Resilient Distributed Real-Time Demand Response via Population Games

Srikantha, Pirathayini; Kundur, Deepa

doi:10.1109/tsg.2016.2526651

Cited by 40 publications

(24 citation statements)

References 24 publications

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“…In Ref. 113, a proficient distributed real‐time DR program is proposed to increase the resilience of the power system. Perfect cooperation among DR contributors is considered in Ref.…”

Section: Enhancing Power System Resiliencymentioning

confidence: 99%

“…Perfect cooperation among DR contributors is considered in Ref. 113 to obtain an aggregated adjustment in power consumption of consumers where specific preferences of consumers are satisfied.…”

Section: Enhancing Power System Resiliencymentioning

confidence: 99%

“…In Ref. 113, a distributed real‐time DR program based on population games is introduced to increase the resiliency of power systems. Strategy revisions of DR agents in this distributed real‐time DR program are dictated by revision protocols with specific probabilities.…”

Section: Taxonomy Of Uncertainty Characterization In Resiliencymentioning

confidence: 99%

See 2 more Smart Citations

A critical reviewon definitions, indices, and uncertainty characterizationin resiliency‐orientedoperation ofpower systems

Izadi

Hosseinian

Dehghan

et al. 2020

Int Trans Electr Energ Syst

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“…In Ref. 113, a proficient distributed real‐time DR program is proposed to increase the resilience of the power system. Perfect cooperation among DR contributors is considered in Ref.…”

Section: Enhancing Power System Resiliencymentioning

confidence: 99%

Section: Enhancing Power System Resiliencymentioning

confidence: 99%

See 1 more Smart Citation

A critical reviewon definitions, indices, and uncertainty characterizationin resiliency‐orientedoperation ofpower systems

Izadi

Hosseinian

Dehghan

et al. 2020

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

“…A load aggregator was also introduced in a day-head market for various objectives [34,35]. Game theories were applied in demand response applications [36][37][38]. A stochastic game approach was proposed to study the interactions among service providers, EV charging stations, and EV owners, and a Nash equilibrium provides a RTP scheme [39].…”

Section: Introductionmentioning

confidence: 99%

Multi-Agent Optimization for Residential Demand Response under Real-Time Pricing

et al. 2019

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Demand response (DR) programs encourage consumers to adapt the time of using electricity based on certain factors, such as cost of electricity, renewable energy availability, and ancillary request. It is one of the most economical methods to improve power system stability and energy efficiency. Residential electricity consumption occupies approximately one-third of global electricity usage and has great potential in DR applications. In this study, we propose a multi-agent optimization approach to incorporate residential DR flexibility into the power system and electricity market. The agents collectively optimize their own interests; meanwhile, the global optimal solution is achieved. The agent perceives its environment, predicts electricity consumption, and forecasts electricity price, based on which it takes intelligent actions to minimize electrical energy cost and time delay of using household appliances. The decision-making action is formulated into a convex program (CP) model. A distributed heuristic algorithm is developed to solve the proposed multi-agent optimization model. Case studies and numerical analysis show promising results with low variation of the aggregated load profile and reduction of electrical energy cost. The proposed approaches can be utilized to investigate various emerging technologies and DR strategies.

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“…Game theory, mechanism design and bi‐level optimisation can be found in the literature to deal with this issue. Game approaches were developed to predict real‐time DR in [28, 29]. A dynamic pricing mechanism was designed in a game theory application [30].…”

Section: Introductionmentioning

confidence: 99%

Layered stochastic approach for residential demand response based on real‐time pricing and incentive mechanism

Wang

Paranjape

Chen

et al. 2019

IET Generation, Transmission & Distribution

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Resilient Distributed Real-Time Demand Response via Population Games

Cited by 40 publications

References 24 publications

A critical reviewon definitions, indices, and uncertainty characterizationin resiliency‐orientedoperation ofpower systems

A critical reviewon definitions, indices, and uncertainty characterizationin resiliency‐orientedoperation ofpower systems

Multi-Agent Optimization for Residential Demand Response under Real-Time Pricing

Layered stochastic approach for residential demand response based on real‐time pricing and incentive mechanism

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