A Multivariate Load Trading Optimization Method for Energy Internet Based on LSTM and Gaming Theory

Energy Internet is a complex nonlinear system. There are many stakeholders in the load trading market, which is usually regarded as a multi-player gaming. Although gaming theory has been introduced to solve Multivariate Load trading problems, different conditions should be considered to accurately o...

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Bibliographic Details
Published in:Energies (Basel) 2021-09, Vol.14 (17), p.5246
Main Authors: Pan, Mingming, Tian, Shiming, Yuan, Jindou, Chen, Songsong, He, Sheng
Format: Article
Language:English
Subjects:
Demand side management
Electric power systems
Electrical loads
Electricity
Electricity consumption
Energy Internet
Energy storage
Game theory
hybrid game
Internet
Long short-term memory
Market prices
Mathematical models
Multivariate analysis
multivariate load
Neural networks
Optimization
orderly power consumption
Purchasing
Reserve capacity
Sales
Scheduling
Strategy
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Summary:Energy Internet is a complex nonlinear system. There are many stakeholders in the load trading market, which is usually regarded as a multi-player gaming. Although gaming theory has been introduced to solve Multivariate Load trading problems, different conditions should be considered to accurately optimize the multivariate load trading problem. For example, the selling side needs to reduce the reserve capacity and improve profits, but the consumer side needs to reduce costs and minimize the impact on its own electricity consumption. These contradictory conditions require multiple Nash equilibrium to achieve obviously. To address this issue, a unified architecture of the power system cloud trading is constructed in this paper, which is combined with the multiple load classification of the power system. In addition, according to the power market operation mechanism, a price-guided multivariate load trading game strategy is designed. More importantly, a multivariate load trading optimization method based on LSTM (Long Short-Term Memory) and gaming theory is proposed in this work. LSTM is introduced for real time prediction, which can be combined with the game theory for strategy searching. The global stability and optimal solution theory prove the feasibility of the proposed neural network, and finally the effectiveness of the proposed method is verified by using numerical simulation.
ISSN:1996-1073
1996-1073
DOI:10.3390/en14175246