CN114285090A - New energy limit consumption capability evaluation method based on single station-partition-whole network - Google Patents
New energy limit consumption capability evaluation method based on single station-partition-whole network Download PDFInfo
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Abstract
The invention discloses a new energy limit consumption capability evaluation method based on single station-partition-whole network, which comprises the following steps: (1) intelligently adjusting the operation mode of the power grid; (2) evaluating the limit consumption capacity of the new energy of the single station; (3) evaluating the limit consumption capacity of the new energy in a subarea; (4) and evaluating and solving the limit consumption capability of the new energy of the whole network. The invention provides an intelligent adjustment method for a power grid operation mode, so that the workload of manual means is greatly reduced, and single-station new energy source limit absorption capacity evaluation systems based on a binary approximation method and an improved particle swarm algorithm are respectively established; a polynomial model considering the influence of the injection power of the new energy node is used for complex constraint in an equivalent provincial network, and based on a single-station calculation result, a limit absorption capacity optimization model of a subarea and the whole network under different safety constraints is established. The method can provide the maximum consumption capacity of the new energy of the whole network and can provide reference opinions for new energy planning and construction.
Description
Technical Field
The invention belongs to the field of new energy of an electric power system, and particularly relates to a new energy limit consumption capability evaluation method based on a single station, a subarea and a whole network.
Background
China puts forward to develop clean energy vigorously, promotes energy power to be converted from high carbon to low carbon, takes fossil energy as a main energy to clean energy as a main energy, and is seriously influenced by factors such as limited system peak regulation capacity, reverse distribution of renewable resources in China, barriers of power transmission channels among provinces and the like, and the phenomena of wind abandonment and light abandonment are serious. Therefore, randomness, intermittence and fluctuation of clean energy output are fully considered, new energy accepting capacity of a power grid in a certain time scale in the future is quantitatively analyzed, reasonable optimization suggestions are provided for new energy planning schemes, and the method is an important premise for guaranteeing safety, reliability, stability and economy of operation of a power system.
On the other hand, related departments still adopt the traditional means to make and analyze the operation mode of the power grid when performing new energy consumption analysis at present, depend on manual experience, have large workload, low efficiency, strong limitation and poor flexibility, and are difficult to meet the future development requirements of the power grid in China. Therefore, based on the above background, it is urgently needed to develop a system new energy limit consumption capability evaluation module, improve the working quality and efficiency of new energy consumption analysis, and better support and service the scheduling operation of the system new energy power system.
Disclosure of Invention
The purpose of the invention is as follows: with the continuous rising of the capacity and the occupation ratio of new energy, the analysis requirement of the limit consumption capacity of the new energy is gradually shown, and the workload of operation mode adjustment and analysis is exponentially increased.
The invention adopts the specific technical scheme that: the new energy limit absorption capacity evaluation method based on single station-partition-whole network is characterized by comprising the following steps: the method comprises the following steps:
step 3, evaluating the limit consumption capacity of the new energy in the subareas;
and 4, evaluating the limit consumption capability of the new energy of the whole network.
Further, the method for intelligently adjusting the power grid operation mode in step 1 includes:
firstly, new energy is accessed in a simulation mode, when the new energy is accessed in the simulation mode, a load with negative active power is accessed in a transformer substation, meanwhile, the accessed reactive power meets a constant power factor, and the 21 st to 30 th positions of an alternating current node card in PSD-BPA are constant loads, so that a new energy output constant power model is superposed on the original power of the transformer substation, the data line where a plant station is located is positioned under a data file according to the node name, the voltage grade and the partition name, and power superposition is carried out under the original load data;
then, the generated output is automatically adjusted, the output of a power plant needs to be reduced in order to keep the active power balance of the system after the new energy is simulated to be accessed, the adjustment is carried out through a P card in BPA (Business Process for production) namely a generated output load percentage modification card, and when the load and the generated output are modified according to the subareas or owners, the modification formula is as follows:
SNew＝SOld×Ratio (1)
wherein: sNewFor modified power, SOldFor raw power, Ratio is the scaling factor, and default is equivalent to a scaling factor of 1.0;
and finally, intelligently extracting an output result, judging the out-of-limit condition of the concerned equipment according to the result after the load flow calculation is finished, so that the power and current information of lines and transformers of various voltage levels needs to be extracted from the output result, and the BPA can specify an output list through a specific control statement and output by adopting a second-level control statement and a third-level control statement.
Further, an objective function with the maximum total amount of a multi-node access new energy source unit as a target is constructed in the step 2, and the expression is as follows:
wherein, PNiRepresents the maximum installed capacity, X, of the new energy cluster accessed at node iiAnd an integer variable is represented, 0 represents that the node i does not access the new energy source unit, and 1 represents that the node i accesses the new energy source unit.
Further, in the step 2, a single-station evaluation model constraint condition is constructed based on an improved particle swarm optimization, a power flow equation is used as an equality constraint condition, and the constraint conditions of inequalities are shown as a formula (3) and a formula (4):
wherein,
represents the minimum value of the conventional unit output at the node i,
represents the maximum value of the output of the conventional unit at the node i, SgRepresenting a collection of conventional generator sets, PliRepresenting the power flow on the ith line, Pli maxRepresents the upper limit value of the power flow of the ith line, SlA set of lines is represented that is,
indicating that the machine set is rotated for standby,
indicating the unit to rotate for standby;
the core idea and steps of the improved particle swarm algorithm are that assuming that a particle swarm is composed of M particles, each particle is defined as a D-dimensional space, and the state attributes of a particle i at the time t are as follows: position of
Speed of rotation
Individual optimum position
Global optimal position
The velocity and position of the particle i at time t +1 can be updated by the following formula:
wherein D is 1,2, …, D; r is1And r2Random numbers uniformly distributed on (0, 1); c. C1And c2Is a learning factor; omega is inertia weight, a nonnegative number is taken, when omega is larger, the global optimization capability is enhanced, and the local optimization capability is weaker; when omega is smaller, the global optimizing capability is weaker, but the local optimizing capability is enhanced, and the global and local searching capabilities can be adjusted by adjusting the size of omega; in order to better control the global and local search capability, an improvement is provided for an adjustment strategy of the inertia weight, and a linear decreasing strategy is adopted for ω value taking, as shown in the following formula:
wherein, ω isstartAnd ωendRespectively an initial inertial weight and a termination inertial weight; t is tmaxIs the maximum iteration number; and t is the current iteration number.
Further, in the step 2, constraint conditions of the single station evaluation model are constructed based on a binary approximation method, wherein the constraint conditions comprise a constraint of a normal current carrying capacity of a 220kV line, a constraint of a load factor of a 220kV transformer and a constraint of N-1, and the constraints are expressed as follows:
in the restriction of the normal current carrying capacity of the 220kV line, for the other 220kV lines except the outgoing line of the power plant, the current of the line in normal operation is required to not exceed the rated current value, and for any line, the rated current is specified by 34 th-37 th bits of an L card; the current or load current percentage of any line can be extracted as a criterion in the overload line list, and the formula is as follows:
wherein, Ii.220Representing the current value of the ith 220kV line; i isi.rated.220The rated current value of the ith line is; n isV＝220The total number of the lines is 220 kV; numi.load.220Is the load current percentage of the ith line;
in the load factor constraint of the 220kV transformer, for the 220kV transformer, the apparent power is required to not exceed the rated capacity or the load factor is required to be lower than 100% when the transformer normally operates, the rated capacity of the transformer is set at 34 th to 37 th bits of a T card, and the information of the apparent power and the load factor can be extracted from an overload transformer list, and the formula is as follows:
wherein S isi.220Representing apparent power of the ith 220kV transformerRate; si.rated.220The rated power value of the ith transformer is obtained; m isV＝220The number of the transformers is 220 kV; numi.S.220The load factor of the ith transformer is obtained;
and for the N-1 constraint, completing N-1 calculation through BPA, sequentially switching off all electrical elements in a specified area during simulation, then performing load flow calculation, searching overload states of the rest elements, and finally outputting a result list.
Further, the specific steps of the evaluation of the single-station new energy limit absorption capacity based on the binary approximation method are as follows:
a. collecting and warehousing annual operation modes and stable quota data files of a power grid, and mining data information in the files in a BPA typical operation mode;
b. the simulation access of new energy and the intelligent adjustment of power generation output are realized;
c. completing load flow calculation and N-1 calculation, and mining relevant load flow information from an output result file according to the name, the voltage level and the partition where the concerned equipment is located;
d. checking constraint conditions, if the current simulation access capacity meets the constraint conditions, continuing an iteration process, wherein the iteration step length adopts a variable step length; the solution process adopts a binary approximation method.
Further, in step 3, calculating results of the new energy single station in step 2, and constructing an objective function with the position of each access point and the optimal capacity as targets when the installed capacity of the future new energy of the subarea is maximum, wherein the expression is as follows:
wherein, PiAccessing the capacity for the new energy of the ith station; and N is the total number of new energy access stations.
Considering that the current and power constraints of 220kV lines and transformers and N-1 constraints are already considered in the evaluation process of the limit absorption capacity of the single station, the grid constraints of 500kV equipment are also considered when a partition optimization model is established, and the constraint conditions are as follows:
(1) and (3) stable quota constraint of 500kV lines:
for a 500kV line, enough margin is reserved to ensure that the 500kV line is not overloaded during accidents, so 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 a limit table is matched and compared to judge whether the limit is out of limit, wherein the formula is as follows:
Ii.500≤Ii.stable.500(i＝1,2,…,nV＝500) (11)
wherein, Ii.500The current value of the ith 500kV line is obtained; i isi.stable.500The stable quota of the ith line; n isV＝500The total number of the lines is 500 kV;
(2) and (3) stable quota constraint of the 500kV transformer:
for 500kV transformers, enough margin is required to be reserved to ensure that the transformers are not overloaded after an accident occurs, the stable quota of each transformer can be obtained from a quota table, and the formula is as follows:
Si.500≤Si.stable.500(i＝1,2,…,mV＝500) (12)
wherein S isi.500The apparent power of the ith 500kV transformer; si.stable.500The stable quota of the ith transformer is set; m isV＝500The number of the transformers is 500 kV.
Constraint equations shown in equations (13) and (14) can be obtained by polynomial equivalence:
Pi≤Pi.max (14)
wherein, Pi.maxAnd the access capacity upper limit value of the ith station is shown.
Further, in step 4, calculating the new energy single station calculation result in step 2 and the calculation result of the partitioned new energy access in step 3, and constructing an objective function with the position and the optimal capacity of each access point when the future installed capacity of the new energy is maximum in the whole network as a target, wherein the expression is as follows:
wherein, PiAccessing the capacity for the new energy of the ith station; n is the total number of new energy access stations;
constraint conditions in the whole network new energy limit absorption capability evaluation model are the same as those in the subarea limit absorption capability evaluation process, and are respectively the current and power constraints of a 220kV line and a transformer, the N-1 constraint and the grid frame constraint of 500kV equipment;
and solving the model in MATLAB and GAMS according to the model to obtain the new energy source limit absorption capacity of the whole network.
Has the advantages that: the method accurately evaluates the maximum receiving capacity of the power grid to the new energy under the current and future time sections so as to improve the utilization rate of renewable energy, and can improve technical support for formulating a reasonable power generation plan and planning scheme under the background of large-scale grid connection of the new energy.
Drawings
FIG. 1 is a flow chart of a method of the present invention;
FIG. 2 is a flow chart of automatic adjustment of the generated output;
FIG. 3 is a flow chart of single station new energy limit absorption capacity evaluation;
FIG. 4 is a flow chart of network-wide new energy limit absorption capacity evaluation;
fig. 5 is a new energy simulation access flow chart;
fig. 6 is a schematic diagram of a proposed access point and its capabilities.
Detailed Description
The technical solution of the present invention is further described below with reference to the accompanying drawings and examples.
As shown in fig. 1, the method for evaluating the limit consumption capability of new energy based on single station-partition-whole network of the present invention includes the following steps:
(1) intelligently adjusting the operation mode of the power grid;
(2) evaluating the limit consumption capacity of the new energy of the single station;
(3) evaluating the limit consumption capacity of the new energy in a subarea;
(4) and evaluating the limit consumption capability of the new energy of the whole network.
In the step (1), the intelligent adjustment method for the operation mode of the power grid comprises the following steps:
firstly, the new energy is accessed in a simulation mode, as shown in fig. 5, when the new energy is accessed in the simulation mode, a load with negative active power is accessed in a transformer substation, meanwhile, the accessed reactive power meets a constant power factor, and 21 st to 30 th positions of an alternating current node card in PSD-BPA are constant loads, so that a new energy output constant power model can be superposed on original power of the transformer substation.
And a second step of automatically adjusting the generated output, as shown in fig. 2, in order to keep the active balance of the system after the new energy is simulated to be accessed, the output of the power plant needs to be reduced, and the output can be adjusted by a P card (generated output load percentage modification card) in the BPA, and the format description of the output is shown in table 1.
TABLE 1 meaning correspondence table for each column of P card
The PZ card is used in the method, and when the load and the power generation output are modified according to the subareas or owners, the modification formula is as follows:
SNew＝SOld×Ratio (1)
in the formula: sNewFor modified power, SOldFor raw power, Ratio is the scaling factor, and the default is equivalent to a scaling factor of 1.0。
When an actual power grid of a certain province is operated, the sequence of the generator set adjustment strategy and the lower limit of output are shown in table 2.
TABLE 2 Adjustable Unit adjustment strategy
In table 2, the units are all thermal power generating units, and the automatic adjustment flow of the generated output is as follows: (1) counting the total capacity of the new energy after simulated access; (2) counting rated capacity and current actual output of the generator set, and calculating actual output of each adjustable unit partition before modification; (3) calculating a modified output adjustment factor; (4) a new PZ card is generated.
And finally, intelligently extracting an output result, judging the out-of-limit condition of the concerned equipment according to the result after the load flow calculation is finished, so that the power and current information of lines and transformers of various voltage levels needs to be extracted from the output result, and the BPA can specify an output list through specific control statements. In addition, the equipment information concerned by the method is information such as load rate and current carrying capacity of the line and the transformer, so that the required report is mainly a list of overload lines and overload transformers.
In the step 2, an objective function with the maximum total amount of a multi-node access new energy source set as a target is constructed, and the expression is as follows:
wherein, PNiRepresents the maximum installed capacity, X, of the new energy cluster accessed at node iiRepresenting an integer variable, 0 representing that the node i does not access the new energy source unit, and 1 representing that the node i accesses the new energy source unit;
in the single-station evaluation model constraint condition based on the improved particle swarm optimization, a power flow equation is used as an equality constraint condition, and the constraint condition of an inequality is shown as a formula (3) and a formula (4):
wherein,
representing the minimum value of the output of the conventional unit at the node i;
representing the maximum value of the output of the conventional unit at the node i; sgRepresenting a collection of conventional generator sets; pliRepresenting the power flow on the ith line,
represents the upper limit value of the power flow of the ith line, SlRepresenting a set of lines;
indicating that the machine set is rotated for standby,
indicating the unit to rotate for standby.
The core idea and steps of the improved particle swarm algorithm are that assuming that a particle swarm is composed of M particles, each particle is defined as a D-dimensional space, and the state attributes of a particle i at the time t are as follows: position of
Speed of rotation
Individual optimum position
Global optimal position
The velocity and position of the particle i at time t +1 can be updated by the following formula:
wherein D is 1,2, …, D; r is1And r2Random numbers uniformly distributed on (0, 1); c. C1And c2Is a learning factor; omega is inertia weight, an inertia factor omega in the traditional algorithm is a non-negative number, when omega is larger, the global optimization capability is enhanced, and the local optimization capability is weaker; when ω is small, the global optimization capability is weak, but the local optimization capability is strong, so adjusting ω can adjust the global and local search capabilities. In order to better control the global and local search capability, the adjustment strategy of the inertial weight is improved, and a linear decreasing strategy is adopted for the value of omega, as shown in the following formula:
wherein, ω isstartAnd ωendRespectively an initial inertial weight and a termination inertial weight; t is tmaxIs the maximum iteration number; and t is the current iteration number.
Compared with an improved particle swarm optimization, the constraint conditions of the single-station evaluation model based on the binary approximation method comprise the constraint of the normal current carrying capacity of the 220kV line, the constraint of the load rate of the 220kV transformer and the constraint of N-1, and the constraints are expressed as follows:
in the restriction of the normal current carrying capacity of the 220kV line, for the other 220kV lines except the outgoing line of the power plant, the current of the line in normal operation is required to not exceed the rated current value, and for any line, the rated current is specified by 34 th-37 th bits of an L card; the current or load current percentage of any line can be extracted from the list of overload lines as a criterion, and the formula is as follows:
wherein, Ii.220Representing the current value of the ith 220kV line; i isi.rated.220The rated current value of the ith line is; n isV＝220The total number of the lines is 220 kV; numi.load.220Is the percentage of the load current of the ith line.
In the load factor constraint of the 220kV transformer, for the 220kV transformer, the apparent power is required to not exceed the rated capacity or the load factor is required to be lower than 100% when the transformer normally operates, the rated capacity of the transformer is set at 34 th to 37 th bits of a T card, and the information of the apparent power and the load factor can be extracted from an overload transformer list, and the formula is as follows:
wherein S isi.220Representing the apparent power of the ith 220kV transformer; si.rated.220The rated power of the ith transformer; m isV＝220The number of the transformers is 220 kV; numi.S.220The load factor of the ith transformer.
For the N-1 constraint, the method completes N-1 calculation through BPA, sequentially switches off all electrical elements in a specified area during simulation, then performs load flow calculation, searches overload states of the rest elements, and finally outputs a result list concerned by a user.
The method comprises the following specific steps of evaluating the limit absorption capacity of the single-station new energy based on a binary approximation method: a. collecting and warehousing data files such as annual operation modes and stable quota of a power grid, and mining data information in the files in a BPA typical operation mode; b. the method realizes the intelligent adjustment of the simulated access and the generated output of new energy according to the method; c. completing load flow calculation and N-1 calculation, and mining relevant load flow information from an output result file according to the name, the voltage level and the partition where the concerned equipment is located; d. checking constraint conditions, if the current simulation access capacity meets the constraint conditions, continuing an iteration process, wherein the iteration step length adopts a variable step length; the solution process adopts a binary approximation method.
The evaluation process of the limit absorption capacity of the single station new energy is shown as the attached figure 3, and the specific implementation steps are as follows:
(1) data mining
And collecting and warehousing data files such as annual operation modes and stable limits of a power grid, and mining data information in the files in a BPA typical operation mode, wherein historical data information to be extracted comprises the rated capacity of an adjustable unit, actual output, names of partitions and owners, new energy access station loads, partition names, voltage levels and outlet bus names of a power station in the current operation mode.
(2) Analog access
The method comprises the steps that the new energy access is simulated, namely a load with negative active power is accessed into a 220kV transformer substation, meanwhile, the accessed reactive power meets a constant power factor, during specific implementation, according to a node name, a voltage grade and a partition name, a data line where a plant station is accessed under a data file is positioned, and the new energy access power is superposed under original load data.
(3) Intelligent adjustment
And (3) in order to keep the active power balance of the system after the new energy is simulated to be accessed, the output of the power plant needs to be reduced, the original output and the actual output of the power plant are obtained from the step (1), the total access capacity of the new energy in the step (2) is calculated, the modified output adjustment factor can be obtained, the adjustment strategy is shown in a table 3.3.2, the output adjustment factor of each adjustable generator set partition is obtained through calculation, and the PZ card in the data file is subjected to positioning modification.
(4) Load flow calculation
The load flow calculation is realized by calling power system analysis software PSD-BPA, an iterative algorithm adopts a Newton-Raphson method, a load flow solution can be obtained if the current power grid operation mode is calculated and converged, and relevant load flow information is mined from an output result file according to the name, the voltage grade and the partition where the concerned equipment is located.
(5) Constraint verification
The constraint conditions comprise normal current carrying capacity constraint of a 220kV line except the outgoing line of the power plant, accident current carrying capacity constraint during N-1 calculation and power constraint during normal operation of a 220kV transformer. If the current simulation access capacity meets the constraint condition, the iteration process is continued, and in order to improve the calculation speed, the iteration step length adopts a variable step length; and a binary approximation method is adopted in the solving process, if any constraint under the current access capacity is not met, the last access capacity in the iteration process is output, and the power at the moment is the maximum access capacity of the station.
The method takes data of a typical summer operation mode of an actual power grid of a certain province as an example, the province has 11 partitions, parameters of each partition are shown in a table 3, and calculation results are shown in a table 4.
TABLE 3 zoning parameter table
Table 4 table of evaluation results of single station new energy limit consumption
In step 3, calculating the new energy single station calculation result in step 2, and constructing an objective function taking the position of each access point and the optimal capacity when the installed capacity of the new energy in the future of the subarea is maximum as a target, wherein the expression is as follows:
wherein, PiIs new for the ith plantEnergy access capacity; and N is the total number of new energy access stations.
Considering that the current and power constraints of 220kV lines and transformers and N-1 constraints are already considered in the evaluation process of the limit absorption capacity of the single station, the grid constraints of 500kV equipment are also considered when a partition optimization model is established, and the constraint conditions are as follows:
(1) and (3) stable quota constraint of 500kV lines:
for 500kV lines, enough margin is usually reserved to ensure that overload is not caused during accidents, so 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 a limit table is matched and compared to judge whether the limit is out of limit or not. The formula is as follows:
Ii.500≤Ii.stable.500(i＝1,2,…,nV＝500) (11)
wherein, Ii.500The current value of the ith 500kV line is obtained; i isi.stable.500The stable quota of the ith line; n isV＝500The total number of the lines is 500 kV.
(2) And (3) stable quota constraint of the 500kV transformer:
for 500kV transformers, enough margin is required to be reserved to ensure that the transformers are not overloaded after an accident occurs, the stable quota of each transformer can be obtained from a quota table, and the formula is as follows:
Si.500≤Si.stable.500(i＝1,2,…,mV＝500) (12)
wherein S isi.500The apparent power of the ith 500kV transformer; si.stable.500The stable quota of the ith transformer is set; m isV＝500The number of the transformers is 500 kV.
Constraint equations shown in equations (13) and (14) can be obtained by polynomial equivalence:
Pi≤Pi.max (14)
wherein, Pi.maxAnd the access capacity upper limit value of the ith station is shown.
In step 4, calculating the new energy single-station calculation result in step 2 and the calculation result of the partitioned new energy access in step 3, and constructing an objective function taking the position and the optimal capacity of each access point when the future installed capacity of the new energy is maximum in the whole network as a target, wherein the expression is as follows:
wherein, PiAccessing the capacity for the new energy of the ith station; and N is the total number of new energy access stations.
Constraint conditions in the whole network new energy limit absorption capability evaluation model are the same as those in the subarea limit absorption capability evaluation process, and are respectively the current and power constraints of 220kV lines and transformers, the N-1 constraint and the grid frame constraint of 500kV equipment.
The evaluation flow of the full-network new energy limit absorption capacity is shown as the attached figure 4, and the specific implementation steps are as follows:
(1) data mining
And collecting and warehousing data files such as annual operation modes and stable limits of a power grid, and mining data information in the files in a BPA typical operation mode, wherein historical data information to be extracted comprises the rated capacity of an adjustable unit, actual output, names of partitions and owners, new energy access station loads, partition names, voltage levels and outlet bus names of a power station in the current operation mode.
(2) Analog access
The method comprises the steps that the new energy access is simulated, namely a negative load with negative active power is accessed into a transformer substation, meanwhile, the accessed reactive power meets a constant power factor, and in specific implementation, according to a node name, a voltage grade and a partition name, a data line where a plant station is located under a data file, and the new energy access power is superposed under original load data. During the subarea simulation, the maximum access capacity of all stations in the subarea is not more than the access limit of the station, and the limit access capacity of each station can be calculated by the limit absorption capacity of a single station; and during the whole network simulation, site selection of the plant station and restriction of an access upper limit are carried out on the basis of a partition optimization result.
(3) Intelligent adjustment
In order to keep the active power balance of the system after the new energy is simulated to be accessed, the output of the power plant needs to be reduced, the original output and the actual output of the power plant are obtained from the step (1), the total access capacity of the new energy in the step (2) is calculated, the modified output adjustment factors can be obtained, the adjustment strategy sequence is shown in the table 2, the output adjustment factors of all adjustable generator set partitions are obtained through calculation, and the PZ card in the data file is subjected to positioning modification.
(4) Load flow calculation
The load flow calculation is realized by calling power system analysis software PSD-BPA, an iterative algorithm adopts a Newton-Raphson method, a load flow solution can be obtained if the current power grid operation mode is calculated and converged, and relevant load flow information is mined from an output result file according to the name, the voltage grade and the partition where the concerned equipment is located.
(5) Fitting of parameters
In order to establish a model of the limit absorption capacity of the new energy, optimization solution is carried out through a numerical analysis method, a polynomial constraint equation is established according to different constraint conditions, and equation coefficients of a polynomial constraint function are fitted, the constraint equation comprises normal current carrying capacity constraint of a 220kV line except for the outgoing line of a power plant, accident current carrying capacity constraint during N-1 calculation, stable quota constraint during normal operation of a 500kV line, power constraint during normal operation of a 220kV transformer and stable quota constraint during normal operation of the 500kV transformer, a least square method is adopted in a fitting algorithm, a training sample is obtained by Monte Carlo sampling to obtain new energy access capacity, load flow calculation is carried out, and the number of the sample is in direct proportion to the number of access stations.
(6) Modeling
And respectively establishing a subarea and whole network new energy limit absorption capacity model, wherein the target function is shown as a formula (15), and the constraint function is shown as a formula (9) to a formula (14).
(7) Optimization solution
And (4) carrying out optimization solution on the nonlinear programming model set up in the step (6), solving the problem by calling a solver, wherein the adopted mathematical optimization algorithm is an interior point method, and if the optimal solution of the current problem can be found, outputting a result, wherein the result comprises the sum of the current new energy access capacity, the site selection of the optimal access plant station and the optimal access capacity of the plant station.
And respectively obtaining the evaluation results of the single-station, the subarea and the whole network new energy source limit absorption capacity, wherein before subarea optimization, a single-station optimization result is required to be provided as a single-station access capacity upper limit, and before whole network optimization, each subarea optimization result is required to be provided as an access plant station primary selection and each station access capacity upper limit.
And solving the model in MATLAB and GAMS according to the model to obtain the new energy source limit absorption capacity of the whole network.
Taking data of a typical summer operation mode of an actual power grid of a certain province as an example, the province has 11 partitions, and parameters of each partition are shown in table 5.
TABLE 5 Total network and partition New energy Limit consumption calculation result Table
Through the single-station-partition-whole-network progressive technical route, the number of access stations meeting the grid security constraint is sequentially reduced, and the optimization result shows that the maximum access capacity of the whole-network new energy is 947 ten thousand watts in the current power grid operation mode, and the optimal access point position and the recommended capacity thereof can be obtained, as shown in fig. 6.
The above embodiments are only for illustrating the technical idea of the present invention, and the protection scope of the present invention is not limited thereby, and any modifications made on the basis of the technical scheme according to the technical idea of the present invention fall within the protection scope of the present invention.
Claims (8)
1. The new energy limit absorption capacity evaluation method based on single station-partition-whole network is characterized by comprising the following steps: the method comprises the following steps:
step 1, simulating a power grid operation mode after new energy is accessed, and performing load flow calculation;
step 2, evaluating the limit consumption capacity of the single station new energy;
step 3, evaluating the limit consumption capacity of the new energy in the subareas;
and 4, evaluating the limit consumption capability of the new energy of the whole network.
2. The method for evaluating the limit absorption capability of new energy based on the single-station-partition-whole network as claimed in claim 1, wherein:
the intelligent adjustment method for the power grid operation mode in the step 1 comprises the following steps:
firstly, performing simulated access on new energy, namely accessing a load with negative active power in a transformer substation when the new energy is simulated, simultaneously enabling the accessed reactive power to meet a constant power factor, superposing a new energy output constant power model on the original power of the transformer substation, positioning the data line where a station is accessed under a data file according to a node name, a voltage grade and a partition name, and performing power superposition under the original load data;
and then, automatically adjusting the generated output, and simulating that the output of the power plant needs to be reduced in order to keep the active balance of the system after the new energy is accessed, namely when the load percentage of the generated output is modified according to the subareas or the owner, the modification formula is as follows:
SNew＝SOld×Ratio (1)
wherein: sNewFor modified power, SOldFor raw power, Ratio is a scaling factor;
and finally, extracting output results, extracting power and current information of lines and transformers of various voltage grades from the output results, and judging the out-of-limit condition of the concerned equipment according to the results.
3. The method for evaluating the limit absorption capability of new energy based on the single-station-partition-whole network as claimed in claim 1, wherein:
in the step 2, an objective function with the maximum total amount of a multi-node access new energy source set as a target is constructed, and the expression is as follows:
wherein, PNiRepresenting the maximum installed capacity accessed by the new energy machine set at the node i, n representing the number of nodes, and XiDenotes an integer variable, XiA value of 0 indicates that node i does not access the new energy bank, XiAnd 1 represents that the node i accesses the new energy machine set.
4. The method for evaluating the limit absorption capability of new energy based on the single-station-partition-whole network as claimed in claim 3, wherein:
in the step 2, a single-station evaluation model constraint condition is constructed based on an improved particle swarm optimization, a power flow equation is used as an equality constraint condition, and the constraint condition of an inequality is shown as a formula (3) and a formula (4):
wherein,
represents the minimum value of the conventional unit output at the node i,
represents the maximum value of the output of the conventional unit at the node i, SgRepresenting a collection of conventional generator sets, PliRepresenting the power flow on the ith line,
represents the upper limit value of the power flow of the ith line, SlA set of lines is represented that is,
indicating that the machine set is rotated for standby,
indicating the unit to rotate for standby;
the improved particle swarm algorithm specifically comprises the following steps: assuming that the particle group consists of M particles, each defined as a D-dimensional space, the state attribute of particle i at time t is as follows: position of
Speed of rotation
Individual optimum position
Global optimal position
The velocity and position of the particle i at time t +1 can be updated by the following formula:
wherein D is 1,2, …, D; r is1And r2Random numbers uniformly distributed on (0, 1); c. C1And c2Is a learning factor; omega is inertia weight, a nonnegative number is taken, when omega is larger, the global optimizing ability is enhanced, and the local optimizing ability is enhancedIs weaker; when omega is smaller, the global optimizing capability is weaker, but the local optimizing capability is enhanced, and the global and local searching capabilities can be adjusted by adjusting the size of omega; the adjustment strategy of the inertia weight is improved, and a linear decreasing strategy is adopted for omega dereferencing:
wherein, ω isstartAnd ωendRespectively an initial inertial weight and a termination inertial weight; t is tmaxIs the maximum iteration number; and t is the current iteration number.
5. The method for evaluating the limit absorption capability of new energy based on the single-station-partition-whole network as claimed in claim 3, wherein:
in the step 2, single-station evaluation model constraint conditions are established based on a binary approximation method, wherein the constraint conditions comprise normal current carrying capacity constraint of a line, transformer load rate constraint and N-1 constraint;
in the restriction of the normal current carrying capacity of the 220kV line, for the other 220kV lines except the outgoing line of the power plant, the current of the line in normal operation is required to not exceed the rated current value, and for any line, the rated current is specified 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, Ii.220Representing the current value of the ith 220kV line; i isi.rated.220The rated current value of the ith line is; n isV＝220The total number of the lines is 220 kV; numi.load.220Is the load current percentage of the ith line;
in the load factor constraint of the 220kV transformer, for the 220kV transformer, the apparent power is required to not exceed the rated capacity or the load factor is required to be lower than 100% when the transformer is in normal operation, the rated capacity of the transformer is set at 34 th to 37 th bits of a T card, and the information of the apparent power and the load factor can be extracted from an overload transformer list:
wherein S isi.220Representing the apparent power of the ith 220kV transformer; si.rated.220The rated power value of the ith transformer is obtained; m isV＝220The number of the transformers is 220 kV; numi.S.220The load factor of the ith transformer is obtained;
and for the N-1 constraint, completing N-1 calculation, sequentially switching off all the electrical elements in the specified area during simulation, then performing load flow calculation, searching the overload state of the rest elements, and finally outputting a result list.
6. The method for evaluating the limit absorption capability of new energy based on the single-station-partition-whole network as claimed in claim 5, wherein:
the method comprises the following specific steps of evaluating the limit absorption capacity of the single-station new energy based on a binary approximation method:
a. collecting and warehousing annual operation modes and stable quota data files of a power grid, and mining data information in the files in a BPA typical operation mode;
b. the simulation access of new energy and the intelligent adjustment of power generation output are realized;
c. completing load flow calculation and N-1 calculation, and mining relevant load flow information from an output result file according to the name, the voltage level and the partition where the concerned equipment is located;
d. checking constraint conditions, if the current simulation access capacity meets the constraint conditions, continuing an iteration process, wherein the iteration step length adopts a variable step length; the solution process adopts a binary approximation method.
7. The method for evaluating the limit absorption capability of new energy based on the single-station-partition-whole network as claimed in claim 1, wherein:
in step 3, calculating the new energy single station calculation result in step 2, and constructing an objective function taking the position of each access point and the optimal capacity when the installed capacity of the new energy in the future of the subarea is maximum as a target, wherein the expression is as follows:
wherein, PiAccessing the capacity for the new energy of the ith station; n is the total number of new energy access stations;
considering that the current and power constraints of 220kV lines and transformers and N-1 constraints are already considered in the evaluation process of the single station limit absorption capacity, the grid constraints of the previous voltage class equipment are also considered when a partition optimization model is established, and the constraint conditions are as follows:
(1) and (3) stable quota constraint of 500kV lines:
for the 500kV line, enough margin is reserved to ensure that the 500kV line is not overloaded during accidents, so 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 a limit table is matched and compared to judge whether the limit is out of limit or not:
Ii.500≤Ii.stable.500(i＝1,2,…,nV＝500) (11)
wherein, Ii.500The current value of the ith 500kV line is obtained; i isi.stable.500The stable quota of the ith line; n isV＝500The total number of the lines is 500 kV;
(2) and (3) stable quota constraint of the 500kV transformer:
for 500kV transformers, enough margin is required to be reserved to ensure that the transformers are not overloaded after an accident occurs, and the stable quota of each transformer can be obtained from a quota table:
Si.500≤Si.stable.500(i＝1,2,…,mV＝500) (12)
wherein S isi.500The apparent power of the ith 500kV transformer; si.stable.500The stable quota of the ith transformer is set; m isV＝500The number of the transformers is 500 kV;
constraint equations shown in equations (13) and (14) can be obtained by polynomial equivalence:
Pi≤Pi.max (14)
wherein, Pi.maxAnd the access capacity upper limit value of the ith station is shown.
8. The method for evaluating the limit absorption capability of new energy based on the single-station-partition-whole network as claimed in claim 1, wherein:
in step 4, calculating the new energy single-station calculation result in step 2 and the calculation result of the partitioned new energy access in step 3, and constructing an objective function taking the position and the optimal capacity of each access point when the future installed capacity of the new energy is maximum in the whole network as a target, wherein the expression is as follows:
wherein, PiAccessing the capacity for the new energy of the ith station; n is the total number of new energy access stations;
constraint conditions in the whole network new energy limit absorption capability evaluation model are the same as those in the subarea limit absorption capability evaluation process, and are respectively the current and power constraints of a 220kV line and a transformer, the N-1 constraint and the grid frame constraint of 500kV equipment;
and solving the model in MATLAB and GAMS according to the model to obtain the new energy source limit absorption capacity of the whole network.
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