CN116316735A

CN116316735A - Multi-time scale optimization method and device for source network storage based on interval time division

Info

Publication number: CN116316735A
Application number: CN202310308574.4A
Authority: CN
Inventors: 韩璟琳; 侯若松; 胡平; 冯喜春; 刘洋; 李洪涛
Original assignee: State Grid Corp of China SGCC; State Grid Hebei Electric Power Co Ltd; Economic and Technological Research Institute of State Grid Hebei Electric Power Co Ltd
Current assignee: State Grid Corp of China SGCC; State Grid Hebei Electric Power Co Ltd; Economic and Technological Research Institute of State Grid Hebei Electric Power Co Ltd
Priority date: 2023-03-27
Filing date: 2023-03-27
Publication date: 2023-06-23

Abstract

The application provides a source network storage multi-time scale optimization method and device based on interval time division. The method comprises the following steps: carrying out time interval division on load power prediction data and photovoltaic power prediction data fluctuation intervals in the day-ahead stage to obtain a discrete device regulation period; based on the regulation period, according to the load power prediction data and the photovoltaic power prediction data, the operation cost of the power distribution network is the minimum as a daily optimization target, and under the constraint of safety indexes, various controllable devices are coordinated and optimized, so that the optimal economic operation scheme in the daily stage is determined; based on the action planning period of the discrete equipment in the day-ahead stage, the full-horizontal scheduling method is used, the minimum running cost loss of the power distribution network in the day-ahead stage is used as the day-ahead optimization target, the day-ahead rolling prediction is carried out on the photovoltaic power prediction data and the load power prediction data, and the day-ahead stage photovoltaic active and reactive power output and the energy storage charge and discharge power are optimized in a rolling mode. The method and the device can fully utilize each controllable resource of the power distribution network and ensure safe and economic operation of the power distribution network.

Description

Multi-time scale optimization method and device for source network storage based on interval time division

Technical Field

The application relates to the technical field of power distribution network operation optimization, in particular to a source network storage multi-time scale optimization method and device based on interval time division.

Background

With devices such as distributed photovoltaic devices, energy storage devices and reactive compensation devices accessing a power distribution network, traditional power distribution networks are gradually evolving into active power distribution networks with numerous controllable resources. However, the power generated by the distributed power source has randomness, intermittence and fluctuation, and the permeability in the power distribution network is continuously improved in recent years, so that the stable operation of the power distribution network faces a great challenge.

At present, most distribution networks are provided with photovoltaic power generation and energy storage unit integrated light storage integrated machines, partial daily load power demand can be met by utilizing photovoltaic power generation in sunshine period, maximum economic benefit is obtained by utilizing low storage and high discharge of the energy storage unit based on time-sharing electricity price, and load fluctuation is stabilized by peak clipping and valley filling. When the deviation between the daily load prediction and the daily load prediction is large, the daily operation cannot realize the economic optimum, and the daily schedule cannot adapt to the daily schedule. Therefore, research on daily rolling optimization of the light storage integrated machine is particularly important, and few research is performed in this aspect nowadays. On the power distribution network side, network reconstruction, on-load voltage regulating transformers and parallel capacitor banks are also important means for operation control, and can change feeder line power flow, improve voltage distribution, balance load, reduce network loss and the like. However, the optimization models are used for carrying out optimization regulation and control on discrete equipment, and the optimization models belong to the problem of mixed integer nonlinear programming.

At present, an optimal power flow method of a power distribution network based on an optical storage integrated machine and discrete equipment has been studied to a certain extent, but certain defects and shortcomings still exist:

(1) The existing power distribution network optimization method cannot fully exert the utility of all controllable resources, has less coordinated optimization research on all-around and multiple links of 'source-network-storage', and the optimization result of model predictive control still depends on a daily reference track to a certain extent in the 'source-storage' daily stage, and when the daily actual load change and the daily predictive deviation are large, the final result still has large error, so that the economic optimum cannot be achieved, and the 'source-storage' resource waste is caused.

(2) The current time period dividing method is mostly not considering the time sequence of the load, the dividing times are also artificial to a certain extent, the current time period dividing is based on the daily prediction precision value, the uncertainty of the daily load and the photovoltaic is not considered, and the problem of time period dividing errors caused by the fluctuation of the daily load cannot be guaranteed.

Disclosure of Invention

The application provides a source network storage multi-time scale optimization method and device based on interval time division, which are used for solving the problem that full-time coordination optimization cannot be realized when a power distribution network operates in the prior art.

In a first aspect, the present application provides a method for optimizing a source network storage multi-time scale based on interval period division, including:

carrying out time interval division on load power prediction data and fluctuation intervals of photovoltaic power prediction data in the day-ahead stage to obtain a regulation and control period of discrete equipment of the power distribution network;

based on the regulation and control period of the discrete equipment of the power distribution network, according to load power prediction data and photovoltaic power prediction data, taking the minimum running cost of the power distribution network as a day-ahead optimization target, and under the constraint of safety indexes, carrying out coordinated optimization on various controllable equipment of the power distribution network, and determining an optimal economic running scheme in a day-ahead stage, wherein the optimal economic running scheme comprises an action planning period of the discrete equipment in the day-ahead stage;

and based on the action planning period of the discrete equipment in the day-ahead stage, adopting a full-horizontal scheduling method, taking the minimum running cost loss of the power distribution network in the day-ahead stage as a day-ahead optimization target, carrying out day-ahead rolling prediction on photovoltaic power prediction data and load power prediction data, and rolling and optimizing the photovoltaic active and reactive power output and the energy storage charge and discharge power in the day-ahead stage.

In a second aspect, the present application provides a source network storage multi-time scale optimization device based on interval period division, including:

The time period division module is used for dividing the time period of the fluctuation interval of the load power prediction data and the photovoltaic power prediction data in the day-ahead stage to obtain the regulation and control period of the discrete equipment of the power distribution network;

the day-ahead optimization module is used for carrying out coordinated optimization on various controllable equipment of the power distribution network under the constraint of a safety index by taking the minimum running cost of the power distribution network as a day-ahead optimization target according to load power prediction data and photovoltaic power prediction data based on the regulation and control period of the discrete equipment of the power distribution network, and determining an optimal economic running scheme of the day-ahead stage, wherein the optimal economic running scheme comprises an action planning period of the discrete equipment of the day-ahead stage;

and the intra-day rolling prediction optimization module is used for performing intra-day rolling prediction on the photovoltaic power prediction data and the load power prediction data by adopting a full-horizontal scheduling method based on the action planning period of the discrete equipment in the day-ahead stage and taking the minimum running cost loss of the power distribution network in the intra-day stage as an intra-day optimization target, and performing rolling optimization on the photovoltaic active and reactive power output and the energy storage charge and discharge power in the intra-day stage.

In a third aspect, the present application provides a terminal comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the method according to the first aspect or any one of the possible implementations of the first aspect when the computer program is executed.

In a fourth aspect, the present application provides a computer readable storage medium storing a computer program which, when executed by a processor, implements the steps of the method as described above in the first aspect or any one of the possible implementations of the first aspect.

The utility model provides a source network storage multi-time scale optimization method and device based on interval period division, which is characterized in that the regulation and control period of discrete equipment is determined by dividing the fluctuation interval period of load power prediction data and photovoltaic power prediction data in the day-ahead stage, the running cost of a power distribution network is the minimum day-ahead optimization target, the optimal economic running scheme in the day-ahead stage is determined under the constraint of safety indexes, and each controllable equipment of the power distribution network is fully utilized; the photovoltaic power prediction data and the load power prediction data are subjected to intra-day rolling prediction, and the photovoltaic active and reactive power output and the energy storage charge and discharge power in the intra-day stage are optimized in a rolling mode, so that safe and economic operation of the power distribution network is ensured; and when the intra-day stage prediction optimization is carried out, the discrete equipment executes according to the action planning period of the discrete equipment in the pre-day stage, wherein the regulation and control period of the discrete equipment is obtained by dividing the period based on the optimal fisher segmentation method of interval data, and the period division ensures that the load in the intra-day stage is still in the correct division period when the fluctuation interval fluctuates to a certain extent, so that the economy of global optimization in the intra-day stage is improved.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required for the embodiments or the description of the prior art will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

Fig. 1 is a flowchart of an implementation of a source network storage multi-time scale optimization method based on interval period division according to an embodiment of the present application;

FIG. 2 is a time scale model diagram provided by an embodiment of the present application;

fig. 3 is a topology diagram of a power distribution network provided in an embodiment of the present application;

FIG. 4 is a graph of time period division results provided by an embodiment of the present application;

FIG. 5 is a graph of the results of optimization at a day-ahead stage provided by an embodiment of the present application;

FIG. 6 is a framework diagram of a "source-web-store" collaborative optimization provided by an embodiment of the present application;

fig. 7 is a schematic structural diagram of a source network storage multi-time scale optimization device based on interval period division according to an embodiment of the present application;

fig. 8 is a schematic diagram of a terminal provided in an embodiment of the present application.

Detailed Description

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

For the purpose of making the objects, technical solutions and advantages of the present application more apparent, the following description will be made with reference to the accompanying drawings by way of specific embodiments.

Fig. 1 is a flowchart of an implementation of a source network storage multi-time scale optimization method based on interval period division according to an embodiment of the present application, which is described in detail below:

in step 101, time interval division is performed on load power prediction data and fluctuation intervals of photovoltaic power prediction data in a day-ahead stage, so as to obtain a regulation period of discrete equipment of the power distribution network.

With reference to fig. 2, the day-ahead stage in the embodiment of the present application is 24 hours a day, and the operation of the power distribution network of 24 hours in the future is optimized with 1 hour as a time interval. The load power prediction data and the photovoltaic power prediction data are 24-hour prediction data to be predicted.

Discrete devices generally refer to devices having only two states, running and stopping of the motor, switching of the two-position valve. The conventional discrete device is simple to control, and the device operation panel is opened by clicking a picture icon, and the device is opened or closed by clicking. Discrete devices in embodiments of the present application include an on-load tap changing transformer, a shunt capacitor bank and a tie switch,

in the embodiment of the application, in the optimization scheduling of the day-ahead stage, the time interval is divided according to the load power prediction data and the corresponding fluctuation interval size of the photovoltaic power prediction data of the day-ahead stage, so that the time interval is used as the regulation and control period of the discrete equipment of the power distribution network, the action times of the discrete equipment such as a switch, an on-load voltage regulating transformer, a parallel capacitor bank and the like are reduced, the solving scale can be reduced, and the operation speed is increased.

In one possible implementation, step 101 may specifically include:

the load power and the photovoltaic power in the day-ahead stage are predicted to obtain load power prediction data and photovoltaic power prediction data in the day-ahead stage, and fluctuation intervals corresponding to the load power prediction data and the photovoltaic power prediction data in the day-ahead stage are combined to obtain a net load fluctuation interval in the day-ahead stage;

and (3) performing time interval division on the net load fluctuation interval based on an optimal fisher segmentation method of interval data to obtain the regulation and control period of the discrete equipment of the power distribution network.

The optimal fisher segmentation method is to use the sum of squares of deviations to represent the degree of difference between similar samples, determine the optimal classification number through simple calculation steps and drawing, minimize the difference between similar samples, maximize the difference between the samples of each class, and check the rationality of the optimal classification number by using an F check method.

In 'source-network-storage' tide optimization, power distribution network side optimization performs optimization regulation and control on discrete equipment, belongs to the mixed integer nonlinear programming problem, and in addition, the problem of difficulty in solving is solved due to the fact that multiple controllable resources of the power distribution network are considered in cooperation, and a period is divided to serve as a regulation and control period of the discrete equipment through an optimal fisher segmentation method based on interval data, so that the solving scale is reduced.

In the embodiment of the application, firstly, load power and photovoltaic power in a day-ahead stage are predicted, load power prediction data and photovoltaic power prediction data in the day-ahead stage are determined, then the obtained prediction data are combined with the corresponding fluctuation interval, so that a net load prediction value in the day-ahead stage and the corresponding fluctuation interval are obtained, and finally, an optimal fisher segmentation method based on interval type data is adopted to divide the net load fluctuation interval in the day-ahead stage in time intervals, so that the regulation and control period of discrete equipment of the power distribution network is obtained.

In the embodiment of the application, the improved IEEE33 node is used to simulate and verify the optimal economic operation scheme in the day-ahead stage, referring to fig. 3, which is a topological diagram of the power distribution network in the embodiment of the application, the voltage regulation range of the secondary side voltage of the on-load tap changer is 1.03 mu u (0.95-1.05), and the step size is 0.0125 mu u; reference value selection S _B ＝10MVA、U _B =12.66 Kv; the nodes 4 and 19 are simply connected into photovoltaic power generation, the

nodes

13, 23 and 31 are connected into a photovoltaic and energy storage integrated light storage integrated machine, and the

nodes

17, 24 and 32 are connected into a parallel capacitor bank.

Referring to fig. 4, in the embodiment of the present application, simulation calculation is performed on the basis of fig. 3, and the payload fluctuation interval is divided into five segments, which are respectively 1:00-8:00, 9:00-15:00, 16:00-17:00, 18:00-21:00, and 22:00-24:00, so that it can be ensured to a certain extent that the daily payload is still in the correct division period when the predicted interval fluctuates.

In one possible implementation manner, the time interval division is performed on the payload fluctuation interval based on an optimal fisher segmentation method of interval type data to obtain a regulation period of discrete equipment of the power distribution network, which may include:

calculating the interval number distance of the net load fluctuation interval through a first formula;

according to the interval number distance, time interval division is carried out on the net load fluctuation interval, and the regulation and control period of the discrete equipment of the power distribution network is obtained;

wherein, the first formula is:

wherein E (u) _d ,u _d+1 -1) interval number distance for the D-th segment, u _d For the d-th period, u _d+1 For the (d+1) th period, n is the number of nodes, j is the j-th node,

load predictive value for the d-th period, < >>

A fluctuation interval size of d-th period, < > for the period of time>

Predicting the mean value for the load of node j, +.>

The predicted mean fluctuation interval size of the node j.

Wherein, for the optimal fisher segmentation method based on interval data: assuming that the full period net power interval is represented by matrix a, a= [ X ₁ ,X ₂ ,…,X _T ] ^T Wherein X is _m ＝[x _m1 ,x _m2 ,…,x _mn ]A load interval set of m time periods, n is the number of nodes,

and->

The load predicted value and the fluctuation interval size are respectively. When dividing the period, the period included in the D segment is defined as { u } _d ,u _d +1,…,u _d+1 -1}，u _k For the first period of the D-th segment, the predicted load mean set corresponding to the D-th segment is V _d ＝[v _d1 ,v _d2 ,…,v _dn ]，/>

And->

And predicting the average value and the fluctuation interval size of the average value for the load of the node j of the D-th segment. The specific calculation formula is shown in formula (1):

wherein, the liquid crystal display device comprises a liquid crystal display device,

for load prediction value, +.>

Is the size of the fluctuation interval.

Wherein, matrix A is the full period net power interval and segment D is the division of the period.

According to the method, time interval division is carried out on the net load fluctuation interval based on the optimal fisher segmentation method of interval type data, the similarity degree of interval data in the section is described by the interval number distance, then the interval data distance in the section of the D-th segment is calculated by a first formula, and according to the calculated interval number distance, the time interval division is carried out on the net load fluctuation interval, so that the regulation and control period of discrete equipment of the power distribution network is obtained. Wherein the weighting factor lambda is used to control the influence of the interval size on the clusters.

In one possible implementation manner, the time interval division is performed on the payload fluctuation interval according to the interval number distance to obtain a regulation period of discrete equipment of the power distribution network, which may include:

determining an optimal time interval division scheme of a net load fluctuation interval through an objective function to obtain a regulation and control period of discrete equipment of the power distribution network, wherein the objective function is as follows:

wherein L [ ] is an objective function, b (T, D) is an optimal time period division scheme divided by D times, D is the time period division times, and T is 24 hours.

According to the embodiment of the application, the similarity degree of the interval data in the section is described by the interval number distance, the objective function is defined, the minimum division mode of the objective function is used as an optimal segmentation scheme, and the optimal segmentation scheme is used as the regulation and control period of the discrete equipment of the power distribution network.

The recurrence formula of the objective function is as follows:

L[b(T,2)]＝min{E(1,a-1)+E(a,n)},a＝2,3,…,T (2)

L[b(T,D)]＝min{L[b(a-1,D-1)]+E(a,T)},a＝D,D+1,…,T (3)

the specific steps of recursion ordered clustering period division include:

step 1: the optimal 2 partitions b (a, 2), a=2, 3, …, T for the first a time payload samples are calculated according to equation (2), respectively.

Step 2: the first a time payload samples are calculated as optimal 3 partitions b (a, 3) (a=3, …, T), optimal 4 partitions b (a, 4) (a=4, …, T), …, optimal D-1 partitions b (a, D-1) (a=d-1, …, T) according to equation (3), respectively.

Step 3: and determining the optimal segmentation points of the T samples.

First, the D-th division point u is determined _d So that it satisfies the following conditions:

L[b(T,D)]＝min{L[b(u _d -1,D-1)]+E(u _d ,T)} (4)

then, find the D-1 st partition point u _d-1 So that it satisfies the following conditions:

L[b(u _d -1,D-1)]＝min{L[b(u _d-1 -1,D-2)]+E(u _d-1 ,u _d -1)｝ (5)

similarly, an optimal time-division scheme can be obtained.

Step 4: the optimal segmentation number is determined, in general, the segmentation number D is increased, the objective function L and the change rate thereof are reduced, and in order to consider the similarity of data in the segments and the action times of the slow-action discrete device, the segmentation number corresponding to the inflection point of the objective function trend graph is defined as the optimal segmentation number by referring to an inflection point method, wherein the inflection point method belongs to the prior art in the embodiment of the present application, and is not described in detail herein.

In step 102, based on the regulation and control period of the discrete equipment of the power distribution network, the operation cost of the power distribution network is taken as a daily optimization target according to the load power prediction data and the photovoltaic power prediction data, and under the constraint of safety indexes, various controllable equipment of the power distribution network is subjected to coordinated optimization, and an optimal economic operation scheme of the daily stage is determined, wherein the optimal economic operation scheme comprises an action planning period of the discrete equipment of the daily stage.

In the embodiment of the present application, based on the regulation period of the discrete device of the power distribution network in step 101, and according to the complete prediction information, that is, the load power prediction data and the photovoltaic power prediction data, which are one day in advance, the running cost of the power distribution network is minimum as a daily optimization target, and under the condition that all safety index constraints are satisfied, the coordinated optimization of all-around and multiple links of each controllable device of the source-network-storage is performed, so as to obtain the optimal economic running scheme of one day in the future, that is, the optimal economic running scheme of the daily stage, wherein the optimal economic running scheme includes the action planning period of the discrete device of the daily stage.

Wherein the control variables include: photovoltaic active/reactive power, tap position of on-load voltage regulating transformer, number of switching groups of parallel capacitor bank, switching state of tie switch and charging and discharging power of optical storage integrated machine.

The operation cost of the power distribution network mainly comprises electricity purchasing cost, network loss cost, photovoltaic active power reduction cost, discrete equipment action cost and operation cost of the optical storage integrated machine.

The daily optimization targets are as follows:

wherein f is the running cost of the power distribution network, deltaT is the time length of the day-ahead period, i is node i, j is node j,

for the unit purchase of electricity for period t, +.>

Interaction power between the main network and the distribution network for period t, < >>

Power loss of distribution network for period t, c _PV For photovoltaic power generation benefits of photovoltaics, +.>

The reduction amount of active power of the photovoltaic is D, the number of divided time periods is c _OLTC Action cost for single-gear adjustment of shaft head, +.>

For the tap position of the on-load tap changer on the d-th divided-period branch ij,

tap position, c of on-load tap-changing transformer on the (d-1) th divided period branch ij _CB Action cost for single-bank switching of capacitors, < >>

The number of capacitor input groups for the d-th divided period node j, is +>

The number of capacitor input groups for the (d-1) th divided period node j, c _S For the action cost of single opening and closing of the switch, < >>

Switch state of the feeder ij for the d-th divided period,/, for>

For the switching state of the d-1 th divided period feeder ij, N _ESS For storing energy unit number c _ESS Invoking energy storage unit price for the optical storage integrated machine, < > >

Charging power for energy storage unit t period in k-ray energy storage integrated machine, < >>

And discharging power in a period of t of an energy storage unit in the k-ray energy storage integrated machine.

The embodiment of the application also comprises a day-ahead optimization constraint, mainly comprising a power flow equation constraint, a safe operation constraint, a photovoltaic active reactive power constraint, a photovoltaic storage integrated machine charge and discharge power constraint, a discrete equipment action constraint and a system active fluctuation constraint of the distribution network.

In order to analyze whether the optimal economic operation scheme of the embodiment of the application in the day-ahead stage is reasonable, four scenes are simulated, specifically referring to table 1, wherein the discrete equipment and the charge and discharge of the optical storage integrated machine are not considered in scene 1, the discrete equipment is only considered in scene 2, the charge and discharge of the optical storage integrated machine is only considered in scene 3, and the day-ahead optimization constraint considered in the embodiment of the application is scene 4. In the scene 1, the photovoltaic output is small in the period of 18-20, the load demand is high, the low-voltage out-of-limit condition occurs in part of nodes, and the condition does not occur in other scenes. Meanwhile, the scene 4 is optimized independently by 'source-network-storage' coordination, the effect is superior to that of discrete equipment such as network reconstruction and an optical storage integrated machine, the global target cost is reduced by 5.34%, and the network loss cost is also greatly reduced.

TABLE 1 comparison of simulation results for economic run plans at different pre-day stages

As can be seen from Table 1, the examples of the present application offer significant advantages over "source", "net", "reservoir" optimization alone. The time interval division provided by the method can effectively reduce the action times of the discrete equipment and accelerate model calculation.

In step 103, based on the action planning period of the discrete equipment in the intra-day stage, adopting a full-horizontal scheduling method, taking the minimum running cost loss of the power distribution network in the intra-day stage as an intra-day optimization target, performing intra-day rolling prediction on the photovoltaic power prediction data and the load power prediction data, and rolling and optimizing the photovoltaic active and reactive power output and the energy storage charge and discharge power in the intra-day stage.

In the embodiment of the application, in the intra-day stage, the action plans of the discrete devices such as the on-load voltage regulating transformer, the parallel capacitor bank and the tie switch are executed according to the action plan time period of the discrete devices in the pre-day stage in the step 102, a full-level scheduling method is adopted, the running cost loss of the power distribution network in the intra-day stage is used as an intra-day optimization target, intra-day rolling prediction is carried out on photovoltaic power prediction data and load power prediction data, and the photovoltaic active and reactive power output and energy storage charge and discharge power in the intra-day stage are optimized in a rolling mode.

In one possible implementation, step 103 may specifically include:

According to the action planning period of the discrete equipment in the day-ahead stage, the running cost loss of the power distribution network in the day-ahead stage is taken as a day-ahead optimization target, the preset optimization time granularity is used as a rolling window to slide in the day-ahead stage, the photovoltaic power prediction data and the load power prediction data in each rolling window are predicted and optimized, and the photovoltaic active and reactive power and the energy storage charge and discharge power in the day-ahead stage are output.

In the embodiment of the application, the action plan of the discrete equipment is executed according to the optimal economic operation scheme in the day-ahead stage, the daily rolling optimization is performed to ensure global daily economy, a full-level scheduling method is adopted, the running cost loss of the distribution network in the residual time in the day is minimum as a daily optimization target, the rolling optimization is performed, the preset optimization time granularity is set as a rolling window, the photovoltaic active reactive power and the energy storage charge-discharge power from the current moment to the moment 24 in the day are predicted, the photovoltaic active reduction of the quick response equipment is performed, the reactive power and the charge-discharge power of the optical storage integrated machine are used as control variables, the regulation instruction in the control time domain is acquired, the execution is started in the 1 st period, the prediction window is moved backwards at intervals of the preset optimization time granularity, and the rolling optimization is repeated. The preset optimization time granularity can be set to 1 hour or 0.5 hour, and the setting of the preset optimization time granularity is not limited in the embodiment of the application.

And the operation cost of the power distribution network in the daily period comprises the main network electricity purchasing cost, the network loss cost, the photovoltaic active power reduction cost and the optical storage integrated machine operation cost from the current moment to the 24 moment, and the daily optimization target is that the sum of the main network electricity purchasing cost, the network loss cost, the photovoltaic active power reduction cost and the optical storage integrated machine operation cost from the current moment to the 24 moment is minimum. The daily optimization targets are as follows:

wherein t is ₀ For the current time, deltat is the time length of the time period in the day, N _ESS For the number of energy storage units,

time-of-use electricity price at g time, +.>

For g time of interaction power of main network and distribution network, < >>

The power loss of the distribution network at the moment g is c _PV For photovoltaic power generation benefits of photovoltaics, +.>

The reduction amount of active power of photovoltaic, c _ESS The energy storage unit price is called for the optical storage integrated machine,

charging power at time g for energy storage unit in k-ray energy storage integrated machine, < >>

And discharging power at the moment of an energy storage unit g in the k-ray energy storage integrated machine.

The on-load tap changers, parallel capacitor banks, and tie-line switches in the intra-day phase discrete device actions and demand response load schemes are determined by the best economic operating scheme in the pre-day phase, and the constraints mainly considered in the intra-day phase include: and the power flow equation constraint, the safe operation constraint, the photovoltaic active reactive power constraint, the energy storage active processing constraint and the like of the power distribution network.

Referring to fig. 5, the embodiment of the application is based on the time-of-use electricity price, the optical storage integrated machine obtains the maximum economic benefit by using low electricity storage and high discharge, and the photovoltaic is more than the user load in the time of 10-15, and because the time is the time of time-of-use electricity price and high price, the redundant photovoltaic output can obtain higher income to the main network for selling electricity, the energy storage is performed in the time of time-of-use electricity price and low price, the photovoltaic output is lower than the load in the time of 16-22 time, and the optical storage and discharge can reduce the electricity purchase quantity of the power distribution network to the main network when the time-of-use electricity price is high price at the moment, so that the power distribution network is more economical.

Referring to table 2, for comparison of the results of the daily rolling optimization simulation in the embodiment of the present application with the results of the execution of the optimal economic operation scheme in the day-ahead stage, the load and photovoltaic fluctuation in the day-ahead stage were increased by the results of the execution of the optimal economic operation scheme in the day-ahead stage, but the daily rolling optimization network loss in the embodiment of the present application is less, and the global objective optimization is reduced by 2.51%.

TABLE 2 comparison of the results of the rolling optimization simulation in the day with the results of the execution of the best economic operation scheme in the day-ahead stage

The framework chart of the "source-network-storage" collaborative optimization in the embodiment of the present application may refer to fig. 6, and a specific implementation process is as follows:

Step one: global optimization at the day-ahead stage.

(1) The optimal fisher segmentation method based on interval data is used for carrying out time interval division on the fluctuation interval size corresponding to the load power prediction data and the photovoltaic power prediction data in the day-ahead stage, and the time interval is used as the regulation and control period of discrete equipment, so that the action times of the discrete equipment such as a switch, an on-load voltage regulating transformer, a parallel capacitor bank and the like are reduced.

(2) Based on the step (1), according to photovoltaic power prediction data, load power prediction data and time-of-use electricity price, the minimum running cost of the distribution network is used as a daily optimization target, and under the constraint of each safety index, all-dimensional and multi-link coordination optimization of each controllable device in the source-network-storage is carried out, so that an optimal economic running scheme in a daily stage is obtained.

Step two: and (5) rolling and optimizing in the day.

(1) In the daytime, the action plan of the on-load voltage regulating transformer, the parallel capacitor bank, the tie switch and other discrete equipment is executed according to the optimal economic operation scheme in the daytime.

(2) And adopting a full-horizontal scheduling method, taking the minimum running cost loss of the power distribution network in the remaining time of the daily stage as a daily optimization target, carrying out daily rolling prediction on the photovoltaic power prediction data and the load power prediction data, and rolling and optimizing the photovoltaic active and reactive power output and the energy storage charging and discharging power of the daily stage to obtain a rolling and optimizing scheme of the daily stage.

The utility model provides a source network storage multi-time scale optimization method based on interval period division, which is characterized in that the regulation and control period of discrete equipment is determined by dividing the fluctuation interval period of load power prediction data and photovoltaic power prediction data in the day-ahead stage, the running cost of a power distribution network is the minimum day-ahead optimization target, the optimal economic running scheme in the day-ahead stage is determined under the constraint of safety indexes, and each controllable equipment of the power distribution network is fully utilized; the photovoltaic power prediction data and the load power prediction data are subjected to intra-day rolling prediction, and the photovoltaic active and reactive power output and the energy storage charge and discharge power in the intra-day stage are optimized in a rolling mode, so that safe and economic operation of the power distribution network is ensured; and when the intra-day stage prediction optimization is carried out, the discrete equipment executes according to the action planning period of the discrete equipment in the pre-day stage, wherein the regulation and control period of the discrete equipment is obtained by dividing the period based on the optimal fisher segmentation method of interval data, and the period division ensures that the regulation and control period of the discrete equipment is still in the correct division period when the load of the intra-day stage fluctuates in the fluctuation interval to a certain extent, so that the economy of global optimization of the intra-day stage is improved.

It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic of each process, and should not limit the implementation process of the embodiment of the present application in any way.

The following are device embodiments of the present application, for details not described in detail therein, reference may be made to the corresponding method embodiments described above.

Fig. 7 is a schematic structural diagram of a source network storage multi-time scale optimization device based on interval period division according to an embodiment of the present application, and for convenience of explanation, only a portion relevant to the embodiment of the present application is shown, which is described in detail below:

as shown in fig. 7, the source network storage multi-time scale optimization device 7 based on interval period division includes:

the period division module 71 is configured to divide a period of fluctuation between load power prediction data and photovoltaic power prediction data in a day-ahead stage to obtain a regulation period of discrete equipment of the power distribution network;

the day-ahead optimization module 72 is configured to coordinate and optimize various controllable devices of the power distribution network under the constraint of a safety index with minimum running cost of the power distribution network as a day-ahead optimization target according to the load power prediction data and the photovoltaic power prediction data based on a regulation and control period of the discrete devices of the power distribution network, and determine an optimal economic running scheme of the day-ahead stage, where the optimal economic running scheme includes an action planning period of the discrete devices of the day-ahead stage;

The intra-day rolling prediction optimization module 73 is configured to perform intra-day rolling prediction on the photovoltaic power prediction data and the load power prediction data by using an all-level scheduling method with minimum running cost loss of the power distribution network in the intra-day stage as an intra-day optimization target based on the action planning period of the discrete device in the pre-day stage, and perform rolling optimization on the photovoltaic active and reactive power output and the energy storage charge and discharge power in the intra-day stage.

The utility model provides a source network stores up multi-time scale optimizing device based on interval period division, through the fluctuation interval period division to the load power forecast data and the photovoltaic power forecast data of the day preceding stage, confirm the regulation and control cycle of discrete equipment, then regard distribution network running cost minimum as the optimization target before day, and under the security index constraint, confirm the best economic operation scheme of the day preceding stage, make full use of each controllable equipment of distribution network; the photovoltaic power prediction data and the load power prediction data are subjected to intra-day rolling prediction, and the photovoltaic active and reactive power output and the energy storage charge and discharge power in the intra-day stage are optimized in a rolling mode, so that safe and economic operation of the power distribution network is ensured; and when the intra-day stage prediction optimization is carried out, the discrete equipment executes according to the action planning period of the discrete equipment in the pre-day stage, wherein the regulation and control period of the discrete equipment is obtained by dividing the period based on the optimal fisher segmentation method of interval data, and the period division ensures that the regulation and control period of the discrete equipment is still in the correct division period when the load of the intra-day stage fluctuates in the fluctuation interval to a certain extent, so that the economy of global optimization of the intra-day stage is improved.

In one possible implementation, the period dividing module may specifically be configured to:

In one possible implementation, the period division module may be further configured to:

wherein, the first formula is:

load predictive value for the d-th period, < >>

A fluctuation interval size of d-th period, < > for the period of time>

Predicting the mean value for the load of node j, +. >

The predicted mean fluctuation interval size of the node j.

In one possible implementation, the distribution network operating costs include electricity purchasing costs, network loss costs, photovoltaic active reduction costs, discrete device action costs, and optical storage all-in-one operating costs; the daily optimization targets are as follows:

for the unit purchase of electricity for period t, +.>

Is the firstThe tap positions of the on-load tap-changing transformer are arranged on the branches ij of d divided time periods,

The number of capacitor input groups for the d-th divided period node j, is +>

Switch state of the feeder ij for the d-th divided period,/, for>

For the switching state of the d-1 th divided period feeder ij, N _ESS For storing energy unit number c _ESS Invoking energy storage unit price for the optical storage integrated machine, < >>

In one possible implementation, the day-ahead optimization module may specifically be configured to:

In one possible implementation, the daily optimization objective is:

/>

time-of-use electricity price at g time, +.>

For g time of interaction power of main network and distribution network, < >>

Fig. 8 is a schematic diagram of a terminal provided in an embodiment of the present application. As shown in fig. 8, the terminal 8 of this embodiment includes: a processor 80, a memory 81 and a computer program 82 stored in the memory 81 and executable on the processor 80. The processor 80 executes the computer program 82 to implement the steps of the source network multi-time scale optimization method embodiments based on interval period division, for example, steps 101 to 103 shown in fig. 1. Alternatively, the processor 80, when executing the computer program 82, implements the functions of the modules in the above-described apparatus embodiments, such as the functions of the modules 71 to 73 shown in fig. 7.

By way of example, the computer program 82 may be partitioned into one or more modules, which are stored in the memory 81 and executed by the processor 80 to complete the present application. The one or more modules may be a series of computer program instruction segments capable of performing the specified functions, which instruction segments describe the execution of the computer program 82 in the terminal 8. For example, the computer program 82 may be split into modules 71 to 73 shown in fig. 7.

The terminal 8 may be a computing device such as a desktop computer, a notebook computer, a palm computer, a cloud server, etc. The terminal 8 may include, but is not limited to, a processor 80, a memory 81. It will be appreciated by those skilled in the art that fig. 8 is merely an example of the terminal 8 and is not intended to limit the terminal 8, and may include more or fewer components than shown, or may combine certain components, or different components, e.g., the terminal may further include input-output devices, network access devices, buses, etc.

The processor 80 may be a central processing unit (Central Processing Unit, CPU), other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

The memory 81 may be an internal storage unit of the terminal 8, such as a hard disk or a memory of the terminal 8. The memory 81 may also be an external storage device of the terminal 8, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the terminal 8. Further, the memory 81 may also include both an internal storage unit and an external storage device of the terminal 8. The memory 81 is used for storing the computer program and other programs and data required by the terminal. The memory 81 may also be used to temporarily store data that has been output or is to be output.

It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions. The functional units and modules in the embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of a software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working process of the units and modules in the above system may refer to the corresponding process in the foregoing method embodiment, which is not described herein again.

In the foregoing embodiments, the descriptions of the embodiments are emphasized, and in part, not described or illustrated in any particular embodiment, reference is made to the related descriptions of other embodiments.

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

In the embodiments provided in the present application, it should be understood that the disclosed apparatus/terminal and method may be implemented in other manners. For example, the apparatus/terminal embodiments described above are merely illustrative, e.g., the division of the modules or units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection via interfaces, devices or units, which may be in electrical, mechanical or other forms.

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

In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.

The integrated modules/units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the present application may implement all or part of the flow of the method of the foregoing embodiment, or may be implemented by instructing related hardware by a computer program, where the computer program may be stored in a computer readable storage medium, where the computer program, when executed by a processor, may implement the steps of each source network storage multiple time scale optimization method embodiment based on interval period division. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth. It should be noted that the computer readable medium may include content that is subject to appropriate increases and decreases as required by jurisdictions in which such content is subject to legislation and patent practice, such as in certain jurisdictions in which such content is not included as electrical carrier signals and telecommunication signals.

The above embodiments are only for illustrating the technical solution of the present application, and are not limiting; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application, and are intended to be included in the scope of the present application.

Claims

1. The source network storage multi-time scale optimization method based on interval time division is characterized by comprising the following steps of: carrying out time interval division on load power prediction data and fluctuation intervals of photovoltaic power prediction data in the day-ahead stage to obtain a regulation and control period of discrete equipment of the power distribution network; based on the regulation and control period of the discrete equipment of the power distribution network, according to load power prediction data and photovoltaic power prediction data, taking the minimum running cost of the power distribution network as a day-ahead optimization target, and under the constraint of safety indexes, carrying out coordinated optimization on various controllable equipment of the power distribution network, and determining an optimal economic running scheme in a day-ahead stage, wherein the optimal economic running scheme comprises an action planning period of the discrete equipment in the day-ahead stage; and based on the action planning period of the discrete equipment in the day-ahead stage, adopting a full-horizontal scheduling method, taking the minimum running cost loss of the power distribution network in the day-ahead stage as a day-ahead optimization target, carrying out day-ahead rolling prediction on photovoltaic power prediction data and load power prediction data, and rolling and optimizing the photovoltaic active and reactive power output and the energy storage charge and discharge power in the day-ahead stage.

2. The method for optimizing the source network storage multi-time scale based on interval time division according to claim 1, wherein the time division is performed on the fluctuation interval corresponding to the daily load and the photovoltaic power data to obtain the regulation period of the slow-motion equipment of the power distribution network, and the method comprises the following steps:

3. The interval period division-based source network storage multi-time scale optimization method of claim 2, wherein the interval period division-based optimal fisher segmentation method is used for performing period division on the payload fluctuation interval to obtain a regulation period of discrete equipment of a power distribution network, and comprises the following steps:

calculating the interval number distance of the payload fluctuation interval through a first formula;

According to the interval number distance, carrying out time interval division on the net load fluctuation interval to obtain a regulation and control period of discrete equipment of the power distribution network;

wherein, the first formula is:

load predictive value for the d-th period, < >>

For the size of the fluctuation interval of the d-th period,

predicting the mean value for the load of node j, +.>

And lambda is a weighting factor for the size of the predicted mean fluctuation interval of the node j.

4. The method for optimizing a source network storage multi-time scale based on interval time division according to claim 3, wherein the performing time division on the payload fluctuation interval according to the interval number distance to obtain a regulation period of a discrete device of a power distribution network comprises:

determining an optimal time interval division scheme of the net load fluctuation interval through an objective function to obtain a regulation and control period of discrete equipment of the power distribution network, wherein the objective function is as follows:

wherein L [ ] is the objective function, b (T, D) is an optimal time interval division scheme divided by D times, D is the time interval division times, and T is 24 hours.

5. The interval period division-based source network storage multi-time scale optimization method according to claim 1, wherein the power distribution network operation cost comprises power purchase cost, network loss cost, photovoltaic active reduction cost, discrete equipment action cost and optical storage integrated machine operation cost; the daily optimization targets are as follows:

for the unit purchase of electricity for period t, +.>

Tap position of on-load tap-changing transformer on branch ij of d-th divided period,/, is provided>

The number of capacitor input groups for the d-th divided period node j, is +>

The switching state of the feeder ij for the d-th divided period,

6. The method for optimizing the source network storage multi-time scale based on interval period division according to claim 1, wherein the operation planning period based on the discrete device in the day-ahead stage adopts a full-level scheduling method, and uses the minimum running cost loss of the power distribution network in the day-ahead stage as an optimization target in the day, so as to perform rolling prediction on the photovoltaic power prediction data and the load power prediction data, and the rolling optimization on the photovoltaic active and reactive power output and the energy storage charge and discharge power in the day-ahead stage comprises the following steps:

according to the action planning period of the discrete equipment in the day-ahead stage, the running cost loss of the power distribution network in the day-ahead stage is taken as a day-ahead optimization target, the preset optimization time granularity is used as a rolling window to slide in the day-ahead stage, the photovoltaic power prediction data and the load power prediction data in each rolling window are predicted and optimized, and the photovoltaic active power reactive power and the energy storage charge and discharge power in the day-ahead stage are output.

7. The method for optimizing the source network storage multi-time scale based on interval period division according to claim 6, wherein the daily optimization objective is:

time-of-use electricity price at g time, +.>

For g time of interaction power of main network and distribution network, < >>

The reduction amount of active power of photovoltaic, c _ESS Invoking energy storage unit price for the optical storage integrated machine, < >>

Store up for k light stores up all-in-oneThe discharge power can be measured in g.

8. The utility model provides a source network stores up multi-time scale optimizing device based on interval period division which characterized in that includes:

9. A terminal comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the source network multi-time scale optimization method based on interval period partitioning as claimed in any one of the preceding claims 1 to 7 when the computer program is executed.

10. A computer readable storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the steps of the source network storage multi-time scale optimization method based on interval period partitioning according to any one of the preceding claims 1 to 7.