CN114123216A - Economic load distribution method, system and medium for new energy power system - Google Patents

Economic load distribution method, system and medium for new energy power system Download PDF

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CN114123216A
CN114123216A CN202111216356.5A CN202111216356A CN114123216A CN 114123216 A CN114123216 A CN 114123216A CN 202111216356 A CN202111216356 A CN 202111216356A CN 114123216 A CN114123216 A CN 114123216A
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population
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new energy
economic load
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赵伟
张晶
褚温家
徐鹏
潘艳
董烨
何珂
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North China Grid Co Ltd
Sichuan Energy Internet Research Institute EIRI Tsinghua University
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Sichuan Energy Internet Research Institute EIRI Tsinghua University
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    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
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    • H—ELECTRICITY
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Abstract

The invention discloses an economic load distribution method, a system and a medium of a new energy power system, wherein the method comprises the following steps: constructing a target function of the new energy power system; initializing a population according to a preset constraint condition; respectively calculating an adaptive value of each particle according to the target function and the position of each particle; calculating the individual optimal value of each particle and the global optimal value of the population according to the adaptive value of each particle; updating the speed and the position of each particle in the population according to a particle swarm algorithm; selecting, crossing and mutating the population by a genetic algorithm to obtain a new population, and updating the crossing probability and the mutating probability; and if the end condition is met, outputting particles corresponding to the global optimal value, and taking the economic load parameters of each generator set in the positions of the particles as the optimal distribution result. The method and the system plan according to the actual situation of the new energy power system, are more suitable for the actual situation of the new energy power system, and can achieve optimal economic load distribution.

Description

Economic load distribution method, system and medium for new energy power system
Technical Field
The present invention relates to a method for distributing an economic load of an electrical power system, and more particularly, to a method, a system, and a medium for distributing an economic load of a new energy electrical power system.
Background
Safety Constrained Economic load distribution (SCED) means that under the condition of meeting the safety constraint of an electric power system, a multi-period unit power generation plan is made by taking the lowest electricity purchasing cost of the system as an optimization target, and the like, so that the aim of minimizing the power generation cost under the condition of meeting the load and operation constraint is achieved, and the SCED has important significance for improving the economy and the reliability of system operation. However, the consumption characteristic is nonlinear and cannot be guided due to the action of the valve point effect of the thermal power generating unit; the problem is essentially a non-convex high-dimensional, non-linear and non-conductive optimization problem because the feasible domain of the problem solution is non-convex due to system operation constraints such as the constraints of the transmission capacity and the stability of the transmission system.
Particle Swarm Optimization (PSO) is simple and convenient to operate, has few dependent empirical parameters, and has been successfully applied to solving multi-dimensional nonlinear function optimization, neural network training, integer optimization, min-max problem and a large number of optimization problems based on industrial background. In power systems, PSOs have been used to solve problems such as reactive power optimization, dynamic safety boundary identification, distribution system state estimation, and compensation capacitor optimization configuration. However, the standard particle swarm optimization is easy to fall into a local optimal solution, so that the calculation result is greatly different from the actual situation.
Under the background of large-scale development of new energy power generation, the connotation, the target, the mode and the method of power grid planning are obviously changed. For power grid planning, different grid structures directly influence the new energy consumption capability of the system, and simultaneously, the grid structures also influence the operation modes of other flexible power supplies of the system, so that the new energy consumption capability is indirectly influenced. Therefore, how to make a reasonable compromise between new energy consumption capability and system investment operation cost is a main research problem for new energy power grid planning.
Disclosure of Invention
The technical problems to be solved by the invention are as follows: aiming at the problems in the prior art, the method, the system and the medium for distributing the economic load of the new energy power system are provided, planning is carried out according to the actual situation of the new energy power system, the optimal solution meets the actual situation, and the optimal economic load distribution can be achieved.
In order to solve the technical problems, the invention adopts the technical scheme that:
an economic load distribution method of a new energy power system comprises the following steps:
s1) constructing an objective function of the new energy power system;
s2) initializing the population according to preset constraint conditions, and setting the position and speed of each particle in the population, wherein the position of each particle is an economic load parameter of each generator set;
s3) respectively calculating an adaptive value of each particle according to the objective function and the position of each particle;
s4) calculating the individual optimal value of each particle and the global optimal value of the population according to the adaptive value of each particle;
s5) updating the speed and the position of each particle in the population according to the particle swarm algorithm;
s6) selecting, crossing and mutating the population by genetic algorithm to obtain a new population, and updating the crossing probability and the mutating probability;
s7) returning to the step S3) until the end condition is met, outputting the particles corresponding to the global optimal value, and taking the economic load parameters of each generator set in the positions of the particles as the optimal distribution result.
Further, the objective function of the new energy power system in step S1) is:
Figure BDA0003310791330000021
in the above formula, j is the serial number of a single unit,
Figure BDA0003310791330000022
is a binary variable and represents the starting state of the jth unit at the moment t, 1 represents that the unit is starting, 0 represents that the unit is not in the starting state,
Figure BDA0003310791330000023
is a binary variable and represents the shutdown state of the jth unit at the moment t, 1 represents that the unit is shutdown, 0 represents that the unit is not in the shutdown state,
Figure BDA0003310791330000024
the output of the jth unit at time t, NjT is a preset time length alpha for participating in optimizing the number of various unitsjFor startup of the jth unit coal consumption, betajFor shutdown of jth unit, coal consumption, ajSlope of coal consumption of a unit varying with power, bjIs a coal consumption constant of a unit, and gamma is a carbon dioxide emission coefficient.
Further, the position expression of each particle in the particle group in step S2) is:
Xi＝(x1，x2，…，xNj)
in the above formula, i is the number of the particle, NjIs the total number of generators in the system, x1，x2，…，xNjFrom 1 st station to N th stationjAnd the economic load parameters of the unit comprise the starting and stopping conditions and the output conditions of the current unit at each moment in time T.
Further, the step of performing genetic algorithm selection, crossing, and mutation operations on the population in step S6) to obtain a new population specifically includes:
taking the particles corresponding to the global optimal value as parent generations, and screening m particles in the population as parent generations;
at cross probability PcThen, respectively crossing each parent and the parent to obtain m child generations, so that the child generations not only contain partial elements in the parent, but also contain partial elements in the parent;
at the mutation probability PmNext, obtaining new progeny particles through the variation of the progeny particles;
and selecting the particles in the original population according to the sequence from high to low of the adaptive value, and forming a new population by the selected particles and the m sub-generation particles so that the number of the particles in the new population is the same as that in the original population.
Further, obtaining new daughter particles through variation of the daughter particles specifically includes: and for the current child particles, randomly selecting a unit, and changing the starting state of the selected unit at a random moment in the time T in the economic load parameter into the stopping state.
Further, the step of updating the cross probability and the mutation probability in step S6) specifically includes: calculating the cross probability P in the next iteration according to the preset annealing coefficientcAnd the mutation probability PmThe function is expressed as follows:
Figure BDA0003310791330000031
in the above formula, λ represents the annealing coefficient, PcTo cross probability, PmIs the mutation probability.
Further, in step S7), the ending condition is that the maximum number of iterations is reached, or the difference between the global optimal values of 5 consecutive iterations is smaller than a preset value.
The invention also provides an economic load distribution system of the new energy power system, which comprises the following components:
the target function constructing program unit is used for constructing a target function of the new energy power system;
the initialization program unit is used for initializing the population according to preset constraint conditions and setting the position and the speed of each particle in the population, wherein the position of each particle is an economic load parameter of each generator set;
an adaptive value calculation program unit for calculating an adaptive value of each particle according to the objective function and the position of each particle, respectively;
an optimal value calculation program unit for calculating an individual optimal value of each particle and a global optimal value of the population according to the adaptive value of each particle;
the particle updating program unit is used for updating the speed and the position of each particle in the population according to a particle swarm algorithm;
the population updating program unit is used for carrying out selection, crossing and mutation operations of a genetic algorithm on the population to obtain a new population and updating the crossing probability and the mutation probability;
and the iterative computation program unit is used for controlling the adaptive value computation program unit, the optimal value computation program unit, the particle update program unit and the population update program unit to perform iterative computation until the end condition is met, outputting particles corresponding to the global optimal value, and taking the economic load parameter of each generator set in the positions of the particles as an optimal distribution result.
The invention also provides an economic load distribution system of the new energy power system, which comprises a computer device, wherein the computer device at least comprises a microprocessor and a memory, the microprocessor is programmed or configured to execute the steps of the economic load distribution method of the new energy power system, and the memory is stored with a computer program which is programmed or configured to execute the economic load distribution method of the new energy power system.
The invention also provides a computer readable storage medium having stored therein a computer program programmed or configured to execute the method of economic load distribution of a new energy power system.
Compared with the prior art, the invention has the following advantages:
1. the invention adopts the genetic algorithm, updates the population aiming at the next iteration in each iteration, jumps out of the dilemma of falling into the local optimum when the standard particle swarm algorithm is adopted for optimizing, breaks through the limitation on candidate results, is more favorable for finding the global optimum solution, and simultaneously adopts the annealing algorithm to calculate the cross probability value and the variation probability value required by the genetic algorithm in the next iteration, so that the cross probability value and the variation probability value are gradually reduced along with the increase of the iteration times, thereby converging to the optimum solution, avoiding the problems that the cross probability and the variation probability are always kept unchanged, the particle structure with high fitness is possibly damaged, and the random search tends to be carried out, and finally reducing the error between the optimum result and the actual condition.
2. The target function is constructed based on the actual situation of the new energy power system, and the generator set in the new energy power system is complex in construction situation and comprises not only a conventional generator set but also the new energy generator set, so that the existing model cannot be suitable for the new energy power system.
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FIG. 1 is a schematic flow chart of an embodiment of the present invention.
Detailed Description
The invention is further described below with reference to the drawings and specific preferred embodiments of the description, without thereby limiting the scope of protection of the invention.
The algorithm flow of the standard PSO is as follows:
step 1: initializing a population of particles (population size N), including random positions and velocities;
step 2: evaluating the fitness of each microparticle;
step 3: for each particle, comparing its fitness value to its experienced best position pbest, and if better, taking it as the current best position pbest;
step 4: for each particle, its fitness value is compared to the global best experienced position, gbest, and if so, gbest is reset
Step 5: the velocity and position of the particles are varied according to the following equation:
Vid＝w×Vid+c1×rand()×(pid-xid)+c2×Rand()×(pgd-xid) (1)
xid＝xid+Vid (2)
wherein: rand () and Rand () are two at [0, 1%]A random function varying within a range, w being an inertial weight (inertiaweight), c1And c2Acceleration constants (accelerationstats);
step 6: if the end condition is not met (typically a good enough adaptation value or a preset maximum number of generations T is reached), then Step2 is returned.
The PSO parameters include: population size N, inertial weight w, acceleration constants c1 and c2, maximum velocity Vmax, and maximum iteration number T. Wherein: the maximum velocity Vmax determines the resolution (or accuracy) of the region between the current position and the best position. If too high, the particles may fly too well; if too small, the particle search speed is slow. For 3 weight factors in the PSO algorithm: inertial weight w, acceleration constants c1 and c 2. The inertial weight w keeps the particles inertial in motion, making them prone to expand the search space with the ability to explore new regions. The acceleration constants c1 and c2 represent the weights of the statistical acceleration terms that push each particle toward the pbest and gbest locations. Introducing inertial weights w may eliminate the need for Vmax because their role is to maintain a balance of global and local search capabilities. Thus, as Vmax increases, a balanced search can be achieved by decreasing w. And a reduction in w may result in a smaller number of iterations being required. In this sense, Vmax can be fixed as the range of variation of the variable per dimension, and only w can be adjusted.
In order to avoid the problem that the standard PSO falls into local optimization, the improvement is carried out on the basis of the standard PSO, and the following steps are added in Step 6:
firstly, obtaining a new population through selection, crossing and mutation operations of a genetic algorithm;
the selection operation of the genetic algorithm is generally finished by a roulette method, the probability of each particle being selected is calculated according to the fitness of each particle in the original population, the set of the selected particles is used as a parent, and the global optimal solution of the PSO is used as a parent;
the crossover operation of the genetic algorithm means that chromosomes between a parent and a mother are exchanged according to crossover probability, so that offspring with partial chromosomes of the parent and the mother are obtained;
mutation operation of the genetic algorithm means that a certain bit or a plurality of bits in a descendant are replaced by random numbers with mutation probability for the descendant;
supplementing the particles with the highest fitness in the original population into a new population, so that the number of the particles of the new population is the same as that of the particles of the original population;
then, the cross probability value and the variation probability value at the next iteration are calculated through an annealing algorithm.
The method has the advantages that the genetic algorithm is added, so that the predicament of falling into local optimum when the standard PSO is adopted can be assisted, the limitation on candidate results in particle swarm is broken through, the global optimum solution can be found more conveniently, meanwhile, the cross probability value and the variation probability value in the genetic algorithm are calculated by adopting the annealing algorithm, the search speed for the optimum solution can be gradually reduced along with the increase of the iteration times, and the optimum solution is converged finally, so that the precision of the optimum solution is ensured, and the error between the calculation result and the actual condition is reduced.
As shown in fig. 1, the present embodiment provides an economic load distribution method for a new energy power system, including the following steps:
s1) constructing an objective function of the new energy power system;
s2) initializing the population according to preset constraint conditions, and setting the position and speed of each particle in the population, wherein the position of each particle is an economic load parameter of each generator set;
s3) respectively calculating an adaptive value of each particle according to the objective function and the position of each particle;
s4) calculating the individual optimal value of each particle and the global optimal value of the population according to the adaptive value of each particle;
s5) updating the speed and the position of each particle in the population according to the particle swarm algorithm;
s6) selecting, crossing and mutating the population by genetic algorithm to obtain a new population, and updating the crossing probability and the mutating probability;
s7) returning to the step S3) until the end condition is met, outputting the particles corresponding to the global optimal value, and taking the economic load parameters of each generator set in the positions of the particles as the optimal distribution result. And taking the economic load parameter of each generator set in the position of the particles as an optimal distribution result.
In step S1) of this embodiment, in order to receive wind energy and solar energy as much as possible, reduce the carbon dioxide emission of the system, and improve the energy saving and emission reduction benefits of the power grid, the objective function is:
Figure BDA0003310791330000061
in the above formula, j is the serial number of a single unit,
Figure BDA0003310791330000062
is a binary variable and represents the starting state of the jth unit at the moment t, 1 represents that the unit is starting, 0 represents that the unit is not in the starting state,
Figure BDA0003310791330000063
is a binary variable and represents the shutdown state of the jth unit at the moment t, 1 represents that the unit is shutdown, 0 represents that the unit is not in the shutdown state,
Figure BDA0003310791330000064
the output of the j machine set at the time t,
Figure BDA0003310791330000065
and
Figure BDA0003310791330000066
are all independent variables, NjT is a preset time length alpha for participating in optimizing the number of various unitsjFor startup of the jth unit coal consumption, betajFor shutdown of jth unit, coal consumption, ajSlope of coal consumption of a unit varying with power, bjIs a coal consumption constant of a unit, gamma is a carbon dioxide emission coefficient, wherein N isj、T、αj、βj、aj、bjThe parameters γ are known constants.
In step S2), initializing the population according to the preset constraint condition, so that each particle in the population includes the economic load parameters of all the generator sets in the new energy power system, comparing the two particles, wherein at least one of the generator sets has a difference in the economic load parameters, and setting Xi＝(x1，x2，…，xNj) Is the position of the ith particle in the population, where NjThe generators include conventional generator sets and new energy generator sets such as wind energy and solar energy, and x is the total number of generators in the system1，x2，…，xNjFrom 1 st station to N th stationjThe economic load parameters of the unit comprise start-stop conditions, output conditions and the like of the ith unit at each moment in time T, and the T-moment starting state of the jth unit can be obtained through the economic load parameters and unit types of all the units in the ith particle
Figure BDA0003310791330000067
Shutdown state of jth unit at time t
Figure BDA0003310791330000068
And the output of the jth unit at the time of t
Figure BDA0003310791330000069
Meanwhile, in step S2) of the present embodiment, the inertia weight w, the acceleration constants c1 and c2, the maximum speed Vmax, and the maximum iteration number T are also initialized.
Different constraint condition functions can be set according to actual conditions, and in the embodiment, the constraint conditions include: the new energy generator set accounts for more than 80%, the new energy power system outputs no less than 1000MW, the shutdown generator set accounts for less than 20% at each moment, and the operation duty ratio of each generator set is more than 80%. Under these constraints, it is desirable to increase the percentage of new-energy power generation units in a new-energy power system while maintaining output, reduce the downtime of each unit, improve the power generation efficiency, and reduce the amount of carbon dioxide emissions.
Step S3) of the present embodiment, the functional expression of the adaptive value for each particle is calculated as follows:
J＝minF(Xi) (4)
and (3) substituting the economic load parameters of all the generator sets in the particles into the result obtained by the formula (3) to obtain the adaptive value of each particle, wherein the result is the carbon dioxide emission of the new energy power system, and the optimizing direction is updated in the subsequent steps according to the adaptive value.
The determination of the individual optimal value and the global optimal value in step S4) of the present embodiment is substantially the same as that of the existing scheme, and will not be described herein too much.
In step S5) of this embodiment, the speed of the next iteration can be calculated according to equation (1) based on the current economic load parameter, the individual optimal value, and the global optimal value of each particle, and the economic load parameter of the next iteration can be calculated according to equation (2) based on the current economic load parameter, the speed of the next iteration of each particle, which are also the existing technical solutions and will not be described herein.
Step S6) of the present embodiment includes the following steps:
selecting m particles in a population as parents by using the particles corresponding to the global optimum value as parents through selection operation in a genetic algorithm, wherein the selection operation specifically comprises a roulette selection method, a random competition selection method, an optimal reservation selection method and the like, which belong to conventional methods in the genetic algorithm and are not described more;
by crossover operations in genetic algorithms, at a crossover probability PcThen, each parent and the mother are crossed to obtain m parentsOffspring so that it contains both the partial elements of the parent and the partial elements of the parent, e.g., X'i＝(x’1，x’2，…，x’Nj) As parent particles, X "i＝(x”1，x”2，…，x”Nj) Is a parent particle, the two are at a cross probability Pc(x ') obtaining a progeny particle'1，x’2，…，x”Nj) The method comprises the economic load parameters of some generators in parent particles and the economic load parameters of some generators in parent particles, and compared with the method of simply adopting a particle swarm algorithm, the method adopts the cross operation to open up a new search field for finding the optimal solution, and the cross operation belongs to a conventional method in a genetic algorithm and is not described more;
for m sub-generation particles, we then operate mutation in the genetic algorithm with a mutation probability PmIn the embodiment, the variation mode specifically includes that for the current child particle, the start-stop condition within the time T in the economic load parameter of the ith unit is randomly changed, the start-up state at partial time is randomly changed into the stop state, the variation operation is adopted to avoid the condition that the key elements of the cross operation for constructing the optimal solution due to deletion fall into the local optimal solution, the variation operation belongs to a conventional method in a genetic algorithm, and excessive description is not provided herein;
selecting particles in the original population according to the sequence of the adaptive values from high to low, and forming a new population by the selected particles and the m sub-generation particles so that the number of the particles in the new population is the same as that of the particles in the original population;
calculating the cross probability P in the next iterationcAnd the mutation probability PmIn this embodiment, the idea of the annealing algorithm is adopted, and the cross probability P in the next iteration is calculated according to the preset annealing coefficientcAnd the mutation probability PmThe function is expressed as follows:
Figure BDA0003310791330000071
in the above formula, λ represents the annealing coefficient, the value range is 0-1, and the cross probability P iscThe value range is 0.4-0.99, and the variation probability PmThe value range is 0.001-0.1, and through the steps, the cross probability P in each iteration is gradually reducedcAnd the mutation probability PmSo that the calculation result of each iteration gradually converges to the optimal value and the cross probability P is avoidedcAnd the mutation probability PmAlways remaining unchanged, may destroy the particle structure with high fitness and tends to the problem of random search.
In this example, λ is 0.5, PcInitial value of 1, PmThe initial value is 0.1, if the iteration times are not reached and the cross probability P in the next iteration is not reachedcAnd the mutation probability PmLess than the lower limit of the respective value range, using the crossover probability PcAnd the mutation probability PmThe lower limit value of each value range is used as the cross probability P in the next iterationcAnd the mutation probability Pm。
Finally, in step S7), after a preset end condition is met, the end condition in this embodiment is that the maximum number of iterations is reached, or a difference between global optimal values of 5 consecutive iterations is smaller than a preset value, particles corresponding to the global optimal values are output, and operations of the units of the new energy power system are configured according to start and stop conditions of the conventional power generator set and the new energy power generator set in the particles at each moment in time T, so that carbon dioxide emission of the new energy power system can be reduced while output electric quantity is maintained, and energy saving and emission reduction benefits of the power grid are improved.
In addition, this embodiment still provides an economic load distribution system of new forms of energy electric power system, includes:
the target function constructing program unit is used for constructing a target function of the new energy power system;
the initialization program unit is used for initializing the population according to preset constraint conditions and setting the position and the speed of each particle in the population, wherein the position of each particle is an economic load parameter of each generator set;
an adaptive value calculation program unit for calculating an adaptive value of each particle according to the objective function and the position of each particle, respectively;
an optimal value calculation program unit for calculating an individual optimal value of each particle and a global optimal value of the population according to the adaptive value of each particle;
the particle updating program unit is used for updating the speed and the position of each particle in the population according to a particle swarm algorithm;
the population updating program unit is used for carrying out selection, crossing and mutation operations of a genetic algorithm on the population to obtain a new population and updating the crossing probability and the mutation probability;
and the iterative computation program unit is used for controlling the adaptive value computation program unit, the optimal value computation program unit, the particle update program unit and the population update program unit to perform iterative computation until the end condition is met, outputting particles corresponding to the global optimal value, and taking the economic load parameter of each generator set in the positions of the particles as an optimal distribution result.
In addition, the present embodiment also provides an economic load distribution system of a new energy power system, including a computer device including at least a microprocessor and a memory, the microprocessor being programmed or configured to perform the steps of the economic load distribution method of the new energy power system. In this embodiment, the memory stores a computer program programmed or configured to execute the economic load distribution method of the new energy power system.
Furthermore, the present embodiment also provides a computer-readable storage medium having stored therein a computer program programmed or configured to execute the economic load distribution method of the new energy power system.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein. The present application is directed to methods, apparatus (systems), and computer program products according to embodiments of the application wherein instructions, which execute via a flowchart and/or a processor of the computer program product, create means for implementing functions specified in the flowchart and/or block diagram block or blocks. These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks. These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The foregoing is considered as illustrative of the preferred embodiments of the invention and is not to be construed as limiting the invention in any way. Although the present invention has been described with reference to the preferred embodiments, it is not intended to be limited thereto. Therefore, any simple modification, equivalent change and modification made to the above embodiments according to the technical spirit of the present invention should fall within the protection scope of the technical scheme of the present invention, unless the technical spirit of the present invention departs from the content of the technical scheme of the present invention.

Claims (10)

1. The economic load distribution method of the new energy power system is characterized by comprising the following steps of:
s1) constructing an objective function of the new energy power system;
s2) initializing the population according to preset constraint conditions, and setting the position and speed of each particle in the population, wherein the position of each particle is an economic load parameter of each generator set;
s3) respectively calculating an adaptive value of each particle according to the objective function and the position of each particle;
s4) calculating the individual optimal value of each particle and the global optimal value of the population according to the adaptive value of each particle;
s5) updating the speed and the position of each particle in the population according to the particle swarm algorithm;
s6) selecting, crossing and mutating the population by genetic algorithm to obtain a new population, and updating the crossing probability and the mutating probability;
s7) returning to the step S3) until the end condition is met, outputting the particles corresponding to the global optimal value, and taking the economic load parameters of each generator set in the positions of the particles as the optimal distribution result.
2. The economic load distribution method of the new energy power system according to claim 1, wherein the objective function of the new energy power system in step S1) is:
Figure FDA0003310791320000011
in the above formula, j is the serial number of a single unit, Yj tIs a binary variable and represents the starting state of the jth unit at the moment t, 1 represents that the unit is starting, 0 represents that the unit is not in the starting state,
Figure FDA0003310791320000012
is a binary variable and represents the shutdown state of the jth unit at the moment t, 1 represents that the unit is in shutdown, 0 represents that the unit is not in the shutdown state, and P represents that the unit is in the shutdown statej tThe output of the jth unit at time t, NjT is a preset time length alpha for participating in optimizing the number of various unitsjFor startup of the jth unit coal consumption, betajFor shutdown of jth unit, coal consumption, ajSlope of coal consumption of a unit varying with power, bjIs a coal consumption constant of a unit, and gamma is a carbon dioxide emission coefficient.
3. The economic load distribution method of the new energy power system according to claim 1, wherein in step S2), the position expression of each particle in the particle group is:
Xi＝(x1，x2，…，xNj)
in the above formula, i is the number of the particle, NjIs the total number of generators in the system, x1，x2，…，xNjFrom 1 st station to N th stationjAnd the economic load parameters of the unit comprise the starting and stopping conditions and the output conditions of the current unit at each moment in time T.
4. The economic load distribution method of the new energy power system according to claim 1, wherein the step of performing genetic algorithm selection, crossing and mutation operations on the population to obtain the new population in step S6) specifically comprises:
taking the particles corresponding to the global optimal value as parent generations, and screening m particles in the population as parent generations;
at cross probability PcThen, respectively crossing each parent and the parent to obtain m child generations, so that the child generations not only contain partial elements in the parent, but also contain partial elements in the parent;
at the mutation probability PmNext, obtaining new progeny particles through the variation of the progeny particles;
and selecting the particles in the original population according to the sequence from high to low of the adaptive value, and forming a new population by the selected particles and the m sub-generation particles so that the number of the particles in the new population is the same as that in the original population.
5. The economic load distribution method of the new energy power system according to claim 4, wherein obtaining new child particles by child particle variation specifically comprises: and for the current child particles, randomly selecting a unit, and changing the starting state of the selected unit at a random moment in the time T in the economic load parameter into the stopping state.
6. The economic load distribution method of the new energy power system according to claim 1, wherein the step of updating the cross probability and the variation probability in step S6) specifically comprises: calculating the cross probability P in the next iteration according to the preset annealing coefficientcAnd the mutation probability PmThe function is expressed as follows:
Figure FDA0003310791320000021
in the above formula, λ represents the annealing coefficient, PcTo cross probability, PmIs the mutation probability.
7. The economic load distribution method of the new energy power system according to claim 1, wherein the ending condition in step S7) is that the maximum number of iterations is reached, or the difference between the global optimal values of 5 consecutive iterations is less than a preset value.
8. An economic load distribution system for a new energy power system, comprising:
the target function constructing program unit is used for constructing a target function of the new energy power system;
the initialization program unit is used for initializing the population according to preset constraint conditions and setting the position and the speed of each particle in the population, wherein the position of each particle is an economic load parameter of each generator set;
an adaptive value calculation program unit for calculating an adaptive value of each particle according to the objective function and the position of each particle, respectively;
an optimal value calculation program unit for calculating an individual optimal value of each particle and a global optimal value of the population according to the adaptive value of each particle;
the particle updating program unit is used for updating the speed and the position of each particle in the population according to a particle swarm algorithm;
the population updating program unit is used for carrying out selection, crossing and mutation operations of a genetic algorithm on the population to obtain a new population and updating the crossing probability and the mutation probability;
and the iterative computation program unit is used for controlling the adaptive value computation program unit, the optimal value computation program unit, the particle update program unit and the population update program unit to perform iterative computation until the end condition is met, outputting particles corresponding to the global optimal value, and taking the economic load parameter of each generator set in the positions of the particles as an optimal distribution result.
9. An economic load distribution system of a new energy power system, comprising a computer device, wherein the computer device at least comprises a microprocessor and a memory, wherein the microprocessor is programmed or configured to execute the steps of the economic load distribution method of the new energy power system according to any one of claims 1 to 7, and the memory stores a computer program programmed or configured to execute the economic load distribution method of the new energy power system according to any one of claims 1 to 7.
10. A computer-readable storage medium having stored therein a computer program programmed or configured to execute the method of allocating economic load of a new energy power system according to any one of claims 1 to 7.
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