Version 1
: Received: 15 June 2018 / Approved: 15 June 2018 / Online: 15 June 2018 (13:01:42 CEST)
How to cite:
Acquah, M.; Yu, B.; Han, S. Online Demand Side Management with PEVs Using Stochastic Optimization. Preprints2018, 2018060254. https://doi.org/10.20944/preprints201806.0254.v1
Acquah, M.; Yu, B.; Han, S. Online Demand Side Management with PEVs Using Stochastic Optimization. Preprints 2018, 2018060254. https://doi.org/10.20944/preprints201806.0254.v1
Acquah, M.; Yu, B.; Han, S. Online Demand Side Management with PEVs Using Stochastic Optimization. Preprints2018, 2018060254. https://doi.org/10.20944/preprints201806.0254.v1
APA Style
Acquah, M., Yu, B., & Han, S. (2018). Online Demand Side Management with PEVs Using Stochastic Optimization. Preprints. https://doi.org/10.20944/preprints201806.0254.v1
Chicago/Turabian Style
Acquah, M., Byeonggu Yu and Sekyung Han. 2018 "Online Demand Side Management with PEVs Using Stochastic Optimization" Preprints. https://doi.org/10.20944/preprints201806.0254.v1
Abstract
This study purposes the use of plug-in electric vehicles for demand side management (DSM) considering uncertainties in demand as well as uncertainties due to mobility of PEV to mitigate peak demand. The solution also seeks to reduce electric cost in addition to reducing the effects of greenhouse gases. In recent years DSM using distributed storage system such as battery energy management system (BESS) and plugged-in electric vehicles (PEV) have become very prevalent with most implementations resorting to deterministic load forecast. These methods do not consider the potential growth in demand making their solutions less robust. In this study we propose a real-time density demand forecast and stochastic optimization for robust operation of PEV for a building. This method accounts for demand uncertainties in addition to uncertainties in mobile energy storage as found in PEV, making the resulting solution robust as compared to the deterministic case. A case study on a real site in South Korea is used for verification and testing. The proposed study is verified and tested against existing algorithms. The result verifies the effectiveness of the proposed approach
Keywords
demand-side management; peak demand control; dynamic-interval density forecast; stochastic optimization; dimension reduction; battery energy-storage system (BESS), plugged-in electric vehicles (PEV); vehicle-to-grid (V2G) ; building energy-management systems (BEMS)
Subject
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.