No abstract

No abstract

No abstract

No abstract

No abstract

In this paper, a novel Smart Energy Management System (SMES) architecture is proposed to solve the multi-objective dispatch of distributed generation problem in a Micro-Grid with different technologies and new concepts such as demand response action. The multi-objective optimization problem is formulated as a constrained mixed integer nonlinear problem in which a set of Pareto optimal solutions will be obtained through the simultaneous optimization of operating cost and the net emission inside the Micro-Grid. The findings indicate that the optimal dispatch of units together with incorporating demand side bidding actions can reduce energy prices for the consumers or increased revenues for the aggregators.

The main purpose of this research is the thermodynamic analysis of the trigeneration system based on energy efficiency, exergy efficiency, and power. The trigeneration system under study consists of three subsystems that including the solar subsystem, Kalina subsystem, and lithium bromide-water absorption chiller subsystem. The proposed system generates power, cooling and hot water by using solar energy. The system studied is designed and analyzed based on the weather condition in Zahedan, Iran. According to the exergy analysis, the most exergy destruction rate takes place in the solar cycle. The results of the base-case analysis demonstrate that energy and exergy efficiencies and total cost rates are 17.37%, 18.82% and 9.63 dollars per hour, respectively. Furthermore, comparison of optimization criteria such as energy efficiency, exergy efficiency, and power are discussed. The results show power is the best criteria for thermodynamic optimization. The results of trigeneration system optimization based on maximum power criterion show that produced power, energy efficiency, exergy efficiency and total cost rate increase 28%, 12.32%, 13.97% and 7.68%, respectively in comparison with the base-case. As a result, this research is proved that thermodynamic investigation is closer to the ideal state in power criterion.

This paper presents a machine learning method for event cause analysis to enhance situational awareness in distribution networks. The data streams are captured using time-synchronized high sampling rates synchro waveform measurement units (SWMU). The proposed method is formulated based on a machine learning method, the convolutional neural network (CNN). This method is capable of capturing the spatiotemporal feature of the measurements effectively and perform the event cause analysis. Several events are considered in this paper to encompass a range of possible events in real distribution networks, including capacitor bank switching, transformer energization, fault, and high impedance fault (HIF). The dataset for our study is generated using the real time digital simulator (RTDS) to simulate real-world events. The event cause analysis is performed using only one cycle of the voltage waveforms after the event is detected. The simulation results show the effectiveness of the proposed machine learning-based method compared to the state-ofthe-art classifiers.