CN117374953A

CN117374953A - Distributed resource power distribution method based on distribution network aggregation boundary

Info

Publication number: CN117374953A
Application number: CN202311355621.7A
Authority: CN
Inventors: 喻振帆; 刘佳乐; 蔡新雷; 孟子杰; 周巍; 程章颖; 李超; 黎可; 郭俊宏; 李欢欢
Original assignee: Guangdong Power Grid Co Ltd; Electric Power Dispatch Control Center of Guangdong Power Grid Co Ltd
Current assignee: Guangdong Power Grid Co Ltd; Electric Power Dispatch Control Center of Guangdong Power Grid Co Ltd
Priority date: 2023-10-18
Filing date: 2023-10-18
Publication date: 2024-01-09

Abstract

The invention discloses a distributed resource power distribution method based on a distribution network aggregation boundary, which comprises the following steps: acquiring the temperature control loads and the operation data of electric vehicles in the distributed resources of each node of the power distribution network; constructing equivalent adjustable characteristic models of each temperature control load and each electric automobile, and respectively obtaining various energy storage models; constructing a distribution network distributed resource aggregation model considering physical network constraint, and solving to obtain an adjustable boundary maximum value in a main network scheduling interval; and combining the maximum value of the adjustable boundary, distributing the power in the various energy storage models through an adjustable time margin sequencing method and a redistribution consistency algorithm, and sending a distribution result to each node of the power distribution network, so that each node of the power distribution network adjusts the power corresponding to each temperature control load in each node of the power distribution network and the power corresponding to each electric automobile according to the distribution result. The invention realizes the distribution network aggregation boundary obtained by effectively aggregating various distributed resources and more accurately distributes various distributed resource powers.

Description

Distributed resource power distribution method based on distribution network aggregation boundary

Technical Field

The invention relates to the field of distribution network distributed resource power distribution, in particular to a distribution network aggregation boundary-based distributed resource power distribution method.

Background

With the increasing number of distributed resource entities, different forms of polymers are a third party medium for achieving benign interaction between the power grid and the distributed resource entities. How to design an aggregation technology, integrate resources of distributed equipment, build an aggregation model, calculate related aggregation parameters, and facilitate centralized scheduling and control of distributed resources of a system, which is a key for effectively applying aggregates such as virtual power plants to the ground.

In the prior art, the final adjustable boundary is formed by the output of logic constraint and optimization means directly according to the running state of the same type of resources, and the power distribution of the same type of resources is controlled by the adjustable boundary. However, this method of distributing power according to the same type of resource aggregation boundary is not suitable for the characteristics of the future distribution network station area distributed resource diversity, and ignores objective physical constraints, so that the feasibility of the obtained result cannot be guaranteed. Thus, there is an urgent need for a method of distributing distributed resource power by considering the distribution network aggregation boundaries of multi-type resource aggregation and grid constraints.

Disclosure of Invention

The invention provides a distribution network aggregation boundary-based distributed resource power distribution method, which realizes that various distributed resource powers are more accurately distributed according to the distribution network aggregation boundary obtained by effectively aggregating various distributed resources.

In order to solve the above technical problems, an embodiment of the present invention provides a distributed resource power allocation method based on a distribution network aggregation boundary, which is characterized by comprising:

acquiring operation data of each temperature control load and operation data of each electric automobile in distributed resources of each node of the power distribution network;

constructing an equivalent adjustable characteristic model of each temperature control load according to the operation data of each temperature control load, and aggregating the equivalent adjustable characteristic models of all the temperature control loads to obtain a first energy storage model of temperature control load homogeneous aggregation;

according to the operation data of each electric automobile, constructing an equivalent adjustable characteristic model of each electric automobile, and aggregating the equivalent adjustable characteristic models of all electric automobiles to obtain a second class energy storage model of electric automobile homogeneous aggregation;

constructing a distribution network distributed resource aggregation model considering physical network constraint according to the first type energy storage model and the second type energy storage model in each node, and solving to obtain an adjustable boundary maximum value in a main network scheduling interval;

According to the maximum value of the adjustable boundary in the main network scheduling interval, distributing the power in the first type of energy storage model by an adjustable time margin sequencing method to obtain a first distribution result, and sending the first distribution result to each temperature control load in each node of the power distribution network so that the first distribution result adjusts the power corresponding to each temperature control load;

and distributing the power in the second type energy storage model through a redistribution consistency algorithm according to the maximum value of the adjustable boundary in the main network scheduling interval to obtain a second distribution result, and sending the second distribution result to each electric automobile in each node of the power distribution network so that the second distribution result adjusts the power corresponding to each electric automobile.

It can be understood that compared with the prior art, the method provided by the invention designs an equivalent energy storage aggregation model aiming at two loads of a temperature control load and an electric vehicle in the current distribution network, builds a distribution network distributed resource aggregation model considering physical network constraint by considering physical topology constraint, equates a class energy storage polymer model of temperature control and electric vehicle load under each node to the description of a class energy storage model in the distribution network distributed resource aggregation model, and finally obtains the aggregation boundary of the whole distribution network by optimizing and solving the model. The layered aggregation framework can firstly realize the aggregation of homogeneous resources in a small range, and is applied to the large-range resource aggregation through the expression of energy storage, so that the aggregation of the whole distribution network is realized, and an effective regulation and control boundary reference is provided for power grid dispatching. According to the effective regulation boundary reference, the temperature control load power and the electric automobile power under each node are redistributed through an adjustable time margin sequencing method and a redistribution consistency algorithm, and the execution of the regulation instruction is ensured while the effective aggregation of the distributed resource adjustable capacity is ensured.

Further, the constructing an equivalent adjustable characteristic model of each temperature control load according to the operation data of each temperature control load specifically includes:

x(t+1)＝κx(t)+γP _cd (t)

-1≤x(t)≤1

wherein x (t+1) is the charge constraint state of the equivalent adjustable characteristic model of each temperature control load; p (P) _cd (t) equivalent energy storage power at time t in the operation data of each temperature control load; delta is the dead zone temperature span in the operation data of each temperature control load; r is the thermal resistance in the operation data of each temperature control load, and C is the heat capacity in the operation data of each temperature control load; COP is the temperature control load energy efficiency coefficient in the operation data of each temperature control load; Δt is a regulation time interval in the operation data of each temperature control load; t (T) _set Setting temperature in the operation data of each temperature control load; t (T) _in (t) is the internal temperature at time t in the operation data of each temperature control load; kappa is a first parameter; gamma is the second parameter.

Further, the aggregation of the equivalent adjustable characteristic models of all the temperature control loads is performed to obtain a first type of energy storage model of the temperature control load homogeneous aggregation, which specifically comprises the following steps:

SOC _AC (t+1)＝αSOC _AC (t)+P _AC (t)

wherein SOC is _AC (t) is a first type of energy storage model of the temperature-controlled load homogeneous polymerization; p (P) _AC (t) is the equivalent charging power of all temperature controlled loads; p (P) _cd，j (t) is the equivalent stored energy power in the operational data of the jth temperature controlled load; r is R _j The thermal resistance in the operation data of the jth temperature control load; c (C) _j The heat capacity in the operation data of the j-th temperature control load; x is x _j (t) is an equivalent adjustable characteristic model of the jth temperature control load; gamma ray _j A second parameter that is a j-th temperature controlled load; alpha is an intermediate parameter.

It can be understood that the method provided by the invention utilizes the thermal resistance model and the load electricity model to perform energy storage equivalence and aggregation on each temperature control load in the distributed resources of each node of the distribution network under the consideration of the temperature dead zone of the temperature control load and the charge-discharge heat energy conversion mechanism, thereby laying a working foundation for better adapting to the energy storage-like expression of the distributed resource characteristics in the distribution network distributed resource aggregation model.

Further, the constructing an equivalent adjustable characteristic model of each electric automobile according to the operation data of each electric automobile specifically includes:

wherein d _EV (t) is the electric quantity injected by the electric automobile at the moment t in the operation data of each electric automobile, and d _min (t) is the lower limit of the energy track at the time t in the running data of each electric automobile, d _max (t) is the upper limit of the energy track at the time t in the running data of each electric automobile, eta is the charging efficiency in the running data of each electric automobile, and P _EV(k) Charging power of the electric automobile in the kth scheduling period in the operation data of each electric automobile, wherein Deltat is the time interval of the scheduling period in the operation data of each electric automobile, and t is _in The electric automobile access time t is the electric automobile access time in the operation data of each electric automobile _out D, the leaving time of the electric automobile in the operation data of each electric automobile is D _expect P is the charging requirement of the electric automobile in the operation data of each electric automobile _EV，max The rated charging power upper limit, P of the battery of the electric automobile in the operation data of each electric automobile _EV (t) is the charging power of the electric automobile at the time t in the operation data of each electric automobile, P _max (t) is the maximum charging power of the electric automobile limited by the energy boundary constraint at the time t in the operation data of each electric automobile, P _min And (t) the minimum charging power of the electric automobile limited by the energy boundary constraint at the time t in the operation data of each electric automobile.

Further, the aggregation of the equivalent adjustable characteristic models of all the electric vehicles to obtain a second type of energy storage model of the electric vehicle homogeneous aggregation specifically comprises:

obtaining the adjustable capacity of each electric automobile according to the maximum charging power and the minimum charging power of the electric automobile in the equivalent adjustable characteristic model of each electric automobile, which are limited by the energy boundary constraint;

And adding the adjustable capacity of each electric automobile to obtain a second type energy storage model of the electric automobile homogeneous aggregation.

It can be understood that the method provided by the invention relies on the charge and discharge process of the electric vehicles to construct the equivalent energy storage model of each electric vehicle and the homogeneous aggregation class energy storage model of the electric vehicles, thereby laying a working foundation for better adapting to the class energy storage expression of the distributed resource characteristics in the distribution network distributed resource aggregation model.

Further, according to the first type energy storage model and the second type energy storage model in each node, a distribution network distributed resource aggregation model considering physical network constraint is constructed, and an adjustable boundary maximum value in a main network scheduling interval is obtained by solving, specifically including:

acquiring a distribution network topological structure, and constructing a distribution network distributed resource aggregation model by taking the maximum value of an adjustable boundary in a main network scheduling interval as an objective function according to the distribution network topological structure;

matching the boundary characteristics of the distributed resources in the operation data of the first type of energy storage model and the operation data of the second type of energy storage model with the boundary characteristics of the distributed resources in the distribution network distributed resource aggregation model;

Determining physical network constraint conditions of the distribution network distributed resource aggregation model through linearized Distflow constraint according to active power, reactive power, active load, reactive load, active output, reactive output, active power flow and reactive power flow in the operation data of the first type energy storage model and the operation data of the second type energy storage model;

and solving an objective function in the distribution network distributed resource aggregation model by combining the distributed resource boundary characteristics and the physical network constraint conditions in the distribution network distributed resource aggregation model to obtain an adjustable boundary maximum value in a main network scheduling interval.

It can be understood that the method provided by the invention realizes unified aggregation of various different types of resources under the premise of considering physical network constraint, forms unified and available distribution network adjustable boundary description meeting distribution network constraint, and provides a foundation for the distribution network to participate in the regulation and control of the power system.

Further, the specific formula of the adjustable boundary maximum value in the main network scheduling interval is:

wherein T is _Sta Indicated as schedule period start time; t (T) _End The end time of the scheduling stage; { S } is the upper layer power supply node set; p (P) _S,it,LO Active power of the jth power supply node in the t stage under the lower bound operation; p (P) _S,it,UP Expressed as the active power of the jth power supply node at the t-th stage under upper bound operation.

Further, the upper-bound resource operation characteristic of the distributed resource boundary characteristic in the distribution network distributed resource aggregation model is specifically:

wherein P is _A,jt,UP,C The charging power of the resource cluster at the node j in the t stage under the upper bound operation is obtained; p (P) _A,jt,UP,D Discharging power of the resource cluster at the node j in the t stage under the upper bound operation; η (eta) _A,jt Equivalent charge and discharge efficiency for the resource clusters; e (E) _A,jt,UP The equivalent electricity storage capacity of the aggregation resource cluster at the node j under the upper bound operation is obtained; e (E) _A,j,initial An equivalent electricity storage capacity is obtained for the aggregation resource cluster of the node j initially;maximum charge-discharge power limit, Z, for aggregated resource cluster of node j _A,jt,UP,C The charging state of the aggregation resource cluster of the node j in the upper bound operation is the charging state of the aggregation resource cluster of the node j in the upper bound operation; z is Z _A,jt,UP,D The discharge state of the stage t of the aggregate resource cluster of the node j under the upper-bound operation; c (C) _A,j The equivalent capacity of the aggregate resource cluster for node j; />Minimum constraint for aggregate resource cluster for node j; />Maximum constraint of aggregate resource cluster for node j;

the lower bound resource operation characteristic of the distributed resource boundary characteristic in the distribution network distributed resource aggregation model is specifically:

Wherein P is _A,jt,LO,C Charging power of the resource cluster at the node j in the t-th stage under the lower bound operation; p (P) _A,jt,LO,D Discharging power of the resource cluster at the node j in the t-th stage under the lower bound operation; e (E) _A,jt,LO The equivalent electricity storage capacity of the aggregation resource cluster at the node j under the lower bound operation is obtained; z is Z _A,jt,LO,C The charging state of the aggregation resource cluster of the node j in the lower bound operation is the charging state of the aggregation resource cluster in the stage t; z is Z _A,jt,LO,D The discharge state of stage t of the aggregate resource cluster of node j is the lower bound operation.

Further, the upper bound operation condition constraint condition in the physical network constraint conditions is specifically:

wherein { S } is the upper layer supply node set; { A } is a node set containing a resource cluster; v is a set formed by all nodes; p (P) _S,it,UP Active power of the jth power supply node in the t stage under the upper bound operation is obtained; q (Q) _S,jt,UP Reactive power of a jth power supply node in a t stage under upper bound operation is obtained; p (P) _L,jt The active load of the node j in the phase t is as follows; q (Q) _L,jt Reactive load of the node j in the phase t; p (P) _A,jt,UP Active power output for aggregate resources under upper bound operation; q (Q) _A,jt,UP Reactive power output of the aggregate resource under the upper-bound operation; h _ijt,UP Active power flow from node i to node j for upper bound lower run phase t; g _ijt,UP Reactive power flow from node i to node j for upper bound run lower phase t; θ _j The power factor of the j node load; u (U) _jt,UP The voltage amplitude of the node j under the upper bound operation is shown; r is R _ij The resistance of branch ij; x is X _ij Reactance for branch ij;a reference voltage amplitude value for an upper layer power supply node j;a minimum active power limit on supply node j; />Limiting the maximum active power at the supply node j; />The minimum reactive limit for power supply node j; />The maximum reactive limit of the power supply node j; />Is the lower bound of the voltage value at node j; />Is the upper bound of the voltage value at node j; pi (j) is the node set of the inflow node j; delta (j) is the node set of the outflow node j; />Is a branch flow limit;

the lower bound operation condition constraint conditions in the physical network constraint conditions are specifically as follows:

wherein P is _S,it,LO Active power of the jth power supply node in the t stage under the lower bound operation; q (Q) _S,jt,LO Reactive power of a jth power supply node in a t stage under lower-limit operation; p (P) _A,jt,LO Active power contribution to aggregate resources under lower bound operation; q (Q) _A,jt,LO Reactive power output of the aggregate resource under lower bound operation; h _ijt,LO Active power flow from node i to node j for lower bound operational phase t; g _ijt,LO Reactive power flow from node i to node j for lower bound operation phase t; u (U) _jt,LO Is the voltage magnitude of node j at lower bound operation.

Further, according to the maximum value of the adjustable boundary in the scheduling interval of the main network, the power in the first type of energy storage model is distributed by an adjustable time margin sorting method to obtain a first distribution result, and the first distribution result is sent to each node of the power distribution network, so that each node of the power distribution network adjusts the power corresponding to each temperature control load in each node of the power distribution network according to the first distribution result, and the method specifically comprises the following steps:

transmitting a response power control instruction to the first type energy storage model according to the adjustable boundary maximum value in the main network scheduling interval;

calculating adjustable time margin of each temperature control node in the first type energy storage model according to the response power control instruction and the temperature change function of the temperature control load;

sequencing the adjustable time margin, and sequentially distributing the power of each node temperature control load in the first type energy storage model according to the sequenced adjustable time margin to obtain a first distribution result;

and sending the first distribution result to each node of the power distribution network, so that each node of the power distribution network adjusts the power corresponding to each temperature control load in each node of the power distribution network according to the first distribution result.

It can be understood that the method provided by the invention realizes the rapid distribution of the internal power of each temperature control load under each node by the sequencing method of the adjustable time margin. The larger the adjustable time margin is, the distributed equipment is explained to be preferentially distributed after participating in power grid regulation, so that the power utilization comfort level of a user is effectively ensured.

Further, according to the maximum value of the adjustable boundary in the main network scheduling interval, distributing the power in the second class energy storage model through a reassignment consistency algorithm to obtain a second distribution result, and sending the second distribution result to each electric automobile in each node of the power distribution network so that the second distribution result adjusts the power corresponding to each electric automobile, which specifically includes:

transmitting a charge and discharge power control instruction to the second type energy storage model according to the adjustable boundary maximum value in the main network scheduling interval;

calculating an adjustable capacity factor according to the battery electric quantity of the electric vehicle and the charging and discharging power of the electric vehicle in the second type of energy storage model;

distributing the power of each node electric vehicle in the second type energy storage model through a redistribution consistency algorithm according to the charge and discharge power control instruction and the adjustable capacity factor to obtain a second distribution result;

And sending the second distribution result to each electric automobile in each node of the power distribution network, so that the second distribution result adjusts the power corresponding to each electric automobile.

Further, the adjustable capacity factor has a specific formula:

wherein,the charge and discharge power of m vehicles in the t period is obtained through a reassignment consistency algorithm; />The battery electric quantity of the mth trolley before the t optimization period begins; SOC (State of Charge) _max The maximum electric quantity of the battery is; SOC (State of Charge) _min A battery minimum power limit; c (C) _max,m Is the characteristic of m vehicles; p (P) _i,t And the sum of the charge and discharge power of the m vehicles at the t period is obtained through the reassignment consistency algorithm.

It can be understood that by means of the reassignment consistency algorithm based on the adjustable capacity factor, when the charge and discharge of each electric automobile in the distributed resources of each node of the distribution network are carried out to a certain extent, the SOC of each electric automobile can effectively tend to be consistent, the expected electric quantity is reached before the expected charge completion time is finished, and the problem of power reassignment caused by the difference between batteries and initial electric quantity of each electric automobile is solved.

Drawings

Fig. 1: the step flow chart of the distributed resource power distribution method based on the distribution network aggregation boundary provided by the embodiment of the invention;

Fig. 2: the step flow chart for constructing the distribution network aggregation boundary in the distribution network aggregation boundary-based distributed resource power distribution method provided by the embodiment of the invention;

fig. 3: the embodiment of the invention provides a schematic diagram of distributed resource aggregation of each node in a distributed resource power allocation method based on a distribution network aggregation boundary.

Detailed Description

The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.

Example 1

Referring to fig. 1, a flowchart of steps of a distributed resource power allocation method based on a distribution network aggregation boundary according to an embodiment of the present invention includes steps S101 to S106, and the steps are specifically as follows.

S101: and acquiring the operation data of each temperature control load and the operation data of each electric automobile in the distributed resources of each node of the power distribution network.

S102: and constructing an equivalent adjustable characteristic model of each temperature control load according to the operation data of each temperature control load, and aggregating the equivalent adjustable characteristic models of all the temperature control loads to obtain a first energy storage model of temperature control load homogeneous aggregation.

It should be noted that, in the embodiment of the present invention, for temperature control modeling, a first-order ordinary differential equation is selected to solve, and a thermodynamic equivalent model describing temperature change is established, where the relationship between temperature and power is between. The method can be concretely expressed as follows:wherein: r is represented by thermal resistance, C is represented by heat capacity, COP is represented by temperature-controlled load energy efficiency coefficient, +.>Electric power, T, expressed as temperature-controlled load _out Expressed as the temperature of the environment in which T _in And (t) is expressed as the on-off state of the temperature control load of S (t) at the internal temperature of the period t, and the value of the temperature control load is '1', and is 'on state', otherwise, the value of the temperature control load is '0', namely 'off state'. Continuously processing the switch control variable in the formula:

in particular, considering that the temperature-controlled load generally has a set temperature, a reference power to maintain the indoor temperature at a preset temperature is determined, which may be calculated by:wherein P is _baseline Expressed as reference power for maintaining a set temperature, T _set Indicated as temperature control load set temperature. Obviously, the power lower than the reference power means that the indoor temperature will gradually develop towards the direction higher than the set temperature, and the power higher than the reference power is expressed as that the indoor temperature develops towards the direction lower than the indoor temperature, so the set equivalent stored energy power is set as follows: p (P) _cd (t)＝P _cont (t)-P _baseline ；Wherein P is _cd And (t) is expressed as equivalent stored energy power. In addition, considering that the temperature-controlled load is usually provided with a dead zone of deviation, i.e. T _min ≤T _in (t)≤T _max ；δ＝T _max -T _min The method comprises the steps of carrying out a first treatment on the surface of the Wherein T is _min Expressed as the dead zone lower limit temperature, T _max Expressed as the dead zone upper limit temperature, and δ expressed as the dead zone temperature span. Considering the charge constraint condition in the equivalent adjustable characteristic model for setting auxiliary variables for each temperature control load, wherein the auxiliary variables are +.>

In this embodiment, the constructing an equivalent adjustable characteristic model of each temperature control load according to the operation data of each temperature control load specifically includes:

x(t+1)＝κx(t)+γP _cd (t)

-1≤x(t)≤1

In this embodiment, the aggregating the equivalent adjustable characteristic models of all the temperature-controlled loads to obtain a first energy storage model of homogeneous aggregation of the temperature-controlled loads specifically includes:

SOC _AC (t+1)＝αSOC _AC (t)+P _AC (t)

It should be noted that the boundary of the temperature-controlled load cluster may be approximately characterized by the following equation:

P _ACs,min ≤P _AC (t)≤P _ACs,max

SOC _ACs,min ≤SOC _AC (t)≤SOC _ACs,max

wherein P is _ACs,min And P _ACs,max Respectively the lower limit and the upper limit of the equivalent charging power of the temperature control load cluster and the SOC _ACs,min And SOC (System on chip) _ACs,max The lower limit and the upper limit of the equivalent SOC of the temperature control load cluster are respectively as follows:

S103: and constructing an equivalent adjustable characteristic model of each electric automobile according to the operation data of each electric automobile, and aggregating the equivalent adjustable characteristic models of all the electric automobiles to obtain a second class energy storage model of the electric automobile homogeneous aggregation.

It should be noted that, for the electric automobile monomer model, its charging requirement S, current battery state of charge SOC, battery capacity C _max The relationship of (2) can be expressed as: s= (1-SOC) C _max The method comprises the steps of carrying out a first treatment on the surface of the Wherein: SOC is expressed as current battery level C and battery capacity C _max Is a ratio of (2). the relationship between SOC and charge/discharge power P at time t can be expressed as:in order to ensure the safety of the battery during the charge and discharge process, the charge and discharge power P must be within the limit of the charge and discharge power of the battery: p (P) _dismax ≤P≤P _char.max The method comprises the steps of carrying out a first treatment on the surface of the Wherein P is _dismax Expressed as maximum discharge power, P, that can be sustained by safe operation of the battery _char.max Expressed as the maximum charge power that the battery can withstand for safe operation.

In the present embodiment, in order to simplify the calculation process and avoid the integral operation, the charge and discharge period is generally discretized, the entire large period is divided into N small periods, the time interval of each small period takes Δt, and the charge and discharge power P is considered to be within the period _t Remaining unchanged, it can be expressed as: During charging, the relation between the reactive power and the active power required by the electric automobile is as follows: />Where λ is the power factor of the charging pile. It is approximately considered that the charging power of each electric automobile is constant and continuously adjustable in each optimized period.

In this embodiment, the constructing an equivalent adjustable characteristic model of each electric automobile according to the operation data of each electric automobile specifically includes:

wherein d _EV (t) is the electric quantity injected by the electric automobile at the moment t in the operation data of each electric automobile, and d _min (t) is the lower limit of the energy track at the time t in the running data of each electric automobile, d _max (t) is the upper limit of the energy track at the time t in the running data of each electric automobile, eta is the charging efficiency in the running data of each electric automobile, and P _EV(k) Charging power of the electric automobile in the kth scheduling period in the operation data of each electric automobile, wherein Deltat is the time interval of the scheduling period in the operation data of each electric automobile, and t is _in The electric automobile access time t is the electric automobile access time in the operation data of each electric automobile _out D, the leaving time of the electric automobile in the operation data of each electric automobile is D _expect P is the charging requirement of the electric automobile in the operation data of each electric automobile _EV,max The rated charging power upper limit, P of the battery of the electric automobile in the operation data of each electric automobile _EV (t) is the charging power of the electric automobile at the time t in the operation data of each electric automobile, P _max (t) is the maximum charging power of the electric automobile limited by the energy boundary constraint at the time t in the operation data of each electric automobile, P _min (t) is the energy edge of the electric automobile at the time t in the running data of each electric automobileThe boundary constrains the minimum charge power.

In this embodiment, the aggregating the equivalent adjustable characteristic models of all electric vehicles to obtain a second class energy storage model of homogeneous aggregation of electric vehicles specifically includes: obtaining the adjustable capacity of each electric automobile according to the maximum charging power and the minimum charging power of the electric automobile in the equivalent adjustable characteristic model of each electric automobile, which are limited by the energy boundary constraint; and adding the adjustable capacity of each electric automobile to obtain a second type energy storage model of the electric automobile homogeneous aggregation.

S104: and constructing a distribution network distributed resource aggregation model considering physical network constraint according to the first type energy storage model and the second type energy storage model in each node, and solving to obtain an adjustable boundary maximum value in a main network scheduling interval.

In this embodiment, please refer to fig. 2, which is a flowchart illustrating steps of constructing a distribution network aggregation boundary in a distribution network aggregation boundary-based distributed resource power allocation method according to an embodiment of the present invention, including steps S201-S204, each step is specifically as follows.

S201: and acquiring a distribution network topological structure, and constructing a distribution network distributed resource aggregation model by taking the maximum value of an adjustable boundary in a main network scheduling interval as an objective function according to the distribution network topological structure.

S202: and matching the boundary characteristics of the distributed resources in the operation data of the first type of energy storage model and the operation data of the second type of energy storage model with the boundary characteristics of the distributed resources in the distribution network distributed resource aggregation model.

In this embodiment, the upper-bound resource operation characteristics of the distributed resource boundary characteristics in the distributed resource aggregation model of the distribution network are specifically:

wherein P is _A,jt,LO,C Charging power of the resource cluster at the node j in the t-th stage under the lower bound operation; p (P) _A,jt,LO,D Discharging power of the resource cluster at the node j in the t-th stage under the lower bound operation; e (E) _A,jt,LO The equivalent electricity storage capacity of the aggregation resource cluster at the node j under the lower bound operation is obtained; z is Z _A,jt,LO,C The state of charge of stage t of the aggregated resource cluster for node j under lower bound operation ；Z _A,jt,LO,D The discharge state of stage t of the aggregate resource cluster of node j is the lower bound operation.

S203: and determining physical network constraint conditions of the distribution network distributed resource aggregation model through linearized Distflow constraint according to active power, reactive power, active load, reactive load, active output, reactive output, active power flow and reactive power flow in the operation data of the first type energy storage model and the operation data of the second type energy storage model.

It should be noted that the linearized Distflow constraint has a more compact tidal current expression, and the problem that the second order cone relaxation depends on minimizing the net loss does not exist.

In this embodiment, the upper bound operation condition constraint condition in the physical network constraint conditions is specifically:

wherein { S } is the upper layer supply node set; { A } is a node set containing a resource cluster; v is a set formed by all nodes; p (P) _S,it,UP Active power of the jth power supply node in the t stage under the upper bound operation is obtained; q (Q) _S,jt,UP Reactive power of a jth power supply node in a t stage under upper bound operation is obtained; p (P) _L,jt The active load of the node j in the phase t is as follows; q (Q) _L,jt Reactive load of the node j in the phase t; p (P) _A,jt,UP Active power output for aggregate resources under upper bound operation; q (Q) _A,jt,UP Reactive power output of the aggregate resource under the upper-bound operation; h _ijt,UP Active power flow from node i to node j for upper bound lower run phase t; g _ijt,UP Reactive power flow from node i to node j for upper bound run lower phase t; θ _j The power factor of the j node load; u (U) _jt,UP The voltage amplitude of the node j under the upper bound operation is shown; r is R _ij The resistance of branch ij; x is X _ij Reactance for branch ij;a reference voltage amplitude value for an upper layer power supply node j;a minimum active power limit on supply node j; />Limiting the maximum active power at the supply node j; />The minimum reactive limit for power supply node j; />For power supply node jMaximum reactive limit; />Is the lower bound of the voltage value at node j; />Is the upper bound of the voltage value at node j; pi (j) is the node set of the inflow node j; delta (j) is the node set of the outflow node j; />Is a branch flow limit;

/>

S204: and solving an objective function in the distribution network distributed resource aggregation model by combining the distributed resource boundary characteristics and the physical network constraint conditions in the distribution network distributed resource aggregation model to obtain an adjustable boundary maximum value in a main network scheduling interval.

It should be noted that, before entering the scheduling time, the upper bound and the lower bound operation modes should be kept consistent, that is, the following constraints are present:

/>

wherein T is _Sta Represented as a scheduling period start time.

In this embodiment, the specific formula of the adjustable boundary maximum value in the main network scheduling interval is:

As a preferred solution, please refer to fig. 3, which is a schematic diagram of distributed resource aggregation of each node in a distributed resource power allocation method based on a distribution network aggregation boundary according to an embodiment of the present invention. Therefore, in the embodiment, the temperature control load monomer model and the electric vehicle monomer model of the node in the power distribution network are acquired at first, and are polymerized to form a temperature control load cluster polymerization model and an electric vehicle load cluster polymerization model respectively. And then, acquiring topological structure parameters of the power distribution network and preset regulation time, and constructing a distribution network hierarchical aggregation boundary.

S105: and distributing the power in the first type of energy storage model by an adjustable time margin sequencing method according to the maximum value of the adjustable boundary in the main network scheduling interval to obtain a first distribution result, and sending the first distribution result to each node of the power distribution network so that each node of the power distribution network adjusts the power corresponding to each temperature control load in each node of the power distribution network according to the first distribution result.

It should be noted that, for the temperature control load, in order to meet the comfort requirement of the user, the time of participating in the regulation of the power grid needs to be limited, and the remaining time is described as an adjustable time margin, where the adjustable time margin is equal to the time of raising or lowering the temperature to the comfort limit value after participating in the regulation of the power grid. Because the working period of the temperature control load is shorter, when the temperature control load responds to the upper power instruction, the outdoor temperature is generally considered to be basically unchanged, and the adjustable time margin of the temperature control load can be specifically calculated according to the temperature change function of the temperature control load.

In this embodiment, when the temperature controlled load responds to the power up command, its adjustable time margin can be described as follows:

wherein t is _up Power up time margin, θ, expressed as temperature controlled load _lo Expressed as user comfort requirement lower limit temperature, θ _o Expressed as outdoor temperature at the current command time, θ _in Expressed as the room temperature at the current command moment.

In this embodiment, when the temperature controlled load responds to the power down command, the adjustable time margin thereof can be described as follows:

wherein t is _down Power up time margin, θ, expressed as temperature controlled load _up Expressed as the upper temperature of the comfort requirement of the user.

In this embodiment, the allocating power in the first type of energy storage model according to the maximum value of the adjustable boundary in the scheduling interval of the main network by using an adjustable time margin ordering method to obtain a first allocation result, and sending the first allocation result to each node of the power distribution network, so that each node of the power distribution network adjusts power corresponding to each temperature control load in each node of the power distribution network according to the first allocation result, which specifically includes: transmitting a response power control instruction to the first type energy storage model according to the adjustable boundary maximum value in the main network scheduling interval; calculating adjustable time margin of each temperature control node in the first type energy storage model according to the response power control instruction and the temperature change function of the temperature control load; sequencing the adjustable time margin, and sequentially distributing the power of each node temperature control load in the first type energy storage model according to the sequenced adjustable time margin to obtain a first distribution result; and sending the first distribution result to each node of the power distribution network, so that each node of the power distribution network adjusts the power corresponding to each temperature control load in each node of the power distribution network according to the first distribution result.

S106: and distributing the power in the second type energy storage model through a redistribution consistency algorithm according to the maximum value of the adjustable boundary in the main network scheduling interval to obtain a second distribution result, and sending the second distribution result to each electric automobile in each node of the power distribution network so that the second distribution result adjusts the power corresponding to each electric automobile.

Since the battery capacity of each vehicle in the group is different from the initial SOC, if the total power is simply distributed equally to each EV, the EV having a smaller individual charge demand is at P _i,t Early battery fill is likely to occur at > 0; EV at P with greater individual charging demand _i,t It is easy for the battery to reach the minimum charge limit when < 0. The above situation may cause that each EV under the cluster cannot fully respond to the upper-layer command request, so that the actual charge and discharge power and the command generate a larger offset, and the EV under the cluster cannot reach the desired electric quantity until the charging process is completed. The present patent therefore proposes a cluster power redistribution algorithm based on "scalability consistency". Wherein the tunable capability factor λ may be defined as follows:

And needs to meet

Wherein,the charge and discharge power of m vehicles in the t period is obtained through a reassignment consistency algorithm; />The battery electric quantity of the mth trolley before the t optimization period begins; SOC (State of Charge) _max The maximum electric quantity of the battery is; SOC (State of Charge) _min A battery minimum power limit; c (C) _max,m Is the characteristic of m vehicles; p (P) _i,t And the sum of the charge and discharge power of the m vehicles at the t period is obtained through the reassignment consistency algorithm. SOC (State of Charge) _max Usually 1 is taken. Considering the problem of loss of battery life due to deep charge and discharge, SOC _min Taking 0.2. In particular, when SOC is equal to SOC _max And P is _i,t Above 0, the case is->Equal to 0, when SOC is equal to SOC _min And P is _i,t When less than 0, < >>Equal to 0. In calculating +.>Then, safety verification and correction are needed to obtain the true charge and discharge power +.>

/>

It is worth mentioning that each clusterThe tunable capacity factor lambda of (c) is varied during the allocation of each period, the value of which is determined by the characteristics (c _max ) The State (SOC) and the total cluster power are determined together. Then calculating to obtain the total charge and discharge power instruction P of each cluster at each moment _i,t And then, the adjustable capacity factor lambda of the same period of the automobile in the same cluster after the total power distribution is consistent and is used as a standard of correct distribution. In the algorithm mode, the automobile with larger SOC is at P _i,t >A larger power share is assumed at 0; automobile with smaller SOC at P _i,t <A larger power share is assumed at 0. When the charge and discharge process is carried out to a certain extent, the SOCs of the EVs in the cluster can effectively tend to be consistent, and the expected electric quantity is reached before the expected charge completion time is finished.

In this embodiment, the allocating power in the second class energy storage model according to the maximum value of the adjustable boundary in the main network scheduling interval by using a reassignment consistency algorithm to obtain a second allocation result, and sending the second allocation result to each node of the power distribution network, so that each node of the power distribution network adjusts the power corresponding to each electric vehicle in each node of the power distribution network according to the second allocation result, which specifically includes: transmitting a charge and discharge power control instruction to the second type energy storage model according to the adjustable boundary maximum value in the main network scheduling interval; calculating an adjustable capacity factor according to the battery electric quantity of the electric vehicle and the charging and discharging power of the electric vehicle in the second type of energy storage model; distributing the power of each node electric vehicle in the second type energy storage model through a redistribution consistency algorithm according to the charge and discharge power control instruction and the adjustable capacity factor to obtain a second distribution result; and sending the second distribution result to each node of the power distribution network, so that each node of the power distribution network adjusts the power corresponding to each electric vehicle in each node of the power distribution network according to the second distribution result.

The method designs an equivalent energy storage aggregation model aiming at two loads of a temperature control load and an electric vehicle in the current distribution network, builds a distribution network distributed resource aggregation model considering physical network constraint by considering physical topology constraint, equates a class energy storage polymer model of the temperature control and the electric vehicle load under each node to the description of the class energy storage model in the distribution network distributed resource aggregation model, and finally obtains the aggregation boundary of the whole distribution network by optimizing and solving the model. The layered aggregation framework can firstly realize the aggregation of homogeneous resources in a small range, and is applied to the large-range resource aggregation through the expression of energy storage, so that the aggregation of the whole distribution network is realized, and an effective regulation and control boundary reference is provided for power grid dispatching. According to the effective regulation boundary reference, the temperature control load power and the electric automobile power under each node are redistributed through an adjustable time margin sequencing method and a redistribution consistency algorithm, and the execution of the regulation instruction is ensured while the effective aggregation of the distributed resource adjustable capacity is ensured.

The foregoing embodiments have been provided for the purpose of illustrating the general principles of the present invention, and are not to be construed as limiting the scope of the invention. It should be noted that any modifications, equivalent substitutions, improvements, etc. made by those skilled in the art without departing from the spirit and principles of the present invention are intended to be included in the scope of the present invention.

Claims

1. The distributed resource power distribution method based on the distribution network aggregation boundary is characterized by comprising the following steps:

according to the maximum value of the adjustable boundary in the main network scheduling interval, distributing the power in the first type of energy storage model by an adjustable time margin sequencing method to obtain a first distribution result, and sending the first distribution result to each node of the power distribution network so that each node of the power distribution network adjusts the power corresponding to each temperature control load in each node of the power distribution network according to the first distribution result;

and distributing the power in the second type energy storage model through a reassignment consistency algorithm according to the maximum value of the adjustable boundary in the main network dispatching interval to obtain a second distribution result, and sending the second distribution result to each node of the power distribution network so that each node of the power distribution network can adjust the power corresponding to each electric automobile in each node of the power distribution network according to the second distribution result.

2. The method for distributing power of distributed resources based on aggregation boundary of distribution network according to claim 1, wherein said constructing an equivalent adjustable characteristic model of each temperature control load according to the operation data of each temperature control load specifically comprises:

x(t+1)＝κx(t)+γP _cd (t)

-1≤x(t)≤1

3. The method for distributing power of distributed resources based on distribution network aggregation boundary as set forth in claim 2, wherein the aggregating the equivalent adjustable characteristic models of all temperature-controlled loads to obtain a first type of energy storage model of temperature-controlled load homogeneous aggregation specifically includes:

SOC _AC (t+1)＝αSOC _AC (t)+P _AC (t)

4. The distributed resource power allocation method based on the distribution network aggregation boundary as claimed in claim 1, wherein the constructing an equivalent adjustable characteristic model of each electric automobile according to the operation data of each electric automobile specifically comprises:

wherein d _EV (t) is the electric quantity injected by the electric automobile at the moment t in the operation data of each electric automobile, and d _min (t) is the lower limit of the energy track at the time t in the running data of each electric automobile, d _max (t) is the upper limit of the energy track at the time t in the running data of each electric automobile, eta is the charging efficiency in the running data of each electric automobile, and P _EV(k) Charging power of the electric automobile in the kth scheduling period in the operation data of each electric automobile, wherein Deltat is the time interval of the scheduling period in the operation data of each electric automobile, and t is _in The electric automobile access time t is the electric automobile access time in the operation data of each electric automobile _out D, the leaving time of the electric automobile in the operation data of each electric automobile is D _expect P is the charging requirement of the electric automobile in the operation data of each electric automobile _EV,max The battery capacity of the electric automobile in the operation data of each electric automobileUpper limit of charging power, P _EV (t) is the charging power of the electric automobile at the time t in the operation data of each electric automobile, P _max (y) is the maximum charging power of the electric automobile limited by the energy boundary constraint at the time t in the operation data of each electric automobile, P _min And (t) the minimum charging power of the electric automobile limited by the energy boundary constraint at the time t in the operation data of each electric automobile.

5. The method for distributing power of distributed resources based on distribution network aggregation boundary as set forth in claim 4, wherein said aggregating equivalent adjustable characteristic models of all electric vehicles to obtain a second type of energy storage model of electric vehicle homogeneous aggregation specifically includes:

6. The method for distributing power of distributed resources based on distribution network aggregation boundary as set forth in claim 1, wherein said constructing a distribution network distributed resource aggregation model considering physical network constraint according to the first class energy storage model and the second class energy storage model in each node, and solving to obtain an adjustable boundary maximum value in a main network scheduling interval specifically includes:

7. The distributed resource power allocation method based on the distribution network aggregation boundary as claimed in claim 6, wherein the adjustable boundary maximum value in the main network scheduling interval is as follows:

8. The distributed resource power allocation method based on the distribution network aggregation boundary as claimed in claim 6, wherein the upper-bound resource operation characteristic of the distributed resource boundary characteristic in the distribution network distributed resource aggregation model is specifically:

wherein P is _A,jt,UP,C The charging power of the resource cluster at the node j in the t stage under the upper bound operation is obtained; p (P) _A,jt,UP,D Discharging power of the resource cluster at the node j in the t stage under the upper bound operation; eta A _, jt is the equivalent charge and discharge efficiency of the resource cluster; e (E) _A,jt,UP The equivalent electricity storage capacity of the aggregation resource cluster at the node j under the upper bound operation is obtained; e (E) _A,j,initial An equivalent electricity storage capacity is obtained for the aggregation resource cluster of the node j initially;maximum charge-discharge power limit, Z, for aggregated resource cluster of node j _A,jt,UP,C The charging state of the aggregation resource cluster of the node j in the upper bound operation is the charging state of the aggregation resource cluster of the node j in the upper bound operation; z is Z _A,jt,UP,D The discharge state of the stage t of the aggregate resource cluster of the node j under the upper-bound operation; c (C) _A,j The equivalent capacity of the aggregate resource cluster for node j; />Minimum constraint for aggregate resource cluster for node j; />Maximum constraint of aggregate resource cluster for node j;

9. The distributed resource power allocation method based on the distribution network aggregation boundary as claimed in claim 6, wherein the upper bound operation condition constraint condition in the physical network constraint condition is specifically:

wherein { S } is the upper layer supply node set; { A } is a node set containing a resource cluster; v is a set formed by all nodes; p (P) _S,it,UP Active power of the jth power supply node in the t stage under the upper bound operation is obtained; q (Q) _S,jt,UP Reactive power of a jth power supply node in a t stage under upper bound operation is obtained; p (P) _L,jt The active load of the node j in the phase t is as follows; q (Q) _L,jt Reactive load of the node j in the phase t; p (P) _A,jt,UP Active power output for aggregate resources under upper bound operation; q (Q) _A,jt,UP Reactive power output of the aggregate resource under the upper-bound operation; h _ijt,UP Active power flow from node i to node j for upper bound lower run phase t; g _ijt,UP Reactive power flow from node i to node j for upper bound run lower phase t; θ _j The power factor of the j node load; u (U) _jt,UP The voltage amplitude of the node j under the upper bound operation is shown; r is R _ij The resistance of branch ij; x is X _ij Reactance for branch ij;a reference voltage amplitude value for an upper layer power supply node j; />A minimum active power limit on supply node j; />Limiting the maximum active power at the supply node j; / >The minimum reactive limit for power supply node j; />The maximum reactive limit of the power supply node j; />Is the lower bound of the voltage value at node j; />Is the upper bound of the voltage value at node j; pi (j) is the node set of the inflow node j; delta (j) is the node set of the outflow node j; />Is a branch flow limit;

10. The method for distributing power of distributed resources based on a distribution network aggregation boundary according to claim 1, wherein the distributing power in the first type of energy storage model according to the maximum value of the adjustable boundary in the main network scheduling interval by an adjustable time margin sequencing method to obtain a first distribution result, and sending the first distribution result to each node of a power distribution network, so that each node of the power distribution network adjusts power corresponding to each temperature control load in each node of the power distribution network according to the first distribution result, specifically comprising:

11. The distributed resource power distribution method based on the distribution network aggregation boundary according to claim 1, wherein the distributing the power in the second type energy storage model according to the adjustable boundary maximum value in the main network scheduling interval by a redistribution consistency algorithm to obtain a second distribution result, and sending the second distribution result to each node of the distribution network, so that each node of the distribution network adjusts the power corresponding to each electric automobile in each node of the distribution network according to the second distribution result, and specifically includes:

and sending the second distribution result to each node of the power distribution network, so that each node of the power distribution network adjusts the power corresponding to each electric vehicle in each node of the power distribution network according to the second distribution result.

12. The method for distributing power of distributed resources based on aggregation boundary of distribution network as set forth in claim 10, wherein the adjustable capacity factor is specifically expressed as the following formula: