Method, device and storage medium for determining access capacity of distributed power source

Abstract

The present invention discloses a method, device and storage medium for determining the access capacity of a distributed power source, which relates to the field of electric power technology, and is mainly capable of improving the accuracy of determining the access capacity planning scheme for the access of distributed power sources to the distribution network, reducing the operating pressure of the distribution network, and avoiding unnecessary losses. The method includes: determining a plurality of access capacity planning schemes for the distributed power source for the distribution network, and obtaining the index data corresponding to different electric energy indicators of the distribution network under the plurality of access capacity planning schemes; using a preset algorithm to process the different index data under the plurality of access capacity planning schemes, and obtaining the index weight coefficients corresponding to the different electric energy indicators; based on the index data and index weight coefficients corresponding to the different electric energy indicators, determining the access capacity coefficients of the plurality of access capacity planning schemes for the distribution network; based on the access capacity coefficient, determining the target access capacity planning scheme that matches the distribution network among the plurality of access capacity planning schemes. The present invention is applicable to the selection of access capacity planning schemes.

Description

Technical Field

[0001] The present invention relates to the field of electric power technology, and in particular to a method, device and storage medium for determining access capacity of a distributed power source. Background Art

[0002] With the continuous development of new energy technologies and the increasing demand for new social loads (such as electric vehicles), more and more distributed power sources have begun to be integrated into the distribution network, including new energy, electric vehicles, microgrids and virtual power plants. The capacity of distributed power sources is relatively small, the grid-connected voltage is low, and they are mostly connected to medium and low voltage distribution networks. However, the structure of medium and low voltage distribution networks is complex, and there are fewer resources that can be directly controlled on the distribution network side. Therefore, its operation and scheduling are more difficult. The access of a large number of volatile distributed power sources has increased the operation and control pressure of medium and low voltage distribution networks, which may bring voltage levels, power quality, equipment operation reliability and other problems to medium and low voltage distribution networks, affecting the safe and stable operation of medium and low voltage distribution networks. Therefore, if you want to safely and stably connect distributed power sources to the distribution network, you need to select a suitable access capacity planning plan.

[0003] At present, the access capacity of distributed power sources is usually determined manually. However, this method of determining the access capacity is greatly influenced by human subjectivity, which may lead to errors in the access capacity determination, thereby increasing the operating pressure of the distribution network and causing unnecessary losses. Summary of the invention

[0004] The present invention provides a method, device and storage medium for determining the access capacity of a distributed power source, which are mainly capable of improving the determination accuracy of the access capacity of a distributed power source, reducing the operating pressure of a distribution network and avoiding unnecessary losses.

[0005] According to a first aspect of the present invention, there is provided a method for determining access capacity of a distributed power source, comprising:

[0006] Determine multiple access capacity planning schemes of distributed power sources for a power distribution network, and obtain indicator data corresponding to different power indicators of the power distribution network under the multiple access capacity planning schemes;

[0007] Using a preset algorithm to process different indicator data under the multiple access capacity planning schemes to obtain indicator weight coefficients corresponding to the different power indicators;

[0008] Determining access capacity coefficients of the plurality of access capacity planning schemes for the power distribution network based on the indicator data and indicator weight coefficients corresponding to the different electric energy indicators;

[0009] Based on the access capacity coefficient, a target access capacity planning scheme matching the power distribution network is determined from among the multiple access capacity planning schemes, and based on the target access capacity planning scheme, the access capacity of the distributed power source for the power distribution network is determined.

[0010] According to a second aspect of the present invention, there is provided a device for determining access capacity of a distributed power source, comprising:

[0011] An acquisition unit, used to determine a plurality of access capacity planning schemes of a distributed power source for a power distribution network, and to acquire index data corresponding to different electric energy indicators of the power distribution network under the plurality of access capacity planning schemes;

[0012] A processing unit, configured to process different indicator data under the plurality of access capacity planning schemes using a preset algorithm to obtain indicator weight coefficients corresponding to the different electric energy indicators;

[0013] A first determining unit, configured to determine access capacity coefficients of the plurality of access capacity planning schemes for the power distribution network based on the indicator data and indicator weight coefficients corresponding to the different electric energy indicators;

[0014] A second determination unit is used to determine a target access capacity planning scheme matching the distribution network from among the multiple access capacity planning schemes based on the access capacity coefficient, and determine the access capacity of the distributed power source for the distribution network based on the target access capacity planning scheme.

[0015] According to a third aspect of the present invention, there is provided a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above method for determining the access capacity of a distributed power source.

[0016] According to a fourth aspect of the present invention, there is provided a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above method for determining the access capacity of distributed power sources when executing the program.

[0017] According to a method, device and storage medium for determining the access capacity of a distributed power source provided by the present invention, compared with the current method of manually selecting an access capacity planning scheme to connect a distributed power source to a distribution network, the present invention determines a plurality of access capacity planning schemes for the distributed power source for the distribution network, and obtains indicator data corresponding to different electric energy indicators of the distribution network under the plurality of access capacity planning schemes; and uses a preset algorithm to process the different indicator data under the plurality of access capacity planning schemes to obtain indicator weight coefficients corresponding to the different electric energy indicators; then, based on the indicator data and indicator weight coefficients corresponding to the different electric energy indicators, determines the access capacity coefficients of the plurality of access capacity planning schemes for the distribution network; finally, based on the access capacity coefficient, determines a target access capacity planning scheme that matches the distribution network from the plurality of access capacity planning schemes, and determines the access capacity of the distributed power source for the distribution network based on the target access capacity planning scheme. Therefore, by obtaining the indicator data corresponding to different electric energy indicators of the distribution network under a variety of access capacity planning schemes, and determining the indicator weights corresponding to different electric energy indicators, the access capacity coefficients of the various access capacity planning schemes are determined according to the indicator weights and indicator data, and finally the access capacity planning scheme that matches the distribution network is selected according to the access capacity coefficient to determine the access capacity. By selecting the access capacity planning scheme through a comprehensive analysis of the indicator data corresponding to different electric energy indicators, the accuracy of determining the access capacity planning scheme can be improved, and then the accuracy of determining the access capacity of distributed power sources can be improved, while reducing the operating pressure of the distribution network, thereby avoiding unnecessary power supply losses. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0019] Figure 1 A flow chart of a method for determining access capacity of a distributed power source provided by an embodiment of the present invention is shown;

[0020] Figure 2 A flow chart of another method for determining the access capacity of a distributed power source provided by an embodiment of the present invention is shown;

[0021] Figure 3 A schematic diagram of a 409-node network provided by an embodiment of the present invention is shown;

[0022] Figure 4 A typical load curve, wind power output and photovoltaic output curve schematic diagram provided by an embodiment of the present invention are shown;

[0023] Figure 5 A schematic diagram of a typical daily load curve of a battery replacement station provided by an embodiment of the present invention is shown;

[0024] Figure 6 A schematic diagram of a typical load curve of a charging station in a residential area provided by an embodiment of the present invention is shown;

[0025] Figure 7 A schematic diagram of a typical daily load curve of a public charging station provided by an embodiment of the present invention is shown;

[0026] Figure 8 A schematic diagram of the structure of a device for determining access capacity of a distributed power source provided by an embodiment of the present invention is shown;

[0027] Fig. 9 A schematic diagram showing the structure of another device for determining the access capacity of a distributed power source provided by an embodiment of the present invention is shown;

[0028] Fig.10 A schematic diagram of the physical structure of a computer device provided by an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0029] The present invention will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that the embodiments and features in the embodiments of the present application can be combined with each other without conflict.

[0030] At present, the access capacity of distributed power sources is determined manually. Since the access capacity is greatly influenced by human subjectivity, it may lead to errors in access capacity determination, thereby increasing the operating pressure of the distribution network and causing unnecessary losses.

[0031] In order to solve the above problems, an embodiment of the present invention provides a method for determining the access capacity of a distributed power source, such as Figure 1 As shown, the method includes:

[0032] 101. Determine various access capacity planning schemes for distributed power sources for the distribution network, and obtain indicator data corresponding to different power indicators of the distribution network under the various access capacity planning schemes.

[0033] Among them, distributed power sources include: new energy, electric vehicles, microgrids, and virtual power plants. Access capacity refers to the power delivered by distributed power sources to the distribution network, which is called the access capacity of distributed power sources. Each access capacity planning scheme includes at least one distributed power source and its corresponding access capacity. There are multiple access capacity planning schemes such as: Scheme 1: The access capacity corresponding to distributed power source 1 is 10MW, the access capacity corresponding to distributed power source 2 is 8MW, and the access capacity corresponding to distributed power source 3 is 15MW; Scheme 2: The access capacity corresponding to distributed power source 1 is 8MW, the access capacity corresponding to distributed power source 2 is 9MW, and the access capacity corresponding to distributed power source 3 is 13MW; Scheme 3: The access capacity corresponding to distributed power source 1 is 12MW, the access capacity corresponding to distributed power source 2 is 11MW, and the access capacity corresponding to distributed power source 3 is 7MW; It should be noted that the access capacity in the above-mentioned planning schemes is within the carrying range of the corresponding distributed power source. Different electric energy indicators refer to indicators selected from multiple angles such as reliability, economy, safety and environmental protection under supply and demand balance, and power quality, economy and safety under operating margin. First, the reliability B11 in the supply and demand balance includes the following indicators: system average power outage frequency indicator, system average power outage duration indicator, system power supply reliability indicator, power shortage expectation indicator, and power shortage expectation indicator. The above indicators evaluate the power and power balance of the distribution network from the supply side and user side of the distribution network respectively; the economy B12 in the supply and demand balance includes the following indicators: distribution network operation cost indicator, power outage loss cost indicator, among which, The distribution network operation cost index describes the overall operating economic status of the distribution network under the supply and demand balance state, and the power outage loss cost index represents the differentiated power supply reliability needs on the demand side; the safety B13 in the supply and demand balance includes the following indicators: N-1 maximum load loss rate index (N-1: single fault), which evaluates the maximum loss rate of user-side demand under the N-1 state, and reflects the degree of imbalance between supply and demand of the distribution network under fault conditions; the environmental protection B14 in the supply and demand balance includes the following indicators: low-carbon benefit index, clean energy ratio index, which reflects the environmental friendliness of the distribution network under the supply and demand balance operation state.Furthermore, the indicators of power quality under operating margin B21 include: the qualified rate index of three-phase unevenness at the access point and the qualified rate index of distribution network voltage. The indicators evaluate the operating status of the distribution network from the perspective of three-phase imbalance and node voltage deviation respectively, and describe the healthy status of the distribution network operation; the indicators of economy under operating margin B22 include: investment cost index and equipment utilization index. The investment cost index evaluates the economy of the distribution network from the perspective of ensuring the safe capacity of the distribution network, and the equipment utilization index evaluates the effective utilization of the distribution network; the indicators of safety under operating margin B23 include: N-1 pass rate and capacity-load ratio. The N-1 pass rate index evaluates the power supply capacity of the distribution network under fault conditions, which indirectly illustrates the operating margin of the distribution network, and the capacity-load ratio index directly evaluates the operating margin of the distribution network. Furthermore, the indicator data under the average power outage frequency index is the average power outage frequency, the indicator data under the system average power outage duration index is the system average power outage duration, the indicator data under the system power supply reliability index is the system power supply reliability rate, the indicator data under the power shortage expectation index is the power shortage expectation value, the indicator data under the power shortage expectation index is the power shortage expectation value, the indicator data under the distribution network operation cost index is the distribution network operation cost, the indicator data under the power outage loss cost index is the power outage loss cost, the indicator data under the N-1 maximum load loss rate index is the N-1 maximum load loss rate, the indicator data under the low-carbon benefit index is the low-carbon benefit, the indicator data under the clean energy ratio index is the clean energy ratio, the indicator data under the access point three-phase unevenness qualified rate index is the access point three-phase unevenness qualified rate, the indicator data under the distribution network voltage qualified rate index is the distribution network voltage qualified rate, the indicator data under the investment cost index is the distribution network investment cost, the indicator data under the equipment utilization rate index is the distribution network utilization, the indicator data under the N-1 pass rate indicator is the N-1 pass rate, and the indicator data under the capacity load ratio indicator is the capacity load ratio. Furthermore, the calculation methods for various indicators of reliability of supply and demand balance are specified as follows: the system average power outage frequency, that is, the average number of continuous power outages experienced by each distributed power source in the distribution network within a specified time. The average power outage frequency of the distribution network can be calculated according to the following formula:.

[0034]

[0035] Among them, C 111 represents the average power outage frequency of the system, λ g represents the annual failure outage frequency of load node g (times / year), constant term, represents the number of users at node g, g represents the load node, Ω F Represents the collection of locations of all types of load points in the distribution network.

[0036] The average power outage duration of the system equipment, that is, the total power outage time experienced by each user side in the system within a specified time, can be calculated according to the following formula:

[0037]

[0038] Among them, C 112 Indicates the average power outage duration of the system, u g is the average power outage duration at load point g (h / year).

[0039] The system power supply reliability rate is the ratio of the total number of uninterrupted power supply hours experienced by the user side within a specified period of time to the total number of power supply hours required by the user side. The system power supply reliability rate can be calculated according to the following formula:

[0040]

[0041] Among them, C 113 Indicates the system power supply reliability rate.

[0042] The power shortage expectation refers to the average annual power shortage capacity, in MW / year. The power shortage expectation can be calculated according to the following formula:

[0043]

[0044] Among them, C 114 Indicates the expected value of power shortage, P g represents the probability of the distribution network state g, S g Indicates the power distribution network status g power outage capacity.

[0045] The power shortage expectation refers to the average annual power shortage, in MWh / year, and the calculation formula is as follows:

[0046]

[0047] Among them, C 115 Indicates the expected value of insufficient power, U g is the duration of state g.

[0048] Furthermore, the calculation methods of various indicators under the economic aspect of supply and demand balance are as follows:

[0049] Grid operation cost C 121 , that is, the grid operation cost includes the electricity generation cost to meet the load demand of the distribution network and the equipment maintenance cost.

[0050] Calculation method of power outage loss cost: Without considering social impact loss, the calculation formula is as follows:

[0051]

[0052] Among them, C 122 represents the power outage loss cost, R g is the average power outage cost of node g under unit power outage, which requires comprehensive consideration of factors such as power shortage, power outage duration, power outage frequency and comprehensive power shortage loss impact; E(g) is the expected value of power shortage at node g per unit time.

[0053] The calculation method of various indicators in terms of the security of supply and demand balance:

[0054] Calculation method of N-1 maximum load loss rate: For example, for the distribution network, the important component accident sequence refers to the 10kV substation N-1 and medium and low voltage line N-1 accident sequence. The load loss rate refers to the proportion of the power shortage of the distribution network per unit time after the fault to the original power. The maximum load loss rate is calculated as follows:

[0055]

[0056] Among them, C 131 It represents the N-1 maximum load loss rate, ΔE is the power shortage per unit time, and E is the original power supply.

[0057] Calculation method of various indicators under the environmental protection aspect of supply and demand balance:

[0058] Calculation method of low-carbon benefit index: Since the large-scale access of distributed power sources changes the inherent energy structure of the regional power system, low-carbon benefits E are generated on the power supply side. C1 At the same time, the current active distribution network actively manages network assets by applying advanced reactive power compensation and power flow control technologies, which also helps to reduce the energy loss generated in the power distribution link. Therefore, the low-carbon benefits of the distribution network are mainly generated by the two links of energy terminals and power distribution, which can be calculated according to the following formula:

[0059] C 141 =E C1 +E C2 +E C3

[0060] Among them, C 141 Indicates the low carbon benefit value, E C1 For the low-carbon benefits of the terminal energy link, E C2 For the low-carbon benefits of electricity distribution, E C3 The low-carbon benefits of distributed power use are defined as:

[0061]

[0062] E C2 =ε c,coal c coal Wloss

[0063] E C3 =Ω ev W EV

[0064] Among them, ε c,coal is the carbon emission coefficient of the distribution network (kg / gce); c coal is the coal consumption per unit of electricity in the distribution network (gce / kWh); η loss is the expected reduction rate of power distribution network loss; T max is the maximum annual utilization hours of distributed power sources; W is the total electricity sales of the distribution enterprise (MWh); Ω ev is the emission reduction coefficient of distributed power generation (kg / kWh), W EV is the total power consumption of distributed generation, P GD,g is the probability of the distributed generation state g in the distribution network.

[0065] Clean energy ratio, that is, clean energy ratio refers to the percentage of clean energy installed capacity of the system to the total installed capacity, which is calculated as follows:

[0066]

[0067] In the formula, C 142 Indicates the proportion of clean energy, C a2 is the installed capacity of clean energy (MW), including distributed wind power, photovoltaic power generation connected to the distribution network, and new energy sources such as wind power and photovoltaic power generation in microgrids and virtual power plants. C is the total capacity of the distribution network, including C a2 , the capacity of other power generation methods and the available capacity of the high-voltage distribution network.

[0068] The calculation method of various indicators in the power quality of the balance of supply and demand of operation margin is as follows:

[0069] Access point three-phase unbalance pass rate C 211 Calculation method: Adopt the international simplified calculation method, as follows:

[0070]

[0071] Among them, n is the total number of nodes, n unbal is the number of nodes with unqualified maximum three-phase unbalance.

[0072] Distribution network voltage qualification rate C 212 Calculation method: Since the access of distributed power sources will change the voltage characteristics of the access point, causing the voltage at the access point to shift, the voltage of the distribution network is related to the power supply quality. In order to evaluate the access capacity of distributed power sources, it is necessary to focus on the impact of distributed power sources on the voltage. The calculation of the voltage shift rate is as follows:

[0073]

[0074] Among them, δ represents the qualified rate of distribution network voltage, U represents the access voltage, and U N Rated voltage for the access point.

[0075] The calculation method of each indicator under the economic aspect of operating margin supply and demand balance is as follows:

[0076] Investment cost 221 Calculation method: Investment cost is an indicator that needs to be considered in planning nodes, which mainly includes the investment cost of distributed power sources, the cost of new lines and other necessary equipment.

[0077] Equipment utilization rate C 222 Calculation method: The equipment utilization rate of the distribution network is calculated by using the weighted average of the average load rate of the distribution network and the lines. The calculation is as follows:

[0078]

[0079] Among them, A i is the average load factor of the distribution network, C i is the investment cost of the distribution network, that is, the average utilization rate of the distribution network and lines is calculated with the investment cost of the equipment or line as the weight, j represents the jth equipment in the distribution network, i represents the ith line in the distribution network, Ω grid Represents a collection of devices in a power distribution network.

[0080] The calculation method of various indicators under the safety aspect of supply and demand balance of operating margin is as follows:

[0081] Calculation method of N-1 pass rate C231: N-1 pass rate refers to the N-1 pass rate of substations and lines in the power distribution network, which is calculated as follows:

[0082]

[0083] Among them, n is the number of individuals in the total N-1 verification set, n not N-1 is the number of individuals that failed.

[0084] Capacity ratio C 232 Calculation method: The capacity-load ratio is the ratio of the total capacity (MVA) of the distribution network in a certain power supply area to the corresponding total load (MW). The determination of the capacity-load ratio should take into account the influence of complex factors such as load dispersion coefficient, average power factor, transformer operation rate, and reserve factor. In engineering, a practical method can be used to estimate the capacity-load ratio. The formula is as follows:

[0085]

[0086] Among them, P max is the maximum predicted load of the entire network at this voltage level, S ei The main transformer capacity of the distribution network i of this voltage level. As an important infrastructure of the city, the urban network should be developed in a moderately advanced manner to meet the needs of urban economic growth and social development. Ensuring the safety and reliability of the power grid and meeting the orderly growth of load are important factors to be considered when determining the urban network capacity-load ratio, which should be controlled between 1.5 and 2.2.

[0087] Therefore, according to the above method, the indicator data corresponding to different electric energy indicators of the distribution network under various access capacity planning schemes can be determined, and then the various indicator data are comprehensively analyzed, and the target access capacity planning scheme that matches the distribution network is selected from the various access capacity planning schemes according to the analysis results. Therefore, by comprehensively analyzing the electric energy indicator data of each indicator to select the access capacity planning scheme, the accuracy of the access capacity planning scheme can be improved, thereby reducing the power supply pressure of the distribution network and avoiding unnecessary losses. At the same time, starting from the supply and demand balance and the operating margin of the distribution network that are closely related to the safe and reliable operation of the power grid, selecting indicators that can characterize the characteristics of distributed power sources and the operating status of the distribution network can further improve the accuracy of the selection of the access capacity planning scheme.

[0088] 102. Use a preset algorithm to process different indicator data under a variety of access capacity planning schemes to obtain indicator weight coefficients corresponding to different power indicators.

[0089] 103. Based on the indicator data and indicator weight coefficients corresponding to different electric energy indicators, determine the access capacity coefficients of the distribution network for various access capacity planning schemes.

[0090] For the embodiments of the present invention, after obtaining the indicator data corresponding to different electric energy indicators of the distribution network under a variety of access capacity planning schemes, in order to select a suitable access capacity planning scheme, it is first necessary to determine the indicator weight coefficients corresponding to the different electric energy indicators, and then calculate the access capacity coefficients corresponding to the different access capacity planning schemes according to the indicator weight coefficients, and finally select the target access capacity planning scheme suitable for access to the distribution network according to the access capacity coefficient. Thus, by comprehensively analyzing the electric energy indicator data of each indicator to select the access capacity planning scheme, the accuracy of determining the access capacity planning scheme can be improved, thereby reducing the power supply pressure of the distribution network and avoiding unnecessary losses.

[0091] 104. Based on the access capacity coefficient, determine a target access capacity planning scheme that matches the distribution network among multiple access capacity planning schemes, and determine the access capacity of the distributed generation for the distribution network based on the target access capacity planning scheme.

[0092] For the embodiments of the present invention, after determining the access capacity coefficients corresponding to different access capacity planning schemes, a target access capacity planning scheme that matches the distribution network is selected from the different access capacity planning schemes based on the access capacity coefficient. Then, according to the target access capacity planning scheme, the access capacity of the distributed power source for the distribution network is determined, thereby avoiding power supply pressure on the distribution network and reducing unnecessary losses.

[0093] According to a method for determining the access capacity of a distributed power source provided by the present invention, compared with the current method of manually determining the access capacity, the present invention determines a plurality of access capacity planning schemes for the distributed power source for a distribution network, and obtains indicator data corresponding to different electric energy indicators of the distribution network under the plurality of access capacity planning schemes; and uses a preset algorithm to process the different indicator data under the plurality of access capacity planning schemes to obtain indicator weight coefficients corresponding to the different electric energy indicators; then, based on the indicator data and indicator weight coefficients corresponding to the different electric energy indicators, determines the access capacity coefficients of the plurality of access capacity planning schemes for the distribution network; finally, based on the access capacity coefficient, determines a target access capacity planning scheme that matches the distribution network from among the plurality of access capacity planning schemes, and determines the access capacity of the distributed power source for the distribution network based on the target access capacity planning scheme. Therefore, by obtaining the indicator data corresponding to different electric energy indicators of the distribution network under a variety of access capacity planning schemes, and determining the indicator weights corresponding to different electric energy indicators, the access capacity coefficients of the various access capacity planning schemes are determined according to the indicator weights and indicator data, and finally the access capacity planning scheme that matches the distribution network is selected according to the access capacity coefficient to determine the access capacity. By selecting the access capacity planning scheme through a comprehensive analysis of the indicator data corresponding to different electric energy indicators, the accuracy of determining the access capacity planning scheme can be improved, and then the accuracy of determining the access capacity of distributed power sources can be improved, while reducing the operating pressure of the distribution network, thereby avoiding unnecessary power supply losses.

[0094] Further, in order to better illustrate the above process of determining the access capacity of the distributed power source, as a refinement and extension of the above embodiment, the embodiment of the present invention provides another method for determining the access capacity of the distributed power source, such as Figure 2 As shown, the method includes:

[0095] 201. Determine a variety of access capacity planning schemes for distributed power sources for a distribution network, and obtain indicator data corresponding to different power indicators of the distribution network under the various access capacity planning schemes.

[0096] According to the embodiment of the present invention, the distributed power sources are pre-connected to the power distribution network according to a plurality of access capacity planning schemes, and then different indicator data of the power distribution network under the plurality of access capacity planning schemes are determined.

[0097] 202. Based on different power indicator data corresponding to multiple access capacity planning schemes, an power indicator matrix is ​​constructed.

[0098] 203. Normalize the index data in the electric energy index matrix to obtain a normalized matrix.

[0099] 204. Based on the normalized matrix, determine the index entropy corresponding to different electric energy indicators.

[0100] For the embodiment of the present invention, firstly, an electric energy index matrix is ​​established. For example, if there are m access capacity planning schemes, S1, S2, ...S m , using n electric energy indicators for evaluation, namely A1, A2, ...A n , the i-th access capacity planning scheme S i At the jth electric energy index A j The indicator data below is P ij , establish the m×n order electric energy index matrix P as follows:

[0101]

[0102] Furthermore, each indicator data in the electric energy indicator matrix is ​​normalized to obtain a normalized matrix, i.e., a dimensionless matrix. Specifically, since the order of magnitude and dimension of each electric energy indicator in the electric energy indicator matrix P are different, it is necessary to perform normalization to make them comparable. Let p j =min{p 1j ,p 2j ,…,p nj}, that is, for a certain power index A, determine the index data corresponding to different access capacity planning schemes, and determine the minimum index data among the index data corresponding to the power index A. The minimum index data is P j , j represents the jth electric energy index, which can determine the minimum index data corresponding to each electric energy index, and then establish a normalized matrix according to the following formula:

[0103]

[0104] F=(f ij ) m×n

[0105] Where F represents the normalized matrix, f ij represents the i-th access capacity planning solution S i At the jth electric energy index Aj Normalized electric energy index data under. Furthermore, it is also necessary to determine the index entropy corresponding to different electric energy indicators based on the normalized matrix. Based on this, the method includes: adding the index data of the target electric energy indicators corresponding to the multiple access capacity planning schemes in the normalized matrix to obtain the sum of the target electric energy index data; adding 1 to the index data corresponding to each target electric energy indicator in the normalized matrix, and dividing the addition results by the sum of the target electric energy index data to obtain the index evaluation value corresponding to the target electric energy indicator; based on the index evaluation value, determining the index entropy corresponding to the target electric energy indicator.

[0106] Specifically, the target power index refers to any one of the power indexes. Specifically, the index entropy corresponding to different power indexes can be calculated according to the following formula:

[0107]

[0108]

[0109] Among them, r ij represents the index evaluation value corresponding to the j-th electric energy index, H j Represents the index entropy corresponding to the j-th electric energy index.

[0110] 205. Based on the index entropy, determine the index weight coefficients corresponding to different electric energy indicators.

[0111] For the embodiment of the present invention, after determining the indicator entropies corresponding to different electric energy indicators, it is also necessary to determine the indicator weight coefficients corresponding to the different electric energy indicators according to the indicator entropies. Based on this, step 205 specifically includes: adding the indicator entropies corresponding to the different electric energy indicators to obtain the total indicator entropy; subtracting the total number of indicators corresponding to different electric energy indicators from the total indicator entropy to obtain a weight evaluation value; subtracting 1 from the indicator entropies corresponding to different electric energy indicators, and dividing the subtraction results by the weight evaluation values ​​to obtain the indicator weight coefficients corresponding to the different electric energy indicators.

[0112] Specifically, after calculating the index entropy corresponding to different electric energy indicators, it is also necessary to calculate the index weight coefficient corresponding to different electric energy indicators according to the index entropy. Specifically, the index weight coefficient can be calculated by the following formula:

[0113]

[0114] Among them, w j represents the index weight coefficient corresponding to the jth electric energy index, and n represents the total number of electric energy indicators

[0115]

[0116] 206. Based on the indicator data and indicator weight coefficients corresponding to different electric energy indicators, determine the access capacity coefficients of the distribution network for various access capacity planning schemes.

[0117] For the embodiment of the present invention, after determining the indicator weight coefficients corresponding to different electric energy indicators, it is also necessary to determine the access capacity coefficients of multiple access capacity planning schemes for the distribution network according to the indicator data and indicator weight coefficients corresponding to the different electric energy indicators. Based on this, step 206 specifically includes: multiplying the indicator data corresponding to the different electric energy indicators by the corresponding indicator weight coefficients to obtain weighted indicator data corresponding to the different electric energy indicators, and constructing a weighted decision matrix from the weighted indicator data; based on the weighted decision matrix, determining the positive ideal indicator data and negative ideal indicator data of the different electric energy indicators corresponding to the multiple access capacity planning schemes; based on the positive ideal indicator data and the negative ideal indicator data, determining the access capacity coefficients of the multiple access capacity planning schemes for the distribution network. Among them, the method for determining the access capacity coefficients of multiple access capacity planning schemes for the distribution network based on the positive ideal indicator data and the negative ideal indicator data includes: subtracting the positive ideal indicator data of different electric energy indicators from the weighted indicator data of the corresponding electric energy indicators of any access capacity planning scheme to obtain the positive ideal difference values ​​of different electric energy indicators corresponding to the arbitrary access capacity planning scheme; determining the positive ideal sum of squares of different positive ideal differences corresponding to the arbitrary access capacity planning scheme, and determining the positive ideal evaluation value corresponding to the arbitrary access capacity planning scheme based on the positive ideal sum of squares; subtracting the negative ideal indicators ... positive ideal sum of squares of different electric energy indicators from the weighted indicator data of the corresponding electric energy indicators of any access capacity planning scheme to obtain the positive ideal difference values ​​of different electric energy indicators corresponding to the arbitrary access capacity planning scheme; determining the positive ideal sum of squares of different positive ideal differences corresponding to the arbitrary access capacity planning scheme, and determining the positive The weighted index data of the corresponding electric energy index of any access capacity planning scheme is subtracted from the target data to obtain the negative ideal difference values ​​of different electric energy indexes corresponding to the arbitrary access capacity planning scheme; the negative ideal sum of squares of different negative ideal differences corresponding to the arbitrary access capacity planning scheme is determined, and based on the negative ideal sum of squares, the negative ideal evaluation value corresponding to the arbitrary access capacity planning scheme is determined; the positive ideal evaluation value is added to the negative ideal evaluation value to obtain the total ideal evaluation value, and the negative ideal evaluation value is divided by the total ideal evaluation value to obtain the access capacity coefficient corresponding to the arbitrary access capacity planning scheme.

[0118] Specifically, the weight vector in step 205 is multiplied by the normalized indicator matrix to obtain a weighted decision matrix. Alternatively, the indicator data corresponding to different electric energy indicators are multiplied by the corresponding indicator weight coefficients to obtain weighted indicator data corresponding to different electric energy indicators, and a weighted decision matrix R=(r ij ) m×n , where R represents the weighted decision matrix, r ijIt represents the weighted index data of the i-th access capacity planning scheme to be evaluated under the j-th power index. The weighted decision matrix is ​​formed by each weighted index data. Then, the positive ideal index data and the negative ideal index data of different power indexes corresponding to the multiple access capacity planning schemes are determined according to the weighted decision matrix. Specifically, the positive ideal index data and the negative ideal index data can be determined according to the following formula:

[0119]

[0120]

[0121] in, represents the positive ideal index data corresponding to the jth electric energy index, S j - Represents the negative ideal indicator data corresponding to the j-th electric energy indicator, max represents the maximum value, and min represents the minimum value. For example, there are 2 access capacity planning schemes and 3 electric energy indicators (indicator A, indicator B and indicator C). Determine the weighted indicator data 1 of the first access capacity planning scheme under indicator A, and determine the weighted indicator data 2 of the second access capacity planning scheme under indicator A. Then, determine the maximum weighted indicator data in the weighted indicator data 1 and the weighted indicator data 2, and determine the maximum weighted indicator data as the positive ideal indicator data corresponding to indicator A. At the same time, determine the minimum weighted indicator data in the weighted indicator data 1 and the weighted indicator data 2, and determine the minimum weighted indicator data as the negative ideal indicator data corresponding to indicator A. Furthermore, it is necessary to calculate the access capacity coefficients of multiple access capacity planning schemes for the distribution network based on the positive ideal indicator data and the negative ideal indicator data. Specifically, the access capacity times can be calculated by the following formula:

[0122]

[0123]

[0124]

[0125] in, represents the positive ideal difference, represents the negative ideal difference, represents the positive ideal evaluation value of the i-th access capacity planning scheme, represents the negative ideal evaluation value of the i-th access capacity planning scheme, η i represents the access capacity coefficient of the i-th access capacity planning scheme, The Prime Minister wants to evaluate the value.

[0126] 207. Based on the access capacity coefficient, determine a target access capacity planning scheme that matches the distribution network among multiple access capacity planning schemes, and determine the access capacity of the distributed generation for the distribution network based on the target access capacity planning scheme.

[0127] For the embodiment of the present invention, after determining the access capacity coefficients corresponding to different access capacity planning schemes, it is necessary to select a suitable access capacity planning scheme according to the access capacity coefficient. Based on this, step 207 specifically includes: determining a target access capacity coefficient greater than a preset threshold in the access capacity coefficient, and determining the access capacity planning scheme corresponding to the target access capacity coefficient as a target access capacity planning scheme that matches the power distribution network.

[0128] The preset threshold is set according to actual needs. Specifically, if the preset threshold is 0.9, the access capacity coefficient corresponding to access capacity planning scheme 1 is 0.5, the access capacity coefficient corresponding to access capacity planning scheme 2 is 0.85, and the access capacity coefficient corresponding to access capacity planning scheme 3 is 0.98, then access capacity planning scheme 3 is finally determined as the target access capacity planning scheme that matches the distribution network, and the access capacity corresponding to each distributed power source in access capacity planning scheme 3 is determined as the access capacity of the corresponding distributed power source for the distribution network.

[0129] In another embodiment of the present invention, for example, a 409-node distribution network system is used for calculation and analysis. The system has 9 distribution network main feeders, 409 load nodes, 6 tie switch lines, and a rated voltage of 10 kV. The 409-node network diagram is as follows: Figure 3As shown, this embodiment establishes a user-side resource access capacity calculation model based on the user-side resource access capacity evaluation index system, that is, an optimization model with the goal of maximizing the fit of each solution after TOPSIS method evaluation, and the capacity and scale of distributed energy or electric vehicle charging / swapping stations at each access point as decision variables. The optimization solution with the greatest fit is the calculation result of the access capacity of distributed energy or electric vehicle charging / swapping stations. The voltage offset constraint and three-phase unbalance constraint of the above model are reflected in the comprehensive evaluation calculation. The key constraint condition for the model solution is the configuration object capacity constraint, specifically the distributed energy single-point access capacity and electric vehicle facility capacity constraint, which will be specifically introduced in the subsequent case analysis. Construct a user-side resource access capacity evaluation index system. It should be noted that due to the data limitation of the 409-node distribution network system, some indicators are difficult to calculate. Computable indicators are selected for example calculation and analysis, including system average power outage frequency C111, system average power outage duration C112, system power supply reliability C113, power grid operation cost (typical day) C121, N-1 maximum load loss rate C131, low-carbon income index (typical day) C141, clean energy ratio C142, access point three-phase voltage imbalance qualification rate C211, distribution network voltage qualification rate C212, investment cost C221. This embodiment performs calculation and analysis on three typical distributed power sources in the distribution network, including wind turbines, photovoltaic panels and gas generators. The alternative access points for wind turbines include (8, 28, 49, 68, 274, 95, 111, 292, 125, 339, 133, 153, 384), the alternative access points for photovoltaic panels include (12, 34, 45, 63, 72, 109, 350, 358, 139, 400), and the alternative access points for gas generators include (18, 32, 44, 264, 88, 295, 326, 137, 159). According to the Technical Regulations for Distributed Power Sources Access to the Grid (QGDW1480-2015), the capacity constraints of each distributed power source are 0 to 6MW, the imbalance is less than 0.013, and the voltage deviation is less than ±7%. Typical load curves, wind power output, and photovoltaic output curves are as follows: Figure 4As shown in the figure, all are per-unit values, which are based on the maximum load value (161.2MW, 177.6MW and 177.4MW for the three phases respectively), the actual configuration capacity of wind power and the actual configuration capacity of photovoltaic power as the benchmark values; the distribution network electricity price implements peak and valley electricity prices, the valley period includes 0:00 to 6:00 and 22:00 to 24:00, the electricity price is 0.487 yuan / kWh, the peak period is 7:00 to 21:00, the electricity price is 0.977 yuan / kWh. The unit configuration capacity is taken as 0.01MW, and the investment costs of wind turbines, photovoltaics and gas generators are taken as 8 million yuan / MW, 9 million yuan / MW and 7 million yuan / MW respectively; the carbon emission cost of electricity purchased by the distribution network is taken as 0.197 yuan / kWh, and the carbon emission cost of gas turbines is taken as 0.18 yuan / kWh; based on the data and the user-side resource access capacity evaluation index system and calculation method proposed in steps 2 and 3, the genetic algorithm is used to solve the optimization model. The optimization results are shown in Table 1, where WT represents a wind turbine, PV represents a photovoltaic panel, and CT represents a gas generator.

[0130] Table 1 Calculation results of three-phase access capacity of distributed energy

[0131]

[0132]

[0133] From the optimization results, it can be seen that under the alternative node set, the access capacity of wind power is 43.95MW, the access capacity of photovoltaic power is 24.90MW, and the access capacity of gas generator is 27.54MW. From the calculation results and the distribution network topology diagram, it can be seen that on the main feeder with light load (F1), the access capacity of distributed energy is relatively weak, that is, the total capacity of wind turbines (node ​​8), photovoltaic power (node ​​12) and gas generators (node ​​18) is relatively small, especially the gas turbine with high operating cost is configured with a capacity of 0; while for the feeder with relatively heavy load (F4), the total capacity of wind turbines (nodes 68, 274), photovoltaic power (nodes 63, 72) and gas generators (node ​​264) is large, and the access capacity of distributed power is relatively high. Under this configuration scheme, the results of each indicator are shown in Table 2.

[0134] Table 2 Evaluation index results of distributed generation access capacity planning scheme

[0135]

[0136]

[0137] Compared with the situation without distributed power supply, the reliability level of the system has been improved to a certain extent, and the N-1 maximum load loss rate C131 has been reduced from 19% to 16%; in terms of other indicators, the typical daily operating cost of the distribution network has dropped from 8.169 million yuan to 7.452 million yuan. The access of new energy has reduced the typical daily network loss of the distribution network by about 50MWh, and further increased the low-carbon benefits of the distribution network; indicators such as the voltage qualification rate are basically not affected by the access of distributed power supply.

[0138] For example, there are many factors affecting the charging load of electric vehicle charging stations, which involve all aspects of power system operation, control and management. The factors affecting the charging load of charging stations include the battery characteristics, operation rules, energy supply methods, development scale and charging control methods of electric vehicles. The charging characteristics of different types of electric vehicles should be analyzed first to calculate their access capacity. Electric vehicle charging methods can be divided into battery replacement and direct charging. The charging load of charging stations is not only related to the charging method, but also deeply affected by the charging habits of users at the location. According to the charging method and location characteristics, this project divides electric vehicle charging stations into three categories: battery replacement stations, residential charging stations and public charging stations.

[0139] (1) Battery replacement stations. This type of charging station is mainly used for public passenger vehicles such as buses and sanitation vehicles. It uses battery replacement to charge electric vehicles. The replaced batteries will be charged in a centralized manner during the low-peak period of the power grid every day using conventional charging methods. Its charging load is relatively stable and can be controlled in a specific way; (2) Residential area charging stations. This type of charging station is located in residential areas and mainly provides charging services to residents. It uses conventional charging methods. Its charging load is relatively stable due to the influence of residents' daily activities. The coverage of residential areas will have a certain impact on the size of the charging load; (3) Public place charging stations. This type of charging station is located in public places such as central business districts and office buildings. It uses fast charging methods. Its charging load is highly random due to the influence of the density of pedestrian and vehicle traffic in public places, and is related to people's daily work and rest habits. Battery replacement stations are mainly used for public passenger vehicles. The operation rules of this type of vehicle are relatively stable, and the battery charging method is conventional charging. A comprehensive analysis of the influencing factors such as the number of public passenger vehicles in operation, the number of batteries in the battery swap station, and the load of the power system is conducted, and the following assumptions are made for the charging rules of the battery swap station: (1) The start time of charging satisfies the uniform distribution from 12:00 to 14:00 on the same day and from 23:00 to 5:00 the next day. (2) The initial state of charge (SOC) satisfies the normal distribution. (3) The battery is charged to full capacity each time. (4) The conventional charging power satisfies the normal distribution. The charging load curve of the battery swap station can be obtained by first calculating the charging load of each battery, then superimposing the start charging time of each battery with the charging time to obtain the charging load per hour in a day, and then calculating the charging load for the entire 24 hours of the day. After that, the charging load obtained by multiple Monte Carlo simulations is averaged to finally obtain the charging load curve for the entire day. The number of batteries charged during the day is 40, the number of batteries charged at night is 100, the number of Monte Carlo simulations is 1000, the battery capacity is 14kWh, and the conventional charging power is expected to be 3.5kWh. The charging load curve of the battery swap station is as follows: Figure 5As shown. The service objects of residential charging stations are residents in residential areas, and the charging method of electric vehicles is conventional charging. After comprehensively analyzing the three important influencing factors of charging power, daily mileage, and starting charging time, the following assumptions are made for the charging rules of residential charging stations: (1) Charging power, daily mileage, and starting charging time are independent random variables; (2) Residents charge immediately after returning from their last trip; (3) Considering the battery capacity and charging power, each charge can charge the battery to full power; (4) Conventional charging power satisfies the normal distribution; (5) Daily mileage satisfies the log-normal distribution, and the starting charging time satisfies the piecewise normal distribution. The charging load curve of the residential charging station can be obtained by first calculating the charging probability of each hour in a day, and then calculating the charging load of each hour according to the number of electric vehicles charged and the size of the charging power, and then calculating the charging load for the whole day for 24 hours. Finally, the charging load of multiple Monte Carlo simulations is averaged to obtain the charging load curve. Selecting the number of Monte Carlo simulations as 10 and the number of electric vehicles as 500, the charging load curve of the residential charging station is as follows Figure 6 As shown. Public charging stations are mainly located in central business districts, and electric vehicles are charged by fast charging. A comprehensive analysis of the three influencing factors of starting charging time, starting SOC, and ending SOC is made, and the following assumptions are made about the charging rules of public charging stations: (1) Starting charging time, starting SOC, and ending SOC are independent random variables; (2) Starting SOC and ending SOC both satisfy the normal distribution; (3) The fast charging power satisfies the normal distribution; (4) The starting charging time of electric vehicles at public charging stations satisfies the uniform distribution of six segments. The number of Monte Carlo simulations is selected as 10, the number of electric vehicles is 300, the expected fast charging power is 24kW / h, and the segmented time periods are 0-7, 7-11, 11-16, 16-19, 19-22, and 22-24, respectively. The charging load curve of the public charging station is as follows: Figure 7 As shown. Based on the above research on the three types of electric vehicle charging loads and the distributed power optimization configuration scheme in Section 1.1, the access capacity of the three types of electric vehicle charging capacity is calculated. Among them, the access points of the battery replacement station are (32, 72, 290, 137), the access method is three-phase access, and the upper limit of the single-point access scale is 100 (i.e. the number of electric vehicles or batteries); the access points of the residential charging station are (23, 38, 57, 78, 100, 292, 358, 139, 415, 395), the access method is single-phase access (phase A), and the upper limit of the single-point access scale is 500; the access points of the public charging station are (12, 48, 94, 339, 384), the access method is three-phase access, and the upper limit of the single-point access scale is 300. The electric vehicle emission reduction coefficient is taken as 0.0426 yuan / kWh, that is, for every kilowatt-hour of electricity consumed by an electric vehicle, the emission cost can be reduced by 0.0426 yuan.

[0140] Table 3 Calculation results of electric vehicle access capacity

[0141]

[0142] The calculation results of the access capacity of three types of electric vehicle charging / swapping stations are shown in Table 3, and the calculation results of each indicator under this scheme are shown in Table 4.

[0143] Table 4 Evaluation index results of electric vehicle access capacity planning scheme

[0144]

[0145]

[0146] Compared with the evaluation index results of the distributed power configuration scheme, it can be seen that the introduction of electric vehicles has increased the operating cost of the power grid, and the N-1 maximum load loss rate has also increased; in terms of low-carbon benefit indicators, electric vehicles use a total of about 100,000 kWh of electricity (on a typical day), increasing carbon emission benefits by 4,752.6 yuan; due to the increase in load, the proportion of clean energy in the distribution network has decreased to a certain extent; in terms of three-phase voltage imbalance, the single-phase access of charging equipment in residential areas has increased the load on phase A of the distribution network, thereby reducing the three-phase voltage imbalance pass rate at the access point of electric vehicle charging facilities. Therefore, it is necessary to reasonably distribute the distribution of charging facilities in residential areas to ensure better voltage quality of the distribution network.

[0147] According to another method for determining the access capacity of a distributed power source provided by the present invention, compared with the current method of connecting a distributed power source to a distribution network by manually selecting an access capacity planning scheme, the present invention determines a plurality of access capacity planning schemes for the distributed power source for the distribution network, and obtains indicator data corresponding to different electric energy indicators of the distribution network under the plurality of access capacity planning schemes; and uses a preset algorithm to process the different indicator data under the plurality of access capacity planning schemes to obtain indicator weight coefficients corresponding to the different electric energy indicators; then, based on the indicator data and indicator weight coefficients corresponding to the different electric energy indicators, determines the access capacity coefficients of the plurality of access capacity planning schemes for the distribution network; finally, based on the access capacity coefficient, determines a target access capacity planning scheme that matches the distribution network from the plurality of access capacity planning schemes, and determines the access capacity of the distributed power source for the distribution network based on the target access capacity planning scheme. Therefore, by obtaining the indicator data corresponding to different electric energy indicators of the distribution network under a variety of access capacity planning schemes, and determining the indicator weights corresponding to different electric energy indicators, the access capacity coefficients of the various access capacity planning schemes are determined according to the indicator weights and indicator data, and finally the access capacity planning scheme that matches the distribution network is selected according to the access capacity coefficient to determine the access capacity. By selecting the access capacity planning scheme through a comprehensive analysis of the indicator data corresponding to different electric energy indicators, the accuracy of determining the access capacity planning scheme can be improved, and then the accuracy of determining the access capacity of distributed power sources can be improved, while reducing the operating pressure of the distribution network, thereby avoiding unnecessary power supply losses.

[0148] Further, as Figure 1 In a specific implementation, an embodiment of the present invention provides a device for determining the access capacity of a distributed power source, such as Figure 8 As shown, the device includes: an acquisition unit 31, a processing unit 32, a first determination unit 33 and a second determination unit 34.

[0149] The acquisition unit 31 may be used to determine a plurality of access capacity planning schemes of the distributed power source for the power distribution network, and to acquire index data corresponding to different electric energy indexes of the power distribution network under the plurality of access capacity planning schemes.

[0150] The processing unit 32 may be configured to process different indicator data under the multiple access capacity planning schemes using a preset algorithm to obtain indicator weight coefficients corresponding to the different electric energy indicators.

[0151] The first determination unit 33 may be configured to determine access capacity coefficients of the plurality of access capacity planning schemes for the power distribution network based on the indicator data and indicator weight coefficients corresponding to the different electric energy indicators.

[0152] The second determination unit 34 can be used to determine a target access capacity planning scheme matching the distribution network among the multiple access capacity planning schemes based on the access capacity coefficient, and determine the access capacity of the distributed power source for the distribution network based on the target access capacity planning scheme.

[0153] In specific application scenarios, in order to determine the indicator weight coefficients corresponding to different power indicators, such as Fig. 9 As shown, the processing unit 32 includes a construction module 321 , a processing module 322 , and a first determination module 323 .

[0154] The construction module 321 may be used to construct an electric energy indicator matrix based on different electric energy indicator data corresponding to the multiple access capacity planning solutions.

[0155] The processing module 322 may be used to perform normalization processing on each indicator data in the electric energy indicator matrix to obtain a normalized matrix.

[0156] The first determination module 323 may be configured to determine the index entropies corresponding to the different electric energy indexes based on the normalization matrix.

[0157] The first determination module 323 may be specifically configured to determine the indicator weight coefficients corresponding to the different electric energy indicators based on the indicator entropy.

[0158] In a specific application scenario, in order to determine the index entropy corresponding to different electric energy indicators, the first determination module 323 can be specifically used to add the index data of the target electric energy indicators corresponding to the multiple access capacity planning schemes in the normalized matrix to obtain the sum of the target electric energy index data; add 1 to the index data corresponding to each target electric energy indicator in the normalized matrix, and divide the addition results by the sum of the target electric energy index data to obtain the index evaluation value corresponding to the target electric energy indicator; based on the index evaluation value, determine the index entropy corresponding to the target electric energy indicator.

[0159] In a specific application scenario, in order to determine the indicator weight coefficients corresponding to different electric energy indicators, the first determination module 323 can be specifically used to add the indicator entropies corresponding to the different electric energy indicators to obtain the total indicator entropy; subtract the total number of indicators corresponding to different electric energy indicators from the total indicator entropy to obtain a weight evaluation value; subtract 1 from the indicator entropies corresponding to different electric energy indicators, and divide the subtraction results by the weight evaluation values ​​to obtain the indicator weight coefficients corresponding to the different electric energy indicators.

[0160] In a specific application scenario, in order to determine the access capacity coefficients of various access capacity planning schemes for a power distribution network, the first determination unit 33 includes a multiplication module 331 and a second determination module 332 .

[0161] The multiplication module 331 can be used to multiply the indicator data corresponding to different electric energy indicators by the corresponding indicator weight coefficients to obtain weighted indicator data corresponding to the different electric energy indicators, and construct a weighted decision matrix based on the weighted indicator data.

[0162] The second determination module 332 may be configured to determine, based on the weighted decision matrix, positive ideal indicator data and negative ideal indicator data of different electric energy indicators corresponding to the multiple access capacity planning schemes.

[0163] The second determination module 332 may be specifically configured to determine access capacity coefficients of the plurality of access capacity planning schemes for the power distribution network based on the positive ideal indicator data and the negative ideal indicator data.

[0164] In a specific application scenario, in order to determine the access capacity coefficients of multiple access capacity planning schemes for the distribution network, the second determination module 332 can be specifically used to subtract the positive ideal indicator data of different electric energy indicators from the weighted indicator data of the corresponding electric energy indicators of any access capacity planning scheme to obtain the positive ideal difference values ​​of different electric energy indicators corresponding to the arbitrary access capacity planning scheme; determine the positive ideal sum of squares of different positive ideal differences corresponding to the arbitrary access capacity planning scheme, and determine the positive ideal evaluation value corresponding to the arbitrary access capacity planning scheme based on the positive ideal sum of squares; subtract the positive ideal difference values ​​of different electric energy indicators from the weighted indicator data of the corresponding electric energy indicators of any access capacity planning scheme to obtain the positive ideal difference values ​​of different electric energy indicators corresponding to the arbitrary access capacity planning scheme; determine the positive ideal sum of squares of different positive ideal differences corresponding to the arbitrary access capacity planning scheme, and determine the positive ideal evaluation value corresponding to the arbitrary access capacity planning scheme based on the positive ideal sum of squares; The ideal indicator data is subtracted from the weighted indicator data of the corresponding electric energy indicator of any access capacity planning scheme to obtain the negative ideal difference values ​​of different electric energy indicators corresponding to the arbitrary access capacity planning scheme; the negative ideal sum of squares of different negative ideal differences corresponding to the arbitrary access capacity planning scheme is determined, and based on the negative ideal sum of squares, the negative ideal evaluation value corresponding to the arbitrary access capacity planning scheme is determined; the positive ideal evaluation value is added to the negative ideal evaluation value to obtain the total ideal evaluation value, and the negative ideal evaluation value is divided by the total ideal evaluation value to obtain the access capacity coefficient corresponding to the arbitrary access capacity planning scheme.

[0165] In a specific application scenario, in order to determine a target access capacity planning scheme that matches the power distribution network, the second determination unit 34 can be specifically used to determine a target access capacity coefficient greater than a preset threshold in the access capacity coefficient, and determine the access capacity planning scheme corresponding to the target access capacity coefficient as the target access capacity planning scheme that matches the power distribution network.

[0166] It should be noted that for other corresponding descriptions of the functional modules involved in the device for determining the access capacity of a distributed power source provided in an embodiment of the present invention, reference can be made to Figure 1 The corresponding description of the method shown will not be repeated here.

[0167] Based on the above Figure 1 The method shown, accordingly, an embodiment of the present invention also provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the following steps are implemented: determining a plurality of access capacity planning schemes for a distributed power source for a distribution network, and obtaining indicator data corresponding to different electric energy indicators of the distribution network under the plurality of access capacity planning schemes; using a preset algorithm to process the different indicator data under the plurality of access capacity planning schemes to obtain indicator weight coefficients corresponding to the different electric energy indicators; based on the indicator data and indicator weight coefficients corresponding to the different electric energy indicators, determining the access capacity coefficients of the plurality of access capacity planning schemes for the distribution network; based on the access capacity coefficient, determining a target access capacity planning scheme that matches the distribution network from the plurality of access capacity planning schemes, and based on the target access capacity planning scheme, determining the access capacity of the distributed power source for the distribution network.

[0168] Based on the above Figure 1 The method shown and Figure 8 The embodiment of the device shown in the figure, the embodiment of the present invention also provides a physical structure diagram of a computer device, such as Fig.10 As shown, the computer device includes: a processor 41, a memory 42, and a computer program stored in the memory 42 and executable on the processor, wherein the memory 42 and the processor 41 are both arranged on a bus 43, and when the processor 41 executes the program, the following steps are implemented: determining a plurality of access capacity planning schemes for a distributed power source to a distribution network, and obtaining indicator data corresponding to different electric energy indicators of the distribution network under the plurality of access capacity planning schemes; processing the different indicator data under the plurality of access capacity planning schemes using a preset algorithm to obtain indicator weight coefficients corresponding to the different electric energy indicators; determining the access capacity coefficients of the plurality of access capacity planning schemes for the distribution network based on the indicator data and indicator weight coefficients corresponding to the different electric energy indicators; determining a target access capacity planning scheme matching the distribution network from among the plurality of access capacity planning schemes based on the access capacity coefficient, and determining the access capacity of the distributed power source for the distribution network based on the target access capacity planning scheme.

[0169] Through the technical solution of the present invention, the present invention determines multiple access capacity planning schemes for distributed power sources for a distribution network, and obtains indicator data corresponding to different electric energy indicators of the distribution network under the multiple access capacity planning schemes; and uses a preset algorithm to process the different indicator data under the multiple access capacity planning schemes to obtain indicator weight coefficients corresponding to the different electric energy indicators; then, based on the indicator data and indicator weight coefficients corresponding to the different electric energy indicators, determines the access capacity coefficients of the multiple access capacity planning schemes for the distribution network; finally, based on the access capacity coefficient, determines a target access capacity planning scheme that matches the distribution network among the multiple access capacity planning schemes, and determines the access capacity of the distributed power source for the distribution network based on the target access capacity planning scheme. Therefore, by obtaining the indicator data corresponding to different electric energy indicators of the distribution network under a variety of access capacity planning schemes, and determining the indicator weights corresponding to different electric energy indicators, the access capacity coefficients of the various access capacity planning schemes are determined according to the indicator weights and indicator data, and finally the access capacity planning scheme that matches the distribution network is selected according to the access capacity coefficient to determine the access capacity. By selecting the access capacity planning scheme through a comprehensive analysis of the indicator data corresponding to different electric energy indicators, the accuracy of determining the access capacity planning scheme can be improved, and then the accuracy of determining the access capacity of distributed power sources can be improved, while reducing the operating pressure of the distribution network, thereby avoiding unnecessary power supply losses.

[0170] Obviously, those skilled in the art should understand that the above modules or steps of the present invention can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, and optionally, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order than here, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0171] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for determining the access capacity of a distributed power source, characterized in that: include: Determine multiple access capacity planning schemes of distributed power sources for a power distribution network, and obtain indicator data corresponding to different power indicators of the power distribution network under the multiple access capacity planning schemes; Using a preset algorithm to process different indicator data under the multiple access capacity planning schemes to obtain indicator weight coefficients corresponding to the different power indicators; Determining access capacity coefficients of the plurality of access capacity planning schemes for the power distribution network based on the indicator data and indicator weight coefficients corresponding to the different electric energy indicators; Based on the access capacity coefficient, determine a target access capacity planning scheme matching the power distribution network from among the multiple access capacity planning schemes, and determine the access capacity of the distributed power source for the power distribution network based on the target access capacity planning scheme; The method of using a preset algorithm to process different indicator data under the multiple access capacity planning schemes to obtain indicator weight coefficients corresponding to the different power indicators includes: Based on different electric energy indicator data corresponding to the multiple access capacity planning schemes, construct an electric energy indicator matrix; Normalizing each indicator data in the electric energy indicator matrix to obtain a normalized matrix; Based on the normalized matrix, determining the index entropy corresponding to the different electric energy indicators respectively; Based on the indicator entropy, determining the indicator weight coefficients corresponding to the different electric energy indicators respectively; Wherein, determining the access capacity coefficients of the multiple access capacity planning schemes for the power distribution network based on the indicator data and indicator weight coefficients corresponding to the different electric energy indicators includes: The index data corresponding to different electric energy indexes are multiplied by the corresponding index weight coefficients to obtain weighted index data corresponding to different electric energy indexes, and a weighted decision matrix is ​​constructed based on the weighted index data; Based on the weighted decision matrix, determining positive ideal indicator data and negative ideal indicator data of different electric energy indicators corresponding to the multiple access capacity planning schemes; Based on the positive ideal index data and the negative ideal index data, access capacity coefficients of the multiple access capacity planning schemes for the power distribution network are determined.

2. The method according to claim 1, characterized in that The determining, based on the normalized matrix, the index entropies corresponding to the different electric energy indicators respectively comprises: Adding the target power index data corresponding to the various access capacity planning schemes in the normalized matrix to obtain the total target power index data; Add 1 to the index data corresponding to each target electric energy index in the normalized matrix, and divide the addition results by the sum of the target electric energy index data to obtain the index evaluation value corresponding to the target electric energy index; Based on the indicator evaluation value, an indicator entropy corresponding to the target electric energy indicator is determined.

3. The method according to claim 1, characterized in that The determining, based on the indicator entropy, indicator weight coefficients corresponding to the different electric energy indicators respectively includes: Adding the index entropies corresponding to the different electric energy indexes to obtain a total index entropy; Subtract the total number of indicators corresponding to different electric energy indicators from the total indicator entropy to obtain a weighted evaluation value; 1 is subtracted from the index entropy corresponding to different electric energy indicators, and the subtraction results are divided by the weight evaluation values ​​to obtain the index weight coefficients corresponding to the different electric energy indicators.

4. The method according to claim 1, characterized in that: The determining, based on the positive ideal index data and the negative ideal index data, access capacity coefficients of the plurality of access capacity planning schemes for the power distribution network comprises: Subtracting the positive ideal index data of different electric energy indicators from the weighted index data of the corresponding electric energy indicators of any access capacity planning scheme to obtain the positive ideal difference values ​​of the different electric energy indicators corresponding to the any access capacity planning scheme; Determine a positive ideal sum of squares of different positive ideal differences corresponding to the arbitrary access capacity planning scheme, and determine a positive ideal evaluation value corresponding to the arbitrary access capacity planning scheme based on the positive ideal sum of squares; Subtracting the negative ideal index data of different electric energy indicators from the weighted index data of the corresponding electric energy indicators of any access capacity planning scheme to obtain the negative ideal difference values ​​of different electric energy indicators corresponding to the any access capacity planning scheme; Determine the negative ideal sum of squares of different negative ideal differences corresponding to the arbitrary access capacity planning scheme, and determine the negative ideal evaluation value corresponding to the arbitrary access capacity planning scheme based on the negative ideal sum of squares; The positive ideal evaluation value is added to the negative ideal evaluation value to obtain a total ideal evaluation value, and the negative ideal evaluation value is divided by the total ideal evaluation value to obtain an access capacity coefficient corresponding to the arbitrary access capacity planning scheme.

5. The method according to claim 1, characterized in that The determining, based on the access capacity coefficient, a target access capacity planning scheme matching the power distribution network from among the multiple access capacity planning schemes comprises: A target access capacity coefficient greater than a preset threshold is determined among the access capacity coefficients, and an access capacity planning scheme corresponding to the target access capacity coefficient is determined as a target access capacity planning scheme matching the power distribution network.

6. A device for determining the access capacity of a distributed power source, characterized in that: include: An acquisition unit, used to determine a plurality of access capacity planning schemes of a distributed power source for a power distribution network, and to acquire index data corresponding to different electric energy indicators of the power distribution network under the plurality of access capacity planning schemes; A processing unit, configured to process different indicator data under the plurality of access capacity planning schemes using a preset algorithm to obtain indicator weight coefficients corresponding to the different electric energy indicators; A first determining unit, configured to determine access capacity coefficients of the plurality of access capacity planning schemes for the power distribution network based on the indicator data and indicator weight coefficients corresponding to the different electric energy indicators; A second determining unit is configured to determine, based on the access capacity coefficient, a target access capacity planning scheme that matches the power distribution network from among the multiple access capacity planning schemes, and determine, based on the target access capacity planning scheme, the access capacity of the distributed power source for the power distribution network; The processing unit is used to construct an electric energy indicator matrix based on the different electric energy indicator data corresponding to the multiple access capacity planning schemes; normalize the various indicator data in the electric energy indicator matrix to obtain a normalized matrix; determine the indicator entropy corresponding to the different electric energy indicators based on the normalized matrix; determine the indicator weight coefficients corresponding to the different electric energy indicators based on the indicator entropy; The first determination unit is used to multiply the indicator data corresponding to different electric energy indicators by the corresponding indicator weight coefficients to obtain weighted indicator data corresponding to the different electric energy indicators, and construct a weighted decision matrix from the weighted indicator data; based on the weighted decision matrix, determine the positive ideal indicator data and negative ideal indicator data of different electric energy indicators corresponding to the multiple access capacity planning schemes; based on the positive ideal indicator data and negative ideal indicator data, determine the access capacity coefficients of the multiple access capacity planning schemes for the distribution network.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

8. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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