Assessing the financial value of real-time energy trading services for privately owned non-commercial electric vehicles

Optimization-based methods are of interest for developing energy management strategies due to their high performance for hybrid electric vehicles. However, these methods are often complicated and may require strong computational efforts, which can prevent them from real-world applications. This paper proposes a novel real-time optimization-based torque distribution strategy for a parallel hybrid truck. The strategy aims to minimize the engine fuel consumption while ensuring battery charge-sustaining by using linear quadratic regulation in a closed-loop control scheme. Furthermore, by reformulating the problem, the obtained strategy does not require the information of the engine efficiency map like the previous works in literature. The obtained strategy is simple, straightforward, and therefore easy to be implemented in real-time platforms. The proposed method is evaluated via simulation by comparison to dynamic programming as a benchmark. Furthermore, the real-time ability of the proposed strategy is experimentally validated by using power hardware-in-the-loop simulation.

Download Full-text

Influence of Features on Accuracy of Anomaly Detection for an Energy Trading System

Sensors ◽

10.3390/s21124237 ◽

2021 ◽

Vol 21 (12) ◽

pp. 4237

Author(s):

Hoon Ko ◽

Kwangcheol Rim ◽

Isabel Praça

Keyword(s):

Anomaly Detection ◽

Real Time ◽

Feature Detection ◽

Trading System ◽

Judgment Accuracy ◽

Energy Trading ◽

Real Time Processing ◽

Detection Model ◽

Time Required ◽

The Relationship

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).

Download Full-text

A Novel Power Market Mechanism Based on Blockchain for Electric Vehicle Charging Stations

Electronics ◽

10.3390/electronics10030307 ◽

2021 ◽

Vol 10 (3) ◽

pp. 307

Author(s):

Zhaoxiong Huang ◽

Zhenhao Li ◽

Chun Sing Lai ◽

Zhuoli Zhao ◽

Xiaomei Wu ◽

...

Keyword(s):

Social Welfare ◽

Real Time ◽

Electric Vehicle ◽

Peak Load ◽

Market Mechanism ◽

Double Auction ◽

Energy Trading ◽

Charging System ◽

Electric Vehicle Charging ◽

System Operator

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.

Download Full-text