CN107611966B - Active power distribution network power supply capacity evaluation method considering difference reliability - Google Patents
Active power distribution network power supply capacity evaluation method considering difference reliability Download PDFInfo
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Abstract
An active power distribution network power supply capacity evaluation method considering difference reliability comprises the following steps: and establishing an active power distribution network power supply capacity evaluation model considering the differential reliability, wherein the target function of the model is that the power supply capacity of the active power distribution network is the maximum, the system power supply capacity comprises a main transformer layer and a medium voltage network layer, the optimization object of the model is the annual load peak value accessed by each feeder line, and the reliability index is used as the constraint condition. The method for solving the power supply capacity evaluation model of the active power distribution network considering the differential reliability comprises the steps of evaluating the reliability of the active power distribution network comprising a main transformer, a distributed photovoltaic system and a storage battery, and performing power supply capacity optimization calculation by taking a reliability evaluation result as an important constraint. The invention can combine the differentiated reliability requirements of different areas and the access of distributed photovoltaic and energy storage, can output a network to meet the maximum power supply capacity constrained by integral and differentiated reliability, realizes the optimization of the integral and local power supply capacity in a subarea, and provides support for the improvement of the power supply capacity under the existing power distribution network structure.
Description
Technical Field
The invention relates to an active power distribution network power supply capacity evaluation method. In particular to an active power distribution network power supply capacity evaluation method considering difference reliability for urban power distribution network planning.
Background
The power supply capacity is the maximum load which can be supplied by the power grid under the condition that certain safety criteria are met. With the development of domestic economy and the rapid expansion of power distribution systems, it is necessary to calculate and evaluate the power supply capacity of the existing power distribution system and plan the future power supply capacity. Meanwhile, the shortage of land resources in urban areas is difficult to meet the requirement of expanding the power distribution network. Therefore, there is an increasing interest in optimizing the power supply capability of existing networks and releasing the power supply potential. With the wide access of renewable distributed energy, the operation mode and the transfer capacity of the power distribution network are influenced significantly, and the power supply capacity is used as an important evaluation index of the power distribution network and also needs to be combined with distributed photovoltaic. Based on this, the evaluation of the power supply capacity of the active power distribution network has wider and practical prospect.
For the evaluation method of the power supply capacity, the early method is mainly based on load flow calculation, such as a capacity-load ratio method for evaluating the power supply capacity of a power distribution system by taking the transformation capacity as a basis, a maximum load multiple method, a load capacity method, a network maximum current method and the like for comprehensively considering the transformation capacity and the load transfer capacity, and the evaluation of the power supply capacity of the distribution-type photovoltaic power distribution network based on a blind number model and a repeated load flow method. The method can obtain the maximum power supply capacity of the network under system constraint conditions such as node voltage, branch power and the like. However, the method based on load flow calculation ignores the requirement of power users on the continuity of power supply, and therefore a power supply capacity evaluation method considering the N-1 safety criterion is developed, the method considers factors such as the interconnection relation of a main transformer, load transfer among transformer stations and the like and the feeder line interconnection structure under the main transformer, and evaluates the maximum power supply capacity of the network under the N-1 criterion, so that the reliability and economic indexes of the power distribution system are considered.
However, for the power distribution system power supply capacity calculation method considering the N-1 safety criterion, the power grid is required to rigidly meet the N-1 check at the peak load moment, but in the actual power grid operation process, the peak load is usually several times of the average load and the duration is very short, so as to further mine the system power supply potential, a power supply capacity evaluation model considering the reliability is provided, the power supply capacity maximization is realized by taking the specific reliability index as the constraint, but the method can only be applied to the traditional power distribution network without the DG and the energy storage, and the reliability is considered in the form of integral constraint. In practical application, due to the fact that different load grades exist, different users have different requirements for power supply reliability, therefore, the method for solving and describing the difference reliability is found, and the access of DG and stored energy is combined, so that the power supply capacity evaluation of the active power distribution network under the constraint of the difference reliability is more practical, the power supply potential can be mined in combination with the actual operation of the power grid, and the economical efficiency of the construction and operation of the power grid is improved.
Disclosure of Invention
The invention aims to solve the technical problem of providing an active power distribution network power supply capacity evaluation method considering differential reliability and capable of providing support for improving power supply capacity under the existing power distribution network structure.
The technical scheme adopted by the invention is as follows: an active power distribution network power supply capacity evaluation method considering differential reliability comprises the following steps:
1) establishing an active power distribution network power supply capacity evaluation model considering differential reliability, wherein the objective function of the model is that the power supply capacity of the active power distribution network is the maximum, the system power supply capacity comprises a main transformer layer and a medium voltage network layer, n main transformers are arranged in the system, the main transformers are numbered as 1,2, …, i and … n, and the number of feeders connected with the ith main transformer is miAnd the number of each feeder line connected with the main transformer is 1,2 …, q, …, miThe objective function of the model is as follows:
the optimization object of the model is the annual load peak value accessed by each feeder line, and the reliability index is used as the constraint condition.
2) The method for solving the power supply capacity evaluation model of the active power distribution network considering the differential reliability comprises the steps of evaluating the reliability of the active power distribution network comprising a main transformer, a distributed photovoltaic system and a storage battery, and performing power supply capacity optimization calculation by taking a reliability evaluation result as an important constraint.
The constraint conditions in the step 1) comprise:
(1) constraint on differential reliability
Selecting the power supply reliability of the feeder line as a basic evaluation unit, wherein the basic evaluation unit is defined as the following: in a unit year, calculating the ratio of the electricity consumption hours of all feeder users to the electricity demand hours of the feeder users, and the power supply reliability of the qth feeder according to the formula:
wherein T is the number of electricity needed in a specified time; u shapejThe annual outage time for load point j; n is a radical ofjThe number of users at the load point j; lqThe total load point number of the q-th feeder line;
different feeder line reliability requirements are different, and a reliability target matrix E is defined as (E)1,E2,…,Eq,…,Em)TIn which EqA q-th feeder line reliability target is obtained; ASAIfeed＝(ASAI1,ASAI2,…,ASAIq,…,ASAIm)TM is the actual feeder reliability index vector and is the system feeder number; the difference reliability constraint is then expressed as:
ASAIfeed≥E (3)；
(2) global reliability constraint
The expected value of the average power supply reliability of the system is selected as an index and is expressed as follows:
wherein p is the total load point number of the system, EsRepresenting a system reliability target;
(3) the system load and the distributed photovoltaic matching are constrained specifically as follows:
in the formula LiIs the real-time load of the ith main transformer, LiqFor real-time loading of the q-th feeder line connected to the i-th main transformer, GiRepresents the ith main transformer distributed photovoltaic sumTotal real time output of accumulator, GiqRepresenting the real-time total output of the distributed photovoltaic and storage battery of the q-th feeder line connected with the ith main transformer, wherein m represents the number of the system feeder lines;
(4) and load rate constraint, which is embodied in the form of:
0≤(Liq-Giq)/Ciq≤1 (7)
0≤(Li-Gi)/Ci≤1 (8)
in the formula CiIndicating the rated capacity, C, of the ith main transformeriqAnd the rated capacity of a q-th feeder line connected with the ith main transformer is shown.
In step 2)
(1) The active power distribution network reliability evaluation of main transformer and distributed photovoltaic and battery, include:
(a) setting simulation years, wherein the system has k elements, all the elements work in normal state at the initial simulation moment, randomly generating k random numbers between 0 and 1, and determining the fault-free operation time T of the k elements according to the fault transfer rate lambda and the exponential distributionTTF；
(b) Finding the minimum fault-free operating time TTTFminGenerating a random number for the failed element, and determining the failure repair time T of the failed element according to the repair transfer rate mu and the exponential distributionTTRAnd generating fault isolation and load transfer time to advance the analog clock to TTTFmin+TTTR；
(c) Analyzing the fault influence according to the type of the fault element, determining 9 partition types in the network, and directly determining the power failure time of the load points in the fault area, the normal area and the upstream isolation area;
(d) dividing the distributed photovoltaic and storage battery in an island operation area into three states of normal operation, outage operation and derating operation, generating random numbers according to the probability of the three states, sampling the operation states of the distributed photovoltaic and storage battery, calculating the real-time load value of a system, the charging and discharging power sequence of the storage battery and the output sequence of the distributed photovoltaic by combining an output model, judging whether the load in an island can be supplied with power by the distributed photovoltaic and storage battery, and determining the power failure time of the load in the island;
(e) for the area needing interconnection transfer, sampling the running state, real-time output and real-time load value of the distributed photovoltaic and storage battery in the interconnection area, and judging whether the load can be transferred or not by taking the minimum isolation area as a unit in combination with load rate constraint;
(f) counting the power failure time of each load point during the system element fault, and sampling the new operation time T of the fault elementTTFnewUpdating the non-failure operation time of the failure element to TTTFmin+TTTR+TTTFnew；
(g) Judging whether the analog clock crosses the year or not, and accumulating the recorded power failure time of the load point to the annual outage time U of the load point j if the analog clock does not cross the yearjPerforming the following steps; calculating the annual reliability index of the feeder line and the system by adopting the calculation formula of the power supply reliability of the q-th feeder line and the calculation formula of the expected value of the average power supply reliability of the system in the case of year crossing, and calculating UjClearing;
(h) judging whether the simulation clock reaches the set simulation years, if not, returning to the step (b), if so, ending the simulation process, counting the reliability index of each simulation year, and further calculating the average power supply availability of the system, namely a reliability evaluation result;
(2) the power supply capacity optimization calculation with the reliability evaluation result as an important constraint optimizes the load distribution of the feeder line to realize the maximization of the power supply capacity under the condition that the reliability evaluation result in the step (1) meets the requirements of overall reliability and differentiation, the power supply capacity optimization calculation adopts a genetic algorithm to solve, and the optimization of the power supply capacity is realized by encoding the load multiple of the feeder line, wherein the power supply capacity is the individual fitness.
The step (c) in the step (1) is to divide the power distribution network into a plurality of minimum isolation areas by combining a feeder line partition method, wherein the minimum isolation areas comprise: the system comprises a fault area, a normal area, an upstream isolation area, an upstream seamless island area, a downstream isolated island area, a downstream seamless island contact area, a downstream isolated island contact area and a contact transfer area, wherein the operating state and the power failure time of a load point in different areas after a fault occurs are different, and the condition that whether the distributed photovoltaic supports island operation is considered; and (4) searching and analyzing the fault influence by taking the minimum isolation region as a unit, and respectively determining the type of each small isolation region and the power failure time of a load point after the occurrence of the fault of the element in the minimum isolation region, the fault of the main transformer, the fault of the bus and the fault of the switch.
The output model of step (1) in step (d) comprises:
(d1) the distributed photovoltaic output model has the following specific expression:
wherein P isbReal-time photovoltaic output; gbtThe light intensity per hour is calculated by an HDKR model; psnRated power for the photovoltaic; gstdRepresents a unit light intensity; rcThe light intensity is light intensity with specific intensity, and represents a turning point of the relation between the distributed photovoltaic output and the light intensity from nonlinearity to linearity;
(d2) load model L at t hour of load pointtComprises the following steps:
Lt＝Lp×Pw×Pd×Ph(t) (10)
in the formula LpThe annual load peak value is an optimized object of the model; pwIs the year-week load percentage factor corresponding to the t hour; pdIs the corresponding weekly-daily load percentage coefficient; ph(t) is the corresponding percent day-hour load factor;
(d3) storage battery model, two-battery model adopting lead-acid storage battery
In a grid-connected state, the storage battery adopts a cyclic charge-discharge strategy, the charge-discharge power is fixed, the storage battery is divided into three stages of charge, discharge and floating charge, the three stages are repeatedly and alternately carried out, and the maximum value of the charge state of the storage battery is SocmaxMinimum value of Socmin(ii) a And in island mode, the charging and discharging power of the storage battery is mainNet exchange power P to be obtained by subtracting distributed photovoltaic contribution from intra-island loadexDetermine when P isexWhen less than 0, the accumulator is charged with electric energy, and when P is less than 0exWhen the voltage is more than 0, the storage battery releases electric energy to the outside;
when the accumulator is charged with electric energy, the absorbed power P is 1hinIs composed of
Pin＝max(Pcmax,Pex)ηc (11)
In the formula etacRepresents the charging efficiency of the battery, PcmaxThe maximum accepted continuous charging power for the storage battery;
when the storage battery discharges electric energy, the power P released in 1houtIs composed of
Pout＝min(Pdmax,Pex)/ηd (12)
Wherein etadRepresents the discharge efficiency, P, of the batterydmaxThe maximum continuous discharge power of the storage battery is obtained;
the charge state of a storage battery at the initial moment of the island and the net exchange power in the island are known, and the energy storage charge-discharge power sequence is repeatedly solved by taking 1h as the step length through the formula.
The step (2) comprises the following steps:
selecting an optimization object as an annual load peak value of each feeder line, setting the load of each feeder line in an initial state to meet N-1 verification of a main transformer and the feeder line at a peak load moment, and generating an initial population, wherein the maximum load of each feeder line is the situation that the main transformer or each feeder line is fully loaded at the peak load moment; the coding object is the ratio of the load of each feeder line in actual operation to the load in the initial state, namely the load access multiple; the gene segments and chromosomes in the biogenetic inheritance respectively represent codes of single feeder load multiple and all feeder load multiple of the network, individuals in the biogenetic inheritance represent annual load peak values of all feeders of the network, namely the power supply capacity of the system, and the population represents a set of a plurality of individuals; the selection operation adopts a roulette method to optimize the power supply capacity, so that the individuals with high adaptability, namely large power supply capacity are ensured to be always handed over to the next generation, the crossing is single-point crossing, and the variation is single-point variation;
and for generating an initial population, selecting, crossing and mutating one generation, and ensuring that individuals with higher fitness are inherited to the next generation until the result is converged to obtain the optimal value of the power supply capacity.
According to the active power distribution network power supply capacity evaluation method considering the differential reliability, the differential reliability requirements of different areas and the access of distributed photovoltaic and energy storage can be combined, the maximum power supply capacity of the whole and differential reliability constraints can be met by an output network, the whole and local optimization of the power supply capacity is realized, the power supply potential is released, the asset utilization efficiency of a power grid is exerted, and the support can be provided for the improvement of the power supply capacity under the existing power distribution network structure.
Drawings
Fig. 1 is a feeder partition of a typical active power distribution network;
FIG. 2 is a cyclic charge and discharge curve;
FIG. 3 is a flow chart of an active power distribution network power supply capability evaluation method considering differential reliability according to the present invention;
FIG. 4 is a diagram of a power distribution network interconnection architecture;
FIG. 5 is a graph of reliability target constraints versus system power supply capability;
fig. 6 shows the optimization result of the system power supply capacity under the differential reliability.
Detailed Description
The following describes in detail an evaluation method for power supply capacity of an active power distribution network considering differential reliability according to the present invention with reference to the following embodiments and accompanying drawings.
As shown in fig. 3, the method for evaluating the power supply capacity of the active power distribution network considering the differential reliability of the present invention includes the following steps:
1) establishing an active power distribution network power supply capacity evaluation model considering differential reliability, wherein the objective function of the model is that the power supply capacity of the active power distribution network is the maximum, the system power supply capacity comprises a main transformer layer and a medium voltage network layer, n main transformers are arranged in the system, the main transformers are numbered as 1,2, …, i and … n, and the number of feeders connected with the ith main transformer is miAnd the number of each feeder line connected with the main transformer is 1,2 …, q, …, miThe objective function of the model is as follows:
the optimization object of the model is the annual load peak value accessed by each feeder line, and the reliability index is used as the constraint condition. The constraint conditions comprise:
(1) constraint on differential reliability
Selecting the power supply reliability of the feeder line as a basic evaluation unit, wherein the basic evaluation unit is defined as the following: in a unit year, calculating the ratio of the electricity consumption hours of all feeder users to the electricity demand hours of the feeder users, and the power supply reliability of the qth feeder according to the formula:
wherein T is the number of electricity needed in a specified time; u shapejThe annual outage time for load point j; n is a radical ofjThe number of users at the load point j; lqThe total load point number of the q-th feeder line;
different feeder line reliability requirements are different, and a reliability target matrix E is defined as (E)1,E2,…,Eq,…,Em)TIn which EqA q-th feeder line reliability target is obtained; ASAIfeed＝(ASAI1,ASAI2,…,ASAIq,…,ASAIm)TM is the actual feeder reliability index vector and is the system feeder number; the difference reliability constraint is then expressed as:
ASAIfeed≥E (3)；
(2) global reliability constraint
The expected value of the average power supply reliability of the system is selected as an index and is expressed as follows:
wherein p is the total load point number of the system, EsRepresenting a system reliability target;
(3) system load and distributed photovoltaic (DG) matching constraints
The constraint condition represents the capacity relationship between each main transformer and the subordinate connecting feeder line in the power distribution network, and the capacity relationship is as follows:
in the formula LiIs the real-time load of the ith main transformer, LiqFor real-time loading of the q-th feeder line connected to the i-th main transformer, GiRepresenting the real-time total output, G, of the distributed photovoltaic and storage battery of the ith main transformeriqRepresenting the real-time total output of the distributed photovoltaic and storage battery of the q-th feeder line connected with the ith main transformer, wherein m represents the number of the system feeder lines;
(4) load rate constraints
The constraint condition represents the value ranges of the main transformer and the feeder load rate of the system, and the specific expression form is as follows:
0≤(Liq-Giq)/Ciq≤1 (7)
0≤(Li-Gi)/Ci≤1 (8)
in the formula CiIndicating the rated capacity, C, of the ith main transformeriqAnd the rated capacity of a q-th feeder line connected with the ith main transformer is shown.
2) The method for solving the power supply capacity evaluation model of the active power distribution network considering the differential reliability comprises the steps of evaluating the reliability of the active power distribution network comprising a main transformer, a distributed photovoltaic system and a storage battery, and performing power supply capacity optimization calculation by taking a reliability evaluation result as an important constraint. Wherein:
(1) the active power distribution network reliability evaluation of main transformer and distributed photovoltaic and battery, include:
(a) setting simulation years, wherein the system has k elements, all the elements work in normal state at the initial simulation moment, randomly generating k random numbers between 0 and 1, and determining the fault-free operation time T of the k elements according to the fault transfer rate lambda and the exponential distributionTTF；
(b) Finding the minimum fault-free operating time TTTFminGenerating a random number for the failed element, and determining the failure repair time T of the failed element according to the repair transfer rate mu and the exponential distributionTTRAnd generating fault isolation and load transfer time to advance the analog clock to TTTFmin+TTTR；
(c) Analyzing the fault influence according to the type of the fault element, determining 9 partition types in the network, and directly determining the power failure time of the load points in the fault area, the normal area and the upstream isolation area;
the method of combining feeder line partition divides the power distribution network into a plurality of minimum isolation areas, and the minimum isolation areas comprise: the system comprises a fault area, a normal area, an upstream isolation area, an upstream seamless island area, a downstream isolated island area, a downstream seamless island contact area, a downstream isolated island contact area and a contact transfer area, wherein the operating state and the power failure time of a load point in different areas after a fault occurs are different, the condition that whether the distributed photovoltaic supports island operation is considered, a DG which does not support island operation can immediately quit operation after a connected feeder line loses power, and the minimum isolation area where the DG is located can not enter the island operation state; and (4) searching and analyzing the fault influence by taking the minimum isolation region as a unit, and respectively determining the type of each minimum isolation region and the power failure time of a load point after the occurrence of the fault of the element in the minimum isolation region, the fault of the main transformer, the fault of the bus and the fault of the switch.
As shown in fig. 1, each minimum isolation region is further divided into: the fault zone is S5 in fig. 1, the normal zone is S1 in fig. 1, the upstream isolation zone is S2 and S3 in fig. 1, the upstream seamless island zone is S4 in fig. 1, the downstream seamless island zone is S7 in fig. 1, the downstream isolated island zone is S8 in fig. 1, the downstream seamless island contact zone is S9 in fig. 1, the downstream isolated island contact zone is S10 in fig. 1, and the contact transfer zone is S9 in fig. 1.
(d) Dividing the distributed photovoltaic and storage battery in an island operation area into three states of normal operation, outage operation and derating operation, generating random numbers according to the probability of the three states, sampling the operation states of the distributed photovoltaic and storage battery, calculating the real-time load value of a system, the charging and discharging power sequence of the storage battery and the output sequence of the distributed photovoltaic by combining an output model, judging whether the load in an island can be supplied with power by the distributed photovoltaic and storage battery, and determining the power failure time of the load in the island;
the output model comprises:
(d1) the distributed photovoltaic output model has the following specific expression:
wherein P isbReal-time photovoltaic output; gbtThe light intensity per hour is calculated by an HDKR model; psnRated power for the photovoltaic; gstdRepresents a unit light intensity; rcThe light intensity is light intensity with specific intensity, and represents a turning point of the relation between the distributed photovoltaic output and the light intensity from nonlinearity to linearity;
(d2) load model L at t hour of load pointtComprises the following steps:
Lt＝Lp×Pw×Pd×Ph(t) (10)
in the formula LpThe annual load peak value is an optimized object of the model; pwIs the year-week load percentage factor corresponding to the t hour; pdIs the corresponding weekly-daily load percentage coefficient; ph(t) is the corresponding percent day-hour load factor;
(d3) storage battery model, two-battery model adopting lead-acid storage battery
Under the grid-connected state, the storage battery adopts a cyclic charge-discharge strategy, and the charge-discharge power is fixedThe method is divided into three stages of charging, discharging and floating charging, which are alternately repeated, as shown in fig. 2, when the period is T, T is T ═ T1+T2+T3. Maximum state of charge of the battery is SocmaxMinimum value of Socmin(ii) a In the island mode, the charge and discharge power of the storage battery is mainly net exchange power P obtained by subtracting the output of the distributed photovoltaic from the load in the islandexDetermine when P isexWhen less than 0, the accumulator is charged with electric energy, and when P is less than 0exWhen the voltage is more than 0, the storage battery releases electric energy to the outside;
when the accumulator is charged with electric energy, the absorbed power P is 1hinIs composed of
Pin＝max(Pcmax,Pex)ηc (11)
In the formula etacRepresents the charging efficiency of the battery, PcmaxThe maximum accepted continuous charging power for the storage battery;
when the storage battery discharges electric energy, the power P released in 1houtIs composed of
Pout＝min(Pdmax,Pex)/ηd (12)
Wherein etadRepresents the discharge efficiency, P, of the batterydmaxThe maximum continuous discharge power of the storage battery is obtained;
the charge state of a storage battery at the initial moment of the island and the net exchange power in the island are known, and the energy storage charge-discharge power sequence is repeatedly solved by taking 1h as the step length through the formula.
(e) For the area needing interconnection transfer, sampling the running state, real-time output and real-time load value of the distributed photovoltaic and storage battery in the interconnection area, and judging whether the load can be transferred or not by taking the minimum isolation area as a unit in combination with load rate constraint;
(f) counting the power failure time of each load point during the system element fault, and sampling the new operation time T of the fault elementTTFnewUpdating the non-failure operation time of the failure element to TTTFmin+TTTR+TTTFnew；
(g) Judging whether the analog clock is year-striding or notAccumulating the recorded power failure time of the load point to the annual outage time U of the load point jjPerforming the following steps; calculating the annual reliability index of the feeder line and the system by adopting the calculation formula of the power supply reliability of the q-th feeder line and the calculation formula of the expected value of the average power supply reliability of the system in the case of year crossing, and calculating UjClearing;
(h) judging whether the simulation clock reaches the set simulation years, if not, returning to the step (b), if so, ending the simulation process, counting the reliability index of each simulation year, and further calculating the average power supply availability of the system, namely a reliability evaluation result;
(2) the power supply capacity optimization calculation with the reliability evaluation result as an important constraint optimizes the load distribution of the feeder line to realize the maximization of the power supply capacity under the condition that the reliability evaluation result in the step (1) meets the requirements of overall reliability and differentiation, the power supply capacity optimization calculation adopts a genetic algorithm to solve, and the optimization of the power supply capacity is realized by encoding the load multiple of the feeder line, wherein the power supply capacity, namely the individual fitness, comprises the following steps:
selecting an optimization object as an annual load peak value of each feeder line, setting the load of each feeder line in an initial state to meet N-1 verification of a main transformer and the feeder line at a peak load moment, and generating an initial population, wherein the maximum load of each feeder line is the situation that the main transformer or each feeder line is fully loaded at the peak load moment; the coding object is the ratio of the load of each feeder line in actual operation to the load in the initial state, namely the load access multiple; the gene segments and chromosomes in the biogenetic inheritance respectively represent codes of single feeder load multiple and all feeder load multiple of the network, individuals in the biogenetic inheritance represent annual load peak values of all feeders of the network, namely the power supply capacity of the system, and the population represents a set of a plurality of individuals; the selection operation adopts a roulette method to optimize the power supply capacity, so that the individuals with high adaptability, namely large power supply capacity are ensured to be always handed over to the next generation, the crossing is single-point crossing, and the variation is single-point variation;
and for generating an initial population, selecting, crossing and mutating one generation, and ensuring that individuals with higher fitness are inherited to the next generation until the result is converged to obtain the optimal value of the power supply capacity.
And establishing a contact model based on the interconnection relation of the main transformers by taking the actual power distribution network structure of a certain area as an example, and expanding the contact model to a feeder line level according to an N-1 criterion and load constraint. The interconnection structure of the added distributed photovoltaic array and the storage battery based on the interconnection relationship of the feeder lines is shown in fig. 4, and the interconnection capacity among the main transformers is shown in table 1. The number of elements on the low-voltage side of each transformer substation is shown in table 2, and distributed photovoltaic and storage batteries are bound and connected in a DG group mode, namely, if a certain minimum isolation area contains DGs, the minimum isolation area comprises a group of photovoltaic and a group of storage batteries.
TABLE 1 communication Capacity between Main transformers
| Number of main transformer of interconnection | Contact Limit Capacity/MVA |
| S11-S12 | 20.0 |
| S12-S21 | 8.0 |
| S12-S31 | 3.0 |
| S21-S22 | 20.0 |
| S21-S31 | 5.0 |
| S21-S32 | 3.0 |
| S22-S31 | 5.0 |
| S22-S32 | 5.0 |
| S31-S32 | 31.5 |
TABLE 2 number of elements carried by the low-voltage side of each substation
And analyzing the reliability of the grid structure of the power grid, and calculating the maximum power supply capacity of the grid structure under the constraint condition of the reliability index. The method mainly considers the influence of the fault of a single element in the grid structure on the load point in the process of performing the reliability calculation, and specifically comprises the following steps: the reliability parameters of various elements such as main transformer faults, bus faults, distribution transformer faults, medium voltage feeder faults, switch faults and the like are shown in a table 3. The fault isolation time and the isolated transfer time are both 1 h; the unit load capacity of the residential load point, the commercial load point and the industrial load point is 0.1802,0.4697 and 0.8472 MW/household, and the initial user number of each load node of the system is 1.
Table 3 main component reliability parameters in the calculation
Because the simulated peak load moment photovoltaic output is 0, in order to meet the load rate constraint, the load is not larger than the line capacity in the normal operation state, the fault of the DG in the normal operation state does not influence the power supply of the system, only the fault condition of the DG after the fault of the non-power element needs to be considered, the distributed photovoltaic and the storage battery adopt a three-state outage model, the outage probability is 0.1, the derated operation probability is 0.05, and the random number sampling DG operation state is generated after the fault of the non-power element. The storage battery adopts a cyclic charge-discharge strategy shown in FIG. 2 under the grid-connected state, wherein SocmaxTake 0.9, SocminTaking 0.1, one charge-discharge period is 20h, T1-T2-10, and T3-0. The basic step size for the reliability simulation is 1 h.
Based on the above description, the overall case of the example includes three substations, six main transformers, 24 feeders, 106 feeder segments, 297 load nodes (10 industrial load nodes, 56 commercial load nodes, 231 residential load nodes), 373 distribution transformers, 65 groups DG (of which 41 groups support islanding operation).
The maximum power supply capacity of the example under different reliability constraints is obtained, 65 groups of DGs are totally arranged in the example system, the capacity of a single group of distributed photovoltaic is only adjusted to be 0.3MW, 1MW and 2MW respectively, the maximum power supply capacity under different overall reliability constraints is output, and a curve graph is drawn to show the variation trend, as shown in FIG. 5.
As can be seen from fig. 5, the reliability constraint of the system has a nonlinear relationship with the maximum power supply capability, the power supply capability decreases after the reliability constraint is improved, and the decrease of the power supply capability increases with the strictness of the reliability constraint under different DG capacities. The lowest point of the power supply capacity corresponding to the three curves is the power supply capacity and the reliability index when the system meets the N-1 criterion, and the highest point is the power supply capacity and the corresponding reliability index when the system is fully loaded. Comparing the three curves, the improvement of the DG capacity can increase the power supply capability of the network under the same reliability constraint from the longitudinal view. From the lateral observation, when the system load is larger, the reliability improvement effect of increasing the capacity of the DG is more obvious. With the increase of the DG capacity, the linear relation between the power supply capacity and the reliability is enhanced, and the effect of reducing the reliability index on the improvement of the power supply capacity is more obvious.
Further introducing differential reliability constraint, taking the capacity of single-component distributed photovoltaic as an example, and when the overall reliability constraint of the system is 99.979%, respectively setting the reliability constraint of each feeder line in 24 feeder lines of the example to 99.970%, and simultaneously keeping the overall reliability constraint of the remaining 23 feeder lines unchanged by 99.979%, and adjusting the load distribution of the feeder lines to obtain the corresponding maximum power supply capacity of the system as shown in fig. 6.
As can be seen from fig. 6, under the condition that the overall reliability level of the remaining 23 feeders is not changed, the effect of reducing the reliability index of different single feeders on improving the power supply capability is different, and for the present example, the effect of reducing the reliability index of the feeder 12 on improving the power supply capability is most obvious, so that a load with a lower importance level can be selected to be connected to the feeder 12, and thus the reliability level of the feeder can be properly reduced, thereby improving the power supply capability significantly.
In the current power grid construction, grid structures are relatively formed, so that effective expansion is difficult to make from the existing grid structures, distributed photovoltaic and storage batteries are connected to the existing grid structures, and through flexible reliability constraint conditions and differentiation processing of the flexible reliability constraint conditions, the power supply capacity can be remarkably improved by reducing the overall reliability index, the differential reliability requirements can be considered, the reliability indexes of different feeders can be reduced, the power supply capacity improvement effect is compared, and the feeder with the most obvious power supply capacity optimization effect is searched.
Claims (4)
1. An active power distribution network power supply capacity evaluation method considering differential reliability is characterized by comprising the following steps:
1) establishing an active power distribution network power supply capacity evaluation model considering differential reliability, wherein the objective function of the model is that the power supply capacity of the active power distribution network is the maximum, the system power supply capacity comprises a main transformer layer and a medium voltage network layer, n main transformers are arranged in the system, the main transformers are numbered as 1,2, …, i and … n, and the number of each main transformer is 1,2, …, i and … nThe number of the feeder lines connected with the ith main transformer is miAnd the number of each feeder line connected with the main transformer is 1,2 …, q, …, miThe objective function of the model is as follows:
the optimization object of the model is an annual load peak value accessed by each feeder line, and the reliability index is used as a constraint condition; the constraint conditions comprise:
(1) constraint on differential reliability
Selecting the power supply reliability of the feeder line as a basic evaluation unit, wherein the basic evaluation unit is defined as the following: in a unit year, calculating the ratio of the electricity consumption hours of all feeder users to the electricity demand hours of the feeder users, and the power supply reliability of the qth feeder according to the formula:
wherein T is the number of electricity needed in a specified time; u shapejThe annual outage time for load point j; n is a radical ofjThe number of users at the load point j; lqThe total load point number of the q-th feeder line;
different feeder line reliability requirements are different, and a reliability target matrix E is defined as (E)1,E2,…,Eq,…,Em)TIn which EqA q-th feeder line reliability target is obtained; ASAIfeed＝(ASAI1,ASAI2,…,ASAIq,…,ASAIm)TM is the actual feeder reliability index vector and is the system feeder number; the difference reliability constraint is then expressed as:
ASAIfeed≥E (3)；
(2) global reliability constraint
The expected value of the average power supply reliability of the system is selected as an index and is expressed as follows:
wherein p is the total load point number of the system, EsRepresenting a system reliability target;
(3) system load and distributed photovoltaic matching constraints
The method comprises the following specific steps:
in the formula LiIs the real-time load of the ith main transformer, LiqFor real-time loading of the q-th feeder line connected to the i-th main transformer, GiRepresenting the real-time total output, G, of the distributed photovoltaic and storage battery of the ith main transformeriqRepresenting the real-time total output of the distributed photovoltaic and storage battery of the q-th feeder line connected with the ith main transformer, wherein m represents the number of the system feeder lines;
(4) and load rate constraint, which is embodied in the form of:
0≤(Liq-Giq)/Ciq≤1 (7)
0≤(Li-Gi)/Ci≤1 (8)
in the formula CiIndicating the rated capacity, C, of the ith main transformeriqThe rated capacity of a q feeder line connected with an ith main transformer is represented;
2) the method for solving the evaluation model of the power supply capacity of the active power distribution network considering the differential reliability comprises the reliability evaluation of the active power distribution network comprising a main transformer, distributed photovoltaic and storage batteries and the power supply capacity optimization calculation taking the reliability evaluation result as the important constraint, wherein the method comprises the following steps of
(1) The active power distribution network reliability evaluation of main transformer and distributed photovoltaic and battery, include:
(a) setting simulation years, wherein the system has k elements, all the elements work in normal state at the initial simulation moment, randomly generating k random numbers between 0 and 1, and determining the fault-free operation time T of the k elements according to the fault transfer rate lambda and the exponential distributionTTF；
(b) Finding the minimum fault-free operating time TTTFminGenerating a random number for the failed element, and determining the failure repair time T of the failed element according to the repair transfer rate mu and the exponential distributionTTRAnd generating fault isolation and load transfer time to advance the analog clock to TTTFmin+TTTR；
(c) Analyzing the fault influence according to the type of the fault element, determining 9 partition types in the network, and directly determining the power failure time of the load points in the fault area, the normal area and the upstream isolation area;
(d) dividing the distributed photovoltaic and storage battery in an island operation area into three states of normal operation, outage operation and derating operation, generating random numbers according to the probability of the three states, sampling the operation states of the distributed photovoltaic and storage battery, calculating the real-time load value of a system, the charging and discharging power sequence of the storage battery and the output sequence of the distributed photovoltaic by combining an output model, judging whether the load in an island can be supplied with power by the distributed photovoltaic and storage battery, and determining the power failure time of the load in the island;
(e) for the area needing interconnection transfer, sampling the running state, real-time output and real-time load value of the distributed photovoltaic and storage battery in the interconnection area, and judging whether the load can be transferred or not by taking the minimum isolation area as a unit in combination with load rate constraint;
(f) counting the power failure time of each load point during the system element fault, and sampling the new operation time T of the fault elementTTFnewUpdating the non-failure operation time of the failure element to TTTFmin+TTTR+TTTFnew；
(g) Judging whether the analog clock is year-crossing or not, and accumulating the recorded power failure time of the load point to the annual outage time of the load point j if the analog clock is not year-crossingInter UjPerforming the following steps; calculating the annual reliability index of the feeder line and the system by adopting the calculation formula of the power supply reliability of the q-th feeder line and the calculation formula of the expected value of the average power supply reliability of the system in the case of year crossing, and calculating UjClearing;
(h) judging whether the simulation clock reaches the set simulation years, if not, returning to the step (b), if so, ending the simulation process, counting the reliability index of each simulation year, and further calculating the average power supply availability of the system, namely a reliability evaluation result;
(2) the power supply capacity optimization calculation with the reliability evaluation result as an important constraint optimizes the load distribution of the feeder line to realize the maximization of the power supply capacity under the condition that the reliability evaluation result in the step (1) meets the requirements of overall reliability and differentiation, the power supply capacity optimization calculation adopts a genetic algorithm to solve, and the optimization of the power supply capacity is realized by encoding the load multiple of the feeder line, wherein the power supply capacity is the individual fitness.
2. The method for evaluating the power supply capacity of the active power distribution network considering the differential reliability as claimed in claim 1, wherein the step (c) in the step 2) and the step (1) is to divide the power distribution network into a plurality of minimum isolation areas by combining a feeder line partition method, and the minimum isolation areas comprise: the system comprises a fault area, a normal area, an upstream isolation area, an upstream seamless island area, a downstream isolated island area, a downstream seamless island contact area, a downstream isolated island contact area and a contact transfer area, wherein the operating state and the power failure time of a load point in different areas after a fault occurs are different, and the condition that whether the distributed photovoltaic supports island operation is considered; and (4) searching and analyzing the fault influence by taking the minimum isolation region as a unit, and respectively determining the type of each small isolation region and the power failure time of a load point after the occurrence of the fault of the element in the minimum isolation region, the fault of the main transformer, the fault of the bus and the fault of the switch.
3. The method according to claim 1, wherein the output model in step (2) and (1) in step (d) comprises:
(d1) the distributed photovoltaic output model has the following specific expression:
wherein P isbReal-time photovoltaic output; gbtThe light intensity per hour is calculated by an HDKR model; psnRated power for the photovoltaic; gstdRepresents a unit light intensity; rcThe light intensity is light intensity with specific intensity, and represents a turning point of the relation between the distributed photovoltaic output and the light intensity from nonlinearity to linearity;
(d2) load model L at t hour of load pointtComprises the following steps:
Lt＝Lp×Pw×Pd×Ph(t) (10)
in the formula LpThe annual load peak value is an optimized object of the model; pwIs the year-week load percentage factor corresponding to the t hour; pdIs the corresponding weekly-daily load percentage coefficient; ph(t) is the corresponding percent day-hour load factor;
(d3) storage battery model, two-battery model adopting lead-acid storage battery
In a grid-connected state, the storage battery adopts a cyclic charge-discharge strategy, the charge-discharge power is fixed, the storage battery is divided into three stages of charge, discharge and floating charge, the three stages are repeatedly and alternately carried out, and the maximum value of the charge state of the storage battery is SocmaxMinimum value of Socmin(ii) a In the island mode, the charge and discharge power of the storage battery is mainly net exchange power P obtained by subtracting the output of the distributed photovoltaic from the load in the islandexDetermine when P isexWhen less than 0, the accumulator is charged with electric energy, and when P is less than 0exWhen the voltage is more than 0, the storage battery releases electric energy to the outside;
when the accumulator is charged with electric energy, the absorbed power P is 1hinIs composed of
Pin＝max(Pcmax,Pex)ηc (11)
In the formula etacRepresents the charging efficiency of the battery, PcmaxThe maximum accepted continuous charging power for the storage battery;
when the storage battery discharges electric energy, the power P released in 1houtIs composed of
Pout＝min(Pdmax,Pex)/ηd (12)
Wherein etadRepresents the discharge efficiency, P, of the batterydmaxThe maximum continuous discharge power of the storage battery is obtained;
the charge state of a storage battery at the initial moment of the island and the net exchange power in the island are known, and the energy storage charge-discharge power sequence is repeatedly solved by taking 1h as the step length through the formula.
4. The method for evaluating the power supply capacity of the active power distribution network considering the differential reliability as claimed in claim 1, wherein the step 2) comprises the following steps in step (2):
selecting an optimization object as an annual load peak value of each feeder line, setting the load of each feeder line in an initial state to meet N-1 verification of a main transformer and the feeder line at a peak load moment, and generating an initial population, wherein the maximum load of each feeder line is the situation that the main transformer or each feeder line is fully loaded at the peak load moment; the coding object is the ratio of the load of each feeder line in actual operation to the load in the initial state, namely the load access multiple; the gene segments and chromosomes in the biogenetic inheritance respectively represent codes of single feeder load multiple and all feeder load multiple of the network, individuals in the biogenetic inheritance represent annual load peak values of all feeders of the network, namely the power supply capacity of the system, and the population represents a set of a plurality of individuals; the selection operation adopts a roulette method to optimize the power supply capacity, so that the individuals with high adaptability, namely large power supply capacity are ensured to be always handed over to the next generation, the crossing is single-point crossing, and the variation is single-point variation;
and for generating an initial population, selecting, crossing and mutating one generation, and ensuring that individuals with higher fitness are inherited to the next generation until the result is converged to obtain the optimal value of the power supply capacity.
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| CN113922367A (en) * | 2021-10-09 | 2022-01-11 | 国网宁夏电力有限公司经济技术研究院 | Power distribution network power supply capacity evaluation method based on differential reliability requirements |
| CN114069618A (en) * | 2021-11-15 | 2022-02-18 | 国网江苏省电力有限公司常州供电分公司 | Power distribution network power supply recovery method based on minimum total power failure loss |
| CN117937474B (en) * | 2024-03-20 | 2024-06-18 | 河北大学 | New energy station energy storage management method and system |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102063657A (en) * | 2010-12-23 | 2011-05-18 | 中国电力科学研究院 | Operating level and power supplying capability evaluation method for urban electric distribution network |
| CN103855707A (en) * | 2014-02-20 | 2014-06-11 | 深圳供电局有限公司 | Power supply reliability assessment method of power distribution network comprising distributed power supply |
| CN103995921A (en) * | 2014-04-22 | 2014-08-20 | 广东电网公司电力科学研究院 | Method for simulating and assessing micro-grid power supply system reliability |
| CN105406470A (en) * | 2015-12-21 | 2016-03-16 | 国家电网公司 | Reliability evaluation method for active power distribution network based on switch boundary subarea division |
| CN105406509A (en) * | 2015-12-21 | 2016-03-16 | 国家电网公司 | Power supply capability evaluation method for power distribution network based on confidence capacity of distributed power supply |
| CN107611966A (en) * | 2017-09-20 | 2018-01-19 | 天津大学 | A kind of active power distribution network evaluation of power supply capability method for considering difference reliability |
-
2017
- 2017-09-20 CN CN201710862635.6A patent/CN107611966B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102063657A (en) * | 2010-12-23 | 2011-05-18 | 中国电力科学研究院 | Operating level and power supplying capability evaluation method for urban electric distribution network |
| CN103855707A (en) * | 2014-02-20 | 2014-06-11 | 深圳供电局有限公司 | Power supply reliability assessment method of power distribution network comprising distributed power supply |
| CN103995921A (en) * | 2014-04-22 | 2014-08-20 | 广东电网公司电力科学研究院 | Method for simulating and assessing micro-grid power supply system reliability |
| CN105406470A (en) * | 2015-12-21 | 2016-03-16 | 国家电网公司 | Reliability evaluation method for active power distribution network based on switch boundary subarea division |
| CN105406509A (en) * | 2015-12-21 | 2016-03-16 | 国家电网公司 | Power supply capability evaluation method for power distribution network based on confidence capacity of distributed power supply |
| CN107611966A (en) * | 2017-09-20 | 2018-01-19 | 天津大学 | A kind of active power distribution network evaluation of power supply capability method for considering difference reliability |
Non-Patent Citations (2)
| Title |
|---|
| 《考虑可靠性的中压配电***供电能力评估》;刘洪等;《电力***自动化》;20170625;第154-159页 * |
| 刘洪等.《考虑可靠性的中压配电***供电能力评估》.《电力***自动化》.2017, * |
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