Day-ahead planning mode reactive power optimization method adaptive to power grid load change

Abstract

The invention discloses a day-ahead planning mode reactive power optimization method adaptive to power grid load change, and belongs to the technical field of electric power systems and automation thereof. According to the method, an independent static reactive power optimization model of a plurality of planning modes is solved through the optimal power flow, and the optimal compensation amount ofeach capacitive reactance device node in each mode is obtained. The day-ahead planning mode is subjected to time period division according to the load change situation and the load change threshold value of different voltage partitions, and the capacitive reactance device node shift with minimal segmented total network loss is solved based on linear programming for different operation modes in the same segment. The number of sections is continuously reduced, and iteration is repeated until all the capacitive reactance device meet the action frequency requirement, the segmented optimal shift of the capacitive reactance device node is fixed, and the power generator reactive power and other continuous solutions of a plurality of day-ahead planning modes are further corrected through the optimal power flow, and finally an optimization mode meeting various constraints is obtained. A day-ahead optimization reactive plan being adaptive to the load change condition of a power grid and meetingthe action frequency requirement of a capacitive reactance device can be provided.

Description

technical field [0001] The invention belongs to the technical field of electric power system automation, and in particular the invention relates to a reactive power optimization method in a day-ahead planning mode that adapts to changes in grid load. Background technique [0002] Day-ahead reactive power optimization refers to formulating an optimized reactive power and voltage dispatching plan that satisfies the grid safety operation constraints and reactive power adjustment equipment action constraints of the next day based on the grid’s day-ahead load forecasting, active power generation, and maintenance plans, so as to achieve the total active power of the grid the next day. The goal of minimizing network loss is an important means to ensure the safe and economical operation of the power system. The traditional static reactive power optimization can only calculate the reactive power optimization results at a certain moment, and the actual load of the power system is ofte...

AI Technical Summary

Problems solved by technology

The traditional static reactive power optimization can only calculate the reactive power optimization results at a certain moment, and the actual load of the power system is often in constant change. Tracking the static reactive power optimization results will lead to frequent switching of discrete control equipment such as reactive power compensation devices. In order to improve the service life of the equipment and avoid the risk of misoperation in the actual power grid, the total number of adjustments within one day is strictly limited

Therefore, after comprehensive consideration of the above factors, the reactive power optimization problem of the multi-planning method is actually a nonlinear mixed integer dynamic optimization problem. Due to the large number of states, it is very difficult to solve directly

[0003] At present, there are two common indirect solution ideas: one is to obtain the static reactive power optimization results of multiple day-ahead planning methods first, and then solve the problem with the goal of minimizing the rounding deviation or the minimum system network loss increment. Considering the integer programming model constrained by the maximum number of switching times of reactive power compensation devices, but it ignores the interaction between capacitive reactors in the solution process, and cannot guarantee the continuity of the capacity curve of capacitive reactors, and the operating frequency may be high. In addition, When there are many planning methods (such as 96 points in general), it is also very difficult to solve the integer programming model containing the absolute value inequality constraints of the number of actions; The day-ahead planning method is divided into time periods, and the switching gears of typical load points in each time period are calculated through static reactive power optimization. are consistent), while ignoring the load characteristics of different sections in the large power grid, and the selection of typical load points in the same section lacks theoretical basis

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