Energy storage and demand response are becoming an increasingly valuable solution for the enhancement of stability and reliability of the electricity grid with high penetration of distributed energy sources such as wind and solar. This paper proposes an intelligent energy management scheme to allow different distributed generation sources and energy storage technologies to actively participate in demand response via the deployed smart meter infrastructure. A Distributed Superconducting Magnetic Energy Storage (D-SMES) device is integrated into the network to deliver instantaneous and large bursts of power to support the grid under shortterm disturbances. The proposed demand response-based energy management algorithm receives, via the smart meter infrastructure, the information from the electricity network such as the energy cost, upper load adjustment limit, and the State-of-Charge (SOC) of the storage system then, under very specific conditions, a decision is formulated as a control signal for charging or discharging the storage system, aiming at minimizing the total energy while avoiding any load shedding. The effectiveness of the proposed demand response approach for controlling generation, storage and demand coordination and energy cost reduction is demonstrated on a sample power system simulated in MATLAB/SimPowerSystems toolbox and evaluated under different case study scenarios.
In recent years, wind power has become one of the most popular sources of renewable electricity generation. However, the wind is, by its nature, a highly intermittent source of energy. To capture the maximum power, wind turbines are generally equipped with a Maximum Power Point Tracking (MPPT) Controller. This paper proposes effective and robust MPPT control strategies based on Fuzzy Logic controller PI (FPI) and Fuzzy logic Fractional Order controller PI (FFOPI). Particle Swarm Optimization (PSO) is used to optimize the membership functions of FPI and FFOPI. The proposed MPPT strategies are validated on a Permanent Magnet Synchronous Generator (PMSG)-variable-speed wind energy conversion system. The overall model of the wind turbine-PMSG and control scheme is developed in MATLAB/Simulink and SimPower Systems toolbox. The results show that the MPPT based on FFOPI control optimized by PSO leads to the best transient response performance and robustness.
Introduction. The widespread use of photovoltaic systems in various applications has spotlighted the pressing requirement for reliability, efficiency and continuity of service. The main impediment to a more effective implementation has been the reliability of the power converters. Indeed, the presence of faults in power converters that can cause malfunctions in the photovoltaic system, which can reduce its performance. Novelty. This paper presents a technique for diagnosing open circuit failures in the switches (IGBTs) of power converters (DC-DC converters and three-phase inverters) in a grid-connected photovoltaic system. Purpose. To ensure supply continuity, a fault-diagnosis process is required throughout all phases of energy production, transfer, and conversion. Methods. The diagnostic approach is based on artificial neural networks and the extraction of features corresponding to the open circuit fault of the IGBT switch. This approach is based on the Clarke transformation of the three-phase currents of the inverter output as well as the calculation of the average value of these currents to determine the exact angle of the open circuit fault. Results. This method is able to effectively identify and localize single or multiple open circuit faults of the DC-DC converter IGBT switch or the three-phase inverter IGBT switches.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.