CN110556862A - Two-stage optimal regulation and control method and device for power distribution network based on photovoltaic cluster - Google Patents

Abstract

The present invention relates to a two-stage optimal control method and device for a distribution network based on photovoltaic clusters. Perform global optimization and regulation on the distribution network; when the node voltage in the photovoltaic cluster of the distribution network exceeds the limit, a second regulation scheme is determined based on the power regulation capabilities of the distributed photovoltaics and energy storage in the cluster, and the second regulation scheme is used to adjust the voltage. The distributed photovoltaic and energy storage in the photovoltaic cluster of the distribution network are regulated. The technical solution provided by the present invention utilizes the global distribution network and the two-stage regulation of the photovoltaic cluster of the distribution network to realize the multi-dimensional coordinated optimal regulation of a variety of regulated resources in the distribution network on the time scale and the space scale, and improves the speed of regulation. Improve the power grid's ability to absorb distributed photovoltaics and ensure the safe and stable operation of the distribution network.

Description

A two-stage optimal control method and device for distribution network based on photovoltaic cluster

technical field

The invention relates to the field of power system operation optimization, in particular to a two-stage optimization control method and device for a power distribution network based on photovoltaic clusters.

Background technique

In recent years, due to the rapid development of photovoltaic power generation technology, distributed photovoltaics have become large-scale and clustered into the distribution network, which will have a significant impact on the safety, stability and low-loss operation of the distribution network. The high-penetration distributed photovoltaic grid-connected changes the power flow distribution of the power grid, causing voltage fluctuations and even overvoltages, resulting in photovoltaic off-grid, which seriously restricts the power grid's ability to absorb distributed photovoltaics. The energy storage system can absorb electricity when the active power of photovoltaics is excessive, and release electricity when the active power is insufficient, which can effectively compensate for the volatility of distributed photovoltaic output. It has also been widely used in distribution networks in recent years.

At present, the optimal regulation method for high-penetration distributed photovoltaic access to the distribution network is researched. For example, some studies have proposed an active distribution network voltage control method based on model predictive control that considers the coordination of multiple devices, and another study has proposed an active power optimization control method of active distribution networks that considers multi-time scale coordination. These research methods all belong to the category of global optimization, which requires each terminal device to continuously upload relevant information to the control center for processing, which brings great pressure to the communication, and the optimization calculation will also consume a lot of time, resulting in the inability to meet the requirements of the system. Optimize control requirements.

SUMMARY OF THE INVENTION

In view of the deficiencies of the prior art, the purpose of the present invention is to provide a two-stage optimal control method and device for a distribution network based on photovoltaic clusters. The global network and the multi-dimensional coordinated and rapid regulation of the cluster on the time scale and the space scale ensure the safe operation of the distribution network and improve the consumption capacity of the distributed power source.

The purpose of this invention is to adopt following technical scheme to realize:

The present invention provides a photovoltaic cluster-based two-stage optimal control method for a distribution network, the improvement of which is that the method includes:

Determine the first control scheme of the distribution network according to the probability of occurrence of each operation scenario of the distribution network;

Controlling the distribution network with the first control scheme;

Wherein, when the node voltage in the photovoltaic cluster of the distribution network exceeds the limit, a second regulation scheme is determined according to the power regulation capability of the distributed photovoltaics and energy storage in the cluster, and the second regulation scheme is used to adjust the voltage in the photovoltaic cluster of the distribution network. The distributed photovoltaic and energy storage are controlled.

Preferably, the first control scheme includes: the reactive power of distributed photovoltaics connected to each node in the distribution network at each moment, the charging and discharging power of the connected energy storage, and the active power of the connected adjustable load ;

The second control scheme includes: a reactive power adjustment amount of distributed photovoltaics and an energy storage charging and discharging power adjustment amount in a photovoltaic cluster of a power distribution network.

Preferably, the photovoltaic cluster is a branch line of a feeder of a power distribution network, wherein the branch line of the feeder is connected with distributed photovoltaic and energy storage devices or adjustable loads.

Further, determining the first control scheme of the distribution network according to the probability of occurrence of each operation scenario of the distribution network includes:

The probability of occurrence of each operation scenario of the distribution network is substituted into the pre-established first regulation model, and the first regulation scheme of the distribution network is obtained by solving.

Further, the objective function in the pre-established first regulation model is determined as follows:

In the above formula, F is the active power lost by the distribution network; p _S,t is the probability that the s-th scenario occurs in the distribution network at time t; S is the total number of typical scenarios retained by the distribution network after scenario analysis; T is the number of moments in the regulation period; c(0) is the end node set of the line with the starting node of the distribution network as the head node; P _0f is the active power flowing from the starting node of the distribution network to the node f; j∈( 1～N), N is the number of nodes in the distribution network; P _PV,j,t is the active power of distributed photovoltaics connected to node j at time t; P _ch,j,t is the connection of node j at time t is the charging power of the incoming energy storage; P _dis,j,t is the discharging power of the energy storage connected by node j at time t; P _CL,j,t is the active power of the adjustable load connected by node j at time t ; P _load,j,t is the active power of the non-adjustable load connected by node j at time t.

Further, the power flow constraints in the pre-established first regulation model are determined as follows:

In the above formula, P _ij,t is the active power of line ij in the distribution network at time t; I _ij,t is the current of line ij in the distribution network at time t; r _ij is the resistance of line ij in the distribution network; x _ij is The reactance of line ij in the distribution network; P _j,t is the active power injected into node j at time t of the distribution network; P _jk,t is the active power of line jk at time t of the distribution network; Q _ij,t is the distribution network Reactive power of line ij at time t; Q _j,t is the reactive power injected into node j at time t of distribution network; Q _jk,t is the reactive power of line jk at time t of distribution network; j∈(1～N ), N is the number of nodes in the distribution network; i∈φ(j), φ(j) is the set of first nodes of the line with node j as the end node in the distribution network; k∈θ(j), θ (j) is the set of end nodes of the line with node j as the head node in the distribution network; P _PV,j,t is the active power of distributed photovoltaics at node j at time t of the distribution network; P _load,j,t is P _ch,j,t is the charging power of the energy storage device at node j of the distribution network at time t; P _dis,j,t is the node of the distribution network at time t The discharge power of the energy storage device at j; Q _PV,j,t is the reactive power of distributed photovoltaics at node j at time t of the distribution network; Q _load,j,t is the adjustable load at node j at time t of the distribution network Reactive power of the device; U _j,t is the voltage amplitude at node j at time t of the distribution network; U _i,t is the voltage amplitude at node i at time t of the distribution network;

The node voltage constraints in the pre-established first regulation model are determined as follows:

In the above formula, is the lower limit of the voltage amplitude of node j in the distribution network; is the upper limit of the voltage amplitude at node j in the distribution network; U _j,t is the voltage amplitude at node j at time t in the distribution network;

The operation constraints of the distributed photovoltaic power station in the pre-established first regulation model are determined as follows:

In the above formula, is the minimum value of the distributed photovoltaic reactive power of node j at time t of the distribution network; is the maximum value of the distributed photovoltaic reactive power of node j at time t in the distribution network;

in, S _PV,j is the distributed photovoltaic inverter capacity of node j in the distribution network; is the predicted value of the active power output of the distributed photovoltaic power station at node j of the distribution network at time t;

The energy storage operation constraints in the pre-established first regulation model are determined as follows:

In the above formula, E _SOC,j,t is the power of the energy storage device at node j of the distribution network at time t; E _SOC,j,t+Δt is the power of the energy storage device at node j of the distribution network at time t+Δt; is the lower limit of the energy storage capacity of the energy storage device of node j in the distribution network; is the upper limit of the energy storage capacity of the energy storage device of node j in the distribution network; is the maximum charging power of the energy storage device at node j in the distribution network; is the maximum discharge power of the energy storage device at node j in the distribution network;

The power constraint condition of the adjustable load device in the pre-established first regulation model is determined as follows:

In the above formula, P _CL,j,t is the actual power consumption of the adjustable load device at node j in the distribution network at time t; W _j,min is the minimum power demand of the adjustable load device at node j in the distribution network; W _j,max is the maximum power demand of the adjustable load device at node j in the distribution network; Δt is the time interval of the first control scheme; T is the number of moments in the control cycle;

in, _εmin is the minimum value of the ratio of the actual power consumption of the adjustable load device in the distribution network to the expected power consumption; _εmax is the maximum value of the ratio of the actual power consumption of the adjustable load device in the distribution network to the expected power consumption; is the expected electric power of the adjustable load device of the distribution network at time t.

Further, the second regulation scheme is determined according to the power regulation capability of distributed photovoltaics and energy storage in the photovoltaic cluster of the power distribution network, and the distributed photovoltaic and energy storage in the distributed photovoltaic cluster are controlled by the second regulation scheme. control, including:

Substitute the maximum and minimum values of the reactive power of the distributed photovoltaics and the maximum and minimum values of the charging and discharging power of the energy storage in the cluster of the distribution network into the pre-established second regulation model, solve the second regulation model, and obtain the distribution network the second control scheme.

Further, the objective function in the pre-established second regulation model is determined as follows:

In the above formula, F _v is the minimum power adjustment amount of distributed photovoltaic and energy storage in the distributed photovoltaic cluster; is the set of all distributed photovoltaics in the cluster; ΔQ _PV,x is the reactive power adjustment of the xth distributed photovoltaic in the cluster; is the maximum reactive power of the xth distributed photovoltaic in the cluster; is the set of all energy storages in the cluster; ΔPch _,y is the charging power adjustment of the yth energy storage in the cluster; is the maximum charging power of the yth energy storage in the cluster; _Dch,y is the charging coefficient of the yth energy storage in the cluster; _Dch,y ∈(0,1); ΔP _dis,y is the charging coefficient of the yth energy storage in the cluster The discharge power adjustment of the yth energy storage; is the maximum discharge power of the yth energy storage in the cluster; D _dis,y is the discharge coefficient of the yth energy storage in the cluster; D _dis,y ∈(0,1).

Further, the constant power constraint condition in the pre-established second regulation model is determined as follows:

In the above formula, is the initial active power value flowing into the starting node of the cluster; PC is the active power value _flowing into the starting node of the cluster after being adjusted by the second regulation scheme; is the initial reactive power value flowing into the starting node of the cluster; _QC is the active power value flowing into the starting node of the cluster after being adjusted by the second regulation scheme;

The power flow constraints in the pre-established second regulation model are determined as follows:

In the above formula, P _αβ,t is the active power of line αβ in the photovoltaic cluster of the distribution network at time t; I _αβ,t is the current of line αβ in the photovoltaic cluster of the grid at time t; r _αβ is the photovoltaic cluster of the distribution network resistance of the middle line αβ; P _β,t is the active power injected into the node β of the photovoltaic cluster in the distribution network at time t; P _βγ,t is the active power of the line βγ in the photovoltaic cluster of the distribution network at time t; Q _{αβ, t} is the reactive power of the line αβ in the photovoltaic cluster of the distribution network at time t; x _αβ is the reactance of the line αβ in the photovoltaic cluster of the distribution network; Q _{β, t} is the node β in the cluster injected into the distribution network at time t. Reactive power; Q _βγ,t is the reactive power of the line βγ in the cluster of the distribution network at time t; β∈(1～M),M is the number of nodes in the photovoltaic cluster of the distribution network; α∈ψ(β ), ψ(β) is the set of line head nodes with node β as the end node in the photovoltaic cluster of the distribution network; is the set of line end nodes with node β as the head node in the photovoltaic cluster of the distribution network; P _PV,β,t is the active power of the distributed photovoltaic connected to node β in the photovoltaic cluster of the distribution network at time t; P _{CL ,β,t} is the active power of the adjustable load at node β in the photovoltaic cluster of the distribution network at time t; P _load,β,t is the active power of the non-adjustable load at the node β in the photovoltaic cluster of the distribution network at time t; P _ch,β,t is the energy storage charging power of the access node β in the photovoltaic cluster of the distribution network at time t; P _dis,β,t is the energy storage of the access node β in the photovoltaic cluster of the distribution network at time t Discharge power; Q _PV,β,t is the reactive power of distributed photovoltaics connected to node β in the photovoltaic cluster of the distribution network at time t; Q _CL,β,t is the node β of the photovoltaic cluster of the distribution network at time t The reactive power of the adjustable load at the place; Q _load,β,t is the reactive power of the non-adjustable load at the node β in the photovoltaic cluster of the distribution network at time t; U _β,t is the node in the photovoltaic cluster of the distribution network β is the voltage amplitude at time t; U _α,t is the voltage amplitude of node α in the distribution network cluster at time t;

The node voltage constraints in the pre-established second regulation model are determined as follows:

In the above formula, is the lower limit of the voltage amplitude of node β in the photovoltaic cluster of the distribution network; is the upper limit of the voltage amplitude of node β in the photovoltaic cluster of the distribution network;

The distributed photovoltaic operation constraints in the pre-established second regulation model are determined as follows:

In the above formula, ΔQ _PV,β,t is the reactive power adjustment value of the distributed photovoltaic connected to node β in the photovoltaic cluster of the distribution network at time t; is the minimum reactive power of distributed photovoltaics connected to node β in the cluster of the distribution network at time t; is the maximum reactive power of distributed photovoltaics connected to node β in the photovoltaic cluster of the distribution network at time t;

in, S _PV,β is the capacity of the photovoltaic inverter at node β in the cluster of the distribution network; P _PV,β,t is the predicted value of the active power output of the distributed photovoltaic at time t of node β in the photovoltaic cluster of the distribution network;

The energy storage operation constraints in the pre-established second regulation model are determined as follows:

In the above formula, E _SOC,β,t is the amount of energy stored in the photovoltaic cluster connected to node β in the distribution network at time t; is the lower limit of the energy storage capacity of the access node β in the photovoltaic cluster of the distribution network; is the upper limit of the energy storage capacity of the access node β in the photovoltaic cluster of the distribution network; ΔPch _,β,t is the charging power adjustment amount of the energy storage of the access node β in the photovoltaic cluster of the distribution network at time t; It is the maximum charging power of the energy storage at the access node β in the photovoltaic cluster of the distribution network; is the maximum discharge power of the energy storage at the access node β in the photovoltaic cluster of the distribution network; ΔP _dis,β,t is the discharge power adjustment amount of the energy storage at the access node β in the cluster of the distribution network at time t.

The present invention provides a photovoltaic cluster-based two-stage optimal control device for distribution network, the improvement of which is that the device includes:

a determining module, configured to determine a first control scheme of the distribution network according to the probability of occurrence of each operation scenario of the distribution network;

a regulation module, configured to regulate the distribution network with the first regulation scheme;

Wherein, when the node voltage in the photovoltaic cluster of the distribution network exceeds the limit, a second regulation scheme is determined according to the power regulation capability of the distributed photovoltaics and energy storage in the cluster, and the second regulation scheme is used to adjust the voltage in the photovoltaic cluster of the distribution network. The distributed photovoltaic and energy storage are controlled.

Preferably, the first control scheme includes: the reactive power of distributed photovoltaics connected to each node in the distribution network at each moment, the charging and discharging power of the connected energy storage, and the active power of the connected adjustable load ;

The second control scheme includes: a reactive power adjustment amount of distributed photovoltaics and an energy storage charging and discharging power adjustment amount in a photovoltaic cluster of a power distribution network.

Preferably, the photovoltaic cluster is a branch line of a feeder of a power distribution network, wherein the branch line of the feeder is connected with distributed photovoltaic and energy storage devices or adjustable loads.

Further, the determining module is used for:

The probability of occurrence of each operation scenario of the distribution network is substituted into the pre-established first regulation model, and the first regulation scheme of the distribution network is obtained by solving.

Further, the objective function in the pre-established first regulation model is determined as follows:

In the above formula, F is the active power lost by the distribution network; p _S,t is the probability that the s-th scenario occurs in the distribution network at time t; S is the total number of typical scenarios retained by the distribution network after scenario analysis; T is the number of moments in the regulation period; c(0) is the end node set of the line with the starting node of the distribution network as the head node; P _0f is the active power flowing from the starting node of the distribution network to the node f; j∈( 1～N), N is the number of nodes in the distribution network; P _PV,j,t is the active power of distributed photovoltaics connected to node j at time t; P _ch,j,t is the connection of node j at time t is the charging power of the incoming energy storage; P _dis,j,t is the discharging power of the energy storage connected by node j at time t; P _CL,j,t is the active power of the adjustable load connected by node j at time t ; P _load,j,t is the active power of the non-adjustable load connected by node j at time t.

Further, the power flow constraints in the pre-established first regulation model are determined as follows:

In the above formula, P _ij,t is the active power of line ij in the distribution network at time t; I _ij,t is the current of line ij in the distribution network at time t; r _ij is the resistance of line ij in the distribution network; x _ij is The reactance of line ij in the distribution network; P _j,t is the active power injected into node j at time t of the distribution network; P _jk,t is the active power of line jk at time t of the distribution network; Q _ij,t is the distribution network Reactive power of line ij at time t; Q _j,t is the reactive power injected into node j at time t of distribution network; Q _jk,t is the reactive power of line jk at time t of distribution network; j∈(1～N ), N is the number of nodes in the distribution network; i∈φ(j), φ(j) is the set of first nodes of the line with node j as the end node in the distribution network; k∈θ(j), θ (j) is the set of end nodes of the line with node j as the head node in the distribution network; P _PV,j,t is the active power of distributed photovoltaics at node j at time t of the distribution network; P _load,j,t is P _ch,j,t is the charging power of the energy storage device at node j of the distribution network at time t; P _dis,j,t is the node of the distribution network at time t The discharge power of the energy storage device at j; Q _PV,j,t is the reactive power of distributed photovoltaics at node j at time t of the distribution network; Q _load,j,t is the adjustable load at node j at time t of the distribution network Reactive power of the device; U _j,t is the voltage amplitude at node j at time t of the distribution network; U _i,t is the voltage amplitude at node i at time t of the distribution network;

The node voltage constraints in the pre-established first regulation model are determined as follows:

In the above formula, is the lower limit of the voltage amplitude of node j in the distribution network; is the upper limit of the voltage amplitude at node j in the distribution network; U _j,t is the voltage amplitude at node j at time t in the distribution network;

The operation constraints of the distributed photovoltaic power station in the pre-established first regulation model are determined as follows:

In the above formula, is the minimum value of the distributed photovoltaic reactive power of node j at time t of the distribution network; is the maximum value of the distributed photovoltaic reactive power of node j at time t in the distribution network;

in, S _PV,j is the distributed photovoltaic inverter capacity of node j in the distribution network; is the predicted value of distributed photovoltaic active power output of node j at time t of the distribution network;

The energy storage operation constraints in the pre-established first regulation model are determined as follows:

In the above formula, E _SOC,j,t is the power of the energy storage device at node j of the distribution network at time t; E _SOC,j,t+Δt is the power of the energy storage device at node j of the distribution network at time t+Δt; is the lower limit of the energy storage capacity of the energy storage device of node j in the distribution network; is the upper limit of the energy storage capacity of the energy storage device of node j in the distribution network; is the maximum charging power of the energy storage device at node j in the distribution network; is the maximum discharge power of the energy storage device at node j in the distribution network;

The power constraint condition of the adjustable load device in the pre-established first regulation model is determined as follows:

In the above formula, P _CL,j,t is the actual power consumption of the adjustable load device at node j in the distribution network at time t; W _j,min is the minimum power demand of the adjustable load device at node j in the distribution network; W _j,max is the maximum power demand of the adjustable load device at node j in the distribution network; Δt is the time interval of the first control scheme; T is the number of moments in the control cycle;

in, _εmin is the minimum value of the ratio of the actual power consumption of the adjustable load device in the distribution network to the expected power consumption; _εmax is the maximum value of the ratio of the actual power consumption of the adjustable load device in the distribution network to the expected power consumption; is the expected electric power of the adjustable load device of the distribution network at time t.

Further, the second regulation scheme is determined according to the power regulation capability of distributed photovoltaics and energy storage in the photovoltaic cluster of the power distribution network, and the distributed photovoltaic and energy storage in the distributed photovoltaic cluster are controlled by the second regulation scheme. control, including:

Substitute the maximum and minimum values of the reactive power of the distributed photovoltaics and the maximum and minimum values of the charging and discharging power of the energy storage in the cluster of the distribution network into the pre-established second regulation model, solve the second regulation model, and obtain the distribution network the second control scheme.

Further, the objective function in the pre-established second regulation model is determined as follows:

In the above formula, F _v is the minimum power adjustment amount of distributed photovoltaic and energy storage in the distributed photovoltaic cluster; is the set of all distributed photovoltaics in the cluster; ΔQ _PV,x is the reactive power adjustment of the xth distributed photovoltaic in the cluster; is the maximum reactive power of the xth distributed photovoltaic in the cluster; is the set of all energy storages in the cluster; ΔPch _,y is the charging power adjustment of the yth energy storage in the cluster; is the maximum charging power of the yth energy storage in the cluster; _Dch,y is the charging coefficient of the yth energy storage in the cluster; _Dch,y ∈(0,1); ΔP _dis,y is the charging coefficient of the yth energy storage in the cluster The discharge power adjustment of the yth energy storage; is the maximum discharge power of the yth energy storage in the cluster; D _dis,y is the discharge coefficient of the yth energy storage in the cluster; D _dis,y ∈(0,1).

Further, the constant power constraint condition in the pre-established second regulation model is determined as follows:

In the above formula, is the initial active power value flowing into the starting node of the cluster; PC is the active power value _flowing into the starting node of the cluster after being adjusted by the second regulation scheme; is the initial reactive power value flowing into the starting node of the cluster; _QC is the active power value flowing into the starting node of the cluster after being adjusted by the second regulation scheme;

The power flow constraints in the pre-established second regulation model are determined as follows:

In the above formula, P _αβ,t is the active power of line αβ in the photovoltaic cluster of the distribution network at time t; I _αβ,t is the current of line αβ in the photovoltaic cluster of the grid at time t; r _αβ is the photovoltaic cluster of the distribution network resistance of the middle line αβ; P _β,t is the active power injected into the node β of the photovoltaic cluster in the distribution network at time t; P _βγ,t is the active power of the line βγ in the photovoltaic cluster of the distribution network at time t; Q _{αβ, t} is the reactive power of the line αβ in the photovoltaic cluster of the distribution network at time t; x _αβ is the reactance of the line αβ in the photovoltaic cluster of the distribution network; Q _{β, t} is the node β in the cluster injected into the distribution network at time t. Reactive power; Q _βγ,t is the reactive power of the line βγ in the cluster of the distribution network at time t; β∈(1～M),M is the number of nodes in the photovoltaic cluster of the distribution network; α∈ψ(β ), ψ(β) is the set of line head nodes with node β as the end node in the photovoltaic cluster of the distribution network; is the set of line end nodes with node β as the head node in the photovoltaic cluster of the distribution network; P _PV,β,t is the active power of the distributed photovoltaic connected to node β in the photovoltaic cluster of the distribution network at time t; P _{CL ,β,t} is the active power of the adjustable load at node β in the photovoltaic cluster of the distribution network at time t; P _load,β,t is the active power of the non-adjustable load at the node β in the photovoltaic cluster of the distribution network at time t; P _ch,β,t is the energy storage charging power of the access node β in the photovoltaic cluster of the distribution network at time t; P _dis,β,t is the energy storage of the access node β in the photovoltaic cluster of the distribution network at time t Discharge power; Q _PV,β,t is the reactive power of distributed photovoltaics connected to node β in the photovoltaic cluster of the distribution network at time t; Q _CL,β,t is the node β of the photovoltaic cluster of the distribution network at time t The reactive power of the adjustable load at the place; Q _load,β,t is the reactive power of the non-adjustable load at the node β in the photovoltaic cluster of the distribution network at time t; U _β,t is the node in the photovoltaic cluster of the distribution network β is the voltage amplitude at time t; U _α,t is the voltage amplitude of node α in the distribution network cluster at time t;

The node voltage constraints in the pre-established second regulation model are determined as follows:

In the above formula, is the lower limit of the voltage amplitude of node β in the photovoltaic cluster of the distribution network; is the upper limit of the voltage amplitude of node β in the photovoltaic cluster of the distribution network;

The distributed photovoltaic operation constraints in the pre-established second regulation model are determined as follows:

In the above formula, ΔQ _PV,β,t is the reactive power adjustment value of the distributed photovoltaic connected to node β in the photovoltaic cluster of the distribution network at time t; is the minimum reactive power of distributed photovoltaics connected to node β in the cluster of the distribution network at time t; is the maximum reactive power of distributed photovoltaics connected to node β in the photovoltaic cluster of the distribution network at time t;

in, S _PV,β is the capacity of the photovoltaic inverter at node β in the cluster of the distribution network; P _PV,β,t is the predicted value of the active power output of the distributed photovoltaic at time t of node β in the photovoltaic cluster of the distribution network;

The energy storage operation constraints in the pre-established second regulation model are determined as follows:

In the above formula, E _SOC,β,t is the amount of energy stored in the photovoltaic cluster connected to node β in the distribution network at time t; is the lower limit of the energy storage capacity of the access node β in the photovoltaic cluster of the distribution network; is the upper limit of the energy storage capacity of the access node β in the photovoltaic cluster of the distribution network; ΔPch _,β,t is the charging power adjustment amount of the energy storage of the access node β in the photovoltaic cluster of the distribution network at time t; It is the maximum charging power of the energy storage at the access node β in the photovoltaic cluster of the distribution network; is the maximum discharge power of the energy storage at the access node β in the photovoltaic cluster of the distribution network; ΔP _dis,β,t is the discharge power adjustment amount of the energy storage at the access node β in the cluster of the distribution network at time t.

Compared with the closest prior art, the present invention has the following beneficial effects:

The invention provides a photovoltaic cluster-based two-stage optimization control method and device for a distribution network, wherein a first control scheme of the distribution network is determined according to the probability of occurrence of each operation scenario of the distribution network; The distribution network is regulated; wherein, when the voltage of the nodes in the cluster of the distribution network exceeds the limit, the second regulation scheme is determined according to the maximum reactive power of the photovoltaic nodes in the cluster of the distribution network and the maximum charge and discharge power of the energy storage nodes , and use the second control scheme to control each node in the cluster of the power distribution network. The technical solution provided by the present invention utilizes the global distribution network and the two-stage regulation of the distribution network cluster to enable the multi-dimensional coordinated optimal regulation of various controllable resources in the distribution network and the distribution network cluster on the time scale and the space scale. , which improves the speed and effectiveness of regulation, helps to improve the grid's ability to absorb distributed photovoltaics, and ensures the safe operation of the distribution network.

Description of drawings

Fig. 1 is a flow chart of a two-stage optimal control method for a distribution network based on photovoltaic clusters provided by the present invention;

2 is a schematic diagram of cluster division provided by the present invention;

FIG. 3 is a structural diagram of a photovoltaic cluster-based two-stage optimal control device for a power distribution network provided by the present invention.

Detailed ways

The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

In order to make the purposes, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments These are some embodiments of the present invention, but not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

The present invention provides a two-stage optimal control method for a distribution network based on photovoltaic clusters, as shown in FIG. 1 , the method includes:

Determine the first control scheme of the distribution network according to the probability of occurrence of each operation scenario of the distribution network;

Controlling the distribution network with the first control scheme;

Wherein, when the node voltage in the photovoltaic cluster of the distribution network exceeds the limit, a second regulation scheme is determined according to the power regulation capability of the distributed photovoltaics and energy storage in the cluster, and the second regulation scheme is used to adjust the voltage in the photovoltaic cluster of the distribution network. The distributed photovoltaic and energy storage are controlled.

Specifically, the first control scheme includes: the reactive power of distributed photovoltaics connected to each node in the distribution network at each moment, the charging and discharging power of the connected energy storage, and the active power of the connected adjustable load ;

The second control scheme includes: a reactive power adjustment amount of distributed photovoltaics and an energy storage charging and discharging power adjustment amount in a photovoltaic cluster of a power distribution network.

The photovoltaic cluster is a branch line of a feeder of a power distribution network, wherein the branch line of the feeder is connected with distributed photovoltaic and energy storage devices or adjustable loads.

In the preferred embodiment of the present invention, due to the strong uncertainty of photovoltaic output and load demand, the prediction error is large, the scene analysis method is used to describe the uncertainty of distributed photovoltaic and load, and the uncertainty The problem is transformed into a deterministic problem to solve, so the first control scheme is based on scenario analysis to coordinate all active and reactive power adjustable units in the distribution network, with Δt as the time interval, the output behavior of each adjustable unit in a future period T is carried out. optimization.

The first control scheme of the power distribution network is determined according to the probability of occurrence of each operation scenario of the power distribution network, including:

The probability of occurrence of each operation scenario of the distribution network is substituted into the pre-established first regulation model, and the first regulation scheme of the distribution network is obtained by solving.

Wherein, the objective function in the pre-established first regulation model is determined as follows:

In the above formula, F is the active power lost by the distribution network; p _S,t is the probability that the s-th scenario occurs in the distribution network at time t; S is the total number of typical scenarios retained by the distribution network after scenario analysis; T is the number of moments in the regulation period; c(0) is the end node set of the line with the starting node of the distribution network as the head node; P _0f is the active power flowing from the starting node of the distribution network to the node f; j∈( 1～N), N is the number of nodes in the distribution network; P _PV,j,t is the active power of distributed photovoltaics connected to node j at time t; P _ch,j,t is the connection of node j at time t is the charging power of the incoming energy storage; P _dis,j,t is the discharging power of the energy storage connected by node j at time t; P _CL,j,t is the active power of the adjustable load connected by node j at time t ; P _load,j,t is the active power of the non-adjustable load connected by node j at time t.

The power flow constraints in the pre-established first regulation model are determined as follows:

In the above formula, P _ij,t is the active power of line ij in the distribution network at time t; I _ij,t is the current of line ij in the distribution network at time t; r _ij is the resistance of line ij in the distribution network; x _ij is The reactance of line ij in the distribution network; P _j,t is the active power injected into node j at time t of the distribution network; P _jk,t is the active power of line jk at time t of the distribution network; Q _ij,t is the distribution network Reactive power of line ij at time t; Q _j,t is the reactive power injected into node j at time t of distribution network; Q _jk,t is the reactive power of line jk at time t of distribution network; j∈(1～N ), N is the number of nodes in the distribution network; i∈φ(j), φ(j) is the set of first nodes of the line with node j as the end node in the distribution network; k∈θ(j), θ (j) is the set of end nodes of the line with node j as the head node in the distribution network; P _PV,j,t is the active power of distributed photovoltaics at node j at time t of the distribution network; P _load,j,t is P _ch,j,t is the charging power of the energy storage device at node j of the distribution network at time t; P _dis,j,t is the node of the distribution network at time t The discharge power of the energy storage device at j; Q _PV,j,t is the reactive power of distributed photovoltaics at node j at time t of the distribution network; Q _load,j,t is the adjustable load at node j at time t of the distribution network Reactive power of the device; U _j,t is the voltage amplitude at node j at time t of the distribution network; U _i,t is the voltage amplitude at node i at time t of the distribution network;

The node voltage constraints in the pre-established first regulation model are determined as follows:

In the above formula, is the lower limit of the voltage amplitude of node j in the distribution network; is the upper limit of the voltage amplitude at node j in the distribution network; U _j,t is the voltage amplitude at node j at time t in the distribution network;

The operation constraints of the distributed photovoltaic power station in the pre-established first regulation model are determined as follows:

In the above formula, is the minimum value of the distributed photovoltaic reactive power of node j at time t of the distribution network; is the maximum value of distributed photovoltaic reactive power at node j at time t of the distribution network; where, S _PV,j is the distributed photovoltaic inverter capacity of node j in the distribution network; is the predicted value of distributed photovoltaic active power output of node j at time t of the distribution network;

The energy storage operation constraints in the pre-established first regulation model are determined as follows:

In the above formula, E _SOC,j,t is the power of the energy storage device at node j of the distribution network at time t; E _SOC,j,t+Δt is the power of the energy storage device at node j of the distribution network at time t+Δt; is the lower limit of the energy storage capacity of the energy storage device of node j in the distribution network; is the upper limit of the energy storage capacity of the energy storage device of node j in the distribution network; is the maximum charging power of the energy storage device at node j in the distribution network; is the maximum discharge power of the energy storage device at node j in the distribution network;

The power constraint condition of the adjustable load device in the pre-established first regulation model is determined as follows:

In the above formula, P _CL,j,t is the actual power consumption of the adjustable load device at node j in the distribution network at time t; W _j,min is the minimum power demand of the adjustable load device at node j in the distribution network; W _j,max is the maximum power demand of the adjustable load device at node j in the distribution network; Δt is the time interval of the first control scheme; T is the number of times in the control cycle; where, _εmin is the minimum value of the ratio of the actual power consumption of the adjustable load device in the distribution network to the expected power consumption; _εmax is the maximum value of the ratio of the actual power consumption of the adjustable load device in the distribution network to the expected power consumption; is the expected electric power of the adjustable load device of the distribution network at time t.

In the preferred embodiment of the present invention, due to the large number of distributed photovoltaics in the distribution network, the global control calculation in the first control scheme is complex and time-consuming, and the purpose of cluster division is to The distribution network is divided into local distribution networks with autonomous capabilities, so as to realize the rapid regulation of power flow and voltage level in the cluster area; each divided object in a cluster must have electrical connections, and each object The electrical distance of the feeder is similar to facilitate the complementary power exchange, so the cluster can be divided according to the following principles: As shown in Figure 2, if a large number of distributed photovoltaics are connected to the branch lines of the feeder, and energy storage devices or adjustable loads are connected at the same time , the branch line is regarded as a distributed photovoltaic cluster with regional autonomy.

In the preferred embodiment of the present invention, the second regulation scheme is determined according to the power regulation capability of distributed photovoltaics and energy storage in the photovoltaic cluster of the distribution network, and the second regulation scheme is used to adjust the power of the distributed photovoltaic cluster. distributed photovoltaics and energy storage for regulation, including:

Substitute the maximum and minimum values of the reactive power of the distributed photovoltaics and the maximum and minimum values of the charging and discharging power of the energy storage in the cluster of the distribution network into the pre-established second regulation model, solve the second regulation model, and obtain the distribution network the second control scheme.

Wherein, the objective function in the pre-established second regulation model is determined as follows:

In the above formula, F _v is the minimum power adjustment amount of distributed photovoltaic and energy storage in the distributed photovoltaic cluster; is the set of all distributed photovoltaics in the cluster; ΔQ _PV,x is the reactive power adjustment of the xth distributed photovoltaic in the cluster; is the maximum reactive power of the xth distributed photovoltaic in the cluster; is the set of all energy storages in the cluster; ΔPch _,y is the charging power adjustment of the yth energy storage in the cluster; is the maximum charging power of the yth energy storage in the cluster; _Dch,y is the charging coefficient of the yth energy storage in the cluster; _Dch,y ∈(0,1); ΔP _dis,y is the charging coefficient of the yth energy storage in the cluster The discharge power adjustment of the yth energy storage; is the maximum discharge power of the yth energy storage in the cluster; D _dis,y is the discharge coefficient of the yth energy storage in the cluster; D _dis,y ∈(0,1).

The constant power constraint in the pre-established second regulation model is determined as follows:

In the above formula, is the initial active power value flowing into the starting node of the cluster; PC is the active power value _flowing into the starting node of the cluster after being adjusted by the second regulation scheme; is the initial reactive power value flowing into the starting node of the cluster; _QC is the active power value flowing into the starting node of the cluster after being adjusted by the second regulation scheme;

The power flow constraints in the pre-established second regulation model are determined as follows:

In the above formula, P _αβ,t is the active power of line αβ in the photovoltaic cluster of the distribution network at time t; I _αβ,t is the current of line αβ in the photovoltaic cluster of the grid at time t; r _αβ is the photovoltaic cluster of the distribution network resistance of the middle line αβ; P _β,t is the active power injected into the node β of the photovoltaic cluster in the distribution network at time t; P _βγ,t is the active power of the line βγ in the photovoltaic cluster of the distribution network at time t; Q _{αβ, t} is the reactive power of the line αβ in the photovoltaic cluster of the distribution network at time t; x _αβ is the reactance of the line αβ in the photovoltaic cluster of the distribution network; Q _{β, t} is the node β in the cluster injected into the distribution network at time t. Reactive power; Q _βγ,t is the reactive power of the line βγ in the cluster of the distribution network at time t; β∈(1～M),M is the number of nodes in the photovoltaic cluster of the distribution network; α∈ψ(β ), ψ(β) is the set of line head nodes with node β as the end node in the photovoltaic cluster of the distribution network; is the set of line end nodes with node β as the head node in the photovoltaic cluster of the distribution network; P _PV,β,t is the active power of the distributed photovoltaic connected to node β in the photovoltaic cluster of the distribution network at time t; P _{CL ,β,t} is the active power of the adjustable load at node β in the photovoltaic cluster of the distribution network at time t; P _load,β,t is the active power of the non-adjustable load at the node β in the photovoltaic cluster of the distribution network at time t; P _ch,β,t is the energy storage charging power of the access node β in the photovoltaic cluster of the distribution network at time t; P _dis,β,t is the energy storage of the access node β in the photovoltaic cluster of the distribution network at time t Discharge power; Q _PV,β,t is the reactive power of distributed photovoltaics connected to node β in the photovoltaic cluster of the distribution network at time t; Q _CL,β,t is the node β of the photovoltaic cluster of the distribution network at time t The reactive power of the adjustable load at the place; Q _load,β,t is the reactive power of the non-adjustable load at the node β in the photovoltaic cluster of the distribution network at time t; U _β,t is the node in the photovoltaic cluster of the distribution network β is the voltage amplitude at time t; U _α,t is the voltage amplitude of node α in the distribution network cluster at time t;

The node voltage constraints in the pre-established second regulation model are determined as follows:

In the above formula, is the lower limit of the voltage amplitude of node β in the photovoltaic cluster of the distribution network; is the upper limit of the voltage amplitude of node β in the photovoltaic cluster of the distribution network;

The distributed photovoltaic operation constraints in the pre-established second regulation model are determined as follows:

In the above formula, ΔQ _PV,β,t is the reactive power adjustment value of the distributed photovoltaic connected to node β in the photovoltaic cluster of the distribution network at time t; is the minimum reactive power of distributed photovoltaics connected to node β in the cluster of the distribution network at time t; is the maximum reactive power of distributed photovoltaics connected to node β in the photovoltaic cluster of the distribution network at time t; where, S _PV,β is the capacity of the photovoltaic inverter at node β in the cluster of the distribution network; P _PV,β,t is the predicted value of the active power output of the distributed photovoltaic at time t of node β in the photovoltaic cluster of the distribution network;

The energy storage operation constraints in the pre-established second regulation model are determined as follows:

In the above formula, E _SOC,β,t is the amount of energy stored in the photovoltaic cluster connected to node β in the distribution network at time t; is the lower limit of the energy storage capacity of the access node β in the photovoltaic cluster of the distribution network; is the upper limit of the energy storage capacity of the access node β in the photovoltaic cluster of the distribution network; ΔPch _,β,t is the charging power adjustment amount of the energy storage of the access node β in the photovoltaic cluster of the distribution network at time t; It is the maximum charging power of the energy storage at the access node β in the photovoltaic cluster of the distribution network; is the maximum discharge power of the energy storage at the access node β in the photovoltaic cluster of the distribution network; ΔP _dis,β,t is the discharge power adjustment amount of the energy storage at the access node β in the cluster of the distribution network at time t.

In the preferred embodiment of the present invention, both the first control stage and the second control stage are non-convex nonlinear problems that are complex and difficult to solve. Converting the problem into a second-order cone programming problem to solve can effectively improve the problem The difficulty of solving is greatly improved, and the solving efficiency is greatly improved;

First, new variables are introduced, and the original regulation model involves the and and are replaced with the following: and

After replacement, only the power flow constraints in the regulation model are is a non-convex nonlinear constraint. Therefore, it is relaxed according to the basic principle of the second-order cone relaxation method. The specific method is as follows:

Rewrite it in the standard second-order conical form, that is:

At this point, the original control model is converted into a second-order cone programming model that can be efficiently solved, and a solver such as Mosek or Cplex can be used to efficiently solve the model.

The present invention provides a two-stage optimal control device for a distribution network based on photovoltaic clusters, as shown in FIG. 3 , the device includes:

a determining module, configured to determine a first control scheme of the distribution network according to the probability of occurrence of each operation scenario of the distribution network;

a regulation module, configured to regulate the distribution network with the first regulation scheme;

Wherein, when the node voltage in the photovoltaic cluster of the distribution network exceeds the limit, a second regulation scheme is determined according to the power regulation capability of the distributed photovoltaics and energy storage in the cluster, and the second regulation scheme is used to adjust the voltage in the photovoltaic cluster of the distribution network. The distributed photovoltaic and energy storage are controlled.

Specifically, the first control scheme includes: the reactive power of distributed photovoltaics connected to each node in the distribution network at each moment, the charging and discharging power of the connected energy storage, and the active power of the connected adjustable load ;

The second control scheme includes: a reactive power adjustment amount of distributed photovoltaics and an energy storage charging and discharging power adjustment amount in a photovoltaic cluster of a power distribution network.

The photovoltaic cluster is a branch line of a feeder of a power distribution network, wherein the branch line of the feeder is connected with distributed photovoltaic and energy storage devices or adjustable loads.

In the preferred embodiment of the present invention, the determining module is used for:

The probability of occurrence of each operation scenario of the distribution network is substituted into the pre-established first regulation model, and the first regulation scheme of the distribution network is obtained by solving.

Wherein, the objective function in the pre-established first regulation model is determined as follows:

In the above formula, F is the active power lost by the distribution network; p _S,t is the probability that the s-th scenario occurs in the distribution network at time t; S is the total number of typical scenarios retained by the distribution network after scenario analysis; T is the number of moments in the regulation period; c(0) is the end node set of the line with the starting node of the distribution network as the head node; P _0f is the active power flowing from the starting node of the distribution network to the node f; j∈( 1～N), N is the number of nodes in the distribution network; P _PV,j,t is the active power of distributed photovoltaics connected to node j at time t; P _ch,j,t is the connection of node j at time t is the charging power of the incoming energy storage; P _dis,j,t is the discharging power of the energy storage connected by node j at time t; P _CL,j,t is the active power of the adjustable load connected by node j at time t ; P _load,j,t is the active power of the non-adjustable load connected by node j at time t.

The power flow constraints in the pre-established first regulation model are determined as follows:

In the above formula, P _ij,t is the active power of line ij in the distribution network at time t; I _ij,t is the current of line ij in the distribution network at time t; r _ij is the resistance of line ij in the distribution network; x _ij is The reactance of line ij in the distribution network; P _j,t is the active power injected into node j at time t of the distribution network; P _jk,t is the active power of line jk at time t of the distribution network; Q _ij,t is the distribution network Reactive power of line ij at time t; Q _j,t is the reactive power injected into node j at time t of distribution network; Q _jk,t is the reactive power of line jk at time t of distribution network; j∈(1～N ), N is the number of nodes in the distribution network; i∈φ(j), φ(j) is the set of first nodes of the line with node j as the end node in the distribution network; k∈θ(j), θ (j) is the set of end nodes of the line with node j as the head node in the distribution network; P _PV,j,t is the active power of distributed photovoltaics at node j at time t of the distribution network; P _load,j,t is P _ch,j,t is the charging power of the energy storage device at node j of the distribution network at time t; P _dis,j,t is the node of the distribution network at time t The discharge power of the energy storage device at j; Q _PV,j,t is the reactive power of distributed photovoltaics at node j at time t of the distribution network; Q _load,j,t is the adjustable load at node j at time t of the distribution network Reactive power of the device; U _j,t is the voltage amplitude at node j at time t of the distribution network; U _i,t is the voltage amplitude at node i at time t of the distribution network;

The node voltage constraints in the pre-established first regulation model are determined as follows:

In the above formula, is the lower limit of the voltage amplitude of node j in the distribution network; is the upper limit of the voltage amplitude at node j in the distribution network; U _j,t is the voltage amplitude at node j at time t in the distribution network;

The operating constraints of the distributed photovoltaics in the pre-established first regulation model are determined as follows:

In the above formula, is the minimum value of the distributed photovoltaic reactive power of node j at time t of the distribution network; is the maximum value of the distributed photovoltaic reactive power of node j at time t in the distribution network;

in, S _PV,j is the distributed photovoltaic inverter capacity of node j in the distribution network; is the predicted value of distributed photovoltaic active power output of node j at time t of the distribution network;

The energy storage operation constraints in the pre-established first regulation model are determined as follows:

In the above formula, E _SOC,j,t is the power of the energy storage device at node j of the distribution network at time t; E _SOC,j,t+Δt is the power of the energy storage device at node j of the distribution network at time t+Δt; is the lower limit of the energy storage capacity of the energy storage device of node j in the distribution network; is the upper limit of the energy storage capacity of the energy storage device of node j in the distribution network; is the maximum charging power of the energy storage device at node j in the distribution network; is the maximum discharge power of the energy storage device at node j in the distribution network;

The power constraint condition of the adjustable load device in the pre-established first regulation model is determined as follows:

In the above formula, P _CL,j,t is the actual power consumption of the adjustable load device at node j in the distribution network at time t; W _j,min is the minimum power demand of the adjustable load device at node j in the distribution network; W _j,max is the maximum power demand of the adjustable load device at node j in the distribution network; Δt is the time interval of the first control scheme; T is the number of moments in the control cycle;

in, _εmin is the minimum value of the ratio of the actual power consumption of the adjustable load device in the distribution network to the expected power consumption; _εmax is the maximum value of the ratio of the actual power consumption of the adjustable load device in the distribution network to the expected power consumption; is the expected electric power of the adjustable load device of the distribution network at time t.

In the preferred embodiment of the present invention, the second regulation scheme is determined according to the power regulation capability of distributed photovoltaics and energy storage in the photovoltaic cluster of the distribution network, and the second regulation scheme is used to adjust the power of the distributed photovoltaic cluster. distributed photovoltaics and energy storage for regulation, including:

Substitute the maximum and minimum values of the reactive power of the distributed photovoltaics and the maximum and minimum values of the charging and discharging power of the energy storage in the cluster of the distribution network into the pre-established second regulation model, solve the second regulation model, and obtain the distribution network the second control scheme.

Wherein, the objective function in the pre-established second regulation model is determined as follows:

In the above formula, F _v is the minimum power adjustment amount of distributed photovoltaic and energy storage in the distributed photovoltaic cluster; is the set of all distributed photovoltaics in the cluster; ΔQ _PV,x is the reactive power adjustment of the xth distributed photovoltaic in the cluster; is the maximum reactive power of the xth distributed photovoltaic in the cluster; is the set of all energy storages in the cluster; ΔPch _,y is the charging power adjustment of the yth energy storage in the cluster; is the maximum charging power of the yth energy storage in the cluster; _Dch,y is the charging coefficient of the yth energy storage in the cluster; _Dch,y ∈(0,1); ΔP _dis,y is the charging coefficient of the yth energy storage in the cluster The discharge power adjustment of the yth energy storage; is the maximum discharge power of the yth energy storage in the cluster; D _dis,y is the discharge coefficient of the yth energy storage in the cluster; D _dis,y ∈(0,1).

The constant power constraint in the pre-established second regulation model is determined as follows:

In the above formula, is the initial active power value flowing into the starting node of the cluster; PC is the active power value _flowing into the starting node of the cluster after being adjusted by the second regulation scheme; is the initial reactive power value flowing into the starting node of the cluster; _QC is the active power value flowing into the starting node of the cluster after being adjusted by the second regulation scheme;

The power flow constraints in the pre-established second regulation model are determined as follows:

In the above formula, P _αβ,t is the active power of line αβ in the photovoltaic cluster of the distribution network at time t; I _αβ,t is the current of line αβ in the photovoltaic cluster of the grid at time t; r _αβ is the photovoltaic cluster of the distribution network resistance of the middle line αβ; P _β,t is the active power injected into the node β of the photovoltaic cluster in the distribution network at time t; P _βγ,t is the active power of the line βγ in the photovoltaic cluster of the distribution network at time t; Q _{αβ, t} is the reactive power of the line αβ in the photovoltaic cluster of the distribution network at time t; x _αβ is the reactance of the line αβ in the photovoltaic cluster of the distribution network; Q _{β, t} is the node β in the cluster injected into the distribution network at time t. Reactive power; Q _βγ,t is the reactive power of the line βγ in the cluster of the distribution network at time t; β∈(1～M),M is the number of nodes in the photovoltaic cluster of the distribution network; α∈ψ(β ), ψ(β) is the set of line head nodes with node β as the end node in the photovoltaic cluster of the distribution network; is the set of line end nodes with node β as the head node in the photovoltaic cluster of the distribution network; P _PV,β,t is the active power of the distributed photovoltaic connected to node β in the photovoltaic cluster of the distribution network at time t; P _{CL ,β,t} is the active power of the adjustable load at node β in the photovoltaic cluster of the distribution network at time t; P _load,β,t is the active power of the non-adjustable load at the node β in the photovoltaic cluster of the distribution network at time t; P _ch,β,t is the energy storage charging power of the access node β in the photovoltaic cluster of the distribution network at time t; P _dis,β,t is the energy storage of the access node β in the photovoltaic cluster of the distribution network at time t Discharge power; Q _PV,β,t is the reactive power of distributed photovoltaics connected to node β in the photovoltaic cluster of the distribution network at time t; Q _CL,β,t is the node β of the photovoltaic cluster of the distribution network at time t The reactive power of the adjustable load at the place; Q _load,β,t is the reactive power of the non-adjustable load at the node β in the photovoltaic cluster of the distribution network at time t; U _β,t is the node in the photovoltaic cluster of the distribution network β is the voltage amplitude at time t; U _α,t is the voltage amplitude of node α in the distribution network cluster at time t;

The node voltage constraints in the pre-established second regulation model are determined as follows:

In the above formula, is the lower limit of the voltage amplitude of node β in the photovoltaic cluster of the distribution network; is the upper limit of the voltage amplitude of node β in the photovoltaic cluster of the distribution network;

The distributed photovoltaic operation constraints in the pre-established second regulation model are determined as follows:

In the above formula, ΔQ _PV,β,t is the reactive power adjustment value of the distributed photovoltaic connected to node β in the photovoltaic cluster of the distribution network at time t; is the minimum reactive power of distributed photovoltaics connected to node β in the cluster of the distribution network at time t; is the maximum reactive power of distributed photovoltaics connected to node β in the photovoltaic cluster of the distribution network at time t;

in, S _PV,β is the capacity of the photovoltaic inverter at node β in the cluster of the distribution network; P _PV,β,t is the predicted value of the active power output of the distributed photovoltaic at time t of node β in the photovoltaic cluster of the distribution network;

The energy storage operation constraints in the pre-established second regulation model are determined as follows:

In the above formula, E _SOC,β,t is the amount of energy stored in the photovoltaic cluster connected to node β in the distribution network at time t; is the lower limit of the energy storage capacity of the access node β in the photovoltaic cluster of the distribution network; is the upper limit of the energy storage capacity of the access node β in the photovoltaic cluster of the distribution network; ΔPch _,β,t is the charging power adjustment amount of the energy storage of the access node β in the photovoltaic cluster of the distribution network at time t; It is the maximum charging power of the energy storage at the access node β in the photovoltaic cluster of the distribution network; is the maximum discharge power of the energy storage at the access node β in the photovoltaic cluster of the distribution network; ΔP _dis,β,t is the discharge power adjustment amount of the energy storage at the access node β in the cluster of the distribution network at time t.

As will be appreciated by those skilled in the art, the embodiments of the present application may be provided as a method, a system, or a computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.

The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present application. It will be understood that each flow and/or block in the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to the processor of a general purpose computer, special purpose computer, embedded processor or other programmable data processing device to produce a machine such that the instructions executed by the processor of the computer or other programmable data processing device produce Means for implementing the functions specified in a flow or flow of a flowchart and/or a block or blocks of a block diagram.

These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory result in an article of manufacture comprising instruction means, the instructions The apparatus implements the functions specified in the flow or flows of the flowcharts and/or the block or blocks of the block diagrams.

These computer program instructions can also be loaded on a computer or other programmable data processing device to cause a series of operational steps to be performed on the computer or other programmable device to produce a computer-implemented process such that The instructions provide steps for implementing the functions specified in the flow or blocks of the flowcharts and/or the block or blocks of the block diagrams.

Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: the present invention can still be Modifications or equivalent replacements are made to the specific embodiments of the present invention, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention shall be included within the protection scope of the claims of the present invention.

Claims (10)

1. A two-stage optimal control method for a power distribution network based on photovoltaic clusters, characterized in that the method comprises: Determine the first control scheme of the distribution network according to the probability of occurrence of each operation scenario of the distribution network; Controlling the distribution network with the first control scheme; Wherein, when the node voltage in the photovoltaic cluster of the distribution network exceeds the limit, a second regulation scheme is determined according to the power regulation capability of the distributed photovoltaics and energy storage in the cluster, and the second regulation scheme is used to adjust the voltage in the photovoltaic cluster of the distribution network. distributed photovoltaic and energy storage for regulation. 2 . The method according to claim 1 , wherein the first control scheme comprises: the reactive power of distributed photovoltaics connected to each node in the distribution network at each moment, and the charge of the connected energy storage. 3 . Discharge power and active power of the connected adjustable load; The second control scheme includes: a reactive power adjustment amount of distributed photovoltaics and an energy storage charging and discharging power adjustment amount in a photovoltaic cluster of a power distribution network. 3 . The method according to claim 1 , wherein the photovoltaic cluster is a branch line of a feeder of a distribution network, wherein the branch line of the feeder is connected with distributed photovoltaic and energy storage devices or adjustable loads. 4 . . 4. The method according to claim 2, wherein the determining the first control scheme of the distribution network according to the probability of occurrence of each operation scenario of the distribution network comprises: The probability of occurrence of each operation scenario of the distribution network is substituted into the pre-established first regulation model, and the first regulation scheme of the distribution network is obtained by solving. 5. method as claimed in claim 4 is characterized in that, the objective function in the first control model established in advance is determined as follows: In the above formula, F is the active power lost by the distribution network; p _S,t is the probability that the s-th scenario occurs in the distribution network at time t; S is the total number of typical scenarios retained by the distribution network after scenario analysis; T is the number of moments in the regulation period; c(0) is the end node set of the line with the starting node of the distribution network as the head node; P _0f is the active power flowing from the starting node of the distribution network to the node f; j∈( 1～N), N is the number of nodes in the distribution network; P _PV,j,t is the active power of distributed photovoltaics connected to node j at time t; P _ch,j,t is the connection of node j at time t is the charging power of the incoming energy storage; P _dis,j,t is the discharging power of the energy storage connected by node j at time t; P _CL,j,t is the active power of the adjustable load connected by node j at time t ; P _load,j,t is the active power of the non-adjustable load connected by node j at time t. 6. method as claimed in claim 4, is characterized in that, the flow constraint condition in the first control model established in advance is determined as follows: In the above formula, P _ij,t is the active power of line ij in the distribution network at time t; I _ij,t is the current of line ij in the distribution network at time t; r _ij is the resistance of line ij in the distribution network; x _ij is The reactance of line ij in the distribution network; P _j,t is the active power injected into node j at time t of the distribution network; P _jk,t is the active power of line jk at time t of the distribution network; Q _ij,t is the distribution network Reactive power of line ij at time t; Q _j,t is the reactive power injected into node j at time t of distribution network; Q _jk,t is the reactive power of line jk at time t of distribution network; j∈(1～N ), N is the number of nodes in the distribution network; i∈φ(j), φ(j) is the set of first nodes of the line with node j as the end node in the distribution network; k∈θ(j), θ (j) is the set of end nodes of the line with node j as the head node in the distribution network; P _PV,j,t is the active power of distributed photovoltaics at node j at time t of the distribution network; P _load,j,t is P _ch,j,t is the charging power of the energy storage device at node j of the distribution network at time t; P _dis,j,t is the node of the distribution network at time t The discharge power of the energy storage device at j; Q _PV,j,t is the reactive power of distributed photovoltaics at node j at time t of the distribution network; Q _load,j,t is the adjustable load at node j at time t of the distribution network Reactive power of the device; U _j,t is the voltage amplitude at node j at time t of the distribution network; U _i,t is the voltage amplitude at node i at time t of the distribution network; The node voltage constraints in the pre-established first regulation model are determined as follows: In the above formula, is the lower limit of the voltage amplitude of node j in the distribution network; is the upper limit of the voltage amplitude at node j in the distribution network; U _j,t is the voltage amplitude at node j at time t in the distribution network; The operating constraints of the distributed photovoltaics in the pre-established first regulation model are determined as follows: In the above formula, is the minimum value of the distributed photovoltaic reactive power of node j at time t of the distribution network; is the maximum value of the distributed photovoltaic reactive power of node j at time t in the distribution network; in, S _PV,j is the distributed photovoltaic inverter capacity of node j in the distribution network; is the predicted value of distributed photovoltaic active power output of node j at time t of the distribution network; The energy storage operation constraints in the pre-established first regulation model are determined as follows: In the above formula, E _SOC,j,t is the power of the energy storage device at node j of the distribution network at time t; E _SOC,j,t+Δt is the power of the energy storage device at node j of the distribution network at time t+Δt; is the lower limit of the energy storage capacity of the energy storage device of node j in the distribution network; is the upper limit of the energy storage capacity of the energy storage device of node j in the distribution network; is the maximum charging power of the energy storage device at node j in the distribution network; is the maximum discharge power of the energy storage device at node j in the distribution network; The power constraint condition of the adjustable load device in the pre-established first regulation model is determined as follows: In the above formula, P _CL,j,t is the actual power consumption of the adjustable load device at node j in the distribution network at time t; W _j,min is the minimum power demand of the adjustable load device at node j in the distribution network; W _j,max is the maximum power demand of the adjustable load device at node j in the distribution network; Δt is the time interval of the first control scheme; T is the number of moments in the control cycle; in, _εmin is the minimum value of the ratio of the actual power consumption of the adjustable load device in the distribution network to the expected power consumption; _εmax is the maximum value of the ratio of the actual power consumption of the adjustable load device in the distribution network to the expected power consumption; is the expected electric power of the adjustable load device of the distribution network at time t. 7. The method according to claim 2, wherein the second regulation scheme is determined according to the power regulation capability of distributed photovoltaics and energy storage in the photovoltaic cluster of the power distribution network, and the second regulation scheme is used to adjust the power. Distributed photovoltaics and energy storage in distributed photovoltaic clusters are regulated, including: Substitute the maximum and minimum values of the reactive power of the distributed photovoltaics and the maximum and minimum values of the charging and discharging power of the energy storage in the cluster of the distribution network into the pre-established second regulation model, solve the second regulation model, and obtain the distribution network the second control scheme. 8. The method of claim 7, wherein the objective function in the second control model established in advance is determined as follows: In the above formula, F _v is the minimum power adjustment amount of distributed photovoltaic and energy storage in the distributed photovoltaic cluster; is the set of all distributed photovoltaics in the cluster; ΔQ _PV,x is the reactive power adjustment of the xth distributed photovoltaic in the cluster; is the maximum reactive power of the xth distributed photovoltaic in the cluster; is the set of all energy storages in the cluster; ΔPch _,y is the charging power adjustment of the yth energy storage in the cluster; is the maximum charging power of the yth energy storage in the cluster; _Dch,y is the charging coefficient of the yth energy storage in the cluster; _Dch,y ∈(0,1); ΔP _dis,y is the charging coefficient of the yth energy storage in the cluster The discharge power adjustment of the yth energy storage; is the maximum discharge power of the yth energy storage in the cluster; D _dis,y is the discharge coefficient of the yth energy storage in the cluster; D _dis,y ∈(0,1). 9. The method of claim 7, wherein the constant power constraint in the second control model established in advance is determined as follows: In the above formula, is the initial active power value flowing into the starting node of the cluster; PC is the active power value _flowing into the starting node of the cluster after being adjusted by the second regulation scheme; is the initial reactive power value flowing into the starting node of the cluster; _QC is the active power value flowing into the starting node of the cluster after being adjusted by the second regulation scheme; The power flow constraints in the pre-established second regulation model are determined as follows: In the above formula, P _αβ,t is the active power of line αβ in the photovoltaic cluster of the distribution network at time t; I _αβ,t is the current of line αβ in the photovoltaic cluster of the grid at time t; r _αβ is the photovoltaic cluster of the distribution network resistance of the middle line αβ; P _β,t is the active power injected into the node β of the photovoltaic cluster in the distribution network at time t; P _βγ,t is the active power of the line βγ in the photovoltaic cluster of the distribution network at time t; Q _{αβ, t} is the reactive power of the line αβ in the photovoltaic cluster of the distribution network at time t; x _αβ is the reactance of the line αβ in the photovoltaic cluster of the distribution network; Q _{β, t} is the node β in the cluster injected into the distribution network at time t. Reactive power; Q _βγ,t is the reactive power of the line βγ in the cluster of the distribution network at time t; β∈(1～M),M is the number of nodes in the photovoltaic cluster of the distribution network; α∈ψ(β ), ψ(β) is the set of line head nodes with node β as the end node in the photovoltaic cluster of the distribution network; is the set of line end nodes with node β as the head node in the photovoltaic cluster of the distribution network; P _PV,β,t is the active power of the distributed photovoltaic connected to node β in the photovoltaic cluster of the distribution network at time t; P _{CL ,β,t} is the active power of the adjustable load at node β in the photovoltaic cluster of the distribution network at time t; P _load,β,t is the active power of the non-adjustable load at the node β in the photovoltaic cluster of the distribution network at time t; P _ch,β,t is the energy storage charging power of the access node β in the photovoltaic cluster of the distribution network at time t; P _dis,β,t is the energy storage of the access node β in the photovoltaic cluster of the distribution network at time t Discharge power; Q _PV,β,t is the reactive power of distributed photovoltaics connected to node β in the photovoltaic cluster of the distribution network at time t; Q _CL,β,t is the node β of the photovoltaic cluster of the distribution network at time t The reactive power of the adjustable load at the place; Q _load,β,t is the reactive power of the non-adjustable load at the node β in the photovoltaic cluster of the distribution network at time t; U _β,t is the node in the photovoltaic cluster of the distribution network β is the voltage amplitude at time t; U _α,t is the voltage amplitude of node α in the distribution network cluster at time t; The node voltage constraints in the pre-established second regulation model are determined as follows: In the above formula, is the lower limit of the voltage amplitude of node β in the photovoltaic cluster of the distribution network; is the upper limit of the voltage amplitude of node β in the photovoltaic cluster of the distribution network; The distributed photovoltaic operation constraints in the pre-established second regulation model are determined as follows: In the above formula, ΔQ _PV,β,t is the reactive power adjustment value of the distributed photovoltaic connected to node β in the photovoltaic cluster of the distribution network at time t; is the minimum reactive power of distributed photovoltaics connected to node β in the cluster of the distribution network at time t; is the maximum reactive power of distributed photovoltaics connected to node β in the photovoltaic cluster of the distribution network at time t; in, S _PV,β is the capacity of the photovoltaic inverter at node β in the cluster of the distribution network; P _PV,β,t is the predicted value of the active power output of the distributed photovoltaic at time t of node β in the photovoltaic cluster of the distribution network; The energy storage operation constraints in the pre-established second regulation model are determined as follows: In the above formula, E _SOC,β,t is the amount of energy stored in the photovoltaic cluster connected to node β in the distribution network at time t; is the lower limit of the energy storage capacity of the access node β in the photovoltaic cluster of the distribution network; is the upper limit of the energy storage capacity of the access node β in the photovoltaic cluster of the distribution network; ΔPch _,β,t is the charging power adjustment amount of the energy storage of the access node β in the photovoltaic cluster of the distribution network at time t; It is the maximum charging power of the energy storage at the access node β in the photovoltaic cluster of the distribution network; is the maximum discharge power of the energy storage at the access node β in the photovoltaic cluster of the distribution network; ΔP _dis,β,t is the discharge power adjustment amount of the energy storage at the access node β in the cluster of the distribution network at time t. 10. A photovoltaic cluster-based two-stage optimal control device for power distribution network, characterized in that the device comprises: a determining module, configured to determine a first control scheme of the distribution network according to the probability of occurrence of each operation scenario of the distribution network; a regulation module, configured to regulate the distribution network with the first regulation scheme; Wherein, when the node voltage in the photovoltaic cluster of the distribution network exceeds the limit, a second regulation scheme is determined according to the power regulation capability of the distributed photovoltaics and energy storage in the cluster, and the second regulation scheme is used to adjust the voltage in the photovoltaic cluster of the distribution network. distributed photovoltaic and energy storage for regulation.