CN114548522A - Two-step system optimization method and device suitable for clearing and checking of provincial standby market - Google Patents

Two-step system optimization method and device suitable for clearing and checking of provincial standby market Download PDF

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CN114548522A
CN114548522A CN202210084451.2A CN202210084451A CN114548522A CN 114548522 A CN114548522 A CN 114548522A CN 202210084451 A CN202210084451 A CN 202210084451A CN 114548522 A CN114548522 A CN 114548522A
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张彦涛
昌力
胡宏
曹荣章
侯勇
丁恰
胡朝阳
周毅
陈新仪
朱敏健
张小白
高志鹏
端伟彬
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NARI Nanjing Control System Co Ltd
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Abstract

The invention provides a two-step system optimization method and a two-step system optimization device suitable for clearing and checking of a provincial reserve market.

Description

Two-step system optimization method and device suitable for clearing and checking of provincial standby market
Technical Field
The invention relates to the technical field of power dispatching automation, in particular to a two-step system optimization method suitable for clearing and checking of a regional power grid inter-provincial standby market.
Background
Aiming at the practical situations that the current power grid operation faces uncertain factor increase and the like caused by more external electricity and rapid development of new energy in a region, the time scale of resource allocation is expanded from the middle to long term to the day before and in the day. When the predicted reserve capacity of a regional provincial (municipal) power grid can not meet the safety requirement of the power grid, a reserve auxiliary service market is started, resources in the whole region are mobilized to support, the main power generation body in the whole network is stimulated in a marketization mode to release the regulation potential, inter-provincial mutual aid is realized, the capability of the regional power grid for resisting uncertain risks is improved, and the regional clean low-carbon development is assisted.
At present, a clearing mechanism of the inter-provincial standby auxiliary service is provided, but the existing safety constraint unit combination and economic dispatching algorithm generally take planning under a three-public dispatching mode of a provincial power grid and bidding clearing under a market mode as optimization objects, or further analysis of the receiving capacity of a regional power grid and the provincial power grid, the combined optimization and planning of the scale of the power grid in the east China area are not really realized, a corresponding optimization model is not established aiming at a standby auxiliary service cross-provincial dispatching mechanism,
disclosure of Invention
The invention provides a two-step optimization method for adapting to clearing checking of the regional power grid inter-provincial standby market aiming at the problem that a safety checking optimization method for adapting to clearing results of the inter-provincial standby market is not provided in the prior art, and a domestic marketized standby auxiliary service cross-provincial dispatching clearing mechanism is combined, solves the problems that the unit combination solving efficiency is low, a non-pertinence optimization model is not provided for checking the regional power grid inter-provincial standby clearing results and the like in the prior art, provides a practical and effective optimization method for checking the safety and feasibility of the regional power grid inter-provincial standby market clearing results, and fills the technical blank in the field of clearing checking of the inter-provincial standby market.
The invention adopts the following technical scheme.
On one hand, the invention provides a two-step system optimization method suitable for clearing and checking of the provincial reserve market, which comprises the following steps:
step S1, establishing inter-province standby market clearing and checking scene data;
step S2, establishing a provincial standby market clearing check model;
step S3, setting a first time period granularity and a first-step optimized decision variable of the check model based on the scene data of S1, and executing a first-step safety constraint unit combination optimization solution to obtain a unit result, wherein the unit result comprises a gas turbine time period granularity start-stop state;
step S4, resetting the second time interval granularity of the checking model and the decision variable of the second step optimization, wherein the second time interval granularity is smaller than the first time interval granularity, and the starting and stopping state of the fixed time interval granularity is set according to the time interval granularity of the gas turbine;
executing a second-step safety constraint economic dispatching optimization solution based on the scene data of S1 to obtain a section, a branch and an equipment load flow;
and step S5, judging whether the cross section, branch or equipment out-of-limit exists, if so, re-clearing is needed, otherwise, indicating that the inter-provincial standby market clearing result can be executed.
Optionally, the method further includes step S6, which is to output the unit combination, the unit output, the port variation and the power flow information obtained by the first step optimization and the second step optimization solution in each time period.
Further, the scene data in step S1 includes system data, unit data, tie line plan data, load data, unit group data, and sensitivity data.
Further, the system data comprises time period information, system load and system standby requirements; the unit data comprises unit basic information, unit calculation parameters, a unit initial state, a unit output limit value and a unit climbing rate; the tie-line plan data includes tie-line basic information and tie-line plan power; the load data comprises a bus load prediction; the machine group data comprises a machine group power limit value and a machine group electric quantity limit value; the sensitivity data comprises generating transfer distribution factors of the unit, the load injection power to the line and the section tide.
Further, in step S2, the objective function of the district power grid inter-provincial standby market clearing check model is as follows:
Figure BDA0003486936280000031
in the formula: n represents the total number of the units; t represents the total time period number; deltai,tThe output deviation cost of the unit i at the moment t is calculated;
Figure BDA0003486936280000032
respectively representing the receiving and sending increments (namely the opening variation) of the provincial power grid a time period t and deciding variables; mTLThe incremental penalty cost of provincial power grid sending is represented; mlA penalty factor representing a network flow constraint for the market clearing optimization line l;
Figure BDA0003486936280000033
respectively representing a forward power flow relaxation variable and a reverse power flow relaxation variable of a line l; NL is the total number of lines; msA penalty factor for representing network flow constraint of the market clearing optimized section s;
Figure BDA0003486936280000034
respectively representing a forward power flow relaxation variable and a reverse power flow relaxation variable of the section s; NS denotes the total number of sections.
The model respectively models the positive deviation and the negative deviation of the unit output adjustment to meet different distribution requirements of the unit on the positive deviation and the negative deviation, and the output adjustment cost of the unit i at the moment t can be obtained as follows:
Figure BDA0003486936280000035
wherein
Figure BDA0003486936280000036
In order to adjust the cost for a positive offset,
Figure BDA0003486936280000037
the cost is adjusted for negative offsets.
Furthermore, the constraint conditions of the export and cleaning check model of the standby market between regional power grids and provinces comprise distribution and power utilization balance constraint, distribution and provincial port sub-balance constraint, unit operation constraint, unit group constraint, network safety constraint and practicality constraint.
The unit operation constraints comprise unit output upper and lower limit constraints, unit maximum starting and stopping times constraints, unit minimum starting and stopping time constraints and unit climbing and landslide constraints.
The group constraint comprises group output constraint and group electric quantity constraint;
the network safety constraint comprises a line flow constraint and a section flow constraint;
the practical constraint comprises the constraint of fixed output of the unit and the constraint of the mode of starting and stopping the unit.
Further, based on the scene data of S1, setting the first time period granularity (time period granularity 1 hour, 1 day, 24 time periods) and the first optimized decision variables (including the start-stop of the combustion engine and the output of the thermal power generating unit) of the check model of step S2, generating a mathematical model suitable for a safety constraint unit combination algorithm, and solving to obtain the start-stop of the combustion engine and the output of the thermal power generating unit at the time period granularity.
Further, step S4 sets, based on the calculation scenario constructed in step S1, the optimized second-period granularity (the second-period granularity is smaller than the first-period granularity, the second-period granularity is 15 minutes, and the period of 1 day is 96) of the checking model in step S2 and the optimized decision variable (the output of the thermal power unit) in step S2, fixes the on-off state of the granularity of 15 minutes according to the on-off result state of the granularity of step S3 in hours, and outputs the checking model to the provincial reserve market and solves the checking model to obtain the 15-minute granularity output plan, the section \ branch and the equipment power flow of the combustion engine and the thermal power.
Further, step S5 determines whether there is a cross-section \ branch \ equipment out-of-limit based on the cross-section \ branch \ equipment power flow and the limit calculated in step S4, and if so, it indicates that the provincial standby market clearing result is unreasonable and needs to be cleared again; if the provincial standby market is not out of limit, the provincial standby market is clear, and tracking execution of each provincial power grid is issued.
Further, step S6 outputs the engine start/stop state calculated in step S3, and the unit output, the section \ the equipment \ the branch power flow and the availability information of the output result calculated in step S4 are sent to the provincial backup auxiliary service technical support system.
On the other hand, the invention also provides a two-step system optimization method device suitable for clearing and checking the spare market among provinces, which comprises the following steps: the system comprises a data acquisition module, a model establishing module, a first optimization solving module, a second optimization solving module and a clear result determining module;
the data acquisition module is used for acquiring inter-provincial standby market clearing and checking scene data;
the model establishing module is used for establishing a provincial standby market clearing check model;
the first optimization solving module is used for setting a first time period granularity and a first-step optimized decision variable of the checking model based on the scene data, and executing a first-step safety constraint unit combination optimization solving on the provincial standby market clearing checking model to obtain a first time period granularity start-up and shut-down state of the gas turbine;
the second optimization solving module is used for setting a second time interval granularity of the checking model and a decision variable of the second step of optimization, the second time interval granularity is smaller than the first time interval granularity, and the on-off state of the second time interval granularity is fixed according to the on-off state of the first time interval granularity of the combustion engine; performing a second-step safety constraint unit combination optimization solution on the provincial standby market clearing check model to obtain a section, a branch and an equipment trend;
and the clearing result determining module is used for judging whether the cross section, the branch or the equipment is out of limit or not, if so, clearing is required again, and if not, the clearing result of the provincial standby market can be executed.
Further, the objective function of the provincial standby market clearing check model established by the model establishing module is represented as follows:
Figure BDA0003486936280000051
in the formula: n represents the total number of the units; t represents the total time period number; deltai,tThe output deviation cost of the unit i at the moment t is calculated;
Figure BDA0003486936280000052
representing the accepted increment of the provincial grid a period t,
Figure BDA0003486936280000053
represents the send-out increment of the provincial grid a period t,
Figure BDA0003486936280000054
and
Figure BDA0003486936280000055
is a decision variable; mlA penalty factor representing a network flow constraint for the market clearing optimization line l;
Figure BDA0003486936280000056
respectively representing a forward power flow relaxation variable and a reverse power flow relaxation variable of a line l; NL is the total number of lines; msA penalty factor for representing network flow constraint of the market clearing optimized section s;
Figure BDA0003486936280000057
respectively representing a forward power flow relaxation variable and a reverse power flow relaxation variable of the section s; NS denotes the total number of sections, MTLAnd (4) representing the incremental penalty cost of provincial power grid delivery.
Compared with the prior art, the invention provides a two-step system optimization method for clearing and checking the inter-provincial standby market of the regional power grid based on the safety constraint unit combination and the economic dispatching algorithm principle, the calculation efficiency of the large-scale power grid safety constraint unit combination is optimized and improved through the unit combination with large time granularity in the first step, then the unit combination is fixed, the accuracy of safety constraint economic dispatching is improved by adopting the economic dispatching with small time granularity in the second step, the optimization model introduces dual balance constraints of provincial dispatching and oral sub-plan, and the integrated adjustment of the regional power grid direct dispatching unit and the provincial power grid dispatching unit is realized. The method relies on the mature theoretical basis of the algorithm, has universality, has high calculation efficiency and accuracy of two-step system optimization modeling, and fills the technical blank in the field of backup clearing and checking among provinces of regional power grids in China.
Drawings
FIG. 1 is a schematic diagram of the segment adjustment cost for positive deviation adjustment of the unit output according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a piecewise-adjusted cost function for positive deviation adjustment of the unit output according to an embodiment of the present invention;
fig. 3 is a two-step optimization method for clearing and checking the spare market among provinces of the regional power grid according to an embodiment of the present invention.
Detailed Description
An embodiment of the present invention is further described below with reference to fig. 3. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
As shown in fig. 3, the embodiment of the present invention includes the following steps:
step S1, establishing a check scene of the regional power grid inter-provincial standby market clearing;
step S2, establishing a district power grid inter-provincial standby market clearing check model;
step S3, setting the time interval granularity and the decision variables of the checking model of the step S2 based on the scene data of S1, and executing the first step of the combined optimization solution of the safety constraint unit;
step S4, setting time interval granularity and decision variables in the step S2 based on the scene data of the step S1 and the unit result of the step S3, and executing the second step of safety constraint economic dispatching optimization solution;
step S5, judging whether a fracture surface \ branch \ equipment out-of-limit exists;
and step S6, outputting the unit combination, the unit output, the port variation and the tide information in each time period.
The basic data in step S1 includes: 1) system data: time interval information and load sharing and saving; 2) the unit data: the method comprises the following steps of (1) generating set basic information, generating set calculation parameters, generating set energy quotation, generating set initial state, generating set electric power constraint and generating set climbing rate; 3) tie-line planning data: tie line basic information, tie line planned power; 4) load data: predicting the load of the bus; 5) a machine group: a machine group power limit value and a machine group electric quantity limit value; 6) sensitivity data: and generating transfer distribution factors of the unit and load injection power to the line and section tide.
The sensitivity data is obtained by obtaining the latest physical model and real-time operation mode data of the power grid and calculating by adopting a PQ decoupling method.
Step S2, the objective function of the district power grid inter-provincial standby market clearing check model is:
Figure BDA0003486936280000071
in the formula: n represents the total number of the units; t represents the total time period number; deltai,tThe output deviation cost of the unit i at the moment t is calculated;
Figure BDA0003486936280000072
respectively representing the receiving and sending increments (namely the opening variation) of the provincial power grid a time period t and deciding variables; mlA penalty factor representing a network flow constraint for the market clearing optimization line l;
Figure BDA0003486936280000073
Figure BDA0003486936280000074
respectively representing a forward power flow relaxation variable and a reverse power flow relaxation variable of a line l; NL is the total number of lines; msA penalty factor for representing network flow constraint of the market clearing optimization section s;
Figure BDA0003486936280000075
respectively representing a forward power flow relaxation variable and a reverse power flow relaxation variable of a section s; NS denotes the total number of sections.
The bias cost in the optimization objective is the result of the bias of the optimized output from the initial output, as further explained below. The model respectively models the positive deviation and the negative deviation of the unit output adjustment so as to realize different distribution requirements of the unit on the positive deviation and the negative deviation.
Unit output adjustment constraint modeling:
Figure BDA0003486936280000076
in the formula: pi,tThe output of the unit i after being adjusted at the moment t;
Figure BDA0003486936280000081
the initial output of the unit i at the time t is obtained;
Figure BDA0003486936280000082
the positive adjustment quantity of the unit i at the time t is obtained;
Figure BDA0003486936280000083
and (4) negative adjustment quantity of the unit i at the time t.
To facilitate the control distribution strategy of deviation, the piecewise adjustment cost of the deviation adjustment quantity of the unit can be introduced, and as shown in the figure, the adjustment cost also increases with the increase of the variation quantity.
After the incremental adjustment cost is increased in sections, the adjustment cost is increased rapidly along with the increase of the output variation of the unit, as shown in fig. 1. Different deviation distribution effects can be achieved by controlling the adjustment cost of each unit in each deviation section.
To pair
Figure BDA0003486936280000084
Segmentation is performed and incremental justification costs are set on each segment. The positive offset adjustment cost is thus:
Figure BDA0003486936280000085
in the formula: s is the total segment number of the piecewise function; delta+ i,t,sThe variable quantity of the unit i on the s-th section of the piecewise function at the moment t is a non-negative value; lambda [ alpha ]+ i,sAnd (5) adjusting the cost of the unit i in the s section of the piecewise function of the unit i.
The variable quantity of the output of the unit is expressed by sectional accumulation:
Figure BDA0003486936280000086
Figure BDA0003486936280000087
in the formula: p+ i,sIs the end power of each segment interval in the piecewise function.
Similar to the modeling method for positive deviations, the negative deviation adjustment cost and the negative deviation amount can be obtained as follows:
Figure BDA0003486936280000088
Figure BDA0003486936280000089
Figure BDA00034869362800000810
so far, the output adjustment cost of the unit i at the time t can be obtained as follows:
Figure BDA00034869362800000811
and step S2, the constraint conditions of the export and cleaning check model of the spare market between the provinces of the power grid in the area comprise distribution and distribution electricity balance constraint, distribution and provincial opening balance constraint, unit operation constraint, unit group constraint, network safety constraint and practicality constraint.
The distribution power distribution balance constraint is as follows:
Figure BDA0003486936280000091
in the formula: pi,tDetermining variables for the unit output at the time t in the period i; saThe method comprises the steps of (1) collecting provincial power grid a units; l isa,tThe system load of the provincial power grid a at the moment t; TLa,tA constant is set for the opening plan of the provincial power grid a at the moment t;
Figure BDA0003486936280000092
respectively representing the receiving increment and the sending increment of the provincial power grid a time period t and deciding variables.
Because the local power grid direct-regulating unit does not directly participate in the distribution and distribution power utilization balance, but superimposes the output of the local power grid direct-regulating unit on the provincial entrance sub-plan, the output adjustment of the local power grid direct-regulating unit is equivalent to the modification of the provincial entrance sub-plan, and in order to ensure that the provincial entrance sub-plan is not changed, the distribution and provincial entrance sub-balance constraint is introduced, wherein the specific expression is as follows:
Figure BDA0003486936280000093
in the formula: k iss,tA change amount of the power flow (a change amount with respect to the initial power flow) for the conservation section s period t; omegaaA provincial section showing that the sending end is provincial a; gamma-shapedaThe input end is represented as a provincial section of a provincial power grid a;
Figure BDA0003486936280000094
respectively representing the receiving and sending increments of the provincial power grid a time period t.
The unit operation constraints comprise unit output upper and lower limit constraints, unit maximum startup and shutdown times constraints, unit minimum startup and shutdown time constraints and unit climbing and landslide constraints.
The upper and lower limits of the unit output are restricted as follows:
Figure BDA0003486936280000095
wherein: alpha is alphai,tRepresenting the running state of the unit i in the time period t, and being an 0/1 variable; if the unit is in a shutdown state, then alphai,tThe unit output can be limited to 0 by the constraint condition; if the unit is in the on state, then alpha i,t1, the constraint condition is the conventional upper and lower limit constraint of output;
Figure BDA0003486936280000096
the maximum output force and the minimum output force of the unit i in the time period t are respectively.
The maximum startup and shutdown times of the unit are constrained as follows:
Figure BDA0003486936280000101
wherein the content of the first and second substances,
Figure BDA0003486936280000102
respectively the maximum starting times and the stopping times of the unit i; etai,tFor whether the unit i is switched to a starting state in a time period t, 0/1 variables are changed, wherein 0 represents that startup switching is not carried out, and 1 represents startup switching; gamma rayi,tIndicating whether the unit i is switched to the shutdown state during the time period t, 0/1 variable, 0 indicating that no shutdown switching is performed, and 1 indicating that shutdown switching is performed.
The minimum start-up and shut-down time constraint of the unit is as follows:
Figure BDA0003486936280000103
Figure BDA0003486936280000104
wherein, TU、TDFor minimum continuous start-up time and minimum continuous stop time, alpha, of the uniti,kRepresenting the running state of the unit i in the time period k.
The unit climbing and landslide restriction is as follows:
Figure BDA0003486936280000105
Figure BDA0003486936280000106
wherein the content of the first and second substances,
Figure BDA0003486936280000107
the maximum upward slope climbing rate of the unit i,
Figure BDA0003486936280000108
for unit i maximum downward ramp rate, Pi,t-1Is the output of the unit i in the t-1 time period.
The group constraint comprises group output constraint and group electric quantity constraint;
the output constraint of the machine group is as follows:
Figure BDA0003486936280000111
wherein the content of the first and second substances,
Figure BDA0003486936280000112
is the maximum and minimum output of the machine group j in the time period t.
The electric quantity constraint of the machine group is as follows:
Figure BDA0003486936280000113
wherein the content of the first and second substances,
Figure BDA0003486936280000114
the upper limit of the electric quantity of the machine group j on the day of birth and departure.
The network safety constraint comprises a line power flow constraint and a section power flow constraint;
the line flow constraint is as follows:
Figure BDA0003486936280000115
wherein the content of the first and second substances,
Figure BDA0003486936280000116
is the tidal current transmission limit of line l; gl-iOutputting a power transfer distribution factor for a generator of a line l by a node where a unit i is located; gl-jTransferring distribution factors for the output power of the node where the tie line j is located to the line l; k is the number of nodes of the system; gl-kThe output power transfer distribution factor for node k to line l; dk,tIs the bus load value of the node k in the time period t; t isj,tRepresenting the active power of the tie j during period t.
The section flow constraint is as follows:
Figure BDA0003486936280000117
wherein the content of the first and second substances,
Figure BDA0003486936280000118
respectively the minimum transmission limit and the maximum transmission limit of the power flow of the section s; gs-iThe generator output power of the section s is transferred to a distribution factor for the node where the unit i is located; gs-jThe output power of the node pair where the tie line j is located transfers distribution factors to the section s; gs-kThe output power transfer distribution factor of the node k to the section s is obtained; dk,tIs the bus load value of the node k in the time period t; t isj,tRepresenting the active power of the tie j during period t.
The practical constraint comprises a unit fixed output constraint and a unit fixed start-stop mode constraint;
the unit fixed output constraint is as follows:
Figure BDA0003486936280000119
in the formula:
Figure BDA0003486936280000121
and (4) representing the output set value of the unit i in the time t.
The fixed start-stop mode of the unit is restricted as follows:
αi,t＝Ui,t (23)
in the formula：Ui,tAnd (4) representing the set value (running or stopping) of the start-stop mode of the unit in the period t.
Step S4 is based on the calculation scene constructed in step S1, the optimization time interval granularity (time interval granularity 15 minutes, 1 day 96 time interval) and the decision variable (thermal power unit output) of the check model in step S2 are set, the start-stop state of the granularity of 15 minutes is fixed according to the start-stop result state of the granularity of the combustion engine in step S3, the check model is cleared and solved for the spare market in province, and the 15-minute granularity output plan, the section \ branch and the equipment power flow of the combustion engine and the thermal power are obtained.
Step S5, based on the section \ branch \ equipment power flow and the quota calculated in step S4, judging whether the section \ branch \ equipment is out of limit, if so, indicating that the clearing result of the provincial standby market is unreasonable and the provincial standby market needs to be cleared again; if the provincial standby market is not out of limit, the provincial standby market is clear, and tracking execution of each provincial power grid is issued.
And S6, outputting the starting and stopping states of the gas turbine calculated in the step S3, and sending the information on the output of the unit, the section, the equipment, the branch power flow and the feasibility information of the output result calculated in the step S4 to a provincial auxiliary service technical support system. Corresponding to the two-step optimization method for the export-clearing check of the spare market in provinces provided by the above embodiment,
the embodiment of the invention also provides a two-step system optimization device suitable for clearing and checking the spare market in provinces, which comprises the following steps: the system comprises a data acquisition module, a model building module, a first optimization solving module, a second optimization solving module and a clear result determining module;
the data acquisition module is used for acquiring clear check scene data of the provincial standby market;
the model establishing module is used for establishing a provincial standby market clearing check model;
the first optimization solving module is used for setting a first time period granularity and a first-step optimized decision variable of the checking model based on the scene data, and executing first-step safety constraint unit combination optimization solving on the provincial standby market clearing checking model to obtain a first time period granularity starting and stopping state of the combustion engine;
the second optimization solving module is used for setting a second time interval granularity of the checking model and a decision variable of the second step of optimization, and fixing the on-off state of the second time interval granularity according to the on-off state of the first time interval granularity of the combustion engine; performing a second-step safety constraint unit combination optimization solution on the provincial standby market clearing check model to obtain a section, a branch and an equipment trend;
and the clearing result determining module is used for judging whether the cross section, the branch or the equipment is out of limit or not, if so, clearing is required again, and if not, the clearing result of the provincial standby market can be executed.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the modules in the system and the apparatus described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
The method is suitable for safety check of the clear results of the regional power grid inter-provincial standby market, and has the characteristics of high calculation efficiency and strong adaptability. The technical scheme of the invention is applied in the center of east China, and the application effect is in line with expectations. The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow in the flow diagrams can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows.
While the present invention has been described with reference to the embodiments shown in the drawings, the present invention is not limited to the embodiments, which are illustrative and not restrictive, and it will be apparent to those skilled in the art that various changes and modifications can be made therein without departing from the spirit and scope of the invention as defined in the appended claims.

Claims (10)

1. The two-step system optimization method suitable for clearing and checking the spare market in provinces is characterized by comprising the following steps of: acquiring clear check scene data of a spare market in provinces;
establishing a clear check model of the provincial standby market;
setting a first time period granularity and a first-step optimized decision variable of the checking model based on the scene data, and executing a first-step safety constraint unit combination optimization solution on the provincial standby market clearing checking model to obtain a first time period granularity starting and stopping state of the combustion engine;
setting a second time interval granularity of the checking model and a decision variable of the second-step optimization, wherein the second time interval granularity is smaller than the first time interval granularity, and fixing the on-off state of the second time interval granularity according to the on-off state of the first time interval granularity of the gas turbine;
performing a second-step safety constraint unit combination optimization solution on the provincial standby market clearing check model to obtain a section, a branch and an equipment trend;
and judging whether the cross section, the branch or the equipment is out of limit, if so, re-clearing is needed, and if not, the clear result of the provincial standby market can be executed.
2. The two-step system optimization method for adaptive provincial standby market clearance check according to claim 1, wherein the regional power grid provincial standby market clearance check scene data comprises: system data, unit data, tie line plan data, load data, unit group data, and sensitivity data.
3. The two-step optimization method for adapting regional power grid inter-provincial backup market clearance check according to claim 1, wherein an objective function of the regional power grid inter-provincial backup market clearance check model is expressed as follows:
Figure FDA0003486936270000011
Figure FDA0003486936270000012
in the formula: n represents the total number of the units; t represents the total time period number; deltai,tThe output deviation cost of the unit i at the moment t is calculated;
Figure FDA0003486936270000013
representing the acceptance of a period t of a provincial power gridThe amount of the compound (A) is,
Figure FDA0003486936270000014
represents the send-out increment of the provincial grid a period t,
Figure FDA0003486936270000021
and
Figure FDA0003486936270000022
is a decision variable; mlA penalty factor for representing a network flow constraint for the market clearing optimization line l;
Figure FDA0003486936270000023
respectively representing a forward power flow relaxation variable and a reverse power flow relaxation variable of a line l; NL is the total number of lines; msA penalty factor for representing network flow constraint of the market clearing optimized section s;
Figure FDA0003486936270000024
respectively representing a forward power flow relaxation variable and a reverse power flow relaxation variable of the section s; NS denotes the total number of sections, MTLAnd (4) representing the incremental penalty cost of provincial power grid delivery.
4. The two-step system optimization method for adaptive inter-provincial standby market clearance check according to claim 3, wherein the constraints of the regional power grid inter-provincial standby market clearance check model comprise provincial power utilization balance constraints, branch provincial aperture sub-balance constraints, unit operation constraints, unit group constraints, network security constraints and pragmatic constraints.
5. The two-step system optimization method for adaptive provincial market liquidation checking according to claim 4, wherein the provincial distribution is represented by the electrical balance constraint as follows:
Figure FDA0003486936270000025
in the formula: p isi,tOutputting the output of the unit in the period i and the time t as a decision variable; saThe method comprises the steps of (1) collecting provincial power grid a units; l isa,tThe system load of the provincial power grid a at the moment t; TLa,tThe opening plan of the provincial power grid a at the time t is a constant;
the fractional aperture sub-balance constraint is expressed as follows:
Figure FDA0003486936270000026
in the formula: ks,tThe power flow variation of the provincial section s time period t relative to the initial power flow; omegaaA provincial section showing that the sending end is provincial a; gamma-shapedaThe input end is represented as a provincial section of a provincial power grid a; haRepresenting a provincial level power grid a generator set.
6. The two-step system optimization method for inter-provincial reserve market clearing check adaptation according to claim 4, wherein the unit operation constraints comprise unit output upper and lower limit constraints, unit maximum on-off times constraints, unit minimum on-off time constraints and unit climbing slope constraints; the machine group constraint comprises a machine group output constraint and a machine group electric quantity constraint; the network safety constraint comprises a line power flow constraint and a section power flow constraint; the practical constraint comprises the constraint of fixed output of the unit and the constraint of the mode of starting and stopping the unit.
7. The two-step system optimization method for export-inventory check of the provincial reserve market according to claim 3, wherein the output deviation cost Δ of the unit i at time ti,tIs represented as follows:
Figure FDA0003486936270000031
wherein
Figure FDA0003486936270000032
The cost is adjusted for the positive offset,
Figure FDA0003486936270000033
the cost is adjusted for negative offsets.
8. The two-step optimization method for export and inventory check of the standby market in the adaptation room according to claim 3, wherein the result obtained by executing the first-step safety constraint unit combination optimization solution further comprises the output of a thermal power unit; and the second step of safety constraint economic dispatching optimization solution, namely obtaining the starting and stopping state of the second granularity combustion engine and the output of the thermal power unit besides the section, the branch and the equipment load flow.
9. A two-step system optimization method device suitable for clearing and checking of a spare market in provinces is characterized by comprising the following steps of: the system comprises a data acquisition module, a model building module, a first optimization solving module, a second optimization solving module and a clear result determining module;
the data acquisition module is used for acquiring clear check scene data of the provincial standby market;
the model establishing module is used for establishing a provincial standby market clearing check model;
the first optimization solving module is used for setting a first time period granularity and a first-step optimized decision variable of the checking model based on the scene data, and executing first-step safety constraint unit combination optimization solving on the provincial standby market clearing checking model to obtain a first time period granularity starting and stopping state of the combustion engine;
the second optimization solving module is used for setting a second time interval granularity of the checking model and a decision variable of the second step of optimization, the second time interval granularity is smaller than the first time interval granularity, and the on-off state of the second time interval granularity is fixed according to the on-off state of the first time interval granularity of the combustion engine; performing a second-step safety constraint unit combination optimization solution on the provincial standby market clearing check model to obtain a section, a branch and an equipment trend;
and the clearing result determining module is used for judging whether the cross section, the branch or the equipment is out of limit or not, if so, clearing is required again, and if not, the clearing result of the provincial standby market can be executed.
10. The apparatus of claim 9, wherein the objective function of the provincial alternate market clearing model established by the model establishing module is represented as follows:
Figure FDA0003486936270000041
in the formula: n represents the total number of the units; t represents the total time period number; deltai,tThe output deviation cost of the unit i at the moment t is calculated;
Figure FDA0003486936270000042
representing the accepted increment of the provincial grid a period t,
Figure FDA0003486936270000043
represents the send-out increment of the provincial grid a period t,
Figure FDA0003486936270000044
and
Figure FDA0003486936270000045
is a decision variable; mlA penalty factor representing a network flow constraint for the market clearing optimization line l;
Figure FDA0003486936270000046
respectively representing a forward power flow relaxation variable and a reverse power flow relaxation variable of a line l; NL is the total number of lines; msA penalty factor for representing network flow constraint of the market clearing optimized section s;
Figure FDA0003486936270000047
respectively representing a forward power flow relaxation variable and a reverse power flow relaxation variable of the section s; NS denotes the total number of sections, MTLAnd (4) representing the incremental penalty cost of provincial power grid delivery.
CN202210084451.2A 2022-01-25 2022-01-25 Two-step system optimization method and device suitable for clearing and checking of provincial standby market Pending CN114548522A (en)

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