Basil H. Jasim
Bio: Basil H. Jasim is an academic researcher from University of Basrah. The author has contributed to research in topics: Microgrid & Computer science. The author has an hindex of 6, co-authored 36 publications receiving 112 citations.
TL;DR: The results show the proposed secondary FTC method's effectiveness in mitigating the faults' effect in multi-agent MG.
TL;DR: A novel decentralized robust control strategy for multi-agent systems (MASs) governed MGs in future Energy Internet is proposed and verified using a hypothetical hybrid DGs connected MG such as photovoltaic or wind turbines in MATLAB Simulink environment.
Abstract: The Energy Internet paradigm is the evolution of the Internet of Things concept in the power system. Microgrids (MGs), as the essential element in an Energy Internet, are expected to be controlled in a corporative and flexible manner. This paper proposes a novel decentralized robust control strategy for multi-agent systems (MASs) governed MGs in future Energy Internet. The proposed controller is based on a consensus algorithm applied with the connected distributed generators (DGs) in the MGs in the energy internet paradigm. The proposed controller’s objectives are the frequency/voltage regulation and proportional reactive/active power-sharing for the hybrid DGs connected MGs. A proposed two-level communication system is implemented to explain the data exchange between the MG system and the cloud server. The local communication level utilizes the transmission control protocol (TCP)/ internet protocol (IP) and the message queuing telemetry transport (MQTT) is used as the protocol for the global communication level. The proposed control strategy has been verified using a hypothetical hybrid DGs connected MG such as photovoltaic or wind turbines in MATLAB Simulink environment. Several scenarios based on the system load types are implemented using residential buildings and small commercial outlets. The simulation results have verified the feasibility and effectiveness of the introduced strategy for the MGs’ various operating conditions.
TL;DR: The results prove the applicability and feasibility of the proposed method for the demand management system in SMG and achieve an improvement of 8.056, 6.629%, and 98.888% for the incremental cost, the total electricity bill, and load energy waste concerning their values without applying the energy management system, respectively.
TL;DR: The results showed that energy management and energy interchange were effective and contributed to cost reductions, CO2 mitigation, and reduction of primary energy consumption, and the newly developed energy management system proved to provide more robust and high performance control than conventional energy management systems.
Abstract: The recent few years have seen renewable energy becoming immensely popular. Renewable energy generation capacity has risen in both standalone and grid-connected systems. The chief reason is the ability to produce clean energy, which is both environmentally friendly and cost effective. This paper presents a new control algorithm along with a flexible energy management system to minimize the cost of operating a hybrid microgrid. The microgrid comprises fuel cells, photovoltaic cells, super capacitors, and other energy storage systems. There are three stages in the control system: an energy management system, supervisory control, and local control. The energy management system allows the control system to create an optimal day-ahead power flow schedule between the hybrid microgrid components, loads, batteries, and the electrical grid by using inputs from economic analysis. The discrepancy between the scheduled power and the real power delivered by the hybrid microgrid is adjusted for by the supervisory control stage. Additionally, this paper provides a design for the local control system to manage local power, DC voltage, and current in the hybrid microgrid. The operation strategy of energy storage systems is proposed to solve the power changes from photovoltaics and houses load fluctuations locally, instead of reflecting those disturbances to the utility grid. Furthermore, the energy storage systems energy management scheme will help to achieve the peak reduction of the houses’ daily electrical load demand. Also, the control of the studied hybrid microgrid is designed as a method to improve hybrid microgrid resilience and incorporate renewable power generation and storage into the grid. The simulation results verified the effectiveness and feasibility of the introduced strategy and the capability of proposed controller for a hybrid microgrid operating in different modes. The results showed that (1) energy management and energy interchange were effective and contributed to cost reductions, CO2 mitigation, and reduction of primary energy consumption, and (2) the newly developed energy management system proved to provide more robust and high performance control than conventional energy management systems. Also, the results demonstrate the effectiveness of the proposed robust model for microgrid energy management.
TL;DR: In this paper, the grey wolf optimizer (GWO), artificial bee colony (ABC) optimization algorithm-based Time of Use (ToU) pricing model is proposed to define the rates for shoulder-peak and on-peak hours.
Abstract: In residential energy management (REM), Time of Use (ToU) of devices scheduling based on user-defined preferences is an essential task performed by the home energy management controller. This paper devised a robust REM technique capable of monitoring and controlling residential loads within a smart home. In this paper, a new distributed multi-agent framework based on the cloud layer computing architecture is developed for real-time microgrid economic dispatch and monitoring. In this paper the grey wolf optimizer (GWO), artificial bee colony (ABC) optimization algorithm-based Time of Use (ToU) pricing model is proposed to define the rates for shoulder-peak and on-peak hours. The results illustrate the effectiveness of the proposed the grey wolf optimizer (GWO), artificial bee colony (ABC) optimization algorithm based ToU pricing scheme. A Raspberry Pi3 based model of a well-known test grid topology is modified to support real-time communication with open-source IoE platform Node-Red used for cloud computing. Two levels communication system connects microgrid system, implemented in Raspberry Pi3, to cloud server. The local communication level utilizes IP/TCP and MQTT is used as a protocol for global communication level. The results demonstrate and validate the effectiveness of the proposed technique, as well as the capability to track the changes of load with the interactions in real-time and the fast convergence rate.
01 Jan 2016
TL;DR: The chaos making a new science is universally compatible with any devices to read, and it is available in the digital library an online access to it is set as public so you can download it instantly.
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TL;DR: In this article , a new framework for the scheduling of microgrids and distribution feeder reconfiguration is presented, taking into account the uncertainties due to the load demand, market price, and renewable power generation.
TL;DR: An overview of MGs technology advancement in recent decades, taking into consideration distributed energy generation (DER), energy storage systems (ESS), EVs, and loads is given.
Abstract: The topic of microgrids (MGs) is a fast-growing and very promising field of research in terms of energy production quality, pollution reduction and sustainable development. Moreover, MGs are, above all, designed to considerably improve the autonomy, sustainability, and reliability of future electrical distribution grid. At the same time, aspects of MGs energy management, taking into consideration distribution generation systems, energy storage devices, electric vehicles, and consumption components have been widely investigated. Besides, grid architectures including DC, AC, or hybrid power generation systems, energy dispatching problems modelling, operating modes (islanded or grid connected), MGs sizing, simulations and problems solving optimization approaches, and other aspects, have been raised as topics of great interest for both electrical and computer sciences research communities. Furthermore, the United Nations Framework Convention on Climate Change and government policies and incentives have paved the way to massive electric vehicle (EV) deployment. Hence, several research studies have been conducted to investigate the integration of EVs in national power grid and future MGs. Specifically, EV charging stations’ bi-directional power flow control and energy management have been considerably explored. These issues index challenging research topics, which are in most cases still under progress. This paper gives an overview of MGs technology advancement in recent decades, taking into consideration distributed energy generation (DER), energy storage systems (ESS), EVs, and loads. It reviews the main MGs architecture, operating modes, sizing and energy management systems (EMS) and EVs integration.
01 Jan 2021
TL;DR: A novel license plate detection and character recognition algorithm based on a combined feature extraction model and BPNN (Backpropagation Neural Network) which is adaptable in weak illumination and complicated backgrounds is proposed.
Abstract: The rapid development of the license plate recognition technology has made great progress for its widespread uses in intelligent transportation system (ITS). This paper has proposed a novel license plate detection and character recognition algorithm based on a combined feature extraction model and BPNN (Backpropagation Neural Network) which is adaptable in weak illumination and complicated backgrounds. Firstly, a preprocessing is first used to strengthen the contrast ratio of original car image. Secondly, the candidate regions of license plate are checked to verify the true plate, and the license plate image is located accurately by the integral projection method. Finally, a new feature extraction model is designed using three sets of features combination, training the feature vectors through BPNN to complete accurate recognition of the license plate characters. The experimental results with different license plate demonstrate effectiveness and efficiency of the proposed algorithm under various complex backgrounds. Compared with three traditional methods, the recognition accuracy of proposed algorithm has increased to 97.7% and the consuming time has decreased to 46.1ms.