Method and system for determining DG output typical state of active power distribution network

Abstract

The invention discloses a DG output typical state determination method and system of an active power distribution network. A DG output typical state probability model of the active power distribution network is constructed, a plurality of typical output states and probabilities thereof in a DG output scene are determined, annual cost of reliability loss in each typical output state is calculated, and the reliability of each typical output state is judged. When the DG output typical state probability model of the active power distribution network is constructed, the problems that the number of DG original output scenes is huge and calculation is complicated are fully considered, the precision condition of the maximum output utilization hours of the DG is set, the maximum daily output curve state and the minimum daily output curve state are fixed to serve as the clustering center, and the DG output typical state probability model of the active power distribution network is constructed. The calculation efficiency and the data change characteristics of the model are considered, the operation scene is reduced, and each typical output state is evaluated by adding the reliability loss annual cost of the DG power abandoning loss annual cost, so that the calculation result is more reliable and accurate.

Description

Technical Field

[0001] The present invention belongs to the field of new power system planning, and in particular relates to a method and system for determining a typical state of DG output of an active distribution network. Background Art

[0002] With the proposal of the "dual carbon" goals and the construction of new power systems, DG (distributed generator) based on renewable energy has been connected to the distribution network in large quantities. Renewable energy has the advantages of low pollution and good environmental benefits. However, compared with traditional thermal power generation with controllable output, the output volatility of renewable energy power sources represented by wind power and photovoltaics is stronger, resulting in randomness, diversity and strong uncertainty in the operation of the power system, which brings huge challenges to the planning and operation of the distribution network.

[0003] In order to reduce the impact of DG output volatility, when considering the analysis process of DG access to the power system, there is an urgent need for a method that can use as few data sets as possible to characterize data characteristics to the greatest extent. At present, the DG output state is usually simulated by a typical day or typical scenario. However, since the changes in wind power and photovoltaic output have certain seasonal and daily periodicities, when extracting representative output scenarios from a large amount of historical wind and solar output data, the extraction method is somewhat subjective and cannot fully represent the output characteristics of DG. Summary of the invention

[0004] The object of the present invention is to provide a method and system for determining a typical state of DG output of an active distribution network in view of the above-mentioned problems existing in the prior art.

[0005] To achieve the above objectives, the technical solution of the present invention is as follows:

[0006] In a first aspect, the present invention provides a method for determining a typical state of DG output of an active distribution network, comprising:

[0007] S1. Construct a DG output typical state probability model for the active distribution network and determine multiple typical output states and their probabilities in the DG output scenario;

[0008] S2. Calculate the annual cost of reliability loss under each typical output state according to multiple typical output states and their probabilities in the determined DG output scenario;

[0009] S3. Determine the reliability of each typical output state according to the annual cost of reliability loss under each typical output state. If each typical output state meets the reliability requirements, determine that the obtained output state is the typical output state of the DG in the active distribution network. Otherwise, return to step S1 to redetermine the typical output state in the DG output scenario.

[0010] In S2, the annual cost of reliability loss under each typical output state includes the annual cost of user power outage loss and the annual cost of DG power abandonment loss, which is calculated using the following formula:

[0011] C xk =C xkl +C xkg ;

[0012]

[0013]

[0014] In the above formula, C xk is the annual cost of reliability loss, C xkl The annual cost of power outage loss for users, C xkg is the annual cost of DG power abandonment loss, N h is the total number of time periods in a day, P l，k is the load size of the kth period in the planning area, N s is the total number of typical DG output states, p i is the probability of the typical state of the i-th DG output, D JCZ，i，k is the reliability factor of planned power outage of overhead lines, k J is the proportion of overhead line in the total length of feeder, D JCZ，i，k ′ is the reliability coefficient of overhead line fault outage, L fm is the average length of the trunk line, k D is the proportion of the cable in the total length of the feeder, D DCZ，i,k is the cable planned outage reliability factor, D DCZ，i，k ′ is the reliability coefficient of cable fault power outage, P g，i，k It is the output value of the i-th DG output state in the k-th period in the planning area.

[0015] The S1 includes:

[0016] S11. According to the daily output curve of DG throughout the year, the following formula is used to calculate the maximum output utilization hours of DG, and the calculation accuracy of the maximum output utilization hours is set:

[0017] T max =W / P max ;

[0018] In the above formula, T max is the annual maximum output utilization hours of DG, W is the annual total power generation, P max is the annual maximum output value;

[0019] S12, set the number of typical states of the daily output curve of DG throughout the year, that is, the number of cluster centers is N s, the maximum number of DG output typical state probability models is N m , initialize the number N of DG output typical state probability models k =1;

[0020] S13. Use the following formula to determine the cluster center of DG throughout the year:

[0021] l1′=l max ;

[0022] l2′=l min ;

[0023] l i ′=l r , i = 3, ..., N s , r=1,…,N s -2;

[0024] In the above formula, l1′ and l2′ are the first cluster center and the second cluster center respectively, l max , l min are the cluster centers of the day with the maximum and minimum output of DG in the whole year, respectively. i ′ is the i-th cluster center, l r is the randomly obtained rth cluster center, l max and l min except;

[0025] S14. Use the following formula to calculate the distance from each day's power curve state to each cluster center:

[0026]

[0027] In the above formula, d j，i is the distance from the jth day power curve state to the i-th cluster center, N h is the total number of time periods in a day, l j，k is the DG output value of the j-th day power curve state in the k-th period, l i，k ′ is the DG output value of the i-th cluster center in the k-th period;

[0028] S15, dividing the daily output curve state into the class to which the nearest cluster center belongs, and calculating the output average value of each typical state at each moment to form a new daily output curve typical state, i.e., a new cluster center;

[0029] S16, return to step S14, recalculate the distance from each daily force curve state to each new cluster center until all cluster centers no longer change, and obtain N s A new typical state of daily output curve;

[0030] S17, count the number of daily output curve state samples in each category, determine the probability of occurrence of each new daily output curve typical state according to the proportion of each category of samples, and thus obtain the Nth k A DG output typical state probability model;

[0031] S18. Calculate the error of the maximum output utilization hours of the DG output typical state probability model. If the error reaches the calculation accuracy of the maximum output utilization hours, the result is used as the final DG output typical state probability model. If the error does not reach the calculation accuracy requirement and N k <N m , then let N k =N k +1, jump to step S13; otherwise jump to step S12, set N s =N s +1.

[0032] The S3 includes:

[0033] If the annual cost of reliability loss under a typical output state is within the set standard range, the typical output state is used as the most representative state in the annual output scenario; if it is not within the standard range, return to S11 to adjust the calculation accuracy of the maximum output utilization hours to redetermine the typical output state in the DG output scenario.

[0034] In a second aspect, the present invention proposes a DG output typical state determination system for an active distribution network, comprising a typical output state determination module, a reliability loss annual cost calculation module, and a most representative state evaluation module;

[0035] The typical output state determination module is used to construct a DG output typical state probability model of the active distribution network, and determine multiple typical output states and their probabilities in the DG output scenario;

[0036] The reliability loss annual cost calculation module is used to calculate the reliability loss annual cost under each typical output state according to multiple typical output states and their probabilities in the determined DG output scenario;

[0037] The most representative state evaluation module is used to determine the reliability of each typical output state according to the annual cost of reliability loss under each typical output state. If each typical output state meets the reliability requirements, the obtained output state is determined to be the typical output state of the DG output of the active distribution network. Otherwise, it returns to the typical output state determination module to re-determine the typical output state in the DG output scenario.

[0038] In the reliability loss annual cost calculation module, the reliability loss annual cost under each typical output state includes the user power outage loss annual cost and the DG power abandonment loss annual cost, which is calculated using the following formula:

[0039] C xk =C xkl +C xkg ;

[0040]

[0041]

[0042] In the above formula, C xk is the annual cost of reliability loss, C xkl The annual cost of power outage loss for users, C xkg is the annual cost of DG power abandonment loss, N h is the total number of time periods in a day, P l，k is the load size of the kth period in the planning area, N s is the total number of typical DG output states, p i is the probability of the typical state of the i-th DG output, D JCZ，i，k is the reliability factor of planned power outage of overhead lines, k J is the proportion of overhead line in the total length of feeder, D JCZ，i，k ′ is the reliability coefficient of overhead line fault outage, L fm is the average length of the trunk line, k D is the proportion of the cable in the total length of the feeder, D DCZ，i，k is the cable planned outage reliability factor, D DCZ，i，k ′ is the reliability coefficient of cable fault power outage, P g，i，k It is the output value of the i-th DG output state in the k-th period in the planning area.

[0043] The typical output state determination module includes a maximum output utilization hours calculation unit, an initialization setting unit, a cluster center determination unit, a distance calculation unit, a cluster center update unit, a typical state determination unit, a model probability determination unit, and an iteration unit;

[0044] The maximum output utilization hours calculation unit is used to calculate the maximum output utilization hours of the DG according to the daily output curve state of the DG throughout the year using the following formula, and set the calculation accuracy of the maximum output utilization hours:

[0045] T max =W / P max ;

[0046] In the above formula, T max is the annual maximum output utilization hours of DG, W is the annual total power generation, P max is the annual maximum output value;

[0047] The initialization setting unit is used to set the number of typical states of the daily power curve of the DG throughout the year, that is, the number of cluster centers is N. s , the maximum number of DG output typical state probability models is N m , initialize the number N of DG output typical state probability models k =1;

[0048] The cluster center determination unit is used to determine the cluster center of the DG throughout the year using the following formula:

[0049] l1′=l max ;

[0050] l2′=l min ;

[0051] l i ′=l r , i = 3, ..., N s , r=1,…,N s -2;

[0052] In the above formula, l1′ and l2′ are the first cluster center and the second cluster center respectively, l max , l min are the cluster centers of the day with the maximum and minimum output of DG in the whole year, respectively. i ′ is the i-th cluster center, l r is the randomly obtained rth cluster center, l max and l min except;

[0053] The distance calculation unit is used to calculate the distance from each daily force curve state to each cluster center using the following formula:

[0054]

[0055] In the above formula, d j，i is the distance from the jth day power curve state to the i-th cluster center, N h is the total number of time periods in a day, l j，k is the DG output value of the j-th day power curve state in the k-th period, l i，k ′ is the DG output value of the i-th cluster center in the k-th period;

[0056] The cluster center updating unit is used to divide the daily power curve state into the class to which the nearest cluster center belongs, and calculate the output average value of each typical state at each moment to form a new daily power curve typical state, that is, a new cluster center;

[0057] The typical state determination unit is used to return to the distance calculation unit to recalculate the distance from each daily force curve state to each new cluster center until all cluster centers no longer change, and obtain N s A new typical state of daily output curve;

[0058] The model probability determination unit is used to count the number of daily output curve state samples in each category, and determine the probability of occurrence of each new daily output curve typical state according to the proportion of each category of samples, so as to obtain the Nth k A DG output typical state probability model;

[0059] The iteration unit is used to calculate the error of the maximum output utilization hours of the obtained DG output typical state probability model. If the error reaches the calculation accuracy of the maximum output utilization hours, the result is used as the final DG output typical state probability model; if the error does not meet the calculation accuracy requirement and N k <N m , then let N k =N k +1, jump to the cluster center determination unit; otherwise jump to the initialization setting unit and set N s =N s +1.

[0060] The most representative state evaluation module is used to, if the annual cost of reliability loss under a typical output state is within a set standard range, use the typical output state as the most representative state in the annual output scenario; if it is not within the standard range, return to the maximum output utilization hours calculation unit, adjust the calculation accuracy of the maximum output utilization hours, and re-determine the typical output state in the DG output scenario.

[0061] In a third aspect, the present invention provides a device for determining a typical state of DG output of an active distribution network, comprising a processor and a memory;

[0062] The memory is used to store computer program code and transmit the computer program code to the processor;

[0063] The processor is used to execute the aforementioned method for determining a typical state of DG output of an active distribution network according to the instructions in the computer program code.

[0064] In a fourth aspect, the present invention provides a computer storage medium having a computer program stored thereon;

[0065] When the computer program is executed by the processor, the steps of the aforementioned method for determining a typical state of DG output of an active distribution network are implemented.

[0066] Compared with the prior art, the present invention has the following beneficial effects:

[0067] 1. The present invention proposes a method and system for determining a typical state of DG output of an active distribution network. The method first determines multiple typical output states and their probabilities in a DG output scenario by constructing a probability model of a typical state of DG output of an active distribution network. Then, according to the multiple typical output states and their probabilities in the determined DG output scenario, the annual cost of reliability loss under each typical output state is calculated. Finally, according to the annual cost of reliability loss under each typical output state, the reliability of each typical output state is determined. If each typical output state meets the reliability requirements, the output state obtained is determined to be a typical state of DG output of the active distribution network. Otherwise, the typical output state in the DG output scenario is re-determined. After determining multiple typical output states and their probabilities in the DG output scenario, the method uses the annual cost of reliability loss to evaluate whether each typical output state is the most representative state in the annual output scenario, so that the obtained typical state of DG output of the active distribution network is more reliable, accurate and practical.

[0068] 2. The present invention proposes a method and system for determining the typical state of DG output of an active distribution network. As the penetration rate of DG access to the distribution network becomes higher and higher, there will be excessive electricity, even far greater than the load. This method adds the annual cost of DG power abandonment loss to the annual cost of reliability loss under each typical output state, thereby completing the missing part when high-penetration DG is accessed and making the calculation result more reasonable.

[0069] 3. The present invention proposes a method and system for determining a typical state of DG output of an active distribution network. When constructing a probability model of a typical state of DG output of an active distribution network, the method, on the one hand, fully considers the problem that the number of DG original output scenarios is huge and the calculation is complicated, sets the accuracy condition of the number of DG maximum output utilization hours, reduces the operation scenarios, and uses as few target states as possible to characterize the characteristics of the data to the greatest extent, thereby improving the solution efficiency of the model; on the other hand, in order to avoid the randomness of determining the number of typical states, the initial number of typical states is set, and each provisional target state number is given a chance to meet the accuracy. If the calculation accuracy is not met, the provisional target state number is increased, and through increment and iteration, the number of target states as few as possible is obtained while ensuring the accuracy; in order to avoid the subjectivity of determining the initial cluster center, the maximum and minimum daily output curve states are fixed as cluster centers, taking into account the calculation efficiency of the model, and reflecting the change characteristics of the initial data to a greater extent. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 This is the annual photovoltaic output curve for the planned area described in Example 1.

[0071] Figure 2 The figure is an overall flow chart of the method of the present invention.

[0072] Figure 3 This is a typical state diagram of ten DG outputs of the planned regional distribution network described in Example 1.

[0073] Figure 4 The structure diagram of the system of the present invention is shown in FIG.

[0074] Figure 5 This is a structural diagram of the device described in Example 3. DETAILED DESCRIPTION

[0075] The present invention is further described in detail below in conjunction with specific implementations and drawings.

[0076] The present invention proposes a method and system for determining a typical state of DG output of an active distribution network. Aiming at distributed power sources with random output fluctuations, the present invention fully considers the problem that the number of DG original output scenarios is huge and the calculation is complicated. A K-means clustering method is used to construct a probability model of a typical state of DG output of an active distribution network. The probability model is used to reflect the characteristics of a large amount of data, and the typical output state and its probability in the DG output scenario are determined. The uncertainty characteristics of DG are described by a few typical scenarios, and the operation scenarios are reduced. According to the annual cost of reliability loss under each typical output state, it is evaluated whether the typical output state is the most representative state in the annual output scenario, so that the calculation result is more reasonable.

[0077] Embodiment 1:

[0078] This embodiment takes a certain planning area as the research object, and reduces the voltage of the 110KV substation in the planning area to 10KV to supply power to users, and obtains its photovoltaic annual output curve as follows: Figure 1 As shown, the typical state of DG output of the active distribution network and its probability are determined by using the method for determining the typical state of DG output of the active distribution network in the planning area.

[0079] like Figure 2 As shown, a method for determining a typical state of DG output of an active distribution network is performed in the following steps:

[0080] 1. For distributed power sources with random output fluctuations, the K-means clustering method is used to construct a DG output typical state probability model for active distribution networks, taking into full consideration the huge number of DG original output scenarios and the complicated calculations. The probability model is used to reflect the characteristics of a large amount of data, and multiple typical output states and their probabilities in DG output scenarios are determined. The uncertainty characteristics of DG are described through a few typical scenarios, and the operation scenarios are reduced;

[0081] According to the daily output curve status sample of DG throughout the year, the maximum output utilization hours of DG are calculated to be 1541h using the following formula, and the calculation accuracy of the maximum output utilization hours is set to 1%:

[0082] T max =W / P max ;

[0083] In the above formula, T max is the annual maximum output utilization hours of DG, W is the annual total power generation, P max is the annual maximum output value;

[0084] Initially set the number of typical states of the DG daily output curve throughout the year, that is, the number of cluster centers is N s , the maximum number of DG output typical state probability models is N m , that is, the number of tentative target states, the number of DG output typical state probability models N is initialized k =1;

[0085] In order to avoid the defects of randomness in determining the target state number and subjectivity in determining the initial cluster center, the maximum and minimum daily power curve states are fixed as cluster centers, taking into account both the calculation efficiency and the data change characteristics, ensuring that the shape and data of the typical state of the daily power curve are similar to the initial state to a large extent. The obtained results can better reflect the characteristics of the original data and are more reasonable when included in the calculation and analysis of the distribution network. The following formula is used to determine the cluster center of DG throughout the year:

[0086] l1′=l max ;

[0087] l2′=l min ;

[0088] l i ′=l r , i = 3, ..., N s , r=1,…,N s -2;

[0089] In the above formula, l1′ and l2′ are the first cluster center and the second cluster center respectively, l max , l min are the cluster centers of the day with the maximum and minimum output of DG in the whole year, respectively. i ′ is the i-th cluster center, l r is the randomly obtained rth cluster center, l max and l min except;

[0090] The following formula is used to calculate the distance from each daily force curve state to each cluster center:

[0091]

[0092] In the above formula, d j，iis the distance from the jth day power curve state to the i-th cluster center, N h is the total number of time periods in a day, l j，k is the DG output value of the j-th day power curve state in the k-th period, l i，k ′ is the DG output value of the i-th cluster center in the k-th period;

[0093] Divide the daily output curve state into the class to which the nearest cluster center belongs, calculate the average output of each typical state at each moment, and form a new typical state of the daily output curve, that is, the new cluster center;

[0094] Return to the distance calculation step from each daily power curve state to each cluster center, recalculate the distance from each daily power curve state to each new cluster center until all cluster centers no longer change, and get N s A new typical state of daily output curve;

[0095] Count the number of daily output curve state samples in each category, and determine the probability of occurrence of each new daily output curve typical state according to the proportion of each category of samples, so as to obtain the Nth k A DG output typical state probability model;

[0096] The error of the maximum output utilization hours of the DG output typical state probability model is calculated. If the error reaches the calculation accuracy of 1% of the maximum output utilization hours, the result is used as the final DG output typical state probability model; if the error does not meet the calculation accuracy requirements and N k <N m , then let N k =N k +1, jump to the step of determining the cluster center of DG throughout the year; otherwise jump to the step of setting the number of typical states of the daily power curve of DG throughout the year, set N s =N s +1, giving each tentative target state number a chance to meet the accuracy. If it does not meet the accuracy, increase the tentative target state number. Through increment and iteration, under the premise of ensuring accuracy, use as few target states as possible to characterize the characteristics of DG output data to the greatest extent, thereby improving the model solving efficiency and the accuracy and practicality of the results;

[0097] The error of the maximum output utilization hours of the DG output typical state probability model is the difference between the maximum output utilization hours of the DG output multi-state probability model and the maximum output utilization hours of the initial annual output curve divided by the maximum output utilization hours of the initial annual output curve.

[0098] 2. Based on multiple typical output states in the determined DG output scenario, calculate the annual cost of reliability loss under each typical output state;

[0099] As the penetration rate of DG access to the distribution network increases, there will be too much electricity, even far greater than the load. Adding the consideration of abandoned power loss to the annual reliability loss cost can make the calculation result of reliability loss cost more reasonable;

[0100] The annual cost of reliability loss under each typical output state includes the annual cost of user power outage loss and the annual cost of DG power abandonment loss, which is calculated using the following formula:

[0101] C xk =C xkl +C xkg ;

[0102] In the above formula, C xk is the annual cost of reliability loss, C xkl The annual cost of power outage loss for users, C xkg Annual cost of DG power curtailment loss;

[0103] When the distribution network equipment fails or is scheduled to be shut down, the load of the non-power-off line section is transferred first, and then the island operation strategy is adopted for the load of the power-off section. Based on different situations of whether an island can be formed, the following formula is used to calculate the annual cost of power outage losses for users:

[0104]

[0105] In the above formula, N h is the total number of time periods in a day, P l，k is the load size of the kth period in the planning area, N s is the total number of typical DG output states, p i is the probability of the typical state of the i-th DG output, k J is the proportion of overhead line in the total length of feeder, D JCZ，i，k is the reliability factor of planned power outage of overhead lines, L fm is the average length of the main line, D JCZ，i，k ′ is the reliability coefficient of power outage caused by overhead line fault, k D is the proportion of the cable in the total length of the feeder, D DCZ，i，k is the cable planned outage reliability factor, D DCZ，i，k ′ is the reliability coefficient of power outage due to cable fault;

[0106] Among them, D JCZ，i，k , D JCZ，i，k ′、D DCZ，i，k , D DCZ，i，k ' is calculated using the following formula:

[0107]

[0108] In the above formula, αZ is the proportion of load that cannot be transferred when the line is out of service, D JCL D is the power outage variable corresponding to the situation that the downstream load of the interconnection line outage section can be fully transferred when the overhead line is planned to be powered off. JCF D is the power outage variable corresponding to the load failure during the planned power outage of the overhead line. JCL ′ is the power outage variable corresponding to the situation that the downstream load of the interconnection line outage section can be fully transferred when the overhead line fails. JCF ′ is the power outage variable corresponding to the load failure when the load cannot be transferred during the power outage due to overhead line fault, D DCL D is the power outage variable corresponding to the situation that the downstream load of the interconnection line outage section can be fully transferred when the cable is planned to be powered off. DCF D is the power outage variable corresponding to the load failure during the planned power outage of the cable. DCL ′ is the power outage variable corresponding to the situation that the downstream load of the interconnection line outage section can be fully transferred when the power outage occurs due to cable fault, D DCF ′ is the power outage variable corresponding to the load failure when the power outage is caused by cable fault;

[0109] All power outage variables in the reliability coefficient of the user's annual power outage loss cost are calculated using the following formula:

[0110]

[0111]

[0112]

[0113]

[0114]

[0115] In the above formula, N i is the average number of sections of the feeder, L i is the total length of the feeder, λ s is the line scheduled outage rate, t ds is the scheduled outage isolation and switching operation time of the line, t s is the average scheduled outage duration, γ i，k is the proportion of load that cannot be supplied by the island in the kth period under the i-th DG output state, λ f is the line failure outage rate, t df is the line fault location, isolation and switching operation time, t f is the average fault outage duration of the line, λ w is the switch failure outage rate, P g，i，k is the output value of the i-th DG output state in the k-th period in the planning area;

[0116] For a feeder with n sections and n connections, if the load rate of each feeder is the same as the load penetration rate of the DG, the load ratio α that cannot be transferred is Z It is calculated using the following formula:

[0117]

[0118]

[0119] In the above formula, η i，k is the feeder load rate in the kth period under the i-th DG output state, N d is the average number of sections of a single feeder, N f is the number of feeder lines, S Lmax The maximum capacity of a single feeder, θ is the power factor of the line load;

[0120] For a feeder with n sections and single interconnection, the load ratio that cannot be transferred is α Z It is calculated using the following formula:

[0121]

[0122] In the above formula, N Y，k Number of feeder sections that can be transferred, N X The number of feeder sections that need to be transferred;

[0123] The annual cost of DG power abandonment loss is calculated using the following formula:

[0124]

[0125] The annual cost of DG power abandonment loss is the proportion of load that cannot be supplied by the island in the kth period under the i-th DG output state. It is calculated using the following formula:

[0126]

[0127] 3. Based on the annual cost of reliability loss under each typical output state, evaluate whether each typical output state is the most representative state in the annual output scenario;

[0128] If the annual cost of reliability loss under a typical output state is within the set standard range, the typical output state will be used as the most representative state in the annual output scenario; if it is not within the standard range, the calculation accuracy of the maximum output utilization hours will be readjusted to redefine the typical output state in the DG output scenario.

[0129] With the maximum utilization hour accuracy of 1% as the goal, after 14 iterations, the maximum output utilization hours of the DG output typical state probability model is obtained to be 1553h, and ten typical DG output states of the planned regional distribution network are determined as follows: Figure 3 As shown, the probability of typical output states of each DG is shown in Table 1;

[0130] Table 1 Probability table of typical output states of each DG

[0131] Status Number 1 2 3 4 5 6 7 8 9 10 Photovoltaic probability / % 0.59 9.97 20.53 7.33 9.68 15.84 8.80 12.32 12.32 2.64 .

[0132] Embodiment 2:

[0133] like Figure 4 As shown, a DG output typical state determination system for an active distribution network includes a typical output state determination module, a reliability loss annual cost calculation module, and a most representative state evaluation module;

[0134] The typical output state determination module is used to construct a DG output typical state probability model of the active distribution network, and determine multiple typical output states and their probabilities in the DG output scenario;

[0135] The reliability loss annual cost calculation module is used to calculate the reliability loss annual cost under each typical output state according to multiple typical output states and their probabilities in the determined DG output scenario;

[0136] The most representative state evaluation module is used to determine the reliability of each typical output state according to the annual cost of reliability loss under each typical output state. If each typical output state meets the reliability requirements, the obtained output state is determined to be the typical output state of the DG output of the active distribution network. Otherwise, it returns to the typical output state determination module to re-determine the typical output state in the DG output scenario.

[0137] The typical output state determination module includes a maximum output utilization hours calculation unit, an initialization setting unit, a cluster center determination unit, a distance calculation unit, a cluster center update unit, a typical state determination unit, a model probability determination unit, and an iteration unit;

[0138] The maximum output utilization hours calculation unit is used to calculate the maximum output utilization hours of the DG according to the daily output curve state of the DG throughout the year using the following formula, and set the calculation accuracy of the maximum output utilization hours:

[0139] T max =W / P max ;

[0140] In the above formula, T max is the annual maximum output utilization hours of DG, W is the annual total power generation, Pmax is the annual maximum output value;

[0141] The initialization setting unit is used to set the number of typical states of the daily power curve of the DG throughout the year, that is, the number of cluster centers is N. s , the maximum number of DG output typical state probability models is N m , initialize the number N of DG output typical state probability models k =1;

[0142] The cluster center determination unit is used to determine the cluster center of the DG throughout the year using the following formula:

[0143] l1′=l max ;

[0144] l2′=l min ;

[0145] l i ′=l r , i = 3, ..., N s , r=1,…,N s -2;

[0146] In the above formula, l1′ and l2′ are the first cluster center and the second cluster center respectively, l max , l min are the cluster centers of the day with the maximum and minimum output of DG in the whole year, respectively. i ′ is the i-th cluster center, l r is the randomly obtained rth cluster center, l max and l min except;

[0147] The distance calculation unit is used to calculate the distance from each daily force curve state to each cluster center using the following formula:

[0148]

[0149] In the above formula, d j，i is the distance from the jth day power curve state to the i-th cluster center, N h is the total number of time periods in a day, l j，k is the DG output value of the j-th day power curve state in the k-th period, l i，k ′ is the DG output value of the i-th cluster center in the k-th period;

[0150] The cluster center updating unit is used to divide the daily power curve state into the class to which the nearest cluster center belongs, and calculate the output average value of each typical state at each moment to form a new daily power curve typical state, that is, a new cluster center;

[0151] The typical state determination unit is used to return to the distance calculation unit to recalculate the distance from each daily force curve state to each new cluster center until all cluster centers no longer change, and obtain N s A new typical state of daily output curve;

[0152] The model probability determination unit is used to count the number of daily output curve state samples in each category, and determine the probability of occurrence of each new daily output curve typical state according to the proportion of each category of samples, so as to obtain the Nth k A DG output typical state probability model;

[0153] The iteration unit is used to calculate the error of the maximum output utilization hours of the obtained DG output typical state probability model. If the error reaches the calculation accuracy of the maximum output utilization hours, the result is used as the final DG output typical state probability model; if the error does not meet the calculation accuracy requirement and N k <N m , then let N k =N k +1, jump to the cluster center determination unit; otherwise jump to the initialization setting unit and set N s =N s +1.

[0154] In the reliability loss annual cost calculation module, the reliability loss annual cost under each typical output state includes the user power outage loss annual cost and the DG power abandonment loss annual cost, which is calculated using the following formula:

[0155] C xk =C xkl +C xkg ;

[0156]

[0157]

[0158] In the above formula, C xk is the annual cost of reliability loss, C xkl The annual cost of power outage loss for users, C xkg is the annual cost of DG power abandonment loss, N h is the total number of time periods in a day, P l，k is the load size of the kth period in the planning area, N s is the total number of typical DG output states, p i is the probability of the typical state of the i-th DG output, D JCZ，i，k is the reliability factor of planned power outage of overhead lines, k J is the proportion of overhead line in the total length of feeder, D JCZ，i，k′ is the reliability coefficient of overhead line fault outage, L fm is the average length of the trunk line, k D is the proportion of the cable in the total length of the feeder, D DCZ，i，k is the cable planned outage reliability factor, D DCZ，i，k ′ is the reliability coefficient of cable fault power outage, P g，i，k It is the output value of the i-th DG output state in the k-th period in the planning area.

[0159] The most representative state evaluation module is used to, if the annual cost of reliability loss under a typical output state is within a set standard range, use the typical output state as the most representative state in the annual output scenario; if it is not within the standard range, return to the maximum output utilization hours calculation unit, adjust the calculation accuracy of the maximum output utilization hours, and re-determine the typical output state in the DG output scenario.

[0160] Embodiment 3:

[0161] like Figure 5 As shown, a device for determining a typical state of DG output of an active distribution network includes a processor and a memory;

[0162] The memory is used to store computer program code and transmit the computer program code to the processor;

[0163] The processor is used to execute the method for determining a typical state of DG output of an active distribution network described in embodiment 1 according to the instructions in the computer program code.

[0164] Embodiment 4:

[0165] A computer storage medium having a computer program stored thereon;

[0166] When the computer program is executed by the processor, the steps of a method for determining a typical state of DG output of an active distribution network described in this solution are implemented.

Claims

1. A method for determining a typical state of DG output in an active distribution network, characterized in that: The method comprises: S1. Construct a DG output typical state probability model for the active distribution network and determine multiple typical output states and their probabilities in the DG output scenario; S2. Calculate the annual cost of reliability loss under each typical output state according to multiple typical output states and their probabilities in the determined DG output scenario; S3. Determine the reliability of each typical output state according to the annual cost of reliability loss under each typical output state. If each typical output state meets the reliability requirements, determine that the obtained output state is the typical output state of the DG in the active distribution network. Otherwise, return to step S1 to redetermine the typical output state in the DG output scenario.

2. The method for determining a typical state of DG output of an active distribution network according to claim 1, characterized in that: In S2, the annual cost of reliability loss under each typical output state includes the annual cost of user power outage loss and the annual cost of DG power abandonment loss, which is calculated using the following formula: C xk =C xkl +C xkg ; In the above formula, C xk is the annual cost of reliability loss, C xkl The annual cost of power outage loss for users, C xkg is the annual cost of DG power abandonment loss, N h is the total number of time periods in a day, P l，k is the load size of the kth period in the planning area, N s is the total number of typical DG output states, p i is the probability of the typical state of the i-th DG output, D JCZ，i，k is the reliability factor of planned power outage of overhead lines, k J is the proportion of overhead line in the total length of feeder, D JCZ，i，k ′ is the reliability coefficient of overhead line fault outage, L fm is the average length of the trunk line, k D is the proportion of the cable in the total length of the feeder, D DCZ，i，k is the cable planned outage reliability factor, D DCZ，i，k ′ is the reliability coefficient of cable fault power outage, P g，i，k It is the output value of the i-th DG output state in the k-th period in the planning area.

3. The method for determining a typical state of DG output of an active distribution network according to claim 1, characterized in that: The S1 includes: S11. According to the daily output curve of DG throughout the year, the following formula is used to calculate the maximum output utilization hours of DG, and the calculation accuracy of the maximum output utilization hours is set: T max =W / P max ; In the above formula, T max is the annual maximum output utilization hours of DG, W is the annual total power generation, P max is the annual maximum output value; S12, set the number of typical states of the daily output curve of DG throughout the year, that is, the number of cluster centers is N s , the maximum number of DG output typical state probability models is N m , initialize the number N of DG output typical state probability models k =1; S13. Use the following formula to determine the cluster center of DG throughout the year: l1′＝l max ； l2′＝l min ； l i ′=l r ,i=3,…,N s ,r=1,…,N s -2; In the above formula, l1′ and l2′ are the first cluster center and the second cluster center respectively, l max , l min are the cluster centers of the day with the maximum and minimum output of DG in the whole year, respectively. i is the i-th cluster center, l r is the randomly obtained rth cluster center, l max and l min except; S14. Use the following formula to calculate the distance from each day's power curve state to each cluster center: In the above formula, d j，i is the distance from the jth day power curve state to the i-th cluster center, N h is the total number of time periods in a day, l j，k is the DG output value of the j-th day power curve state in the k-th period, l i，k ′ is the DG output value of the i-th cluster center in the k-th period; S15, dividing the daily output curve state into the class to which the nearest cluster center belongs, and calculating the output average value of each typical state at each moment to form a new daily output curve typical state, i.e., a new cluster center; S16, return to step S14, recalculate the distance from each daily force curve state to each new cluster center until all cluster centers no longer change, and obtain N s A new typical state of daily output curve; S17, count the number of daily output curve state samples in each category, determine the probability of occurrence of each new daily output curve typical state according to the proportion of each category of samples, and thus obtain the Nth k A DG output typical state probability model; S18. Calculate the error of the maximum output utilization hours of the DG output typical state probability model. If the error reaches the calculation accuracy of the maximum output utilization hours, the result is used as the final DG output typical state probability model. If the error does not reach the calculation accuracy requirement and N k <N m , then let N k =N k +1, jump to step S13; otherwise jump to step S12, set N s =N s +1.

4. The method for determining a typical state of DG output of an active distribution network according to claim 1, characterized in that: The S3 includes: If the annual cost of reliability loss under a typical output state is within the set standard range, the typical output state is used as the most representative state in the annual output scenario; if it is not within the standard range, return to S11 to adjust the calculation accuracy of the maximum output utilization hours to redetermine the typical output state in the DG output scenario.

5. A DG output typical state determination system for an active distribution network, characterized in that: The system includes a typical output state determination module, a reliability loss annual cost calculation module, and a most representative state evaluation module; The typical output state determination module is used to construct a DG output typical state probability model of the active distribution network, and determine multiple typical output states and their probabilities in the DG output scenario; The reliability loss annual cost calculation module is used to calculate the reliability loss annual cost under each typical output state according to multiple typical output states and their probabilities in the determined DG output scenario; The most representative state evaluation module is used to determine the reliability of each typical output state according to the annual cost of reliability loss under each typical output state. If each typical output state meets the reliability requirements, the obtained output state is determined to be the typical output state of the DG output of the active distribution network. Otherwise, it returns to the typical output state determination module to re-determine the typical output state in the DG output scenario.

6. The DG output typical state determination system of an active distribution network according to claim 5, characterized in that: In the reliability loss annual cost calculation module, the reliability loss annual cost under each typical output state includes the user power outage loss annual cost and the DG power abandonment loss annual cost, which is calculated using the following formula: C xk =C Cxkl +C xkg ; In the above formula, C xk is the annual cost of reliability loss, C xkl The annual cost of power outage loss for users, C xkg is the annual cost of DG power abandonment loss, N h is the total number of time periods in a day, P l，k is the load size of the kth period in the planning area, N s is the total number of typical DG output states, p i is the probability of the typical state of the i-th DG output, D JCZ，i，k is the reliability factor of planned power outage of overhead lines, k J is the proportion of overhead line in the total length of feeder, D JCZ，i，k is the reliability factor of power outage due to overhead line fault, L fm is the average length of the trunk line, k D is the proportion of the cable in the total length of the feeder, D DCZ，i，k is the cable planned outage reliability factor, D DCZ，i，k ′ is the reliability coefficient of cable fault power outage, P g，i，k It is the output value of the i-th DG output state in the k-th period in the planning area.

7. The DG output typical state determination system of an active distribution network according to claim 5, characterized in that: The typical output state determination module includes a maximum output utilization hours calculation unit, an initialization setting unit, a cluster center determination unit, a distance calculation unit, a cluster center update unit, a typical state determination unit, a model probability determination unit, and an iteration unit; The maximum output utilization hours calculation unit is used to calculate the maximum output utilization hours of the DG according to the daily output curve state of the DG throughout the year using the following formula, and set the calculation accuracy of the maximum output utilization hours: T max =W / P max ; In the above formula, T max is the annual maximum output utilization hours of DG, W is the annual total power generation, P max is the annual maximum output value; The initialization setting unit is used to set the number of typical states of the daily power curve of the DG throughout the year, that is, the number of cluster centers is N. s , the maximum number of DG output typical state probability models is N m , initialize the number N of DG output typical state probability models k =1; The cluster center determination unit is used to determine the cluster center of the DG throughout the year using the following formula: l1′＝l max ； l2′＝l min ； l i ′=l r ,i=3,…,N s ,r=1,…,N s -2; In the above formula, l1′ and l2′ are the first cluster center and the second cluster center respectively, l max , l min are the cluster centers of the day with the maximum and minimum output of DG in the whole year, respectively. i is the i-th cluster center, l r is the randomly obtained rth cluster center, l max and l min except; The distance calculation unit is used to calculate the distance from each daily force curve state to each cluster center using the following formula: In the above formula, d j，i is the distance from the jth day power curve state to the i-th cluster center, N h is the total number of time periods in a day, l j，k is the DG output value of the j-th day power curve state in the k-th period, l i，k ′ is the DG output value of the i-th cluster center in the k-th period; The cluster center updating unit is used to divide the daily power curve state into the class to which the nearest cluster center belongs, and calculate the output average value of each typical state at each moment to form a new daily power curve typical state, that is, a new cluster center; The typical state determination unit is used to return to the distance calculation unit to recalculate the distance from each daily force curve state to each new cluster center until all cluster centers no longer change, and obtain N s A new typical state of daily output curve; The model probability determination unit is used to count the number of daily output curve state samples in each category, and determine the probability of occurrence of each new daily output curve typical state according to the proportion of each category of samples, so as to obtain the Nth k A DG output typical state probability model; The iteration unit is used to calculate the error of the maximum output utilization hours of the obtained DG output typical state probability model. If the error reaches the calculation accuracy of the maximum output utilization hours, the result is used as the final DG output typical state probability model; if the error does not meet the calculation accuracy requirement and N k <N m , then let N k =N k +1, jump to the cluster center determination unit; otherwise jump to the initialization setting unit and set N s =N s +1.

8. The DG output typical state determination system of an active distribution network according to claim 7, characterized in that: The most representative state evaluation module is used to take a typical output state as the most representative state in the annual output scenario if the annual reliability loss cost under a typical output state is within a set standard range; If it is not within the standard range, the maximum output utilization hours calculation unit is returned to adjust the calculation accuracy of the maximum output utilization hours to redetermine the typical output state in the DG output scenario.

9. A device for determining a typical state of DG output of an active distribution network, characterized in that: including a processor and a memory; The memory is used to store computer program code and transmit the computer program code to the processor; The processor is used to execute the method for determining the typical state of DG output of an active distribution network according to any one of claims 1 to 4 according to the instructions in the computer program code.

10. A computer storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a method for determining a typical state of DG output of an active distribution network according to any one of claims 1 to 4 are implemented.

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