A power distribution network distributed photovoltaic site selection and capacity determination method considering photovoltaic reactive power support

Abstract

The application discloses a distributed photovoltaic site selection and capacity determination method for distribution network considering photovoltaic reactive power support, belongs to the field of distributed photovoltaic planning of distribution network, and can consider the static voltage stability index and loss sensitivity of nodes when site selection of the distributed photovoltaic of the distribution network is carried out, and consider the reactive power output condition of the photovoltaic power supply and the service life damage of the power device when capacity determination is carried out, so that the operation cost and equipment investment cost of the power grid can be reduced.

Description

Technical Field

[0001] This invention belongs to the field of distributed photovoltaic planning in distribution networks, and specifically relates to a method for site selection and capacity determination of distributed photovoltaic in distribution networks that takes into account photovoltaic reactive power support. Background Technology

[0002] In recent years, both international and domestic demands for energy conservation, emission reduction, and the vigorous development of clean energy have become increasingly urgent. New energy power generation, represented by wind power and photovoltaics, has become an important component of my country's efforts to adjust its energy structure and achieve its "dual carbon" goals. However, new energy power generation is characterized by randomness, volatility, indirectness, and weak regulation; large-scale grid connection can impact the stability of the power grid.

[0003] For distribution networks, distributed photovoltaic (PV) integration changes the unidirectional power flow, transforming a radial passive grid into an active ring network. Compared to radial distribution networks, planning a distribution network with distributed generation is more challenging. Research results show that the topology, line parameters, and distributed access points and capacities of the distribution system determine the impact of distributed integration on network losses, power quality, and reliability. Load node voltage variations are closely related to the distance between distributed access points and the bus, the access capacity, and the active and reactive power ratio. Rational planning of distributed PV access locations and capacities, as well as effectively coordinating the active and reactive power ratios of distributed generation output, can ensure that distributed generation output provides good support for the distribution network voltage, especially the terminal voltage. Therefore, scientific and intelligent distributed generation planning schemes can reduce the impact of distributed generation on the safe and economical operation of the distribution network.

[0004] To address the existing problems, this invention proposes a method for site selection and capacity determination of distributed photovoltaic (PV) power in distribution networks that considers reactive power support. First, based on the electrical distances between nodes in the distribution network, the electrical distance from each node to the first node is calculated. Considering the power balance within the cluster, distributed PV clusters are divided in the distribution network considering reactive power support. An optimized site selection scheme for distributed PV is obtained using a comprehensive evaluation index considering static voltage stability and loss sensitivity. The lifetime damage of PV power sources, PV access capacity, and annual network losses of the distribution network are introduced into the objective function, establishing an optimized configuration model for distributed PV capacity in the distribution network considering reactive power support. Finally, a site selection and capacity determination scheme for distributed PV power in the distribution network considering reactive power support is obtained. This method considers the static voltage stability index and loss sensitivity of nodes when selecting distributed PV sites in the distribution network, and takes into account the reactive power output of PV power sources and the lifetime damage of power devices when determining capacity, which helps reduce the operating costs and equipment investment costs of the power grid. Summary of the Invention

[0005] The purpose of this invention is to propose a method for site selection and capacity determination of distributed photovoltaic (PV) power grids that considers PV reactive power support, such as... Figure 1As shown in the attached diagram. The following is a detailed explanation of each step.

[0006] Historical data of the distribution network is acquired, including: power load data, photovoltaic output data, distribution network irradiance, ambient temperature, topology, and line parameters, and this data is used as a basis for subsequent reactive power and voltage optimization control of the distribution network.

[0007] Using the first node of the distribution network as the reference node, the electrical distance from each node to the first node is calculated based on the electrical distance between each node in the distribution network. Taking into account the power balance within the cluster, the distribution network cluster division result is obtained.

[0008] Electrical distance L between nodes ij The calculation method is as follows:

[0009]

[0010] In the formula, ΔV is the voltage change; ΔP is the change in active power injected between nodes; S VP The sensitivity of voltage amplitude to changes in active power; S VP，ij The sensitivity of the voltage magnitude at node i to changes in the injected active power at node j; d ij Let be the ratio of the voltage change at node j to the voltage change at node i when the active power at node j changes.

[0011] The steps for dividing a distributed photovoltaic cluster in a distribution network considering photovoltaic reactive power support are as follows: take the first node of the distribution network as the reference node, calculate the electrical distance from each node to the first node based on the electrical distance between each node of the distribution network, and take into account the power balance constraints within the same cluster, so that the number of nodes within each cluster is similar.

[0012] Using static voltage stability index and loss sensitivity as indicators, the entropy weight method is used to obtain the comprehensive evaluation index for distributed photovoltaic (PV) site selection in distribution networks that takes into account PV reactive power support.

[0013] Static voltage stability index (SVSI):

[0014] The SVSI (Static Voltage Indicator) is used to determine the impact of distributed photovoltaic (PV) grid connections on voltage support. The closer the SVSI value is to 1, the worse the system's static voltage stability. Therefore, deploying distributed PV at nodes with higher SVSI values ​​makes it easier to reduce node voltage deviation.

[0015] In the formula, SVSI is the static voltage stability index; P b,load Q b,load These represent the active and reactive loads of node b, respectively; Xab U is the branch reactance of branch ab; a Let be the node voltage of node a.

[0016] Loss sensitivity factors (LSFs):

[0017] From the perspective of grid losses, network nodes with high loss sensitivity are more likely to reduce system grid losses after being connected to distributed photovoltaics. The mathematical model of LSFs is as follows:

[0018] In the formula, LSFs is the loss sensitivity; P ab,L R represents the active power loss of branch ab; ab U is the branch resistance of branch ab; b Let be the node voltage of node b.

[0019] The entropy weight method is used to assign weights to the static voltage stability index and loss sensitivity.

[0020] Entropy weighting method weighting steps:

[0021] a. Data Standardization: First, the various indicators are dedimensionalized. Assume there are m indicators:

[0022] X1,X2,…,X m

[0023] In the formula, X i ={x1,x2,…,x n}

[0024] Assume the standardized values ​​of each indicator are Y1, Y2, ..., Y m

[0025] So (When the indicator is positive) or (When the indicator is negative)

[0026] b. Calculate the ratio of each indicator under each scheme, that is, the proportion of the j-th indicator in the i-th scheme, also known as calculating the variation of the indicator.

[0027]

[0028] Calculate the information entropy of each indicator: According to the definition of information entropy in information theory, the information entropy of a set of data is:

[0029] In the formula, E j ≥0; if p ij =0, define E j =0.

[0030] Determine the weights of each indicator: Based on the formula for calculating information entropy, calculate the information entropy of each indicator as E1, E2, ... E m

[0031] The weights of each indicator are calculated using information entropy:

[0032] In the formula, k is the number of indicators, i.e., k = m.

[0033] Finally, calculate the comprehensive score for each solution.

[0034] Based on historical data of the distribution network, the results of distribution network cluster division, and the distribution network distributed photovoltaic site selection optimization scheme, a distribution network distributed photovoltaic capacity optimization configuration model considering photovoltaic reactive power support is established.

[0035] The objective function consists of photovoltaic power generation lifetime degradation, photovoltaic grid connection capacity, and annual distribution network losses:

[0036]

[0037] In the formula, ω1, ω2, and ω3 are weighting coefficients; f1, f2, and f3 represent sub-objective functions; LC represents lifetime damage, used to describe the lifetime loss of power devices; P represents annual network loss; loss,t,n For cluster n in the distribution network, the network loss of each line in a certain time period t; S represents the sum of the distributed photovoltaic capacity connected to each node; PV,i,n This represents the photovoltaic capacity connected to node i in cluster n.

[0038] Constraints:

[0039]

[0040]

[0041]

[0042]

[0043]

[0044]

[0045]

[0046]

[0047]

[0048] SOC min ≤SOC≤SOC max

[0049] 0≤P ES (t)≤P ES-max

[0050]

[0051]

[0052] In the formula, ω1, ω2, and ω3 are weighting coefficients; j, k, and l are distribution network bus indices; J(k) and L(k) are the parent node and child node, respectively; P jk Q jk r jk x jk with I jk These are the active power, reactive power, line resistance, line inductance, and line current from bus j to bus k, respectively. V k These are the photovoltaic active power at bus k, the reactive power of the photovoltaic inverter, the active load, the reactive load, and the bus voltage, respectively. V is the upper limit of the line current between bus j and bus k; min V max These are the upper and lower limits of the distribution network voltage. The photovoltaic grid connection capacity at busbar k; SOC max SOC min These are the upper and lower limits of the state of charge of the energy storage device, respectively; P ES-max , These represent the maximum charging / discharging power and installed capacity of the energy storage device at node i, respectively; P DPVi P load η represents the active power connected to node i and the total system load, respectively, and η is the ratio of the total connected capacity to the total load.

[0053] A method for site selection and capacity determination of distributed photovoltaic power in distribution networks that considers photovoltaic reactive power support and the reactive power output of photovoltaic power sources.

[0054] Output of photovoltaic power supply in reactive power compensation mode:

[0055]

[0056] In the formula, where This refers to the output phase voltage of the inverter. R is the three-phase phase voltage of the grid; R+jX is the grid-connected buffer inductor impedance of the grid-connected inverter; δ is the angle between the output phase voltage of the inverter and the three-phase phase voltage of the grid.

[0057]

[0058] The active and reactive power injected into the distribution network by photovoltaic power sources are as follows:

[0059]

[0060] a. When δ<0, corresponding to P>0, the inverter absorbs power from the grid, part of which compensates for the inverter's consumption, and the other part is used for reactive power compensation equipment, thereby increasing the voltage of the bus.

[0061] b. When δ>0, corresponding to P<0, the inverter provides power to the grid, that is, the photovoltaic power supply is in the power generation state, and the bus voltage value will decrease to a certain extent.

[0062] c. When V s <V g When Q < 0, the inverter outputs reactive power (capacitive).

[0063] d. When V s >V g When Q>0, the inverter absorbs reactive power (inductive).

[0064] The distributed photovoltaic capacity optimization model for distribution networks that considers photovoltaic reactive power support takes power device lifetime damage as one of the optimization objectives.

[0065] During operation, power devices are subjected to varying degrees of thermal stress on different materials, which, over time, can lead to thermal fatigue failure. The lifespan of a power device is related to the junction temperature fluctuation amplitude ΔT. j and the average junction temperature T during the thermal shock cycle m Furthermore, fluctuations in the junction temperature of power devices and increases in the average junction temperature will reduce the lifespan of power devices.

[0066] The historical data of the distribution network, the distribution network cluster division results, and the distribution network distributed photovoltaic site selection optimization scheme are input into the distribution network distributed photovoltaic capacity optimization configuration model considering photovoltaic reactive power support described in S5. The improved particle swarm algorithm is used to solve the model to obtain the distribution network distributed photovoltaic access capacity optimization scheme that minimizes the photovoltaic power source lifetime damage of the target node and the annual network loss of the distribution network, and maximizes the distributed photovoltaic access capacity.

[0067] As can be seen, the distributed photovoltaic (PV) power grid location and capacity determination method for distribution networks considering the reactive power support capability of PV power sources disclosed in this application constructs a comprehensive evaluation index for distributed PV power grid location considering reactive power support based on static voltage stability index and loss sensitivity. Using historical data of the distribution network and the distribution network cluster division results, the indexes of each node are calculated to determine the optimal location scheme for distributed PV power grid location. A distributed PV capacity optimization configuration model considering reactive power support is constructed with PV power source lifetime damage, PV access capacity, and annual network loss of the distribution network as optimization objectives. Combining historical data of the distribution network, distribution network cluster division results, and the distributed PV power grid location optimization scheme, the optimal configuration model for distributed PV capacity considering reactive power support is solved to obtain an optimal distributed PV access capacity scheme that minimizes the PV power source lifetime damage and annual network loss of the target nodes while maximizing the distributed PV access capacity. Finally, a distributed PV power grid location and capacity determination scheme considering reactive power support is obtained. Attached Figure Description

[0068] Figure 1 Overall Flowchart

[0069] Figure 2 69-node distribution network topology

[0070] Figure 3 Relationship between power device temperature and its lifetime

[0071] Figure 4 Flowchart of the improved particle swarm optimization algorithm

[0072] Figure 5 Vector Relationship of Photovoltaic Inverters in Reactive Power Compensation Mode Detailed Implementation

[0073] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0074] Obtain historical data of the power distribution network, including: power load data, photovoltaic output data, power distribution network irradiance, ambient temperature, topology, and line parameters.

[0075] Using the first node of the distribution network as the reference node, the electrical distance from each node to the first node is calculated based on the electrical distance between each node of the distribution network. Considering the power balance within the cluster, the distributed photovoltaic cluster of the distribution network considering photovoltaic reactive power support is divided, and the distribution network cluster division result is obtained.

[0076] Using static voltage stability index and loss sensitivity as indicators, the entropy weight method is used to determine the comprehensive evaluation index for distributed photovoltaic (PV) site selection in distribution networks that takes into account PV reactive power support. The historical data of the distribution network and the results of distribution network cluster division are then substituted into the comprehensive evaluation index to determine the optimal site selection scheme for distributed PV in distribution networks.

[0077] Establish a distributed photovoltaic capacity optimization configuration model for distribution networks that takes into account photovoltaic reactive power support.

[0078] By combining historical data of the distribution network, the results of distribution network cluster division, and the optimization scheme for distributed photovoltaic site selection in the distribution network, an improved particle swarm optimization algorithm is used to solve the problem and obtain the optimal access capacity scheme.

[0079] The above operation process can yield the optimal access location scheme and its optimal access capacity scheme for distributed photovoltaic power.

[0080] This application discloses a distributed photovoltaic (PV) site selection and capacity determination method considering PV reactive power support. A comprehensive evaluation index for distributed PV site selection in a distribution network considering PV reactive power support is constructed based on the static voltage stability index and loss sensitivity. An optimal configuration model for distributed PV capacity in a distribution network considering PV reactive power support is constructed with PV power source lifetime damage, PV access capacity, and annual network loss of the distribution network as objective functions. The optimal configuration model is solved based on historical data of the distribution network, distribution network cluster division results, and distributed PV site selection optimization schemes to obtain the optimal access location scheme and the optimal access capacity scheme for distributed PV.

Claims

1. A method for site selection and capacity determination of distributed photovoltaic (PV) power grids considering PV reactive power support, characterized in that, This method mainly includes the following steps: S1: Obtain historical data of the distribution network, including: power load data, photovoltaic output data, distribution network irradiance, ambient temperature, topology, and line parameters; S2: Divide the distribution network cluster. Using the historical data of the distribution network obtained in S1, take the first node of the distribution network as the reference node. Based on the electrical distance between each node of the distribution network, calculate the electrical distance from each node to the first node. Considering the power balance within the cluster, divide the distributed photovoltaic cluster of the distribution network considering the reactive power support of photovoltaics, and obtain the distribution network cluster division result. S3: Determine the comprehensive evaluation index. Using the static voltage stability index and loss sensitivity as indicators, determine the comprehensive evaluation index for distributed photovoltaic site selection in the distribution network considering photovoltaic reactive power support through the entropy weight method. S4: Determine the photovoltaic site selection optimization scheme. Using the historical data of the distribution network obtained in S1 and the distribution network cluster division results obtained in S2, calculate the comprehensive evaluation index of distributed photovoltaic site selection of the distribution network considering photovoltaic reactive power support as described in S3, and determine the distributed photovoltaic site selection optimization scheme of the distribution network. S5: Construct a photovoltaic capacity optimization configuration model, taking photovoltaic power source lifetime damage, photovoltaic access capacity and annual network loss of distribution network as optimization objectives, and construct a distributed photovoltaic capacity optimization configuration model for distribution network that considers photovoltaic reactive power support; S6: Determine the photovoltaic access capacity optimization scheme. Input the historical data of the distribution network obtained in S1, the cluster division results obtained in S2, and the distribution network distributed photovoltaic site selection optimization scheme described in S4 into the distribution network distributed photovoltaic capacity optimization configuration model considering photovoltaic reactive power support described in S5. Solve the model using the improved particle swarm algorithm to obtain the distribution network distributed photovoltaic access capacity optimization scheme that minimizes the photovoltaic power source lifetime damage of the target node and the annual network loss of the distribution network, and maximizes the distributed photovoltaic access capacity. S7: Determine the photovoltaic site selection and capacity allocation scheme. Combining the distribution network distributed photovoltaic site selection optimization scheme obtained in S4 and the distribution network distributed photovoltaic access capacity optimization scheme in S6, the final distribution network distributed photovoltaic site selection and capacity allocation scheme considering photovoltaic reactive power support is obtained.

2. The method for site selection and capacity determination of distributed photovoltaic power in a distribution network considering photovoltaic reactive power support according to claim 1, characterized in that, The specific process for dividing the distributed photovoltaic (PV) clusters in the distribution network considering PV reactive power support is as follows: Taking the first node of the distribution network as the reference node, the electrical distance from each node to the first node is calculated based on the electrical distances between nodes in the distribution network. Simultaneously, considering the location of each node in the distribution network and the power balance constraints within the same cluster, the number of nodes within each cluster is similar, and the electrical distance L between nodes is minimized. ij The expression is: In the formula, ΔV is the voltage change; ΔP represents the change in active power injected between nodes; S VP The sensitivity of voltage amplitude to changes in active power; S VP，ij The sensitivity of the voltage magnitude at node i to changes in the injected active power at node j; d ij Let be the ratio of the voltage change at node j to the voltage change at node i when the active power at node j changes.

3. The method for site selection and capacity determination of distributed photovoltaic power in a distribution network considering photovoltaic reactive power support according to claim 1, characterized in that, The comprehensive evaluation index for distributed photovoltaic (PV) site selection in distribution networks considering PV reactive power support and the optimized configuration model for distributed PV capacity in distribution networks considering PV reactive power support are as follows: Comprehensive evaluation indicators: In the formula, η1 and η2 are weighting coefficients, respectively; SVSI is the static voltage stability index; P b,load Q b,load These represent the active and reactive loads of node b, respectively; X ab R is the branch reactance of branch ab; ab U is the branch resistance of branch ab; a U is the node voltage of node a; b The node voltage at node b; LSFs is the loss sensitivity; P ab,L The active power loss of branch ab; Objective function: In the formula, ω1, ω2, and ω3 are weighting coefficients; f1, f2, and f3 are sub-objective functions; and LC represents lifetime damage, used to describe the lifetime loss of power devices. Annual network loss; P loss,t,n For cluster n in the distribution network, the network loss of each line in a certain time period t; S is the sum of the distributed photovoltaic capacity connected to each node; PV,i,n Let be the photovoltaic capacity connected to node i in cluster n; Constraints: In the formula, j, k, and l are the bus indices of the distribution network, respectively; J(k) and L(k) are the parent node and child node, respectively; P jk Q jk r jk x jk I jk These are the active power, reactive power, line resistance, line inductance, and line current from bus j to bus k, respectively. V k These are the photovoltaic active power at bus k, the reactive power of the photovoltaic inverter, the active load, the reactive load, and the bus voltage, respectively. V is the upper limit of the line current between bus j and bus k; min V max These are the upper and lower limits of the distribution network voltage, respectively. The photovoltaic grid connection capacity at busbar k; SOC max SOC min These are the upper and lower limits of the state of charge of the energy storage device, respectively; P ES-max , These represent the maximum charging / discharging power and installed capacity of the energy storage device at node i, respectively; P DPVi P load η represents the active power connected to node i and the total system load, respectively; η is the ratio of the total connected capacity to the total load.

4. The method for site selection and capacity determination of distributed photovoltaic power in a distribution network considering photovoltaic reactive power support according to claim 1, characterized in that, The entropy weight method is used to assign weights to each objective in the comprehensive evaluation index of distributed photovoltaic (PV) site selection in the distribution network, and the comprehensive evaluation index of all nodes in the distribution network is calculated based on historical data of the distribution network. This yields an optimized distributed PV site selection scheme that minimizes the static voltage stability index and loss sensitivity of the target nodes.

5. The method for site selection and capacity determination of distributed photovoltaic power in a distribution network considering photovoltaic reactive power support according to claim 1, characterized in that, The distributed photovoltaic (PV) capacity optimization configuration model for the distribution network, which optimizes PV power source lifetime damage, grid connection capacity, and annual network loss of the distribution network, uses the entropy weight method to assign weights to each optimization objective, thereby transforming the multi-objective optimization problem into a single-objective optimization problem. An improved particle swarm optimization algorithm is then used to solve the distributed PV capacity optimization configuration model, resulting in an optimized scheme for PV grid connection capacity that minimizes PV power source lifetime damage and annual network loss at the target nodes while maximizing distributed PV grid connection capacity.

6. The method for site selection and capacity determination of distributed photovoltaic power in a distribution network considering photovoltaic reactive power support according to claim 1, characterized in that, The distributed photovoltaic (PV) capacity optimization configuration model for distribution networks considering PV reactive power support takes into account the reactive power output of PV power sources, including: Output of photovoltaic power supply in reactive power compensation mode: The active and reactive power injected into the distribution network by photovoltaic power sources are as follows: In the formula, This refers to the output phase voltage of the photovoltaic inverter. R+jX is the three-phase phase voltage of the distribution network; R+jX is the grid-connected buffer inductance impedance of the photovoltaic inverter; δ is the angle between the output phase voltage of the photovoltaic inverter and the three-phase phase voltage of the distribution network.

7. The method for site selection and capacity determination of distributed photovoltaic power in a distribution network considering photovoltaic reactive power support according to claim 1, characterized in that, Considering the lifetime damage of power devices, the number of failure cycles is calculated based on the power device lifetime model, specifically as follows: In the formula, N f T is the number of power cycle cycles; jm ΔT represents the average junction temperature of the power device. j For junction temperature fluctuations in power devices; t on Heating time; α, β0, β1, γ, C, ar, k b E a f d These are parameters for the power device lifetime model; The lifetime damage of power devices is estimated based on the Miner linear cumulative damage criterion, which assumes that each temperature cycle causes damage to the power device, and that this damage has a linear cumulative characteristic; the power device fails when the cumulative lifetime damage exceeds 1. The expression for the cumulative damage degree of the power device is: In the formula, n i The number of cycles under a certain thermal stress; (N) f ) i The number of failure cycles calculated for the lifetime model.

Images