Thermal power generating unit monthly power generation plan optimization method considering new energy access

Abstract

The invention provides a thermal power generating unit monthly power generation plan optimization method considering new energy access, and relates to the field of power generation scheduling of a power system. The method comprises the following steps: firstly, acquiring information of each thermal power generating unit in a provincial power system, and establishing a monthly thermal power generating unit combination optimization model; making a monthly startup rule of the thermal power generating unit, and checking the maximum and minimum total output of the thermal power generating unit to obtain a monthly startup plan of the thermal power generating unit; solving the model to obtain a monthly output plan of the thermal power generating unit; optimizing a unit shutdown plan based on a monthly shutdown rule of the thermal power unit; and finally, obtaining a monthly power generation plan optimization scheme of the thermal power generating unit composed of the three plans. According tothe method, the monthly power generation plan meeting the actual engineering requirements can be efficiently calculated, the actual production requirements of power dispatching under tens of thousands of variable levels of provincial power companies are effectively met, and the method has important practical significance.

Description

technical field [0001] The invention relates to the field of power system power generation dispatching, in particular to a method for optimizing a thermal power unit's monthly power generation plan considering the access of new energy. Background technique [0002] Power system optimal scheduling refers to rationally arranging the operation mode of units under the conditions of meeting various technical and safety constraints, meeting the load demand, taking into account the requirements of new energy consumption, and seeking to meet the actual project production, Safe and reliable power generation plan. In the process of promoting the transformation and upgrading of the energy structure and promoting the development of the renewable energy industry, the existing power system scheduling model based on traditional energy cannot fully meet the grid-connected operation requirements of fluctuating renewable energy such as wind power and photovoltaic power generation. The flexib...

AI Technical Summary

Problems solved by technology

[0004] The core task of formulating a monthly power generation plan is to arrange the monthly unit combination, and the monthly unit combination is essentially a high-dimensional, non-convex, nonlinear, and strongly constrained mixed integer programming problem. Although there are many optimization methods including the mixed integer programming method Analytical methods such as dynamic programming, Lagrangian relaxation, and intelligent optimization algorithms such as ant colony algorithm, particle swarm algorithm, and genetic algorithm have all been applied to solve this problem, but the effect in practical application is not Unsatisfactory, as the system scale increases, it is difficult to obtain an accurate feasible solution in a limited time

When mixed integer programming is directly used in practical problems, the efficiency is low, and the problem must be effectively decomposed; dynamic programming is a widely used unit combination algorithm, but it is difficult to consider time-related constraints, and with the scheduling cycle and thermal power unit The increase of the number is easy to cause the "curse of dimensionality"; the Lagrangian relaxation method cannot be robust to the oscillation in the iteration, the acquisition of feasible solutions, and the convergence conditions during the solution process.

Intelligent optimization methods are essentially unconstrained optimization algorithms, which are difficult to deal with discrete start-stop constraints in unit combinations, and when faced with the monthly unit combination problem with strong constraints, the efficiency of searching for feasible solutions is low

Therefore, with the increase of the number of units and the scheduling time, the conventional unit combination solution method is likely to cause the curse of dimensionality, the efficiency of the solution process is low, and the obtained results cannot meet the actual production requirements

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