Enhanced-decision energy trading for island renewable microgrids considering large interruptible refrigeration load intra-hour switching

Author(s):  
Quan Sui ◽  
Feiyu Li ◽  
Xiangning Lin ◽  
Fanrong Wei ◽  
Chuantao Wu ◽  
...  
Keyword(s):  
2016 ◽  
Vol 11 (4) ◽  
pp. 381
Author(s):  
Madan Mohan Tripathi ◽  
Anil Kumar Pandey ◽  
Amit Verma ◽  
Krishan Gopal Upadhyay ◽  
Dinesh Chandra

Author(s):  
Dafeng Zhu ◽  
Bo Yang ◽  
Qi Liu ◽  
Kai Ma ◽  
Shanying Zhu ◽  
...  
Keyword(s):  

2015 ◽  
Vol 62 (4) ◽  
pp. 2551-2559 ◽  
Author(s):  
David Gregoratti ◽  
Javier Matamoros

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4237
Author(s):  
Hoon Ko ◽  
Kwangcheol Rim ◽  
Isabel Praça

The biggest problem with conventional anomaly signal detection using features was that it was difficult to use it in real time and it requires processing of network signals. Furthermore, analyzing network signals in real-time required vast amounts of processing for each signal, as each protocol contained various pieces of information. This paper suggests anomaly detection by analyzing the relationship among each feature to the anomaly detection model. The model analyzes the anomaly of network signals based on anomaly feature detection. The selected feature for anomaly detection does not require constant network signal updates and real-time processing of these signals. When the selected features are found in the received signal, the signal is registered as a potential anomaly signal and is then steadily monitored until it is determined as either an anomaly or normal signal. In terms of the results, it determined the anomaly with 99.7% (0.997) accuracy in f(4)(S0) and in case f(4)(REJ) received 11,233 signals with a normal or 171anomaly judgment accuracy of 98.7% (0.987).


Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 307
Author(s):  
Zhaoxiong Huang ◽  
Zhenhao Li ◽  
Chun Sing Lai ◽  
Zhuoli Zhao ◽  
Xiaomei Wu ◽  
...  

This work presents a novel blockchain-based energy trading mechanism for electric vehicles consisting of day-ahead and real-time markets. In the day-ahead market, electric vehicle users submit their bidding price to participate in the double auction mechanism. Subsequently, the smart match mechanism will be conducted by the charging system operator, to meet both personal interests and social benefits. After clearing the trading result, the charging system operator uploads the trading contract made in the day-ahead market to the blockchain. In the real-time market, the charging system operator checks the trading status and submits the updated trading results to the blockchain. This mechanism encourages participants in the double auction to pursue higher interests, in addition to rationally utilize the energy unmatched in the auction and to achieve the improvement of social welfare. Case studies are used to demonstrate the effectiveness of the proposed model. For buyers and sellers who successfully participate in the day-ahead market, the total profit increase for buyer and seller are 22.79% and 53.54%, respectively, as compared to without energy trading. With consideration of social welfare in the smart match mechanism, the peak load reduces from 182 to 146.5 kW, which is a 19.5% improvement.


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