CN116263897A - Distributed power supply optimal configuration method based on multi-target particle swarm algorithm - Google Patents

Distributed power supply optimal configuration method based on multi-target particle swarm algorithm Download PDF

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CN116263897A
CN116263897A CN202211614879.XA CN202211614879A CN116263897A CN 116263897 A CN116263897 A CN 116263897A CN 202211614879 A CN202211614879 A CN 202211614879A CN 116263897 A CN116263897 A CN 116263897A
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朱重希
花志伟
李鑫
胡遨洋
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State Grid Zhejiang Electric Power Co Ltd Tongxiang Power Supply Co
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Abstract

The invention mainly aims to solve the problem that the traditional distributed power supply optimal configuration algorithm cannot achieve the multi-objective of reducing investment operation cost, network loss and node voltage deviation, and discloses a distributed power supply optimal configuration method based on a multi-objective particle swarm algorithm.

Description

Distributed power supply optimal configuration method based on multi-target particle swarm algorithm
Technical Field
The invention relates to the technical field of power system planning, in particular to a distributed power supply optimal configuration method based on a multi-target particle swarm algorithm.
Background
The distributed power supply is completely different from the traditional power supply mode, has the advantages of low pollution, flexible and convenient installation and the like, and can effectively relieve the problems of energy exhaustion and environmental pollution. With the increase of the capacity of the distributed power supply, adverse effects such as network loss increase, line flow backflow, short-circuit current increase, power quality damage and the like are brought to the power distribution network, meanwhile, uncertainty of power distribution network planning is increased, and the adverse effects and the uncertainty are closely related to the optimal configuration of the distributed power supply in the power distribution network. Therefore, the access position and the capacity of the distributed power supply are scientifically and reasonably determined, and the method has important significance for the economic, safe, stable and effective operation of the power distribution network.
At present, a plurality of scholars at home and abroad propose different solutions to the problem of optimizing the configuration of the distributed power supply. The solution algorithm for the position and capacity of distributed power supply access can be roughly divided into classical mathematical programming methods and modern intelligent optimization algorithms. However, the algorithm cannot achieve multiple objectives of reducing investment and operation cost, network loss and node voltage deviation, and has limitations.
Disclosure of Invention
The invention mainly aims at solving the problem that the traditional distributed power supply optimal configuration algorithm cannot achieve the multi-objective of reducing investment operation cost, network loss and node voltage deviation, and provides a distributed power supply optimal configuration method based on a multi-objective particle swarm algorithm.
In order to achieve the above object, the present invention adopts the following technical scheme.
A distributed power supply optimal configuration method based on a multi-target particle swarm algorithm comprises the following steps:
step S1: establishing an optimal configuration model with minimum total cost of investment operation of a distributed power supply, minimum total active network loss of a power distribution network and minimum node voltage deviation as objective functions;
step S2: solving a multi-target distributed power supply optimizing configuration model by adopting a particle swarm algorithm of a self-adaptive weight updating strategy, and outputting a configuration result, wherein the configuration result comprises the installation position and capacity of a distributed power supply;
aiming at the configuration problem of the installation position and capacity of the distributed power supply, the invention establishes a multi-target distributed power supply optimizing configuration model by taking the minimum investment operation total cost, the minimum total active network loss and the minimum node voltage deviation as objective functions, and solves and analyzes the established model by adopting a multi-target particle swarm algorithm of a self-adaptive weight updating strategy under the constraint conditions of taking power balance, node voltage power, line transmission power, node installation capacity and the like into consideration to obtain a configuration result, wherein the configuration result comprises the installation position and the capacity of the distributed power supply. The invention considers the total cost of investment operation, the total active network loss of the power distribution network and the node voltage deviation, effectively reduces the cost of investment operation, the network loss and the node voltage deviation, and realizes multi-objective planning; the invention realizes scientific and reasonable optimal configuration of the distributed power supply, and is beneficial to the economic, safe and stable operation of the power distribution network; according to the method, the particle swarm algorithm of the self-adaptive weight updating strategy is applied to the established multi-objective distributed power supply optimization configuration model for solution analysis, so that the flying speed of particles can be better restrained and the optimal solution can be found more quickly.
Preferably, in step S1, the objective function of the multi-objective distributed power supply optimization configuration model is:
C＝minf 1 +minf 2 +minΔU
wherein f 1 Representing the total cost of investment operation of the distributed power supply; f (f) 2 Representing the total active power loss of the power distribution network; Δu represents the node voltage deviation; the invention establishes a multi-objective distributed power supply optimizing configuration model by taking the minimum total cost of the distributed power supply investment operation, the minimum total active network loss of the power distribution network and the minimum node voltage deviation as objective functions and taking the power balance, the node voltage power, the line transmission power and the installation capacity as constraint conditions for restraining the nodes of the power distribution network.
Preferably, the distributed power investment operation total cost f 1 The calculation formula of (2) is as follows:
Figure BDA0003998225040000021
wherein t is the t-th year of the planned service life; r is annual rate; x is x i The random variable is 0-1, and is expressed as whether the node i is connected with a distributed power supply or not; n (N) 1 The number of installation nodes for the distributed power supply; n (N) 2 Planning operation years for the distributed power supply; p (P) i The installation capacity of the distributed power supply at the node i is set; a is that C The unit acquisition cost of the distributed power supply; c (C) C The unit infrastructure cost; i C The unit installation cost; l (L) C Cost per unit land use.
Preferably, the total active power loss f of the power distribution network 2 The calculation formula of (2) is as follows:
Figure BDA0003998225040000022
Figure BDA0003998225040000023
Figure BDA0003998225040000024
wherein N is the total node number; r is R ij And X ij The resistance and reactance between the lines ij respectively; p and Q are the active and reactive power flowing into the node, respectively; v is the voltage amplitude of the node; θ is the node voltage phase angle.
Preferably, the calculation formula of the node voltage deviation Δu is:
Figure BDA0003998225040000031
wherein Δu is the node voltage deviation amount; u (U) i The actual voltage value of the node i; n is the total number of nodes;
Figure BDA0003998225040000032
a voltage expected value of the node i; />
Figure BDA0003998225040000033
Is the maximum deviation of the allowed voltage.
Preferably, the constraint conditions of the multi-objective distributed power supply optimization configuration model include:
1) Power balance constraint
Figure BDA0003998225040000034
Figure BDA0003998225040000035
Wherein R is a set formed by connecting a node i with a node j; p (P) i Active power flowing in at node i; q (Q) i Reactive power flowing in at node i; u (U) i The voltage amplitude of the node i; g ij Is the conductance between nodes ij; b (B) ij Is susceptance between nodes ij; θ ij Is the voltage-to-angle difference between nodes ij;
2) Node voltage constraint
U imin ≤U i ≤U imax
Wherein U is imax 、U imin Respectively the maximum value and the minimum value of the voltage at the node i;
3) Installation capacity constraints
Figure BDA0003998225040000036
Wherein P is i Active capacity of the i-th node; η is the maximum value of the total load capacity of the distributed power supply; p is the total load capacity;
4) Line transmission constraints
Figure BDA0003998225040000037
Wherein P is ij Is the transmission power between the lines ij;
Figure BDA0003998225040000038
is the maximum transmission power between the lines ij.
Preferably, in step S2, the particles are updated by using adaptive inertial weights, and the iterative formula is as follows:
v i+1 ＝ω×v i +c 1 ×r×(pbest i -x i )+c 2 ×r×(gbest i -x i )
Figure BDA0003998225040000039
wherein c 1 And c 2 C is a learning factor 1 ＝2.5+(0.5-2.5)×t/g，c 2 =0.5+ (2.5-0.5) ×t/g; t is the current iteration number of the particles; g is the total iteration number of the particles; r is a random number between 0 and 1; pbest (p best) i Is the optimal position of the particles; gbest (g best) i Is the global optimal position of the group; x is x i Is the current position of the particle; omega max And omega min Is the maximum value of the inertial weight; f is the fitness value of the particles; f (f) avg Fitness for particle swarmThe average value; f (f) min Is the minimum value of the fitness;
the invention updates the particles by adopting the self-adaptive inertia weight, and can update and restrict the particles better than the linear change speed inertia weight factor. The self-adaptive updating weight can effectively solve the problem of multi-objective optimal configuration of the distributed power supply, and has the advantages of high convergence speed and high adaptability, and the adopted algorithm has certain superiority.
Preferably, the specific process of the step S2 includes the following steps:
step S21: parameter initialization including, but not limited to, population size, maximum number of iterations, control variables, and algorithm base parameters; step S22: initializing the position and speed of the particles to enable the particles to be in the agreed boundary conditions;
step S23: obtaining the optimal particles and the optimal positions of the individuals;
step S24: calculating a self-adaptive weight coefficient and updating the particle speed, judging whether the particle speed is out of range, and taking a boundary value if the particle speed is out of range;
step S25: judging whether all particles are updated, if not, returning to the step S23; if yes, go to step S26;
step S26: obtaining a Pareto solution set and outputting a configuration result;
the Pareto non-inferior solution set obtained by the method can be closer to an optimal solution. Aiming at the distributed power supply optimal configuration problem, a particle swarm algorithm of a self-adaptive weight updating strategy is applied to the established multi-objective distributed power supply optimal configuration model for solving and analyzing, so that the flying speed of particles can be better restrained and an optimal solution can be found more quickly.
Therefore, the invention has the advantages that:
(1) The invention realizes scientific and reasonable optimal configuration of the distributed power supply, and is beneficial to the economic, safe and stable operation of the power distribution network;
(2) The invention considers the total cost of investment operation, the total active network loss of the power distribution network and the node voltage deviation, effectively reduces the cost of investment operation, the network loss and the node voltage deviation, and realizes multi-objective planning;
(3) According to the method, the particle swarm algorithm of the self-adaptive weight updating strategy is applied to the established multi-objective distributed power supply optimization configuration model for solution analysis, so that the flying speed of particles can be better restrained and the optimal solution can be found more quickly.
Drawings
Fig. 1 is a flowchart of a distributed power supply optimization configuration method based on a multi-target particle swarm algorithm in an embodiment of the invention.
FIG. 2 is a flowchart of a particle swarm algorithm for solving a multi-objective distributed power optimization configuration model using an adaptive weight update strategy according to an embodiment of the present invention.
Detailed Description
The invention is further described below with reference to the drawings and detailed description.
A distributed power supply optimal configuration method based on a multi-target particle swarm algorithm is shown in fig. 1, and comprises the following steps: step S1: establishing an optimal configuration model with minimum total cost of investment operation of a distributed power supply, minimum total active network loss of a power distribution network and minimum node voltage deviation as objective functions;
step S2: solving a multi-target distributed power supply optimizing configuration model by adopting a particle swarm algorithm of a self-adaptive weight updating strategy, and outputting a configuration result, wherein the configuration result comprises the installation position and capacity of a distributed power supply;
aiming at the configuration problem of the installation position and capacity of the distributed power supply, the invention establishes a multi-objective distributed power supply optimizing configuration model by taking the minimum investment operation total cost, the minimum total active network loss and the minimum node voltage deviation as objective functions, solves and analyzes the established model by adopting a multi-objective particle swarm algorithm of a self-adaptive weight updating strategy under the constraint conditions of taking power balance, node voltage power, line transmission power, node installation capacity and the like into consideration, obtains a configuration result, comprises the installation position and the capacity of the distributed power supply, realizes scientific and reasonable optimizing configuration of the distributed power supply, effectively reduces investment operation cost, network loss and node voltage deviation, and realizes multi-objective planning.
In step S1, the objective function of the multi-objective distributed power supply optimization configuration model is:
C＝mf 1 +minf 2 +minΔU
wherein f 1 Representing the total cost of investment operation of the distributed power supply; f (f) 2 Representing the total active power loss of the power distribution network; Δu represents the node voltage deviation; the invention establishes a multi-objective distributed power supply optimizing configuration model by taking the minimum total cost of the distributed power supply investment operation, the minimum total active network loss of the power distribution network and the minimum node voltage deviation as objective functions and taking the power balance, the node voltage power, the line transmission power and the installation capacity as constraint conditions for restraining the nodes of the power distribution network.
Total cost f of distributed power investment operation 1 The calculation formula of (2) is as follows:
Figure BDA0003998225040000051
wherein t is the t-th year of the planned service life; r is annual rate; x is x i The random variable is 0-1, and is expressed as whether the node i is connected with a distributed power supply or not; n (N) 1 The number of installation nodes for the distributed power supply; n (N) 2 Planning operation years for the distributed power supply; p (P) i The installation capacity of the distributed power supply at the node i is set; a is that C The unit acquisition cost of the distributed power supply; c (C) C The unit infrastructure cost; i C The unit installation cost; l (L) C Cost per unit land use.
Total active loss f of distribution network 2 The calculation formula of (2) is as follows:
Figure BDA0003998225040000052
Figure BDA0003998225040000061
Figure BDA0003998225040000062
wherein N is the total node number; r is R ij And X ij The resistance and reactance between the lines ij respectively; p and Q are the active and reactive power flowing into the node, respectively; v is the voltage amplitude of the node; θ is the node voltage phase angle.
The calculation formula of the node voltage deviation deltau is:
Figure BDA0003998225040000063
wherein Δu is the node voltage deviation amount; u (U) i The actual voltage value of the node i; n is the total number of nodes;
Figure BDA0003998225040000064
a voltage expected value of the node i; />
Figure BDA0003998225040000065
Is the maximum deviation of the allowed voltage.
Constraints of the multi-objective distributed power supply optimization configuration model include:
1) Power balance constraint
Figure BDA0003998225040000066
Figure BDA0003998225040000067
Wherein R is a set formed by connecting a node i with a node j; p (P) i Active power flowing in at node i; q (Q) i Reactive power flowing in at node i; u (U) i The voltage amplitude of the node i; g ij Is the conductance between nodes ij; b (B) ij Is susceptance between nodes ij; θ ij Is the voltage-to-angle difference between nodes ij;
2) Node voltage constraint
U imin ≤U i ≤U imax
Wherein U is imax 、U imin Respectively the maximum value and the minimum value of the voltage at the node i;
3) Installation capacity constraints
Figure BDA0003998225040000068
Wherein P is i Active capacity of the i-th node; η is the maximum value of the total load capacity of the distributed power supply; p is the total load capacity;
4) Line transmission constraints
Figure BDA0003998225040000069
Wherein P is ij Is the transmission power between the lines ij;
Figure BDA00039982250400000610
is the maximum transmission power between the lines ij.
In step S2, the particles are updated by adopting adaptive inertial weights, and the iterative formula is as follows:
v i+1 ＝ω×v i +c 1 ×r×(pbest i -x i )+c 2 ×r×(gbest i -x i )
Figure BDA0003998225040000071
wherein c 1 And c 2 C is a learning factor 1 ＝2.5+(0.5-2.5)×t/g，c 2 =0.5+ (2.5-0.5) ×t/g; t is the current iteration number of the particles; g is the total iteration number of the particles; r is a random number between 0 and 1; pbest (p best) i Is the optimal position of the particles; gbest (g best) i Is the global optimal position of the group; x is x i Is the current position of the particle; omega max And omega min Is the maximum value of the inertial weight; f is the fitness value of the particles; f (f) avg Is the particle swarm fitness mean value; f (f) min Is the minimum value of the fitness;
the invention updates the particles by adopting the self-adaptive inertia weight, and can update and restrict the particles better than the linear change speed inertia weight factor.
The specific process of step S2, as shown in fig. 2, includes the following steps:
step S21: parameter initialization including, but not limited to, population size, maximum number of iterations, control variables, and algorithm base parameters;
step S22: initializing the position and speed of the particles to enable the particles to be in the agreed boundary conditions;
step S23: obtaining the optimal particles and the optimal positions of the individuals;
step S24: calculating a self-adaptive weight coefficient and updating the particle speed, judging whether the particle speed is out of range, and taking a boundary value if the particle speed is out of range;
step S25: judging whether all particles are updated, if not, returning to the step S23; if yes, go to step S26;
step S26: obtaining a Pareto solution set and outputting a configuration result;
the Pareto non-inferior solution set obtained by the method can be closer to an optimal solution. Aiming at the distributed power supply optimal configuration problem, a particle swarm algorithm of a self-adaptive weight updating strategy is applied to the established multi-objective distributed power supply optimal configuration model for solving and analyzing, so that the flying speed of particles can be better restrained and an optimal solution can be found more quickly.
The foregoing is merely specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily think about changes or substitutions within the technical scope of the present application, and the changes and substitutions are intended to be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims (8)

1. The distributed power supply optimal configuration method based on the multi-target particle swarm algorithm is characterized by comprising the following steps of:
step S1: establishing an optimal configuration model with minimum total cost of investment operation of a distributed power supply, minimum total active network loss of a power distribution network and minimum node voltage deviation as objective functions;
step S2: and solving a multi-target distributed power supply optimizing configuration model by adopting a particle swarm algorithm of a self-adaptive weight updating strategy, and outputting a configuration result, wherein the configuration result comprises the installation position and the capacity of the distributed power supply.
2. The distributed power supply optimization configuration method based on the multi-objective particle swarm algorithm according to claim 1, wherein in step S1, the objective function of the multi-objective distributed power supply optimization configuration model is:
C＝minf 1 +minf 2 +minΔU
wherein f 1 Representing the total cost of investment operation of the distributed power supply; f (f) 2 Representing the total active power loss of the power distribution network; Δu represents the node voltage deviation.
3. The method for optimizing configuration of distributed power supply based on multi-objective particle swarm algorithm according to claim 2, wherein the total cost f of investment operation of the distributed power supply 1 The calculation formula of (2) is as follows:
Figure FDA0003998225030000011
wherein t is the t-th year of the planned service life; r is annual rate; x is x i The random variable is 0-1, and is expressed as whether the node i is connected with a distributed power supply or not; n (N) 1 The number of installation nodes for the distributed power supply; n (N) 2 Planning operation years for the distributed power supply; p (P) i The installation capacity of the distributed power supply at the node i is set; a is that C The unit acquisition cost of the distributed power supply; c (C) C The unit infrastructure cost; i C The unit installation cost; l (L) C Cost per unit land use.
4. A distributed power supply optimizing configuration method based on a multi-objective particle swarm algorithm according to claim 2 or 3, characterized in that the configuration is thatTotal active power loss f of power grid 2 The calculation formula of (2) is as follows:
Figure FDA0003998225030000012
Figure FDA0003998225030000013
Figure FDA0003998225030000014
wherein N is the total node number; r is R ij And X ij The resistance and reactance between the lines ij respectively; p and Q are the active and reactive power flowing into the node, respectively; v is the voltage amplitude of the node; θ is the node voltage phase angle.
5. The distributed power supply optimization configuration method based on the multi-objective particle swarm algorithm according to claim 2 or 3, wherein the calculation formula of the node voltage deviation Δu is as follows:
Figure FDA0003998225030000021
wherein Δu is the node voltage deviation amount; u (U) i The actual voltage value of the node i; n is the total number of nodes;
Figure FDA0003998225030000022
a voltage expected value of the node i; />
Figure FDA0003998225030000023
Is the maximum deviation of the allowed voltage.
6. A distributed power supply optimization configuration method based on a multi-objective particle swarm algorithm according to claim 2 or 3, wherein constraints of the multi-objective distributed power supply optimization configuration model include:
1) Power balance constraint
Figure FDA0003998225030000024
Figure FDA0003998225030000025
Wherein R is a set formed by connecting a node i with a node j; p (P) i Active power flowing in at node i; q (Q) i Reactive power flowing in at node i; u (U) i The voltage amplitude of the node i; g ij Is the conductance between nodes ij; b (B) ij Is susceptance between nodes ij; θ ij Is the voltage-to-angle difference between nodes ij;
2) Node voltage constraint
U imin ≤U i ≤U imax
Wherein U is imax 、U imin Respectively the maximum value and the minimum value of the voltage at the node i;
3) Installation capacity constraints
Figure FDA0003998225030000026
Wherein P is i Active capacity of the i-th node; η is the maximum value of the total load capacity of the distributed power supply; p is the total load capacity;
4) Line transmission constraints
Figure FDA0003998225030000027
Wherein P is ij Is the transmission power between the lines ij;
Figure FDA0003998225030000028
is the maximum transmission power between the lines ij.
7. The distributed power supply optimization configuration method based on the multi-objective particle swarm algorithm according to claim 1, wherein in step S2, the particles are updated by adopting adaptive inertia weights, and an iterative formula is as follows:
v i+1 ＝ω×v i +c 1 ×r×(pbest i -x i )+c 2 ×r×(gbest i -x i )
Figure FDA0003998225030000031
wherein c 1 And c 2 C is a learning factor 1 ＝2.5+(0.5-2.5)×t/g，c 2 =0.5+ (2.5-0.5) ×t/g; t is the current iteration number of the particles; g is the total iteration number of the particles; r is a random number between 0 and 1; pbest (p best) i Is the optimal position of the particles; gbest (g best) i Is the global optimal position of the group; x is x i Is the current position of the particle; omega max And omega min Is the maximum value of the inertial weight; f is the fitness value of the particles; f (f) avg Is the particle swarm fitness mean value; f (f) min Is the minimum value of the adaptation degree.
8. The method for optimizing configuration of a distributed power supply based on a multi-objective particle swarm algorithm according to claim 1 or 7, wherein the specific process of step S2 comprises the following steps:
step S21: parameter initialization including, but not limited to, population size, maximum number of iterations, control variables, and algorithm base parameters;
step S22: initializing the position and speed of the particles to enable the particles to be in the agreed boundary conditions;
step S23: obtaining the optimal particles and the optimal positions of the individuals;
step S24: calculating a self-adaptive weight coefficient and updating the particle speed, judging whether the particle speed is out of range, and taking a boundary value if the particle speed is out of range;
step S25: judging whether all particles are updated, if not, returning to the step S23; if yes, go to step S26;
step S26: and obtaining a Pareto solution set and outputting a configuration result.
CN202211614879.XA 2022-12-14 2022-12-14 Distributed power supply optimal configuration method based on multi-target particle swarm algorithm Pending CN116263897A (en)

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117557009A (en) * 2024-01-12 2024-02-13 东莞市华灏技术有限公司 Power efficiency monitoring method and system
CN117557009B (en) * 2024-01-12 2024-05-07 东莞市华灏技术有限公司 Power efficiency monitoring method and system

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