Abstract
This paper investigates two problems for demand response: demand allocation market and demand shedding market. By utilizing reinforcement learning, stochastic approximation and secure multi-party computation, we propose two distributed algorithms to solve the induced games respectively. The proposed algorithms are able to protect the privacy of the market participants, including the system operator and end users. The algorithm convergence is formally ensured and the algorithm performance is verified via numerical simulations.
| Original language | English (US) |
|---|---|
| Article number | 6939191 |
| Journal | IEEE Power and Energy Society General Meeting |
| Volume | 2014-October |
| Issue number | October |
| DOIs | |
| State | Published - Oct 29 2014 |
| Event | 2014 IEEE Power and Energy Society General Meeting - National Harbor, United States Duration: Jul 27 2014 → Jul 31 2014 |
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All Science Journal Classification (ASJC) codes
- Energy Engineering and Power Technology
- Nuclear Energy and Engineering
- Renewable Energy, Sustainability and the Environment
- Electrical and Electronic Engineering
Cite this
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Distributed demand response algorithms against semi-honest adversaries. / Zhu, Minghui.
In: IEEE Power and Energy Society General Meeting, Vol. 2014-October, No. October, 6939191, 29.10.2014.Research output: Contribution to journal › Conference article
TY - JOUR
T1 - Distributed demand response algorithms against semi-honest adversaries
AU - Zhu, Minghui
PY - 2014/10/29
Y1 - 2014/10/29
N2 - This paper investigates two problems for demand response: demand allocation market and demand shedding market. By utilizing reinforcement learning, stochastic approximation and secure multi-party computation, we propose two distributed algorithms to solve the induced games respectively. The proposed algorithms are able to protect the privacy of the market participants, including the system operator and end users. The algorithm convergence is formally ensured and the algorithm performance is verified via numerical simulations.
AB - This paper investigates two problems for demand response: demand allocation market and demand shedding market. By utilizing reinforcement learning, stochastic approximation and secure multi-party computation, we propose two distributed algorithms to solve the induced games respectively. The proposed algorithms are able to protect the privacy of the market participants, including the system operator and end users. The algorithm convergence is formally ensured and the algorithm performance is verified via numerical simulations.
UR - http://www.scopus.com/inward/record.url?scp=84931003407&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84931003407&partnerID=8YFLogxK
U2 - 10.1109/PESGM.2014.6939191
DO - 10.1109/PESGM.2014.6939191
M3 - Conference article
VL - 2014-October
JO - IEEE Power and Energy Society General Meeting
JF - IEEE Power and Energy Society General Meeting
SN - 1944-9925
IS - October
M1 - 6939191
ER -