Optimization method for user side battery energy storage configuration and operation

Abstract

The invention discloses an optimization method for user side battery energy storage configuration and operation. According to the method, an energy storage configuration optimization model combining demand defense and peak load shifting is constructed on the basis of charging rules of large industrial user electricity charges. For different users, the most appropriate energy storage configurationcan be selected by utilizing investment return rate evaluation. A energy storage operation performance constraints are added when the optimization model is constructed, so that the operation loss canbe reduced, and the energy storage life is prolonged. During energy storage daily operation optimization, load data is updated in real time, an intra-day operation scheduling optimization model is constructed, and real-time rolling optimization is carried out; secondly, a monthly demand defense value updating model is constructed, the monthly demand defense value is updated in time, and the influence of load prediction errors is continuously corrected; examples prove that the rolling optimization algorithm provided by the invention can more reasonably schedule energy storage and effectively reduce the power consumption cost of users.

Description

technical field [0001] The invention belongs to the technical field of power systems, and specifically relates to a configuration and operation optimization method for user-side battery energy storage. Background technique [0002] The cost of electricity consumption is an important part of the operating costs of large power users. Battery energy storage (hereinafter referred to as "energy storage") can adopt the strategy of "low storage and high discharge", that is, to store electric energy when the user's power load and electricity price are low, and to release electric energy when the user's power load and electricity price are high. In the case of changing the user's electricity consumption behavior, the maximum monthly demand value of the user's load is reduced, and the basic electricity fee is reduced while generating peak-shaving and valley-filling benefits. Moreover, the user-side energy storage service objects are clear, and there are fewer changing factors, which ...

AI Technical Summary

Problems solved by technology

There are also some studies that predetermine the scheduling strategy for energy storage capacity allocation and day-ahead operation planning. This method is not only difficult to obtain the optimal allocation capacity of energy storage, but also does not consider the influence of power load forecast errors, resulting in the actual operation of energy storage within a day. poor performance

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