Distributed demand response algorithms against semi-honest adversaries

    Research output: Contribution to journalConference article

    7 Citations (Scopus)

    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 languageEnglish (US)
    Article number6939191
    JournalIEEE Power and Energy Society General Meeting
    Volume2014-October
    Issue numberOctober
    DOIs
    StatePublished - Oct 29 2014
    Event2014 IEEE Power and Energy Society General Meeting - National Harbor, United States
    Duration: Jul 27 2014Jul 31 2014

    Fingerprint

    Reinforcement learning
    Parallel algorithms
    Mathematical operators
    Computer simulation

    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

    @article{dfca8269d5ae43c180d374ee22f79df0,
    title = "Distributed demand response algorithms against semi-honest adversaries",
    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.",
    author = "Minghui Zhu",
    year = "2014",
    month = "10",
    day = "29",
    doi = "10.1109/PESGM.2014.6939191",
    language = "English (US)",
    volume = "2014-October",
    journal = "IEEE Power and Energy Society General Meeting",
    issn = "1944-9925",
    number = "October",

    }

    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 journalConference 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 -