CN109711644B - Thermal power generating unit load optimization distribution method based on improved pollen algorithm - Google Patents
Thermal power generating unit load optimization distribution method based on improved pollen algorithm Download PDFInfo
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Abstract
The invention discloses a thermal power generating unit load optimal distribution method based on an improved pollen algorithm, which comprises the following steps: and fitting a coal consumption characteristic curve according to the coal consumption characteristic parameters of the generator sets, establishing a load optimization distribution model, converting the coal consumption characteristic curve into a non-constraint problem through a penalty function method, distributing the total power generation power to each generator set by combining an improved pollen algorithm, and finally obtaining the optimal load distribution result of each generator set. The invention can scientifically and reasonably distribute the optimal value of each generator set under the condition of total power supplied to the generator set, thereby reducing the power supply coal consumption consumed by the generator set.
Description
Technical Field
The invention belongs to a group intelligent algorithm application technology, relates to a thermal power plant load optimization technology, and particularly relates to a thermal power plant load optimization distribution method based on an improved pollen algorithm.
Background
The optimal load distribution of the thermal power generating unit is taken as a typical optimization problem of the power system, and the optimal load distribution optimally configures the output active power of each generator on the premise of considering the total demand and various constraint conditions, so that the power generation cost is reduced, the power grid loss is reduced, the utilization rate of fuel is improved, and the safety and reliability of the power system are further improved. Because of the non-linearity, multi-dimensionality, multi-constraint and other attributes of the problem, it is also a key and difficult point to discuss and make a reasonable scheduling plan.
Compared with the traditional scheduling method, the emerging group intelligence algorithm is more flexible, and various optimization algorithms based on biological group intelligence are derived along with the development of the method and are applied to various fields.
The pollen algorithm is a novel heuristic algorithm based on a pollen pollination process in nature, which is proposed in 2012 by a british scholar YANG X.S. and has the advantages of few parameters, simple structure and easy realization, and has good optimization capability. The improved pollen algorithm proposed herein is an improved algorithm for the pollen algorithm.
Disclosure of Invention
The invention aims to provide a thermal power generating unit load optimal distribution method based on an improved pollen algorithm, and solves the problem that the economic effect of the existing scheduling method and the power system is difficult to adjust.
The technical scheme adopted by the invention is as follows: a thermal power generating unit load optimal distribution method based on an improved pollen algorithm is implemented according to the following steps:
the specific form taking the minimum coal consumption of the thermal power generating unit as an objective function is as follows:
in the formula, n is the total number of the generators; p is i The active power of the unit i; a is i 、b i 、c i And the energy consumption characteristic coefficient of the unit i. In addition, the influence of the valve point effect on the power generation cost should be considered, so the formula should be changed into:
in the formula, e i 、f i The characteristic coefficient of coal consumption of the unit i is obtained; p imin The lower limit of the output power of the unit i is set.
The related inequality constraint is the unit operation constraint, and the related formula is as follows:
P imin ≤P i ≤P imax (3)
in the formula, P imin 、P imax The minimum and maximum active power output of the unit i are respectively.
The actual problem of load balance of each unit is brought into an objective function of the coal consumption characteristics of the unit by a penalty function method, and the objective function after the penalty function is introduced is as follows:
where n is the total number of generators, σ is a penalty factor, typically between 100 and 150, and F (P) i ) The standard coal consumption of the unit i is obtained; p i The active power of the unit i; and D is the total scheduling load.
The optimization distribution method is optimized by taking a coal consumption characteristic equation established according to the load and the coal consumption of each unit as an objective function, and incorporates the given total power into one part of the objective function through a penalty function method, and comprises the following specific steps of:
1) Determining actual parameters of the generator set, and randomly generating a group of load initial values within the load limiting range of each generator set;
2) Comparing coal consumption values corresponding to all loads, and determining an initial optimal load distribution scheme;
3) Determining whether the plants are self-pollinated or cross-pollinated according to the conversion probability of the improved pollen algorithm, so as to update the load of each unit;
4) Performing a cross pollination optimization process on the optimal load;
5) Comparing the coal consumption values corresponding to the loads, and determining the optimal load distribution scheme of the iteration;
6) And outputting the optimal load value of each unit until the iteration condition is met.
Compared with the prior art, the method can scientifically and reasonably distribute the optimal value of each generator set under the condition of the total power supplied to the generator set, thereby reducing the power supply coal consumption consumed by the generator set.
Drawings
FIG. 1 is a flow chart of a thermal power generating unit load optimization distribution method for improving a pollen algorithm;
FIG. 2 is a graph comparing the results of the pollen algorithm and the improved pollen algorithm for a total power of 660MW as practiced in the present invention;
FIG. 3 is a comparison graph of the results of the pollen algorithm and the improved pollen algorithm at a total power of 770MW as practiced in the present invention;
FIG. 4 is a graph showing the comparison of the results of the pollen algorithm and the improved pollen algorithm when the total power of the invention is 900 MW.
Detailed Description
The invention is described in detail below with reference to the specific figures and embodiments, with reference to fig. 1.
1. Mathematical modeling based on penalty function
1. Firstly, determining a coal consumption characteristic curve fitted according to coal consumption characteristic parameters of a generator set and establishing a load optimization distribution model, namely a specific form taking the minimum coal consumption of the thermal power generating unit as an objective function:
in the formula, n is the total number of the generators; p is i The active power of the unit i is obtained; a is i 、b i 、c i And the energy consumption characteristic coefficient of the unit i. In addition, the influence of the valve point effect on the power generation cost should be considered, so the above formula should be changed into:
in the formula, e i 、f i The characteristic coefficient of coal consumption of the unit i is obtained; p imin The lower limit of the output power of the unit i is set.
The following two constraints are mainly considered in the objective function:
1) Load balancing constraints of the system: i.e. the sum of the active power P of the units i The requirements for the total load D should be met:
2) And (3) upper and lower limit constraint of unit output power:
P imin ≤P i ≤P imax (4)
in the formula, P imin 、P imax The minimum and maximum active power output of the unit i are respectively.
2. Modeling an objective function by penalty function method
Since the optimization process needs to consider the constraint conditions, a penalty function is introduced to convert the objective function into an unconstrained problem.
The method adopts an external penalty function construction method to add the load balance constraint into the objective function.
The objective function after introducing the penalty function is as follows:
where n is the total number of generators, σ is a penalty factor, typically between 100 and 150, and F (P) i ) The standard coal consumption of the unit i is obtained; p is i The active power of the unit i; and D is the total scheduling load.
2. Thermal power generating unit load optimal distribution based on improved pollen algorithm
1. Improved pollen algorithm (MFPA)
Firstly, the pollen algorithm is a novel heuristic algorithm based on a pollen pollination process in nature, which is proposed by English scholars YANG X.S. in 2012, and has the advantages of few parameters, simple structure and easy realization, and good optimization capability.
The pollen algorithm is characterized in that the optimization process of self-pollination or cross-pollination is determined according to the conversion probability p, and the correlation formula is as follows:
in the formula (I), the compound is shown in the specification,
represents the position of the pollen i in the t +1 cycle>
The active power of the jth group of generators corresponding to the current optimal value is represented, L represents the pollination strength degree, the essence of the pollination strength degree is a step size factor subjected to column dimension distribution, and the related formula is as follows:
where Γ (λ) is a standard gamma function, and λ is typically 1.5, the distribution falls within a reasonable category when s > 0. Due to frequent and wide range of changes, the process belongs to global optimization.
In the formula (I), the compound is shown in the specification,
and &>
Is different from pollen in the same species>
Is epsilon U (0,1). The process is essentially a random walk process in a limited neighborhood, and thus belongs to local optimization.
The improved pollen algorithm provided by the invention is characterized in that the cross pollination process aiming at the optimal flower individual is added to the original algorithm, so that the local optimization capability is improved, and the later convergence speed is accelerated.
And (3) carrying out the cross pollination process on the optimal individual after the individual value is updated, wherein the related formula is as follows:
the steps for improving the pollen algorithm are as follows:
1) Setting initial parameters of the flowers, and randomly generating initial positions of the flowers within an allowable range;
2) Evaluating the adaptive value of the individual, and selecting the optimal flower individual;
3) Determining whether the flower is self-pollinated or cross-pollinated according to the conversion probability, and updating the individual value;
4) Performing a cross-pollination process on the optimal individuals;
5) Comparing the adaptive values of all related individuals, and updating an optimal value;
6) If the iteration condition is met, stopping optimization and outputting the optimal solution, otherwise, turning to 2).
2. Thermal power generating unit load optimal distribution based on MFPA
The specific distribution process is as follows:
1) Determining a mathematical model of thermal power generating unit load optimization distribution based on a penalty function;
2) Setting upper and lower limits of the generated power of each unit;
3) Randomly initializing the load of each unit;
4) Obtaining the optimal distribution load of each unit by improving a pollen algorithm;
the method is realized by MATLAB, and the performance parameters and the upper and lower load limits of each generator set are fitted according to the historical data of three generator sets of a certain power plant, wherein the detailed data are shown in the following table 1;
given three total loads of 660MW, 770MW, 900MW, the modified pollen algorithm was calculated separately and compared to the results calculated by the pollen algorithm and the mean distribution. The algorithm population scale was set to 200, the number of evolutionary iterations was 1000, and the results are shown in tables 2-4:
TABLE 1 Performance parameters and Upper and lower load limits of each Unit
Table 2 optimization results for total load D =660MW
Table 3 optimization results for total load D =770MW
Table 4 optimization results for total load D =900MW
The data of the load distribution is a load value obtained through algorithm optimization; the coal consumption is the power supply coal consumption consumed by the corresponding load; the deviation refers to the total coal consumption deviation value based on the improved pollen algorithm.
By observing the deviation values in the table, the improved pollen algorithm can find a better solution than the pollen algorithm, is more obvious than average distribution, and can better exert the performances of different units under different total load states.
FIGS. 2-4 are graphs comparing the results of the MFPA and FPA algorithms for total loads of 660MW, 770MW, and 900MW, respectively. Therefore, the MFPA algorithm finds the optimal value before the 100 th iteration, which is obviously faster than the FPA algorithm, so that the later convergence speed is faster, and the optimization time is shortened.
Claims (2)
1. A thermal power generating unit load optimal distribution method based on an improved pollen algorithm is characterized by comprising the following steps:
step one, mathematical modeling based on penalty function
Firstly, fitting a coal consumption characteristic curve according to the coal consumption characteristic parameters of the generating set and establishing a load optimization distribution model, namely the concrete form of taking the minimum coal consumption of the thermal power generating unit as an objective function is as follows:
in the formula, n is the total number of the generators; p i The active power of the unit i is obtained; a is a i 、b i 、c i In order to obtain the energy consumption characteristic coefficient of the unit i, the influence of the valve point effect on the power generation cost should be considered, so the formula should be changed into:
in the formula, e i 、f i The characteristic coefficient of coal consumption of the unit i is obtained; p imin The lower limit of the output power of the unit i is set;
secondly, modeling the target function by a penalty function method, and converting the target function into an unconstrained problem
And (3) adopting an external penalty function construction method to add the load balance constraint condition into the objective function, wherein the objective function after the penalty function is introduced is as follows:
where n is the total number of generators, σ is a penalty factor, typically between 100 and 150, and F (P) i ) The standard coal consumption of the unit i is obtained; p i The active power of the unit i; d is the total scheduling load;
step two, thermal power unit load optimal distribution based on improved pollen algorithm
Firstly, the steps for improving the pollen algorithm are as follows:
1) Setting initial parameters of the flowers, and randomly generating initial positions of the flowers within an allowable range;
2) Evaluating the adaptive value of the individual, and selecting the optimal flower individual;
3) Determining whether the flower is self-pollinated or cross-pollinated according to the conversion probability, thereby updating the individual value;
4) Performing a cross-pollination process on the optimal individuals;
and (3) carrying out the cross pollination process on the optimal individual when the individual value is updated, wherein the related formula is as follows:
wherein the content of the first and second substances,
is the current t thThe active power of the jth group generator corresponding to the optimal value is substituted>
Is the active power of the jth group generator corresponding to the optimal value of the t +1 th generation of evolution, and is selected>
And &>
Is different from pollen in the same species respectively>
epsilon.U (0,1;
5) Comparing the adaptive values of all related individuals, and updating an optimal value;
6) If the iteration condition is met, stopping optimization and outputting the optimal solution, otherwise turning to 2),
secondly, the load of the thermal power generating unit is optimally distributed, and the distribution process is as follows:
(1) Determining a mathematical model of thermal power unit load optimization distribution based on a penalty function;
(2) Setting upper and lower limits of the generated power of each unit;
(3) Randomly initializing the load of each unit;
(4) And obtaining the optimal distribution load of each unit by an improved pollen algorithm.
2. The thermal power generating unit load optimal distribution method based on the improved pollen algorithm as claimed in claim 1, wherein the following two constraints are mainly considered in the objective function of minimum coal consumption of the thermal power generating unit:
1) Load balancing constraints of the system: i.e. the sum of the active power P of the units i The requirements for the total load D should be met:
2) And (3) upper and lower limit constraint of unit output power:
P imin ≤P i ≤P imax (4)
in the formula, P imin 、P imax The minimum and maximum active power output of the unit i are respectively.
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| CN107370188A (en) * | 2017-09-11 | 2017-11-21 | 国网山东省电力公司莱芜供电公司 | A kind of power system Multiobjective Scheduling method of meter and wind power output |
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