of peak shaving and frequency regulation using hybrid energy storage system considering degeneration characteristic is proposed. Firstly, incorporating degradation costs of the hybrid energy storage system with respect to the depth of discharge and cycle lifetime, long‐term costs of battery energy storage and flywheel energy storage are modelled and transformed to short‐term costs related to real‐time operation. Then, a joint scheduling model is proposed for hybrid energy storage system to perform peak shaving and frequency regulation services to coordinate and optimize the output strategies of battery energy storage and flywheel energy storage, and minimize the total operation cost of microgrid. In addition, three optimal dispatching strategies for hybrid energy storage system to participate in the auxiliary service of power grid are proposed based on the established auxiliary service model. Simulation studies demonstrate that the proposed joint scheduling method can optimize the power of different energy storage, alleviate the degradation process of battery energy storage. Compared with the scenario without scheduling hybrid energy storage system, scheduling hybrid energy storage system can reduce the total operation cost, and the electricity cost of microgrid is reduced by 13.13%.

Research on intelligent control system of permanent magnet motor for high-speed flywheel energy storage system based on deep learning

With the continuous development of society, more and more people pay attention to energy issues, and the realization of energy storage has become a hot research direction today. Despite advancements, the control system of the high-speed flywheel energy storage system’s permanent magnet motor still encounters issues in effectively regulating the magnetic suspension bearing and motor speed. In addressing this issue, a technical solution involves the implementation of an intelligent control system for the high-speed flywheel energy storage system’s permanent magnet motor, utilizing deep learning principles. This innovative approach employs deep neural networks to model, optimize, and regulate the flywheel energy storage system. The essence of flywheel energy storage lies in the conversion of electrical energy into mechanical energy, followed by its reconversion into electrical energy during output. It has the advantages of high energy density, high power density, long cycle life, fast charging and discharging, maintenance-free and environmental protection. A permanent magnet motor is a motor that uses permanent magnets to generate a magnetic field. It has the characteristics of high efficiency, high power density, and low rotor loss. It remains the most widely utilized motor in flywheel energy storage systems. An intelligent control system is characterized by its use of artificial intelligence technology to adapt, self-learn, and self-organize complex systems. This system is distinguished by its robust nonlinear processing capabilities and resilience to faults. The high-speed flywheel energy storage system permanent magnet motor intelligent control system based on deep learning can improve the performance, efficiency and reliability of the flywheel energy storage system, reduce costs and risks, and is suitable for electric vehicles, rail transit, power grid frequency regulation and other fields. In this paper, the convolutional neural network and PSO algorithm are used to obtain the PSNN neural network structure to predict the speed of the motor, so as to achieve its control. And the reliability of the structure is verified by experiments.

AI-Based Control of Storage Capacity in High-Power-Density Energy Storage Systems, Used in Electric Vehicles

Exempting batteries from supplying power transients in Electric Vehicles (EVs) is beneficial to extend their useful lifespan. The adaptive capacity of High Power-density Energy Storage Systems (HPESSs) like ultracapacitors or high-speed Flywheel Energy Storage Systems (FESSs) could fulfill the targets in this context. This paper proposes a sizing/control methodology and real-time AI-based control of the storage capacity for the adaptive capacity HPESSs, used in EVs. The sizing approach consists of an optimal energy management strategy and a sizing algorithm applied to a Variable-Step HPESS (VS-HPESS). This methodology derives the battery/VS-HPESS power-split and sizes of the storage capacities. In addition, a Nonlinear Autoregressive Neural Network with eXogenous inputs (NARX-NN) is trained offline to switch the desirable capacity of the VS-HPESS in real-time operation. Finally, an experiment is designed to evaluate the proposed real-time control scheme, in which the EV power transients are emulated and applied to a dual-capacitance ultracapacitor as the VS-HPESS. The results confirm the capability of the proposed approach to meet the considered targets.

Dual-Mode Coordinated Control of Magnetic Suspension Flywheel Battery Based on Vehicle Driving Conditions Characteristics

Dual-Mode Coordinated Control of Magnetic Suspension Flywheel Battery Based on Vehicle Driving Conditions Characteristics

Dynamic Modeling of the Support System for Vehicle-Mounted Spherical Magnetic Suspension Flywheel Battery

Dynamic Modeling of the Support System for Vehicle-Mounted Spherical Magnetic Suspension Flywheel Battery

Research on Control Strategy of High-Speed Grid-Connected FESS (Flywheel Energy Storage System) Based on Dual-PWM Converter

Research on Control Strategy of High-Speed Grid-Connected FESS (Flywheel Energy Storage System) Based on Dual-PWM Converter

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.

Hybrid energy storage configuration method for wind power microgrid based on EMD decomposition and two-stage robust approach

Data centers are usually characterized by high energy loads, which raises increasing sustainability concerns in both academic and daily usage. To mitigate the uncertainty and high volatility of distributed wind energy generation, this paper proposes a hybrid energy storage allocation strategy by means of the Empirical Mode Decomposition (EMD) technique and the two-stage robust method. First, this paper conducts the evolution analyses for the over- and under-evaluated uncertainty of wind power fluctuation under different time scales. Second, we employ the EMD technique to configure a high-frequency flywheel energy storage device, realizing the wind power transformation from large fluctuations to small fluctuations and the convergence of the wind power fluctuation curves in minute- and hour levels. Finally, based on the hour-level wind energy stable power curves, we carry out two-stage robust planning for the equipment capacity of low-frequency cold storage tanks and lithium bromide chillers. The case study on a data center microgrid in northeastern China confirms the effectiveness of our proposed strategy.

Study of Geometries and Stability for Energy Density in Electromechanical Battery Flywheels with a Gaussian Shape

A study on flywheels materials and geometries is presented here with the use of finite element modeling (FEM) simulations. The study analyzes the stress behavior of flywheel rotors subject to the rotational speed that creates the maximum stress that the flywheel can support, and then calculates the rotational energy mass ratio for each geometry. The obtained geometry was Gaussian section for the Flywheel rotor. The material used in this case study is carbon fiber Hexcel UHM 12000. The best results were a rotational speed of ≈ 279.000 rpm and rotational energy density of ≈ 440 Wh/kg. Then, to increase the total energy, Flywheel with 2 and 3 rotors were analyzed. Then the stability under rotation of these Flywheels were analyzed, some instability was found and a solution was presented.

Optimum Electrical Machine Selection for Spacecraft Electro-Mechanical Battery

—Electro-Mechanical Batteries have important advantages comparing chemical batteries and using them is popularized specially in Low Earth Orbit satellites recently. Different type of Permanent Magnet machines are used in these systems as Motor/Generator. Simplified parametric design method is given for synchronous permanent magnet and brushless DC in this paper and finite element based simulations are used to confirm design presses. Finally proper machine is selected based on important parameters in spacecraft.

Cogging Torque Reduction in PM machines Used in Electro-Mechanical Battery

Electro-Mechanical Batteries have important advantages comparing chemical batteries and using them is popularized specially in Low Earth Orbit satellites recently. External rotor Permanent Magnet machines are used in these systems as Motor/Generator. The main problem of these machines is their cogging torque originated from high strength PM's. Different methods for cogging torque reduction are available, anyway not all of them are applicable in spacecraft EMB’s machines. In this paper different method of cogging torque will be investigated and proper manners will be extracting. Finite Element Method (FEM) simulations are used for this aim.

Retracted: A Research on the Control System of High-Speed Homopolar Motor with Solid Rotor Based on Flywheel Energy Storage

Retracted: A Research on the Control System of High-Speed Homopolar Motor with Solid Rotor Based on Flywheel Energy Storage

Simplified Design of PM Machine for Spacecraft Electro-Mechanical Batteries

Electromechanical Batteries have important advantages comparing to chemical batteries and using them is popularized specially in Low Earth Orbit satellites recently. External rotor Permanent Magnet machines are used in these systems as Motor/Generator. Simplified parametric design method is given for PM machine design in this paper. Finite element based simulations are used to confirm design presses and show less than 7% error in parametric design that is acceptable for simple primary design.

Using a flywheel associated to PV power plant in order to increase the integration of PV into island electrical grid

In a context where renewable energies from both wind and solar energy have low predictability, low controllability and strong variability, their massive integration into power systems may cause instabilities for these grids. The use of storage energy system is a promising solution. In the present article, we propose to focus on a detailed photovoltaic model in order to test flywheel energy storage system (FESS) models. FESS is used to smooth photovoltaic energy for power quality. A 17-kW photovoltaic grid connected power plant is modelled with Matlab/Simulink/SimPowerSystems.