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Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
}
TY - GEN
T1 - Consensus-based Hierachical Demand Side Management in Microgrid
AU - Ma, Jie
AU - Ma, Xiandong
N1 - ©2019 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
PY - 2019/11/11
Y1 - 2019/11/11
N2 - The increasing penetration of renewable power generators has brought a great challenge to develop an appropriate energy dispatch scheme in a microgrid system. This paper presents a hierarchical energy management scheme by integrating renewable energy forecast results and distributed consensus algorithm. A multiple aggregated prediction algorithm (MAPA) is implemented based on satellite weather forecast data to obtain a short-term local solar radiance forecast curve, which outperforms the multiple linear regression model. A distributed consensus algorithm is then incorporated into the HVAC (heating ventilation air conditioning) units as the adjustable loads in order to dynamically regulate power consumption of each HVAC unit, based on solar power forecast in a day. The scheme aims to alleviate the local supply-demand power mismatch by varying demand response of the HVAC units. Two case studies are performed to demonstrate the feasibility and robustness of the algorithms.
AB - The increasing penetration of renewable power generators has brought a great challenge to develop an appropriate energy dispatch scheme in a microgrid system. This paper presents a hierarchical energy management scheme by integrating renewable energy forecast results and distributed consensus algorithm. A multiple aggregated prediction algorithm (MAPA) is implemented based on satellite weather forecast data to obtain a short-term local solar radiance forecast curve, which outperforms the multiple linear regression model. A distributed consensus algorithm is then incorporated into the HVAC (heating ventilation air conditioning) units as the adjustable loads in order to dynamically regulate power consumption of each HVAC unit, based on solar power forecast in a day. The scheme aims to alleviate the local supply-demand power mismatch by varying demand response of the HVAC units. Two case studies are performed to demonstrate the feasibility and robustness of the algorithms.
UR - http://www.cacsuk.co.uk/application/files/5315/6657/8219/Parallel_Session.pdf
U2 - 10.23919/IConAC.2019.8895118
DO - 10.23919/IConAC.2019.8895118
M3 - Conference contribution/Paper
SN - 9781728125183
BT - 2019 25th IEEE International Conference on Automation & Computing (ICAC)
PB - IEEE
ER -