CN114285090B - New energy source limit absorption capacity assessment method based on single station-partition-whole network - Google Patents

Abstract

The present invention discloses a method for evaluating the limit absorption capacity of new energy based on a single station-district-whole network, comprising the following steps: (1) intelligent adjustment of the operation mode of the power grid; (2) evaluation of the limit absorption capacity of new energy at a single station; (3) evaluation of the limit absorption capacity of new energy at a district; (4) evaluation and solution of the limit absorption capacity of new energy at the whole network. The present invention proposes an intelligent adjustment method for the operation mode of the power grid, thereby greatly reducing the workload of manual means, and establishes a single-station limit absorption capacity evaluation system for new energy based on a bisection approximation method and an improved particle swarm algorithm respectively; proposes to use a polynomial model that considers the influence of the injected power of new energy nodes for complex constraints in an equivalent provincial network, and based on the calculation results of a single station, establishes a limit absorption capacity optimization model for districts and the whole network under different safety constraints. The method can give the limit absorption capacity of new energy for the whole network, and can provide reference opinions for the planning and construction of new energy.

Description

Evaluation method of new energy extreme absorption capacity based on single station-region-whole network

Technical Field

The present invention belongs to the field of new energy for electric power systems, and in particular relates to a method for evaluating the limit absorption capacity of new energy based on a single station, a partition, and a whole network.

Background technique

my country has proposed to vigorously develop clean energy and promote the transformation of energy and electricity from high-carbon to low-carbon, and from fossil energy to clean energy. However, due to factors such as limited system peak-shaving capacity, reverse distribution of renewable resources in my country, and barriers to inter-provincial transmission channels, the phenomenon of wind and solar power abandonment is serious. Therefore, it is an important prerequisite to ensure the safety, reliability, stability, and economy of power system operation to fully consider the randomness, intermittency, and volatility of clean energy output, quantitatively analyze the new energy acceptance capacity of the power grid within a certain time scale in the future, and put forward reasonable optimization suggestions for new energy planning schemes.

On the other hand, at present, relevant departments still use traditional means to formulate and analyze the operation mode of the power grid when conducting new energy consumption analysis, relying on manual experience, with large workload, low efficiency, strong limitations, and poor flexibility, which is difficult to meet the future development needs of my country's power grid. Therefore, based on the above background, it is urgent to develop a system new energy limit consumption capacity assessment module to improve the work quality and efficiency of new energy consumption analysis, and better support and serve the dispatching and operation of the system new energy power system.

Summary of the invention

Purpose of the invention: With the continuous increase in the capacity and proportion of new energy, the need for analysis of the limit absorption capacity of new energy has become increasingly apparent, and the workload of operation mode adjustment and analysis has increased exponentially. The present invention aims at the evaluation of the limit absorption capacity of new energy, realizes the evaluation of the limit absorption capacity of new energy for a single station, a partition and the entire network, optimizes and analyzes the best access site and optimal access capacity of new energy in the system, and provides a reference for the access planning of new energy in the future.

The specific technical solution adopted by the present invention is: a new energy limit absorption capacity assessment method based on a single station-region-whole network, characterized in that the method comprises the following steps:

Step 1: Intelligent adjustment of power grid operation mode;

Step 2: Evaluation of the maximum absorption capacity of new energy at a single station;

Step 3: Assessment of the maximum absorption capacity of new energy in different regions;

Step 4: Evaluate the maximum renewable energy absorption capacity of the entire network.

Furthermore, the intelligent adjustment method of the power grid operation mode in step 1 is:

First, simulate the access of new energy. When simulating the access of new energy, it is equivalent to accessing a load with negative active power in the substation. At the same time, its access reactive power satisfies the constant power factor. The 21st to 30th bits of the AC node card in PSD-BPA are constant loads. Therefore, a new energy output constant power model is superimposed on the original power of the substation. According to the node name, voltage level, and partition name, locate the data row where the access plant is located in the data file, and perform power superposition under the original load data.

Then the power generation output is automatically adjusted to simulate the need to reduce the power plant output to maintain the active power balance of the system after the access of new energy. The P card in BPA, that is, the power generation output load percentage modification card, is used for adjustment. When the load and power generation output are modified according to the partition or owner, the modification formula is:

S _New = S _Old × Ratio (1)

Where: S _New is the modified power, S _Old is the original power, Ratio is the proportional coefficient, and the default is equivalent to a proportional coefficient of 1.0;

Finally, the output results are extracted intelligently. After the power flow calculation is completed, it is necessary to judge the over-limit situation of the equipment according to the results. Therefore, it is necessary to extract the power and current information of the lines and transformers of each voltage level from the output results. BPA can specify the output list through specific control statements and use the second and third level control statements to specify the output.

Furthermore, in step 2, an objective function is constructed with the goal of maximizing the total amount of multi-node access to new energy units, and its expression is:

Among them, P _Ni represents the maximum installed capacity of the new energy unit connected to the node i, _Xi represents an integer variable, 0 means that the node i is not connected to the new energy unit, and 1 means that the node i is connected to the new energy unit.

Furthermore, in step 2, the single-station evaluation model constraint conditions are constructed based on the improved particle swarm algorithm, the power flow equation is used as the equality constraint condition, and the inequality constraint conditions are shown in formula (3) and formula (4):

in, represents the minimum output of conventional units at node i, represents the maximum output of conventional units at node i, _Sg represents the set of conventional generators, _Pli represents the power flow on the i-th line, represents the upper limit of the power flow of the ith line, S _l represents the line set, Indicates that the unit is on spinning standby. Indicates that the unit is in spinning standby mode;

The core idea and steps of the improved particle swarm algorithm are as follows: Assume that the particle swarm consists of M particles, and each particle is defined as a D-dimensional space. Then the state attributes of particle i at time t are as follows: Position speed Individual optimal position Global optimal position Then the velocity and position of particle i at time t+1 can be updated by the following formula:

Wherein, d = 1, 2, ..., D; r ₁ and r ₂ are random numbers uniformly distributed on (0, 1); c ₁ and c ₂ are learning factors; ω is the inertia weight, which takes a non-negative number. When ω is large, the global optimization ability is enhanced and the local optimization ability is weak; when ω is small, the global optimization ability is weak, but the local optimization ability is enhanced. Adjusting the size of ω can adjust the global and local search capabilities. In order to better control the global and local search capabilities, the adjustment strategy of the inertia weight is improved, and the ω value adopts a linear decreasing strategy, as shown in the following formula:

Among them, ω _start and ω _end are the initial inertia weight and the ending inertia weight respectively; t _max is the maximum number of iterations; and t is the current number of iterations.

Furthermore, in step 2, the constraints of the single-station evaluation model are constructed based on the bisection approximation method, including the normal current carrying capacity constraint of the 220kV line, the load rate constraint of the 220kV transformer, and the N-1 constraint, and the constraints are expressed as follows:

In the normal current carrying capacity constraint of 220kV lines, for the remaining 220kV lines except the power plant outgoing line, it is required that the line current shall not exceed its rated current value during normal operation. For any line, its rated current is specified by the 34th to 37th bits of the L card; in the overload line list, the current or load current percentage of any line can be extracted as a criterion, and the formula is as follows:

Among them, I _i.220 represents the current value of the i-th 220 kV line; I _i.rated.220 is the rated current value of the i-th line; n _V＝220 is the total number of 220 kV lines; num _i.load.220 is the load current percentage of the i-th line;

In the 220kV transformer load rate constraint, for the 220kV transformer, it is required that its apparent power does not exceed its rated capacity or the load rate is less than 100% during normal operation. The rated capacity of the transformer is set in the 34th to 37th positions of the T card. The apparent power and load rate information can be extracted from the overload transformer list. The formula is as follows:

Among them, S _i.220 represents the apparent power of the i-th 220 kV transformer; S _i.rated.220 is the rated power value of the i-th transformer; m _V=220 is the number of 220 kV transformers; Num _iS220 is the load rate of the i-th transformer;

For N-1 constraints, N-1 calculations are completed through BPA. During the simulation, all electrical components in the specified area are disconnected in sequence, and then the power flow calculation is performed and the overload status of the remaining components is searched, and finally a list of results is output.

Furthermore, the specific steps of evaluating the maximum absorption capacity of new energy for a single station based on the bisection approximation method are as follows:

a. Collect and store the annual operation mode and stability limit data files of the power grid, and mine the data information in the files under the typical operation mode of BPA;

b. Realize simulated access to new energy and intelligent adjustment of power generation output;

c. Complete the power flow calculation and N-1 calculation, and mine the relevant power flow information from the output result file according to the name, voltage level, and partition of the device of interest;

d. Check the constraints. If the current simulated access capacity meets the constraints, the iteration process continues with a variable step size. The solution process uses the bisection approximation method.

Furthermore, in step 3, the calculation results of the new energy single station in step 2 are taken into account to construct an objective function with the location and optimal capacity of each access point when the future installed capacity of new energy in the partition is the largest, and its expression is:

Among them, _Pi is the new energy access capacity of the i-th plant; N is the total number of new energy access plants.

Considering that the current and power constraints of 220kV lines and transformers and N-1 constraints have been taken into account in the evaluation of the single-station maximum absorption capacity, the grid constraints of 500kV equipment should also be considered when establishing the partition optimization model. The constraints are as follows:

(1) 500kV line stability limit constraints:

For 500kV lines, sufficient margin must be left to ensure that there is no overload in the event of an accident. Therefore, a specific stability limit is set. After the current value of the 500kV line is extracted from the result, it is matched and compared with the stability limit of each line in the limit table to determine whether it exceeds the limit. The formula is as follows:

I _i.500 ≤I _i.stable.500 (i＝1,2,…,n _V＝500 ) (11)

Among them, I _i.500 is the current value of the i-th 500 kV line; I _i.stable.500 is the stability limit of the i-th line; n _V=500 is the total number of 500 kV lines;

(2) 500kV transformer stability limit constraints:

For 500kV transformers, sufficient margin is required to ensure that there is no overload after an accident occurs. The stability limit of each transformer can be obtained from the limit table. The formula is as follows:

S _i.500 ≤S _i.stable.500 (i＝1,2,…,m _V＝500 ) (12)

Among them, S _i.500 is the apparent power of the i-th 500 kV transformer; S _i.stable.500 is the stability limit of the i-th transformer; m _V=500 is the number of 500 kV transformers.

Through polynomial equivalence, we can obtain the constraint equations shown in formulas (13) and (14):

_Pi ≤ _Pi.max (14)

Among them, _Pi.max represents the upper limit of the access capacity of the i-th plant.

Furthermore, in step 4, the calculation results of the new energy single station in step 2 and the calculation results of the partitioned new energy access in step 3 are taken into account to construct an objective function with the location and optimal capacity of each access point when the future new energy installed capacity of the entire network is the largest. The expression is:

Where, _Pi is the new energy access capacity of the i-th plant; N is the total number of new energy access plants;

The constraints in the whole network new energy extreme absorption capacity assessment model are the same as those in the regional extreme absorption capacity assessment process, which are the current and power constraints of 220kV lines and transformers, as well as the N-1 constraints and the grid constraints of 500kV equipment.

According to the above model, the model is solved in MATLAB and GAMS to obtain the maximum new energy absorption capacity of the entire network.

Beneficial effects: The present invention accurately evaluates the maximum acceptance capacity of the power grid for new energy in the current and future time sections, thereby improving the utilization rate of renewable energy, and can provide technical support for the formulation of reasonable power generation plans and planning schemes in the context of large-scale grid connection of new energy.

BRIEF DESCRIPTION OF THE DRAWINGS

Fig. 1 is a flow chart of the method of the present invention;

FIG2 is a flowchart of automatic adjustment of power generation output;

Figure 3 is a flow chart of the evaluation of the maximum absorption capacity of new energy at a single station;

Figure 4 is a flow chart of the evaluation of the maximum absorption capacity of new energy for the entire network;

Figure 5 is a flowchart of new energy simulation access;

FIG6 is a schematic diagram of the proposed access points and their capacities.

Detailed ways

The technical solution of the present invention is further described below in conjunction with the accompanying drawings and embodiments.

As shown in FIG1 , the present invention provides a method for evaluating the limit absorption capacity of new energy based on a single station, a partition, and a whole network, comprising the following steps:

(1) Intelligent adjustment of power grid operation mode;

(2) Evaluation of the maximum absorption capacity of new energy at a single station;

(3) Assessment of the maximum absorption capacity of new energy in different regions;

(4) Assessment of the entire network’s maximum new energy absorption capacity.

In step (1), the method for intelligently adjusting the operation mode of the power grid is:

First, simulate the access of new energy, as shown in Figure 5. When simulating the access of new energy, it is equivalent to accessing a load with negative active power in the substation. At the same time, its access reactive power satisfies the constant power factor. The 21st to 30th bits of the AC node card in PSD-BPA are constant loads. Therefore, a new energy output constant power model can be superimposed on the original power of the substation. When implementing it, it is necessary to locate the data row where the access plant is located under the data file according to the node name, voltage level, and partition name, and perform power superposition under the original load data.

The second step is to automatically adjust the power generation output, as shown in Figure 2. After simulating the access of new energy, in order to maintain the active power balance of the system, the power plant output needs to be reduced. This can be adjusted through the P card (power generation output load percentage modification card) in BPA. The format description is shown in Table 1.

Table 1 P card column meaning correspondence table

This method uses the PZ card. When modifying the load and power output according to the partition or owner, the modification formula is:

S _New = S _Old × Ratio (1)

Where: S _New is the modified power, S _Old is the original power, and Ratio is the proportional coefficient. The default is equivalent to a proportional coefficient of 1.0.

When operating the actual power grid in a certain province, the order of adjustment strategies and the lower limit of output of its generator sets are shown in Table 2.

Table 2 Adjustment strategy for adjustable units

The units in Table 2 are all thermal power units. The process of automatic adjustment of power generation output is as follows: (1) Count the total capacity after the simulated access of new energy; (2) Count the rated capacity and current actual output of the generator units, and calculate the actual output of each adjustable unit before modification; (3) Calculate the output adjustment factor after modification; (4) Generate a new PZ card.

Finally, the output results are intelligently extracted. After the power flow calculation is completed, it is necessary to judge the over-limit situation of the equipment of concern based on the results. Therefore, it is necessary to extract the power and current information of the lines and transformers of each voltage level from the output results. BPA can specify the output list through specific control statements. In this method, the second and third level control statements are used to specify the output. In addition, the equipment information concerned by this method is the load rate, current carrying capacity and other information of the lines and transformers, so the required reports are mainly the list of overloaded lines and overloaded transformers.

In step 2, an objective function is constructed with the goal of maximizing the total amount of new energy units connected to multiple nodes, and its expression is:

Among them, P _Ni represents the maximum installed capacity of the new energy unit connected at node i, _Xi represents an integer variable, 0 means that node i is not connected to the new energy unit, and 1 means that node i is connected to the new energy unit;

In the constraints of the single-station evaluation model based on the improved particle swarm algorithm, the power flow equation is used as the equality constraint, and the inequality constraint is shown in formula (3) and formula (4):

in, Represents the minimum output of conventional units at node i; represents the maximum output of the conventional unit at node i; _Sg represents the set of conventional generators; _Pli represents the power flow on the i-th line, represents the upper limit of the power flow of the ith line, and S _l represents the line set; Indicates that the unit is on spinning standby. Indicates that the unit is in spinning standby.

The core idea and steps of the improved particle swarm algorithm are as follows: Assume that the particle swarm consists of M particles, and each particle is defined as a D-dimensional space. Then the state attributes of particle i at time t are as follows: Position speed Individual optimal position Global optimal position Then the velocity and position of particle i at time t+1 can be updated by the following formula:

Where, d = 1, 2, ..., D; r ₁ and r ₂ are random numbers uniformly distributed on (0, 1); c ₁ and c ₂ are learning factors; ω is the inertia weight. In the traditional algorithm, the inertia factor ω takes a non-negative number. When ω is large, the global optimization ability is enhanced and the local optimization ability is weak; when ω is small, the global optimization ability is weak, but the local optimization ability is enhanced. Therefore, adjusting the size of ω can adjust the global and local search capabilities. In order to better control the global and local search capabilities, this paper proposes an improvement to the adjustment strategy of the inertia weight. The ω value adopts a linear decreasing strategy, as shown in the following formula:

Among them, ω _start and ω _end are the initial inertia weight and the ending inertia weight respectively; t _max is the maximum number of iterations; and t is the current number of iterations.

Compared with the improved particle swarm optimization algorithm, the constraints of the single-station evaluation model based on the bisection approximation method include the normal current carrying capacity constraint of the 220kV line, the load rate constraint of the 220kV transformer, and the N-1 constraint. The constraints are expressed as follows:

In the normal current carrying capacity constraint of 220kV lines, for the remaining 220kV lines except the power plant outgoing line, it is required that the line current shall not exceed its rated current value during normal operation. For any line, its rated current is specified by the 34th to 37th bits of the L card; in the overload line list, the current or load current percentage of any line can be extracted as a criterion, and the formula is as follows:

Among them, I _i.220 represents the current value of the i-th 220 kV line; I _i.rated.220 is the rated current value of the i-th line; n _V=220 is the total number of 220 kV lines; num _i.load.220 is the load current percentage of the i-th line.

In the 220kV transformer load rate constraint, for the 220kV transformer, it is required that its apparent power does not exceed its rated capacity or the load rate is less than 100% during normal operation. The rated capacity of the transformer is set in the 34th to 37th positions of the T card. The apparent power and load rate information can be extracted from the overload transformer list. The formula is as follows:

Among them, S _i.220 represents the apparent power of the i-th 220 kV transformer; S _i.rated.220 is the rated power of the i-th transformer; m _V=220 is the number of 220 kV transformers; Num _iS220 is the load rate of the i-th transformer.

For N-1 constraints, this method completes the N-1 calculation through BPA. During the simulation, all electrical components in the specified area are disconnected in sequence, and then the flow calculation is performed and the overload status of the remaining components is searched. Finally, a list of results that the user is concerned about is output.

The specific steps of the single-station renewable energy extreme absorption capacity assessment based on the bisection approximation method are as follows: a. Collect and store data files such as the annual operation mode and stability limit of the power grid, and mine the data information in the files under the typical operation mode of BPA; b. According to the method proposed in this paper, simulated access of renewable energy and intelligent adjustment of power generation output are realized; c. Complete the flow calculation and N-1 calculation, and mine relevant flow information from the output result file according to the name, voltage level and partition of the equipment in question; d. Check the constraints. If the current simulated access capacity meets the constraints, the iterative process continues, and the iterative step size adopts a variable step size; the solution process adopts the bisection approximation method.

The evaluation process of the maximum absorption capacity of new energy for a single station is shown in Figure 3, and the specific implementation steps are as follows:

(1) Data Mining

The data files such as the annual operation mode and stability limit of the power grid are collected and stored, and the data information in the files under the typical operation mode of BPA is mined. The historical data information to be extracted includes the rated capacity, actual output, partition and owner name of the adjustable units under the current operation mode, the load of the new energy access plant, partition name, voltage level, and the name of the power plant outgoing bus.

(2) Analog access

Simulating the access of new energy is equivalent to accessing a load with negative active power in a 220kV substation, and at the same time its access reactive power satisfies the constant power factor. During the specific implementation, it is necessary to locate the data row where the access plant is located under the data file according to the node name, voltage level, and partition name, and superimpose the new energy access power under the original load data.

(3) Intelligent adjustment

After simulating the access of new energy, in order to maintain the active power balance of the system, it is necessary to reduce the output of the power plant. The original output and actual output of the power plant are obtained by (1). By calculating the total access capacity of new energy in (2), the modified output adjustment factor can be obtained. The adjustment strategy is shown in Table 3.3.2. The output adjustment factor of each adjustable generator group is calculated and located and modified in the PZ card in the data file.

(4) Power flow calculation

The power flow calculation is implemented by calling the power system analysis software PSD-BPA. Its iterative algorithm adopts the Newton-Raphson method. If the calculation of the current power grid operation mode converges, the power flow solution can be obtained, and relevant power flow information can be mined from the output result file according to the name, voltage level and partition of the equipment of interest.

(5) Constraint Verification

The constraints include the normal current carrying capacity of 220kV lines except the outgoing line of the power plant, the accident current carrying capacity during N-1 calculation, and the power constraint during normal operation of the 220kV transformer. If the current simulated access capacity meets the constraints, the iteration process continues. In order to improve the calculation speed, the iteration step size adopts a variable step size; the solution process adopts the binary approximation method. If any constraint is not met under the current access capacity, the previous access capacity in the iteration process is output, and the power at this time is the maximum access capacity of the station.

This method uses the typical summer operation mode data of a province’s actual power grid as an example. The province has 11 zones. The parameters of each zone are shown in Table 3. The calculation results are shown in Table 4.

Table 3 Parameters of each partition

Table 4 Single-station new energy limit consumption assessment results

In step 3, the calculation results of the new energy single station in step 2 are taken into account to construct an objective function with the location and optimal capacity of each access point when the future installed capacity of new energy in the partition is the largest. The expression is:

Among them, _Pi is the new energy access capacity of the i-th plant; N is the total number of new energy access plants.

Considering that the current and power constraints of 220kV lines and transformers and N-1 constraints have been taken into account in the evaluation of the single-station maximum absorption capacity, the grid constraints of 500kV equipment should also be considered when establishing the partition optimization model. The constraints are as follows:

(1) 500kV line stability limit constraints:

For 500kV lines, sufficient margin is usually reserved to ensure that there is no overload in the event of an accident. Therefore, a specific stability limit is set. After the current value of the 500kV line is extracted from the result, it is matched and compared with the stability limit of each line in the limit table to determine whether it exceeds the limit. The formula is as follows:

I _i.500 ≤I _i.stable.500 (i＝1,2,…,n _V＝500 ) (11)

Among them, I _i.500 is the current value of the i-th 500 kV line; I _i.stable.500 is the stability limit of the i-th line; n _V=500 is the total number of 500 kV lines.

(2) 500kV transformer stability limit constraints:

For 500kV transformers, sufficient margin is required to ensure that there is no overload after an accident occurs. The stability limit of each transformer can be obtained from the limit table. The formula is as follows:

S _i.500 ≤S _i.stable.500 (i＝1,2,…,m _V＝500 ) (12)

Among them, S _i.500 is the apparent power of the i-th 500 kV transformer; S _i.stable.500 is the stability limit of the i-th transformer; m _V=500 is the number of 500 kV transformers.

Through polynomial equivalence, we can obtain the constraint equations shown in formulas (13) and (14):

_Pi ≤ _Pi.max (14)

Among them, _Pi.max represents the upper limit of the access capacity of the i-th plant.

In step 4, the calculation results of the new energy single station in step 2 and the calculation results of the partitioned new energy access in step 3 are taken into account to construct an objective function with the location and optimal capacity of each access point when the future new energy installed capacity of the entire network is the largest. The expression is:

Among them, _Pi is the new energy access capacity of the i-th plant; N is the total number of new energy access plants.

The constraints in the whole network's new energy extreme absorption capacity assessment model are the same as those in the regional extreme absorption capacity assessment process, namely, the current and power constraints of 220kV lines and transformers, as well as the N-1 constraints and the grid constraints of 500kV equipment.

The evaluation process of the maximum absorption capacity of new energy for the entire network is shown in Figure 4, and the specific implementation steps are as follows:

(1) Data Mining

The data files such as the annual operation mode and stability limit of the power grid are collected and stored, and the data information in the files under the typical operation mode of BPA is mined. The historical data information to be extracted includes the rated capacity, actual output, partition and owner name of the adjustable units under the current operation mode, the load of the new energy access plant, partition name, voltage level, and the name of the power plant outgoing bus.

(2) Analog access

Simulating the access of new energy is equivalent to accessing a negative load with negative active power in the substation, and its access reactive power meets the constant power factor. In specific implementation, it is necessary to locate the data row where the access plant is located under the data file according to the node name, voltage level, and partition name, and superimpose the access power of new energy under the original load data. During the partition simulation, the maximum access capacity of all stations in the partition shall not be greater than the access limit of the station, and the maximum access capacity of each station can be calculated from the maximum absorption capacity of a single station; during the full network simulation, the site selection and access upper limit constraints of the plant should be carried out based on the partition optimization results.

(3) Intelligent adjustment

After simulating the access of new energy, in order to maintain the active power balance of the system, it is necessary to reduce the output of the power plant. The original output and actual output of the power plant are obtained by (1). By calculating the total access capacity of new energy in (2), the modified output adjustment factor can be obtained. The order of adjustment strategies is shown in Table 2. The output adjustment factors of each adjustable generator group are calculated and modified in the PZ card in the data file.

(4) Power flow calculation

The power flow calculation is implemented by calling the power system analysis software PSD-BPA. Its iterative algorithm adopts the Newton-Raphson method. If the calculation of the current power grid operation mode converges, the power flow solution can be obtained, and relevant power flow information can be mined from the output result file according to the name, voltage level and partition of the equipment of interest.

(5) Parameter fitting

In order to establish a model of the maximum absorption capacity of new energy, it is optimized and solved through numerical analytical methods. It is necessary to establish polynomial constraint equations for different constraints and fit the equation coefficients of the polynomial constraint function. The constraint equations include the normal current-carrying capacity constraint of the 220kV line excluding the power plant outgoing line, the accident current-carrying capacity constraint during N-1 calculation, the stability limit constraint during normal operation of the 500kV line, the power constraint during normal operation of the 220kV transformer, and the stability limit constraint during normal operation of the 500kV transformer. The fitting algorithm adopts the least squares method, and the training samples are obtained by Monte Carlo sampling to obtain the new energy access capacity for power flow calculation, and the number of samples is proportional to the number of access plants.

(6) Model building

The regional and network-wide new energy maximum absorption capacity models are established respectively, and their objective functions are shown in formula (15), and the constraint functions are shown in formulas (9) to (14).

(7) Optimization solution

The nonlinear programming model constructed in (6) is optimized and solved by calling the solver. The mathematical optimization algorithm adopted is the interior point method. If the optimal solution to the current problem can be found, the results are output. The results include the total current new energy access capacity, the optimal access plant site selection, and the optimal access capacity of the plant.

The above steps can respectively obtain the evaluation results of the maximum new energy absorption capacity of a single station, a sub-zone and the entire network. Among them, before the sub-zone optimization, the single-station optimization result needs to be provided as the upper limit of the single-station access capacity. Before the whole network optimization, the optimization results of each sub-zone need to be provided as the preliminary selection of access plants and the upper limit of the access capacity of each station.

According to the above model, the model is solved in MATLAB and GAMS to obtain the maximum new energy absorption capacity of the entire network.

Taking the typical summer operation mode data of a province’s actual power grid as an example, there are 11 zones in the province, and the parameters of each zone are shown in Table 5.

Table 5 Calculation results of the maximum consumption of new energy in the whole network and in different regions

Through the above-mentioned progressive technical route of single station-partition-whole network, the number of access sites that meet the grid safety constraints will be reduced successively. The optimization results show that the maximum access capacity of new energy in the whole network under the current grid operation mode is 9.47 million kilowatts, and the location of the best access point and its recommended capacity can be known, as shown in Figure 6.

The above embodiments are only for illustrating the technical idea of the present invention, and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution in accordance with the technical idea proposed by the present invention shall fall within the protection scope of the present invention.

Claims (4)

1. The new energy source limit absorption capacity assessment method based on single station-partition-whole network is characterized by comprising the following steps of: the method comprises the following steps:

Step 1, simulating a power grid operation mode after new energy is accessed, and carrying out tide calculation; the intelligent adjustment method of the power grid operation mode comprises the following steps:

Firstly, new energy is simulated and connected, when the new energy is simulated and connected, the load with negative active power is connected in a transformer substation, meanwhile, the connected reactive power meets a constant power factor, a new energy output constant power model is superimposed on the original power of the transformer substation, the data line of the station connected under a data file is positioned according to the node name, the voltage class and the partition name, and the power superposition is carried out under the original load data;

Then, automatically adjusting the power generation output, and after the new energy is simulated to be connected, in order to keep the active balance of the system, the output of the power plant needs to be reduced, namely, when the load percentage of the power generation output is modified according to the partition or the owner, the modification formula is as follows:

S_New＝S_Old×Ratio (1)

Wherein: s _New is modified power, S _Old is original power, and Ratio is a proportionality coefficient;

finally, extracting output results, extracting power and current information of the circuit and the transformer of each voltage class from the output results, and judging out-of-limit conditions of the concerned equipment according to the results;

step 2, evaluating the new energy source limit absorbing capacity of a single station, which specifically comprises the following steps:

constructing an objective function with the maximum total amount of the multi-node access new energy unit as a target, wherein the expression is as follows:

wherein P _Ni represents the maximum installed capacity of the new energy unit accessed at the node i, n represents the number of nodes, X _i represents an integer variable, X _i is 0, the node i is not accessed to the new energy unit, and X _i is 1, the node i is accessed to the new energy unit;

Constructing a single-station evaluation model constraint condition based on an improved particle swarm algorithm or a bipartite approximation method;

based on the improved particle swarm algorithm, constructing a single-station evaluation model constraint condition, wherein a tide equation is used as an equality constraint condition, and the inequality constraint condition is shown in a formula (3) and a formula (4):

Wherein, Representing the minimum value of the conventional unit output at node i,Representing the maximum value of the conventional genset output at node i, S _g representing the set of conventional gensets, P _li representing the flow of power on the ith line,The upper limit value of the power flow of the ith line is represented, S _l represents the line set,Indicating the rotation on the machine set for standby,Indicating the rotation under the unit for standby;

The improved particle swarm algorithm specifically comprises the following steps: assuming that a particle group consists of M particles, each defined as a D-dimensional space, the state properties of particle i at time t are as follows: position of Speed of speedIndividual optimum positionGlobal optimum positionThe velocity, position of particle i at time t+1 can be updated by:

Wherein d=1, 2, …, D; r ₁ and r ₂ are random numbers uniformly distributed on (0, 1); c ₁ and c ₂ are learning factors; omega is inertia weight, a non-negative number is taken, when omega is larger, the global optimizing capability is enhanced, and the local optimizing capability is weaker; when omega is smaller, the global optimizing capability is weaker, but the optimizing capability is enhanced, and the global searching capability and the local searching capability can be adjusted by adjusting the omega; the adjustment strategy of the inertia weight is improved, and the omega value adopts a linear decreasing strategy:

Wherein ω _start and ω _end are the initial inertial weight and the terminating inertial weight, respectively; t _max is the maximum number of iterations; t is the current iteration number;

constructing a single-station evaluation model constraint condition based on a bipartite approximation method, wherein the constraint condition comprises a line normal current carrying capacity constraint, a transformer load rate constraint and an N-1 constraint;

In the constraint of normal current carrying capacity of 220kV lines, for the rest 220kV lines except the outlet of a power plant, the line current is required not to exceed the rated current value during normal operation, and for any line, the rated current is regulated by 34 th-37 th bits of an L card; the current or load current percentage of any line can be extracted in the overload line list as a criterion:

Wherein I _i.220 represents the current value of the ith 220kV line; i _i.rated.220 is the rated current value of the ith line; n _V＝220 is the total number of 220kV lines; num _i.load.220 is the load current percentage of the ith line;

In 220kV transformer load factor constraint, for 220kV transformer, the apparent power is required not to exceed its rated capacity or the load factor is required to be lower than 100% in normal operation, the rated capacity of the transformer is set at 34-37 bits of T card, and the apparent power and load factor information can be extracted from overload transformer list:

Wherein S _i.220 represents the apparent power of the ith 220kV transformer; s _i.rated.220 is the rated power value of the ith transformer; m _V＝220 is the number of 220kV transformers; num _i.S.220 is the load factor of the i-th transformer;

For N-1 constraint, completing N-1 calculation, sequentially switching off all electric elements in a designated area during simulation, then carrying out load flow calculation, searching overload states of the rest elements, and finally outputting a result list;

step 3, evaluating the new energy source limit absorption capacity of the partition;

and 4, evaluating the full-network new energy source limit absorption capacity.

2. The single-station-partition-full-network-based new energy source limit absorption capacity assessment method according to claim 1, wherein:

the single-station new energy source limit absorption capacity evaluation method based on the bipartite approximation method comprises the following specific steps of:

a. collecting and warehousing the annual operation mode and the stable quota data file of the power grid, and mining data information in the file under the BPA typical operation mode;

b. the simulation access of new energy and the intelligent adjustment of the power generation output are realized;

c. finishing load flow calculation and N-1 calculation, and mining relevant load flow information from an output result file according to the name, voltage level and the partition of the concerned equipment;

d. Checking constraint conditions, and if the current analog access capacity meets the constraint conditions, continuing an iteration process, wherein the iteration step length adopts a variable step length; the solving process adopts a bipartite approximation method.

3. The single-station-partition-full-network-based new energy source limit absorption capacity assessment method according to claim 1, wherein:

in step 3, according to the new energy single-station calculation result in step 2, constructing an objective function which aims at partitioning the position and the optimal capacity of each access point when the future new energy installed capacity is maximum, wherein the expression is as follows:

Wherein P _i is the new energy access capacity of the ith station; n is the total number of new energy access stations;

Considering that the current and power constraints and N-1 constraints of 220kV lines and transformers are already considered in the single-station limit capacity evaluation process, the grid constraints of the last voltage class device are also considered when the partition optimization model is established, and the constraint conditions are as follows:

(1) 500kV line stability quota constraint:

For 500kV lines, enough margin is reserved to ensure that overload is not caused during accidents, a specific stability limit is set, and after the current value of the 500kV line is extracted from the result, the stability limit of each line in the limit table is matched and compared to judge whether the limit is exceeded or not:

I_i.500≤I_i.stable.500(i＝1,2,…,n_V＝500) (11)

Wherein, I _i.500 is the current value of the ith 500kV line; i _i.stable.500 is the stability quota of the ith line; n _V＝500 is the total number of 500kV lines;

(2) 500kV transformer stability quota constraint:

For 500kV transformers, enough margin is required to be left to ensure that no overload occurs after an accident occurs, and the stability quota of each transformer can be obtained from a quota table:

S_i.500≤S_i.stable.500(i＝1,2,…,m_V＝500) (12)

S _i.500 is apparent power of the ith 500kV transformer; s _i.stable.500 is the stability quota of the ith transformer; m _V＝500 is the number of 500kV transformers;

constraint equations as shown in formulas (13), (14) can be obtained by polynomial equivalent:

P_i≤P_i.max (14)

Wherein, P _i.max represents the upper limit value of the access capacity of the ith station.

4. The single-station-partition-full-network-based new energy source limit absorption capacity assessment method according to claim 1, wherein:

In step 4, according to the new energy single-station calculation result in step2 and the calculation result of the partition new energy access in step 3, constructing an objective function which aims at the position and the optimal capacity of each access point when the installed capacity of the new energy in the whole network is maximum, wherein the expression is as follows:

Wherein P _i is the new energy access capacity of the ith station; n is the total number of new energy access stations;

The constraint conditions in the full-network new energy source limit absorption capacity evaluation model are the same as those in the partition limit absorption capacity evaluation process, and are the current and power constraint of a 220kV line and a transformer, the N-1 constraint and the grid constraint of 500kV equipment respectively;

and according to the model, solving the model in MATLAB and GAMS to obtain the new energy source limit absorption capacity of the whole network.