Bargaining‐based cooperative game among multi‐aggregators with overlapping consumers in incentive‐based demand response

Zheng, Shunlin; Yi, Sun; Li, Bin; Qi, Bing; Shi, Kun; Li, Yuanfei; Du, Yi

doi:10.1049/iet-gtd.2019.1084

Cited by 11 publications

(2 citation statements)

References 29 publications

(67 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The GO anticipates total unbalanced power (surplus power or deficit power) that cannot be fed by energy supplier in an upcoming short period, and subsequently releases the information about unbalanced power to trading centre, which is responsible for broadcasting the information to all subscribed DRAs. Then, each DRA submits its bidding strategy to the market for the share of providing balancing power, the strategy including maximal balancing power and lowest acceptable compensation price for providing balancing power [31]. Based on the bidding strategies of DRAs, the trading centre releases the results of clearing, including required balancing power that a DRA has to provide during appointed time periods

false(normalΔ L_{k}^{req} false)

and corresponding remuneration

false(ℜ_{k} false)

if the required balancing power is provided accurately.…”

Section: Framework Of Ibdr With Esumentioning

confidence: 99%

Stochastic programming model for incentive‐based demand response considering complex uncertainties of consumers

Zheng

et al. 2020

IET Generation, Transmission & Distribution

Self Cite

View full text Add to dashboard Cite

false(normalΔ L_{k}^{req} false)

and corresponding remuneration

false(ℜ_{k} false)

if the required balancing power is provided accurately.…”

Section: Framework Of Ibdr With Esumentioning

confidence: 99%

Stochastic programming model for incentive‐based demand response considering complex uncertainties of consumers

Zheng

et al. 2020

IET Generation, Transmission & Distribution

Self Cite

View full text Add to dashboard Cite

“…For instance, a Stackelberg game based energy management framework was proposed in [18] for joint operation of combined heat and power and photovoltaic prosumers. The work in [19] proposed a bargaining-based cooperative game to solve the irreconcilable pricing problem of multiple aggregators with overlapping consumers. In work [20], a game theoretical-based incentivizing strategy was proposed to help a demand response aggregator determine the optimal incentive for each customer based on their identified characteristics.…”

Section: Introductionmentioning

confidence: 99%

Assessing the Feasibility of Game-Theory-Based Demand Response Management by Practical Implementation

Zhang

Jiang

et al. 2021

IEEE Access

View full text Add to dashboard Cite

Demand response (DR) has been widely recognized as an effective solution to help mitigate the stresses imposed on power grids. As new concepts evolve, DR induces various interactions among multiple emerging entities, which further complicates the decision-making processes in grid operations. Recently, game theory (GT) has received great attention in DR management, due to its ability to handle complex decision-making problems. Numerous theoretical GT-based approaches have been proposed for addressing various DR issues, but the feasibility of these theoretical approaches in practical implementation remains in doubt. To bridge the gap between theoretical studies and practical implementations, we first provide specific guidelines regarding how to construct a DR-oriented facility, and then investigate the effectiveness of deploying a Stackelberg game theory-based DR algorithm to manage the energy consumption of the facility, wherein the energy management center (EMC) serves as the leader and multiple devices act as the followers. The experimental evaluation results show that the GT-based DR algorithm achieved great performance in practical DR management, including optimal load control in responding to real-time price (RTP), and peak load reduction with a peak-to-average ratio (PAR) of 1.59.

show abstract

Dual-channel recycling e-waste pricing decision under the impact of recyclers’ loss aversion and consumers’ bargaining power

Jia

Meng

2021

Environ Dev Sustain

View full text Add to dashboard Cite

Bargaining‐based cooperative game among multi‐aggregators with overlapping consumers in incentive‐based demand response

Cited by 11 publications

References 29 publications

Stochastic programming model for incentive‐based demand response considering complex uncertainties of consumers

Stochastic programming model for incentive‐based demand response considering complex uncertainties of consumers

Assessing the Feasibility of Game-Theory-Based Demand Response Management by Practical Implementation

Dual-channel recycling e-waste pricing decision under the impact of recyclers’ loss aversion and consumers’ bargaining power

Contact Info

Product

Resources

About