Combination operation control method of sending-end power grid units with new energy participating in power balance

Abstract

The invention relates to a method for controlling combined operation of sending-end power grid units in which new energy participates in electricity balance, comprising the following steps: 1) Obtaining the predicted output confidence interval of new energy and the start-up priority list of traditional generating sets; 2) Introducing a pool of standby units and abandoned power unit pool, while considering new energy participation balance, spinning reserve constraints, and minimum start-stop time constraints, to control the state of the unit; 3) The unit state obtained in step 2) is used as the input of the particle swarm optimization algorithm, based on considering the unit ramp rate The position of the particles is adjusted according to the power balance of the particle swarm algorithm, and the optimal output of the particle swarm algorithm is obtained, and the operating state of the unit combination is controlled according to the optimal output. Compared with the prior art, the invention has the advantages of improving the economy of system operation and the like.

Description

technical field [0001] The invention relates to the field of automatic control of power systems, in particular to a combined operation control method for power grid units at the sending end where new energy sources participate in power balance. Background technique [0002] The unit combination problem (UC problem) generally refers to determining the start-stop and output arrangement of each unit in each period of the research period under the constraints of satisfying the system load demand, reserve capacity and minimum start-stop time, so as to minimize the total power generation cost. The essence of the model is a high-dimensional, discrete, non-convex mixed integer nonlinear programming model, and it is not easy to obtain the theoretical optimal solution. With the expansion of the system scale, the complexity of the model solution space increases, the model solution time and the optimality of the solution decrease, and some methods cannot even obtain feasible solutions. ...

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Problems solved by technology

The above patents only use a single priority method or intelligent algorithm, or directly call a mature mathematical programming solver to solve UC problems, so they cannot solve the inherent defects of low computational efficiency when the above-mentioned methods are applied to large-scale UC problems.

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