A method for evaluating new energy consumption capacity of a mountainous power distribution network

Abstract

The present application relates to the technical field of new energy consumption in mountainous distribution network, and particularly relates to a method for evaluating new energy consumption capacity in mountainous distribution network, comprising: obtaining distribution network data, clustering to generate a typical source-load operation scenario set covering different seasons; constructing a consumption capacity evaluation model with the optimization target of maximizing the total active power of new energy access and the branch power flow equation of second-order cone relaxation as the constraint; solving to obtain an initial consumption estimation value and performing full-node voltage verification; for voltage out-of-limit, calculating a voltage-reactive power sensitivity matrix, and iteratively introducing the adjustment capacity of the reactive power compensation node to correct until the voltage is qualified or the resources are exhausted; and outputting the feeder-level consumption limit capacity and the available capacity of the transformer area. The method guarantees the solving accuracy and convergence through second-order cone relaxation, fully releases the consumption space of mountainous power grid by using the sensitivity-driven reactive power excavation mechanism, and the evaluation result is accurate and reliable.

Description

Technical Field

[0001] This invention relates to the field of renewable energy absorption technology in mountainous power distribution networks, and more specifically, to a method for assessing the renewable energy absorption capacity of mountainous power distribution networks. Background Technology

[0002] With the advancement of the "dual carbon" goals, distributed photovoltaic power generation has experienced explosive growth in rural and mountainous power distribution networks. Due to geographical limitations, mountainous power distribution networks generally have characteristics such as long power supply radius, high line impedance ratio, radial grid structure, and weak voltage support capacity at the end.

[0003] The existing technologies for assessing the renewable energy absorption capacity of power distribution networks have the following three main shortcomings: 1. The assessment model is mismatched with the physical characteristics of the power grid in mountainous areas.

[0004] Conventional absorption capacity assessment methods are mostly based on urban distribution network models, focusing on line thermal stability limits (current carrying capacity) and transformer capacity constraints. However, the impedance ratio of distribution networks in mountainous areas is significantly higher than that in urban areas, making them extremely sensitive to voltage amplitude changes caused by active power fluctuations. If existing methods ignore this high impedance ratio characteristic and directly apply conventional DC power flow models or simplified models that ignore the strong voltage-power coupling relationship, the assessment results will be overly optimistic, and in actual operation, the risk of grid disconnection can easily occur due to the terminal voltage exceeding the upper limit.

[0005] 2. There is a contradiction between convergence and accuracy in nonlinear power flow calculation.

[0006] To pursue computational speed, existing technologies often employ sensitivity coefficient weighting or linearized power flow for absorption boundary estimation. However, under the conditions of weak power grids in mountainous areas, the power flow equations exhibit significant nonlinear characteristics, and simple linear weighting algorithms cannot capture abrupt inflection points when voltage exceeds limits. On the other hand, traditional AC power flow nonlinear iteration faces problems such as ill-conditioned Jacobian matrix due to high impedance ratios and convergence difficulties, making it difficult to apply to large-scale node assessment.

[0007] 3. Insufficient exploration of potential reactive power support capabilities.

[0008] Current assessments often treat distribution networks as passive power receiving networks, assuming a static boundary after the operation of grid voltage control equipment (such as on-load tap-changing transformers and capacitors). However, in mountainous distribution networks, the rapid reactive power response capabilities of power electronic devices such as distributed energy storage and static var generators are not fully incorporated into the assessment models. The lack of a closed-loop assessment method that incorporates a dynamic correction mechanism based on "voltage-reactive power sensitivity" into the process of approaching the absorption boundary leads to conservatively estimated absorption capacities, failing to fully realize the potential absorption capacity of mountainous power grids.

[0009] Therefore, there is an urgent need for a new energy consumption capacity assessment method that can accurately adapt to the characteristics of high impedance ratio grid structures in mountainous areas, take into account both calculation accuracy and convergence, and dynamically tap the potential for reactive power regulation. Summary of the Invention

[0010] The purpose of this invention is to provide a method for assessing the renewable energy absorption capacity of power distribution networks in mountainous areas, so as to solve the above-mentioned problems.

[0011] To achieve the above objectives, the embodiments of this application provide the following technical solutions: This application provides a method for assessing the renewable energy absorption capacity of a mountainous distribution network. The method includes: acquiring the network topology parameters, load data of each node, and historical time-series data of renewable energy output of the mountainous distribution network; generating a set of typical source-load joint operation scenarios representing different seasons and operating modes using a clustering algorithm; establishing an absorption capacity assessment model with the objective function of maximizing the total active power of distributed renewable energy access within the mountainous distribution network; and introducing second-order cone relaxation DistFlow power flow equation constraints into the absorption capacity assessment model; wherein the second-order cone relaxation is used to address the non-convexity problem of power flow calculation caused by the high impedance ratio characteristics of mountainous distribution network lines; and calculating... Solve the absorption capacity assessment model to obtain the initial maximum renewable energy absorption power estimate for each node, and perform a safety check on the voltage amplitude of each node in the distribution network; in response to the situation where the voltage amplitude of a node exceeds the limit, calculate the voltage-reactive power sensitivity coefficient matrix of the node exceeding the limit to each reactive power compensation node, determine the voltage support regulation resources to be unlocked based on the sensitivity coefficient matrix, and substitute the reactive power regulation amount of the regulation resources as a new constraint variable into the absorption capacity assessment model for iterative correction until the voltage of all nodes returns to the allowable range or all available regulation resources are exhausted; output the final renewable energy absorption capacity assessment value corresponding to each feeder and transformer area in the mountainous distribution network.

[0012] Optionally, the specific method for generating a typical source-load joint operation scenario set is as follows: Obtain the net load power curve of each node within a preset period, wherein the net load power is the difference between the load power and the distributed new energy power generation power; The net load power curve was clustered and reduced using the K-Medoids clustering algorithm. Typical daily data corresponding to each cluster center were selected to construct source-load joint operation scenario sets for spring and autumn irrigation, summer flood season, and winter dry season, respectively, to cover the output coupling characteristics of small hydropower and photovoltaic power in mountainous areas in different seasons.

[0013] Optionally, the second-order cone-relaxed DistFlow power flow equation constraint is specifically expressed as: For any branch (i,j), the following constraints apply: ; in, , The active and reactive power transmitted at the beginning of the branch; , These represent the squares of the voltage magnitudes at nodes i and j, respectively; Represents the square of the branch current amplitude; , For branch resistance and reactance.

[0014] Optionally, the voltage-reactive power sensitivity coefficient matrix is ​​calculated as follows: Based on the Jacobian matrix J in the Newton-Raphson power flow calculation, the partial derivative submatrix of voltage magnitude with respect to reactive power is extracted. ; For the submatrix Inverting the matrix yields the sensitivity matrix. Matrix elements That is, the sensitivity of the effect of the reactive power change at node k on the voltage amplitude at node i; Based on the sensitivity matrix, the reactive power compensation node that provides the greatest support for the voltage of the over-limit node is selected, and the reactive power regulation capacity of the node is preferentially used to release the space for new energy consumption.

[0015] Optionally, the step of substituting the reactive power regulation of the regulating resources as a new constraint variable into the absorption capacity assessment model for iterative correction specifically involves: The reactive power output increment of the reactive power compensation node As variables to be optimized, they are introduced into the constraint set of the objective function to establish the correction constraint relationship for the voltage amplitude: ; in, This represents the base value of the node voltage under the current operating mode. This is the voltage-reactive power sensitivity coefficient.

[0016] Optionally, the assessment value of the new energy absorption capacity includes the feeder-level absorption limit capacity and the transformer area open capacity. The feeder-level absorption limit capacity is used to characterize the maximum total installed capacity of distributed photovoltaic and hydropower that can be connected to a single 10kV feeder without causing voltage over-limit and reverse overload of equipment. The transformer area open capacity is used to characterize the new household photovoltaic grid-connected capacity that can be added to the low-voltage side of each distribution transformer, based on meeting the safety constraints of the upper feeder.

[0017] The beneficial effects of this invention are as follows: This invention addresses the high impedance ratio of power distribution networks in mountainous areas by introducing a second-order cone-relaxed DistFlow power flow equation constraint. By transforming the non-convex nonlinear power flow equation into a second-order cone programming problem, it preserves the computational accuracy of the strong coupling relationship between active power, voltage, and reactive power, while also solving the numerical problem of the traditional Newton's method's difficulty in convergence under high impedance ratio networks in mountainous areas. This ensures that the evaluated absorption boundary is no longer a coarse static capacity ratio, but rather a precise capture of the ultimate carrying capacity at the voltage over-limit inflection point.

[0018] Secondly, this invention overcomes the limitations of traditional methods that treat the power grid as a static, passive component. By calculating and introducing a voltage-reactive power sensitivity coefficient matrix, it can intelligently identify the reactive power compensation node that provides the strongest support for over-limit voltage at the end. This "sensitivity-driven" iterative correction mechanism transforms the reactive power regulation capabilities of static var generators and energy storage converters into direct variables for releasing the space for renewable energy consumption. This process simulates the operational logic of reactive power compensation devices actively supporting voltage in actual operation, resulting in an estimated absorption capacity upper limit that is 5%-15% higher than that obtained using static methods, effectively delaying investment in the transformation of power grids in mountainous areas.

[0019] Secondly, this invention fully considers the unique characteristics of mountainous areas where small hydropower and photovoltaic power coexist as dual new energy sources. During the scenario generation stage, K-Medoids clustering is used to extract three typical scenarios: spring and autumn irrigation, summer flood season, and winter dry season. This ensures that the evaluation method can address the challenges of full hydropower generation and high voltage risks during the high-water season, while also covering the difficulties of photovoltaic backfeeding and low-voltage grid connection during the dry season, achieving a comprehensive scan of the annual operational risks of the mountainous power distribution network.

[0020] This method ultimately outputs not just a single number, but a tiered heat map and list containing feeder-level absorption limits and available capacity for distribution areas. Power grid planners can directly use this list to determine which distribution areas still have capacity to accommodate residential photovoltaic applications, and which feeders have reached voltage bottlenecks and require SVG installation or line upgrades. This provides a strong quantitative basis for differentiated and precise investment in mountainous distribution networks.

[0021] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing embodiments of the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings. Attached Figure Description

[0022] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is a schematic diagram of a method for assessing the renewable energy absorption capacity of a mountain power distribution network, as described in an embodiment of the present invention. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0025] Example 1:

[0026] like Figure 1 As shown in the figure, this embodiment provides a method for assessing the renewable energy absorption capacity of a mountainous power distribution network. The method includes: Step S100: Obtain the network topology parameters, load of each node and historical time series data of new energy output of the mountain power distribution network, and generate a set of typical source-load joint operation scenarios representing different seasons and operating modes through clustering algorithm; The specific method for generating the typical source-load joint operation scenario set is as follows: Step S110: Obtain the net load power curve of each node within a preset period, wherein the net load power is the difference between the load power and the distributed new energy power generation power. Step S120: The net load power curve is clustered and reduced using the K-Medoids clustering algorithm. Typical daily data corresponding to each cluster center are selected to construct source-load joint operation scenario sets for spring and autumn irrigation scenarios, summer flood season scenarios, and winter dry season scenarios, so as to cover the output coupling characteristics of small hydropower and photovoltaic power in different seasons in mountainous areas.

[0027] Step S200: Establish a power absorption capacity assessment model with the objective function of maximizing the total active power of distributed new energy access in the mountainous distribution network, and introduce second-order cone relaxation DistFlow power flow equation constraints into the power absorption capacity assessment model; wherein, the second-order cone relaxation is used to deal with the non-convexity problem of power flow calculation caused by the high impedance ratio characteristics of mountainous distribution network lines; the second-order cone relaxation DistFlow power flow equation constraints are specifically expressed as follows: For any branch (i,j), the following constraints apply: ; in, , The active and reactive power transmitted at the beginning of the branch; , These represent the squares of the voltage magnitudes at nodes i and j, respectively; Represents the square of the branch current amplitude; , For branch resistance and reactance.

[0028] Step S300: Solve the absorption capacity assessment model to obtain the initial maximum absorption capacity estimate of new energy at each node, and perform a safety verification on the voltage amplitude at each node of the distribution network. Step S400: In response to the situation where the voltage amplitude of a node exceeds the limit, calculate the voltage-reactive power sensitivity coefficient matrix of the node exceeding the limit to each reactive power compensation node, determine the voltage support regulation resources to be unlocked based on the sensitivity coefficient matrix, and substitute the reactive power regulation amount of the regulation resources as a new constraint variable into the absorption capacity assessment model for iterative correction until all node voltages return to the allowable range or all available regulation resources are exhausted. The voltage-reactive power sensitivity coefficient matrix is ​​calculated as follows: Step S410: Based on the Jacobian matrix J in the Newton-Raphson power flow calculation, extract the partial derivative submatrix of voltage magnitude with respect to reactive power. ; Step S420: For the sub-matrix Inverting the matrix yields the sensitivity matrix. Matrix elements That is, the sensitivity of the effect of the reactive power change at node k on the voltage amplitude at node i; Step S430: Based on the sensitivity matrix, select the reactive power compensation node that provides the greatest support for the voltage of the over-limit node, and prioritize the use of the reactive power regulation capacity of the node to release the space for new energy consumption.

[0029] Secondly, the reactive power regulation of the adjustment resources is substituted as a new constraint variable into the absorption capacity assessment model for iterative correction, specifically as follows: The reactive power output increment of the reactive power compensation node As variables to be optimized, they are introduced into the constraint set of the objective function to establish the correction constraint relationship for the voltage amplitude: ; in, This represents the base value of the node voltage under the current operating mode. This is the voltage-reactive power sensitivity coefficient.

[0030] Step S500: Output the final assessment value of the renewable energy absorption capacity of each feeder and transformer substation in the mountainous distribution network. The assessment value of renewable energy absorption capacity includes the feeder-level absorption limit capacity and the substation open capacity. The feeder-level absorption limit capacity is used to characterize the maximum total installed capacity of distributed photovoltaic and hydropower that can be connected to a single 10kV feeder without causing voltage over-limit and reverse overload of equipment. The substation open capacity is used to characterize the new household photovoltaic grid-connected capacity that can be added to the low-voltage side of each distribution transformer, based on meeting the safety constraints of the upper feeder.

[0031] This embodiment addresses the high impedance ratio of distribution network lines in mountainous areas by introducing a second-order cone-relaxed DistFlow power flow equation constraint. By transforming the non-convex nonlinear power flow equation into a second-order cone programming problem, it preserves the calculation accuracy of the strong coupling relationship between active power, voltage, and reactive power, while solving the numerical problem of the traditional Newton's method's difficulty in convergence under high impedance ratio networks in mountainous areas. This makes the evaluated absorption boundary no longer a coarse static capacity ratio, but rather a precise capture of the ultimate carrying capacity at the voltage over-limit inflection point.

[0032] Secondly, this embodiment overcomes the limitation of traditional methods that treat the power grid as a static, passive component. By calculating and introducing a voltage-reactive power sensitivity coefficient matrix, it can intelligently identify the reactive power compensation node that provides the strongest support for over-limit voltage at the end. This "sensitivity-driven" iterative correction mechanism transforms the reactive power regulation capabilities of static var generators and energy storage converters into direct variables for releasing the space for renewable energy consumption. This process simulates the active voltage support logic of reactive power compensation devices in actual operation, resulting in an estimated absorption capacity upper limit that is 5%-15% higher than that obtained using static methods, effectively delaying the investment in upgrading power grids in mountainous areas.

[0033] Secondly, this embodiment fully considers the unique characteristics of mountainous areas where small hydropower and photovoltaic power coexist as dual new energy sources. During the scenario generation stage, K-Medoids clustering is used to extract three typical scenarios: spring and autumn irrigation, summer flood season, and winter dry season. This ensures that the evaluation method can address the challenges of full hydropower generation and high voltage risks during the high-water season, while also covering the difficulties of photovoltaic power backfeeding and low-voltage grid connection during the dry season, achieving a comprehensive scan of the annual operational risks of the mountainous power distribution network.

[0034] This solution ultimately outputs not just a single number, but a tiered heat map and list including feeder-level absorption limits and available capacity for distribution areas. Power grid planners can directly use this list to determine which distribution areas still have capacity to accommodate residential photovoltaic applications, and which feeders have reached voltage bottlenecks and require SVG installation or line upgrades. This provides a strong quantitative basis for differentiated and precise investment in mountainous distribution networks.

[0035] Example 2:

[0036] This embodiment, based on Embodiment 1, provides a method for assessing the renewable energy absorption capacity of a mountainous distribution network. This method is applied to a typical 10kV distribution network feeder system in a mountainous area. The feeder includes one 35kV / 10kV substation, one run-of-river small hydropower station, three village-level centralized photovoltaic power stations, and scattered household photovoltaic access points along the line.

[0037] Step S1: Obtain the network topology parameters, load of each node and historical time series data of new energy output of the mountain power distribution network, and generate a set of typical source-load joint operation scenarios representing different seasons and operating modes through clustering algorithms.

[0038] In practice, the following data are first exported from the distribution network geographic information system (GIS) and the data acquisition and monitoring control system (SCADA): Network topology parameters: line resistance, reactance, node connection relationship, rated capacity of distribution transformer and allowable reverse load rate.

[0039] Historical time-series data: Over the past year, the active power of the load, the output of small hydropower units, and the output of photovoltaic power stations were sampled every 15 minutes at each node.

[0040] Considering the significant impact of seasonal characteristics in mountainous areas on hydropower and photovoltaic output (e.g., full hydropower generation and strong photovoltaic irradiance during the summer flood season, and hydropower shutdown but photovoltaic output remaining during the winter dry season), this step does not use a single typical day analysis, but instead uses the K-Medoids clustering algorithm to cluster and reduce the "net load power curve" of each node.

[0041] The net load power Defined as: ; in, For the active power of the load, To provide power to small hydropower units, Contribute to photovoltaic power plants.

[0042] By selecting typical daily data corresponding to the cluster centers, the following three typical source-load joint operation scenario sets covering the annual operation characteristics are constructed: Scenario A (Summer Flood Season): Hydropower is operating at full capacity, photovoltaic power is strong, and the load is at a moderate level. This scenario mainly tests the risk of voltage exceeding the upper limit at the end of the distribution network.

[0043] Scenario B (Spring and Autumn Irrigation Scenario): Hydropower output is moderate, photovoltaic output fluctuates greatly, and agricultural load is heavy. This scenario tests the stability of the voltage at the end of the line.

[0044] Scenario C (Winter Dry Season): Hydropower is almost shut down, photovoltaic power generation is high in the afternoon, and the load is mainly for residential heating. In this scenario, transformer reverse overload is likely to occur due to reverse power flow.

[0045] Step S2: Establish a power absorption capacity assessment model with the objective function of maximizing the total active power of distributed new energy access in the mountainous distribution network, and introduce the second-order cone-relaxed DistFlow power flow equation constraint into the power absorption capacity assessment model.

[0046] The core of this step is to construct an optimized mathematical model that adapts to the characteristics of high impedance ratio (larger R / X) in mountainous areas.

[0047] Objective function: Establish a mathematical expression with the objective of maximizing the total incremental increase of active power from renewable energy sources allowed to be connected to the system: ; in, This refers to the set of new energy nodes whose access capacity is to be evaluated. The potential increase in the output of new energy sources at node i based on the current level.

[0048] Key Constraint – DistFlow Power Flow Equations with Second-Order Cone Relaxation: To address the issue that the resistance and reactance of power distribution lines in mountainous areas are of similar magnitude and conventional power flow calculations are prone to divergence, this embodiment adopts a second-order cone programming (SOCP) approach to relax the power flow equations, thereby significantly improving the solution efficiency while ensuring the global optimal solution.

[0049] For any branch (i,j), the constraints are stated as follows: ; in, , The active and reactive power transmitted at the beginning of the branch; , These represent the squares of the voltage magnitudes at nodes i and j, respectively; Represents the square of the branch current amplitude; , For branch resistance and reactance.

[0050] By using the aforementioned second-order cone relaxation, the nonlinear voltage-power relationship is transformed into a convex cone constraint, enabling the model to accurately capture the sensitive characteristics of voltage variation with active power in high impedance ratio lines.

[0051] Step S3: Solve the absorption capacity assessment model to obtain the initial maximum absorption capacity estimate of new energy at each node, and perform a safety verification on the voltage amplitude of each node in the distribution network.

[0052] The model constructed in step S2 is solved using a commercial solver based on the interior point method (such as IBM CPLEX or MOSEK).

[0053] During the initial solution process, the model may not yet include voltage limit cutoff constraints, or it may only include static capacity constraints. After the solver converges quickly, it outputs a set of initial estimates of the maximum renewable energy absorption capacity. .

[0054] Subsequently, the squares of the voltage amplitudes at each node under the estimated operating condition are extracted. Converted to per-unit voltage value According to national standards, the acceptable voltage range is set to [0.93, 1.07] pu (per unit value). If all node voltages are within this range, proceed directly to step S5; if any node voltage exceeds the upper or lower limit, proceed to step S4 for correction iteration.

[0055] Step S4: In response to the situation where the voltage amplitude of a node exceeds the limit, calculate the voltage-reactive power sensitivity coefficient matrix of the node exceeding the limit to each reactive power compensation node, determine the voltage support regulation resource to be unlocked based on the sensitivity coefficient matrix, and substitute the reactive power regulation amount of the regulation resource as a new constraint variable into the absorption capacity evaluation model for iterative correction.

[0056] This step is one of the core creative points of this invention, used to simulate the release effect of reactive power compensation device on the absorption space in actual operation.

[0057] 4.1 Calculation of the voltage-reactive power sensitivity coefficient matrix.

[0058] Based on the power flow profile obtained in step S3, calculate the Jacobian matrix J in the Newton-Raphson method. Extract the partial derivative submatrix of voltage magnitude with respect to reactive power variation. Invert this submatrix to obtain the sensitivity matrix: ; Elements in the matrix It characterizes the support effect on the voltage amplitude of the over-limit node i when a unit reactive power is injected at node k. The larger the value, the higher the "cost-effectiveness" of configuring a Static Var Generator (SVG) or Regulating Energy Storage Converter (PCS) at node k.

[0059] 4.2 Iterative correction strategy.

[0060] In this embodiment, the regulating resources mainly refer to the reactive power output (QESS) of distributed energy storage and the dedicated static var generator (QSVG). These reactive power output increments are introduced into the model as new variables to be optimized.

[0061] For weak nodes where the voltage exceeds the upper limit, a voltage constraint equation with sensitivity correction is established: ; in, These are the base values ​​of the node voltages before any corrections were applied. It is a set of reactive power compensation nodes with adjustment capabilities.

[0062] The constraint is activated in the model, and the solver is called again for iteration. This process simulates the logic of a power grid dispatch automation system: prioritizing the use of reactive power sources with the highest sensitivity coefficients to nodes exceeding limits, achieving the greatest voltage improvement effect with the least reactive power increment, thereby raising the cutoff threshold of voltage constraints on renewable energy output.

[0063] Repeat steps S3 and S4 until all node voltages return to the range of [0.93, 1.07] pu, or until all available QESS and QSVG reactive power regulation capacities have been exhausted.

[0064] Step S5: Output the final assessment value of the renewable energy absorption capacity of each feeder and transformer area in the mountainous distribution network.

[0065] After the above iterations converge, the model outputs... This refers to the precise absorption capacity after taking into account the actual voltage sensitivity characteristics and reactive power support capacity in mountainous areas.

[0066] The results output in this embodiment include two dimensions of quantitative indicators: Feeder-level absorption limit capacity: refers to the maximum total installed capacity of distributed power sources allowed to be connected to the feeder without causing voltage overruns at any node of the 10kV main line or reverse overload of the main transformer (unit: kW or MW). Evaluation results show that the absorption limit of the mountain feeder in this embodiment is 3.2MW without considering reactive power support. After adopting the method of this invention, due to unlocking the reactive power compensation capability of the terminal SVG, the absorption limit is increased to 3.75MW.

[0067] List of Available Capacity for Distribution Areas: This refers to the permitted new grid-connected capacity of residential photovoltaic (PV) systems on the low-voltage side (400V side) of each distribution transformer along the power line, provided that the safety constraints of the upstream feeder are met. For example, the assessment results show that the #12 public transformer area, located furthest from the substation, has a 0kW available capacity (already saturated) due to a high risk of voltage exceeding limits. Planners can use this information to install SVG or modify the power lines at this location, rather than blindly accepting new grid-connection applications. Finally, a heat map of the distribution network absorption capacity containing the above information is generated for dispatching and planning personnel to use for visualization reference.

[0068] Technical effectiveness verification: To verify the effectiveness of this invention, the method of this embodiment was compared with traditional method one (fixed ratio method: estimated based on 25% of transformer capacity) and traditional method two (conventional optimal power flow method that does not consider high impedance ratio convergence). The results are shown in Table 1: Table 1 Comparison of Results from Different Evaluation Methods Traditional Method 1 (Fixed Proportion Method) 2.5 MW Actual operation exceeded limits extremely short The results are crude and can easily lead to misjudgments in mountainous areas, causing operational risks. Traditional Method Two (Conventional Optimal Power Flow) Unable to converge N / A time out Due to the high impedance ratio, the Jacobian matrix is ​​ill-conditioned, and the calculation fails. Method of the present invention (Example 1) 3.75 MW 100% 2.3 seconds Precise convergence fully unleashed the reactive power regulation potential, and the evaluation results are safe and reliable. As shown in Table 1, the method of the present invention not only solves the problem of computational failure of traditional algorithms in mountainous areas, but also improves the reactive power absorption capacity assessment value by 50% compared with the rough estimate and by 17% compared with the uncorrected value through the sensitivity-driven reactive power correction mechanism, which has significant technical progress and engineering application value.

[0069] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for assessing the renewable energy absorption capacity of a mountainous power distribution network, characterized in that, The method includes: The network topology parameters, load of each node and historical time-series data of renewable energy output of the mountain power distribution network are obtained, and a set of typical source-load joint operation scenarios representing different seasons and operating modes is generated by clustering algorithm; A power absorption capacity assessment model is established with the objective function of maximizing the total active power of distributed new energy access in mountainous distribution networks. A second-order cone relaxation DistFlow power flow equation constraint is introduced into the power absorption capacity assessment model. The second-order cone relaxation is used to deal with the non-convexity problem of power flow calculation caused by the high impedance ratio characteristics of mountainous distribution network lines. Solve the absorption capacity assessment model to obtain the initial maximum absorption capacity estimate of new energy at each node, and perform a safety verification on the voltage amplitude at each node of the distribution network. In response to the situation where the voltage amplitude of a node exceeds the limit, the voltage-reactive power sensitivity coefficient matrix of the node exceeding the limit to each reactive power compensation node is calculated. Based on the sensitivity coefficient matrix, the voltage support regulation resources to be unlocked are determined, and the reactive power regulation amount of the regulation resources is substituted into the absorption capacity assessment model as a new constraint variable for iterative correction until all node voltages return to the allowable range or all available regulation resources are exhausted. Output the final assessment value of the renewable energy absorption capacity of each feeder and transformer area in the mountainous power distribution network.

2. The method for assessing the renewable energy absorption capacity of mountain power distribution networks according to claim 1, characterized in that, The specific method for generating a typical source-load joint operation scenario set is as follows: Obtain the net load power curve of each node within a preset period, wherein the net load power is the difference between the load power and the distributed new energy power generation power; The net load power curve was clustered and reduced using the K-Medoids clustering algorithm. Typical daily data corresponding to each cluster center were selected to construct source-load joint operation scenario sets for spring and autumn irrigation, summer flood season, and winter dry season, respectively, to cover the output coupling characteristics of small hydropower and photovoltaic power in mountainous areas in different seasons.

3. The method for assessing the renewable energy absorption capacity of mountain power distribution networks according to claim 1, characterized in that, The second-order cone-relaxed DistFlow power flow equation constraint is specifically expressed as follows: For any branch (i,j), the following constraints apply: ； in, , The active and reactive power transmitted at the beginning of the branch; , These represent the squares of the voltage magnitudes at nodes i and j, respectively; Represents the square of the branch current amplitude; , For branch resistance and reactance.

4. The method for assessing the renewable energy absorption capacity of mountain power distribution networks according to claim 1, characterized in that, The voltage-reactive power sensitivity coefficient matrix is ​​calculated as follows: Based on the Jacobian matrix J in the Newton-Raphson power flow calculation, the partial derivative submatrix of voltage magnitude with respect to reactive power is extracted. ; For the submatrix Inverting the matrix yields the sensitivity matrix. Matrix elements That is, the sensitivity of the effect of the reactive power change at node k on the voltage amplitude at node i; Based on the sensitivity matrix, the reactive power compensation node that provides the greatest support for the voltage of the over-limit node is selected, and the reactive power regulation capacity of the node is preferentially used to release the space for new energy consumption.

5. The method for assessing the renewable energy absorption capacity of mountain power distribution networks according to claim 4, characterized in that, The step of iteratively refining the reactive power regulation of the adjustment resources by substituting them into the absorption capacity assessment model as a new constraint variable is as follows: The reactive power output increment of the reactive power compensation node As variables to be optimized, they are introduced into the constraint set of the objective function to establish the correction constraint relationship for the voltage amplitude: ； in, This represents the base value of the node voltage under the current operating mode. This is the voltage-reactive power sensitivity coefficient.

6. The method for assessing the renewable energy absorption capacity of mountain power distribution networks according to claim 1, characterized in that, The assessment value of the new energy absorption capacity includes the feeder-level absorption limit capacity and the transformer area open capacity. The feeder-level absorption limit capacity is used to characterize the maximum total installed capacity of distributed photovoltaic and hydropower that can be connected to a single 10kV feeder without causing voltage over-limit and reverse overload of equipment. The transformer area open capacity is used to characterize the new household photovoltaic grid-connected capacity that can be added to the low-voltage side of each distribution transformer, based on meeting the safety constraints of the upper feeder.

Images