CN116388271A - Power supply unit distributed power supply acceptance performance processing method, equipment and medium - Google Patents
Power supply unit distributed power supply acceptance performance processing method, equipment and medium Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/381—Dispersed generators
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/04—Circuit arrangements for ac mains or ac distribution networks for connecting networks of the same frequency but supplied from different sources
- H02J3/06—Controlling transfer of power between connected networks; Controlling sharing of load between connected networks
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
- H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/20—The dispersed energy generation being of renewable origin
- H02J2300/22—The renewable source being solar energy
- H02J2300/24—The renewable source being solar energy of photovoltaic origin
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/20—The dispersed energy generation being of renewable origin
- H02J2300/28—The renewable source being wind energy
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/40—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation wherein a plurality of decentralised, dispersed or local energy generation technologies are operated simultaneously
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Abstract
The invention provides a power supply unit distributed power supply acceptance performance processing method, equipment and medium, comprising the following steps: constructing a distributed power supply admission capacity optimization framework considering network reconstruction; establishing a distributed power supply admittance mathematical model taking a power supply unit as an object according to an optimization framework; and solving by combining a genetic algorithm and traversal, and substituting the solution into the distributed power supply capacity. The invention aims at the maximum admission capacity of the distributed power supply of the power supply unit, takes the access position and the access capacity of the distributed power supply as decision variables, meets the constraint that various indexes of the power distribution network are not out of limit under the switching state of the interconnection switch and the sectionalized switch which consider the mutual economy between feed lines and the mutual economy between stations, solves the access position and the access capacity which obtain the maximum admission capacity, and finally can rapidly calculate the admission capacity of the distributed power supply of the power supply unit.
Description
Technical Field
The invention belongs to the technical field of new energy grid connection of an electric power system, and particularly relates to a distributed power supply admission capacity calculation method of a power supply unit based on network reconstruction.
Background
The installed amount of clean renewable energy power generation is rapidly increased, and more distributed power sources such as wind power generation, solar photovoltaic power generation and the like are operated in a grid-connected mode.
However, due to the unmatched characteristics of the output and the load demand of the distributed power supply, the effective utilization of the installed capacity of the grid-connected distributed power supply is hindered, so that the local consumption level of the distributed power supply is lower, the resource waste and the resource loss are caused, the sustainable development of new energy sources is not facilitated in the long term, and the clean transformation of the energy sources is finally realized.
The traditional method for calculating the admission capacity of the distributed power source has the following technical problems: the single feeder lines are used as research objects, the influence of mutual aid among the feeder lines on the receiving capacity of the distributed power supply is not considered, key contents such as main body transfer and communication are omitted, and a result is greatly error.
Disclosure of Invention
Aiming at the technical problems, the invention provides a power supply unit distributed power supply admission capacity calculation method, equipment and medium based on network reconstruction.
The technical scheme of the invention is as follows: a power supply unit distributed power supply acceptance performance processing method comprises the following steps:
s1, constructing a distributed power supply admission capacity optimization framework considering network reconstruction;
s2, establishing a mathematical model of the receiving capacity of the distributed power supply taking the power supply unit as an object according to an optimization framework;
and S3, solving by combining a genetic algorithm and traversal, and substituting the solution into the solution to obtain the distributed power supply capacity.
Preferably, in S1, the step of optimizing the framework:
s11, optimizing decision variable distributed power supply access positions and access capacities by taking the maximum sum of the distributed power supply access capacities as a target;
s12, constraint judgment is carried out in consideration of network reconstruction, the switch state combination of the tie switch and the sectionalizer switch is traversed, whether the combination meeting constraint conditions exists or not is searched, and a judgment result is obtained;
s13, if a solution meeting the conditions exists, describing that the distributed power supply admission capacity distribution network of the power supply unit under the network reconstruction can be consumed, if the solution meeting the conditions does not exist, describing that the solution can not be consumed, and then correcting until the solution can be obtained;
s14, correcting the corresponding individuals by using a genetic algorithm according to the judging result, and alternating until an optimal result is achieved.
Preferably, in S2, the maximum sum of the distributed power supply access capacities of the power supply units is taken as an objective function, the access positions and the access capacities of the distributed power supplies are taken as decision variables, each index of the power distribution network is not out of limit under the condition of considering the connection state of the tie switch and the disconnection state of the sectionalizing switch as a constraint condition, and the constraint of the switching state and the switching times and the constraint of the capacity of the equipment are considered to establish a distributed power supply admission capacity calculation model.
Preferably, in S2 the objective function is:
wherein P is DGi Is distributed intoThe installed capacity of a node i on a feeder line m of a power supply is used as the maximum power generation power of a distributed power supply power generation curve in calculation; m is the number of feeder lines in the power supply unit; i is the number of nodes of the feeder line of the power supply unit.
Preferably, the constraint condition includes:
1) Switch state constraints
The initial state of the tie switch is open, and the initial state of the sectionalizing switch is closed;
wherein X is w ins For this reason, the on-off state of the w-th tie switch in the power supply unit is that 1 is in the on state and 0 is in the off state; x is X v ses For this reason, the v-th sectionalizer in the power supply unit is in an open state, 1 is in a closed state, and 0 is in an open state; w is the number of the connecting switches in the power supply unit; v is the number of sectionalizing switches in the power supply unit; n (N) lin The number of branches is the number of branches;
2) Number of switch-over times
Wherein S is max Switching the switch for the maximum number of times in one day;
3) Tidal current constraint of power distribution network
Wherein P is i 、Q i Active and reactive injection is performed at the node i; u (U) i 、U j The voltage amplitude values of the nodes i and j are obtained; g ij 、B ij The conductance and susceptance of branch ij; θ ij For the phase angle of voltage between nodes i and jDifference;
4) Node voltage constraint
U imin ＜U i ＜U imax (6)
In U imin The lower limit value of the node voltage is 0.9, U imax The upper limit value of the node voltage is 1.07;
5) Branch tide constraint
P ij (t)≤P ijmax (7)
Wherein P is ij (t) is the single-phase active power transmitted by the line ij at the time t; p (P) ijmax For the limit value of the single-phase active power transmitted by the line ij at the time t, the value can be obtained by the maximum current-carrying capacity in the line parameters, and the calculation formula is as follows:
wherein U is the rated voltage of the line, I max Taking 0.9 for the maximum current-carrying capacity of the line, wherein cos phi is a power factor;
6) Short circuit current restraint
I SCL ＜I SCLmax (9)
Wherein I is SCL And I SCLmax The maximum short-circuit current of the circuit and the maximum short-circuit current of the breaker are respectively;
7) Device capacity constraints
P DGmin ≤P DGi ≤P DGmax (10)
Wherein P is DGmax The maximum limit value of the distributed power supply access in the line is set; p (P) max For this purpose, the maximum limit value accommodated by the power supply system and the energy storage device in the power supply unit.
Preferably, in S3, a genetic algorithm and traversal are used in combination to solve, and the specific steps include:
s31, initializing a population, wherein the genes of each individual are access positions and access capacities;
s32, constraint judgment is carried out on each individual, and acceptance can be carried out under the condition that each constraint is met under the condition that whether a combination of states of a tie switch and a sectionalizing switch exists or not is searched through traversal;
s33, if the power distribution network is provided with the power distribution network, the individual can accept the distributed power supply capacity under the condition that all indexes of the power distribution network are not out of limit; if not, the individual is stated to consider that the network reconfiguration still cannot accommodate the capacities, and the individual which does not meet the constraint is replaced by the individual which meets the requirement; the selected and corrected individuals enter an iterative process of a genetic algorithm;
and S34, taking the sum of the admission capacities of the distributed power supplies as a fitness value, calculating the fitness value of each individual, reserving elite, then selecting, crossing and mutating the individual, repeating the steps S32 and S33 to obtain a preliminary next generation population, calculating the fitness value of each individual in the preliminary next generation population, comparing whether the fitness value of the optimal individual of the previous generation is larger than the fitness value of the worst individual of the current generation, if so, replacing to obtain a second generation population, and then continuing optimizing the second generation population by using the same method until the iteration termination condition is reached.
Preferably, the roulette algorithm is adopted in S34 to perform selection operation, the individuals are selected from the parent population as the parent individuals,
preferably, the method of crossover and mutation in S34:
single-point crossover inheritance and mutation are carried out on the selected male parent individuals, wherein the crossover and mutation probability adopts an improved self-adaptive operator, the individuals lower than the fitness average value adopt higher crossover and mutation probability, the individuals higher than the fitness average value adopt lower crossover and mutation probability, and the probability adjustment formula:
wherein: f (f) max Is the maximum value of the fitness value; f (f) av Average fitness value for each generation of population; f' is the fitness value of the current operation individual, and the crossover operation is 2 to be crossedThe individual has larger fitness value, and the mutation operation is the fitness value of the individual to be mutated; p (P) adu And P adl Is the upper and lower limits of probability adjustment.
Preferably, the iteration termination condition in S34 is: reaching the preset iteration times.
An arithmetic device comprises a memory, a processor, a main board and a computer program stored in the memory and executed by the processor, wherein the processor realizes any one of the power supply unit distributed power supply acceptance calculation methods when executing the computer program.
A computer readable storage medium having stored thereon a computer program which when executed implements any of the power supply unit distributed power supply capability calculation methods described herein.
The invention has the beneficial effects that: the method is characterized in that the maximum admission capacity of the distributed power supply of the power supply unit is taken as a target, the access position and the access capacity of the distributed power supply are taken as decision variables, the constraint that various indexes of the power distribution network are not out of limit under the switching state of a tie switch and a sectionalized switch which consider mutual economy between feed lines and mutual economy between stations is met, the access position and the access capacity which obtain the maximum admission capacity are solved, the obtained result is continuously optimized by adopting a genetic algorithm and traversal combined solution, and finally the optimal distribution power supply admission scheme of the power supply unit can be rapidly calculated.
Drawings
Figure 1 is a technical framework of the present invention,
fig. 2 is a solution flow chart of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made apparent and fully in view of the accompanying drawings, in which some, but not all embodiments of the invention are shown. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, the present embodiment provides a power supply unit distributed power supply acceptance performance processing method, which includes the following steps:
s1, constructing a distributed power supply admission capacity optimization framework considering network reconstruction;
s2, establishing a mathematical model of the receiving capacity of the distributed power supply taking the power supply unit as an object according to an optimization framework;
and S3, solving by combining a genetic algorithm and traversal, and substituting the solution into the solution to obtain the distributed power supply capacity.
In this embodiment, in S1, the optimization framework specifically includes the steps of:
s11, optimizing decision variable distributed power supply access positions and access capacities by taking the maximum sum of the distributed power supply access capacities as a target;
s12, constraint judgment is carried out in consideration of network reconstruction, the switch state combination of the tie switch and the sectionalizer switch is traversed, whether the combination meeting constraint conditions exists or not is searched, and a judgment result is obtained;
s13, if a solution meeting the conditions exists, describing that the distributed power supply admission capacity distribution network of the power supply unit under the network reconstruction can be consumed, if the solution meeting the conditions does not exist, describing that the solution can not be consumed, and then correcting until the solution can be obtained;
s14, correcting the corresponding individuals by using a genetic algorithm according to the judging result, and alternating until an optimal result is achieved.
The function relation of states of the interconnection switch and the sectionalizer on the admittance capacity is considered, so that the admittance capacity of the distributed power supply is further improved.
In this embodiment, in S2, the maximum sum of the distributed power supply access capacities of the power supply units is taken as an objective function, the access positions and the access capacities of the distributed power supplies are taken as decision variables, all indexes of the power distribution network are not out of limit under the condition that the connection state of the tie switch and the sectionalized switch are considered, and the switch state, the switching frequency constraint and the equipment capacity constraint are considered, so as to establish a distributed power supply admission capacity calculation model.
In this embodiment, in S2, the objective function is that the sum of access capacities of all nodes of all feeder lines of the power supply unit is the largest, so the objective function is shown in equation (1).
Wherein P is DGi Taking the installed capacity of the node i of the distributed power supply on the feeder line m as the maximum power generation power of a power generation curve of the distributed power supply in calculation; m is the number of feeder lines in the power supply unit; i is the number of nodes of the feeder line of the power supply unit.
Wherein the constraint conditions
And traversing to find the switch state combination of the tie switch and the sectionalizer with or without various indexes of the power distribution network not exceeding the limit.
1) Switch state constraints
The initial state of the tie switch is open, and the initial state of the sectionalizing switch is closed;
wherein X is w ins For this reason, the on-off state of the w-th tie switch in the power supply unit is that 1 is in the on state and 0 is in the off state; x is X v ses For this reason, the v-th sectionalizer in the power supply unit is in an open state, 1 is in a closed state, and 0 is in an open state; w is the number of the connecting switches in the power supply unit; v is the number of sectionalizing switches in the power supply unit; the number of the interconnecting switches and the sectionalizing switches is determined by the wiring form of the power supply unit;
2) Number of switch-over times
Taking into account switching losses, a constraint of the number of switching times is carried out
Wherein S is max The maximum number of times is switched for one day for switching.
3) Tidal current constraint of power distribution network
Wherein P is i 、Q i Active and reactive injection is performed at the node i; u (U) i 、U j The voltage amplitude values of the nodes i and j are obtained; g ij 、B ij The conductance and susceptance of branch ij; θ ij Is the voltage phase angle difference between the nodes i and j.
4) Node voltage constraint
U imin ＜U i ＜U imax (6)
In U imin The lower limit value of the node voltage is 0.9, U imax The upper limit of the node voltage was 1.07.
5) Branch tide constraint
P ij (t)≤P ijmax (7)
Wherein P is ij (t) is the single-phase active power transmitted by the line ij at the time t; p (P) ijmax For the limit value of the single-phase active power transmitted by the line ij at the time t, the value can be obtained by the maximum current-carrying capacity in the line parameters, and the calculation formula is as follows:
wherein U is the rated voltage of the line, I max For the maximum current-carrying capacity of the line, cos phi is the power factor and 0.9 is taken.
The three constraints of the power distribution network, namely the power flow constraint, the node voltage constraint and the branch power flow constraint are network operation constraints, so that the safety problem of distributed power supply access is avoided.
6) Short circuit current restraint
The power supply is connected to the power grid to increase the level of the short-circuit current of the power grid, and when the feeder line fails, the short-circuit current flowing through the circuit breaker is equal to the sum of the short-circuit current of the system and the short-circuit current of the power supply, so that the short-circuit current of the line after the distributed power supply is connected does not exceed the limit value of the voltage class.
I SCL ＜I SCLmax (9)
Wherein I is SCL And I SCLmax The maximum line current and the maximum breaker current are respectively.
7) Device capacity constraints
The distributed power supply capacity is taken as an optimization variable, the maximum constraint exists, and the maximum admittance capacity of the distributed power supply in the power supply unit is smaller than the sum of the power grid and the energy storage device, and the method is as follows:
P DGmin ≤P DGi ≤P DGmax (10)
wherein P is DGmax The maximum limit value of the distributed power supply access in the line is set; p (P) max For this purpose, the maximum limit value accommodated by the power supply system and the energy storage device in the power supply unit.
Referring to fig. 2, in this embodiment, in S3, a genetic algorithm and traversal are used to solve, and the specific steps include:
s31, initializing a population, wherein the gene of each individual is DG access position and access capacity;
s32, constraint judgment is carried out on each individual, and acceptance can be carried out under the condition that each constraint is met under the condition that whether a combination of states of a tie switch and a sectionalizing switch exists or not is searched through traversal;
s33, if the power distribution network is provided with the power distribution network, the individual can accept the distributed power supply capacity under the condition that all indexes of the power distribution network are not out of limit; if not, the individual is stated to consider that the network reconfiguration still cannot accommodate the capacities, and the individual which does not meet the constraint is replaced by the individual which meets the requirement; the selected and corrected individuals enter an iterative process of a genetic algorithm;
s34, taking the objective function value as the fitness value, namely the sum of the access capacities of the distributed power supplies, calculating the fitness value of each individual, keeping elite, then selecting, crossing and mutating the individual, repeating the steps S32 and S33 to obtain a preliminary next generation population, calculating the fitness value of each individual in the preliminary next generation population, comparing whether the fitness value of the optimal individual of the previous generation is larger than the fitness value of the worst individual of the current generation, if so, replacing to obtain a second generation population, and then continuing optimizing the second generation population by using the same method until the preset maximum iteration number is reached. Finally, the optimized population is obtained, and the optimized distributed power DG access position and access capacity are obtained.
In this embodiment, in S34, a roulette algorithm is used to perform selection operation, and an individual is selected from a parent population as a father individual, where the roulette algorithm, which is called roulette selection method, is the simplest and most commonly used selection method, and in this method, the selection probability of each individual is proportional to the fitness value thereof, and the larger the fitness is, the larger the selection probability is. Single-point crossover inheritance and mutation are carried out on the selected male parent individuals, wherein the crossover and mutation probability adopts an improved self-adaptive operator, the individuals lower than the fitness average value adopt higher crossover and mutation probability, the individuals higher than the fitness average value adopt lower crossover and mutation probability, and the probability adjustment formula:
wherein: f (f) max Is the maximum value of the fitness value; f (f) av Average fitness value for each generation of population; f' is the fitness value of the current operation individual, the crossover operation is the larger fitness value of 2 individuals to be crossed, and the mutation operation is the fitness value of the individuals to be mutated; p (P) adu And P adl Is the upper and lower limits of probability adjustment.
In this embodiment, an computing device is provided, including a memory, a processor, a motherboard, and a computer program stored on the memory and executed by the processor, where the processor implements a power supply unit distributed power supply acceptance computing method when executing the computer program.
In this embodiment, a computer-readable storage medium is provided, on which a computer program is stored, which when executed implements a power supply unit distributed power supply acceptance calculation method.
The invention aims at the maximum admission capacity of the distributed power supply of the power supply unit, takes the access position and the access capacity of the distributed power supply as decision variables, meets the constraint that various indexes of the power distribution network are not out of limit under the switching state of a tie switch and a sectionalized switch which consider mutual economy between feed lines and mutual economy between stations, solves the access position and the access capacity which obtain the maximum admission capacity, adopts a genetic algorithm and traversal to combine and solve the obtained result, continuously optimizes the obtained result, and finally can rapidly calculate the optimal distribution power supply admission scheme of the power supply unit.
The invention is not limited to the above embodiments, and based on the technical solution disclosed in the invention, a person skilled in the art may make some substitutions and modifications to some technical features thereof without creative effort according to the technical content disclosed, and all the substitutions and modifications are within the protection scope of the invention.
Claims (11)
1. The power supply unit distributed power supply acceptance performance processing method is characterized by comprising the following steps of:
s1, constructing a distributed power supply admission capacity optimization framework considering network reconstruction;
s2, establishing a mathematical model of the receiving capacity of the distributed power supply taking the power supply unit as an object according to an optimization framework;
and S3, solving by combining a genetic algorithm and traversal, and substituting the solution into the solution to obtain the distributed power supply capacity.
2. The power supply unit distributed power supply acceptance performance processing method according to claim 1, wherein in S1, the step of optimizing the framework:
s11, optimizing decision variable distributed power supply access positions and access capacities by taking the maximum sum of the distributed power supply access capacities as a target;
s12, constraint judgment is carried out in consideration of network reconstruction, the switch state combination of the tie switch and the sectionalizer switch is traversed, whether the combination meeting constraint conditions exists or not is searched, and a judgment result is obtained;
s13, if a solution meeting the conditions exists, describing that the distributed power supply admission capacity distribution network of the power supply unit under the network reconstruction can be consumed, if the solution meeting the conditions does not exist, describing that the solution can not be consumed, and then correcting until the solution can be obtained;
s14, correcting the corresponding individuals by using a genetic algorithm according to the judging result, and alternating until an optimal result is achieved.
3. The method for processing distributed power supply acceptance performance of a power supply unit according to claim 2, wherein in S2, a maximum sum of distributed power supply access capacities of the power supply unit is taken as an objective function, a distributed power supply access position and an access capacity are taken as decision variables, various indexes of the power distribution network are not out of limit under the condition of considering the on-off states of the tie switch and the sectionalizing switch as constraint conditions, and a distributed power supply acceptance capacity calculation model is established under the condition of considering the on-off states, the switching times constraint and the equipment capacity constraint.
4. A power supply unit distributed power supply acceptance performance processing method according to claim 3, wherein in S2, the objective function is:
wherein P is DGi Taking the installed capacity of the node i of the distributed power supply on the feeder line m as the maximum power generation power of a power generation curve of the distributed power supply in calculation; m is the number of feeder lines in the power supply unit; i is the number of nodes of the feeder line of the power supply unit.
5. A power supply unit distributed power supply acceptance performance processing method according to claim 3, wherein said constraint condition comprises:
1) Switch state constraints
The initial state of the tie switch is open, and the initial state of the sectionalizing switch is closed;
wherein X is w ins For this reason, the on-off state of the w-th tie switch in the power supply unit is that 1 is in the on state and 0 is in the off state; x is X v ses For this reason, the v-th sectionalizer in the power supply unit is in an open state, 1 is in a closed state, and 0 is in an open state; w is the number of the connecting switches in the power supply unit; v is the number of sectionalizing switches in the power supply unit; n (N) lin The number of branches is the number of branches;
2) Number of switch-over times
Wherein S is max Switching the switch for the maximum number of times in one day;
3) Tidal current constraint of power distribution network
Wherein P is i 、Q i Active and reactive injection is performed at the node i; u (U) i 、U j The voltage amplitude values of the nodes i and j are obtained; g ij 、B ij The conductance and susceptance of branch ij; θ ij The voltage phase angle difference between the nodes i and j is obtained;
4) Node voltage constraint
U imin ＜U i ＜U imax (6)
In U imin The lower limit value of the node voltage is 0.9, U imax The upper limit value of the node voltage is 1.07;
5) Branch tide constraint
P ij (t)≤P ijmax (7)
Wherein P is ij (t) is the single-phase active power transmitted by the line ij at the time t; p (P) ijmax For the limit value of the single-phase active power transmitted by the line ij at the time t, the value can be obtained by the maximum current-carrying capacity in the line parameters, and the calculation formula is as follows:
wherein U is the rated voltage of the line, I max Taking 0.9 for the maximum current-carrying capacity of the line, wherein cos phi is a power factor;
6) Short circuit current restraint
I SCL ＜I SCLmax (9)
Wherein I is SCL And I SCLmax The maximum short-circuit current of the circuit and the maximum short-circuit current of the breaker are respectively;
7) Device capacity constraints
P DGmin ≤P DGi ≤P DGmax (10)
Wherein P is DGmax The maximum limit value of the distributed power supply access in the line is set; p (P) max For this purpose, the maximum limit value accommodated by the power supply system and the energy storage device in the power supply unit.
6. The method for processing the distributed power supply acceptance performance of the power supply unit according to claim 1, wherein in S3, a combination of genetic algorithm and traversal is used for solving, and the specific steps include:
s31, initializing a population, wherein the genes of each individual are access positions and access capacities;
s32, constraint judgment is carried out on each individual, and acceptance can be carried out under the condition that each constraint is met under the condition that whether a combination of states of a tie switch and a sectionalizing switch exists or not is searched through traversal;
s33, if the power distribution network is provided with the power distribution network, the individual can accept the distributed power supply capacity under the condition that all indexes of the power distribution network are not out of limit; if not, the individual is stated to consider that the network reconfiguration still cannot accommodate the capacities, and the individual which does not meet the constraint is replaced by the individual which meets the requirement; the selected and corrected individuals enter an iterative process of a genetic algorithm;
and S34, taking the sum of the admission capacities of the distributed power supplies as a fitness value, calculating the fitness value of each individual, reserving elite, then selecting, crossing and mutating the individual, repeating the steps S32 and S33 to obtain a preliminary next generation population, calculating the fitness value of each individual in the preliminary next generation population, comparing whether the fitness value of the optimal individual of the previous generation is larger than the fitness value of the worst individual of the current generation, if so, replacing to obtain a second generation population, and then continuing optimizing the second generation population by using the same method until the iteration termination condition is reached.
7. The method for processing distributed power supply acceptance performance of a power supply unit according to claim 6, wherein the selection operation is performed by adopting a roulette algorithm in S34, and the individual is selected from a parent population as a parent individual.
8. The method for processing distributed power supply acceptance performance of a power supply unit according to claim 6, wherein the method for cross-over and mutation in S34:
single-point crossover inheritance and mutation are carried out on the selected male parent individuals, wherein the crossover and mutation probability adopts an improved self-adaptive operator, the individuals lower than the fitness average value adopt higher crossover and mutation probability, the individuals higher than the fitness average value adopt lower crossover and mutation probability, and the probability adjustment formula:
wherein: f (f) max Is the maximum value of the fitness value; f (f) av Average fitness value for each generation of population; f' is the fitness value of the current operation individual, the crossover operation is the larger fitness value of 2 individuals to be crossed, and the mutation operation is the fitness value of the individuals to be mutated; p (P) adu And P adl Is the upper and lower limits of probability adjustment.
9. The method for processing distributed power supply acceptance performance of a power supply unit according to claim 1, wherein the iteration termination condition in S34 is: reaching the preset iteration times.
10. An arithmetic device comprising a memory, a processor, a motherboard and a computer program stored on the memory and executed by the processor, characterized in that: the processor, when executing the computer program, implements the power supply unit distributed power supply capability calculation method of any one of claims 1 to 9.
11. A computer-readable storage medium having stored thereon a computer program, characterized in that the program when executed implements the power supply unit distributed power supply capacity calculation method of any one of claims 1 to 9.
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