CN115603330A - Power distribution network optimization method based on self-adaptive discrete particle swarm algorithm - Google Patents
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Abstract
The invention relates to a digital power grid intelligent operation and control technology, and aims to provide a power distribution network optimization method based on a self-adaptive discrete particle swarm algorithm. The optimization method is used for adjusting the optimal output values of reactive compensation equipment in the power distribution network under different load levels and power generation levels, so that the sum of the overall network loss of the power distribution network and the voltage deviation of each node is comprehensively minimized; after a particle position matrix is constructed on the basis of reactive output power or adjustment gears of reactive compensation equipment, network loss and voltage deviation of a power distribution network at different particle positions are calculated by using a self-adaptive discrete particle swarm algorithm, a global optimal particle position matrix is obtained through calculation after multiple iterations, and reactive power values or adjustment gears to be output by each reactive compensation equipment are obtained and used for achieving reactive power adjustment. The invention can optimize continuous variables and discrete variables simultaneously, can greatly improve the efficiency of algorithm solution, enhances the capability of particle swarm algorithm to search global optimal solution, and is more suitable for actual power distribution network scenes.
Description
Technical Field
The invention relates to a power distribution network optimization method based on a self-adaptive discrete particle swarm algorithm, and belongs to the digital power grid intelligent operation and control technology.
Background
The new energy represented by photovoltaic power generation and wind power generation has rapid power development and wide prospect, but due to the inherent characteristics of illumination and wind energy, the photovoltaic power generation and the wind power generation have strong randomness and volatility, and great challenge is brought to the economic and safe operation of a power distribution network under the high penetration of new energy.
With the acceleration of the digital transformation pace of the power grid and the rapid upgrade of the communication technology, the information interaction between different nodes in the power distribution network has stronger instantaneity and stability, which lays a solid foundation for the appearance of a new method and a new technology. The heuristic optimization algorithm is widely applied to operation optimization of the power distribution network, the existing power distribution network optimization method mainly comprises a basic particle swarm algorithm, a genetic algorithm, a simulated annealing algorithm and the like, but the methods have the problems that a global optimal solution cannot be found accurately, the calculation time is too long and the like, cannot meet the requirements of the power distribution network on real-time performance, reliability, safety and the like, and have very limited application value.
Disclosure of Invention
The invention aims to solve the technical problem of overcoming the defects in the prior art and provides a power distribution network optimization method based on a self-adaptive discrete particle swarm algorithm.
In order to solve the technical problem, the solution of the invention is as follows:
the method is characterized in that the optimal output values of reactive compensation equipment in the power distribution network under different load levels and power generation levels are adjusted, so that the sum of the overall network loss of the power distribution network and the voltage deviation of each node is comprehensively minimized; the optimization method specifically comprises the following steps:
(1) Acquiring parameters and operation data of reactive compensation equipment participating in reactive power optimization of the power distribution network, and constructing a particle position matrix by using reactive output power or adjustment gears of the reactive compensation equipment;
(2) Randomly initializing to form a plurality of particle position matrixes and a plurality of particle speed matrixes, and updating the particle positions and setting a fitness function; calculating the network loss and voltage deviation of the power distribution network at different particle positions by using a self-adaptive discrete particle swarm algorithm, and calculating after multiple iterations to obtain a global optimal particle position matrix g;
(3) Calculating by using the global optimal particle position matrix g to obtain a reactive power value or an adjustment gear which each reactive compensation device should output; and the local controller of each reactive compensation device adjusts respective reactive power output according to the instruction issued by the control center, so that reactive adjustment on the whole situation of the power distribution network is realized to stabilize the node voltage of the power distribution network and reduce the network loss.
Compared with the prior art, the invention has the beneficial effects that:
1. the method is characterized in that a continuous updating method and a discrete updating method are respectively used for the continuously changing particle positions and the discretely changing particle positions, the basic particle swarm algorithm and the discrete particle swarm algorithm are integrated, the continuous variable and the discrete variable can be simultaneously optimized, and the method is more suitable for actual power distribution network scenes.
2. According to the invention, the key coefficients in the algorithm are dynamically adjusted based on the current state of the particles, so that the algorithm solving efficiency is greatly improved, and the capability of the particle swarm algorithm for searching the global optimal solution is enhanced.
3. The invention can carry out comprehensive calculation aiming at different types and quantities of reactive compensation equipment in the power distribution network, and solve the optimal reactive compensation strategy of the power distribution network under different operating conditions, thereby reducing the network loss and voltage deviation of the power distribution network, and improving the power quality and the power supply reliability.
Drawings
FIG. 1 is a schematic diagram of a 33-node power distribution network;
FIG. 2 is a flow chart of an adaptive discrete particle swarm algorithm.
Detailed Description
The principles and features of this invention are described below in conjunction with the following drawings, which are set forth by way of illustration only and not by way of limitation with respect to the scope of the invention.
The invention provides a power distribution network optimization method based on a self-adaptive discrete particle swarm algorithm, which is used for adjusting optimal output values of reactive compensation equipment in a power distribution network under different load levels and power generation levels so as to minimize the comprehensive sum of the overall network loss of the power distribution network and the voltage deviation of each node; the optimization method specifically comprises the following steps:
(1) Acquiring parameters and operation data of reactive compensation equipment participating in reactive power optimization of the power distribution network, and constructing a particle position matrix by using reactive output power or adjustment gears of the reactive compensation equipment;
the reactive compensation equipment comprises two types of compensation equipment for continuously adjusting reactive output and equipment for adjusting reactive output by stepping. The former can be selected as a static var generator or a photovoltaic inverter, and the position of the particle is the reactive power value which the particle should output; the latter can be selected as an on-load tap changer or a capacitor, and the particle position is the gear to be selected.
(2) Randomly initializing to form a plurality of particle position matrixes and a plurality of particle speed matrixes, and updating the particle positions and setting a fitness function; calculating the network loss and voltage deviation of the power distribution network at different particle positions by using a self-adaptive discrete particle swarm algorithm, and calculating after multiple iterations to obtain a global optimal particle position matrix g;
the specific calculation steps of the self-adaptive discrete particle swarm algorithm are as follows:
(2.1) counting the number m of devices participating in reactive power regulation in the power distribution network, and randomly initializing to form an initial particle swarm position matrix X = [ X ] 1 ，x 2 ，…，x m ]Initial velocity matrix V = [ V ] corresponding to particle 1 ，v 2 ，…，v m ](ii) a Wherein x is m 、v m Are column vectors of size n × 1, n representing the number of particles;
(2.2) initializing to form an individual optimal particle position matrix P = [ P ] 1 ，p 2 ，…，p m ](ii) a Wherein p is m Is a column vector of size n × 1; initializing to form an individual fitness extreme value pbest which is a column vector with the size of n multiplied by 1; initializing to form a global fitness extreme value gbest;
(2.3) calculating the fitness f (X (i)) of each particle, wherein f is a fitness function, and X (i) is the ith particle;
if f (X (i)) > pbest (i), let pbest (i) = f (X (i)) and P (i) = X (i); when all the particles in the particle swarm are calculated, let gbest = max (pbest); wherein max represents a function of taking a maximum value, and assigns the particle position with the fitness of gbest to a global optimal particle position matrix g;
the fitness function f is specifically represented by the following formula:
in the formula, ω p 、ω u Is a weight coefficient; p loss 、P′ loss Are respectively provided withThe network loss of the power distribution network before and after optimization; du, du' are the voltage deviation before and after optimizing respectively, calculate according to following formula and obtain:
in the formula, m is the total number of the node voltages; u. of N Is the rated voltage value of the node; u. of i Is the actual voltage value of the ith node.
The inertia coefficient w is dynamically adjusted according to the fitness of the current particle, which is specifically as follows:
in the formula, w max 、w min The maximum value and the minimum value of the inertia coefficient are obtained; f i The fitness of the ith particle is f (X (i)); f av′ Is an average individual fitness limit value of
F av Is the average fitness of individuals, and has a value of
F m The maximum fitness is max (F) i )；k 1 、k 2 To adjust the coefficients.
(2-4) updating the velocity matrix of the particle population according to:
v k+1( i，j)＝w*v k (i，j)+c 1 *rand()*(p k (i，j)-x k (i，j))+c 2 *rand()*(g-x k (i，j)) (1.1)
in the formula, v k+1 (i，j)、v k (i, j) are values corresponding to the ith row and the jth column of the velocity matrix V in the (k + 1) th iteration and the kth iteration respectively; x is the number of k (i, j) is a particle swarm location matrix X, p at the kth iteration k (i, j) is the particle individual optimal position matrix P ith row jth column correspondingA value of (d); w is the coefficient of inertia; c. C 1 、c 2 Is an acceleration factor; rand () is a random number generating function for generating random numbers in the range of 0 to 1;
(2.5) according to whether the reactive compensation equipment can continuously perform reactive power regulation, respectively adopting different methods to update the positions of the particle swarms:
for continuously adjustable devices, the position matrix of the population of particles is updated according to:
x k+1 (i，j)＝x k (i，j)+v k+1 (i，j) (1.2)
for equipment which cannot continuously perform reactive power regulation, updating the position matrix of the particle swarm according to the following formula:
in the formula, x k+1 (i, j) is a value corresponding to the ith row and the jth column of the particle swarm location matrix X in the (k + 1) th iteration; tanh () is a hyperbolic tangent function; else means that rand () < tan h (v) is removed k+1 (i, j)) and-rand () > tanh (v) k+1 (i, j)) other than the case;
(2.6) repeating the steps (2.3) to (2.5) until the maximum iteration number, wherein the global optimal particle position matrix g is the optimal output value of the reactive power compensation equipment and is a column vector with the size of n multiplied by 1, and each reactive power compensation device correspondingly adjusts and outputs reactive power or gears according to the value in the global optimal particle position matrix g.
(3) Calculating by using the global optimal particle position matrix g to obtain a reactive power value or an adjustment gear which each reactive compensation device should output; and the local controller of each reactive compensation device adjusts respective reactive power output according to the instruction issued by the control center, so that reactive adjustment on the whole situation of the power distribution network is realized to stabilize the node voltage of the power distribution network and reduce the network loss.
The implementation process of the present invention is further explained by specific application examples as follows:
the embodiment provides a power distribution network optimization method based on a self-adaptive discrete particle swarm algorithm, which can comprehensively calculate different types and numbers of reactive compensation equipment in a power distribution network and solve the optimal reactive compensation strategy of the power distribution network under different operation conditions, thereby reducing the network loss and voltage deviation of the power distribution network and improving the power quality and the power supply reliability.
The 33-node power distribution network and the node numbers optimized in this embodiment are shown in fig. 1, where node 1 is a balance node, the other nodes are PQ nodes, and an on-load tap changer is installed between node 1 and node 2. Photovoltaic power is connected to the node 7 and the node 25, wind power generators are connected to the node 22 and the node 33, static var generators are connected to the node 9 and the node 29, and capacitors are connected to the node 16 and the node 31.
The equipment participating in reactive power regulation is as follows: the static var generators SVC1 and SVC2 of the node 9 and the node 29 have the adjusting range of-0.5 Mvar to 0.5Mvar; the adjustment ranges of the capacitors SC1 and SC2 at the node 16 and the node 31 are both 5 multiplied by 0.1Mvar; the on-load tap changer (OLTC) has an adjustment range of-4 multiplied by 1.25% to 4 multiplied by 1.25%.
The flow of the adaptive discrete particle swarm algorithm is shown in fig. 2, in this embodiment, the number of devices participating in reactive power regulation is 5, the particle swarm size is 100, and the maximum iteration number is 100.
Firstly, randomly initializing to generate a particle swarm position matrix X = [ X ] 1 ，x 2 ，…，x 5 ]Sum velocity matrix V = [ V ] 1 ，v 2 ，…，v 5 ]Wherein x is 1 ～x 5 A column vector of 100 × 1, representing the output reactive power of SVC1 and SVC2 and the gear of SC1, SC2 and OLTC, x 1 、x 2 Is a continuous variable, x 3 、x 4 、x 5 Are discrete variables. v. of 1 ～v 5 The column vector, 100 x 1, represents the velocity of the corresponding particle position, and is a continuous variable.
Forming a particle individual optimal position matrix P = [ P ] through initialization 1 ，p 2 ，…，p 5 ]Wherein p is 1 ～p 5 Column vectors of size 100 × 1; initializing to form an individual fitness extreme value pbest, wherein the size of the column vector is 100 multiplied by 1; and initializing to form a global fitness extreme value gbest.
Calculating the fitness f (X (i)) of each particle, wherein f is a fitness function and is defined as the following formula:
in the formula, ω p 、ω u In this embodiment, ω is taken as a weight coefficient p ＝ω u ＝1；P loss 、P′ loss Respectively performing network loss before and after optimization; du and du' are voltage deviation before and after optimization respectively, and du is defined as the following formula:
in the formula, v N Is a rated voltage value; u. of i Is the actual voltage value of the ith node.
For the ith particle X (i), if f (X (i)) > pbest (i), let pbest (i) = f (X (i)) and P (i) = X (i), when all particles in the particle population are calculated, let gbest = max (pbest), where max represents taking the maximum function, and assign the particle position with fitness gbest to the global optimal particle position matrix g.
Updating the velocity matrix for the particle swarm according to:
v k+1 (i，j)＝w*v k (i，j)+2*rand()*(p(i，j)-x k (i，j))+2*rand()*(g-x k (i，j))
in the formula, v k+1 (i，j)、v k (i, j) are respectively the values corresponding to the ith row and the jth column of the velocity matrix V in the (k + 1) th iteration and the kth iteration; x is the number of k (i，j)、p k (i, j) are values corresponding to a particle swarm position matrix X and an ith row and a jth column of the particle individual optimal position matrix P in the kth iteration respectively; w is the coefficient of inertia; and rand () is a random number generating function, which generates random numbers in the range of 0 to 1.
And w is dynamically adjusted according to the fitness of the current particle, and the adjustment strategy is shown as the following formula:
in the formula, F i The fitness of the ith particle is f (X (i)); f av′ Is an average individual fitness limit value of
F av Is the average individual fitness of a value of
F m The maximum fitness is max (F) i ).
For continuous variable x 1 、x 2 The position is updated according to:
x k+1 (i，j)＝x k (i，j)+v k+1 (i，j) (j＝1，2)
for discrete variable x 3 、x 4 、x 5 The position is updated according to:
in the formula, x k+1 (i, j) is a value corresponding to the ith row and the jth column of the particle swarm location matrix X in the (k + 1) th iteration; tanh () is a hyperbolic tangent function.
Repeating the steps until the maximum iteration number is reached, wherein the global optimal particle position matrix g = [ x ] g1 ，x g2 ，x g3 ，x g4 ，x g5 ]I.e. the optimal output value of the reactive power compensation equipment, wherein x g1 、x g2 Is the optimal reactive power output value, x, of SVC1, SVC2 g3 、x g4 Is the optimal gear position of SC1, SC2, x g5 Is the optimal gear of the OLTC.
In conclusion, the power distribution network optimization method based on the self-adaptive discrete particle swarm algorithm can optimize continuous variables and discrete variables simultaneously. And the inertia coefficient in the algorithm can be dynamically adjusted based on the current state of the particles, the inertia coefficient is reduced for the particles with higher fitness, the inertia coefficient is increased for the particles with better fitness, the algorithm solving efficiency is greatly improved, the capability of the particle swarm algorithm for searching the global optimal solution is enhanced, and the method is more suitable for actual power distribution network scenes.
Claims (7)
1. A power distribution network optimization method based on a self-adaptive discrete particle swarm algorithm is characterized in that the optimal output values of reactive compensation equipment in a power distribution network under different load levels and power generation levels are adjusted, so that the sum of the overall network loss of the power distribution network and the voltage deviation of each node is comprehensively minimized; the optimization method specifically comprises the following steps:
(1) Acquiring parameters and operation data of reactive compensation equipment participating in reactive power optimization of the power distribution network, and constructing a particle position matrix by using reactive output power or adjustment gears of the reactive compensation equipment;
(2) Randomly initializing to form a plurality of particle position matrixes and a plurality of particle speed matrixes, and updating the particle positions and setting a fitness function; calculating the network loss and voltage deviation of the power distribution network at different particle positions by using a self-adaptive discrete particle swarm algorithm, and calculating after multiple iterations to obtain a global optimal particle position matrix g;
(3) Calculating by using the global optimal particle position matrix g to obtain a reactive power value or an adjustment gear which each reactive compensation device should output; and the local controller of each reactive compensation device adjusts respective reactive power output according to the instruction issued by the control center, so that reactive adjustment on the whole situation of the power distribution network is realized to stabilize the node voltage of the power distribution network and reduce the network loss.
2. The method according to claim 1, wherein in step (1), the reactive compensation equipment comprises two types: compensation equipment for continuously adjusting reactive output and equipment for adjusting reactive output by stepping; for the compensation equipment for continuously adjusting reactive output, the position of the particle is the value of the reactive power to be output; and for the equipment which participates in reactive output regulation in the stepping position, the particle position is the gear to be selected.
3. The method according to claim 2, characterized in that the compensation device for continuously regulating reactive power output is a static var generator or a photovoltaic inverter; the equipment which is used for regulating the reactive output by the gear positions is an on-load tap changing transformer or a capacitor.
4. The method according to claim 1, wherein in the step (2), the specific calculation steps of the adaptive discrete particle swarm algorithm are as follows:
(2.1) counting the number m of devices participating in reactive power regulation in the power distribution network, and randomly initializing to form an initial particle swarm position matrix X = [ X ] 1 ，x 2 ，…，x m ]Initial velocity matrix V = [ V ] corresponding to particle 1 ，v 2 ，…，v m ](ii) a Wherein x is m 、v m Are column vectors of size n × 1, n representing the number of particles;
(2.2) initializing to form an individual optimal particle position matrix P = [ P ] 1 ，p 2 ，…，p m ](ii) a Wherein p is m Is a column vector of size n × 1; initializing to form an individual fitness extreme value pbesi which is a column vector with the size of n multiplied by 1; initializing to form a global fitness extreme value gbest;
(2.3) calculating the fitness f (X (i)) of each particle, wherein f is a fitness function, and X (i) is the ith particle;
let pbest (i) = f (X (i)) and P (i) = X (i) if f (X (i)) > pbest (i); let gbest = max (pbest) when all particles in the particle swarm have been calculated; wherein max represents a function of taking a maximum value, and assigns the particle position with the fitness of gbest to a global optimal particle position matrix g;
(2.4) updating the velocity matrix of the population of particles according to:
v k+1 (i，j)＝w*v k (i，j)+c 1 *rand()*(p k (i，j)-x k (i，j))+c 2 *rand()*(g-x k (i，j)) (1.1)
in the formula, v k+1 (i，j)、v k (i, j) are values corresponding to the ith row and the jth column of the velocity matrix V in the (k + 1) th iteration and the kth iteration respectively; x is the number of k (i, j) is a particle swarm location matrix X, p at the kth iteration k (i, j) is the value corresponding to the ith row and the jth column of the particle individual optimal position matrix P; w is the coefficient of inertia; c. C 1 、c 2 Is an acceleration factor; rand () is a random number generating function for generating random numbers in the range of 0 to 1;
(2.5) according to whether the reactive compensation equipment can continuously perform reactive power regulation, respectively adopting different methods to update the positions of the particle swarms:
for continuously adjustable devices, the position matrix of the population of particles is updated according to:
x k+1 (i，j)＝x k (i，j)+v k+1 (i，j) (1.2)
for equipment which cannot continuously perform reactive power regulation, updating the position matrix of the particle swarm according to the following formula:
in the formula, x k+1 (i, j) is a value corresponding to the ith row and the jth column of the particle swarm location matrix X in the (k + 1) th iteration; tanh () is a hyperbolic tangent function; else means that rand () < tan h (v) is removed k+1 (i, j)) and-rand () > tanh (v) k+1 (i, j)) other than the above cases;
(2.6) repeating the steps (2.3) to (2.5) until the maximum iteration number, wherein the global optimal particle position matrix g is the optimal output value of the reactive power compensation equipment and is a column vector with the size of n multiplied by 1, and each reactive power compensation device correspondingly adjusts and outputs reactive power or gears according to the value in the global optimal particle position matrix g.
5. The method according to claim 4, characterized in that in step (2.3), the fitness function f is specifically represented by the following formula:
in the formula, omega p 、ω u Is a weight coefficient; p loss 、P′ loss Respectively optimizing the network loss of the power distribution network before and after optimization; du, du' are the voltage deviations before and after optimization respectively.
6. The method of claim 5, wherein the voltage offset is calculated according to the following equation:
in the formula, m is the total number of the node voltages; u. u N Is the rated voltage value of the node; u. of i Is the actual voltage value of the ith node.
7. The method according to claim 4, wherein in step (2.4), the inertia coefficient w is dynamically adjusted according to the fitness of the current particle, as follows:
in the formula, w max 、w min The maximum value and the minimum value of the inertia coefficient are obtained; f i The fitness of the ith particle is f (X (i)); f av′ Is an average individual fitness limit value of
F av Is the average fitness of individuals, and has a value of
F m The maximum fitness is max (F) i )；k 1 、k 2 To adjust the coefficients.
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| CN116683471B (en) * | 2023-04-28 | 2024-06-11 | 国网河北省电力有限公司电力科学研究院 | Configuration method, device and equipment of reactive power compensation resource |
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