Year-end Sale % OFF 
Abstract
Demand response (DR) aggregator controlling and aggregating flexible resource of residential users to participate in DR market will contribute the performance of DR project. However, DR aggregator has to face the risk that users may break the contract signed with aggregator and refuse to be controlled by aggregator due to the uncertainty factors of electricity consumption. Therefore, in this paper, community operator (i.e., DR aggregator) is proposed to equip auxiliary equipment, such as energy storage and gas boiler, to compensate for power shortage caused by users’ breach behavior. DR aggregated resource with different auxiliary equipment will have different characteristics, such as breach rate of DR resource. In the proposed DR framework, for selling the aggregated resource, community operator has to compete the market share with other operators in day-ahead DR market. In the competition, each operator will try its best to make the optimal bidding strategy by knowing as much information of its opponents as possible. But, some information of community operator (e.g., DR resource’s characteristic) belongs to privacy information, which is unknown to other operators. Accordingly, this paper focuses on the application of incomplete information game-theoretic framework to model the competition among community operators in DR bidding market. To optimize bidding strategy for the high profit with incomplete information, a Bayesian game approach is formulated. And, an effective iterative algorithm is also presented to search the equilibrium for the proposed Bayesian game model. Finally, a case study is performed to show the effectiveness of the proposed framework and Bayesian game approach.
Highlights
The fast-growing electricity demand driven by the development of social economy has made the current power grid face serious challenges, such as grid security and power supply reliability [1,2].Since electricity cannot be largely stored, power grid has to constantly balance energy supply and demand, reducing supply when generation exceeds consumption and increasing supply when consumption exceeds generation
According to the received information, community operators will make the optimal strategy to maximize the self-profit with Bayesian game theory and summit the bidding strategy to demand response (DR)
For community operators in the DR market, they are willing to participate in the optimization of bidding strategy
Summary
The fast-growing electricity demand driven by the development of social economy has made the current power grid face serious challenges, such as grid security and power supply reliability [1,2]. While in [14], an optimization model was proposed for planning and operating DR resource managed by a price-maker aggregator participating in the market, through both a deterministic and stochastic approach. This paper mainly concentrates on the optimization of bidding strategy for community operators (i.e., DR aggregator) in DR market considering the incomplete information. By formulating the incomplete information game model, community operators can obtain the optimal profit in the bidding market by speculating opponents’ information. A Bayesian game based approach is formulated for the proposed scenario with incomplete information to promote the participants’ profit, and an iterative algorithm is designed to obtain the bidding equilibrium among community operators. A case study is carried out to verify the performance and effectiveness of the proposed Bayesian game approach by simulating the bidding strategy optimization of 3 community operators.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
