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

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

Calculation method, device and system for evaluating new energy consumption capacity of power grid based on multi-scenario generation technology

technical field

The invention belongs to the technical field of power system dispatching automation, and relates to a calculation method for evaluating new energy consumption capacity of a power grid, in particular to a calculation method for evaluating new energy consumption capacity of a power grid based on a multi-scenario generation technology.

Background technique

The penetration rate of new energy in the power system is gradually increasing, making the dispatching resources in the system more and more abundant and the operation mode increasingly complex. However, the traditional distribution network still has problems such as insufficient new energy consumption capacity, which seriously restricts the high penetration of new energy. , which is not conducive to the optimization and adjustment of the energy structure. In addition, the integration of new energy into the grid places higher requirements on the power system's adjustment capability. However, conventional power grid systems are usually dominated by thermal power, and flexible adjustment resources are insufficient, resulting in limited adjustment capabilities of the power system and insufficient to cope with the uncertainty of new energy output. . In order to ensure the safe and stable operation of the power system, during the peak period of new energy output, the system will be forced to abandon wind and light, which greatly reduces the utilization rate of new energy.

After the new energy is connected to the power grid, the primary problem of the optimal scheduling of units is how to establish a reliable mathematical model of the Security Constraint Unit Commitment (SCUC) problem with new energy. The objective function of the SCUC model is to minimize the total operating cost in the scheduling period for a long period of time, which mainly includes the operating cost of conventional units and the cost of starting and stopping. With the development of the electricity market, based on different starting points, the form of the objective function of the SCUC model has undergone new changes. Due to the reduced utilization rate of new energy, the objective function may require that the grid-connected capacity of new energy that the system can accept is as high as possible. In terms of constraints, the form of constraints has also changed correspondingly according to different energy types and objective functions.

The SCUC problem itself is a multi-dimensional, nonlinear, multi-period coupled mixed integer programming problem, and the new energy itself has uncertainties such as intermittent and volatility, and its large-scale grid connection makes the solution of the SCUC problem more complicated. The traditional new energy absorptive capacity assessment is calculated based on a single forecasted scenario, but the actual power grid has multiple new energy stations and their predicted output is uncertain, and the consumption capacity assessment only considering a single forecasted scenario of new energy is no longer sufficient. The demand for diversified development of the power grid will significantly affect the optimization results.

With the development of mathematical optimization algorithms and the great progress in the computational performance of commercial solvers, Mixed Integer Programming (MIP) has become the main method for solving optimization scheduling problems. The advantages of MIP are: (1) global optimality; (2) direct measurement of the optimality of the solution; (3) more flexible and accurate modeling capabilities. The optimization model based on MIP algorithm is more and more widely used in the field of power dispatching.

At present, the idea of the new energy consumption capacity evaluation algorithm of the power grid based on the multi-scenario generation technology is mainly to use the scenario generation technology to model the uncertainty of the new energy forecast, aiming at the maximum output of the new energy that can be accepted by the system. The scene, all variables and all constraints are unifiedly established as a standard mathematical optimization model, and the optimization solver is directly invoked to perform large-scale mixed integer programming calculations on the standardized model.

The random error of new energy forecast is a key factor restricting whether the calculation model for evaluating the power grid's new energy consumption capacity based on multi-scenario generation technology can obtain the optimal decision-making scheme. Combination scenarios are issues that need to be paid attention to in the research of optimal operation and scheduling with large-scale new energy sources; on the other hand, the traditional calculation model for evaluating the power grid's new energy consumption capacity based on multi-scenario generation technology does not consider the SCUC problem, which makes the optimization results impossible. Provide protection for the safe operation of the power grid.

SUMMARY OF THE INVENTION

In view of the above problems, the present invention proposes a new energy consumption capacity evaluation calculation method of a power grid based on a multi-scenario generation technology, which can improve the validity and reference of the consumption capacity assessment results under the premise of ensuring the safety and stability of the system.

In order to realize the above-mentioned technical purpose and achieve the above-mentioned technical effect, the present invention is realized through the following technical solutions:

In a first aspect, the present invention provides a calculation method for evaluating the new energy consumption capacity of a power grid based on a multi-scenario generation technology, including:

Determine the power grid range and calculation period and other calculation boundaries that need to be carried out for the assessment and calculation of the new energy consumption capacity of the power grid;

Through the free arrangement and combination of fields, a large number of combined scenes with multiple new energy power plants are generated;

The scene reduction technology of the backward reduction method is adopted to merge similar scenes, reduce the number of combined scenes, and generate typical combined scenes;

Merge similar time periods, reduce the number of time periods entering the optimization, and reduce the size of the calculation time period of the optimization model;

For each typical combination scenario, establish the SCUC dimensionality reduction model after time period merge, and solve it to obtain the unit combination result;

Based on the unit combination results, a full-time SCED model is established and solved, and finally the new energy consumption results under each typical combination scenario are obtained, and the evaluation and calculation of the new energy consumption capacity of the power grid based on the multi-scenario generation technology is completed.

Optionally, the generating process of the combined scene includes:

Assuming that the total number of new energy stations in a power grid is N _w , there are N _p types of predicted output scenarios for each new energy station, and the probability of occurrence of each predicted output scene is pr _ij (i=1,2,...,N _w ; j=1,2,...,N _p );

Arrange and combine the output scenarios of all new energy stations to obtain the final number of new energy output scenarios _Na ,

The probability of occurrence of the combined scene is the product of the probability of occurrence of the corresponding output scene.

Optionally, the method for generating the typical combined scene includes:

Initialize the deleted scene set J to be empty, the number of scenes to be deleted is K, and the scene to be deleted in the kth iteration is l _k ;

Repeat the following steps until the end of the iteration:

Calculate the Kantorovich distance, so that l takes the scene l _k to obtain the minimum value of the following formula. The calculation formula of the Kantorovich distance is:

where J is the set of deleted scenes; pi is the probability of scene _i ; ξ ⁱ corresponds to scene sequence i; T is the number of segments in the scene time scale; c _T (ξ ⁱ ,ξ ^j ) represents the difference between scene sequence ξ ⁱ and the distance of the scene sequence ξ ^j ,

Delete scene l _k , let J ^k =J ^k-1 ∪{l _k }, and accumulate the probability of scene l _k to the scene closest to it;

If k<K, then let k=k+1.

Optionally, the merging of similar time periods includes the following steps:

Let L _t be the system load of time period t, then the system load change rate of adjacent time periods t and t+1 is:

Calculate the system load change rate for all time periods, find the time period corresponding to the minimum change rate ΔL _t , and combine time periods t and t+1 into a new time period;

According to the change rate of the system load, the time period merge is repeated until the minimum change rate ΔL _t is greater than the set threshold, or the number of time periods remaining after the merge reaches the preset number, and the merge process ends.

Optionally, the objective function of the SCUC dimensionality reduction model is the maximization of the total power generation of new energy generating units, which is expressed as:

In the formula, N _W is the set of new energy units; T is the set of time periods included after the time period is merged; P _w,t is the maximum power receiving capacity of the new energy unit w in time period t;

The constraints of the SCUC dimensionality reduction model include load balance constraints, upper and lower output limits of conventional units, minimum start-up and shutdown time constraints of units, upper and lower output limits of new energy units, power grid security constraints, and system reserve capacity constraints:

P _i,min u _i,t ≤P _i,t ≤P _i,max u _i,t

y _i,t -z _i,t =ui _,t -u _i,t-1

y _i,t +z _i,t ≤1

P _w,t ≤P _0,w,t

In the formula, N _i is the total number of thermal power units; N _w is the total number of new energy units; N _l is the total number of external connection lines; P _i,t is the active power output of conventional unit i in time period t; P _{w, t} is the active power output of the new energy unit w in the period t; P _l,t is the active power output of the tie line l in the period t; L _t is the load value of the system in the period t; u _i,t is the unit i in the period t. Start-stop state; P _i,min is the lower power limit of unit i, P _i,max is the upper power limit of unit i; UT _i and DT _i are the minimum startup time and minimum shutdown time of unit i respectively; y _i,t is the unit i, t Whether i has a sign that the state changes from shutdown to startup in time period t; zi _,t is whether the unit i has a sign of change from startup to shutdown state in time period t; P _0,w,t is the prediction of new energy unit w in time period t output; _{Li ij} represents the upper limit of the power flow of branch ij; M is the set of grid computing nodes; li _,t is the node load power; S _i,j,t is the sensitivity of the injected power of node i to branch ij; R _{t, u} is the lower limit of the positive reserve capacity of the system in the time period t; R _t,d is the lower limit of the negative reserve capacity of the system in the time period t.

Optionally, the objective function of the SCED model is the maximization of the total power generation of new energy generating units, which is expressed as:

In the formula, T is the set of all time periods; N _w is the total number of new energy units; P _w,t is the active power output of new energy units w in time period t;

The constraints include load balance constraints, upper and lower output constraints of conventional units, upper and lower output constraints of new energy units, power grid security constraints, and system reserve capacity constraints:

P _i,min u _i,t ≤P _i,t ≤P _i,max u _i,t

P _w,t ≤P _0,w,t

In the formula, N _i is the total number of thermal power units; N _w is the total number of new energy units; N _l is the total number of external connection lines; P _i,t is the active power output of conventional unit i in time period t; P _{w, t} is the active power output of the new energy unit w in the period t; P _l,t is the active power output of the tie line l in the period t; L _t is the load value of the system in the period t; u _i,t is the unit i in the period t. Start-stop state; P _i,min is the lower power limit of unit i, P _i,max is the upper power limit of unit i; UT _i and DT _i are the minimum startup time and minimum shutdown time of unit i respectively; P _0,w,t is the predicted output of the new energy unit w in the time period t; _{Li ij} represents the upper limit of the power flow of the branch ij; M is the set of grid computing nodes; li _,t is the node load power; S _i,j,t is the injected power of the node i Sensitivity to branch ij; R _t,u is the lower limit of positive reserve capacity of the system in time period t; R _t,d is the lower limit of negative reserve capacity of the system in time period t.

In a second aspect, the present invention provides a computing device for evaluating new energy consumption capacity of a power grid based on a multi-scenario generation technology, including:

Determine the unit, determine the power grid range and calculation period for the calculation of the new energy consumption capacity evaluation calculation of the power grid, and other calculation boundaries;

The first generation unit is used to generate a large number of combined scenarios including multiple new energy power plants through free arrangement and combination between fields;

The second generation unit is used for the scene reduction technology of the backward reduction method to merge similar scenes, reduce the number of combined scenes, and generate typical combined scenes;

The merging unit is used to merge similar time periods, reduce the number of time periods entering the optimization, and reduce the scale of the calculation time period of the optimization model;

The first solving unit is used for establishing the SCUC dimensionality reduction model after time merging for each typical combination scenario, and solving to obtain the unit combination result;

The second solving unit is used to establish and solve the full-time SCED model based on the unit combination results, and finally obtain the new energy consumption results under each typical combination scenario, and complete the evaluation calculation of the new energy consumption capacity of the power grid based on the multi-scenario generation technology.

Optionally, the objective function of the SCUC dimensionality reduction model is the maximization of the total power generation of new energy generating units, which is expressed as:

In the formula, N _W is the set of new energy units; T is the set of time periods included after the time period is merged; P _w,t is the maximum power receiving capacity of the new energy unit w in time period t;

The constraints of the SCUC dimensionality reduction model include load balance constraints, upper and lower output limits of conventional units, minimum start-up and shutdown time constraints of units, upper and lower output limits of new energy units, power grid security constraints, and system reserve capacity constraints:

P _i,min u _i,t ≤P _i,t ≤P _i,max u _i,t

y _i,t -z _i,t =ui _,t -u _i,t-1

y _i,t +z _i,t ≤1

P _w,t ≤P _0,w,t

In the formula, N _i is the total number of thermal power units; N _w is the total number of new energy units; N _l is the total number of external connection lines; P _i,t is the active power output of conventional unit i in time period t; P _{w, t} is the active power output of the new energy unit w in the period t; P _l,t is the active power output of the tie line l in the period t; L _t is the load value of the system in the period t; u _i,t is the unit i in the period t. Start-stop state; P _i,min is the lower power limit of unit i, P _i,max is the upper power limit of unit i; UT _i and DT _i are the minimum startup time and minimum shutdown time of unit i respectively; y _i,t is the unit i, t Whether i has a sign that the state changes from shutdown to startup in time period t; zi _,t is whether the unit i has a sign of change from startup to shutdown state in time period t; P _0,w,t is the prediction of new energy unit w in time period t output; _{Li ij} represents the upper limit of the power flow of branch ij; M is the set of grid computing nodes; li _,t is the node load power; S _i,j,t is the sensitivity of the injected power of node i to branch ij; R _{t, u} is the lower limit of the positive reserve capacity of the system in the time period t; R _t,d is the lower limit of the negative reserve capacity of the system in the time period t.

Optionally, the objective function of the SCED model is the maximization of the total power generation of new energy generating units, which is expressed as:

In the formula, T is the set of all time periods; N _w is the total number of new energy units; P _w,t is the active power output of new energy units w in time period t;

The constraints include load balance constraints, upper and lower output constraints of conventional units, upper and lower output constraints of new energy units, power grid security constraints, and system reserve capacity constraints:

P _i,min u _i,t ≤P _i,t ≤P _i,max u _i,t

P _w,t ≤P _0,w,t

In the formula, N _i is the total number of thermal power units; N _w is the total number of new energy units; N _l is the total number of external connection lines; P _i,t is the active power output of conventional unit i in time period t; P _{w, t} is the active power output of the new energy unit w in the period t; P _l,t is the active power output of the tie line l in the period t; L _t is the load value of the system in the period t; u _i,t is the unit i in the period t. Start-stop state; P _i,min is the lower power limit of unit i, P _i,max is the upper power limit of unit i; UT _i and DT _i are the minimum startup time and minimum shutdown time of unit i respectively; P _0,w,t is the predicted output of the new energy unit w in the time period t; _{Li ij} represents the upper limit of the power flow of the branch ij; M is the set of grid computing nodes; li _,t is the node load power; S _i,j,t is the injected power of the node i Sensitivity to branch ij; R _t,u is the lower limit of positive reserve capacity of the system in time period t; R _t,d is the lower limit of negative reserve capacity of the system in time period t.

In a third aspect, the present invention provides a computing system for evaluating new energy consumption capacity of a power grid based on a multi-scenario generation technology, including a storage medium and a processor;

the storage medium is used for storing instructions;

The processor is adapted to operate in accordance with the instructions to perform the steps of the method according to any of the first aspects.

Compared with the prior art, the beneficial effects of the present invention:

The invention firstly determines the power grid range and calculation period that need to carry out the evaluation and calculation of the new energy consumption capacity of the power grid based on the multi-scenario generation technology, and determines the calculation boundary; through the free arrangement and combination between the fields, a large number of combined scenarios containing multiple new energy power plants are generated; The scene reduction technology of the backward reduction method is used to generate typical combined scenes; then the merge analysis of similar periods is carried out to reduce the number of periods entered into SCUC calculation; for each typical combined scene, a SCUC dimensionality reduction model after period merge is established to quickly solve and obtain the unit Combination results; based on the unit combination results, a full-time SCED model is established and solved; the new energy consumption results under each typical combination scenario are finally obtained, which can improve the validity and reference of the assessment results of the consumption capacity under the premise of ensuring the safety and stability of the system sex.

The invention utilizes combination scene reduction, similar time period merging and invalid start-stop variable identification technology to reduce the time period scale and variable dimension of entering SCUC optimization calculation, reduce the state combination space of unit combination, and reduce the cycle of SCUC-SCED optimization solution. times, effectively improving the solution efficiency of the entire absorptive capacity evaluation;

The invention performs unit combination dimension reduction and clearing and full-time economic dispatch calculation based on multiple new energy combination scenario generation technologies, obtains calculation results under different combination scenarios and different occurrence probabilities, and reflects the consumption of new energy in more detail;

The invention performs the evaluation and calculation of the new energy consumption capacity of the power grid based on the multi-scenario generation technology based on the combination of the safety constraints, ensuring that the new energy consumption results meet various power grid operation constraints, and the model calculation results are stable. It provides an effective way of thinking and has certain practical value.

Description of drawings

In order to make the content of the present invention easier to be understood clearly, the present invention will be described in further detail below according to specific embodiments and in conjunction with the accompanying drawings, wherein:

FIG. 1 is a schematic flowchart of a calculation method for evaluating new energy consumption capacity of a power grid based on a multi-scenario generation technology according to an embodiment of the present invention.

Detailed ways

In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the protection scope of the present invention.

The application principle of the present invention will be described in detail below with reference to the accompanying drawings.

In the process of evaluating and calculating the new energy consumption capacity of the power grid, it is necessary to adopt the technology of safety constraint unit combination to optimize the unit combination and new energy output results in the monthly 744 period. The optimization process not only requires careful consideration of various security constraints in power grid operation, such as unit operation constraints, power grid security constraints, and load balance constraints, but also requires that the new energy output scenario can describe the actual situation of dispatching operations as much as possible, and the calculation process can be used by operators. It can be finished within an acceptable time, so that the calculation accuracy and calculation time can meet the actual requirements of the project.

Example 1

The invention proposes a new energy consumption capacity evaluation calculation method of the power grid based on the multi-scenario generation technology, which preprocesses various probability scenarios of the new energy, and takes into account the structural characteristics of the power system. Processing, merging similar calculation periods and combined scenarios, identifying invalid integer variables, reducing the number of combined scenarios, narrowing the optimization range of mixed integer programming and the iteration times of new energy absorption capacity evaluation, improving solution efficiency and effectiveness. The method provided by the present invention will be described in detail below through specific examples.

Specifically, the method for evaluating and calculating the new energy consumption capacity of the power grid based on the multi-scenario generation technology includes the following steps:

Step 1) Consider the assessment of the new energy consumption capacity of the power system in a certain area. The power grid includes 190 generator sets that need to be calculated by state combination. The calculation process needs to consider 29 transmission sections, and there are 12 new energy partitions. Each partition contains For 3 probability prediction scenarios, the new energy consumption capacity evaluation takes 1 hour as a calculation period, and the number of calculation periods is 744. Firstly, data preparation is performed to obtain parameters such as the upper and lower output limits of the generator sets in the power grid, the minimum start and stop time, and other parameters such as the topology structure of the power grid, the components of the transmission section, and the transmission limit. At the same time, various planning data are obtained, including load forecast, maintenance plan, standby demand, new energy forecast output and its probability, etc., to determine the boundary of the power grid new energy consumption capacity evaluation calculation.

Step 2) According to the predicted output under different probabilities of each new energy source, a large number of combined scenarios containing multiple new energy power plants are generated through free arrangement and combination between fields, and the probability of occurrence of each combined scenario is calculated;

In a specific implementation of the embodiment of the present invention, the specific process of generating the combined scene includes the following steps:

Assuming that the total number of new energy stations in a power grid is N _w , there are N _p types of predicted output scenarios for each new energy station, and the probability of occurrence of each predicted output scene is pr _ij (i=1,2,...,N _w ; j=1,2,...,N _p );

By arranging and combining the output scenarios of all new energy stations, the final number N _a of new energy output scenarios can be obtained as follows:

The probability of occurrence of the combined scene is the product of the probability of occurrence of the corresponding output scene.

Step 3) adopt the scene reduction technology of the backward reduction method, merge similar scenes, reduce the number of combined scenes, and generate typical combined scenes;

From the perspective of new energy output scenarios reflecting the uncertainty of new energy output, similar scenarios can provide similar uncertainty information, but at the same time, it will also increase the amount of unnecessary calculation and affect the calculation efficiency. Therefore, it is necessary to perform scene reduction on the basis of the generated combined scene set, remove some scenes with low probability, and merge similar scenes. Scenario reduction is essentially a method to improve computing efficiency at the expense of computing accuracy. Therefore, the effectiveness of new energy output scenarios should be guaranteed to the greatest extent during scenario reduction.

The basic principle of scene reduction is to minimize the probability distance between the reduced scene set and the scene set before reduction. Probabilistic distance is a way of weighing the distance of each scene and the probability of the scene, which makes the information expressed by the scene before reduction and the scene after reduction the closest, even if the precision loss caused by the reduction process is the lowest. The probabilistic distance of this optimization model is described by the Kantorovich distance D _k , which is expressed as _follows :

where J is the set of deleted scenes; pi is the probability of scene _i ; ξ ⁱ corresponds to scene sequence i; T is the number of segments in the scene time scale; c _T (ξ ⁱ ,ξ ^j ) represents the difference between scene sequence ξ ⁱ and The distance of the scene sequence ξ ^j , namely:

The backward reduction method is mainly divided into the following steps:

Step1: Initialize the deleted scene set J to be empty, the number of scenes to be deleted is K, and the deleted scene of the k-th iteration is l _k ;

Step2: Calculate the Kantorovich distance, so that the following formula obtains the minimum value when l takes the scene l _k ;

Step3: delete scene l _k , let J ^k =J ^k-1 ∪{l _k }, and accumulate the probability of scene l _k to the scene closest to it;

Step4: If k<K, set k=k+1, and return to Step2, otherwise the iteration ends.

Step 4) According to the change trend of the system load in different time periods in the calculation cycle, merge the similar calculation time periods, reduce the number of time periods entering the optimization, and reduce the scale of the calculation time period of the optimization model; According to the optimizable state of the unit, identify the combination of safety-constrained units The must-open and must-stop group and buffer group in decision-making;

The specific process of time period merging is: Let L _t be the system load of time period t, then the system load change rate of adjacent time periods t and t+1 is:

Calculate the system load change rate for all time periods, find the time period corresponding to the minimum change rate ΔL _t , and combine time periods t and t+1 into a new time period. According to the change rate of the system load, the time period merge is repeated until the minimum change rate ΔL _t is greater than the set threshold, or the number of time periods remaining after the merge reaches the preset number, and the merge process ends.

The must-start and must-stop units in the decision-making of the safety-constrained unit combination are those that must be developed or must be stopped during the operation of the power grid, and there is no need to make an optimization decision on the unit state in the security-constrained unit combination; the buffer unit cannot be determined in advance. For the generator set in the state, it is necessary to make the optimal decision of the state of the generator set in the combination of safety constraints. According to the provided unit state data, the buffer unit and the must-start must-stop unit are identified to reduce the number of integer variables, narrow the optimization range of mixed integer programming, and improve the solution efficiency.

Step 5) For a typical combination scenario, the on-off state of the buffer unit is used as the combination decision-making variable, considering the transmission limit constraints of the effective section, and the SCUC dimension reduction model after time period merge is established, and the mixed integer programming algorithm is used to quickly solve the problem. The result of the start-stop status of the buffer unit.

Monthly evaluation and calculation of new energy consumption capacity optimizes the start-up, shutdown and output of thermal power units at each time period, as well as the output plan of new energy units to meet the demand of the load curve. Taking the new energy units in the area as the specific evaluation object, based on the total power generation of each new energy unit, the total output of the new energy units in the study area is obtained. The amount of electricity produced by the power unit. The objective function of the SCUC dimensionality reduction model can be expressed as:

In the formula, N _W is the set of new energy units; T is the set of time periods included after the time period is merged; P _w,t is the maximum power receiving capacity of the new energy unit w in time period t.

Constraints include load balance constraints, upper and lower output constraints of conventional units, minimum start-up and shutdown time constraints of units, upper and lower output constraints of new energy units, power grid security constraints, and system reserve capacity constraints:

P _i,min u _i,t ≤P _i,t ≤P _i,max u _i,t

y _i,t -z _i,t =ui _,t -u _i,t-1

y _i,t +z _i,t ≤1

P _w,t ≤P _0,w,t

In the formula, N _i is the total number of thermal power units; N _w is the total number of new energy units; N _l is the total number of external connection lines; P _i,t is the active power output of conventional unit i in time period t; P _{w, t} is the active power output of the new energy unit w in the period t; P _l,t is the active power output of the tie line l in the period t; L _t is the load value of the system in the period t; u _i,t is the unit i in the period t. Start-stop state; P _i,min is the lower power limit of unit i, P _i,max is the upper power limit of unit i; UT _i and DT _i are the minimum startup time and minimum shutdown time of unit i respectively; y _i,t is the unit i, t Whether i has a sign that the state changes from shutdown to startup in time period t; zi _,t is whether the unit i has a sign of change from startup to shutdown state in time period t; P _0,w,t is the prediction of new energy unit w in time period t output; _{Li ij} represents the upper limit of the power flow of branch ij; M is the set of grid computing nodes; li _,t is the node load power; S _i,j,t is the sensitivity of the injected power of node i to branch ij; R _{t, u} is the lower limit of the positive reserve capacity of the system in the time period t; R _t,d is the lower limit of the negative reserve capacity of the system in the time period t.

Step 6) On the basis of the start-stop state results of the buffer unit and the start-stop state of the must-open must-stop group, establish a SCED model that considers the entire calculation period, and use a linear programming algorithm to solve it; If satisfied, add the period to the calculation period set, and go to step 5), otherwise, go to step 7).

The objective function of the full-time SCED model is to maximize the total power generation of new energy units, which can be expressed as:

In the formula, T is the set of all time periods.

The constraints include load balance constraints, upper and lower output constraints of conventional units, upper and lower output constraints of new energy units, power grid security constraints, and system reserve capacity constraints:

P _i,min u _i,t ≤P _i,t ≤P _i,max u _i,t

P _w,t ≤P _0,w,t

In the formula, N _i is the total number of thermal power units; N _w is the total number of new energy units; N _l is the total number of external connection lines; P _i,t is the active power output of conventional unit i in time period t; P _{w, t} is the active power output of the new energy unit w in the period t; P _l,t is the active power output of the tie line l in the period t; L _t is the load value of the system in the period t; u _i,t is the unit i in the period t. Start-stop state; P _i,min is the lower power limit of unit i, P _i,max is the upper power limit of unit i; UT _i and DT _i are the minimum startup time and minimum shutdown time of unit i respectively; P _0,w,t is the predicted output of the new energy unit w in the time period t; _{Li ij} represents the upper limit of the power flow of the branch ij; M is the set of grid computing nodes; li _,t is the node load power; S _i,j,t is the injected power of the node i Sensitivity to branch ij; R _t,u is the lower limit of positive reserve capacity of the system in time period t; R _t,d is the lower limit of negative reserve capacity of the system in time period t.

Step 7) If traversing all typical combination scenes, then enter step 8), otherwise according to the next typical combination scene, enter step 5);

Step 8) Generate the maximum output of each new energy and its occurrence probability under each typical combination scenario, and the total output of the new energy of the system, and the calculation of the new energy consumption capacity evaluation calculation of the power grid is completed.

In order to verify the effectiveness of the proposed model, the optimization model based on the extreme scene method is set as scheme 1, and the optimization model based on multi-scenario generation technology mentioned in the article is set as scheme 2. The power grid includes 190 generator sets that need to carry out state combination calculation, the number of calculation periods is 744, the total number of generator set state discrete variables is 141360 (190*744), and the number of transmission section constraints is 21576 (29*744) indivual. The number of calculation periods of the optimization model after the merging of similar periods is 93, and the integer variables that are invalid are identified. The number of discrete variables of the unit state of the optimization model after dimensionality reduction is reduced to 15252. , the average total computation time is within 30 minutes.

Scheme 1 takes the maximum probability and minimum probability scenarios of each new energy partition as two combined scenarios based on the extreme scenario method, and the average value of the objective function is 1847121; The total number of new energy combination scenarios is 312, which is reduced to 5 after the reduction, and the average value of the objective function is 1,865,524. The average value of the objective function of scheme 1 is low, because the probability of occurrence of the scenarios with the lowest probability of each partition is very small, resulting in too conservative optimization results, which affects the objectivity of the optimization results. Combination scenarios are more reasonable and can represent typical prediction scenarios. Therefore, the optimization method proposed in this paper is more in line with the actual scheduling operation and has certain reference significance. Moreover, the reduction of scenarios and the combination of time periods significantly improve the solution efficiency and meet the requirements of practical applications.

The method of the present invention is a research and attempt of a calculation method for evaluating the new energy consumption capacity of a power grid based on a multi-scenario generation technology under the actual power grid data. This method reduces the number of combined scenarios through multi-probability scenario combination preprocessing of new energy, narrows the optimization range of mixed integer programming through time period merging, quickly obtains unit combinations and active power output results that meet computing requirements, and applies the safety constraint unit combination technology. In the evaluation and calculation of the new energy consumption capacity of the power grid, considering the relationship between new energy consumption and network security, a more reliable optimization result can be obtained. The calculation speed of this method can meet the needs of practical applications, effectively solve the disadvantage of traditional new energy consumption that is only evaluated according to a single forecast scenario, enhance the safety and rationality of new energy optimal scheduling, and has broad prospects for promotion.

Example 2

Based on the same inventive concept as Embodiment 1, an embodiment of the present invention provides a computing device for evaluating new energy consumption capacity of a power grid based on a multi-scenario generation technology, including:

Determine the unit, determine the power grid range and calculation period for the calculation of the new energy consumption capacity evaluation calculation of the power grid, and other calculation boundaries;

The first generation unit is used to generate a large number of combined scenarios including multiple new energy power plants through free arrangement and combination between fields;

The second generation unit is used for the scene reduction technology of the backward reduction method to merge similar scenes, reduce the number of combined scenes, and generate typical combined scenes;

The merging unit is used to merge similar time periods, reduce the number of time periods entering the optimization, and reduce the scale of the calculation time period of the optimization model;

The first solving unit is used for establishing the SCUC dimensionality reduction model after time merging for each typical combination scenario, and solving to obtain the unit combination result;

The second solving unit is used to establish and solve the full-time SCED model based on the unit combination results, and finally obtain the new energy consumption results under each typical combination scenario, and complete the evaluation calculation of the new energy consumption capacity of the power grid based on the multi-scenario generation technology.

In a specific embodiment of the embodiment of the present invention, the generating process of the combined scene includes:

Assuming that the total number of new energy stations in a power grid is N _w , there are N _p types of predicted output scenarios for each new energy station, and the probability of occurrence of each predicted output scene is pr _ij (i=1,2,...,N _w ; j=1,2,...,N _p );

Arrange and combine the output scenarios of all new energy stations to obtain the final number of new energy output scenarios _Na ,

The probability of occurrence of the combined scene is the product of the probability of occurrence of the corresponding output scene.

In a specific implementation of the embodiment of the present invention, the method for generating the typical combined scene includes:

Initialize the deleted scene set J to be empty, the number of scenes to be deleted is K, and the scene to be deleted in the kth iteration is l _k ;

Repeat the following steps until the end of the iteration:

Calculate the Kantorovich distance, so that l takes the scene l _k to obtain the minimum value of the following formula. The calculation formula of the Kantorovich distance is:

In the formula: J is the set of deleted scenes; pi is the probability of scene _i ; ξ ⁱ corresponds to scene sequence i; T is the number of segments of the scene time scale; c _T (ξ ⁱ ,ξ ^j ) represents scene sequence ξi and scene the distance of the sequence ξ ^j ,

Delete scene l _k , let J ^k =J ^k-1 ∪{l _k }, and accumulate the probability of scene l _k to the scene closest to it;

If k<K, then let k=k+1.

In a specific implementation of the embodiment of the present invention, the merging of similar time periods includes the following steps:

Let L _t be the system load of time period t, then the system load change rate of adjacent time periods t and t+1 is:

Calculate the system load change rate for all time periods, find the time period corresponding to the minimum change rate ΔL _t , and combine time periods t and t+1 into a new time period;

According to the change rate of the system load, the time period merge is repeated until the minimum change rate ΔL _t is greater than the set threshold, or the number of time periods remaining after the merge reaches the preset number, and the merge process ends.

In a specific implementation of the embodiment of the present invention, the objective function of the SCUC dimensionality reduction model is the maximization of the total power generation of new energy generating units, which is expressed as:

In the formula, N _W is the set of new energy units; T is the set of time periods included after the time period is merged; P _w,t is the maximum power receiving capacity of the new energy unit w in time period t;

The constraints of the SCUC dimensionality reduction model include load balance constraints, upper and lower output limits of conventional units, minimum start-up and shutdown time constraints of units, upper and lower output limits of new energy units, power grid security constraints, and system reserve capacity constraints:

P _i,min u _i,t ≤P _i,t ≤P _i,max u _i,t

y _i,t -z _i,t =ui _,t -u _i,t-1

y _i,t +z _i,t ≤1

P _w,t ≤P _0,w,t

In the formula, N _i is the total number of thermal power units; N _w is the total number of new energy units; N _l is the total number of external connection lines; P _i,t is the active power output of conventional unit i in time period t; P _{w, t} is the active power output of the new energy unit w in the period t; P _l,t is the active power output of the tie line l in the period t; L _t is the load value of the system in the period t; u _i,t is the unit i in the period t. Start-stop state; P _i,min is the lower power limit of unit i, P _i,max is the upper power limit of unit i; UT _i and DT _i are the minimum startup time and minimum shutdown time of unit i respectively; y _i,t is the unit i, t Whether i has a sign that the state changes from shutdown to startup in time period t; zi _,t is whether the unit i has a sign of change from startup to shutdown state in time period t; P _0,w,t is the prediction of new energy unit w in time period t output; _{Li ij} represents the upper limit of the power flow of branch ij; M is the set of grid computing nodes; li _,t is the node load power; S _i,j,t is the sensitivity of the injected power of node i to branch ij; R _{t, u} is the lower limit of the positive reserve capacity of the system in the time period t; R _t,d is the lower limit of the negative reserve capacity of the system in the time period t.

In a specific implementation of the embodiment of the present invention, the objective function of the SCED model is the maximization of the total power generation of new energy generating units, which is expressed as:

In the formula, T is the set of all time periods; N _w is the total number of new energy units; P _w,t is the active power output of new energy units w in time period t;

The constraints include load balance constraints, upper and lower output constraints of conventional units, upper and lower output constraints of new energy units, power grid security constraints, and system reserve capacity constraints:

P _i,min u _i,t ≤P _i,t ≤P _i,max u _i,t

P _w,t ≤P _0,w,t

In the formula, N _i is the total number of thermal power units; N _w is the total number of new energy units; N _l is the total number of external connection lines; P _i,t is the active power output of conventional unit i in time period t; P _{w, t} is the active power output of the new energy unit w in the period t; P _l,t is the active power output of the tie line l in the period t; L _t is the load value of the system in the period t; u _i,t is the unit i in the period t. Start-stop state; P _i,min is the lower power limit of unit i, P _i,max is the upper power limit of unit i; UT _i and DT _i are the minimum startup time and minimum shutdown time of unit i respectively; P _0,w,t is the predicted output of the new energy unit w in the time period t; _{Li ij} represents the upper limit of the power flow of the branch ij; M is the set of grid computing nodes; li _,t is the node load power; S _i,j,t is the injected power of the node i Sensitivity to branch ij; R _t,u is the lower limit of positive reserve capacity of the system in time period t; R _t,d is the lower limit of negative reserve capacity of the system in time period t.

Example 3

Based on the same inventive concept as Embodiment 1, the present invention provides a computing system for evaluating new energy consumption capacity of a power grid based on a multi-scenario generation technology, including 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 of Embodiment 1.

In summary:

On the premise of considering the actual dispatching operation and ensuring system safety requirements, the present invention generates combined scenarios and their probabilities according to the predicted output of a single new energy plant and its probability, and reduces the scenarios to ensure the effectiveness of generating multiple new energy scenarios . Based on this, the mature mixed integer linear programming algorithm software is called to calculate the SCUC model in each combination scenario, determine the unit combination state, and further calculate the system's consumption capacity under the access of large-scale new energy, so as to ensure the safety and stability of the system. , to improve the validity and reference of the assessment results of absorptive capacity.

As will be appreciated by those skilled in the art, the embodiments of the present application may be provided as a method, a system, or a 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, etc.) 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 present application. It will be understood that each flow and/or block in the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to the processor of a general purpose computer, special purpose computer, embedded processor or other programmable data processing device to produce a machine such that the instructions executed by the processor of the computer or other programmable data processing device produce Means for implementing the functions specified in a flow or flow of a flowchart and/or a block or blocks of a block diagram.

These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory result in an article of manufacture comprising instruction means, the instructions The apparatus implements the functions specified in the flow or flow of the flowcharts and/or the block or blocks of the block diagrams.

These computer program instructions can also be loaded on a computer or other programmable data processing device to cause a series of operational steps to be performed on the computer or other programmable device to produce a computer-implemented process such that The instructions provide steps for implementing the functions specified in the flow or blocks of the flowcharts and/or the block or blocks of the block diagrams.

The embodiments of the present invention have been described above with reference to the accompanying drawings, but the present invention is not limited to the above-mentioned specific embodiments. The above-mentioned specific embodiments are only illustrative rather than restrictive. Under the inspiration of the present invention, without departing from the scope of protection of the present invention and the claims, many forms can be made, which all belong to the protection of the present invention.

The basic principles and main features of the present invention and the advantages of the present invention have been shown and described above. Those skilled in the art should understand that the present invention is not limited by the above-mentioned embodiments, and the descriptions in the above-mentioned embodiments and the description are only to illustrate the principle of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have Various changes and modifications fall within the scope of the claimed invention. The claimed scope of the present invention is defined by the appended claims and their equivalents.

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 N_wThe predicted output scene of each new energy station has N_pThe occurrence probability of each predicted contribution scene is pr_ij(i＝1,2,…,N_w；j＝1,2,…,N_p)；

Arranging and combining the output scenes of all the new energy stations to obtain the final number N of the new energy output scenes_a，

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 l_k；

The following steps are repeated until the iteration is finished:

calculating the distance of Kantorovzval to make l take the scene l_kObtaining a minimum value by using a time formula, wherein the computing formula of the Kantouvyqi distance is as follows:

in the formula: j is the deleted scene set; p is a radical of_iIs the probability of scene i ξⁱCorresponding to a scene sequence i; t is the number of segments of the scene timescale; c. C_T(ξⁱ,ξ^j) Representing a sequence of scenes ξⁱAnd scene sequence ξ^jThe distance of (a) to (b),

deleting scene l_kLet J^k＝J^k-1∪{l_kAnd will scene l_kThe 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 L_tFor the system load of time period t, the system load change rate of adjacent time periods t and t +1 is:

calculating the change rate of the system load in all time periods to find the minimum change rate delta L_tMerging 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 L_tAnd 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:

in the formula, N_WIs a new energy machine set; t is a time interval set contained after time interval merging; p_w,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:

P_i,minu_i,t≤P_i,t≤P_i,maxu_i,t

y_i,t-z_i,t＝u_i,t-u_i,t-1

y_i,t+z_i,t≤1

P_w,t≤P_0,w,t

in the formula, N_iThe total number of the thermal power generating units; n is a radical of_wThe total number of the new energy units; n is a radical of_lThe total number of the external connecting lines; p_i,tThe active power output of the conventional unit i in the time period t is obtained; p_w,tThe active power output of the new energy unit w in the time period t is obtained; p_l,tL for the active power of the tie line l in the time period t_tIs the load value of the system in the time period t; u. of_i,tStarting and stopping a unit i at a time t; p_i,minLower power limit, P, of unit i_i,maxThe upper power limit of the unit i is set; UT (unified device)_iAnd DT_iRespectively the minimum starting time and the minimum stopping time of the unit i; y is_i,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 of_i,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; p_0,w,tL predicted output for new energy unit w in time period t_ijRepresenting the upper current limit of branch ij; m is a power grid computing node set; l_i,tLoad power for the node; s_i,j,tSensitivity of injected power to branch ij for node i; r_t,uA positive spare capacity lower limit for the system at time period t; r_t,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:

in the formula, T is a set of all time periods; n is a radical of_wThe total number of the new energy units; p_w,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:

P_i,minu_i,t≤P_i,t≤P_i,maxu_i,t

P_w,t≤P_0,w,t

in the formula, N_iThe total number of the thermal power generating units; n is a radical of_wThe total number of the new energy units; n is a radical of_lThe total number of the external connecting lines; p_i,tThe active power output of the conventional unit i in the time period t is obtained; p_w,tThe active power output of the new energy unit w in the time period t is obtained; p_l,tL for the active power of the tie line l in the time period t_tIs the load value of the system in the time period t;u_i,tstarting and stopping a unit i at a time t; p_i,minLower power limit, P, of unit i_i,maxThe upper power limit of the unit i is set; UT (unified device)_iAnd DT_iRespectively the minimum starting time and the minimum stopping time of the unit i; p_0,w,tL predicted output for new energy unit w in time period t_ijRepresenting the upper current limit of branch ij; m is a power grid computing node set; l_i,tLoad power for the node; s_i,j,tSensitivity of injected power to branch ij for node i; r_t,uA positive spare capacity lower limit for the system at time period t; r_t,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:

in the formula, N_WIs a new energy machine set; t is a time interval set contained after time interval merging; p_w,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:

P_i,minu_i,t≤P_i,t≤P_i,maxu_i,t

y_i,t-z_i,t＝u_i,t-u_i,t-1

y_i,t+z_i,t≤1

P_w,t≤P_0,w,t

in the formula, N_iThe total number of the thermal power generating units; n is a radical of_wThe total number of the new energy units; n is a radical of_lThe total number of the external connecting lines; p_i,tThe active power output of the conventional unit i in the time period t is obtained; p_w,tThe active power output of the new energy unit w in the time period t is obtained; p_l,tL for the active power of the tie line l in the time period t_tIs the load value of the system in the time period t; u. of_i,tStarting and stopping a unit i at a time t; p_i,minLower power limit, P, of unit i_i,maxThe upper power limit of the unit i is set; UT (unified device)_iAnd DT_iRespectively the minimum starting time and the minimum stopping time of the unit i; y is_i,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 of_i,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; p_0,w,tL predicted output for new energy unit w in time period t_ijRepresenting the upper current limit of branch ij; m is a power grid computing node set; l_i,tLoad power for the node; s_i,j,tSensitivity of injected power to branch ij for node i; r_t,uA positive spare capacity lower limit for the system at time period t; r_t,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:

in the formula, T is a set of all time periods; n is a radical of_wThe total number of the new energy units; p_w,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:

P_i,minu_i,t≤P_i,t≤P_i,maxu_i,t

P_w,t≤P_0,w,t

in the formula, N_iThe total number of the thermal power generating units; n is a radical of_wThe total number of the new energy units; n is a radical of_lThe total number of the external connecting lines; p_i,tThe active power output of the conventional unit i in the time period t is obtained; p_w,tThe active power output of the new energy unit w in the time period t is obtained; p_l,tL for the active power of the tie line l in the time period t_tIs the load value of the system in the time period t; u. of_i,tStarting and stopping a unit i at a time t; p_i,minLower power limit, P, of unit i_i,maxThe upper power limit of the unit i is set; UT (unified device)_iAnd DT_iRespectively the minimum starting time and the minimum stopping time of the unit i; p_0,w,tL predicted output for new energy unit w in time period t_ijRepresenting the upper current limit of branch ij; m is a power grid computing node set; l_i,tLoad power for the node; s_i,j,tSensitivity of injected power to branch ij for node i; r_t,uA positive spare capacity lower limit for the system at time period t; r_t,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.

Images