| Autor: |
Jens Schoene, Moein Lak, Minqi Zhong, Gary Sun, Noah Badayos, Brenden Russell, Alaa M. Zewila, Josh Bui, Muhammad Humayun, Armando Salazar, Christopher R. Clarke |
| Rok vydání: |
2020 |
| Předmět: |
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| Zdroj: |
2020 SoutheastCon. |
| DOI: |
10.1109/southeastcon44009.2020.9249706 |
| Popis: |
Southern California Edison (SCE) is in the process of implementing Smart Grid applications as part of an ongoing grid modernization effort to improve reliability and asset utilization, avoid DER-caused problems, and timely demand response to market and/or other signals. How many sensors are needed, where to deploy them and what data do they need to provide are questions that have a considerable impact on the effectiveness of these applications and significant economic consequences for SCE and other utilities. This paper presents a stochastic methodology that allows utilities to quantify the accuracy of DSSE results for simulated sensor placement and operational forecasting scenarios. We applied this methodology to six real-world distribution circuits located in SCE's service territory to inform the deployment of sensors and operational forecasting that yield sufficiently accurate DSSE results with respect to achieving optimal and violation-free execution of the Volt-Var Optimization (VVO) Smart Grid application. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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