CN117543569A

CN117543569A - Cross-regional power scheduling method

Info

Publication number: CN117543569A
Application number: CN202311681509.2A
Authority: CN
Inventors: 江海燕; 蒋一博; 史文博; 马亚辉; 邹风华; 孟诗语; 陈爱康
Original assignee: State Grid Suzhou Urban Energy Research Institute Co ltd
Current assignee: State Grid Suzhou Urban Energy Research Institute Co ltd
Priority date: 2023-12-08
Filing date: 2023-12-08
Publication date: 2024-02-09

Abstract

The invention relates to the technical field of power dispatching, in particular to a trans-regional power dispatching method, which comprises the following steps: the lower layer dispatching center integrates the information in the area into three types of time-varying relation curves; constructing a day-ahead economic dispatch model according to the three time-varying relation curves; and constructing an intra-day economic dispatch model according to the correction quantity form corresponding to the three types of time-varying relation curves, taking the result of the pre-day economic dispatch model as a starting point, and correcting the result of the pre-day economic dispatch model by using the intra-day economic dispatch model. The invention can effectively protect the unit and load information in the independent area and improve the safety of dispatching interaction information data. And according to the daily economic scheduling models and the daily economic scheduling models constructed by the three time-varying relation curves, the cross-regional standby optimization global optimal scheduling decision can be effectively supported.

Description

Cross-regional power scheduling method

Technical Field

The invention relates to the technical field of power dispatching, in particular to a trans-regional power dispatching method.

Background

The existing power system dispatching in China has two characteristics, namely a vertical upper and lower layered dispatching system and a multi-time scale dispatching framework from day to day.

Based on the vertical upper and lower layered dispatching system, in production practice, each regional power grid comprises a plurality of provincial power grids connected through provincial interconnecting lines. The inter-provincial power grid company has line parameters and dispatching control authority of inter-provincial connecting lines, is used for optimizing and dispatching power and standby interaction quantity among the provincials, and does not dispatch specific units in the provincials nor acquire the inter-provincial units and the line parameters. On the basis of the determined interactive power and standby plan, the provincial power grid company optimally schedules the output of the provincial generator set according to the specific parameters of the provincial generator set, the parameters of the provincial power transmission line and the provincial power transmission grid structure by taking the economical efficiency optimization of the provincial power grid company as a scheduling target. The provincial power grid needs to keep secret on the unit and line parameters in the provincial, and only the interactive information among provincial parts is uploaded to the cross-provincial dispatching.

The information interaction content of provincial power grid companies and cross-provincial power grid companies can influence the efficiency and quality of spare resource allocation in power dispatching to a great extent. The existing cross-regional standby optimal scheduling model cannot be suitable for a China scheduling system, so that the model cannot be truly applied to the operation and the actual scheduling operation of a China power system.

Disclosure of Invention

Therefore, the technical problem to be solved by the invention is to solve the problem that a cross-region standby optimal scheduling model in the prior art cannot be suitable for the existing constitution scheduling mechanism in China.

In order to solve the technical problems, the invention provides a cross-regional power scheduling method, which comprises the following steps:

the lower-layer dispatching center integrates the information in the area into three types of time-varying relation curves, including a relation curve between the output of the area total fuel unit and the total fuel cost, a relation curve between the output of the area fuel unit and the area standby providing capacity, a relation curve between the system reserved upward standby capacity and the system load shedding expected relation curve and a relation curve between the system reserved downward standby capacity and the system wind curtailment expected relation curve;

constructing a day-ahead economic dispatch model according to the three time-varying relation curves, wherein the method comprises the following steps:

the lower layer scheduling center uploads the three types of time-varying relation curves and wind power and load prediction curves of all areas to the upper layer scheduling center, the upper layer scheduling center constructs a day-ahead upper layer scheduling objective function according to the three types of time-varying relation curves and the total running cost of the whole system, calculates the boundary interaction active power of all areas, the upward and downward reserve capacities of boundary interaction, the total active output of the areas and the upward and downward reserve capacities under the condition of the minimum total running cost of the whole system, and serves as a day-ahead scheduling plan of the upper layer scheduling center;

The lower layer dispatching center builds a day-ahead lower layer dispatching objective function according to the total fuel cost of the area, and solves the day-ahead planned active output of the fuel unit in the area under the condition of the minimum total fuel cost of the area according to the day-ahead dispatching plan of the upper layer dispatching center;

constructing an intra-day economic dispatch model according to the correction quantity form corresponding to the three types of time-varying relation curves, taking the result of the pre-day economic dispatch model as a starting point, and correcting the result of the pre-day economic dispatch model by using the intra-day economic dispatch model, wherein the method comprises the following steps:

the lower layer scheduling center uploads correction forms corresponding to the three types of time-varying relation curves to the upper layer scheduling center, and the upper layer scheduling center corrects the day-ahead scheduling plan under the condition that the total correction cost of the whole system is minimum by taking the total correction cost of the whole system as a construction day-ahead upper layer scheduling objective function according to the correction forms corresponding to the three types of time-varying relation curves and the day-ahead scheduling plan, the day-ahead and day-ahead prediction error correction;

and the lower layer scheduling center constructs a daily lower layer scheduling objective function according to the regional total fuel correction cost, and corrects the daily planned active output of the regional fuel unit under the condition that the regional total fuel correction cost is minimum.

In one embodiment of the present invention, the day-ahead upper layer scheduling objective function is:

wherein,for the total running cost of the whole system, m represents an AREA, AREA represents an AREA set, T represents a scheduling period, and T represents a scheduling period; PC (personal computer) _m,t A curve representing the relation between the total fuel assembly output and the total fuel cost of zone m during period t,/->Representing the total active force of region m over a period t; />Represents the cut load price of region m, +.>RLS representing the wind curtailment price of region m _m,t Representing the expected relation curve of the upward reserve capacity of the system and the system cut load of the region m in the period t, RWC _m,t Representing a system reserved downward standby capacity and system waste wind expected relation curve of the region m in a t period;and->Respectively representUpward and downward spare capacity of region m in t period,/->And->Representing the upward and downward reserve capacities of the boundary interaction of region m during the period t, respectively;

the constraint conditions of the day-ahead upper layer scheduling objective function comprise:

wherein PRup _m,t And PRdn _m,t The relationship between fuel assembly output and upward and downward backup capacity over the period t for region m is shown,representing the inverse of the cumulative distribution function of the probability model;

the balance constraint conditions of the day-ahead upper layer scheduling objective function comprise:

Wherein,represents the boundary interaction active power of region m within the t period,/for>Indicates that region m is wind power before day within t period,/->A predicted value representing the load of the region m in the t period;

calculating the total running cost of the whole system by using the day-ahead upper layer scheduling objective functionIn the smallest caseAnd->And->Boundary interaction active power +.f. of area m planned before day within period t is obtained>Upward and downward reserve capacity of the area m planned before the day in the period t +.>And->The area m planned before the day is bordered by interactive up and down reserve capacity +.>And->

In one embodiment of the present invention, the constraint condition of the day-ahead upper layer scheduling objective function further includes:

inter-zone tie constraint:

wherein,and->Representing the maximum and minimum transmission capacity of inter-zone link l, respectively, < >>Nodes representing a network of inter-area links inject power transfer factors.

In one embodiment of the present invention, the day-ahead lower layer scheduling objective function is:

wherein,g is the total fuel cost of the region _m Representing a set of fuel assemblies, a, within region m _i 、b _i And c _i For the fuel cost factor of the fuel assembly i, +.>Representing the planned active output of the fuel unit i in the t period;

The constraint conditions of the day-ahead lower layer scheduling objective function comprise:

wherein,represents the wind power of the region m in the period t, k represents the node, BUS _m Representing the set of nodes within region m, +.>Within the representation area mNode k interacts active power at the day front boundary within the period t; />Node injection power transfer factor for representing network of inter-node links>Representing the day-ahead planned active force of node k during period t, +.>Represents the wind power of node k in t period, D _k,t Representing a load predicted value of the node k in a t period; />Andrespectively representing the upward and downward reserve capacities of the fuel assembly i during the period t;

calculating the total fuel cost of the area by using the day-ahead lower layer scheduling objective functionIn the smallest caseObtaining the daily planned active output of the fuel unit i in the t period>

In one embodiment of the present invention, the intra-day upper layer scheduling objective function is:

wherein,for total correction cost of whole system, < > for>Correction form of the curve representing the total fuel assembly output versus the total fuel cost of the region m during the t period, +.>Correction quantity form of relation curve of reserved upward reserve capacity of system and system cut load expected in t period of representing region m >A correction quantity form of a relation curve of reserved downward standby capacity of the system and expected system waste wind of the region m in a t period is represented; ΔP _m,t Indicating that region m is active force correction amount in t period,/->And->Correction amounts respectively representing upward and downward reserve capacities of the region m in the t period, +.>And->Correction amounts respectively representing upward and downward reserve capacities of boundary interaction of the region m in the t period;

the constraint conditions of the intra-day upper layer scheduling objective function comprise:

wherein,and->A correction quantity form of a relation curve of output of the fuel unit and upward and downward standby providing capability in a t period of a region m in a day-ahead schedule is respectively shown; ΔP _m,t Indicating an active force correction amount of the region m in the t period;

the correction constraint conditions of the intra-day upper layer scheduling objective function comprise:

wherein,representing pre-and intra-day prediction error fixesPositive amount, ->A correction amount representing boundary interaction active power of the region m in the t period;

calculating total correction cost of the whole system by using the daily upper layer scheduling objective functionCorrection amount Δp in the minimum case _m,t 、/>And->And->

In one embodiment of the present invention, the pre-and intra-day prediction error correction amounts Wherein (1)>Represents the wind power predicted value of the intra-day region m in the period t, And representing the wind power predicted value of the region m before the day in the period t.

In one embodiment of the present invention, the intra-day lower layer scheduling objective function is:

wherein,correction costs for regional total fuel, < >>A correction amount representing the planned active output of the fuel assembly i in the t period;

calculating the total fuel correction cost of the area by using the daily lower layer scheduling objective functionCorrection in the minimum case +.>

In one embodiment of the present invention, the functional expression of the plot of regional total fuel unit output versus total fuel cost is:

wherein,is the total fuel cost of the fuel unit, a _i 、b _i And c _i For the fuel cost factor of the fuel assembly i, +.>Representing the planned active output of the fuel assembly i during the period t.

In one embodiment of the invention, the functional expression of the relationship between regional fuel unit output and regional backup capacity is:

wherein,and->Respectively representing the total upward and downward reserve capacity that the region m can provide in the t period, G _m Representing the fuel assembly set in region m, +.>Representing the planned active force of the fuel assembly i during the t period, ->Andrespectively representing the upper limit and the lower limit of the active power output of the fuel unit i _i And RD (RD) _i Indicating the upward and downward climbing capabilities of the fuel assembly i, respectively.

In one embodiment of the present invention, the functional expression of the system reserve capacity versus system cut load expected relationship is:

wherein,indicating a system cut load desire during the t period, < >>Indicating that the system in period t reserves upward reserve capacity, < > for the period t>Representing a historical maximum systematic error in a historical new energy data box, and f (x) represents a probability density function of a probability model;

the functional expression of the system reserved downward spare capacity and system abandoned wind expected relation curve is as follows:

wherein,indicating the system wind abandon expectations during period t, < >>Indicating that the system within period t reserves downward reserve capacity, < >>Representing the historical minimum systematic error in the historical new energy data box.

Compared with the prior art, the technical scheme of the invention has the following advantages:

according to the trans-regional power scheduling method, based on the relation between upper and lower levels of actual scheduling in China and an information interaction mechanism, information in regions is integrated into three types of time-varying relation curves according to the difference of input information in different scheduling time scales, a lower scheduling center only uploads the three types of time-varying relation curves to an upper scheduling center, the upper scheduling center builds economic scheduling models before and during the day according to the three types of time-varying relation curves uploaded by each region, unit and line parameter information in each region of the lower scheduling center are protected, and scheduling interaction information data security is improved. The daily economic scheduling model and the daily economic scheduling model can efficiently distribute power and standby resources in the whole system to different areas on a space scale, and can gradually correct inter-area transaction amount and intra-area unit output plans on a time scale, thereby effectively supporting cross-area standby optimization global optimal scheduling decisions.

Drawings

In order that the invention may be more readily understood, a more particular description of the invention will be rendered by reference to specific embodiments thereof that are illustrated in the appended drawings.

FIG. 1 is a schematic diagram of an optimization control object of an upper layer scheduling center in a Chinese vertical scheduling system;

FIG. 2 is a schematic diagram of the information interaction process between the upper and lower dispatching centers in the China vertical dispatching system;

FIG. 3 is a flow chart of a cross-regional power scheduling method of the present invention;

FIG. 4 is a fuel assembly consumption profile;

FIG. 5 is a graph of the relationship between the regional fuel unit output and the consumed microincrement rate based on the principle of equal microincrement rate, wherein the graph (a) is a graph of the relationship between the consumed microincrement rate and the fuel unit output after the upper and lower limit constraints of the output and the climbing constraint of a cross-time section are considered by three fuel units, and the graph (b) is a graph of the relationship between the regional total fuel unit output and the consumed microincrement rate;

FIG. 6 is a graph of regional total fuel unit output versus total fuel cost;

FIG. 7 is a schematic diagram of the coupling relationship between the fuel assembly operating point and the alternate providing capability;

FIG. 8 is a graph of regional fuel unit output versus regional backup capacity;

FIG. 9 is a graph of area up backup versus area cut load expected value, area down backup versus area discard load expected value, where graph (a) is a graph of area up backup versus area cut load expected value, and graph (b) is a graph of area down backup versus area discard load expected value;

fig. 10 is a three-zone system configuration diagram in the second embodiment;

fig. 11 is a graph of prediction error history data of wind farms in three areas in the second embodiment, where (a) is a graph of prediction error of wind power in area 1 during a scheduling period, (b) is a graph of prediction error of wind power in area 2 during a scheduling period, and (c) is a graph of prediction error of wind power in area 3 during a scheduling period;

FIG. 12 is a graph of wind power prediction curves and load prediction curves before and during the day in the second embodiment, wherein (a) is a graph of load prediction during a scheduling period, (b) is a graph of wind power output prediction during the scheduling period, and (c) is a graph of net load prediction during the scheduling period;

FIG. 13 is a graph showing the relationship between the total active power output value of the thermal power plant and the fuel cost, the upward reserve capacity provided by the region and the downward reserve capacity provided by the region in three regions in the second embodiment, wherein the graph (a) is a graph showing the relationship between the total active power output value of the thermal power plant and the fuel cost, the graph (b) is a graph showing the relationship between the total active power output value of the thermal power plant and the upward reserve capacity provided by the region, and the graph (c) is a graph showing the relationship between the total active power output value of the thermal power plant and the downward reserve capacity provided by the region;

FIG. 14 is a graph of planned output of six thermal power plants under multi-time scale scheduling in the second embodiment, wherein FIG. (a) is a graph of planned output of six thermal power plants under the time scale before day, and FIG. (b) is a graph of planned output of six thermal power plants under the time scale during day;

FIG. 15 is a graph of the total planned output of a thermal power plant in three regions under multi-time scale scheduling in the second embodiment, wherein (a) is a graph of the total planned output of a thermal power plant in three regions under a time scale before day, and (b) is a graph of the total planned output of a thermal power plant in three regions under a time scale within day;

FIG. 16 is a graph of inter-zone link power tide for multi-time scale scheduling in embodiment two, wherein graph (a) is a graph of inter-zone link power tide for the time scale before day and graph (b) is a graph of inter-zone link power tide for the time scale within day;

FIG. 17 is a diagram showing the situation of providing upward backup capability and having upward backup resources for each region in the second embodiment, wherein the diagram (a) shows the situation of providing upward backup capability and having upward backup resources for region 1, the diagram (b) shows the situation of providing upward backup capability and having upward backup resources for region 2, and the diagram (c) shows the situation of providing upward backup capability and having upward backup resources for region 3;

FIG. 18 is a diagram showing the situation of providing downward spare capacity and having downward spare resources for each zone in the second embodiment, wherein the diagram (a) shows the situation of providing downward spare capacity and having downward spare resources for zone 1, the diagram (b) shows the situation of providing downward spare capacity and having downward spare resources for zone 2, and the diagram (c) shows the situation of providing downward spare capacity and having downward spare resources for zone 3;

fig. 19 is an analysis chart of the spare capacity allocation of the area 1 in the second embodiment in different scheduling periods, wherein the chart (a) is an upward spare capacity chart possessed by the area 1 in the scheduling period, the chart (b) is a graph of the upward spare capacity versus the expected value of the cut load possessed by the area 1 at the point A1 in the chart (a), and the chart (c) is a graph of the upward spare capacity versus the expected value of the cut load possessed by the area 1 at the point A2 in the chart (a); fig. (d) is a graph of downward spare capacity possessed by the area 1 in the scheduling period, fig. (e) is a graph of downward spare capacity possessed by the area 1 at point B1 in fig. (d) versus the expected value of the waste wind, and fig. (f) is a graph of downward spare capacity possessed by the area 1 at point B2 in fig. (d) versus the expected value of the waste wind;

fig. 20 is a graph showing the effect of cross-regional allocation of upward spare resources for each region at the same time section in the second embodiment, where (a) is a graph of upward spare capacity owned by each region in the same scheduling period, (b) is a graph of upward spare capacity owned by region 1 at point 1 in (a) versus expected tangential load, and (c) is a graph of upward spare capacity owned by region 2 at point 2 in (a) versus expected tangential load, and (d) is a graph of upward spare capacity owned by region 3 at point 3 in (a) versus expected tangential load;

Fig. 21 is a graph showing the effect of cross-regional allocation of downward spare resources for each region at the same time section in the second embodiment, where (a) is a graph of downward spare capacity owned by each region in the same scheduling period, (b) is a graph of downward spare capacity owned by region 1 at point 1 in (a) versus expected value of discarded air, (c) is a graph of downward spare capacity owned by region 2 at point 2 in (a) versus expected value of discarded air, and (d) is a graph of downward spare capacity owned by region 3 at point 3 in (a) versus expected value of discarded air;

FIG. 22 is a graph of wind power, load and net load prediction for the day before and during the day in embodiment III, wherein graph (a) is a graph of load prediction for a scheduled time period, graph (b) is a graph of wind power output prediction for a scheduled time period, and graph (c) is a graph of net load prediction for a scheduled time period;

FIG. 23 is a graph showing the relationship between the total active power output value of the thermal power plant and the fuel cost, the upward reserve capacity provided by the region and the downward reserve capacity provided by the region in three regions in the third embodiment, wherein the graph (a) is a graph showing the relationship between the total active power output value of the thermal power plant and the fuel cost, the graph (b) is a graph showing the relationship between the total active power output value of the thermal power plant and the upward reserve capacity provided by the region, and the graph (c) is a graph showing the relationship between the total active power output value of the thermal power plant and the downward reserve capacity provided by the region;

FIG. 24 is a diagram showing the situation of each region providing upward backup capability versus the situation of having upward backup resources in the third embodiment, wherein the diagram (a) shows the situation of providing upward backup capability versus the situation of having upward backup resources in the region 1, the diagram (b) shows the situation of providing upward backup capability versus the situation of having upward backup resources in the region 2, and the diagram (c) shows the situation of providing upward backup capability versus the situation of having upward backup resources in the region 3;

FIG. 25 is a diagram showing the situation of providing downward spare capacity and having downward spare resources for each area in the third embodiment, wherein the diagram (a) shows the situation of providing downward spare capacity and having downward spare resources for the area 1, the diagram (b) shows the situation of providing downward spare capacity and having downward spare resources for the area 2, and the diagram (c) shows the situation of providing downward spare capacity and having downward spare resources for the area 3;

FIG. 26 is a graph comparing scheduling results using a complete information statistics model and an economic scheduling model according to the present invention;

fig. 27 is a calculation time statistics diagram of an economic dispatch model according to the present invention, in which fig. a is a calculation time statistics diagram of an upper dispatch center in a day-ahead economic dispatch model, fig. b is a calculation time statistics diagram of a lower dispatch center in a day-ahead economic dispatch model, fig. c is a calculation time statistics diagram of an upper dispatch center in a day-ahead economic dispatch model, and fig. d is a calculation time statistics diagram of a lower dispatch center in a day-ahead economic dispatch model.

Detailed Description

The present invention will be further described with reference to the accompanying drawings and specific examples, which are not intended to be limiting, so that those skilled in the art will better understand the invention and practice it.

Example 1

In order to adapt to the traditional power dispatching system in China, the invention provides a transregional power dispatching method. In order to understand the technical scheme provided by the invention in depth, the present application firstly introduces the current power dispatching system in China.

The traditional power system dispatching system in China comprises a vertical upper and lower layered dispatching system and a multi-time scale dispatching framework from day front to day inside.

The vertical hierarchical upper and lower scheduling architecture includes a lower layer scheduling center (Local control center, LCC) and an upper layer scheduling center (Upper control center, UCC). In the application, the lower-layer dispatching center corresponds to a provincial power grid dispatching center in production practice, and the upper-layer dispatching center corresponds to a provincial power grid control center.

Fig. 1 illustrates, by way of example, four lower level dispatch centers, and the scheduling and control process of the lower level dispatch centers by the upper level dispatch centers. Fig. 2 shows the information interaction process of the upper layer scheduling center and the lower layer scheduling center.

Along with the large-scale access of renewable energy sources with uncertainty such as wind power, the influence of prediction errors on the traditional scheduling of the Chinese power system is increasingly remarkable. Because the expected value of the prediction error gradually decreases along with the approach of time, the scheduling framework with multiple time scales can gradually relieve the power imbalance problem of the Chinese power system caused by the prediction error by utilizing the continuously updated prediction information. The present embodiment selects an economic schedule of a time scale before day and an economic schedule within day advanced by 4 hours within day as subjects of discussion.

The day-ahead economic dispatch is the basis of the day-ahead economic dispatch. The generator set parameters, the intra-area and inter-area tie line parameters and the grid structure information are common input information of daily economic dispatch and daily economic dispatch. The difference is that the day-ahead economic schedule takes day-ahead prediction information and historical data of corresponding time scales as input, and the day-ahead economic schedule takes day-ahead optimal scheduling results, day-ahead prediction information and historical data of 4-hour time scales in the day as input. In general, daily economic dispatch is required to be performed on the basis of daily economic dispatch output results, and the daily economic dispatch results are corrected according to updated prediction information and historical data of daily time scales, so that unbalanced power of a system caused by prediction errors is reduced.

Referring to fig. 3, the invention provides a cross-regional power dispatching method, which integrates information in a region into three types of time-varying relation curves, including a relation curve between the output of a regional total fuel unit and the total fuel cost, a relation curve between the output of the regional fuel unit and the regional standby providing capability, a relation curve between the upward standby capacity reserved by a system and the load shedding expected relation curve of the system and a relation curve between the downward standby capacity reserved by the system and the wind curtailment expected relation curve of the system. And the upper layer scheduling center performs optimized scheduling according to the three types of time-varying relation curves uploaded by each lower layer scheduling center, and specific unit parameters in the lower layer scheduling center area are not needed. Each class of time-varying relationship curves contains a form for a day-ahead scheduling problem and a form for an intra-day scheduling problem.

The first type of time-varying relationship, namely the integration method of the relationship between regional total fuel unit output and total fuel cost, is described below.

The consumption characteristic curve of the fuel unit is a curve for reflecting the relation between the energy input and the energy output in unit time of the fuel unit. In the actual energy conversion process, the input data of the boiler corresponding to the fuel unit is fuel consumption (t standard coal/h), and the corresponding output data is steam quantity (t/h). Subsequently, the steam is injected into a turbo generator to generate electric power. FIG. 4 reflects a relationship between the power generation (MW) of a fuel unit and the coal burning rate (t standard coal/h) of the fuel unit, namely, the consumption characteristic curve of the fuel unit, and the corresponding function analytical formula is:

Wherein a is _i 、b _i And c _i Is the fuel cost factor for the fuel assembly i,representing the planned active output of the fuel assembly i during the period t.

The slope of any point on the consumption curve of the fuel unit is the corresponding consumption micro-increment rate of the fuel unit at the point. Taking an area containing three fuel units as an example, the consumption micro-increment rate lambda expression of the three fuel units is as follows:

the relation between the fuel consumption micro-increment rate of the fuel unit and the active output of the unit is obtained by the consumption micro-increment rate expression of the fuel unit:

as is known from the equal-minute-rate criteria, when the load is divided among a plurality of fuel assemblies and the consumption minute-rate of each fuel assembly is equal, the total fuel consumption in this region is minimized, and the total fuel cost in the corresponding region is minimized.

When lambda is ₁ ＝λ ₂ ＝λ ₃ When the fuel machine is in operation, the active output of the three fuel machine sets can be obtainedAnd->The relational expression for the consumption microincrement rate λ:

according to the set of relational expressions, a relational curve of each fuel unit with the active force as an abscissa and lambda as an ordinate can be drawn. In consideration of the operation scheduling of an actual power system, the constraint on the active output of the fuel unit comprises the upper limit constraint and the lower limit constraint of the output and the climbing constraint of a cross-time section, and the constraint on the active output of the fuel unit Is limited by the value of (c).

The upper and lower limits of the output force are constrained as follows:

/>

wherein,representing the minimum active force, < > of the fuel assembly i>Indicating the maximum active output of the fuel assembly i.

The climbing constraint of the cross-time section is as follows:

wherein,and->The maximum upward and downward hill climbing capacities of the fuel assembly i during the unit scheduling period are respectively represented.

Referring to fig. 5, a graph (a) is a graph of the consumption micro-increment rate λ and the output of the fuel unit after the upper and lower limits of the output and the climbing constraint of the cross-section of time are considered, and a graph (b) is shown in the graph (a) in which the total output of the fuel unit is calculated as a region.

The relation between the total fuel unit output and the consumption micro-increment rate lambda of the area is obtained by superposition of relation curves of the single fuel units, and by utilizing the relation, when the total active output required to be born by the area is known, the relation can be deduced according to the reverse direction of superposition to obtain how the area distributes the total active output required to be born to each fuel unit.

Under the condition that the active power output specifically born by each fuel unit in the known area, the total fuel cost of all the fuel units in the area can be calculated, thereby establishing a relation curve PC between the output of the total fuel units in the area and the total fuel cost in the area _m,t As shown in fig. 6. Relation PC _m,t The functional expression of (2) is:

in order to adapt to the Chinese multi-time scale scheduling framework, the application also provides a relation curve PC _m,t In the form of a correction, ΔPC _x 。ΔPC _x Representing a relationship PC _m,t The amount of change in the vicinity at the upper point x represents the relationship between the total output change of the regional fuel unit at that point and the total fuel cost change of that region. PC (personal computer) _m,t On the dayApplication in front economic dispatch model, delta PC _x The method is applied to an economic scheduling model in the day.

The second type of time-varying relationship, namely the integration method of the relationship between regional fuel unit output and regional backup capacity, is described below.

Referring to FIG. 7, the reserve capacity within a unit scheduling period of a fuel assembly provides a range within which the assembly can adjust the active force up or down at this time. Therefore, the upward and downward standby capability that each fuel assembly in the area can provide is limited not only by the current operating point of the fuel assembly, but also by the upper and lower limits of the active force and the climbing capability. Taking an area containing three fuel units as an example, the constraint expression is:

after knowing the relation between the spare capacity and the output of the fuel units, the operating points of each fuel unit in the output and the spare capacity of the regional fuel units can be obtained according to the inverse pushing method in the first time-varying relation curve, and then the total upward and downward spare capacities which can be provided by the regional are obtained by calculation according to the constraint, as shown in fig. 8, namely the second time-varying relation curve.

Also, when the total active force to be borne by a region is known, it is possible to obtain how the region distributes the active force to be borne to a specific fuel assembly.

The relationship between the zone fuel unit output and the zone up and down stand-by capability is designated as PRup _m,t And PRdn _m,t The functional expression is:

wherein,and->Respectively representing the total upward and downward reserve capacity that the region m can provide in the t period, G _m Representing a set of fuel units within region m, RU _i And RD (RD) _i Indicating the upward and downward climbing capabilities of the fuel assembly i, respectively.

The curve of the relation between the output of the domain fuel unit and the upward and downward reserve supply capacity of the domain also has a corresponding correction quantity form, namely delta PRup _x And ΔPRdn _x 。

The third time-varying relation curve, namely the relation curve of the upward reserve capacity reserved by the system and the expected load shedding of the system, and the integration method of the downward reserve capacity reserved by the system and the expected wind curtailment relation curve of the system are introduced as follows.

In order to realize efficient allocation of inter-area standby in a hierarchical scheduling system, relevant information of a dynamic standby demand assessment model is also required to be uploaded to an upper layer scheduling center by each area. System reserve upward backup capacity and system cut load expected relationship curve RLS _m,t System reserved downward reserve capacity and system wind curtailment expected relation curve RWC _m,t The effect of reserving spare capacity for different scheduling periods for each region can be described.

The "region up backup-region cut load expected" and "region down backup-region cut load expected" curve libraries obtained by statistics of the historical data for each region are shown in fig. 9, where fig. (a) is a graph of the relationship between the region up backup and the region cut load expected value, and fig. (b) is a graph of the relationship between the region down backup and the region cut load expected value. The curve feature coverage in the curve library describes "new" in that regionThe total relation between the system uncertainty 'and' new energy resource waste and load loss event caused by standby deficiency 'of the energy prediction error, load prediction error and line flow randomness'. The lower layer dispatching center of each area will screen the relation curve RLS meeting the requirements in the curve library based on the predicted information such as the time period of dispatching, the load predicted value, the new energy predicted value and the like _m,t RWC of relation curve _m,t The method and the device are used, so that the information interaction redundancy is greatly reduced, and the operation of a dispatcher is facilitated. The upper layer dispatch center uses the relation curve RLS _m,t RWC of relation curve _m,t Based on this, upward and downward standby resources are efficiently allocated among the zones.

The system reservation upward reserve capacity and system load shedding expected relation curve RLS _m,t The functional expression of (2) is:

wherein,indicating a system cut load desire during the t period, < >>Indicating that the system in period t reserves upward reserve capacity, < > for the period t>Representing the historical maximum systematic error in the historical new energy data box, and f (x) represents the probability density function of the probability model.

The system reserves a downward reserve capacity and a system wind abandon expected relation curve RWC _m,t The functional expression of (2) is:

/>

The system reservation upward reserve capacity and system load shedding expected relation curve RLS _m,t The corresponding correction quantity is of the form DeltaRLS _x System reserve downward reserve capacity and system wind curtailment expected relation curve RWC _m,t The corresponding correction is of the form DeltaRWC _x 。

The method is used for modeling the optimization strategies of the upper layer and the lower layer scheduling center under each time scale under the vertical scheduling system of China aiming at the daily economic scheduling and the daily economic scheduling models in the China multi-time scale scheduling framework. It should be noted that the integration method of three types of time-varying relation curves provided in the present application is not only applicable to the scheduling problem of two time scales discussed in this embodiment, but also applicable to the scheduling problem of other time scales. In this embodiment, only the two time scales are taken as examples, and information interaction processes between scheduling centers of all layers in the vertical scheduling system under different time scales are shown.

The following describes a day-ahead economic dispatch model constructed from the three types of time-varying relationships.

The input information of the economic dispatch problem of the upper layer before the day comprises three types of time-varying relation curves uploaded by each area and wind power and load prediction curves of each area.

And the upper layer scheduling center constructs a daily upper layer scheduling objective function according to the three types of time-varying relation curves and the total operation cost of the whole system, calculates the boundary interaction active power of each region, the upward and downward reserve capacities of boundary interaction, the total active output of the region and the upward and downward reserve capacities under the condition of the minimum total operation cost of the whole system, and uses the boundary interaction active power, the upward and downward reserve capacities as a daily scheduling plan of the upper layer scheduling center.

The total system operation cost comprises three parts, namely total system fuel cost, expected total system cut load and expected total system waste wind, and an upper layer scheduling objective function is constructed according to the following steps:

wherein,the method comprises the steps that an objective function of day-ahead upper-layer scheduling is used for calculating total running cost of the whole system, m represents an AREA, AREA represents an AREA set, T represents a scheduling period, and T represents a scheduling period; PC (personal computer) _m,t A curve representing the relation between the total fuel assembly output and the total fuel cost of zone m during period t,/- >Representing the total active force of region m over a period t;the cut load price of the region m is expressed in $/MWh; />The punishment price of the abandoned wind in the region m is expressed in the unit of $/MWh; RLS (radio link failure) _m,t Representing the expected relation curve of the upward reserve capacity of the system and the system cut load of the region m in the period t, RWC _m,t Representing a system reserved downward standby capacity and system waste wind expected relation curve of the region m in a t period; />And->Representing the upward and downward sparing of region m during period t, respectivelyCapacity of (I)>And->Representing the upward and downward reserve capacities of the boundary interaction of region m during the t period, respectively.

When (when)And->When taking positive value, it indicates standby delivery, when +.>And->Taking a negative value indicates that redundancy is obtained from the outer region.

The basic constraint conditions of the objective function of the day-ahead upper layer scheduling comprise:

the ability of each zone to provide up and down reserve capacity is limited by the total active force of the zone:

the upper layer dispatching center needs to ensure that the upward and downward spare capacity owned by each area meets the lowest security constraint under a certain confidence coefficient:

wherein PRup _m,t And PRdn _m,t The regions m are indicated respectively. A relationship between fuel assembly output and upward and downward backup capacity during period t,representing the inverse of the cumulative distribution function of the probability model.

The inter-area tie constraint condition of the daily upper layer scheduling objective function comprises the following steps:

direct current flow constraint of inter-regional tie lines:

on the basis of direct current flow constraint of inter-region interconnecting lines, cross-region interactive power is further limited, so that enough remaining transmission channels exist for upward and downward reserve capacity reserved in the cross-region during actual use, and constraint expressions are as follows:

wherein,and->Representing the maximum and minimum transmission capacity of inter-zone link l, respectively, < >>Nodes representing a network of inter-area links inject power transfer factors. />Representing the boundary interaction active power of region m within the t period.

The balance constraint conditions of the objective function of the day-ahead upper layer scheduling include:

/>

wherein,indicates that region m is wind power before day within t period,/->The predicted value of the load of the region m in the t period is represented.

Calculating the total running cost of the whole system by using the day-ahead upper layer scheduling objective functionMinimum +.>And->And->Boundary interaction active power +.f. of area m planned before day within period t is obtained>Upward and downward reserve capacity of the area m planned before the day in the period t +.>And->The area m planned before the day is bordered by interactive up and down reserve capacity +. >And->

The lower layer dispatching center builds a day-ahead lower layer dispatching objective function according to the total fuel cost of the area, and solves the day-ahead planned active output of the fuel unit in the area under the condition of the minimum total fuel cost of the area according to the day-ahead dispatching plan of the upper layer dispatching center. Each area can construct a corresponding day-ahead lower layer scheduling objective function according to own operation plan and related regulations, and each area is not required to be kept consistent.

The present embodiment provides the most basic day-ahead lower layer scheduling objective function:

wherein,for the objective function of the day-ahead lower layer schedule to calculate the total fuel cost of the zone，G _m Representing a set of fuel assemblies, a, within region m _i 、b _i And c _i For the fuel cost factor of the fuel assembly i, +.>Representing the planned active output of the fuel assembly i during the period t.

power balance constraint within region m:

direct current power flow constraint in region m:

the sum of the upward and downward reserve capacities provided by each fuel assembly in region m is equal to the reserve provisioning task of the upper layer scheduling allocation:

wherein,represents the wind power of the region m in the period t, k represents the node, BUS _m Representing the set of nodes within region m, +. >Representing the daily front boundary interactive active power of a node k in a region m in a period t; />Node injection power transfer factor for representing network of inter-node links>Representing the day-ahead planned active force of node k during period t, +.>Represents the wind power of node k in t period, D _k,t Representing a load predicted value of the node k in a t period; />Andrepresenting the upward and downward reserve capacities of the fuel assembly i during the t-period, respectively.

The daily economic dispatch model takes the result of the daily economic dispatch model as a starting point, and corrects the result of the daily economic dispatch model. The following describes an intra-day economic dispatch model constructed according to the correction quantity form of the three types of time-varying relation curves.

And uploading correction forms corresponding to the three types of time-varying relation curves to an upper layer scheduling center by the lower layer scheduling center, and correcting the daily front scheduling plan under the condition that the total correction cost of the whole system is minimum by taking the total correction cost of the whole system as a construction daily upper layer scheduling objective function according to the correction forms corresponding to the three types of time-varying relation curves and the daily front scheduling plan, the daily front and daily inner prediction error correction.

The expression of the pre-day and intra-day prediction error correction amounts is as follows:

wherein,wind power prediction value representing the time period t of the time-of-day region m, < >>And representing the wind power predicted value of the region m before the day in the period t.

In the daily economic dispatch model, an upper dispatch center aims at minimizing the total correction cost of the whole system. The total system correction cost comprises the fuel cost of the output adjustment of the whole system fuel unit, the whole system load shedding correction expected cost and the whole system air discarding correction expected cost. The daily upper layer scheduling objective function is as follows:

wherein,for total correction cost of whole system, < > for>Correction form of the curve representing the total fuel assembly output versus the total fuel cost of the region m during the t period, +.>Indicating that region m is reserved to the system in t periodCorrection of the upper backup capacity versus system load demand, in the form of a +>A correction quantity form of a relation curve of reserved downward standby capacity of the system and expected system waste wind of the region m in a t period is represented; ΔP _m,t Indicating that region m is active force correction amount in t period,/->And->Correction amounts respectively representing upward and downward reserve capacities of the region m in the t period, +.>And- >The correction amounts of the upward and downward reserve capacities of the boundary interaction of the region m in the period t are respectively represented.

constraint on spare provisioning capability for each zone:

wherein,and->Respectively represent the fuel in the region m in the day-ahead schedule in the period tA correction quantity form of a relation curve of unit output and upward and downward standby providing capability; ΔP _m,t The active force correction amount of the region m in the t period is shown.

the physical meaning of the intra-day power correction, namely, the expression that the power balance of each area under the condition of updating the prediction information is satisfied by adjusting the active power output of each area is as follows:

correction amountAnd->The relation of the corresponding day-ahead dispatching results is as follows:

wherein,indicating the pre-and intra-day prediction error correction, < >>Representing the correction of the boundary interaction active power of the region m in the period t.

And the lower layer scheduling center constructs a daily lower layer scheduling objective function according to the regional total fuel correction cost, and corrects the daily planned active output of the regional fuel unit under the condition that the regional total fuel correction cost is minimum. As with the day-ahead economic dispatch model, each region can construct a corresponding day-ahead lower layer dispatch objective function according to its own operation plan and related regulations, without requiring that each region remain consistent.

Taking the day-ahead lower layer scheduling objective function provided in this embodiment as an example, the corresponding day-ahead lower layer scheduling objective function is:

/>

wherein,correction costs for regional total fuel, < >>A correction amount representing the planned active output of the fuel assembly i in the t period.

According to the trans-regional power dispatching method, the lower dispatching center integrates regional information into three time-varying relation curves, and performs information interaction with the upper dispatching center, so that the unit and load information in an independent region can be effectively protected, and the dispatching interaction information data security is improved. And according to the daily economic scheduling models and the daily economic scheduling models constructed by the three time-varying relation curves, the cross-regional standby optimization global optimal scheduling decision can be effectively supported.

Example two

In order to verify the effectiveness of the transregional power scheduling method, the implementation constructs an economic scheduling model which spans two time scales of 24 hours before the day and 4 hours in the day, the result of the economic scheduling model before the day is the input information of the economic scheduling model in the day, and three time-varying relation curves are mainly analyzed to be used as the accuracy of integrated information and the standby distribution effect of the economic scheduling model before the day and the economic scheduling model in the day constructed according to the three time-varying relation curves.

The present embodiment will introduce the results of the day-ahead economic dispatch model, and the optimization results of the first round of day-ahead economic dispatch model, where the data includes the active power and standby allocation results for 16 dispatch periods during 0:00 to 4:00 of day.

The application focuses on the information integration and interaction of each region in the power dispatching system, when the two-region system displays the integrated information of each region, only the data of two regions in each type of time-varying relation curve are used as comparison, and the information difference is displayed and compared more singly, so that the embodiment adopts a three-region system as shown in fig. 10 to carry out small-system simulation analysis.

The three-area system comprises three wind power stations and six thermal power units. Parameters of the six thermal power generating units are shown in table 1.

TABLE 1 basic parameters of thermal power generating unit

As can be seen from Table 1, a thermal power unit with a climbing capacity of 100MW installed and 12.50MW/15min and a thermal power unit with a climbing capacity of 80MW installed and 10MW/15min are configured in each area. Meanwhile, the fuel cost of the unit under the same active output condition is G ₅ ≥G ₃ ≥G ₁ And G ₆ ≥G ₄ ≥G ₂ . Thus, the fuel cost for zone 1 is the lowest for the full system and the highest for zone 3, assuming the same load demand.

Referring to fig. 10, a wind farm is connected to each area, and the corresponding access nodes are node 3, node 6 and node 9. The installed capacity of the wind farm 1 and the wind farm 2 is 40MW, and the installed capacity of the wind farm 3 is 80MW, namely, the installed capacity of the wind in the region 3 in the system is the largest. Historical prediction error data for three wind farms is shown in fig. 11, where plot (a) is a plot of wind power prediction error for region 1 during a scheduled time period, plot (b) is a plot of wind power prediction error for region 2 during a scheduled time period, and plot (c) is a plot of wind power prediction error for region 3 during a scheduled time period. Comparing the graphs (a), (b) and (c) in fig. 11, it can be seen that the uncertainty introduced by the wind power prediction error in the region 3 is significantly larger than in the other two regions. The duty cycle of the three zones in the system was 30.10%,28.80% and 40.10%, respectively. In addition, the transmission capacity of the three inter-zone interconnections is 100MW.

FIG. 12 shows load prediction curves, wind power prediction curves and net load prediction curves for time scales before and during the day, wherein graph (a) is a prediction curve of load during a scheduling period, graph (b) is a prediction curve of wind power output during the scheduling period, and graph (c) is a prediction curve of net load during the scheduling period. The data curves presented in fig. 12 are each without regard to load prediction error, where the black curve represents the day-ahead data and the gray curve represents the day-in data. As can be seen from fig. 12 (a), the load demand in the region 3 is the largest. As can be seen from fig. 12 (b), the wind power output is maximum in the region 3. As can be seen from fig. 12 (c), the area 3 payload requirement is the highest for the whole system. The payload of region 1 is greater than region 2 between scheduling periods 0 to 10, while the payload of region 2 is greater than region 1 between scheduling periods 10 to 16.

According to the method for integrating three types of time-varying relation curves, the first two types of time-varying relation curves of the three areas in FIG. 10, namely PC, are calculated _m,t ，PRup _m,t PRdn _m,t As shown in fig. 13. FIG. 13 is a graph showing the relationship between the total active power output value of the thermal power plant and the fuel cost in three regions, the region providing upward standby capability and the region providing downward standby capability, respectively, wherein the graph (a) is a relationship between the total active power output value of the thermal power plant and the fuel cost PC _m,t Graph (b) is a relation curve PRup of total active output value and area of thermal power unit for providing upward standby capability _m,t Graph (c) is a relation curve PRdn of total active output value and area of thermal power unit for providing downward standby capability _m,t A drawing. Taking the graph (a) in fig. 13 as an example, the abscissa of the subgraph is the total active power output value of the thermal power generating unit in the corresponding region, and the ordinate is the total fuel cost in the corresponding region. Comparing the first time-varying relationship of the three zones, it can be seen that when the three zones provide the same active force, zone 3 has the highest fuel cost, zone 2 times, and zone 1 is the lowest. This is similar to the "fuel cost G under the same active output" obtained from analysis of specific parameters of each thermal power generating unit ₅ ≥G ₃ ≥G ₁ And G ₆ ≥G ₄ ≥G ₂ The results of "are consistent.

Through simulation calculation, planned daily and daily output of six thermal power units in the system are shown in FIG. 14, wherein graph (a) is a planned output curve chart of six thermal power units on a daily time scale, and graph (b) is dailyPlanned output curves of six thermal power generating units under a time scale. As can be seen from a comparison of fig. 14 (a) and (b), the output plans of the respective generator sets are adjusted by a certain amount due to the update of the prediction information. Wherein, G with large installed capacity and strong climbing capacity ₁ 、G ₃ And G ₅ The main load of the system is borne. Compared with the thermal power units with the same installed capacity and climbing capacity, the G with the lowest fuel cost can be known ₁ G with the highest load and highest fuel cost ₅ The load is least. Similarly, G ₂ ，G ₄ And G ₆ A similar trend is presented. This trend is shown in units of the same installed capacity in the figure, G ₁ 、G ₂ The magnitudes of the curves are all higher than G ₃ 、G ₄ Curve sum G ₅ 、G ₆ The magnitude of the curve. The distribution trend shows that the time-varying relation curve of the first kind can be used for describing the difference in load bearing efficiency of each region. To more intuitively show this trend, the total planned active power output of the thermal power generating unit in each region is shown in fig. 15, where fig. (a) is a total planned power output curve graph of the thermal power generating unit in three regions on a time scale before day, and fig. (b) is a total planned power output curve graph of the thermal power generating unit in three regions on a time scale before day.

Fig. 16 shows the planned power transfer curve on the inter-zone tie at both the day-ahead and day-in time scales, where fig. (a) is the inter-zone tie power tide graph at the day-ahead time scale and fig. (b) is the inter-zone tie power tide graph at the day-in time scale. The selection rules of the positive direction of the tidal current among the three areas are as follows: in fig. 10, (1) the power supplied from the line taking area 1 to the area 2 is positive, (2) the power supplied from the line taking area 1 to the area 3 is positive, and (3) the power supplied from the line taking area 2 to the area 3 is positive. As can be seen from the trend results in fig. 16, during this period, zone 1 provides power support to both zone 2 and zone 3, and zone 2 is also delivering power to zone 3. This is mainly because the net load in the region 3 is the largest in the whole system, and the fuel cost of the thermal power generating unit in the region 3 is the highest in the whole system, so that the other two regions supply power to the region 3 can reduce the total running cost of the whole system. Further comparing the power flow plans on the (1) line and the (3) line, it is known that the active power transmitted from the region 2 to the region 3 is greater than the active support provided from the region 1 to the region 2 in the scheduling period 0 to 10. This is because during this period, zone 2 has a lower payload and less active demand on its own, so zone 2 is also providing active support for zone 3. However, during the scheduling periods 10 to 16, as the payload in region 2 increases, the active power provided by region 2 to region 3 is progressively lower than the active support provided by region 1 to region 2. This indicates that during this period zone 1 is delivering power to both zone 2 and zone 3, thereby ensuring that the total fuel cost of the system is minimal.

Fig. 17 shows the comparison of the upward reserve capacity provided by each of the areas with the upward reserve resource situation obtained by the final allocation, wherein fig. (a) provides the upward reserve capacity with the upward reserve resource situation for area 1, fig. (b) provides the upward reserve capacity with the upward reserve resource situation for area 2, and fig. (c) provides the upward reserve capacity with the upward reserve resource situation for area 3. Fig. 18 shows the downward backup capacity provided by each and the resulting downward backup resource situation allocated, wherein fig. (a) provides a downward backup capacity versus downward backup resource situation owned by region 1, fig. 2 provides a downward backup capacity versus downward backup resource situation owned by region 2, and fig. 3 provides a downward backup capacity versus downward backup resource situation owned by region 3.

As can be seen from fig. 17, the upward reserve capacity provided by both area 1 and area 2 is greater than the upward reserve resources allocated by both areas, indicating that area 1 and area 2 are upward reserve output areas in the system. In contrast, the upward backup capacity provided by the area 3 is always smaller than the upward backup resource allocated thereto in the period, that is, the area 3 is an input area of the upward backup resource in the system. As can be seen from the three area prediction error histories shown in fig. 11, the allocation result shows that after the cross-regional power scheduling method of the present invention is adopted, the system still has high-efficiency spare resource allocation capability, and upward spare resources in the whole system are allocated to the area 3 with larger prediction error more, so that the load shedding cost expected value of the whole system is reduced. Similarly, FIG. 18 illustrates the allocation of the system to the lower standby resources. Because the positive and negative errors of wind power prediction in the area 3 are the maximum of the whole system, based on the transregional power scheduling method, the upper layer of system scheduling also distributes the downward standby resources in the system to the area 3 more, thereby reducing the expected value of the wind discarding cost of the whole system.

Further comparing the magnitudes of the solid lines represented by the reserve capacity provided by each of the zones in fig. 17 and 18, it can be seen that zone 3 provides the reserve capacity upwardly that is equal to zone 2 and greater than zone 1, while zone 3 provides the reserve capacity downwardly that is the lowest throughout the system. This is because, although the thermal power plants of each zone are assembled identically, zone 3 assumes the least load requirements within the overall system. Thus, both thermal power plants in region 3 are at a lower operating point. As can be seen from the schematic coupling relation between the output point and the standby providing capability of each thermal power unit shown in fig. 7, two thermal power units in the area 3 have a larger upward space to provide upward standby capacity for the system, and a relatively smaller downward space to provide downward standby capacity for the system. The standby providing capability level shown in fig. 17 and 18 is presented.

Two points A1 and A2 shown in fig. 19 (a) are selected on the curve a of fig. 17 (a). The wind power prediction value in the corresponding scheduling period of the point A1 and the point A2 can be used for taking out a relation curve of reserved upward standby-region load shedding expected in a corresponding curve library, as shown in a graph (b) and a graph (c) in fig. 19, wherein the graph (b) is a relation curve of upward standby capacity and load shedding expected value, which are owned by a region 1 of the point A1 in the graph (a), and the graph (c) is a relation curve of upward standby capacity and load shedding expected value, which are owned by a region 1 of the point A2 in the graph (a). Comparing the graph (b) with the graph (c), it can be seen that when the upward spare capacity with the same size is reserved, the cut load of the area in the corresponding period of the point A1 is expected to be higher. Therefore, the economic dispatch model of the invention distributes more upward spares at the point A1, thereby reducing the total possible cut load of the area. Similarly, fig. 19 (d), e) and f) analyze the downward backup, where fig. d is a graph of the downward backup capacity possessed by the area 1 in the scheduling period, fig. e is a graph of the downward backup capacity possessed by the area 1 at the point B1 in fig. d, and fig. f is a graph of the downward backup capacity possessed by the area 1 at the point B2 in fig. d, and the expected value of the abandoned wind. The above analysis illustrates that the information-integrated scheduling model still has the ability to efficiently allocate spare resources across scheduling periods.

Fig. 20 is a graph of the effect of cross-regional allocation of upward standby resources in each region under the same time section, taking the upward standby capacity obtained by planning allocation of three regions corresponding to a scheduling period 0 as an example, in the scheduling period 0, the "reserved upward standby-region load shedding expected" relationship curves in the region 1, the region 2, the region 10 and the region 3 can be taken out according to the predicted values of the three wind power plants in the region 1, the region 2 and the region 3, as shown in the figures (b), (c) and (d), respectively. Wherein graph (b) is a graph of upward backup capacity versus cut load expected value for region 1 at point 1 in graph (a), graph (c) is a graph of upward backup capacity versus cut load expected value for region 2 at point 2 in graph (a), and graph (d) is a graph of upward backup capacity versus cut load expected value for region 3 at point 3 in graph (a). As can be seen from curve comparison, when the same upward spare capacity is reserved, the corresponding area cut load is expected to be area 3> area 1> area 2, i.e. the effect of reserving the upward spare capacity in area 3 is better than that of the other two areas. The standby allocation result obtained by adopting the day-ahead and day-ahead economic scheduling model of the invention is just that more upward standby resources are allocated to the area 3 at the moment.

Similarly, fig. 21 is a graph showing the effect of cross-regional allocation of downward spare resources for each region at the same time section, where fig. (a) is a graph of downward spare capacity owned by each region in the same scheduling period, fig. b is a graph of downward spare capacity owned by region 1 at point 1 in fig. (a) versus expected value of waste wind, fig. c is a graph of downward spare capacity owned by region 2 at point 2 in fig. (a) versus expected value of waste wind, and fig. d is a graph of downward spare capacity owned by region 3 at point 3 in fig. (a) versus expected value of waste wind. As with the analysis process of fig. 20, fig. 21 illustrates that the information-integrated scheduling model still has the ability to efficiently allocate spare resources across regions.

In order to fully verify the effect of intra-day optimization on inter-area power and standby interaction quantity correction in the economic dispatch model and the importance of reserved standby transmission channels of the tie lines, the embodiment sets the following two comparison models.

The comparison model 1 is a daily time scale tie line power correction process of removing upper layer scheduling on the basis of the economic scheduling model. The inter-area power interaction quantity and the inter-area standby interaction quantity planned before each area day are not corrected on the intra-day scale, and the running point of the thermal power unit is readjusted only in the area so as to stabilize unbalanced power introduced by the update of the prediction information.

The comparison model 2 is based on the economic dispatch model of the invention, and does not consider the influence of the spare transmission capacity on the line on the tide. I.e. not reserving sufficient link transmission channels for cross-regional up and down spare capacity. It should be noted that the comparative example needs to recalculate the truly available cross-regional upward and downward reserve capacity after obtaining the scheduling results of the model.

In addition to the two comparative models, in order to more accurately analyze the influence of the transmission capability of the inter-area interconnecting line on different models, three sets of line parameters are set as input data for simulation analysis. The Case 1 keeps original parameters unchanged, the maximum transmission capacity of the three inter-area connecting lines is 100MW, the Case 2 compresses the transmission capacity of the connecting lines of the number (2) and the number (3) connected with the area 3 to 15MW, and the Case 3 further compresses the transmission capacity of the connecting lines of the number (2) and the number (3) to 10MW. Tables 2, 3 and 4 show the system operating costs for the three models under the three examples, respectively.

Table 2, economic analysis of comparative model 1

Table 3, economic analysis of comparative model 2

TABLE 4 economic analysis of economic dispatch model according to the invention

Comparing tables 2 and 4, compared with the comparison model 1, the economic dispatch model of the invention has the advantages that the running cost of the whole system is reduced by 0.14% under Case 1, 2.17% under Case 2 and 4.54% under Case 3. This is because after the intra-day prediction information is corrected, the interactive power and the spare allocation result of each area of the original plan can no longer ensure the minimum total running cost of the whole system, and the influence is more obvious along with the shrinking of the transmission capacity of the inter-area connecting line. As can be seen from comparing table 3 with table 4, when the transmission capacity of the inter-area connection line is sufficient, the system operation cost calculated by the comparison model 2 and the economic dispatch model according to the present invention is consistent. And when the transmission capacity of the connecting line is gradually contracted, compared with the total operation cost of the comparison model 2, the economic dispatch model is reduced by 5.28% under Case 2 and 9.62% under Case 3. This is because the comparative model 2 fails to reserve transmission channels for the cross-regional reserve capacity in advance in the optimization process, resulting in that the partially reserved cross-regional reserve capacity is not usable due to transmission blockage, thereby increasing the cut load and the expected cost of wind curtailment of the system.

Example III

The standard IEEE 118 node system is adopted as a representative of an actual power system, so that the applicability of the transregional power scheduling method in the actual system is verified. Because the resource allocation effect of the economic dispatch model disclosed by the invention is fully verified in the three-area small system simulation example of the second embodiment, the accuracy of the integrated information of three types of time-varying relation curves is emphasized.

In this embodiment, a partitioning method of a standard IEEE 118 node system is adopted, where the system includes three wind farms, which are respectively located in a node 12 in a region 1, a node 54 in a region 2, and a node 106 in a region 3, and installed capacities of the three wind farms are divided into 500MW, 1000MW, and 500MW. Furthermore, the load distribution ratios of the three regions were 22.70%, 41.60% and 35.70%. FIG. 22 shows load prediction curves, wind power prediction curves and net load prediction information of each region on a day-ahead and day-ahead time scale, wherein (a) is a prediction curve of load in a scheduling period, (b) is a prediction curve of wind power output in the scheduling period, and (c) is a prediction curve of net load in the scheduling period, and the gray curve represents day-ahead information and the black curve represents day-ahead information. As can be seen from fig. 22, the load demand of the region 1 is the minimum system-wide, and the load demand of the region 2 is the maximum system-wide. The wind power output level of region 2 is much higher than the other two regions. In combination with wind power and load forecast information, region 2 has the highest overall system payload requirement, while region 1 has the lowest overall system payload requirement.

Fig. 23 shows the first two types of time-varying relationships obtained by the method of the present invention, namely, the "regional total thermal power unit output-regional total fuel cost", "regional total thermal power unit output-regional provide upward reserve capability", and "regional total thermal power unit output-regional provide downward reserve capability" relationship. Wherein, the graph (a) is a graph of the relation between the total active output value and the fuel cost of the thermal power unit, the graph (b) is a graph of the relation between the total active output value and the area of the thermal power unit, which provides upward reserve capacity, and the graph (c) is a graph of the relation between the total active output value and the area of the thermal power unit, which provides downward reserve capacity. Taking the graph (a) in fig. 23 as an example, it can be seen that the region 1 curve corresponds to the highest region total fuel cost for the same region total active power, which is determined by the fuel cost coefficient of the thermal power generation unit in region 1. The curve illustrates that the unit cost of power generation for zone 1 is higher, while zones 2 and 3 are adapted to bear more of the active demands within the system.

FIG. 24 shows the upward backup capacity provided by each region in the 118 node system versus the resulting upward backup resource situation for the final allocation, wherein FIG. (a) provides an upward backup capacity versus an upward backup resource situation for region 1, FIG. (b) provides an upward backup capacity versus an upward backup resource situation for region 2, and FIG. 3 provides an upward backup capacity versus an upward backup resource situation for region 3. Fig. 25 shows the downward backup capability provided by each of the 118 node systems versus the resulting downward backup resource situation allocated, wherein fig. (a) provides a downward backup capability versus downward backup resource situation owned by region 1, fig. 2 provides a downward backup capability versus downward backup resource situation owned by region 2, and fig. 3 provides a downward backup capability versus downward backup resource situation owned by region 3.

As can be seen from fig. 24, the upward reserve capacity provided by the area 1 and the area 3 is greater than the upward reserve resource allocated by the area 1 and the area 3, that is, the upward reserve resource output area of the system. In contrast to zones 1 and 3, zone 2 provides less than half of its allocated upward reserve capacity, i.e., zone 2 is the upward reserve resource entry area of the system. The system allocates more upward backup resources to areas containing more wind power, thereby reducing the cut load expected cost of the whole system. Similarly, FIG. 25 illustrates the allocation of the system to the lower standby resources. Therefore, when the time-varying relation curve is used as the interaction information of the upper and lower scheduling centers, the upper scheduling center can still realize the efficient allocation of the standby resources.

In order to fully verify the accuracy of the integrated information of the three types of time-varying relation curves, the embodiment establishes a complete information tuning model which is not layered as a comparison model. The model mainly shows the cross-region optimization effect under the condition of lossless information interaction. The operating costs for each zone and the overall system for both models are shown in tables 5 and 6.

TABLE 5 general tuning model economic analysis of complete information for this example

TABLE 6 economic dispatch model economic analysis according to the invention

As can be seen from tables 5 and 6, compared with the hierarchical economic dispatch model constructed by three time-varying relation curves and the unified dispatch model of non-hierarchical complete information based on the invention, the two models have only 0.015% difference between the optimized system running cost, and the difference mainly comes from numerical calculation errors in the optimizing process.

Fig. 26 further shows the total thermal power unit output results of each region calculated by using the complete information tuning model and the economic dispatch model according to the present invention, wherein the solid line represents the complete information tuning model, and the dotted line represents the economic dispatch model according to the present invention. As can be seen from fig. 26, the active output plans obtained by the two models are basically consistent, which illustrates that the invention can accurately depict the complex relationships among the output, the fuel cost, the upward and downward standby providing capability and the regional wind curtailment and the load shedding expectation of the thermal power unit in the region, and realize the efficient distribution of power and standby resources while protecting the specific unit information of each region.

In the embodiment, under the condition of different input data, the calculation time of the economic dispatch model is counted. Fig. 27 shows the calculation time of the solution of the economic dispatch model according to the present invention under twenty sets of different input data, where fig. (a) is a calculation time statistical diagram of an upper dispatch center in a day-ahead economic dispatch model, fig. b is a calculation time statistical diagram of a lower dispatch center in a day-ahead economic dispatch model, fig. c is a calculation time statistical diagram of an upper dispatch center in a day-ahead economic dispatch model, and fig. d is a calculation time statistical diagram of a lower dispatch center in a day-ahead economic dispatch model.

The data shows that in the day-ahead scheduling, the average time for cross-regional power and standby collaborative optimization is 8.45 seconds by the upper layer scheduling center, and the average time for economic scheduling in the region is 2.00 seconds by the lower layer scheduling center. In daily scheduling, the upper layer scheduling center needs 2.27 seconds to finish solving on average, and the lower layer scheduling center needs 1.11 seconds to finish on average. The statistical result proves that the calculation speed of the economic dispatch model meets the requirement of an actual power system.

It is apparent that the above examples are given by way of illustration only and are not limiting of the embodiments. Other variations and modifications of the present invention will be apparent to those of ordinary skill in the art in light of the foregoing description. It is not necessary here nor is it exhaustive of all embodiments. And obvious variations or modifications thereof are contemplated as falling within the scope of the present invention.

Claims

1. A cross-regional power scheduling method, comprising:

the lower layer scheduling center uploads the three types of time-varying relation curves and wind power and load prediction curves of all areas to the upper layer scheduling center, the upper layer scheduling center constructs a daily upper layer scheduling objective function according to the three types of time-varying relation curves and the total running cost of the whole system, calculates boundary interaction active power of all areas, upward and downward standby capacities of boundary interaction, total active output of the areas and upward and downward standby capacities under the condition of minimum total running cost of the whole system, and serves as a daily scheduling plan of the upper layer scheduling center;

2. The transregional power scheduling method of claim 1, wherein the day-ahead upper layer scheduling objective function is:

wherein,for the total running cost of the whole system, m represents an AREA, AREA represents an AREA set, T represents a scheduling period, and T represents a scheduling period; PC (personal computer) _m,t A curve representing the relation between the total fuel assembly output and the total fuel cost of zone m during period t,/->Representing the total active force of region m over a period t; />Represents the cut load price of region m, +. >RLS representing the wind curtailment price of region m _m,t Representing the expected relation curve of the upward reserve capacity of the system and the system cut load of the region m in the period t, RWC _m,t Representing a system reserved downward standby capacity and system waste wind expected relation curve of the region m in a t period; />And->Representing the upward and downward reserve capacity of zone m during period t, < >>And->Representing the upward and downward reserve capacities of the boundary interaction of region m during the period t, respectively;

wherein,represents the boundary interaction active power of region m within the t period,/for>Representing the wind power generation before day of the region m in the t periodRate of->A predicted value representing the load of the region m in the t period;

calculating the total running cost of the whole system by using the day-ahead upper layer scheduling objective functionIn the smallest caseAnd->And->Boundary interaction active power +.f. of area m planned before day within period t is obtained >Upward and downward reserve capacity of the area m planned before the day in the period t +.>And->The area m planned before the day is bordered by interactive up and down reserve capacity +.>And->

3. The transregional power scheduling method of claim 2, wherein the constraint condition of the day-ahead upper layer scheduling objective function further comprises:

inter-zone tie constraint:

4. A transregional power scheduling method according to claim 3, wherein the day-ahead lower layer scheduling objective function is:

wherein,represents the wind power of the region m in the period t, k represents the node, BUS _m Representing the set of nodes within region m, +.>Representing the daily front boundary interactive active power of a node k in a region m in a period t; / >Node injection power transfer factor for representing network of inter-node links>Representing the daily planned active force of node k during period t,represents the wind power of node k in t period, D _k,t Representing a load predicted value of the node k in a t period; />And->Respectively representing the upward and downward reserve capacities of the fuel assembly i during the period t;

calculating the total fuel cost of the area by using the day-ahead lower layer scheduling objective functionMinimum +.>Obtaining the daily planned active output of the fuel unit i in the t period>

5. The transregional power scheduling method of claim 4, wherein the intra-day upper layer scheduling objective function is:

wherein,total correction cost for whole system，/>Correction form of the curve representing the total fuel assembly output versus the total fuel cost of the region m during the t period, +.>Correction quantity form of relation curve of reserved upward reserve capacity of system and system cut load expected in t period of representing region m>A correction quantity form of a relation curve of reserved downward standby capacity of the system and expected system waste wind of the region m in a t period is represented; ΔP _m,t Indicating that region m is active force correction amount in t period,/->And->Correction amounts respectively representing upward and downward reserve capacities of the region m in the t period, +. >And->Correction amounts respectively representing upward and downward reserve capacities of boundary interaction of the region m in the t period;

wherein,indicating the pre-and intra-day prediction error correction, < >>A correction amount representing boundary interaction active power of the region m in the t period;

6. The method of transregional power scheduling of claim 5, wherein the pre-and intra-day prediction error correction amountsWherein (1)>Wind power prediction value representing the time period t of the time-of-day region m, < >>And representing the wind power predicted value of the region m before the day in the period t.

7. The transregional power scheduling method of claim 4, wherein the intra-day lower layer scheduling objective function is:

8. The transregional power scheduling method of claim 1, wherein the functional expression of the relationship between regional total fuel unit output and total fuel cost is:

wherein,is the total fuel cost of the fuel unit, a _i 、b _i And c _i Is the fuel cost factor for the fuel assembly i,representing the planned active output of the fuel assembly i during the period t.

9. The transregional power scheduling method of claim 1, wherein the functional expression of the relationship between regional fuel unit output and regional backup capacity is:

10. The transregional power scheduling method of claim 1, wherein the functional expression of the system reservation upward backup capacity versus system cut load expected relationship is: