With the continuous changes in China's investment environment, new changes in power grid investment patterns have emerged, requiring the transformation and upgrading of traditional businesses and the continuous development of new businesses. This paper analyses the coupling relationship between traditional business and new business based on coupling degree theory, and analyses the factors influencing the investment demand of traditional business and new business of power grid enterprises. With the goal of maximizing the investment value of traditional and new business, we consider the impact of investment projects on the economy, reliability and security of power grid, and add the capital constraint to the objective function in the form of penalty function, so as to build an optimization model of power grid investment based on the integration of traditional and new business. The practicality and accuracy of the model are verified through case analysis based on genetic algorithm.

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Each subject in the integrated energy system has different interests and demands, and it is necessary to optimize the energy dispatching with the help of multi-subject game theory. In order to solve the above problems, this paper proposes a reinforcement learning-based multi-object operation optimization method for integrated energy systems. Firstly, a multi-subject integrated energy system model including energy suppliers, park service providers and users is constructed; secondly, a game search method based on reinforcement signals is proposed to improve the speed of multi-subject game solution; finally, a simulation is conducted with an integrated energy system as an example to verify the effectiveness and rapidity of the proposed method.