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• Operation mechanism for multi-energy virtual power plant to participate in the peak regulating market is established. • A day-ahead robust bidding model is established to minimize the cost of multi-energy virtual power plant. • Multiple energies such as electricity, cooling energy, thermal energy, and natural gas are involved in the peak-regulation market. Multi-energy virtual power plant (MEVPP) can aggregate flexible resources such as energy storage and flexible loads that decentralized in the region to meet the access conditions in the peak-regulation ancillary service market. However, the uncertainties in energy sources and loads bring adverse impact on the operation of MEVPP. Therefore, this paper proposes a day-ahead robust bidding strategy for MEVPP to participate in the peak-regulation market. Firstly, this paper analyzes the impact of uncertainties for MEVPP on the peak-regulation market. On this basis, the operation mechanism for MEVPP in the peak-regulation market is proposed by considering the integrated demand response (IDR). Additionally, the day-ahead two-stage robust bidding model is established to minimize the operation cost of MEVPP. Finally, the case studies show that the day-ahead robust bidding strategy can effectively reduce the peak-regulation deviation penalty compared with traditional deterministic optimization. Specifically, with the proposed robust bidding strategy, the total revenue in the actual operation stage is increased by 5.16% and 8.45% on sunny day and raised by 8.28% and 15.35% on cloudy day when the predicted deviations are respectively 20% and 30%, comparing with traditional deterministic optimization. [ABSTRACT FROM AUTHOR]

To improve the coordination and complementarity of multiple energy sources, balancing the interests of different participants in a multi-energy system is of great importance. However, traditional centralized optimization can hardly reflect the game relationship between supply side and demand sides. A trading model based on the Stackelberg game model is proposed in this paper to balance the interests of the supply side and demand side and reduce the carbon emissions. First of all, the process of trading between the supply side and demand side based on smart contracts is described. A contractual consensus is obtained through an internal game, and the transaction is completed automatically. Secondly, a bilevel optimization model is established to coordinate the benefits of both parties based on the Stackelberg game model. The energy operator acts as a leader, and considers the two objectives, i.e., maximizing net income and minimizing carbon emissions, and uses the linear weighting method to convert the dual objectives into single objective. Users act as followers and aim to increase the comprehensive benefits, including energy cost and comfort. Then, Karush–Kuhn–Tucker optimality condition is used to transform the bilevel optimization model into an equivalent single-level model. Finally, simulation results show that the proposed method can coordinate the economic interests of both sides of supply and demand and effectively reduce the carbon emissions of the energy operator. [ABSTRACT FROM AUTHOR]

With the rapid development of distributed energy resources and natural gas power generation, multi-energy microgrid (MEMG) is considered as a critical technology to increase the penetration of renewable energy and achieve the target of carbon emission reduction. Therefore, this paper proposes a low-carbon economic dispatch model for MEMG to minimize the daily operation cost by considering integrated demand response (IDR) and multistep carbon trading. Specifically, IDR operation includes shifting of shiftable electric load, adjusting of flexible thermal load and cooling load, and it is employed to decrease operation cost. Besides, the multistep carbon trading means that different carbon trading prices correspond to different carbon trading volumes, which is applied to stringently restrict carbon emission. The simulation results show that the proposed model can effectively reduce the carbon emission while greatly decrease the operation cost. [ABSTRACT FROM AUTHOR]

• A two-level incentive mechanism based peer-to-peer energy sharing community model is established. • Individual modified internal prices are proposed based on the proof of credit consensus and game theory. • A comparison with other models is given to show economic superiority of the proposed model. • Performance analysis is carried out by changing community scale and prosumer ratio. Peer-to-peer trading mode is one of the most potential future energy trading scheme. However, there are some issues still waiting to be solved, e.g. privacy of energy data, asynchrony of households' desire and incomplete understanding of its operation features, etc. These issues may hinder its further application and development. To address these requirements, a research to analyze the characteristics of a typical peer-to-peer energy sharing community model is carried out. The model is built on the basis of the proof of credit consensus in blockchain technology and Shapley value in game theory. A two-level hierarchical incentive mechanism is proposed to motivate participants to obey the energy smart contract and involve in electricity peak-shifting. A series of indexes are applied to evaluate the operation performance of the proposed model, from the aspects of the economy, technique, environment and market efficiency. The validity of the incentive mechanism and the proposed model are verified, and the impacts of important factors, i.e. community scale and prosumer ratio, on the proposed model are assessed. This work would provide reference to the constructors of future peer-to-peer energy market about arranging the optimal appliance scale and distributed energy resources ratio to make it become more friendly, efficient and economical. [ABSTRACT FROM AUTHOR]