A Distributed Power Plant Planning Method Based on Improved Light Robust Model

Abstract

The invention relates to a distributed power station planning method based on an improved light robust model. The method comprises the following steps: 1) acquiring a target function and a constraint condition of a distributed power station to be planned; 2) establishing a distributed power station planning model according to the target function and the constraint condition of the distributed power station to be planned, and converting the distributed power station planning model into the improved light robust model; and 3) solving the improved light robust model to acquire an optimal distributed power station capacity planning layout with lowest cost. Compared with the prior art, the method has the advantages of fastness in solving, small calculation amount and the like.

Description

technical field [0001] The invention relates to the field of power dispatching, in particular to a distributed power station planning method based on an improved light robust model. Background technique [0002] The scheduling and planning of the power system includes various uncertain factors such as load fluctuations, sudden failures, and intermittent power generation from new energy sources such as wind power and photovoltaics. Mathematically, it is a planning problem with uncertain parameters. [0003] Compared with the deterministic mathematical programming method, the mathematical programming method that takes into account the uncertainty of parameters more truly describes and reflects the objective practical problems because the development and operation process of many practical problems itself contains the uncertain fluctuation process of parameters. The actual process makes its solution more practical, so it has been paid more and more attention by theorists and en...

AI Technical Summary

Problems solved by technology

However, stochastic optimization has the following problems in practical applications: First, the probability distribution function of the uncertainty parameter must be known in advance to perform scene simulation, but in practical problems, the estimation of the probability distribution function is usually very difficult or impossible to achieve, even if Sufficient historical data, obtained through statistical analysis and fitting, cannot be guaranteed to be completely consistent with the actual situation, so the solution can only be an approximate solution; secondly, the scene of stochastic optimization simulation is discrete, and the parameter uncertainty in reality Most of them are continuously changing. In order to obtain high simulation accuracy, enough scenarios have to be simulated, which makes the scale of the model extremely large, and the difficulty of solving is much greater than that of deterministic programming problems. Although it can be simplified to a certain extent, it is at the expense of certain At the cost of accuracy, even so, the calculation amount is still an order of magnitude different from that of deterministic problems

However, regardless of the basic light robust model or the heuristic light robust model, there are problems such as (1) they cannot be solved independently, and the amount of calculation is large; (2) there is no guarantee that the conservatism can be improved; (3) the constraints may be excessively violated, etc.

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