Abstract
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.
Highlights
With the rapid development of new energy, adjustable load, energy storage, V2G (Vehicle-to-grid) charging station and other heterogeneous flexible resources in power system [1], the deviation between new energy output and load forecasting, the uncertainty of intermittent energy and load operation bring challenges to power system market operation and system regulation and control, and increase the complexity of power system regulation and control, which provides a new regulation means for the power system
With the complexity and diversity of participants in the electricity market, traditional methods based on probability statistics cannot accurately predict the data with complex subject characteristics
It can be applied to the time series price prediction in the spot market to provide more lean trading assistant decision-making suggestions for the market participants
Summary
With the rapid development of new energy, adjustable load, energy storage, V2G (Vehicle-to-grid) charging station and other heterogeneous flexible resources in power system [1], the deviation between new energy output and load forecasting, the uncertainty of intermittent energy and load operation bring challenges to power system market operation and system regulation and control, and increase the complexity of power system regulation and control, which provides a new regulation means for the power system. In the multi-party game electricity trading market, the uncertainty and randomness of demand-side resources have always been a problem that needs to be considered in the existing distributed control research. In this work, according to the power market operation mechanism, a price-guided multivariate load trading game strategy is designed. The contributions of this paper are: (1) Based on the development of energy Internet technology, combined with the multiple load classification of the power system, a unified architecture of the power system trading is constructed; (2) According to the power market operation mechanism, a price-guided Multivariate Load trading game strategy is designed; (3) A multivariate load trading optimization method based on LSTM and gaming theory is proposed. With the participation of complex entities such as virtual power plant and active load, each entity in the power market needs to obtain more market price influence factors, train data more suitable for the characteristics of complex entities to reduce the forecast deviation, and provide more lean trading auxiliary decision-making suggestions for market entities
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