Showing 7 results for Energy Storage
A. Hajizadeh,
Volume 9, Issue 1 (3-2013)
Abstract
This paper presents modeling and control of a hybrid distributed energy sources including photovoltaic (PV), fuel cell (FC) and battery energy storage (BES) in a microgrid which provides both real and reactive power to support an unbalanced utility grid. The overall configuration of the microgrid including dynamic models for the PV, FC, BES and its power electronic interfacing are briefly described. Then controller design methodologies for the power conditioning units to control the power flow from the hybrid power plant to the unbalanced utility grid are presented. In order to distribute the power between power sources, the neuro-fuzzy power controller has been developed. Simulation results are presented to demonstrate the effectiveness and capability of proposed control strategy.
A. A. Khodadoost Arani, B. Zaker, G. B. Gharehpetian,
Volume 13, Issue 1 (3-2017)
Abstract
The Micro-Grid (MG) stability is a significant issue that must be maintained in all operational modes. Usually, two control strategies can be applied to MG; V/f control and PQ control strategies. MGs with V/f control strategy should have some Distributed Generators (DGs) which have fast responses versus load changes. The Flywheel Energy Storage System (FESS) has this characteristic. The FESS, which converts the mechanical energy to electrical form, can generate electrical power or absorb the additional power in power systems or MGs. In this paper, the FESS structure modeled in detail and two control strategies (V/f and PQ control) are applied for this application. In addition, in order to improve the MG frequency and voltage stability, two complementary controllers are proposed for the V/f control strategy; conventional PI and Fuzzy Controllers. A typical low voltage network, including FESS is simulated for four distinct scenarios in the MATLAB/ Simulink environment. It is shown that fuzzy controller has better performance than conventional PI controller in islanded microgrid.
P. Asgharian, R. Noroozian,
Volume 15, Issue 1 (3-2019)
Abstract
Microturbine generation system is one of the most promising and a fast growing distributed generation sources. It is used in various applications thanks to high efficiency, quick start and high reliability. Combination of the microturbine and storage system (e.g. battery bank) is desirable selection to satisfy the load requirements under all conditions and hence the battery bank can play an important role in restoring balance between source and demand. In this paper, modeling of the microturbine with battery energy storage system is presented to supply sensitive loads. Appropriate power exchange between battery and the microturbine is an essential issue so, a new control method is proposed for battery energy storage based on instantaneous value of DC-link voltage. In this new strategy, DC-link voltage as well as battery parameters (current and voltage) are used in order to produce desirable DC-DC switching. A control scheme based on voltage, current and frequency measurement is presented for the corresponding inverter. Simulations are carried out in MATLAB/Simulink software and the results show that storage along with proper control improves system reliability to supply sensitive load. The proposed configuration can be used as a remote power, emergency power and also in micro-grid.
S. M. Hoseini, N. Vasegh, A. Zangeneh,
Volume 16, Issue 2 (6-2020)
Abstract
In this paper, a robust local controller has been designed to balance the power for distributed energy resources (DERs) in an islanded microgrid. Three different DER types are considered in this study; photovoltaic systems, battery energy storage systems, and synchronous generators. Since DER dynamics are nonlinear and uncertain, which may destabilize the power system or decrease the performance, distributed robust nonlinear controllers are designed for the DERs. They are based on the Lyapunov stabilization theory and super-twisting integral sliding mode control which guarantees system stability and optimality simultaneously. The reference signals for each DER are generated by a supervisory controller as a power management system. The controllers proposed in this work are robust, have fast response times, and most importantly, the control signals satisfy physical system constraints. The designed controller stability and effectiveness are also verified using numerical simulations.
M. Habibzadeh, S. M. Mirimani,
Volume 17, Issue 4 (12-2021)
Abstract
The role of energy management in hybrid and electric vehicles (EVs) is an important concern to enhance operational performance and provide the defined efficiency targets in transportation. The power conversion stage as an interface between storage units and the DC-link of the three-phase inverter forms a major challenge in EVs. In this study, a control approach for DC-bus voltage, which utilizes a hybrid energy storage system (HESS) for EV applications, has been proposed. A high-energy-density battery pack and an ultra-capacitor, which owns a high-power density, form the hybrid energy storage system. The proposed approach allows full utilization of the stored energy in the storage devices, and also adds a voltage boost feature to the DC-bus. In the proposed control structure, a motor drive based on SVM-DTC is used to track the flux and torque components using regulators with the space vector modulation. The optimal DC-bus voltage can be tracked by incorporating the motor drive stage with a HESS. This integration results in less processed power. This article presents the simulation results toward confirming and verifying the effectiveness of the proposed approach.
Hamid Salarvand, Meysam Doostizadeh, Farhad Namdari,
Volume 18, Issue 4 (12-2022)
Abstract
Owing to the portability and flexibility of mobile energy storage systems (MESSs), they seem to be a promising solution to improve the resilience of the distribution system (DS). So, this paper presents a rolling optimization mechanism for dispatching MESSs and other resources in microgrids in case of a natural disaster occurrence. The proposed mechanism aims to minimize the total system cost based on the updated information of the status of the DS and transportation network (TN). In addition, the characteristics of the protection system in DS (i.e., relays with fixed protection settings), the constraints related to the protection coordination are examined under pre- and post-event conditions. The coordinated scheduling at each time step is formulated as a two-stage stochastic mixed-integer linear program (MILP) with temporal-spatial and operation constraints. The proposed model is carried out on the Sioux Falls TN and the IEEE 33-bus test system. The results demonstrate the effectiveness of MESS mobility in enhancing DS resilience due to the coordination of mobile and stationary resources.
Nasreddine Attou, Sid-Ahmed Zidi, Samir Hadjeri, Mohamed Khatir,
Volume 19, Issue 3 (9-2023)
Abstract
Demand-side management has become a viable solution to meet the needs of the power system and consumers in the past decades due to the problems of power imbalance and peak demand on the grid. This study focused on an improved decision tree-based algorithm to cover off-peak hours and reduce or shift peak load in a grid-connected microgrid using a battery energy storage system (BESS), and a demand response scheme. The main objective is to provide an efficient and optimal management strategy to mitigate peak demand, reduce the electricity price, and replace expensive reserve generation units. The developed algorithm is evaluated with two scenarios to see the behavior of the management system throughout the day, taking into account the different types of days (weekends and working days), the random profile of the users' demand, and the variation of the energy price (EP) on the grid. The simulation results allowed us to reduce the daily consumption by about 30% to 40% and to fill up to 12% to 15% of the off-peak hours with maximum use of renewable energies, demonstrating the control system's performance in smoothing the load curve.
