Abstract
As a new form of smart grid, the energy transmission mode of the Energy Internet (EI) has changed from one direction to the interconnected form. Centralized scheduling of traditional power grids has the problems of low communication efficiency and low system resilience, which do not contribute to long‐term development in the future. Owing to the fact that it is difficult to achieve an optimal operation for centralized control, we propose a decentralized energy flow control framework for regional Energy Internet. Through optimal scheduling of regional EI, large‐scale utilization and sharing of distributed renewable energy can be realized, while taking into consideration the uncertainty of both demand side and supply side. Combing the multiagent system with noncooperative game theory, a novel electricity price mechanism is adopted to maximize the profit of the regional EI. We prove that Nash equilibrium of theoretical noncooperative game can realize consensus in the multiagent system. The numerical results of real‐world traces show that the regional EI can better absorb the renewable energy under the optimized control strategy, which proves the feasibility and economy of the proposed decentralized energy flow control framework.
Highlights
Due to the diversi cation of load patterns and stochastic nature of renewable energy sources in Energy Internet (EI), the traditional centralized optimization scheduling method is di cult to apply in practice in actual operation
A strategy of optimal operation in EI is proposed based on the multiagent system (MAS) combined with noncooperative game theory to realize the decentralized control of the ELN system. e real-time electricity price is obtained by iterative optimization, which maximizes the overall profit of the EI system
A decentralized energy flow control framework of optimal operation considering the uncertainty of the supply side and demand side has been proposed for the Energy Internet
Summary
ELN is a collection of a complete future-oriented energy system constructed from the aspects of energy production, transmission, distribution, transformation, and consumption It is a power-centric interactive and shared platform for all kinds of energy which enables smart mutual supply of different types of loads. Where PtAR,i represents adsorption refrigerator; the output cooling QtAR,i represents the power of the heat absorbed by an adsorption refrigerator from a gas turbine; and ηAR represents the refrigeration efficiency of the adsorption refrigeration machine. We use the reduction method of typical scenes to characterize the uncertainty of load and output of renewable generation [18]. The predicted value of RES output at period T in the future is expressed as time series based on the method of backward reduction. Based on the short-time prediction results, the active power of RES output and basic load is shown as PPV,i P1PV,i, P2PV,i, . Awnhdertehue tgEridI,isryesptermes;enPttEsCt,ihereipnrteesreancttisotnhpe oewleecrtrbiectwreeferingei rEaLtoNr power which provides cold load for the system; and umgraidx,i represents the power constraint of timeline
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