User real-time autonomous energy management optimization method based on near-end strategy optimization

Abstract

The invention discloses a user real-time autonomous energy management optimization method based on near-end strategy optimization. The management optimization method comprises the steps of S1, classifying and modeling user DER equipment; S2, modeling a user real-time autonomous energy management optimization problem into a sequential decision problem based on classification and modeling of the user DER equipment in the step S1; S3, extracting the future trend of real-time time sequence data by using a long short-term memory neural network, and assisting the deep reinforcement learning in the steps S4 and S5 to carry out strategy optimization; S4, inputting the future trend extracted in the S3 and internal state characteristics observed by an energy management intelligent agent into a strategy function based on a deep neural network, enabling the energy management intelligent agent to learn discrete and continuous actions at the same time, and achieving the control of the equipment; and S5, learning an energy management optimization strategy in the discrete and continuous actions in the step S4 by adopting a near-end strategy optimization algorithm. According to the management optimization method, the adaptability of the strategy to uncertainty is improved while the power consumption cost is minimized.

Description

technical field [0001] The invention relates to the field of home energy management, in particular to a user real-time autonomous energy management optimization method based on near-end strategy optimization. Background technique [0002] In recent years, with the widespread popularization of distributed resources such as distributed photovoltaics, electric vehicles and other flexible loads and energy storage in residents' smart electricity consumption, residents' energy management and optimal control are facing challenges brought by various uncertain factors . At the same time, the rapid development of technologies such as smart meters and communications has provided key technical support for monitoring and controlling distributed equipment of residential users. The development of big data and artificial intelligence technologies has provided new data-driven approaches for energy management optimization. . [0003] As the embodiment of energy management technology on the ...

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Problems solved by technology

However, the performance of model-based energy management optimization methods depends on the accuracy of operating models for various DER (distributed energy resource, DER) equipment; the pursuit of refined modeling tends to make the optimization problem have non-convex and non-smooth characteristics, increasing The large solution difficulty and computational burden make the obtained strategy more suitable for offline applications, and it is difficult to achieve the goal of real-time energy management optimization

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