Energy storage capacity competition-based demand response method in blockchain ancillary service market

With the development of new energy, the ancillary service demand of power grid is increasing. The published literature focuses on building a blockchain ancillary service market to ensure the safety and efficiency of ancillary service transactions. However, due to the high threshold and poor scalability, the existing blockchain market is hard to meet the demand of the response rate of ancillary services. Therefore, this paper proposes an energy storage capacity competition-based demand response method in blockchain ancillary service market. Firstly, a new market structure is proposed, which uses the Proof of Capacity (POC) as the consensus mechanism. The structure uses POC to realize the competition among users' energy storage capacity. Secondly, the demand response process of ancillary service market is designed. During the transaction processes, the probability of users participating in the transaction is changed according to the users' storage capacity. Dealing with different demands, the market chooses the user with the largest capacity or the combination of users to respond. The simulation shows that, compared with the traditional ancillary service market, the capacity competition-based market demand response rate is increased by 20%, the time to fulling all users' capacity in the market is reduced by 2 h, and the call rate of user's capacity is increased by 10%.(c) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Automated summary

This paper proposes an energy storage capacity competition-based demand response method in the blockchain ancillary service market. It introduces a new market structure that utilizes Proof of Capacity (POC) as the consensus mechanism to enable competition among users' energy storage capacity. The demand response process of the ancillary service market is also designed to select users with the largest capacity or combinations of users to respond to different demands. Simulation results show significant improvements in the response rate, time to fulfill users' capacity, and call rate of user's capacity compared to the traditional ancillary service market.