CN111400918A - Power grid new energy consumption capability evaluation and calculation method, device and system based on multi-scene generation technology - Google Patents

Power grid new energy consumption capability evaluation and calculation method, device and system based on multi-scene generation technology Download PDF

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CN111400918A
CN111400918A CN202010197840.7A CN202010197840A CN111400918A CN 111400918 A CN111400918 A CN 111400918A CN 202010197840 A CN202010197840 A CN 202010197840A CN 111400918 A CN111400918 A CN 111400918A
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new energy
unit
scene
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power
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涂杉杉
李利利
江长明
陈之栩
孙田
史普鑫
涂孟夫
张丙金
丁恰
昌力
杨鹏程
曹益奇
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NARI Group Corp
North China Grid Co Ltd
Nari Technology Co Ltd
NARI Nanjing Control System Co Ltd
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North China Grid Co Ltd
Nari Technology Co Ltd
NARI Nanjing Control System Co Ltd
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Abstract

The invention discloses a power grid new energy consumption capability evaluation calculation method, a device and a system based on a multi-scene generation technology, which comprises the steps of determining the power grid range and the calculation period of power grid new energy consumption capability evaluation calculation to be carried out and other calculation boundaries; generating a large number of combined scenes containing multiple new energy power plants by freely arranging and combining the fields; merging similar scenes by adopting a scene reduction technology of backward reduction, reducing the number of combined scenes and generating a typical combined scene; merging analysis is carried out at similar time intervals, and the number of time intervals entering optimization is reduced; aiming at each typical combination scene, establishing a SCUC dimension reduction model after time interval merging, and solving to obtain a unit combination result; and establishing a full-time SCED model based on the unit combination result and solving, finally obtaining a new energy consumption result under each typical combination scene, and completing the evaluation and calculation of the new energy consumption capability of the power grid based on the multi-scene generation technology. The invention improves the validity and the referential property of the digestion capability evaluation result on the premise of ensuring the safety and the stability of the system.

Description

Power grid new energy consumption capability evaluation and calculation method, device and system based on multi-scene generation technology
Technical Field
The invention belongs to the technical field of electric power system dispatching automation, relates to a power grid new energy consumption capability evaluation and calculation method, and particularly relates to a power grid new energy consumption capability evaluation and calculation method based on a multi-scene generation technology.
Background
The permeability of new energy in an electric power system is gradually increased, so that scheduling resources in the system are more abundant, the operation mode is increasingly complex, the traditional power distribution network still has the problems of insufficient new energy consumption and the like, the high permeability of the new energy is seriously restricted, and the optimization and adjustment of an energy structure are not facilitated. In addition, the new energy grid connection puts higher requirements on the regulation capacity of the power system, however, the conventional power grid system usually mainly uses thermal power and has insufficient flexible regulation resources, so that the regulation capacity of the power system is limited and cannot cope with the uncertainty of the new energy output. In order to ensure the safe and stable operation of the power system, the system is forced to abandon wind and light at the peak time of new energy output, and the utilization rate of new energy is greatly reduced.
After the new energy is accessed to the power grid, the primary problem of the Unit optimization scheduling is how to establish a reliable mathematical model of a new energy Security Constraint Unit (SCUC) problem. The objective function of the SCUC model minimizes the total operation cost in a scheduling period in a long period of time, wherein the total operation cost mainly comprises the operation cost and the start-stop cost of a conventional unit. With the development of the power market, the target function form of the SCUC model is changed newly based on different starting points. Due to the fact that the utilization rate of the new energy is reduced, the objective function possibly requires that the grid-connected capacity of the new energy which can be accepted by the system is as high as possible. In the aspect of constraint conditions, the form of the constraint conditions is correspondingly changed according to different energy types and different objective functions.
The SCUC problem is a mixed integer programming problem with multiple dimensions, nonlinearity and multi-time-interval coupling, the new energy has uncertainty such as intermittence and volatility, and the large-scale grid connection of the new energy makes the solution of the SCUC problem more complex. The traditional new energy consumption capability assessment is calculated based on a single predicted scene, a plurality of new energy stations exist in an actual power grid, the predicted output of the new energy stations is uncertain, the consumption capability assessment only considering a single predicted scene of new energy cannot meet the requirement of diversified development of the power grid, and the optimization result can be obviously influenced.
With the development of mathematical optimization algorithm and the great progress of the calculation performance of a commercial solver, a mixed integer programming Method (MIP) becomes a main method for solving an optimized scheduling problem, the MIP has the advantages of ① global optimization, ② direct solution optimality measurement and calculation, ③ more flexible and accurate modeling capacity, and an optimization model based on the MIP algorithm is more and more widely applied to the field of power scheduling.
The idea of the existing power grid new energy consumption capability evaluation algorithm based on the multi-scene generation technology is mainly to utilize the scene generation technology to model the prediction uncertainty of new energy, take the maximum output of the new energy which can be accepted by the system as a target, uniformly establish all scenes, all variables and all constraints as a standard mathematical optimization model according to the requirements, directly call an optimization solver, and perform large-scale mixed integer programming calculation on the standard model.
The random error of new energy prediction is a key factor for restricting whether an optimal decision scheme can be obtained by a power grid new energy consumption capability evaluation calculation model based on a multi-scene generation technology, and how to reasonably consider and depict a plurality of new energy combination scenes in an original scheduling mode is a problem which needs to be concerned in optimization operation scheduling research containing large-scale new energy; on the other hand, the SCUC problem is not considered in the traditional power grid new energy consumption capability evaluation calculation model based on the multi-scene generation technology, so that the optimization result cannot provide guarantee for the safe operation of the power grid.
Disclosure of Invention
Aiming at the problems, the invention provides a power grid new energy consumption capability evaluation calculation method based on a multi-scene generation technology, which can improve the validity and the referential property of the consumption capability evaluation result on the premise of ensuring the safety and the stability of a system.
In order to achieve the technical purpose and achieve the technical effects, the invention is realized by the following technical scheme:
in a first aspect, the invention provides a power grid new energy consumption capability assessment and calculation method based on a multi-scenario generation technology, which comprises the following steps:
determining a power grid range and a calculation period of power grid new energy consumption capability evaluation calculation to be carried out and other calculation boundaries;
generating a large number of combined scenes containing multiple new energy power plants by freely arranging and combining the fields;
merging similar scenes by adopting a scene reduction technology of backward reduction, reducing the number of combined scenes and generating a typical combined scene;
merging the similar time periods, reducing the number of time periods entering optimization, and reducing the scale of the calculation time period of the optimization model;
aiming at each typical combination scene, establishing a SCUC dimension reduction model after time interval merging, and solving to obtain a unit combination result;
and establishing a full-time SCED model based on the unit combination result and solving, finally obtaining a new energy consumption result under each typical combination scene, and completing the evaluation and calculation of the new energy consumption capability of the power grid based on the multi-scene generation technology.
Optionally, the generating process of the combined scene includes:
the total number of new energy stations of a certain power grid is assumed to be NwThe predicted output scene of each new energy station has NpThe occurrence probability of each predicted contribution scene is prij(i＝1,2,…,Nw；j＝1,2,…,Np)；
Arranging and combining the output scenes of all the new energy stations to obtain the final number N of the new energy output scenesa，
Figure BDA0002418266390000021
The combined scene occurrence probability is the product of the corresponding contribution scene occurrence probabilities.
Optionally, the method for generating the typical combination scenario includes:
initializing a deleted scene set J to be null, wherein the number of scenes needing to be deleted is K, and the number of scenes deleted in the K iteration is lk；
The following steps are repeated until the iteration is finished:
calculating the distance of Kantorovzval to make l take the scene lkObtaining a minimum value by using a time formula, wherein the computing formula of the Kantouvyqi distance is as follows:
Figure BDA0002418266390000031
in the formula: j is the deleted scene set; p is a radical ofiIs the probability of scene i ξiCorresponding to a scene sequence i; t is the number of segments of the scene timescale; c. CT(ξi,ξj) Representing a sequence of scenes ξiAnd scene sequence ξjThe distance of (a) to (b),
Figure BDA0002418266390000032
deleting scene lkLet Jk＝Jk-1∪{lkAnd will scene lkThe probability of (c) is accumulated to the scene closest to it;
if K < K, let K be K + 1.
Optionally, the merging the similar time periods includes:
let LtFor the system load of time period t, the system load change rate of adjacent time periods t and t +1 is:
Figure BDA0002418266390000033
calculating the change rate of the system load in all time periods to find the minimum change rate delta LtMerging the time interval t and the time interval t +1 into a new time interval in the corresponding time interval;
according to the change rate of the system load, the time interval merging is repeated until the minimum change rate delta LtAnd if the number of the time intervals is larger than the set threshold value or the number of the time intervals left after the merging reaches the preset number, the merging process is ended.
Optionally, the objective function of the SCUC dimension reduction model is a maximization of a total power generation amount of the new energy unit, and is expressed as:
Figure BDA0002418266390000034
in the formula, NWIs a new energy machine set; t is a time interval set contained after time interval merging; pw,tThe maximum power receiving capacity of the new energy source unit w in the time period t;
the constraint conditions of the SCUC dimension reduction model comprise load balance constraint, conventional unit output upper and lower limit constraint, unit minimum start-up and shut-down time constraint, new energy unit output upper and lower limit constraint, power grid safety constraint and system reserve capacity constraint:
Figure BDA0002418266390000035
Pi,minui,t≤Pi,t≤Pi,maxui,t
Figure BDA0002418266390000036
Figure BDA0002418266390000041
yi,t-zi,t＝ui,t-ui,t-1
yi,t+zi,t≤1
Pw,t≤P0,w,t
Figure BDA0002418266390000042
Figure BDA0002418266390000043
Figure BDA0002418266390000044
in the formula, NiThe total number of the thermal power generating units; n is a radical ofwAs a new energy unitThe total number; n is a radical oflThe total number of the external connecting lines; pi,tThe active power output of the conventional unit i in the time period t is obtained; pw,tThe active power output of the new energy unit w in the time period t is obtained; pl,tL for the active power of the tie line l in the time period ttIs the load value of the system in the time period t; u. ofi,tStarting and stopping a unit i at a time t; pi,minLower power limit, P, of unit ii,maxThe upper power limit of the unit i is set; UT (unified device)iAnd DTiRespectively the minimum starting time and the minimum stopping time of the unit i; y isi,tWhether the unit i has a sign of change from a shutdown state to a startup state in a time period t or not is marked; z is a radical ofi,tWhether the unit i has a sign of change from a starting state to a stopping state in a time period t or not is marked; p0,w,tL predicted output for new energy unit w in time period tijRepresenting the upper current limit of branch ij; m is a power grid computing node set; li,tLoad power for the node; si,j,tSensitivity of injected power to branch ij for node i; rt,uA positive spare capacity lower limit for the system at time period t; rt,dThe negative spare capacity lower limit for the system at time t.
Optionally, the objective function of the SCED model is a maximization of a total power generation of the new energy unit, and is expressed as:
Figure BDA0002418266390000045
in the formula, T is a set of all time periods; n is a radical ofwThe total number of the new energy units; pw,tThe active power output of the new energy unit w in the time period t is obtained;
the constraint conditions comprise load balance constraint, conventional unit output upper and lower limit constraint, new energy unit output upper and lower limit constraint, power grid safety constraint and system reserve capacity constraint:
Figure BDA0002418266390000046
Pi,minui,t≤Pi,t≤Pi,maxui,t
Pw,t≤P0,w,t
Figure BDA0002418266390000047
Figure BDA0002418266390000048
Figure BDA0002418266390000051
in the formula, NiThe total number of the thermal power generating units; n is a radical ofwThe total number of the new energy units; n is a radical oflThe total number of the external connecting lines; pi,tThe active power output of the conventional unit i in the time period t is obtained; pw,tThe active power output of the new energy unit w in the time period t is obtained; pl,tL for the active power of the tie line l in the time period ttIs the load value of the system in the time period t; u. ofi,tStarting and stopping a unit i at a time t; pi,minLower power limit, P, of unit ii,maxThe upper power limit of the unit i is set; UT (unified device)iAnd DTiRespectively the minimum starting time and the minimum stopping time of the unit i; p0,w,tL predicted output for new energy unit w in time period tijRepresenting the upper current limit of branch ij; m is a power grid computing node set; li,tLoad power for the node; si,j,tSensitivity of injected power to branch ij for node i; rt,uA positive spare capacity lower limit for the system at time period t; rt,dThe negative spare capacity lower limit for the system at time t.
In a second aspect, the present invention provides a power grid new energy consumption capability assessment computing device based on a multi-scenario generation technology, including:
the determining unit is used for determining the power grid range and the calculation period of the power grid new energy consumption capability evaluation calculation to be carried out and other calculation boundaries;
the first generation unit is used for generating a large number of combination scenes containing multiple new energy power plants through free arrangement and combination among fields;
the second generation unit is used for merging similar scenes by adopting a scene reduction technology of backward reduction, reducing the number of combined scenes and generating a typical combined scene;
the merging unit is used for merging the similar time intervals, reducing the number of time intervals entering optimization and reducing the scale of the calculation time intervals of the optimization model;
the first solving unit is used for establishing a SCUC dimension reduction model after time interval merging aiming at each typical combination scene and solving to obtain a unit combination result;
and the second solving unit is used for establishing a full-time SCED model based on the unit combination result and solving the full-time SCED model to finally obtain a new energy consumption result under each typical combination scene, and finishing the evaluation and calculation of the new energy consumption capability of the power grid based on the multi-scene generation technology.
Optionally, the objective function of the SCUC dimension reduction model is a maximization of a total power generation amount of the new energy unit, and is expressed as:
Figure BDA0002418266390000052
in the formula, NWIs a new energy machine set; t is a time interval set contained after time interval merging; pw,tThe maximum power receiving capacity of the new energy source unit w in the time period t;
the constraint conditions of the SCUC dimension reduction model comprise load balance constraint, conventional unit output upper and lower limit constraint, unit minimum start-up and shut-down time constraint, new energy unit output upper and lower limit constraint, power grid safety constraint and system reserve capacity constraint:
Figure BDA0002418266390000061
Pi,minui,t≤Pi,t≤Pi,maxui,t
Figure BDA0002418266390000062
Figure BDA0002418266390000063
yi,t-zi,t＝ui,t-ui,t-1
yi,t+zi,t≤1
Pw,t≤P0,w,t
Figure BDA0002418266390000064
Figure BDA0002418266390000065
Figure BDA0002418266390000066
in the formula, NiThe total number of the thermal power generating units; n is a radical ofwThe total number of the new energy units; n is a radical oflThe total number of the external connecting lines; pi,tThe active power output of the conventional unit i in the time period t is obtained; pw,tThe active power output of the new energy unit w in the time period t is obtained; pl,tL for the active power of the tie line l in the time period ttIs the load value of the system in the time period t; u. ofi,tStarting and stopping a unit i at a time t; pi,minLower power limit, P, of unit ii,maxThe upper power limit of the unit i is set; UT (unified device)iAnd DTiRespectively the minimum starting time and the minimum stopping time of the unit i; y isi,tWhether the unit i has a sign of change from a shutdown state to a startup state in a time period t or not is marked; z is a radical ofi,tWhether the unit i has a sign of change from a starting state to a stopping state in a time period t or not is marked; p0,w,tL predicted output for new energy unit w in time period tijRepresenting the upper current limit of branch ij; m is a power grid computing node set; li,tLoad power for the node; si,j,tSensitivity of injected power to branch ij for node i; rt,uA positive spare capacity lower limit for the system at time period t; rt,dThe negative spare capacity lower limit for the system at time t.
Optionally, the objective function of the SCED model is a maximization of a total power generation of the new energy unit, and is expressed as:
Figure BDA0002418266390000067
in the formula, T is a set of all time periods; n is a radical ofwThe total number of the new energy units; pw,tThe active power output of the new energy unit w in the time period t is obtained;
the constraint conditions comprise load balance constraint, conventional unit output upper and lower limit constraint, new energy unit output upper and lower limit constraint, power grid safety constraint and system reserve capacity constraint:
Figure BDA0002418266390000068
Pi,minui,t≤Pi,t≤Pi,maxui,t
Pw,t≤P0,w,t
Figure BDA0002418266390000071
Figure BDA0002418266390000072
Figure BDA0002418266390000073
in the formula, NiThe total number of the thermal power generating units; n is a radical ofwThe total number of the new energy units; n is a radical oflThe total number of the external connecting lines; pi,tThe active power output of the conventional unit i in the time period t is obtained; pw,tThe active power output of the new energy unit w in the time period t is obtained; pl,tL for the active power of the tie line l in the time period ttIs the load value of the system in the time period t; u. ofi,tStarting and stopping a unit i at a time t; pi,minLower power limit, P, of unit ii,maxOf a unit iAn upper power limit; UT (unified device)iAnd DTiRespectively the minimum starting time and the minimum stopping time of the unit i; p0,w,tL predicted output for new energy unit w in time period tijRepresenting the upper current limit of branch ij; m is a power grid computing node set; li,tLoad power for the node; si,j,tSensitivity of injected power to branch ij for node i; rt,uA positive spare capacity lower limit for the system at time period t; rt,dThe negative spare capacity lower limit for the system at time t.
In a third aspect, the invention provides a power grid new energy consumption capability evaluation computing system based on a multi-scenario generation technology, which comprises a storage medium and a processor;
the storage medium is used for storing instructions;
the processor is configured to operate in accordance with the instructions to perform the steps of the method according to any one of the first aspects.
Compared with the prior art, the invention has the beneficial effects that:
firstly, determining the power grid range and the calculation period of power grid new energy consumption capability evaluation calculation based on a multi-scene generation technology, and determining a calculation boundary; generating a large number of combined scenes containing multiple new energy power plants by freely arranging and combining the fields; generating a typical combined scene by adopting a scene reduction technology of backward reduction; then carrying out merging analysis of similar time periods, and reducing the number of time periods entering SCUC calculation; aiming at each typical combination scene, establishing a SCUC dimension reduction model after time interval merging, and quickly solving to obtain a unit combination result; establishing a full-time SCED model based on the unit combination result and solving; and finally, obtaining new energy consumption results in each typical combination scene, and improving the effectiveness and the referential of the consumption capability evaluation result on the premise of ensuring the safety and stability of the system.
According to the SCUC-SCED optimization calculation method, the combined scene reduction, the merging of similar time periods and the invalid start-stop variable identification technology are utilized, the time period scale and variable dimension entering SCUC optimization calculation are reduced, the state combination space of unit combination is reduced, the cycle number of SCUC-SCED optimization solution is also reduced, and the solution efficiency of the whole estimation of the absorption capacity is effectively improved;
the method is based on a plurality of new energy combination scene generation technologies to perform unit combination dimension reduction clearing and all-time economic dispatching calculation, obtains calculation results under different combination scenes and different occurrence probabilities, and reflects the consumption condition of new energy in more detail;
the method carries out power grid new energy consumption capability evaluation calculation based on the multi-scene generation technology based on the safety constraint unit combination, ensures that new energy consumption results meet various power grid operation constraints, has stable model calculation results, provides an effective thought for solving the practical problems of new energy consumption, power grid safety and the like, and has certain practical value.
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In order that the present disclosure may be more readily and clearly understood, reference is now made to the following detailed description of the present disclosure taken in conjunction with the accompanying drawings, in which:
fig. 1 is a schematic flow chart of a method for evaluating and calculating new energy consumption capability of a power grid based on a multi-scenario generation technology according to an embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and do not limit the scope of the invention.
The following detailed description of the principles of the invention is provided in connection with the accompanying drawings.
In the evaluation and calculation process of the new energy consumption capability of the power grid, a safety constraint unit combination technology needs to be adopted to optimize the unit combination and new energy output result in a month-level 744 period. The optimization process not only requires to precisely consider various safety constraint limits in the operation of the power grid such as unit operation constraint, power grid safety constraint, load balance constraint and the like, but also requires that a new energy output scene can describe the actual situation of scheduling operation as much as possible, and the calculation process can be finished within the time acceptable by operators, so that the calculation precision and the calculation time meet the actual engineering requirements.
Example 1
The invention provides a power grid new energy consumption capability evaluation and calculation method based on a multi-scene generation technology, which is used for preprocessing multiple probability scenes of new energy, considering the structural characteristics of a power system, merging similar calculation time periods and combined scenes by generating and processing the combined scenes of multiple new energy, identifying invalid integer variables, reducing the number of the combined scenes, reducing the optimization range of mixed integer programming and the iteration times of new energy consumption capability evaluation, and improving the solving efficiency and effectiveness. The method provided by the present invention is described in detail below by way of specific examples.
Specifically, the power grid new energy consumption capability evaluation and calculation method based on the multi-scenario generation technology comprises the following steps:
step 1) considering new energy consumption capability evaluation of a power system in a certain area, wherein the power grid comprises 190 generator sets needing state combination calculation, 29 power transmission sections need to be considered in the calculation process, the number of new energy partitions is 12, each partition comprises 3 probability prediction scenes, 1 hour serves as a calculation time period for the new energy consumption capability evaluation, and the number of the calculation time periods is 744. Firstly, data preparation is carried out, parameters such as upper and lower output limits of a generator set in the power grid, minimum start-up and shut-down time and the like are obtained, and parameter information such as a topological structure of the power grid, a power transmission section forming device, a power transmission limit value and the like is obtained. Meanwhile, various plan data including information such as load prediction, maintenance plans, standby requirements, new energy predicted output and probability thereof are obtained so as to determine the boundary of the evaluation and calculation of the new energy consumption capability of the power grid.
Step 2) generating a large number of combination scenes containing multiple new energy power plants through inter-field free permutation and combination according to the predicted output of each new energy under different probabilities, and calculating the occurrence probability of each combination scene;
in a specific implementation manner of the embodiment of the present invention, the specific process of generating the combination scenario includes the following steps:
the total number of new energy stations of a certain power grid is assumed to be NwOf each new energy stationPredicted contribution scenario has NpThe occurrence probability of each predicted contribution scene is prij(i＝1,2,…,Nw；j＝1,2,…,Np)；
The output scenes of all the new energy stations are arranged and combined to obtain the final number N of the new energy output scenesaThe following formula:
Figure BDA0002418266390000091
the combined scene occurrence probability is the product of the corresponding contribution scene occurrence probabilities.
Step 3) merging similar scenes by adopting a scene reduction technology of backward reduction, reducing the number of combined scenes and generating a typical combined scene;
from the perspective that the new energy output scene reflects the uncertainty of the new energy output, the uncertainty information provided by similar scenes is also similar, but meanwhile, unnecessary calculation amount is increased, and calculation efficiency is affected. Therefore, it is necessary to perform scene reduction on the basis of the generated combined scene set, remove a part of scenes with low probability, and merge similar scenes. Scene reduction is essentially a method for improving the computational efficiency at the expense of the computational accuracy, and therefore, the effectiveness of a new energy output scene should be ensured to the greatest extent when the scene reduction is performed.
The basic principle of scene reduction is: and the probability distance between the reduced scene set and the scene set before reduction is minimized. The probability distance is a mode for balancing the distance of each scene and the probability of the scene, and enables the information expressed by the scene before reduction and the scene after reduction to be closest, even if the precision loss caused by the reduction process is the lowest. The probability distance of the optimization model adopts a Kantorovici distance DkDescription, DkThe expression is as follows:
Figure BDA0002418266390000092
in the formula: j is the deleted scene set; p is a radical ofiIs a scenei probability of ξiCorresponding to a scene sequence i; t is the number of segments of the scene timescale; c. CT(ξi,ξj) Representing a sequence of scenes ξiAnd scene sequence ξjI.e.:
Figure BDA0002418266390000093
the backward reduction method is mainly divided into the following steps:
step1, initializing the deleted scene set J to be empty, wherein the number of the scenes needing to be deleted is K, and the K-th iteration deleted scenes are lk；
Step2, calculating the distance of Kantoroviz, and taking scene lkObtaining a minimum value according to the following formula;
step3 deletion of scene lkLet Jk＝Jk-1∪{lkAnd will scene lkThe probability of (c) is accumulated to the scene closest to it;
and Step4, if K is less than K, making K equal to K +1, and returning to Step2, otherwise, ending the iteration.
Step 4) merging the approximate calculation time intervals according to the variation trends of the system load in different time intervals in the calculation cycle, reducing the number of time intervals entering optimization, and reducing the calculation time interval scale of the optimization model; identifying a unit which must be started and stopped and a buffer unit in the safety constraint unit combination decision according to the optimizable state of the unit;
the specific process of time interval merging is set as LtFor the system load of time period t, the system load change rate of adjacent time periods t and t +1 is:
Figure BDA0002418266390000101
calculating the change rate of the system load in all time periods to find the minimum change rate delta LtAccording to the change rate of the system load, the time interval merging is repeated until the minimum change rate delta LtGreater than a set threshold, or left after mergingThe number of the next time period reaches the preset number, and the merging process is finished.
The unit which must be started and stopped in the safety constraint unit combination decision is a unit which must be developed or a unit which must be stopped in the operation of the power grid, and the optimization decision of the unit state is not needed in the safety constraint unit combination; the buffer unit is a generator unit which cannot determine the starting and stopping state in advance, and the optimization decision of the unit state needs to be carried out in the safety constraint unit combination. And identifying the buffer unit and the unit which must be started and stopped according to the provided unit state data so as to reduce the number of integer variables, reduce the optimization range of mixed integer programming and improve the solving efficiency.
And step 5) aiming at a certain typical combination scene, taking the start-stop state of the buffer unit as a combined decision variable, considering the power transmission limit constraint of an effective section, establishing a SCUC dimension reduction model after time period merging, and rapidly solving to obtain the start-stop state result of the buffer unit by adopting a mixed integer programming algorithm.
And evaluating, calculating and optimizing the start-stop and output of the thermal power generating unit and the output plan of the new energy unit at each time interval so as to meet the requirement of a load curve. And taking the new energy machine sets in the region as a specific evaluation object, and obtaining the total output of the new energy machine sets in the research region based on the total sum of the generated power of each new energy machine set, wherein the optimization aim is to maximize the generated energy of the new energy machine sets under the condition of meeting various constraints. The objective function of the SCUC dimension reduction model can be expressed as:
Figure BDA0002418266390000111
in the formula, NWIs a new energy machine set; t is a time interval set contained after time interval merging; pw,tThe maximum power capacity of the new energy source unit w in the time period t.
The constraint conditions comprise load balance constraint, conventional unit output upper and lower limit constraint, unit minimum on-off time constraint, new energy unit output upper and lower limit constraint, power grid safety constraint and system reserve capacity constraint:
Figure BDA0002418266390000112
Pi,minui,t≤Pi,t≤Pi,maxui,t
Figure BDA0002418266390000113
Figure BDA0002418266390000114
yi,t-zi,t＝ui,t-ui,t-1
yi,t+zi,t≤1
Pw,t≤P0,w,t
Figure BDA0002418266390000115
Figure BDA0002418266390000116
Figure BDA0002418266390000117
in the formula, NiThe total number of the thermal power generating units; n is a radical ofwThe total number of the new energy units; n is a radical oflThe total number of the external connecting lines; pi,tThe active power output of the conventional unit i in the time period t is obtained; pw,tThe active power output of the new energy unit w in the time period t is obtained; pl,tL for the active power of the tie line l in the time period ttIs the load value of the system in the time period t; u. ofi,tStarting and stopping a unit i at a time t; pi,minLower power limit, P, of unit ii,maxThe upper power limit of the unit i is set; UT (unified device)iAnd DTiRespectively the minimum starting time and the minimum stopping time of the unit i; y isi,tWhether the unit i has a sign of change from a shutdown state to a startup state in a time period t or not is marked; z is a radical ofi,tWhether the unit i has a sign of change from a starting state to a stopping state in a time period t or not is marked; p0,w,tL predicted output for new energy unit w in time period tijRepresenting the upper current limit of branch ij; m is a power grid computing node set; li,tLoad power for the node; si,j,tSensitivity of injected power to branch ij for node i; rt,uA positive spare capacity lower limit for the system at time period t; rt,dThe negative spare capacity lower limit for the system at time t.
Step 6) establishing an SCED model considering all calculation time intervals on the basis of the start-stop state result of the buffer unit and the start-stop state of the unit which must be started and stopped, and solving by adopting a linear programming algorithm; if the system balance constraint of partial time interval can not be met, adding the time interval into the calculation time interval set, and entering the step 5), or entering the step 7).
The objective function of the full-time SCED model is the maximization of the total power generation of the new energy unit, and can be expressed as:
Figure BDA0002418266390000121
where T is the set of all periods.
The constraint conditions comprise load balance constraint, conventional unit output upper and lower limit constraint, new energy unit output upper and lower limit constraint, power grid safety constraint and system reserve capacity constraint:
Figure BDA0002418266390000122
Pi,minui,t≤Pi,t≤Pi,maxui,t
Pw,t≤P0,w,t
Figure BDA0002418266390000123
Figure BDA0002418266390000124
Figure BDA0002418266390000125
in the formula, NiThe total number of the thermal power generating units; n is a radical ofwThe total number of the new energy units; n is a radical oflThe total number of the external connecting lines; pi,tThe active power output of the conventional unit i in the time period t is obtained; pw,tThe active power output of the new energy unit w in the time period t is obtained; pl,tL for the active power of the tie line l in the time period ttIs the load value of the system in the time period t; u. ofi,tStarting and stopping a unit i at a time t; pi,minLower power limit, P, of unit ii,maxThe upper power limit of the unit i is set; UT (unified device)iAnd DTiRespectively the minimum starting time and the minimum stopping time of the unit i; p0,w,tL predicted output for new energy unit w in time period tijRepresenting the upper current limit of branch ij; m is a power grid computing node set; li,tLoad power for the node; si,j,tSensitivity of injected power to branch ij for node i; rt,uA positive spare capacity lower limit for the system at time period t; rt,dThe negative spare capacity lower limit for the system at time t.
Step 7), if all the typical combination scenes are traversed, entering step 8), otherwise, entering step 5) according to the next typical combination scene;
and 8) generating the maximum output and the occurrence probability of each new energy under each typical combination scene, the total output of the new energy of the system and the evaluation and calculation of the new energy consumption capability of the power grid.
In order to verify the effectiveness of the proposed model, an optimization model based on a limit scene method is set as a scheme 1, and an optimization model based on a multi-scene generation technology is set as a scheme 2. The power grid comprises 190 generator sets needing state combination calculation, the number of calculation time periods is 744, the number of total state discrete variables of the generator sets is 141360(190 × 744), and the number of power transmission section constraint conditions is 21576(29 × 744). The number of the calculation time intervals of the optimization model after the merging of the similar time intervals is 93, invalid integer variables are identified, and the number of the unit state discrete variables of the optimization model after dimensionality reduction is 15252. The average total calculation time is within 30 minutes.
In the scheme 1, the maximum probability and the minimum probability of each new energy partition are respectively taken as 2 combined scenes based on a limit scene method, and the average value of an objective function is 1847121; according to the scheme 2, based on a power grid new energy consumption capability evaluation technology of a multi-scene generation technology, the total number of new energy combination scenes is 312, the number of new energy combination scenes is reduced to 5 after reduction, and the average value of an objective function is 1865524. The average value of the objective function of the scheme 1 is low because the probability that the scenes with the lowest probability of each partition are considered together is very small, so that the optimization result is too conservative, the objectivity of the optimization result is influenced, and the combination scene generated by combining and reducing the scenes is more reasonable and can represent a typical prediction scene, so that the optimization method provided by the scheme is more consistent with the actual scheduling operation and has certain reference significance; and the scene reduction and the time interval combination obviously improve the solving efficiency and meet the requirements of practical application.
The method is used for researching and trying the power grid new energy consumption capability evaluation calculation method based on the multi-scene generation technology and developed under actual power grid data. The method reduces the number of combined scenes by preprocessing the multi-probability scene combination of new energy, reduces the optimization range of mixed integer programming by time period merging, quickly obtains the unit combination and active output results meeting the calculation requirements, applies the safety constraint unit combination technology to the evaluation and calculation of the new energy consumption capability of the power grid, and considers the relationship between the new energy consumption and the network safety to further obtain a more reliable optimization result. The calculation speed of the method can meet the requirement of practical application, the defect that the traditional new energy consumption is only evaluated according to a single prediction scene is effectively overcome, the safety and the reasonability of the new energy optimization scheduling are enhanced, and the method has wide popularization prospect.
Example 2
Based on the same inventive concept as embodiment 1, the embodiment of the present invention provides a power grid new energy consumption capability evaluation and calculation apparatus based on a multi-scenario generation technology, including:
the determining unit is used for determining the power grid range and the calculation period of the power grid new energy consumption capability evaluation calculation to be carried out and other calculation boundaries;
the first generation unit is used for generating a large number of combination scenes containing multiple new energy power plants through free arrangement and combination among fields;
the second generation unit is used for merging similar scenes by adopting a scene reduction technology of backward reduction, reducing the number of combined scenes and generating a typical combined scene;
the merging unit is used for merging the similar time intervals, reducing the number of time intervals entering optimization and reducing the scale of the calculation time intervals of the optimization model;
the first solving unit is used for establishing a SCUC dimension reduction model after time interval merging aiming at each typical combination scene and solving to obtain a unit combination result;
and the second solving unit is used for establishing a full-time SCED model based on the unit combination result and solving the full-time SCED model to finally obtain a new energy consumption result under each typical combination scene, and finishing the evaluation and calculation of the new energy consumption capability of the power grid based on the multi-scene generation technology.
In a specific embodiment of the present invention, the generating process of the combined scene includes:
the total number of new energy stations of a certain power grid is assumed to be NwThe predicted output scene of each new energy station has NpThe occurrence probability of each predicted contribution scene is prij(i＝1,2,…,Nw；j＝1,2,…,Np)；
Arranging and combining the output scenes of all the new energy stations to obtain the final number N of the new energy output scenesa，
Figure BDA0002418266390000141
The combined scene occurrence probability is the product of the corresponding contribution scene occurrence probabilities.
In a specific implementation manner of the embodiment of the present invention, the method for generating the typical combination scenario includes:
initializing a deleted scene set J to be null, wherein the number of scenes needing to be deleted is K, and the number of scenes deleted in the K iteration is lk；
The following steps are repeated until the iteration is finished:
calculating the distance of Kantorovzval to make l take the scene lkObtaining a minimum value by using a time formula, wherein the computing formula of the Kantouvyqi distance is as follows:
Figure BDA0002418266390000142
in the formula: j is the deleted scene set; p is a radical ofiIs the probability of scene i ξiCorresponding to a scene sequence i; t is the number of segments of the scene timescale; c. CT(ξi,ξj) Representing scene sequence ξ i and scene sequence ξjThe distance of (a) to (b),
Figure BDA0002418266390000143
deleting scene lkLet Jk＝Jk-1∪{lkAnd will scene lkThe probability of (c) is accumulated to the scene closest to it;
if K < K, let K be K + 1.
In a specific implementation manner of the embodiment of the present invention, the merging the similar time periods includes the following steps:
let LtFor the system load of time period t, the system load change rate of adjacent time periods t and t +1 is:
Figure BDA0002418266390000144
calculating the change rate of the system load in all time periods to find the minimum change rate delta LtMerging the time interval t and the time interval t +1 into a new time interval in the corresponding time interval;
according to the change rate of the system load, the time interval merging is repeated until the minimum change rate delta LtGreater than a set thresholdOr the number of the remaining time periods after merging reaches the preset number, and the merging process is finished.
In a specific implementation manner of the embodiment of the present invention, an objective function of the SCUC dimension reduction model is a maximization of a total power generation amount of the new energy unit, and is expressed as:
Figure BDA0002418266390000151
in the formula, NWIs a new energy machine set; t is a time interval set contained after time interval merging; pw,tThe maximum power receiving capacity of the new energy source unit w in the time period t;
the constraint conditions of the SCUC dimension reduction model comprise load balance constraint, conventional unit output upper and lower limit constraint, unit minimum start-up and shut-down time constraint, new energy unit output upper and lower limit constraint, power grid safety constraint and system reserve capacity constraint:
Figure BDA0002418266390000152
Pi,minui,t≤Pi,t≤Pi,maxui,t
Figure BDA0002418266390000153
Figure BDA0002418266390000154
yi,t-zi,t＝ui,t-ui,t-1
yi,t+zi,t≤1
Pw,t≤P0,w,t
Figure BDA0002418266390000155
Figure BDA0002418266390000156
Figure BDA0002418266390000157
in the formula, NiThe total number of the thermal power generating units; n is a radical ofwThe total number of the new energy units; n is a radical oflThe total number of the external connecting lines; pi,tThe active power output of the conventional unit i in the time period t is obtained; pw,tThe active power output of the new energy unit w in the time period t is obtained; pl,tL for the active power of the tie line l in the time period ttIs the load value of the system in the time period t; u. ofi,tStarting and stopping a unit i at a time t; pi,minLower power limit, P, of unit ii,maxThe upper power limit of the unit i is set; UT (unified device)iAnd DTiRespectively the minimum starting time and the minimum stopping time of the unit i; y isi,tWhether the unit i has a sign of change from a shutdown state to a startup state in a time period t or not is marked; z is a radical ofi,tWhether the unit i has a sign of change from a starting state to a stopping state in a time period t or not is marked; p0,w,tL predicted output for new energy unit w in time period tijRepresenting the upper current limit of branch ij; m is a power grid computing node set; li,tLoad power for the node; si,j,tSensitivity of injected power to branch ij for node i; rt,uA positive spare capacity lower limit for the system at time period t; rt,dThe negative spare capacity lower limit for the system at time t.
In a specific implementation manner of the embodiment of the present invention, an objective function of the SCED model is a maximization of a total power generation amount of the new energy unit, and is expressed as:
Figure BDA0002418266390000161
in the formula, T is a set of all time periods; n is a radical ofwThe total number of the new energy units; pw,tThe active power output of the new energy unit w in the time period t is obtained;
the constraint conditions comprise load balance constraint, conventional unit output upper and lower limit constraint, new energy unit output upper and lower limit constraint, power grid safety constraint and system reserve capacity constraint:
Figure BDA0002418266390000162
Pi,minui,t≤Pi,t≤Pi,maxui,t
Pw,t≤P0,w,t
Figure BDA0002418266390000163
Figure BDA0002418266390000164
Figure BDA0002418266390000165
in the formula, NiThe total number of the thermal power generating units; n is a radical ofwThe total number of the new energy units; n is a radical oflThe total number of the external connecting lines; pi,tThe active power output of the conventional unit i in the time period t is obtained; pw,tThe active power output of the new energy unit w in the time period t is obtained; pl,tL for the active power of the tie line l in the time period ttIs the load value of the system in the time period t; u. ofi,tStarting and stopping a unit i at a time t; pi,minLower power limit, P, of unit ii,maxThe upper power limit of the unit i is set; UT (unified device)iAnd DTiRespectively the minimum starting time and the minimum stopping time of the unit i; p0,w,tL predicted output for new energy unit w in time period tijRepresenting the upper current limit of branch ij; m is a power grid computing node set; li,tLoad power for the node; si,j,tSensitivity of injected power to branch ij for node i; rt,uA positive spare capacity lower limit for the system at time period t; rt,dThe negative spare capacity lower limit for the system at time t.
Example 3
Based on the same inventive concept as embodiment 1, the invention provides a power grid new energy consumption capability evaluation computing system based on a multi-scene generation technology, which comprises a storage medium and a processor, wherein the processor is used for processing the power grid new energy consumption capability evaluation computing system;
the storage medium is used for storing instructions;
the processor is configured to operate in accordance with the instructions to perform the steps of the method according to any of embodiment 1.
In summary, the following steps:
according to the method, on the premise of considering the actual dispatching operation and ensuring the safety requirement of the system, a combined scene and the probability thereof are generated according to the predicted output and the probability thereof of the new energy single plant, and scene reduction is performed to ensure the effectiveness of generation of a plurality of new energy scenes. Based on the method, mature mixed integer linear programming algorithm software is called to calculate the SCUC model under each combination scene, the combination state of the unit is determined, and the consumption capability of the system under the condition of large-scale new energy access is further calculated, so that the validity and the referential property of the consumption capability evaluation result are improved on the premise of ensuring the safety and stability of the system.
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 and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block 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 and/or block diagram block or blocks.
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 and/or block diagram block or blocks.
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 and/or block diagram block or blocks.
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.
The foregoing shows and describes the general principles and broad features of the present invention and advantages thereof. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.

Claims (10)

1. A power grid new energy consumption capability assessment and calculation method based on a multi-scenario generation technology is characterized by comprising the following steps:
determining a power grid range and a calculation period of power grid new energy consumption capability evaluation calculation to be carried out and other calculation boundaries;
generating a large number of combined scenes containing multiple new energy power plants by freely arranging and combining the fields;
merging similar scenes by adopting a scene reduction technology of backward reduction, reducing the number of combined scenes and generating a typical combined scene;
merging the similar time periods, reducing the number of time periods entering optimization, and reducing the scale of the calculation time period of the optimization model;
aiming at each typical combination scene, establishing a SCUC dimension reduction model after time interval merging, and solving to obtain a unit combination result;
and establishing a full-time SCED model based on the unit combination result and solving, finally obtaining a new energy consumption result under each typical combination scene, and completing the evaluation and calculation of the new energy consumption capability of the power grid based on the multi-scene generation technology.
2. The method for evaluating and calculating the new energy consumption capability of the power grid based on the multi-scenario generation technology according to claim 1, wherein: the generation process of the combined scene comprises the following steps:
the total number of new energy stations of a certain power grid is assumed to be NwThe predicted output scene of each new energy station has NpThe occurrence probability of each predicted contribution scene is prij(i＝1,2,…,Nw；j＝1,2,…,Np)；
Arranging and combining the output scenes of all the new energy stations to obtain the final number N of the new energy output scenesa，
Figure FDA0002418266380000011
The combined scene occurrence probability is the product of the corresponding contribution scene occurrence probabilities.
3. The method for evaluating and calculating the new energy consumption capability of the power grid based on the multi-scenario generation technology according to claim 1, wherein: the reduction method of the typical combination scene comprises the following steps:
initializing a deleted scene set J to be null, wherein the number of scenes needing to be deleted is K, and the number of scenes deleted in the K iteration is lk；
The following steps are repeated until the iteration is finished:
calculating the distance of Kantorovzval to make l take the scene lkObtaining a minimum value by using a time formula, wherein the computing formula of the Kantouvyqi distance is as follows:
Figure FDA0002418266380000012
in the formula: j is the deleted scene set; p is a radical ofiIs the probability of scene i ξiCorresponding to a scene sequence i; t is the number of segments of the scene timescale; c. CT(ξi,ξj) Representing a sequence of scenes ξiAnd scene sequence ξjThe distance of (a) to (b),
Figure FDA0002418266380000013
deleting scene lkLet Jk＝Jk-1∪{lkAnd will scene lkThe probability of (c) is accumulated to the scene closest to it;
if K < K, let K be K + 1.
4. The method for evaluating and calculating the new energy consumption capability of the power grid based on the multi-scenario generation technology according to claim 1, wherein: the merging of the similar time periods comprises the following steps:
let LtFor the system load of time period t, the system load change rate of adjacent time periods t and t +1 is:
Figure FDA0002418266380000021
calculating the change rate of the system load in all time periods to find the minimum change rate delta LtMerging the time interval t and the time interval t +1 into a new time interval in the corresponding time interval;
according to the change rate of the system load, the time interval merging is repeated until the minimum change rate delta LtAnd if the number of the time intervals is larger than the set threshold value or the number of the time intervals left after the merging reaches the preset number, the merging process is ended.
5. The method for evaluating and calculating the new energy consumption capability of the power grid based on the multi-scenario generation technology according to claim 1, wherein: the objective function of the SCUC dimension reduction model is the maximization of the total power generation of the new energy unit, and is represented as follows:
Figure FDA0002418266380000022
in the formula, NWIs a new energy machine set; t is a time interval set contained after time interval merging; pw,tThe maximum power receiving capacity of the new energy source unit w in the time period t;
the constraint conditions of the SCUC dimension reduction model comprise load balance constraint, conventional unit output upper and lower limit constraint, unit minimum start-up and shut-down time constraint, new energy unit output upper and lower limit constraint, power grid safety constraint and system reserve capacity constraint:
Figure FDA0002418266380000023
Pi,minui,t≤Pi,t≤Pi,maxui,t
Figure FDA0002418266380000024
Figure FDA0002418266380000025
yi,t-zi,t＝ui,t-ui,t-1
yi,t+zi,t≤1
Pw,t≤P0,w,t
Figure FDA0002418266380000026
Figure FDA0002418266380000027
Figure FDA0002418266380000031
in the formula, NiThe total number of the thermal power generating units; n is a radical ofwThe total number of the new energy units; n is a radical oflThe total number of the external connecting lines; pi,tThe active power output of the conventional unit i in the time period t is obtained; pw,tThe active power output of the new energy unit w in the time period t is obtained; pl,tL for the active power of the tie line l in the time period ttIs the load value of the system in the time period t; u. ofi,tStarting and stopping a unit i at a time t; pi,minLower power limit, P, of unit ii,maxThe upper power limit of the unit i is set; UT (unified device)iAnd DTiRespectively the minimum starting time and the minimum stopping time of the unit i; y isi,tWhether the unit i has a sign of change from a shutdown state to a startup state in a time period t or not is marked; z is a radical ofi,tWhether the unit i has a sign of change from a starting state to a stopping state in a time period t or not is marked; p0,w,tL predicted output for new energy unit w in time period tijRepresenting the upper current limit of branch ij; m is a power grid computing node set; li,tLoad power for the node; si,j,tSensitivity of injected power to branch ij for node i; rt,uA positive spare capacity lower limit for the system at time period t; rt,dThe negative spare capacity lower limit for the system at time t.
6. The method for evaluating and calculating the new energy consumption capability of the power grid based on the multi-scenario generation technology according to claim 1, wherein: the objective function of the SCED model is the maximization of the total power generation of the new energy unit, and is represented as follows:
Figure FDA0002418266380000032
in the formula, T is a set of all time periods; n is a radical ofwThe total number of the new energy units; pw,tThe active power output of the new energy unit w in the time period t is obtained;
the constraint conditions comprise load balance constraint, conventional unit output upper and lower limit constraint, new energy unit output upper and lower limit constraint, power grid safety constraint and system reserve capacity constraint:
Figure FDA0002418266380000033
Pi,minui,t≤Pi,t≤Pi,maxui,t
Pw,t≤P0,w,t
Figure FDA0002418266380000034
Figure FDA0002418266380000035
Figure FDA0002418266380000036
in the formula, NiThe total number of the thermal power generating units; n is a radical ofwThe total number of the new energy units; n is a radical oflThe total number of the external connecting lines; pi,tThe active power output of the conventional unit i in the time period t is obtained; pw,tThe active power output of the new energy unit w in the time period t is obtained; pl,tL for the active power of the tie line l in the time period ttIs the load value of the system in the time period t;ui,tstarting and stopping a unit i at a time t; pi,minLower power limit, P, of unit ii,maxThe upper power limit of the unit i is set; UT (unified device)iAnd DTiRespectively the minimum starting time and the minimum stopping time of the unit i; p0,w,tL predicted output for new energy unit w in time period tijRepresenting the upper current limit of branch ij; m is a power grid computing node set; li,tLoad power for the node; si,j,tSensitivity of injected power to branch ij for node i; rt,uA positive spare capacity lower limit for the system at time period t; rt,dThe negative spare capacity lower limit for the system at time t.
7. A power grid new energy consumption capability assessment and calculation device based on multi-scenario generation technology is characterized by comprising the following steps:
the determining unit is used for determining the power grid range and the calculation period of the power grid new energy consumption capability evaluation calculation to be carried out and other calculation boundaries;
the first generation unit is used for generating a large number of combination scenes containing multiple new energy power plants through free arrangement and combination among fields;
the second generation unit is used for merging similar scenes by adopting a scene reduction technology of backward reduction, reducing the number of combined scenes and generating a typical combined scene;
the merging unit is used for merging the similar time intervals, reducing the number of time intervals entering optimization and reducing the scale of the calculation time intervals of the optimization model;
the first solving unit is used for establishing a SCUC dimension reduction model after time interval merging aiming at each typical combination scene and solving to obtain a unit combination result;
and the second solving unit is used for establishing a full-time SCED model based on the unit combination result and solving the full-time SCED model to finally obtain a new energy consumption result under each typical combination scene, and finishing the evaluation and calculation of the new energy consumption capability of the power grid based on the multi-scene generation technology.
8. The device for evaluating and calculating the new energy consumption capability of the power grid based on the multi-scenario generation technology according to claim 7, wherein: the objective function of the SCUC dimension reduction model is the maximization of the total power generation of the new energy unit, and is represented as follows:
Figure FDA0002418266380000041
in the formula, NWIs a new energy machine set; t is a time interval set contained after time interval merging; pw,tThe maximum power receiving capacity of the new energy source unit w in the time period t;
the constraint conditions of the SCUC dimension reduction model comprise load balance constraint, conventional unit output upper and lower limit constraint, unit minimum start-up and shut-down time constraint, new energy unit output upper and lower limit constraint, power grid safety constraint and system reserve capacity constraint:
Figure FDA0002418266380000042
Pi,minui,t≤Pi,t≤Pi,maxui,t
Figure FDA0002418266380000043
Figure FDA0002418266380000044
yi,t-zi,t＝ui,t-ui,t-1
yi,t+zi,t≤1
Pw,t≤P0,w,t
Figure FDA0002418266380000051
Figure FDA0002418266380000052
Figure FDA0002418266380000053
in the formula, NiThe total number of the thermal power generating units; n is a radical ofwThe total number of the new energy units; n is a radical oflThe total number of the external connecting lines; pi,tThe active power output of the conventional unit i in the time period t is obtained; pw,tThe active power output of the new energy unit w in the time period t is obtained; pl,tL for the active power of the tie line l in the time period ttIs the load value of the system in the time period t; u. ofi,tStarting and stopping a unit i at a time t; pi,minLower power limit, P, of unit ii,maxThe upper power limit of the unit i is set; UT (unified device)iAnd DTiRespectively the minimum starting time and the minimum stopping time of the unit i; y isi,tWhether the unit i has a sign of change from a shutdown state to a startup state in a time period t or not is marked; z is a radical ofi,tWhether the unit i has a sign of change from a starting state to a stopping state in a time period t or not is marked; p0,w,tL predicted output for new energy unit w in time period tijRepresenting the upper current limit of branch ij; m is a power grid computing node set; li,tLoad power for the node; si,j,tSensitivity of injected power to branch ij for node i; rt,uA positive spare capacity lower limit for the system at time period t; rt,dThe negative spare capacity lower limit for the system at time t.
9. The device for evaluating and calculating the new energy consumption capability of the power grid based on the multi-scenario generation technology according to claim 7, wherein: the objective function of the SCED model is the maximization of the total power generation of the new energy unit, and is represented as follows:
Figure FDA0002418266380000054
in the formula, T is a set of all time periods; n is a radical ofwThe total number of the new energy units; pw,tThe active power output of the new energy unit w in the time period t is obtained;
the constraint conditions comprise load balance constraint, conventional unit output upper and lower limit constraint, new energy unit output upper and lower limit constraint, power grid safety constraint and system reserve capacity constraint:
Figure FDA0002418266380000055
Pi,minui,t≤Pi,t≤Pi,maxui,t
Pw,t≤P0,w,t
Figure FDA0002418266380000056
Figure FDA0002418266380000057
Figure FDA0002418266380000058
in the formula, NiThe total number of the thermal power generating units; n is a radical ofwThe total number of the new energy units; n is a radical oflThe total number of the external connecting lines; pi,tThe active power output of the conventional unit i in the time period t is obtained; pw,tThe active power output of the new energy unit w in the time period t is obtained; pl,tL for the active power of the tie line l in the time period ttIs the load value of the system in the time period t; u. ofi,tStarting and stopping a unit i at a time t; pi,minLower power limit, P, of unit ii,maxThe upper power limit of the unit i is set; UT (unified device)iAnd DTiRespectively the minimum starting time and the minimum stopping time of the unit i; p0,w,tL predicted output for new energy unit w in time period tijRepresenting the upper current limit of branch ij; m is a power grid computing node set; li,tLoad power for the node; si,j,tSensitivity of injected power to branch ij for node i; rt,uA positive spare capacity lower limit for the system at time period t; rt,dFor negative standby of the system during time period tThe lower limit of the capacity.
10. A power grid new energy consumption capability evaluation and calculation system based on a multi-scenario generation technology is characterized by comprising a storage medium and a processor;
the storage medium is used for storing instructions;
the processor is configured to operate in accordance with the instructions to perform the steps of the method according to any one of claims 1 to 6.
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