Demand response (DR) can offer a wide range of advantages for electrical systems by facilitating the interaction and balance between supply and demand. However, DR always requires a central agent, giving rise to issues of security and trust. Besides this, differences in user response cost characteristics are not taken into consideration during incentive pricing, which would affect the equal participation of users in DR and increase the costs borne by the electricity retail company. In this paper, a blockchain-enabled DR scheme with an individualized incentive pricing mode is proposed. First, a blockchain-enabled DR framework is proposed to promote the secure implementation of DR. Next, a dual-incentive mechanism is designed to successfully implement the blockchain to DR, which consists of a profit-based and a contribution-based model. An individualized incentive pricing mode is adopted in the profit-based model to decrease the imbalance in response frequency of users and reduce the costs borne by the electricity retail company. Then, the Stackelberg game model is constructed and Differential Evolution (DE) is used to produce equilibrium optimal individualized incentive prices. Finally, case studies are conducted. The results demonstrate that the proposed scheme can reduce the cost borne by the electricity retail company and decrease the imbalance among users in response frequency.
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication.
The promising power-to-gas (P2G) technology makes it possible for wind farms to absorb carbon and trade in multiple energy markets. Considering the remoteness of wind farms equipped with P2G systems and the isolation of different energy markets, the scheduling process may suffer from inefficient coordination and unstable information. An automated scheduling approach is thus proposed. Firstly, an automated scheduling framework enabled by smart contract is established for reliable coordination between wind farms and multiple energy markets. Considering the limited logic complexity and insufficient calculation of smart contracts, an off-chain procedure as a workaround is proposed to avoid complex on-chain solutions. Next, a non-linear model of the P2G system is developed to enhance the accuracy of scheduling results. The scheduling strategy takes into account not only the revenues from multiple energy trades, but also the penalties for violating contract items in smart contracts. Then, the implementation of smart contracts under a blockchain environment is presented with multiple participants, including voting in an agreed scheduling result as the plan. Finally, the case study is conducted in a typical two-stage scheduling process—i.e., day-ahead and real-time scheduling—and the results verify the efficiency of the proposed approach.
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