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1. Introduction As a kind of clean renewable energy, photovoltaic power generation has been more and more widely used in the world [1, 2].Photovoltaic power generation system uses the principle of solar energy conversion into electricity, with environmental
Model predictive control for power management in a plug-in hybrid electric vehicle with a hybrid energy storage system Appl Energy, 185 ( 2017 ), pp. 1654 - 1662, 10.1016/j.apenergy.2015.12.035 View PDF View article View in
A FESS is an electromechanical system that stores energy in form of kinetic energy. A mass rotates on two magnetic bearings in order to decrease friction at high speed, coupled with an electric machine. The entire structure is placed in a vacuum to reduce wind shear [118], [97], [47], [119], [234].
A more accurate hourly prediction of day-ahead wind power can effectively reduce the uncertainty of wind power integration and improve the competitiveness of wind power in power auction markets. However, due to the inherent stochastic and intermittent nature of wind energy, it is very difficult to sharply improve the multi-step wind power forecasting
In the preceding optimization-based strategies, DP is used as a benchmark for comparison with the newly developed energy management strategies [1], [8], [25]. However, DP cannot be implemented in
The optimization control reaches multi-objective optimization including the stability of autonomous area''s exchange power and optimized power allocation of hybrid energy
Energy supply is the energy consumption of energy storage system during driving process and energy storage is the energy recovery during regenerative braking process. Thus, the total power consumption of the system is taken as one of the cost functions to ensure the energy utilization efficiency of the energy storage system in the
The lithium battery acts as an energy storage device, supplying additional power when necessary or recuperating braking energy. The PEMFC-lithium battery hybrid power system has multiple advantages, such as improved fuel utilization efficiency, reduced operating costs, and decreased emissions impact on the environment.
Forecasting solar power production accurately is critical for effectively planning and managing renewable energy systems. This paper introduces and investigates novel hybrid deep learning models for solar power forecasting using time series data. The research analyzes the efficacy of various models for capturing the complex patterns
This paper presents a model predictive control (MPC) approach for energy management of a hybrid energy storage system (HESS), in an electric vehicle (EV). HESS constitutes the battery and the supercapacitor (SC) where the latter is
From the literature it has been identified that, the hybrid deep learning method (CNN-BiLSTM) has not yet been used for prediction of hybrid power generation. There has been no standardized assessment and validation of several deep learning techniques (CNN, LSTM, Bi-LSTM, CNN-Bi-LSTM) in the wind and solar energy forecasting domain.
Papers [7, 8] presented the design approaches of hybrid electrical energy storage (HEES), where the power processed by HEES was separated into the low-frequency and the high-frequency parts. In the proposed methods, the low-frequency part was levelled by energy storage batteries while the high-frequency part was
This study proposed an integrated optimal power management approach for a PHEV launched with a hybrid energy storage system. In the proposed approach, the output power between the APU and HESS is determined according to the rule-based power management strategy, and the output power of the battery pack is determined according
Some studies have proposed the adaptive ECMS (AECMS) to improve the charge maintenance ability of energy storage equipment [20, 21]. Zeng et al. [22] proposed an optimization-oriented AECMS based on the demand power prediction of the multi-step
The use of multiple energy sources as power supply of an electric vehicle allows to improve its performance by increasing its autonomy and extending life cycle of on-board battery pack, which is the most expensive element of this type of automobile. In this work, it is proposed the use of computational intelligence techniques in the management of a
In wind farms, hybrid energy storage (HES) can effectively mitigate the fluctuation and intermittency of wind power output and effectively compensate for the prediction errors of wind power. However, the high cost of HES has prevented its large-scale adoption. Inspired by the sharing economy, this paper introduces the concept of
Fig. 1 shows the power system structure established in this paper. In this system, the load power P L is mainly provided by the output power of the traditional power plant P T and the output power of the wind farm P
This paper analyzes a hybrid power system containing a fuel cell (FC) and proposes an improved scheme involving the replacement of a single energy storage system with a hybrid energy storage
In this paper, we present the results of using four different regression mo dels for solar power pre diction: linear regression, logistic regression, Lasso regression, and elastic regression. Our
Model predictive control of a hybrid energy storage system using load prediction Abstract: Hybrid Energy Storage Systems (HESS) enable the use of the advantages
a Battery/Supercapacitor Hybrid Energy Storage System in Autonomous DC Microgrid," IEEE International Symposium on Industrial Electr on- ics, vol. 2018-June, no. 7, pp. 19–24, 2018.
Lithium batteries are widely used in energy storage power systems such as hydraulic, thermal, wind and solar power stations, as well as power tools, military equipment, aerospace and other fields. The traditional fusion prediction algorithm for the cycle life of energy storage in lithium batteries combines the correlation vector machine,
Firstly, a discrete-time prediction model of the hybrid energy storage system is established; secondly, a sequential structure model is used for hierarchical elimination of weight coefficients to select the best switching state while eliminating the weight factor of
Conclusion and future work. In this paper, the STAM-LSTM hybrid model, random forest algorithm and wavelet denoising are applied to wind power prediction. A STAM-LSTM model for wind power prediction with feature selection is proposed. Wavelet denoising eliminates high-frequency noise and burr points.
As the hydrogen energy gradually receives more attention, this paper constructs the structure of a hybrid hydrogen energy storage system shared by an IES alliance in a dynamic pricing mode. A bi-level optimization model for the shared hybrid hydrogen energy storage system (SHHESS) is proposed to optimize the capacity
Abstract: In this paper, a real-time energy management strategy is proposed for a plug-in hybrid electric vehicle with the hybrid energy storage system including a Ni-Co-Mn Li-ion battery pack and a Lithium-Titanium-Oxide battery pack.
This study proposes a novel control strategy for a hybrid energy storage system (HESS), as a part of the grid-independent hybrid renewable energy system (HRES) which comprises diverse renewable energy resources and HESS – combination of battery energy storage system (BESS) and supercapacitor energy storage system (SCESS).
Better prediction models for the upcoming supply of renewable energy are important to decrease the need of controlling energy provided by conventional power plants. Especially for successful power grid integration of the highly volatile wind power production, a reliable forecast is crucial.
Deployed in an off-grid hybrid energy system, the hybrid GES/BAT system operates in charge/discharge modes depending on the power excess/shortage in the power plant.
To satisfy the high-rate power demand fluctuations in the complicated driving cycle, electric vehicle (EV) energy storage systems should have both high power density and high energy density. In order to obtain better energy and power performances, a combination of battery and supercapacitor are utilized in this work to form a semi-active
With the increasing influence of new energy power system, the prediction of Photovoltaic (PV) output power becomes more and more important In this paper, it is the first time to put forward a hybrid modeling method combining long–short term memory recurrent neural network (LSTM) and stochastic differential equation (SDE).
However, hybrid energy storage systems have more complicated topologies and energy management strategies, and a more reliable and robust RDT prediction is required. Aiming at this requirement, this paper developed a framework of fractional-order model-based RDT prognostic for the battery and ultra-capacitor hybrid
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