Electric Vehicle Interaction at the Electrical Circuit Level
2018
- 46Usage
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Usage46
- Downloads40
- Abstract Views6
Report Description
A large growth in energy demand has increased renewable energy penetration into existing power grid infrastructures, as well as spurring increased research into demand response programs. But before implementing an efficient demand response program, it is first necessary to understand the power usage behaviors of a consumer. This paper presents a real-time data acquisition system for the collection and storage of power data that will allow the study of demand response in an urban area, which can be applied to the efficient use of electric vehicle supply equipment (EVSE). Demand response programs are an ideal alternative to costly energy storage and spinning reserves. Detailed power consumption data is necessary to study proper demand response programs and implement efficient control decisions. A pilot system has been implemented on the island of Oahu in Hawaii to prove the feasibility of a data collection system in an urban environment. The pilot program has deployed a smart metering device that is collecting power data at a high resolution and transmitting it in real-time to a server for load forecasting analysis. There were two purposes to this study. The first being to study and investigate the feasibility of implementing real-time data acquisition on a large scale as it relates to the study of present and future demand response programs. And the second being the utilization of a bottom-up approach to collect and analyze data for usage in load prediction and forecasting in combination with demand response techniques.
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