CN115085276A - Power generation scheduling method and system in power system - Google Patents
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Abstract
The invention discloses a power generation dispatching method and a power generation dispatching system in a power system, wherein the method comprises the following steps: obtaining model parameters; establishing a power generation dispatching model according to the model parameters; solving a power generation scheduling model to obtain a power generation scheduling scheme; the method for solving the power generation scheduling model comprises the following steps: dividing a particle swarm composed of the generated power of each generator into three sub-populations; updating each sub-population by adopting different updating strategies; the particles in the different sub-populations are exchanged in an iterative process. The method adopts three updating strategies of balance, biased local mining and biased global exploration to update various groups through a multi-group information sharing strategy, and enables various groups to learn the advantages of other groups and carry out coordinated updating through a communication exchange mechanism.
Description
Technical Field
The invention relates to the technical field of electric power, in particular to a power generation scheduling method and system in an electric power system.
Background
The power generation dispatching is one of the most basic energy management problems in the power system, and aims to realize the supply and demand balance of the total output power and the load demand of the generator set at the minimum cost under the premise of ensuring the safe and stable operation of the power grid and under the comprehensive consideration of the constraints of the physical properties of each generator set, the load end and even the power transmission link.
Since the concept of optimal distribution of unit power generation was proposed in 1919, power workers made a lot of work and made a lot of major breakthroughs. The formula of equal consumption micro-increment rate as proposed in 1934, the classical coordination equation considering network loss as proposed in 1952, and the newton's method as proposed in 60's of 20 th century are milestone achievements and are called as classical economic dispatching methods. Classical economic dispatch has strict requirements on initial values, and improper assignment can cause algorithm divergence. In addition, the classical method has long calculation time and poor convergence, is difficult to process system safety constraint, and is only suitable for small-scale power systems. Based on the defects of the classical economic dispatching method, various traditional optimization methods are provided, such as linear programming, a direct search algorithm, a lambda iterative algorithm and the like, which can effectively solve an objective function and are the economic dispatching problem of a convex function. However, the traditional optimization algorithm generally assumes that the cost function is a monotonous continuous piecewise linear function, and is difficult to handle the non-linear and non-continuous conditions. Furthermore, in systems with hybrid power generation units on a large scale, the conventional algorithm suffers from oscillation problems, resulting in long convergence times. In order to avoid these problems, some random search algorithms with heuristic characteristics are proposed, such as evolutionary programming, differential evolution algorithm, genetic algorithm, particle swarm algorithm, and the like. The method has strong global optimization capability and is suitable for solving the optimization problem which cannot be solved by the conventional optimization algorithm in the complex environment.
However, the intelligent optimization algorithm, such as a particle swarm algorithm, an evolutionary algorithm, a harmony search algorithm, an ant colony algorithm, and the like, still has the disadvantages of low optimization precision and slow algorithm speed when dealing with the economic scheduling nonlinear optimization operation problem with numerous complex constraints, such as power balance constraint, power generation capacity constraint, forbidden operation region constraint, climbing rate limitation, and the like.
Disclosure of Invention
The embodiment of the invention provides a power generation scheduling method and system in a power system, which are used for solving the problems of low precision and low speed of an intelligent optimization method in the prior art.
In one aspect, an embodiment of the present invention provides a power generation scheduling method in an electric power system, including:
obtaining model parameters;
establishing a power generation dispatching model according to the model parameters;
solving a power generation scheduling model to obtain a power generation scheduling scheme;
the method for solving the power generation scheduling model comprises the following steps:
dividing a particle swarm composed of the generated power of each generator into three sub-populations;
updating each sub-population by adopting different updating strategies;
and exchanging the particles in different sub-populations in the iteration process until the iteration is finished.
In another aspect, an embodiment of the present invention provides a power generation scheduling system in an electric power system, including:
the parameter acquisition module is used for acquiring model parameters;
the model establishing module is used for establishing a power generation dispatching model according to the model parameters;
the model solving module is used for solving the power generation scheduling model to obtain a power generation scheduling scheme;
wherein, the model solving module comprises:
the group division submodule is used for dividing a particle swarm formed by the power generation power of each generator into three sub-groups;
the population updating submodule is used for updating each sub population by adopting different updating strategies;
and the particle exchange submodule is used for exchanging the particles in different sub-populations in the iteration process until the iteration is finished.
The power generation scheduling method and system in the power system have the following advantages:
the method comprises the steps of averagely dividing a population into three parts through a multi-population information sharing strategy, updating various populations by using three updating strategies of balance, biased local mining and biased global exploration respectively, evaluating through state indexes of the various populations, adaptively selecting the populations to perform particle exchange, enabling the various populations to learn the advantages of other populations through a communication exchange mechanism, performing coordinated updating, combining the strategy with a particle swarm algorithm, and applying the strategy to the problem of processing more complex and multi-constraint optimized operation such as economic dispatching, so as to achieve the purposes of improving the optimization speed, improving the optimization accuracy and avoiding the trapping of local optimal solution.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flowchart of a power generation scheduling method in an electric power system according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 is a flowchart of a power generation scheduling method in an electric power system according to an embodiment of the present invention. The embodiment of the invention provides a power generation scheduling method in a power system, which comprises the following steps:
and S100, obtaining model parameters.
Illustratively, the model parameters include the total system load P, and the parameter α of each generator set j 、β j 、γ j 、e j 、f j And of generated powerUpper and lower limits
Wherein, j 1,2.
In an embodiment of the present invention, the total system load is set to 850MW, while the other model parameters are shown in table 1.
TABLE 13 model parameters for a genset
Further, besides the need to obtain model parameters, it is also necessary to obtain user-defined data, including a proportional value rate, a transfer period T, and a maximum number of iterations T max Maximum number of successive iterations T', number of particles N per population, inertial weight w max 、w min 。
In the embodiment of the invention, the rate is 5%, the transfer period T is 200, and the maximum iteration number T max 5000, 200 maximum continuous iteration times T', 10 particle number N of each sub-population, and maximum and minimum values w of inertia weight max ＝0.7、w min 0.2, 1.5 of the lavi flight coefficient beta, and an initial coefficient z (0) Taking an arbitrary value (but not 0.5) within the range of 0 to 1.
And S110, establishing a power generation dispatching model according to the model parameters.
And S120, solving the power generation scheduling model to obtain a power generation scheduling scheme.
Exemplarily, S120 specifically includes: s121, dividing particle swarms formed by the power generation power of each generator into three sub-populations; s122, updating each sub-population by adopting different updating strategies; and S123, exchanging the particles in different sub-populations in the iteration process until the iteration is finished.
Before solving the power generation scheduling model, the current iteration time t needs to be set to 0. Then randomly generating 3N particles in a constraint range, and randomly and averagely dividing the population into three sub-populations, wherein x is respectively Ai 、x Bi 、x Ci Also referred to as first, second and third sub-populations, or a, B and C sub-populations, wherein Ai 1,2,.. said N, Bi 1,2,.. said. N, Ci 1,2,. said.n.
After the solution is started, firstly calculating the fitness of each particle to obtain and record the current global optimal solution x gbest I.e. the current best power generation scheduling scheme, the best and worst particles x per sub-population Abest 、x Bbest 、x Cbest And x Aworst 、x Bworst 、x Cworst And the historical optimal value p of the ith particle in the jth dimension in the A sub-population A,ij . Then the particles in each sub-population are updated.
In the embodiment of the invention, when the particles in each sub-population are updated, different speed updating strategies are firstly adopted to update the speed of the particles in each sub-population; and then updating the position of the particles in each sub-population according to the updated particle velocity.
Specifically, the particle velocity update can be performed by adopting a balancing strategy, a biased local mining strategy and a biased global exploration strategy for A, B and C sub-populations respectively.
For the A sub-population, the particle velocity can be updated as follows:
v A，ij (t+1) ＝w(t)v A，ij (t) +c 1 (t)r 1 (t) [p A，ij (t) -x A，ij (t) ]+c 2 (t)r 2 (t) [x Abest，j (t) -x A，ij (t) ]
where w is the dynamic inertial weight of a concave function model, c 1 、c 2 Is a learning factor, r 1 、r 2 Is a random number with a size of 0-1. Through dynamic adjustment of the learning factor, the A sub-population has stronger global search capability in the early stage of iteration, and the search precision in the later stage of iteration is improved, so that balanced updating is achieved. Therefore, updating the particles of the a sub-population by the above formula is called an equalization strategy.
For the B sub-population, particle velocity update is performed according to the following formula:
z (t+1) ＝4z (t) (1-z (t) )
v’ B，ij (t+1) ＝V B，ij (t) +r 3 (2z (t+1) -1)(2 Bbest，ij (t) -x B，ij (t) -x Bworst，ij (t) )
wherein r is 3 、r 4 Is a random number between 0 and 1, when the initial coefficient z (0) In [0, 1 ]]And when the value of z is not equal to 0.5 in the interval, the value of z can never be repeated, and the generated uniform random sequence is crucial to improving the performance of the mutation operation. The B sub-population is subjected to slight variation through a chaotic algorithm, and then the whole population approaches to the optimal solution of the population, so that the mining capability of the local optimal solution is improved. Therefore, updating the particles of the B sub-population by the above formula is referred to as a biased local mining strategy.
For the C sub-population, the velocity of each particle in the C sub-population is updated according to the following formula:
RL＝0.05×S
RB＝r 5
wherein beta is the distribution coefficient of the Laevir flight between 0 and 2, and r 5 Is a random number between 0 and 1. The C sub-population aims to improve the global exploration capability by utilizing the excellent exploration efficiency and performance of the levy flight algorithm, and therefore, updating the particles of the C sub-population through the formula is called a biased global exploration strategy.
After the velocity update of the particles in each sub-population is completed, the particle positions of each population may be updated according to the following formula:
in the embodiment of the present invention, after the velocity and position of the particles in each sub-population are updated, boundary processing needs to be performed on the particles in each sub-population. Specifically, the boundary processing may be performed on the particles according to the following formula:
after completing the position update of the particles in each sub-population, it is also necessary to: determining the fitness of each particle; for the particles in the A sub-population, updating the part of the particles with the highest fitness by using a greedy algorithm; for the particles in the B sub-population, updating all the particles by a greedy algorithm; and for the particles in the C sub-population, keeping the optimal particles in the updated C sub-population.
Specifically, for the a sub-population, only half of the particles with the highest fitness are updated by the greedy algorithm, that is, if the new particle fitness is inferior to the original particle, the particle is not updated. Simultaneously recording the optimal particles x of the current sub-population Abest . For the B sub-population, all the particles are updated by a greedy algorithm, and the optimal particle x of the current sub-population is recorded at the same time Bbest . For the C sub-population, only the optimal particles in the updated C sub-population are reserved, and the optimal particles x of the current sub-population are recorded Cbest 。
After the above treatment is completed, the particles in different sub-populations can be exchanged. The switching method specifically comprises the following steps: after each iteration, the optimal particles x in the B and C sub-populations are determined Bbest And x Cbest Any two particles in the A sub-population are replaced, so that the information of the optimal solution obtained by the B sub-population in local mining and the C sub-population in global exploration is obtained at the same time, and the A sub-population is helped to be updated better and evenly; when the optimal particles in the B sub-population are not changed for a plurality of times continuously (namely when the update of the sub-population is stagnated), replacing any one particle in the A and C sub-populations with any two particles in the B sub-population to increase the diversity of the B sub-population and help the B sub-population jump out of a local optimal point, and when the particles in the A and C sub-populations are superior to the optimal particles in the B sub-population, immediately replacing any one particle in the B sub-population with the particles, so that the B sub-population is excavated next to the current global optimal point; and when the ratio of the difference value of the fitness of the worst particle and the optimal particle in the C sub-population to the optimal particle is smaller than a set ratio value rate, replacing one particle in the C sub-population with the worst particle in the A sub-population so as to increase the diversity of the C sub-population and prevent the C sub-population from excessively converging.
Specifically, each time T iterations are performed, particle replacement is required, and when the optimal particles in the second sub-population are not changed for T consecutive times, particle replacement is required, and after each iteration, if it is found that particles existing in the first and third sub-populations are better than the optimal particles in the second sub-population, particle replacement is also required; and when the ratio of the fitness difference value of the worst particle and the optimal particle in the C sub-population to the optimal particle is smaller than a set ratio value rate, particle replacement is required. Moreover, random substitution is adopted when particle substitution is carried out.
After each iteration is finished, the current iteration times are overlapped by 1, the operation of updating the particle speed to the particle substitution is repeated until the current iteration times reach the set maximum iteration times, and then the global optimal solution x can be output gbest And the optimal solution is a globally optimal power generation scheduling scheme.
An embodiment of the present invention further provides a power generation scheduling system in an electric power system, where the system includes:
the parameter acquisition module is used for acquiring model parameters;
the model establishing module is used for establishing a power generation dispatching model according to the model parameters;
the model solving module is used for solving the power generation scheduling model to obtain a power generation scheduling scheme;
wherein, the model solving module comprises:
the group division submodule is used for dividing a particle swarm formed by the power generation power of each generator into three sub-groups;
the population updating submodule is used for updating each sub population by adopting different updating strategies;
and the particle exchange submodule is used for exchanging the particles in different sub-populations in the iteration process until the iteration is finished.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (7)
1. A method for scheduling power generation in a power system is characterized by comprising the following steps:
obtaining model parameters;
establishing a power generation scheduling model according to the model parameters;
solving the power generation scheduling model to obtain a power generation scheduling scheme;
the method for solving the power generation scheduling model comprises the following steps:
dividing a particle swarm composed of the generated power of each generator into three sub-populations;
updating each sub population by adopting different updating strategies;
and exchanging the particles in different sub-populations in the iteration process until the iteration is finished.
2. The method according to claim 1, wherein the updating with different updating strategies for each sub-population comprises:
updating the speed of the particles in each sub-population by adopting different speed updating strategies;
and updating the position of the particle in each sub-population according to the updated particle speed.
3. The method according to claim 2, wherein the updating the speed of the particles in each of the sub-populations by using different speed updating strategies comprises:
and respectively updating the particle speed of the first sub population, the second sub population and the third sub population by adopting a balance strategy, a biased local mining strategy and a biased global exploration strategy.
4. The method according to claim 2, further comprising, after updating the positions of the particles in each of the sub-populations according to the updated particle velocities:
determining the fitness of each particle;
for the particles in the first sub-population, updating the part of the particles with the highest fitness by using a greedy algorithm;
for the particles in the second sub-population, updating all the particles by a greedy algorithm;
for particles in a third of said sub-populations, the optimal particles in the sub-population after the update are retained.
5. The method according to claim 4, wherein exchanging the particles in the different sub-populations comprises:
after each iteration, replacing any two particles in the first sub-population with the optimal particles in the second and third sub-populations;
replacing any particle in the first and third said sub-populations with any two particles in the second said sub-population when the optimum particle in the second said sub-population has not changed a number of consecutive times, and replacing any particle in the second said sub-population with a particle when there is a particle in the first and third said sub-populations that is better than the optimum particle in the second said sub-population;
and when the fitness ratio of the difference value between the worst particle and the optimal particle in the third sub-population to the optimal particle is smaller than a set ratio value, replacing a particle in the third sub-population with the worst particle in the first sub-population.
6. The method according to claim 2, further comprising, after updating the positions of the particles in each of the sub-populations according to the updated particle velocities:
and carrying out boundary treatment on the particles in each sub-population.
7. A power generation scheduling system in an electric power system, comprising:
the parameter acquisition module is used for acquiring model parameters;
the model establishing module is used for establishing a power generation dispatching model according to the model parameters;
the model solving module is used for solving the power generation scheduling model to obtain a power generation scheduling scheme;
wherein the model solving module comprises:
the group division submodule is used for dividing a particle swarm formed by the power generation power of each generator into three sub-groups;
the population updating submodule is used for updating each sub population by adopting different updating strategies;
and the particle exchange submodule is used for exchanging the particles in different sub-populations in the iteration process until the iteration is finished.
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