CN115189378A

CN115189378A - Distributed power supply and energy storage grid-connected locating and sizing method and device and electronic equipment

Info

Publication number: CN115189378A
Application number: CN202210922009.2A
Authority: CN
Inventors: 王献志; 李铁成; 曾四鸣; 张卫明; 赵宇皓; 郭少飞; 王心蕊
Original assignee: State Grid Corp of China SGCC; Electric Power Research Institute of State Grid Hebei Electric Power Co Ltd; State Grid Hebei Energy Technology Service Co Ltd
Current assignee: State Grid Corp of China SGCC; Electric Power Research Institute of State Grid Hebei Electric Power Co Ltd; State Grid Hebei Energy Technology Service Co Ltd
Priority date: 2022-08-02
Filing date: 2022-08-02
Publication date: 2022-10-14

Abstract

The invention provides a distributed power supply, an energy storage grid-connected locating and sizing method, a device and electronic equipment. The method comprises the following steps: establishing a first objective function by taking the minimum investment cost of the power distribution network as a target, establishing a second objective function by taking the minimum network loss of the power distribution network as a target, and establishing a third objective function by taking the minimum difference value between the energy storage access capacity and the distributed power supply access capacity in the power distribution network as a target to obtain a multi-objective function; establishing constraint conditions of a multi-objective function; solving a multi-objective function based on the constraint condition to obtain an optimal solution; the optimal solution comprises the access positions and the access capacities of the distributed power sources and the stored energy in the power distribution network. The invention can fully utilize renewable energy, relieve the severe situation of the traditional fossil energy supply, improve the voltage quality of the power distribution network, simultaneously give consideration to the economy and the voltage quality of the power distribution network, and can determine the capacity configuration scheme of the distributed power supply and the stored energy under different conditions.

Description

Distributed power supply and energy storage grid-connected locating and sizing method and device and electronic equipment

Technical Field

The invention relates to the technical field of electric power, in particular to a distributed power supply, an energy storage grid-connected location and volume fixing method and device and electronic equipment.

Background

In recent years, the traditional fossil energy is gradually exhausted, and the requirements of efficient energy utilization and clean production cannot be met by adopting the traditional fossil energy for power generation. The distributed power supply widely utilizes clean renewable energy sources, so that the consumption of fossil energy and the emission of harmful gases can be reduced; and meanwhile, the distributed power supply is positioned at a user side, so that the construction cost and the loss of a power transmission and distribution network can be reduced. The distributed power supply has the advantages of being environment-friendly, flexible in configuration, capable of enhancing power supply reliability and capable of meeting sustainable development, and the distributed power supply can be connected to the power distribution network to be a supplementary and perfecting means of a centralized power generation mode.

With the development of distributed power supplies and energy storage technologies, the proportion of the distributed power supplies and energy storage equipment connected to a power distribution network is continuously increased, and on one hand, the voltage of the power distribution network is supported; on the other hand, the voltage fluctuation of the power distribution network can be enhanced or reduced, so that the system voltage is difficult to control, the stability of the system voltage is not facilitated, and the normal power utilization of users is influenced; and the distributed power supply is widely distributed and is not uniform when the power distribution network is stopped and started, so that the output power fluctuates to influence the normal operation of the power system, and the adverse effect is caused to the power distribution network. The magnitude of this effect is closely related to the access location and access capacity of the distributed power supply and the stored energy.

Therefore, while considering both the economy and the voltage quality of the power distribution network, the configuration schemes of the distributed power supply and the stored energy under different conditions need to be determined, so that a basis is provided for accessing the distributed power supply and the stored energy.

Disclosure of Invention

The embodiment of the invention provides a distributed power supply and energy storage grid-connected site selection method, a distributed power supply and energy storage grid-connected site selection device and electronic equipment, and aims to determine the optimal access position and the optimal access capacity of a distributed power supply and energy storage under the condition of considering both economy and distribution network voltage quality.

In a first aspect, an embodiment of the present invention provides a distributed power supply and energy storage grid-connected location and volume fixing method, including:

establishing a first objective function by taking the minimum investment cost of the power distribution network as a target, establishing a second objective function by taking the minimum network loss of the power distribution network as a target, and establishing a third objective function by taking the minimum difference value between the energy storage access capacity and the distributed power supply access capacity in the power distribution network as a target to obtain a multi-objective function;

establishing constraint conditions of the multi-objective function;

solving the multi-objective function based on the constraint conditions to obtain an optimal solution; the optimal solution comprises the access positions and the access capacities of the distributed power sources and the stored energy in the power distribution network.

In one possible implementation, the calculation formula of the multi-objective function is:

wherein, the first and the second end of the pipe are connected with each other,

is a calculation formula of the first objective function,

is a calculation formula of the second objective function,

is a calculation formula of the third objective function, C is the investment cost of the power distribution network, P is the network loss of the power distribution network, Z is the difference value of the energy storage access capacity and the distributed power supply access capacity, and N is _D Set of nodes for accessing distributed power, N _S For accessing the node set of the energy storage, T is any time in a typical period of the power distribution network, T is the time of the typical period of the power distribution network, n is the node set in the power distribution network, L is the set of lines in the power distribution network,

to access the investment cost of a k-node distributed power supply,

in order to access the investment cost of energy storage of k nodes,

for the cost of fuel consumed when the power distribution grid is operating,

for pollution penalty of emissions, R _l Is the resistance of line l in the distribution network, I _l (t) is the current flowing in line l at time t, P _DG,k For distributed power capacity of access node k, P _E,k Is the energy storage capacity of the access node k.

In one possible implementation, the constraint conditions include a system power flow constraint, a voltage deviation constraint, a voltage fluctuation constraint, a branch power constraint, a distributed power supply installation capacity constraint and an energy storage constraint; wherein the content of the first and second substances,

the system flow constraint is as follows:

wherein i and j respectively represent any node in the power distribution network, and P _i For the active power, Q, flowing in at node i _i For reactive power, P, flowing in at node i _L.i Active power, Q, for the access load at node i _L.i For reactive power, P, flowing in at node i _DG.i Active power, Q, injected for distributed power supply at node i _DG.i Reactive power, U, injected for distributed power supply at node i _i For node i corresponds to the voltage across the branch, U _j For node j corresponding to the voltage across the branch, G _ij Conductance values of the branches corresponding to nodes i and j, B _ij Susceptance value, θ, for the branches corresponding to nodes i and j _ij Is a phase angle;

the voltage deviation constraint is:

U _N (1-ε ₁ )≤U _m ≤U _N (1+ε ₂ ),m∈n

wherein, U _m Is the voltage at node m, U _N For the nominal voltage, epsilon, of the distribution network ₁ And ε ₂ All are standard specified deviation values;

the voltage fluctuation constraint is:

d _m ％≤d _m.max ％,m∈n

wherein d is _m % is the voltage fluctuation at node m, d _m.max % is the maximum value of voltage fluctuation permitted by regulation;

the branch power constraints are:

S _l ≤S _l.max ,l∈L

wherein S is _l Power transmitted for branch l, S _l.max In order to allow the maximum power transmitted by the branch I, L is the collection of all the branches in the power distribution network;

the constraints of the installation capacity of the distributed power supply are as follows:

G _DG.k ≤G _DG.max ,k∈N _D

wherein G is _DG.k Distributed power capacity, G, for access node k _DG.max Maximum capacity, N, of distributed power sources allowed to be accessed for each node _D A node set for accessing a distributed power supply;

the energy storage constraint is:

wherein the content of the first and second substances,

k∈N _S for energy-storage capacity constraint, E _S.k The amount of electric power stored by the energy storage system for node k,

is the upper limit of the electric quantity of the energy storage system,

is the lower limit of the electric quantity of the energy storage system, N _S A node set for accessing energy storage;

k∈N _S for energy storage power constraint, P _SS.k (t) charging power of energy storage system of access node k at time t, P _SR.k (t) the discharge power of the energy storage system of the access node at time t,

the maximum value of the charging power of the energy storage system,

the minimum value of the charging power of the energy storage system,

is the maximum value of the discharge power of the energy storage system,

is the minimum value of the discharge power of the energy storage system, u _S (t) is the state of charge of the energy storage system at time t, u _R And (t) is the discharge state of the energy storage system at the moment t.

In one possible implementation, the calculation formula of the voltage at the node m in the voltage deviation constraint is:

U _m ＝U _N ×(1+ΔU _m )

wherein, U _m Is the voltage at node m, U _N For nominal voltage, Δ U, of the distribution network _m Is the voltage deviation at node m, R ₀ +jX ₀ Is the equivalent impedance, R, on the side of the power system ₀ Is a resistance on the side of the power system, X ₀ Is reactance of power system side, R _i +jX _i Equivalent impedance, R, of the branch corresponding to node i _i Resistance of the branch, X, for node i _i Reactance, P, of branch corresponding to node i _L.j +jQ _L.j Work flowing in for the branch corresponding to node jRate, P _L.j Active power, Q, flowing into the branch corresponding to node j _L.j Reactive power, P, flowing into the corresponding branch for node j _DG.j +jQ _DG.j For the power injected by the distributed power supply at node j, P _DG.j Active power, Q, injected for distributed power supply at node j _DG.j Reactive power, P, injected for distributed power supply at node j _E.j +jQ _E.j Power stored for node j, P _E.j Active power, Q, for storing energy at node j _E.j And (4) storing the reactive power at the node j.

In one possible implementation manner, the calculation formula of the voltage fluctuation at the node m in the voltage fluctuation constraint is as follows:

wherein d is _m % is the voltage fluctuation at node m, d _PV.m % is the voltage fluctuation, d, caused by the photovoltaic power supply in the distributed power supply at node m _W.m % is voltage fluctuation U caused by fan in distributed power supply at node m _N For the nominal voltage, P, of the distribution network _PV.j Power injected for photovoltaic power supply at node j, P _W.j +jQ _W.j Power injected for fan at node j, P _W.j Active power, Q, injected for fan at node j _W.j Reactive power, R, injected for fans at node j _i +jX _i Equivalent impedance, R, of the branch corresponding to node i _i Resistance of the branch, X, for node i _i The reactance of the corresponding branch for node i.

In one possible implementation manner, the algorithm for solving the multi-objective function based on the constraint condition is an improved particle swarm optimization algorithm of a hybrid simulated annealing algorithm;

the process of solving the multi-objective function is as follows:

setting relevant parameters of an improved particle swarm optimization algorithm of a hybrid simulated annealing algorithm, wherein the relevant parameters comprise the particle swarm size, the initial temperature, the annealing rate, the iteration times and the maximum iteration times;

randomly generating initial positions of particles in the particle swarm, calculating an adaptive value of each particle according to the initial position of each particle, and determining the optimal position of each particle in the particle swarm and the optimal position of the swarm according to the adaptive value of each particle;

step three, updating the position of each particle once according to the optimal position of each particle and the optimal position of the group of particle swarms, and calculating the adaptive value of each particle after updating once;

fourthly, performing neighborhood disturbance on each particle after the primary updating to obtain a new position of each particle, and calculating an adaptive value corresponding to the new position;

determining whether the positions of the particles are updated for the second time according to a Metropolis criterion and adaptive values before and after neighborhood disturbance, and taking the positions of the particles updated for the second time as the optimal positions of the particles;

step six, determining the optimal position of the particle swarm according to the optimal position of each particle in the particle swarm, and adding 1 to the current iteration times;

step seven, judging whether the iteration times reach the maximum iteration times, if the iteration times do not reach the maximum iteration times, performing temperature reduction according to the temperature reduction rate, and skipping to the step three;

and step eight, if the iteration times reach the maximum iteration times, stopping the iteration, and determining the optimal position of the current particle swarm as the optimal solution of the multi-target function.

In one possible implementation, the optimal solution is a plurality of optimal solutions;

after obtaining the optimal solution, the method further comprises:

respectively carrying out normalization processing on each objective function value of the optimal solutions;

calculating the maximum difference value between each objective function value in each optimal solution and the corresponding objective function expected value after normalization processing according to the preset expected value of each objective function;

and determining the optimal solution with the minimum corresponding maximum difference value as the expected optimal solution.

In a second aspect, an embodiment of the present invention provides a distributed power supply and energy storage grid-connected location and volume fixing device, including:

the establishing module is used for establishing a first objective function by taking the minimum investment cost of the power distribution network as a target, establishing a second objective function by taking the minimum network loss of the power distribution network as a target, and establishing a third objective function by taking the minimum difference value between the energy storage access capacity and the distributed power supply access capacity in the power distribution network as a target to obtain a multi-objective function; and establishing constraint conditions of the multi-objective function;

the calculation module is used for solving the multi-target function based on the constraint condition to obtain an optimal solution; the optimal solution comprises the access positions and the access capacities of the distributed power sources and the stored energy in the power distribution network.

In a third aspect, an embodiment of the present invention provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor executes the computer program to implement the steps of the method according to the first aspect or any one of the possible implementation manners of the first aspect.

In a fourth aspect, the present invention provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the computer program implements the steps of the method according to the first aspect or any one of the possible implementation manners of the first aspect.

The embodiment of the invention provides a distributed power supply and an energy storage grid-connected site selection method, renewable energy is fully utilized by considering access capacity and energy storage access capacity of the distributed power supply, the severe situation of the traditional fossil energy supply is relieved, and meanwhile, the voltage quality of a power distribution network can be improved by utilizing the distributed power supply and the energy storage; the method has the advantages that the solution is carried out by taking the minimum investment cost of the power distribution network, the minimum network loss of the power distribution network and the minimum difference value between the access capacity of the stored energy in the power distribution network and the access capacity of the distributed power supply as a multi-objective function, the economical efficiency and the voltage quality of the power distribution network can be considered simultaneously, and the capacity allocation schemes of the distributed power supply and the stored energy under different conditions can be determined.

Drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.

Fig. 1 is a flowchart of an implementation of a distributed power supply and an energy storage grid-connected location and sizing method according to an embodiment of the present invention;

fig. 2 is a schematic structural diagram of a distributed power supply and an energy storage access distribution network according to an embodiment of the present invention;

fig. 3 is a schematic structural diagram of an IEEE33 node according to an embodiment of the present invention;

FIG. 4 is a spatial distribution diagram of Pareto fronts provided by an embodiment of the present invention;

fig. 5 is a schematic structural diagram of a distributed power supply and an energy storage grid-connected location and sizing device provided in the embodiment of the present invention;

fig. 6 is a schematic diagram of an electronic device provided in an embodiment of the present invention.

Detailed Description

In the following description, for purposes of explanation and not limitation, specific details are set forth such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.

To make the objects, technical solutions and advantages of the present invention more apparent, the following description will be made by way of specific embodiments with reference to the accompanying drawings.

Fig. 1 is a flowchart of an implementation of a distributed power supply and an energy storage grid-connected location and capacity determination method according to an embodiment of the present invention, which is detailed as follows:

step 101, establishing a first objective function with the minimum investment cost of the power distribution network as a target, establishing a second objective function with the minimum network loss of the power distribution network as a target, and establishing a third objective function with the minimum difference value between the energy storage access capacity and the distributed power supply access capacity in the power distribution network as a target to obtain a multi-objective function.

In this embodiment, a first objective function is established with the minimum investment cost of the power distribution network as a target, the economic problem of the investment of the power distribution network is considered, a second objective function is established with the minimum network loss of the power distribution network as a target, the voltage quality problem of the power distribution network after the distributed power supply and the energy storage system are accessed is considered, a third objective function is established with the minimum difference value between the access capacity of the energy storage in the power distribution network and the access capacity of the distributed power supply as a target, and the access capacity of the distributed power supply and the access capacity of the energy storage are considered, so that renewable energy is fully utilized.

And 102, establishing constraint conditions of the multi-objective function.

In this embodiment, the constraint conditions may include a system power flow constraint, a voltage deviation constraint, a voltage fluctuation constraint, a branch power constraint, a distributed power supply installation capacity constraint, and an energy storage constraint, and the multi-objective function is constrained from multiple aspects, so as to solve to obtain an optimal solution of the objective function.

103, solving a multi-objective function based on constraint conditions to obtain an optimal solution; the optimal solution comprises the access positions and the access capacities of the distributed power sources and the stored energy in the power distribution network.

In the embodiment, a multi-objective function is obtained according to the first objective function, the second objective function and the third objective function, the multi-objective function is solved, the three objective functions are considered at the same time, the economy and the voltage quality of the power distribution network can be considered, and then the capacity allocation scheme of the distributed power supply and the energy storage under different conditions can be determined.

According to the embodiment of the invention, the access capacity of the distributed power supply and the access capacity of the stored energy are considered, renewable energy is fully utilized, the severe situation of the traditional fossil energy supply is relieved, and meanwhile, the voltage quality of the power distribution network can be improved by utilizing the distributed power supply and the stored energy; the method has the advantages that the solution is carried out by taking the minimum investment cost of the power distribution network, the minimum network loss of the power distribution network and the difference value between the access capacity of the distributed power supply and the access capacity of the stored energy as a multi-objective function, the economical efficiency and the voltage quality of the power distribution network can be considered simultaneously, and the capacity allocation schemes of the distributed power supply and the stored energy under different conditions can be determined.

In one possible implementation manner, the distributed power supply comprises a fan and a photovoltaic power supply, and the output of the fan and the output of the photovoltaic power supply both have volatility and also present certain complementarity; meanwhile, the energy storage system can balance the influence of the two distributed power supplies, namely the fan and the photovoltaic power supply, on the power distribution network.

Because the output of the fan and the photovoltaic power supply has fluctuation, a scheduling strategy of the energy storage system can be set, and charging, discharging or no charging and discharging are carried out in different time periods; specifically, the scheduling policy may be set as: taking a natural day as a typical cycle, namely a typical day, charging is carried out in a range of 14.

Thus, considering 24 hours in a typical day, the calculation formula of the multi-objective function can be set as:

wherein the content of the first and second substances,

is a calculation formula for the first objective function,

is a calculation formula for the second objective function,

is a calculation formula of a third objective function, C is the investment cost of the power distribution network, P is the network loss of the power distribution network, and Z is the difference value of the energy storage access capacity and the distributed power supply access capacity，N _D Set of nodes for accessing distributed power, N _S In order to access the node set of the energy storage, T is any time in a typical period of the power distribution network, T is the time of the typical period of the power distribution network, m is any node in the power distribution network, n is the node set in the power distribution network, L is the set of lines in the power distribution network,

to access the investment cost of the k-node distributed power supply,

in order to access the investment cost of energy storage of the k nodes,

for the cost of fuel consumed when the power distribution grid is operating,

for pollution penalty of emissions, R _l Is the resistance of line l in the distribution network, I _l (t) is the current flowing in line l at time t, P _DG,k Distributed power capacity, P, for access node k _E,k Is the energy storage capacity of the access node k.

Specifically, the investment of the distributed power supply, the investment of energy storage, the fuel cost consumed by each node and the pollution punishment of emissions are simultaneously considered in the first objective function, so that the investment cost is comprehensively considered in the subsequent calculation, and the economy of the final scheme is ensured; calculating network loss through the current and the resistance of each node in the second objective function, and setting the second objective function to ensure that the voltage quality of a scheme can be considered in subsequent calculation and ensure better voltage quality of the power distribution network; and in the third objective function, the sizes of the distributed power supply access capacity and the energy storage access capacity are considered, so that the renewable energy can be fully utilized in subsequent calculation.

In one possible implementation, the constraint conditions include system power flow constraint, voltage deviation constraint, voltage fluctuation constraint, branch power constraint, distributed power supply installation capacity constraint and energy storage constraint; and constraint conditions are established from six aspects, so that the constraint can be comprehensively carried out, and the final scheme can be ensured to adapt to different requirements and conditions.

Wherein, the system power flow constraint is as follows:

wherein i and j respectively represent any node in the power distribution network, and P _i For the active power, Q, flowing in at node i _i For reactive power, P, flowing in at node i _L.i Active power, Q, for the access load at node i _L.i For reactive power flowing in at node i, P _DG.i Active power injected for distributed power supply at node i, Q _DG.i Reactive power, U, injected for distributed power supply at node i _i For node i to correspond to the voltage across the branch, U _j For node j corresponds to the voltage across the branch, G _ij Conductance values of branches corresponding to nodes i and j, B _ij Susceptance value, θ, for the branches corresponding to nodes i and j _ij Is the phase angle.

Specifically, the system power flow constraint can clarify the power distribution condition of each line, prevent the phenomena that part of lines are overloaded and part of lines are lightly loaded, ensure the static stability of the power system and ensure that the power grid resources can be fully utilized.

The voltage deviation constraint is:

U _N (1-ε ₁ )≤U _m ≤U _N (1+ε ₂ ),m∈n

wherein, U _m Is the voltage at node m, U _N For nominal voltage, epsilon, of the distribution network ₁ And ε ₂ Are all standard deviation values.

Specifically, after a large number of distributed power sources and energy storage are connected to the power distribution network, the intermittent nature and the fluctuation of the distributed power sources and the energy storage can greatly influence the tide distribution in the power distribution network, so that voltage deviation and fluctuation occur, and the stability of the power distribution network is influenced.

The voltage fluctuation constraint is:

d _m ％≤d _m.max ％,m∈n

wherein d is _m % is the voltage fluctuation at node m, d _m.max % is the maximum value of voltage fluctuation permitted by regulation.

Specifically, as can be seen from the foregoing, the voltage may fluctuate due to the connection of the distributed power supply and the energy storage, which affects the stability of the power distribution network, so that in order to avoid the voltage from fluctuating too much, the power distribution network is operated under the condition of a higher or lower voltage, which causes an uneconomical or unsafe problem, the voltage fluctuation of each node is constrained to ensure the stability of the node voltage, and the power distribution network can be operated fully and safely.

The branch power constraints are:

S _l ≤S _l.max ,l∈L

wherein S is _l Power transmitted for branch l, S _l.max To allow maximum power for branch L to transmit, L is the collection of all branches in the distribution network.

Specifically, when the power in the line is too high, the temperature of the line or the access device may exceed the allowable range, which may damage the insulation performance outside the line or the access device, and accelerate aging; in severe cases, a combustion phenomenon may occur, thereby causing a fire. Therefore, in order to ensure the safety and stability of the line, the power transmitted by the line needs to be restricted.

G _DG.k ≤G _DG.max ,k∈N _D

wherein, G _DG.k Distributed power capacity for access node k, G _DG.max Maximum capacity, N, of distributed power sources allowed to be accessed for each node _D Is a collection of nodes that access the distributed power supply.

Specifically, by restricting the installation capacity of the distributed power supply, the distribution network is prevented from being affected too much on the premise of ensuring the access capacity of the distributed power supply.

The energy storage constraint is:

is the upper limit of the electric quantity of the energy storage system,

k∈N _S for energy storage power constraint, P _SS.k (t) is the charging power of the energy storage system of the access node k at time t, P _SR.k (t) the discharge power of the energy storage system of the access node at time t,

the maximum value of the charging power of the energy storage system,

the minimum value of the charging power of the energy storage system,

is the maximum value of the discharge power of the energy storage system,

Specifically, the electric quantity and the power of the energy storage system are restricted, so that the access capacity of the energy storage system is accurately calculated, and the economy of the final scheme and the stability of the power distribution network are guaranteed.

In a possible implementation manner, the voltage deviation is caused by the access of the distributed power supply and the access of the energy storage, so that the voltage deviation caused by the distributed power supply and the energy storage needs to be fully considered.

Referring to a schematic structural diagram of a distributed power supply and energy storage access power distribution network shown in fig. 2, the power distribution network has n nodes, each node is a power distribution network bus, and each node is simultaneously accessed with a load, a distributed power supply and energy storage, and when a power value is set to 0, it indicates that the node is not accessed with the load, the distributed power supply or the energy storage, wherein R is ₀ +jX ₀ Is the equivalent impedance, R, on the side of the power system _i +jX _i Equivalent impedance, P, for the branch corresponding to node i _L.j +jQ _L.j The power, P, flowing in for the branch corresponding to node j _DG.j +jQ _DG.j For the power injected by the distributed power supply at node j, P _E.j +jQ _E.j The power stored at node j.

When the distributed power supply and the stored energy are not connected, the voltage deviation delta U at any node m _m The calculation formula of (2) is as follows:

when only the distributed power supply is connected to the power distribution network, the voltage deviation delta U at any node m after the distributed power supply is connected is caused by the negative load _m The calculation formula of (c) is:

when the stored energy is also connected to the power distribution network, the energy storage system is similar to the distributed power supply in a discharging state, and the voltage deviation delta U of any node m at the moment _m The calculation formula of (2) is as follows:

when the energy storage system is in a charging state, the voltage deviation delta U of any node m is similar to that of a load _m % is calculated as:

therefore, the voltage at node m in the voltage deviation constraint is calculated by:

U _m ＝U _N ×(1+ΔU _m )

in the above formula, U _m Is the voltage at node m, U _N For nominal voltage, Δ U, of the distribution network _m Is the voltage deviation at node m, R ₀ +jX ₀ Is an equivalent impedance, R, on the power system side ₀ Resistance on the side of the power system, X ₀ Is reactance of power system side, R _i +jX _i Equivalent impedance, R, of the branch corresponding to node i _i Resistance of the branch, X, for node i _i Reactance of branch corresponding to node i, P _L.j +jQ _L.j The power, P, flowing in for the branch corresponding to node j _L.j Active power, Q, flowing into the branch corresponding to node j _L.j Reactive power, P, flowing into the branch corresponding to node j _DG.j +jQ _DG.j For the power injected by the distributed power supply at node j, P _DG.j Active power, Q, injected for distributed power supply at node j _DG.j Reactive power, P, injected for distributed power supply at node j _E.j +jQ _E.j Power stored for node j, P _E.j Active power, Q, for storing energy at node j _E.j And (4) storing the reactive power at the node j.

The voltage at the node m is accurately calculated by considering the voltage deviation caused by the access of the distributed power supply and the access of the stored energy, so that the voltage is restrained, and the stability of the voltage is ensured.

In one possible implementation, when the photovoltaic power output changes instantaneously, the ratio of the output change to the rated output is λ _PV Then the voltage fluctuation at node m caused by the photovoltaic power supply alone is:

the fan power supply generally adopts a direct grid connection mode in a power distribution network, the influence of the reactive power of the fan power supply on voltage fluctuation needs to be considered, and the ratio of the instantaneous change of the fan output to the rated output is lambda _W Then, the voltage fluctuation at node m caused only by the fan power supply is:

therefore, when calculating the voltage fluctuation at the node m, the influence of the photovoltaic power supply and the wind power supply on the node m needs to be considered at the same time, and the voltage fluctuation at the node m is:

d _m ％＝d _PV.m ％+d _W.m ％

the lambda can be selected according to the fluctuation of the output of the photovoltaic and the fan _PV And λ _W For example, λ can be selected _PV =0.5 and λ _W =0.6; because the energy storage system is flexible and controllable, the expected planned output can be tracked to carry out charging and discharging planning, and voltage fluctuation is stabilized, so that the influence generated after the distributed power supply is connected to the grid is reduced. Therefore, according to the matching situation of the energy storage and the distributed power supply, the lambda can be selected _PV =0.2 and λ _W =0.4, the voltage fluctuates by aboutThe calculation formula of the voltage fluctuation at node m in the beam is:

in the above formula, d _m % is the voltage fluctuation at node m, d _PV.m % is the voltage fluctuation caused by the photovoltaic power supply in the distributed power supply at node m, d _W.m % is voltage fluctuation U caused by fan in distributed power supply at node m _N For the nominal voltage, P, of the distribution network _PV.j Power injected for photovoltaic power supply at node j, P _W.j +jQ _W.j Power injected for fan at node j, P _W.j Active power, Q, injected for fan at node j _W.j Reactive power, R, injected for fans at node j _i +jX _i Equivalent impedance, R, of the branch corresponding to node i _i Resistance of the branch, X, for node i _i The reactance of the corresponding branch for node i.

By considering the influence of the access of different distributed power supplies on voltage fluctuation and the matching condition of the distributed power supplies and energy storage, the voltage fluctuation at the node m is accurately calculated, so that the voltage fluctuation at the node m can be restrained, the stability of voltage is ensured, and the voltage quality of a power distribution network is ensured.

In one possible implementation mode, the algorithm for solving the multi-objective function based on the constraint condition is an improved particle swarm optimization algorithm of a hybrid simulated annealing algorithm;

the process of solving the multi-objective function is as follows:

step three, updating the position of each particle once according to the optimal position of each particle and the optimal position of the particle swarm of each particle, and calculating the adaptive value of each particle after updating once;

determining whether the positions of the particles are updated for the second time according to the Metropolis criterion and the adaptive values before and after the neighborhood disturbance, and taking the positions of the particles updated for the second time as the optimal positions of the particles of each particle, so that poor solutions can be accepted with a certain probability, local optimal solutions can be skipped, and global optimal solutions can be found more quickly and accurately;

step seven, judging whether the iteration times reach the maximum iteration times, if the iteration times do not reach the maximum iteration times, showing that the current group optimal position is not necessarily the global optimal position, namely, the global optimal solution is not necessarily found, and the global optimal position needs to be continuously searched, then carrying out temperature reduction according to the temperature reduction rate, and jumping to the step three;

and step eight, if the iteration times reach the maximum iteration times, the current group optimal position is possibly the global optimal position, namely the global optimal solution is found, the iteration is stopped, and the group optimal position of the current particle swarm is determined to be the optimal solution of the multi-target function.

Specifically, the injection power of the distributed power supply and the energy storage at each node is considered as a basic particle, and the specific expression is as follows:

wherein P is a particle group, x _MN The magnitude of the injected power at the Nth node, R, of the Mth particle, represented as a distributed power source and stored energy _M(N+1) The adaptive value of the Mth particle; and calculating the adaptive value, namely calculating the injection power value of each particle at each node.

In a possible implementation manner, the related parameters further include the velocity of the particle, and the calculation formula for updating the velocity and the position of each particle is as follows:

is the position of the ith particle of the kth generation of particle, V _i ^k+1 The velocity of the ith particle in the kth generation of particle,

is the position of the ith particle of the kth generation of particle, V _i ^k Is the velocity of the ith particle of the kth generation of particle group, w is a weighting coefficient, c ₁ 、c ₂ The value of the learning factor in this embodiment is 2; rand is a random number between (0, 1), pbest is the particle optimal position for each particle, i.e., the population optimal position for the population of gbest particle populations, i.e., the global optimal solution, relative to the optimal solution for that particle.

In a possible implementation manner, according to the Metropolis criterion and the adaptive values before and after the neighborhood disturbance, the calculation formula for determining the particle update is as follows:

wherein, E _n An adaptive value, E, corresponding to the new position after each particle neighborhood perturbation _c And (3) an adaptive value corresponding to the position of each particle before the disturbance, wherein t is the current temperature.

By performing neighborhood disturbance on each particle and determining the optimal position of each particle according to the Metropolis criterion and the adaptive values before and after the neighborhood disturbance, a poor solution can be accepted with a certain probability, so that a local optimal solution is skipped, the convergence speed is increased, and the optimal solution is found quickly and accurately.

In a possible implementation manner, the optimal solution is a plurality of optimal solutions, and after obtaining the optimal solution, the method includes: respectively carrying out normalization processing on each objective function value of the optimal solutions; calculating the maximum difference value between each objective function value in each optimal solution and the corresponding objective function expected value after normalization processing according to the preset expected value of each objective function; and determining the optimal solution with the minimum corresponding maximum difference value as the expected optimal solution.

Specifically, the calculation formula for normalizing each objective function value is as follows:

value of i-th objective function normalized for a certain scheme, f _i ^max For the maximum value of the ith objective function value in the optimal solution set, f _i ^min The minimum value of the ith objective function value in the optimal solution set.

By normalizing the objective function values in the optimal solution, the subsequent comparison and judgment with the expected optimal solution can be facilitated, so that the optimal solution which is more in line with the decision expectation can be found.

The calculation formula for determining the desired optimal solution is:

wherein D (x) is the determined optimal solution, μ _ri And omega is a set of optimal solutions for a preset ith objective function expectation value.

And determining the difference between the optimal solution and the expected value by comparing the maximum value of the objective function value and the expected value in each optimal solution, and selecting the optimal solution with the minimum difference from the optimal solutions, namely the optimal solution which is closest to the expected value or the preference of a decision maker.

By way of example, the IEEE33 node system shown in fig. 3 is taken as an example to verify the distributed power supply and the energy storage grid-connected location determination method provided by the present application. The rated voltage of the node system is 12.66kV, the power reference value is 100MW, the total active load is 3.715MW, and the total reactive load is 2.3MVar.

In this embodiment, points with lower voltage deviation indexes and larger node loads are taken as selection principles, and it can be considered that the

nodes

22 and 31 are photovoltaic alternative nodes, the

nodes

9 and 26 are fan alternative nodes, and the

nodes

5 and 17 are energy storage alternative nodes; aiming at the fluctuation of the output of the fan and the photovoltaic, the stabilizing effect of the energy storage access on the distributed power supply is considered, and the lambda can be selected _PV =0.2 and λ _W =0.4; in addition, the permissible voltage deviation value can be selected from epsilon ₁ And ε ₂ All are 0.7, the maximum value d of voltage fluctuation _m.max % may be chosen to be 3%.

According to the maximum access capacity of the distributed power supply, calculating by using an improved particle swarm optimization algorithm of a hybrid simulated annealing algorithm to obtain the calculation results of the access capacity and the position of the distributed power supply and the stored energy, wherein the calculation results are specifically shown in table 1:

table 1 table of optimal access capacity of each node

As can be seen from table 1, under the constraint of voltage deviation and voltage fluctuation, the limit access capacity of the distributed power supply and the stored energy is 2.523MW, and the ratio of the limit access capacity to the total active load is 67.9%.

According to the multi-objective function and the constraint condition, solving is performed by using an improved particle swarm optimization algorithm of a hybrid simulated annealing algorithm to obtain a set of Pareto optimal solutions, specifically, as shown in fig. 4, the set is a spatial distribution of Pareto frontiers, and all solutions in the space are optimal solutions of the multi-objective function problem.

As can be seen from fig. 4, the solution set has a good distribution. When the access difference value between the distributed power supply and the stored energy is increased, the total investment cost of the power distribution network is increased, and the network loss of the power distribution network is reduced; however, when the difference of access is reduced, the network loss of the distribution network is increased, and the cost is reduced correspondingly. The solutions in the solution set are all optimal solutions and can be selected according to the requirements of a decision maker.

Specifically, the expected value may be preset according to the requirement of the decision maker. For example, 1) the expected value of an objective function of the network loss of the power distribution network is set to be 0.9, and the expected values of the other two objective functions are set to be 0.7; 2) Setting the target function expectation value of the investment cost of the power distribution network to be 0.9, and setting the other two target function expectation values to be 0.7; 3) And setting the expected value of an objective function of the difference value between the energy storage access capacity and the distributed power supply access capacity in the power distribution network to be 0.9, and setting the expected values of the other two objective functions to be 0.7. The multi-objective planning results for each of the contemplated scenarios are shown in table 2:

TABLE 2 Multi-objective planning results table

As can be seen from table 2, the objective function value of the objective function with a higher expectation value in each scheme is slightly smaller than the other two objective function values, for example, if the expectation of the objective function of the network loss of the distribution network in scheme 1 is higher, the network loss of the distribution network in this scheme is the least of the 3 schemes. By comparing the objective function values of the schemes, the influence of the access of the distributed power supply and the stored energy on the network loss and the influence of the total access capacity are basically similar, but mutually restricted with the objective function of the investment cost. And when the objective function is only set to maximize the access capacity of the distributed power supply in the power distribution network, although the total access capacity in the planning result is far greater than that of the multi-objective planning result, the total network loss is high. Therefore, the multi-objective planning method provided by the invention can ensure that the investment is small and the voltage quality is good, can also realize the minimum difference value between the energy storage access capacity and the distributed power supply access capacity in the power distribution network, namely the maximum capacity access of the distributed power supply and the minimum capacity access of the energy storage system, and can also select a satisfactory planning scheme from all solutions according to actual requirements.

According to the embodiment of the invention, the access capacity of the distributed power supply and the access capacity of the stored energy are considered, renewable energy is fully utilized, the severe situation of the traditional fossil energy supply is relieved, and meanwhile, the voltage quality of the power distribution network can be improved by utilizing the distributed power supply and the stored energy; solving by taking the minimum investment cost of the power distribution network, the minimum network loss of the power distribution network and the minimum difference value between the access capacity of the stored energy in the power distribution network and the access capacity of the distributed power supply as a multi-objective function, and establishing constraint conditions by taking system power flow constraint, voltage deviation constraint, voltage fluctuation constraint, branch power constraint, distributed power supply installation capacity constraint and stored energy constraint as constraint conditions from six aspects, so that the constraint can be comprehensively carried out, and the final scheme can be ensured to adapt to different requirements and conditions; when the multi-objective function is solved, the improved particle swarm optimization algorithm of hybrid simulated annealing is adopted, so that the defects that the simulated annealing algorithm is too long in time and the particle swarm optimization algorithm is easy to fall into local oscillation and the like can be overcome, the convergence speed and the convergence precision can be improved, the optimal solution can be quickly and accurately found, and the access position and the access capacity of the distributed power supply and the stored energy are determined; after a plurality of optimal solutions are solved, each optimal solution is normalized, an expected optimal solution is calculated, and the optimal solution meeting the requirements or expected by a decision maker can be quickly and accurately found.

It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.

The following are embodiments of the apparatus of the invention, reference being made to the corresponding method embodiments described above for details which are not described in detail therein.

Fig. 5 shows a schematic structural diagram of a distributed power supply and an energy storage grid-connected location determining device provided in an embodiment of the present invention, and for convenience of description, only parts related to the embodiment of the present invention are shown, which are detailed as follows:

as shown in fig. 5, the distributed power supply and energy storage grid-connected locating and sizing device 5 includes:

the establishing module 51 is configured to establish a first objective function with the minimum investment cost of the power distribution network as a target, establish a second objective function with the minimum network loss of the power distribution network as a target, and establish a third objective function with the minimum difference between the energy storage access capacity and the distributed power supply access capacity in the power distribution network as a target to obtain a multi-objective function; and establishing constraint conditions of the multi-objective function.

The calculation module 52 is configured to solve the multi-objective function based on the constraint condition to obtain an optimal solution; the optimal solution comprises the access positions and the access capacities of the distributed power sources and the stored energy in the power distribution network.

is a calculation formula for the first objective function,

is a calculation formula for the second objective function,

is a calculation formula of a third objective function, C is the investment cost of the power distribution network, P is the network loss of the power distribution network, Z is the difference value of the energy storage access capacity and the distributed power supply access capacity, and N is _D Set of nodes for accessing distributed power, N _S For accessing the node set of the energy storage, T is any time in a typical period of the power distribution network, T is the time of the typical period of the power distribution network, n is the node set in the power distribution network, L is the set of lines in the power distribution network,

to access the investment cost of a k-node distributed power supply,

in order to access the investment cost of energy storage of the k nodes,

for the cost of fuel consumed when the power distribution grid is operating,

In one possible implementation manner, the constraint conditions comprise system power flow constraint, voltage deviation constraint, voltage fluctuation constraint, branch power constraint, distributed power supply installation capacity constraint and energy storage constraint; wherein the content of the first and second substances,

the system flow constraint is as follows:

wherein i and j respectively represent any node in the power distribution network, P _i For the active power, Q, flowing in at node i _i For reactive power, P, flowing in at node i _L.i Active power, Q, for the access load at node i _L.i For reactive power flowing in at node i, P _DG.i Active power, Q, injected for distributed power supply at node i _DG.i Reactive power, U, injected for distributed power supply at node i _i For node i to correspond to the voltage across the branch, U _j For node j corresponds to the voltage across the branch, G _ij Conductance values of the branches corresponding to nodes i and j, B _ij Susceptance value, θ, for the branches corresponding to nodes i and j _ij Is the phase angle.

The voltage deviation constraint is:

U _N (1-ε ₁ )≤U _m ≤U _N (1+ε ₂ ),m∈n

wherein, U _m Is the voltage at node m, U _N For the nominal voltage, epsilon, of the distribution network ₁ And ε ₂ Are all standard deviation values.

The voltage fluctuation constraint is:

d _m ％≤d _m.max ％,m∈n

wherein, d _m % is the voltage fluctuation at node m, d _m.max % is the maximum value of voltage fluctuation permitted by regulation.

The branch power constraints are:

S _l ≤S _l.max ,l∈L

G _DG.k ≤G _DG.max ,k∈N _D

The energy storage constraint is:

wherein the content of the first and second substances,

is the upper limit of the electric quantity of the energy storage system,

the maximum value of the charging power for the energy storage system,

the minimum value of the charging power of the energy storage system,

is the maximum value of the discharge power of the energy storage system,

In one possible implementation, the voltage at node m in the voltage deviation constraint is calculated by:

U _m ＝U _N ×(1+ΔU _m )

wherein, U _m Is the voltage at node m, U _N For nominal voltage, Δ U, of the distribution network _m Is the voltage deviation at node m, R ₀ +jX ₀ Is the equivalent impedance, R, on the side of the power system ₀ Is a resistance on the side of the power system, X ₀ Is reactance of power system side, R _i +jX _i Equivalent impedance, R, of the branch corresponding to node i _i Resistance of the branch, X, for node i _i Reactance, P, of branch corresponding to node i _L.j +jQ _L.j The power, P, flowing in for the branch corresponding to node j _L.j Active power, Q, flowing into the corresponding branch for node j _L.j Reactive power, P, flowing into the branch corresponding to node j _DG.j +jQ _DG.j For the power injected by the distributed power supply at node j, P _DG.j Active power, Q, injected for distributed power supply at node j _DG.j Reactive power, P, injected for distributed power supply at node j _E.j +jQ _E.j Power stored for node j, P _E.j Active power, Q, for storing energy at node j _E.j And (4) storing the reactive power at the node j.

In one possible implementation, the calculation formula of the voltage fluctuation at the node m in the voltage fluctuation constraint is:

wherein d is _m % is the voltage fluctuation at node m, d _PV.m % is the voltage fluctuation caused by the photovoltaic power supply in the distributed power supply at node m, d _W.m % is voltage fluctuation, U, caused by fan in distributed power supply at node m _N For nominal voltage, P, of the distribution network _PV.j Power injected for photovoltaic power supply at node j, P _W.j +jQ _W.j Power injected for fan at node j, P _W.j Active power, Q, injected for fan at node j _W.j Reactive power, R, injected for fans at node j _i +jX _i Equivalent impedance, R, of the branch corresponding to node i _i The resistance, X, of the branch corresponding to node i _i The reactance of the corresponding branch for node i.

the calculation module 52 is specifically configured to:

step five, determining whether the positions of the particles are updated for the second time according to Metropolis criterion and adaptive values before and after neighborhood disturbance, and taking the positions of the particles updated for the second time as the optimal positions of the particles;

In a possible implementation manner, the optimal solution is a plurality of optimal solutions, and after obtaining the optimal solution, the calculating module 52 is further configured to:

respectively carrying out normalization processing on each objective function value of the optimal solutions; calculating the maximum difference value between each objective function value in each optimal solution and the corresponding objective function expected value after normalization processing according to the preset expected value of each objective function; and determining the optimal solution with the minimum corresponding maximum difference value as the expected optimal solution.

Fig. 6 is a schematic diagram of an electronic device provided in an embodiment of the present invention. As shown in fig. 6, the electronic apparatus 6 of this embodiment includes: a processor 60, a memory 61, and a computer program 62 stored in the memory 61 and operable on the processor 60. The processor 60 executes the computer program 62 to implement the steps in each of the embodiments of the distributed power supply and energy storage grid-connected location determination method described above, such as steps S101 to S103 shown in fig. 1. Alternatively, the processor 60, when executing the computer program 62, implements the functions of the respective modules in the above-described respective apparatus embodiments, such as the functions of the modules 51 to 52 shown in fig. 5.

Illustratively, the computer program 62 may be partitioned into one or more modules/units, which are stored in the memory 61 and executed by the processor 60 to implement the present invention. One or more of the modules/units may be a series of computer program instruction segments capable of performing specific functions that describe the execution of the computer program 62 in the electronic device 6. For example, the computer program 62 may be divided into the modules 51 to 52 shown in fig. 5.

The electronic device 6 may include, but is not limited to, a processor 60, a memory 61. Those skilled in the art will appreciate that fig. 6 is merely an example of an electronic device 6, and does not constitute a limitation of the electronic device 6, and may include more or less components than those shown, or combine certain components, or different components, e.g., the electronic device may also include input-output devices, network access devices, buses, etc.

The Processor 60 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

The memory 61 may be an internal storage unit of the electronic device 6, such as a hard disk or a memory of the electronic device 6. The memory 61 may also be an external storage device of the electronic device 6, such as a plug-in hard disk provided on the electronic device 6, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like. Further, the memory 61 may also include both an internal storage unit of the electronic device 6 and an external storage device. The memory 61 is used for storing computer programs and other programs and data required by the electronic device. The memory 61 may also be used to temporarily store data that has been output or is to be output.

It should be clear to those skilled in the art that, for convenience and simplicity of description, the foregoing division of the functional units and modules is only used for illustration, and in practical applications, the above function distribution may be performed by different functional units and modules as needed, that is, the internal structure of the device is divided into different functional units or modules, so as to perform all or part of the above described functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.

In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.

Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.

In the embodiments provided in the present invention, it should be understood that the disclosed apparatus/electronic device and method may be implemented in other ways. For example, the above-described apparatus/electronic device embodiments are merely illustrative, and for example, a module or a unit may be divided into only one logic function, and may be implemented in other ways, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.

Units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.

The integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow in the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium and used by a processor to implement the steps of the above-described embodiments of the method. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying computer program code, recording medium, U.S. disk, removable hard disk, magnetic diskette, optical disk, computer Memory, read-Only Memory (ROM), random Access Memory (RAM), electrical carrier wave signal, telecommunications signal, software distribution medium, etc.

The above examples are only intended to illustrate the technical solution of the present invention, and not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present invention, and are intended to be included within the scope of the present invention.

Claims

1. A distributed power supply and energy storage grid-connected locating and sizing method is characterized by comprising the following steps:

establishing constraint conditions of the multi-objective function;

solving the multi-target function based on the constraint condition to obtain an optimal solution; the optimal solution comprises the access positions and the access capacities of the distributed power sources and the stored energy in the power distribution network.

2. The distributed power supply and energy storage grid-connected locating and sizing method according to claim 1, wherein a calculation formula of the multi-objective function is as follows:

wherein the content of the first and second substances,

is a calculation formula of the first objective function,

is a calculation formula of the second objective function,

is a calculation formula of the third objective function, C is the investment cost of the power distribution network, P is the network loss of the power distribution network, Z is the difference value of the energy storage access capacity and the distributed power supply access capacity, and N is _D Set of nodes for accessing distributed power, N _S In order to access the node set of the energy storage, T is any time in a typical period of the power distribution network, T is the time of the typical period of the power distribution network, m is any node in the power distribution network, n is the node set in the power distribution network, L is the set of lines in the power distribution network,

to access the investment cost of a k-node distributed power supply,

in order to access the investment cost of energy storage of k nodes,

for the cost of fuel consumed when the power distribution grid is operating,

for pollution penalty of emissions, R _l Is the resistance of line l in the distribution network, I _l (t) is the current flowing in line l at time t, P _DG,k For distributed power capacity of access node k, P _E,k Is the energy storage capacity of access node k.

3. The distributed power supply and energy storage grid-connected localization and sizing method according to claim 1, wherein the constraint conditions comprise system power flow constraint, voltage deviation constraint, voltage fluctuation constraint, branch power constraint, distributed power supply installation capacity constraint and energy storage constraint; wherein the content of the first and second substances,

the system flow constraint is as follows:

wherein i and j respectively represent any node in the power distribution network, and P _i For the active power, Q, flowing in at node i _i For reactive power flowing in at node i, P _L.i Active power, Q, for the access load at node i _L.i For reactive power, P, flowing in at node i _DG.i Active power, Q, injected for distributed power supply at node i _DG.i Reactive power, U, injected for distributed power supply at node i _i For node i corresponds to the voltage across the branch, U _j For node j corresponds to the voltage across the branch, G _ij Conductance values of the branches corresponding to nodes i and j, B _ij Susceptance value, θ, for the branches corresponding to nodes i and j _ij Is a phase angle;

the voltage deviation constraint is:

U _N (1-ε ₁ )≤U _m ≤U _N (1+ε ₂ ),m∈n

the voltage fluctuation constraint is:

d _m ％≤d _m.max ％,m∈n

the branch power constraints are:

S _l ≤S _l.max ,l∈L

G _DG.k ≤G _DG.max ,k∈N _D

wherein, G _DG.k Distributed power capacity, G, for access node k _DG.max Maximum capacity, N, of distributed power sources allowed to be accessed for each node _D A node set for accessing the distributed power supply;

the energy storage constraints are:

for energy-storage capacity constraint, E _S.k The amount of electricity stored for the energy storage system of node k,

is the upper limit of the electric quantity of the energy storage system,

for energy storage power constraint, P _SS.k (t) charging power of energy storage system of access node k at time t, P _SR.k (t) the discharge power of the energy storage system of the access node at time t,

the maximum value of the charging power of the energy storage system,

the minimum value of the charging power of the energy storage system,

is the maximum value of the discharge power of the energy storage system,

4. The distributed power supply and energy storage grid-connected localization and sizing method according to claim 3, wherein a calculation formula of the voltage at the node m in the voltage deviation constraint is as follows:

U _m ＝U _N ×(1+ΔU _m )

wherein, U _m Is the voltage at node m, U _N For nominal voltage, Δ U, of the distribution network _m At node mDeviation of voltage, R ₀ +jX ₀ Is the equivalent impedance, R, on the side of the power system ₀ Resistance on the side of the power system, X ₀ Is reactance of power system side, R _i +jX _i Equivalent impedance, R, of the branch corresponding to node i _i Resistance of the branch, X, for node i _i Reactance of branch corresponding to node i, P _L.j +jQ _L.j Power, P, flowing into the branch corresponding to node j _L.j Active power, Q, flowing into the branch corresponding to node j _L.j Reactive power, P, flowing into the branch corresponding to node j _DG.j +jQ _DG.j For the power injected by the distributed power supply at node j, P _DG.j Active power, Q, injected for distributed power supply at node j _DG.j Reactive power, P, injected for distributed power supply at node j _E.j +jQ _E.j For the power stored at node j, P _E.j Active power, Q, for storing energy at node j _E.j And the reactive power stored at the node j is obtained.

5. The distributed power supply and energy storage grid-connected locating and sizing method according to claim 3, wherein a calculation formula of voltage fluctuation at a node m in the voltage fluctuation constraint is as follows:

wherein, d _m % is the voltage fluctuation at node m, d _PV.m % is the voltage fluctuation caused by the photovoltaic power supply in the distributed power supply at node m, d _W.m % is voltage fluctuation U caused by fan in distributed power supply at node m _N For nominal voltage, P, of the distribution network _PV.j Power injected for photovoltaic power supply at node j, P _W.j +jQ _W.j Power injected for fan at node j, P _W.j Active power, Q, injected for fan at node j _W.j Reactive power, R, injected for fan at node j _i +jX _i Equivalent impedance, R, of the branch corresponding to node i _i Resistance of the branch, X, for node i _i As node i pairThe reactance of the branch should be taken.

6. The distributed power supply and energy storage grid-connected localization and sizing method according to claim 1, wherein the algorithm for solving the multi-objective function based on the constraint condition is an improved particle swarm optimization algorithm of a hybrid simulated annealing algorithm;

the process of solving the multi-objective function is as follows:

step four, performing neighborhood disturbance on each particle after the primary update to obtain a new position of each particle, and calculating an adaptive value corresponding to the new position;

and step eight, if the iteration times reach the maximum iteration times, stopping iteration, and determining the optimal position of the group of the current particle swarm as the optimal solution of the multi-objective function.

7. The distributed power supply and energy storage grid-connected locating and sizing method according to claim 1, characterized in that the optimal solution is a plurality of optimal solutions;

after obtaining the optimal solution, the method further comprises:

8. A distributed power supply and energy storage grid-connected locating and sizing device is characterized by comprising:

the establishing module is used for establishing a first objective function by taking the minimum investment cost of the power distribution network as a target, establishing a second objective function by taking the minimum network loss of the power distribution network as a target, and establishing a third objective function by taking the minimum difference value between the energy storage access capacity and the distributed power supply access capacity in the power distribution network as a target to obtain a multi-objective function; establishing constraint conditions of the multi-objective function;

9. An electronic device comprising a memory for storing a computer program and a processor for invoking and running the computer program stored in the memory, wherein the processor implements the steps of the method of any of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7 above.