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Hybrid scheduling method based on maximum consumption of new energy and optimal power generation cost

A technology of power generation cost and hybrid scheduling, applied in the fields of genetic laws, genetic models, electrical components, etc., can solve the problems of insufficient utilization of clean energy, abandonment of wind and solar energy, etc., to achieve improved distribution range and uniformity, and accurate power generation costs. Effect

Pending Publication Date: 2022-04-15
STATE GRID LIAONING ELECTRIC POWER RES INST +2
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

At present, my country's new energy is connected to the grid on a large scale, and the installed capacity of wind power and photovoltaics ranks among the top in the world. Due to the large-scale grid connection of clean energy, it cannot be fully utilized, and a large number of abandoned wind and light have occurred.

Method used

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  • Hybrid scheduling method based on maximum consumption of new energy and optimal power generation cost
  • Hybrid scheduling method based on maximum consumption of new energy and optimal power generation cost
  • Hybrid scheduling method based on maximum consumption of new energy and optimal power generation cost

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Effect test

Embodiment 1

[0113] Such as figure 1 As shown, a hybrid scheduling method based on the maximum consumption of new energy and the optimal cost of power generation, the steps include:

[0114] Step 1. Establish an optimization model based on new energy consumption capacity and power generation cost, construct the maximum objective function of new energy consumption and the minimum operating cost of the integrated energy system, energy balance constraints, system energy supply equipment constraints, and energy storage devices Constraints and spinning reserve constraints;

[0115] Step 2, set the population size pop, the number of iterations gen, the number of objective functions M, the number of decision variables V, the inertia weight w, the individual learning coefficient c1, the global learning coefficient c2, and the velocity vector fori;

[0116] Step 3, through the genetic algorithm of non-dominated sorting, iteratively solve the optimal value of the new energy consumption capacity and...

Embodiment 2

[0123] Such as figure 2 As shown, the operation steps of the non-dominated sorting genetic algorithm NSGA-II are as follows:

[0124] Step a: Firstly, random simulation operation is performed to generate an initial population P with a scale value of N, followed by the generation of offspring population Q, and then the combination of population P and offspring population Q, and finally a population M with a scale value of 2N;

[0125] Step b, combine the parent and child populations, perform non-dominated sorting, and calculate the crowding degree of all individuals in the non-dominated layer, and then select the appropriate individual from the non-dominated relationship and individual crowding degree to form a new parent Population P 1 ;

[0126] Step c, generate a new offspring population Q through a genetic algorithm, and transfer the new parent population P 1 with the new offspring population Q 1 combine to form a new population M 1 , repeat the above steps until the ...

Embodiment 3

[0128] The operation steps of the multi-objective particle swarm optimization algorithm MOPSO are as follows:

[0129] Step a, for each particle in the particle swarm, initialize its velocity and position;

[0130] Step b, calculate the target vector value of each particle, and add the non-dominated solution to the external memory;

[0131] Step c, determine the initial global best position gbest and individual best position pbest of the particle;

[0132] Step d, update the velocity and position of the particle, and take certain measures to ensure that the particle flies in the search space;

[0133] Step e, calculate the target vector of each particle, and adjust the individual best position pbest of the particle;

[0134] Step f, update the external memory, and select the global best position gbest for each particle at the same time;

[0135] Step g, judge whether the algorithm termination condition is satisfied, if satisfied, the algorithm stops running; otherwise, retu...

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Abstract

The invention discloses a hybrid scheduling method based on maximum consumption of new energy and optimal power generation cost. The method comprises the following steps: firstly, constructing an objective function, an energy balance constraint condition, a system energy supply equipment constraint condition, an energy storage device constraint condition and a spinning reserve constraint condition with maximum new energy consumption and minimum integrated energy system operation cost; taking the constraint conditions as constraint conditions of a non-dominated sorting genetic algorithm and a multi-objective particle swarm algorithm, and solving an optimal solution for the multi-objective function by fusing the non-dominated sorting genetic algorithm and the multi-objective particle swarm algorithm; finally, in the iteration process, the populations are sorted according to the crowding distance, the whole population is divided into two parts according to the sorting result, the best half of the population is optimized through a non-dominated sorting genetic algorithm, the other half of the population is optimized through a multi-target particle swarm algorithm, and the populations are converged around the optimal solution. The method provided by the invention can effectively promote the consumption of new energy and reduce the operation cost of the system.

Description

technical field [0001] The invention relates to the technical field of grid planning, in particular to a hybrid scheduling method based on the maximum consumption of new energy and the optimization of power generation costs. Background technique [0002] With the increasingly prominent environmental problems and the continuous improvement of new energy power generation technology, large-scale development and utilization of new energy power generation is a new direction for power system dispatching. At present, my country's new energy is connected to the grid on a large scale, and the installed capacity of wind power and photovoltaics ranks among the top in the world. Due to the large-scale grid connection of clean energy, it cannot be fully utilized, and a large number of abandoned wind and light have occurred. Due to the uncertainty of wind and wind power generation, the realization of large-scale grid connection of wind and wind puts forward higher requirements on the powe...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H02J3/38H02J3/46H02J3/32G06N3/00G06N3/12G06Q10/04
CPCH02J3/003H02J3/0075H02J3/381H02J3/466H02J3/32G06Q10/04G06N3/126G06N3/006H02J2203/20H02J2300/20H02J2300/24H02J2300/28H02J2300/40
Inventor 李胜辉赵清松郝建成马辉孙峰杨安全戈阳阳张强董鹤楠张冠锋谢赐戬程绪可张潇桐谢冰王超袁鹏李平
Owner STATE GRID LIAONING ELECTRIC POWER RES INST
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