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A dc hybrid power source based on the combination of ultracapacitor and lead–acid battery is considered in this paper. The various control systems for such hybrid power source reported in the technical literature thus far are rather complex. A low complexity control system for such hybrid power source is proposed in this paper. The key feature of t...
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... of each power source and to extend life expectancy as stresses due to undesired load currents drawn from one type of power source can be minimised. Batteries or fuel cells are usually used as the main power source while the ultracapacitors/supercapacitors are used as the secondary power source in order to supply/store fast, large bursts of power. Fig. 1 illustrates an example of using a battery bank and an ultracapacitor bank in a grid connected photovoltaic power system. Here, the batteries and the ultracapacitors can compensate for the stochastic nature of the photovoltaic power output and/or also support the grid ...
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... response of the proposed controller to sinusoidal, square and sawtooth waveforms emulating the total required output current is presented in Fig. 10. Since none of the constraints are reached, in this case the actual total current matches the required total ...
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... results in Fig. 11 show the operation of the proposed control system in response to a random total required output current. The ultracapacitor responds to fast current changes and the battery mainly to slow current changes. When some of the current limits are reached during the operation the actual total current does not match the total required current. ...
Citations
... As a result, an efficient control system is required for the HES to operate optimally with fluctuating load demand. Numerous control algorithms [12]- [17] have been recorded in existing literature, focusing on governing the distribution of power between the battery and the SC. This includes techniques like model predictive control [14], fuzzy logic control [12], and adaptive neuro-fuzzy control [13]. ...
... Numerous control algorithms [12]- [17] have been recorded in existing literature, focusing on governing the distribution of power between the battery and the SC. This includes techniques like model predictive control [14], fuzzy logic control [12], and adaptive neuro-fuzzy control [13]. The wavelet based frequency decoupling technique was proposed by Dusmez and Khaligh [15] for HES control in electric vehicle (EV) applications. ...
... Expression (11) represents the cumulative current requirement that the HES must fulfill to maintain a consistent DC-link voltage. The voltage control loop produces this total current Itot, which is obtained and represented in the equation below (12). ...
... In [9] and [10], MPC used in the converters model could maintain the limitation of current, voltage, and SOC. Authors in [11]- [13] have presented that the optimal power allocation can be achieved by optimizing the objective function. MPC can better reduce current fluctuation and prolong the battery life. ...
... active HESS topology is extensively reviewed and expanded by the researchers with distinct energy management algorithms [28][29][30]. In contrast, in the cascaded full-active HESS, as illustrated in Figure 4c, the battery and SC are connected in series, and the two DC-DC converters are cascaded with their corresponding ESS elements and are controlled with distinct voltage ratings. ...
... Both the ESS components operate with distinct voltages, which are isolated from the DC bus. Due to this advantage, the parallel full-active HESS topology is extensively reviewed and expanded by the researchers with distinct energy management algorithms [28][29][30]. In contrast, in the cascaded full-active HESS, as illustrated in Figure 4c, the battery and SC are connected in series, and the two DC-DC converters are cascaded with their corresponding ESS elements and are controlled with distinct voltage ratings. ...
In the case of microgrid (MG) systems, the choice of the right configuration plays a vital role to meet grid/load necessities when integrating low voltage, non-linear and highly sensitive (to environmental conditions) power sources such as solar PV modules, batteries and supercapacitors (SCs), etc. In the case of MG systems, the choice of the right configuration and the appropriate type of power converters in any application can have a significant impact on the optimum performance. Numerous architectures have been proposed for the integration of various energy sources to achieve optimum performance. A large number of research articles have been published in these areas. In this article, the detailed organization of various architectures based on the arrangement of various sources and detailed analyses is presented along with a discussion on those architectures. Moreover, the suitability of all the reviewed architectures based on driving factors such as (a) high conversion gain, (b) good power decoupling, (c) high efficiency, (d) isolation, (e) power-handling capabilities and (f) compact design is presented in the discussions section. The critical examination and comparative study presented in this work can assist both industry personnel and academicians in selecting the best architectural and power converter topologies required for optimum performance.
... These can be generally categorized into rule-based and optimization-based approaches [4]. Rule-based methods [5][6][7] are based on experience and empirical evidence, and are widely used for real-time operation because of their simplicity. However, rule-based methods are limited by the short-vision and may not accurately reflect the actual conditions of ESS in the long run. ...
This paper develops a multi-timescale coordinated operation method for microgrids based on modern deep reinforcement learning. Considering the complementary characteristics of different storage devices, the proposed approach achieves multi-timescale coordination of battery and supercapacitor by introducing a hierarchical two-stage dispatch model. The first stage makes an initial decision irrespective of the uncertainties using the hourly predicted data to minimize the operational cost. For the second stage, it aims to generate corrective actions for the first-stage decisions to compensate for real-time renewable generation fluctuations. The first stage is formulated as a non-convex deterministic optimization problem, while the second stage is modeled as a Markov decision process solved by an entropy-regularized deep reinforcement learning method, i.e., the Soft Actor-Critic. The Soft Actor-Critic method can efficiently address the exploration–exploitation dilemma and suppress variations. This improves the robustness of decisions. Simulation results demonstrate that different types of energy storage devices can be used at two stages to achieve the multi-timescale coordinated operation. This proves the effectiveness of the proposed method.
... [1]. Dual active bridge converters [17], interleaved bidirectional converters [18], and bidirectional SEPIC converters [19] are also finding application in HESSs. Non-isolated buck-boost converters are employed between the source and load to provide bidirectional operation. ...
... Bidirectional DC-DC boost converter topologies are more accurate for HESS applications [1]. Dual active bridge converters [17], interleaved bidirectional converters [18], and bidirectional SEPIC converters [19] are also finding application in HESSs. Non-isolated buck-boost converters are employed between the source and load to provide bidirectional operation. ...
Power availability from renewable energy sources (RES) is unpredictable, which should be managed effectively for better utilization. The role of a hybrid energy storage system (HESS) plays a vital role in this context. Renewable energy sources along with hybrid energy storage systems can give better power management in the DC microgrid environment. In this paper, the optimal PI control-based hybrid energy storage system for the DC microgrid is proposed for the effective utilization of renewable power. In this model, the proposed optimal PI controller is developed using the particle swarm optimization (PSO) approach. A 72 W DC microgrid system is considered to validate the effectiveness of the proposed optimal PI controller. The proposed model is implemented in the MATLAB/SIMULINK platform. To show the effectiveness of the proposed model, the results are validated with a conventional PI controller-based hybrid energy storage system.
... SC elements during the step-changed load (Hredzak et al., 2014;Mukherjee and Strickland, 2016;Abeywardana et al., 2017). ...
... HESSs of different time scales constitute a major concern nowadays as compared to the single-type ESS (Manandhar et al., 2018). HESS is widely accepted in electric vehicles as well as for grid-tied ESS (Hredzak et al., 2014;Abeywardana et al., 2017). A combination of two or more ESS can help to improve the system performance for power grid applications. ...
The present work describes a control methodology for a hybrid energy storage system (HESS) to improve its transient performance under dynamic load conditions. The proposed coordination control enhanced life cycle performance by segregating the power between battery energy storage systems (BESS) and a supercapacitor (SC). The BESS and SC are connected parallel to each other, and two individual DC–DC bidirectional converters connect them to a common DC bus. The coordination control is established between the controllers of BESS and the SC of HESS, which helps to utilise the usable energy capacity of the HESS. The charging/discharging current of the BESS is controlled within the allowable safety range based on the slope and magnitude of the BESS current. The high-frequency power component is handled by the SC, which helps to reduce the extra exhaustion on the BESS during operation with a higher current. The proposed coordination control of HESS is validated through simulation and the results show the effectiveness of the proposed controller.
... The conventional model predictive method, which is based on a discrete model of such control scheme and objective functions, is computationally expensive. In [29], a low-complexity control method for a hybrid BESS-SC dc power supply was proposed. The main advantage is that it can achieve performance equivalent to that stated in [28] with lower complexity and computational burden. ...
Energy storage devices and renewable resources especially rooftop photovoltaic (PV) are vital in operation of nanogrids. In this study, energy management strategy (EMS) for battery energy storage system(BESS), PV, super-capacitor (SC) is presented. The proposed control strategy is designed to optimize BESS flow rate, discharge and charge cycles energy system using Meta-heuristic Jaya algorithm by properly coordinating SC and PV. SC was employed in HESS to fulfill the transient energy mismatches and reduce the transitory high charge/discharge impact on BESS using EMS. The proposed controller also has the benefit of keeping the battery’s SoC within limitations for a long period. In order to extend the life of BESS, the EMS is aimed at minimizing deep charging and discharging. PV generation is reduced, particularly under light load (no-load) situations and when battery SoC ≥ SoC max . Similarly, SC charging and select able load shedding are employed when SoC ≤ SoC min to avoid deep discharge. This controller method is validated in both simulation environment and hardware-prototype under different perturbation and operating conditions.
... Here, MPC is used to produce the reference current for the power converters, and tracking of references is done by hysteresis control, as shown in Figure 10. Similarly, another efficient power management technique was proposed in [51] with less complicated control by using the MPC. Here, the MPC control helps to limit the current for both battery and supercapacitor and also allocates the high-frequency power requirements to the supercapacitor. ...
... A basic fuzzy logic-based power management scheme was proposed in Figure 10. PMS based on model predictive control [51]. ...
... PMS based on model predictive control[51]. ...
The limited availability of fossil fuel and the growing energy demand in the world creates global energy challenges. These challenges have driven the electric power system to adopt the renewable source-based power production system to get green and clean energy. However, the trend of the introduction of renewable power sources increases the uncertainty in the production, control, and operation of power systems due to the erratic nature of the environment. To overcome these meteorological conditions, some support systems, such as storage devices, are integrated with renewable energy sources (RES). A number of storage devices are hybridized to get the hybrid energy storage system (HESS) to get a potential solution for these microgrid problems. For maintaining the robustness and reliability of the power system, proper control, and management of power in the microgrid is very important. In this paper, an analytical study related to power management strategies is given along with different interconnection topologies for the HESS. Analysis and control of storage devices are necessary to avoid the premature degradation of the devices and to get their optimal utilization. Therefore, this article attempts to include different power management schemes used in AC/DC microgrids. Furthermore, various control techniques specific to different energy storage devices are reviewed extensively, which would serve as a complete guide for the design and implementation of a hybrid AC/DC microgrid.
... Técnica de Gestión MPC LINEALES [26], [33], [34], [40], [50], [51], NO LINEALES [26], [35], [36], [37], [52], [53], EMPC [33], [34], [44], [45], [46], [47], [48], [49], OTRAS [15], [16], [17], [25], [27], [29], [31], [32], [37], [39], [41], [42], [54], [55], [56], [57], Modelo del controlador Lineal [25], [26], [29], [33], [39], [40], [44], [46], [49], [50], [51], No lineal [15], [16], [26], [27], [32], [34], [35], [36], [37], [45], [47], [48], [52], [53], Otros [17], [31], [41], [42], [54], [55], [56], Función de peso utilizada Cuadrática [35], [36], [48], [50], [51], Otras [16], [17], [26], [27], [29], [33], [37], [39], [40], [47], [49], [52], [53], GESTIÓN ENERGÉTICA DEL VEHICULO Hibrido [15], [25], [26], [27], [2], [29], [30], [36], [37], [41], [42], [53], [56], Fuel Cells [31], [32], [39], [50], [52], [57], [58], Eléctricos [16], [17], [33], [39], [40], [54], [57], TIPOS DE VEHICULO Serie [15], [16], [17], [25], [26], [27], [41], [42], Paralelos [15], [17], [25], [26], [37], [39], MODO DE ALMACENAR LA ENERGÍA Baterías [15], [16], [17], [26], [28], [29], [30], [37], [39], [40], [41], [42], [49], [50], [52], [55], [56], Supercondensadores [30], [31], [32], [49], [50], [52], [55], [56], [58], Tabla 1. Estado del arte sobre control de vehículos. ...
... Técnica de Gestión MPC LINEALES [26], [33], [34], [40], [50], [51], NO LINEALES [26], [35], [36], [37], [52], [53], EMPC [33], [34], [44], [45], [46], [47], [48], [49], OTRAS [15], [16], [17], [25], [27], [29], [31], [32], [37], [39], [41], [42], [54], [55], [56], [57], Modelo del controlador Lineal [25], [26], [29], [33], [39], [40], [44], [46], [49], [50], [51], No lineal [15], [16], [26], [27], [32], [34], [35], [36], [37], [45], [47], [48], [52], [53], Otros [17], [31], [41], [42], [54], [55], [56], Función de peso utilizada Cuadrática [35], [36], [48], [50], [51], Otras [16], [17], [26], [27], [29], [33], [37], [39], [40], [47], [49], [52], [53], GESTIÓN ENERGÉTICA DEL VEHICULO Hibrido [15], [25], [26], [27], [2], [29], [30], [36], [37], [41], [42], [53], [56], Fuel Cells [31], [32], [39], [50], [52], [57], [58], Eléctricos [16], [17], [33], [39], [40], [54], [57], TIPOS DE VEHICULO Serie [15], [16], [17], [25], [26], [27], [41], [42], Paralelos [15], [17], [25], [26], [37], [39], MODO DE ALMACENAR LA ENERGÍA Baterías [15], [16], [17], [26], [28], [29], [30], [37], [39], [40], [41], [42], [49], [50], [52], [55], [56], Supercondensadores [30], [31], [32], [49], [50], [52], [55], [56], [58], Tabla 1. Estado del arte sobre control de vehículos. ...
... Técnica de Gestión MPC LINEALES [26], [33], [34], [40], [50], [51], NO LINEALES [26], [35], [36], [37], [52], [53], EMPC [33], [34], [44], [45], [46], [47], [48], [49], OTRAS [15], [16], [17], [25], [27], [29], [31], [32], [37], [39], [41], [42], [54], [55], [56], [57], Modelo del controlador Lineal [25], [26], [29], [33], [39], [40], [44], [46], [49], [50], [51], No lineal [15], [16], [26], [27], [32], [34], [35], [36], [37], [45], [47], [48], [52], [53], Otros [17], [31], [41], [42], [54], [55], [56], Función de peso utilizada Cuadrática [35], [36], [48], [50], [51], Otras [16], [17], [26], [27], [29], [33], [37], [39], [40], [47], [49], [52], [53], GESTIÓN ENERGÉTICA DEL VEHICULO Hibrido [15], [25], [26], [27], [2], [29], [30], [36], [37], [41], [42], [53], [56], Fuel Cells [31], [32], [39], [50], [52], [57], [58], Eléctricos [16], [17], [33], [39], [40], [54], [57], TIPOS DE VEHICULO Serie [15], [16], [17], [25], [26], [27], [41], [42], Paralelos [15], [17], [25], [26], [37], [39], MODO DE ALMACENAR LA ENERGÍA Baterías [15], [16], [17], [26], [28], [29], [30], [37], [39], [40], [41], [42], [49], [50], [52], [55], [56], Supercondensadores [30], [31], [32], [49], [50], [52], [55], [56], [58], Tabla 1. Estado del arte sobre control de vehículos. ...
Los sistemas de control predictivos son ampliamente usados en la industria en la actualidad. El sistema de control predictivo económico permite incluir en la función de coste los datos de cada uno de los subsistemas de control. En el presente trabajo, usando la metodología del control predictivo económico EMPC, se toman dos fuentes de almacenamiento de energía como lo son los supercapacitores y las baterías, mismas que complementan el sistema de propulsión de hidrógeno mediante un motor eléctrico. Usando el frenado regenerativo se puede absorber la energía que se convertiría en calor, y mediante el cumplimiento de la ecuación de potencia y energía se cumplen los perfiles de conducción. Se toma en cuenta varios escenarios y el perfil escogido para el vehículo de masa ligera es el NEDC (nuevo ciclo de conducción europea). En los escenarios con distintos pesos de control, se cumplen los perfiles con cada una de las fuentes y los porcentajes de absorción de energía quedan representados mediante el balance de fuerzas.
... The authors claim that employing an energy loop avoids the nonlinearities that appear when using a voltage loop controller, thus making the calculation of the HESS parameters and sizing of the SC easier. Works that make use of an SC voltage loop can be found in [17,[40][41][42][43]. ...
The Filter-Based Method (FBM) is one of the most simple and effective approaches for energy management in hybrid energy storage systems (HESS) composed of batteries and supercapacitors (SC). The FBM has evolved from its conventional form in such a manner that more flexibility and functionalities have been added. A comparative study and analysis of the most recent and relevant proposals based on the FBM for HESS are provided in this paper. In this way, the improvements for this energy management system (EMS) are in the form of adaptive filters, rules, Fuzzy logic control, sharing coefficients, and additional control loops. It is shown how these enhancements seek to avoid the premature degradation of the storage devices that are caused by deep discharge, overcharge, and fast current variations in the case of batteries and overcharge in the SC case. Therefore, the enhancements are focused on keeping the battery and SC working within safe operational limits. This paper presents new comparisons regarding the SoC evolution in the storage devices, specifically how the SC SoC is used in the EMS to establish the power sharing. Numerical simulations are added to compare the performance of the different EMS structures. The analysis of the results shows the effectiveness of the FBM in achieving power allocation and how the latest proposed improvements help to add flexibility to HESS as well as to avoid premature degradation of the storage devices.
