A method for calculating mutual exclusion coefficient, bearing capacity evaluation and fast evaluation of bearing capacity with energy storage for distribution network distributed power supply access

Abstract

The application firstly proposes a mutual exclusion coefficient calculation method for distribution network distributed power access, and based on the quantitative calculation of the mutual exclusion coefficient, proposes a load carrying capacity evaluation method for the distribution network distributed power; and for the energy storage scene, a load carrying capacity fast calculation method for the distribution network distributed power based on the mutual exclusion structure inheritance is proposed, and after the energy storage is accessed, only the single node carrying capacity is recalculated, and a small number of possible changed node pairs are selected for correction. The above method can balance the calculation efficiency and physical interpretability, and realizes the fast evaluation of the load carrying capacity under the distribution network distributed power access and energy storage configuration.

Description

Technical Field

[0001] This invention relates to the field of distributed generation technology in distribution networks, and in particular to a method for calculating mutual exclusion coefficients, assessing carrying capacity, and rapidly assessing carrying capacity including energy storage for distributed generation access in distribution networks. Background Technology

[0002] With the advancement of the "dual-carbon" goals, the scale of distributed generation (DG) in distribution networks is continuously increasing. The integration of large amounts of distributed photovoltaic (PV) and other power sources significantly alters the operation of distribution networks, affecting voltage distribution, power flow direction, and network losses. A high proportion of DG integration can easily lead to voltage exceeding limits and line overloads, thus limiting the system's further absorption capacity. Therefore, how to reasonably assess the distribution network's capacity to support DG has become an important research issue.

[0003] Extensive research has been conducted on the issue of carrying capacity assessment from various perspectives. One type of method is based on deterministic optimization models, which solve for the upper limit of the system's connectable capacity by constructing power flow constraints and capacity variables. Another type of method introduces probabilistic models or scenario analysis to characterize the uncertainties of load and renewable energy output, thereby improving the reliability of the assessment results. In addition, some studies have used sensitivity analysis or simulation methods to assess the impact of distributed power source access on voltage and lines.

[0004] Meanwhile, energy storage systems, due to their power regulation and energy buffering capabilities, are considered an important means to enhance the carrying capacity of power distribution networks. Related research has explored aspects such as energy storage configuration, dispatch strategies, and multi-source coordinated control, demonstrating that energy storage can mitigate voltage fluctuations and improve system operational stability to a certain extent.

[0005] Despite the significant achievements of the aforementioned research, some shortcomings remain in scenarios involving simultaneous multi-node access. Existing methods primarily rely on "single-node capacity" or "total system capacity" as their main results, lacking a quantitative description of the interactions between different nodes. In practical planning, due to the shared line impedance among nodes, simultaneous access of multiple nodes often generates coupling effects, complicating capacity allocation. In such cases, relying solely on single-point evaluation results is insufficient to directly guide multi-node capacity configuration; typically, it requires repeated trial calculations or enumeration of combinations to obtain feasible solutions, resulting in low computational efficiency.

[0006] Furthermore, in multi-node configuration problems, the mutual constraints between different access points exhibit distinct structural characteristics. For example, nodes with closer electrical distances often show strong capacity competition, while nodes with greater electrical distances are relatively independent. Although existing research has observed this phenomenon from a simulation perspective, it lacks an intuitive and quantifiable metric to describe the capacity coupling relationship between nodes. Summary of the Invention

[0007] In view of this, one objective of this invention is to provide a quantitative calculation method for the node mutual exclusion coefficient for distributed generation access in distribution networks; a second objective is to provide a carrying capacity assessment method for distributed generation access in distribution networks; and a third objective is to provide a rapid carrying capacity assessment method for distributed generation access in distribution networks with energy storage. These methods balance computational efficiency and physical interpretability, and based on the quantitative calculation of the node mutual exclusion coefficient, they enable rapid assessment of the carrying capacity under distributed generation access and energy storage configuration in distribution networks.

[0008] One of the objectives of this invention is achieved through the following technical solution: This method for quantitatively calculating the node mutual exclusion coefficient for distributed generation access in distribution networks includes the following steps: Step S1: Set the installed capacity of the i-th distributed power access node in the system to C. i Set the installed capacity of all distributed power sources connected to the node (except for i) to 0, and find the node C that satisfies the node voltage, current, and SOC constraints in all time periods. i The maximum value of is taken as the maximum bearing capacity of point i, denoted as . ; Step S2: For any distributed power supply access node pair (i,j) within the system, record the total installed capacity of the node pair as S. i,j Set the installed capacity of all DG nodes except i and j to 0, and find the S that satisfies the node voltage, current, and SOC constraints in all time periods. i,j The maximum value is taken as the sum of the maximum bearing capacities of node pairs i and j, denoted as . ; Step S3: If node i and node j do not affect each other, then there should be ≈ + When the installed capacity of two nodes is mutually constrained, the sum of the capacities of both nodes connected simultaneously will be less than the sum of the capacities of a single node connected individually. Therefore, a mutual exclusion coefficient R is defined for node pairs. i,j for: A large mutual exclusion coefficient indicates that the two nodes "reject each other"; a small mutual exclusion coefficient indicates that the two nodes have little mutual influence.

[0009] Furthermore, the node voltage, current, and SOC constraints are as follows: At each time period t, the node voltage magnitude |V is obtained through power flow calculation of the distribution network. i (t)|and branch current amplitude|I j (t)∣, then the node voltage constraint is: The line current constraint is: The SOC constraint is: .

[0010] The second objective of this invention is achieved through the following technical solution: This capacity assessment method for distributed generation access in distribution networks includes the following steps: Step S1: Obtain distribution network parameters and set relevant constraints and distributed power generation nodes; Step S2: Calculate the maximum bearing capacity of a single node and the bearing capacity of a node pair one by one; Step S3: Based on the quantitative calculation method of node mutual exclusion coefficient, construct mutual exclusion coefficients from the node pair capacity loss to characterize the capacity coupling relationship between different access nodes, and establish a linear programming model; Step S4: Solve the model to obtain the initial solution and substitute it into the power flow verification. If the capacity scheme meets the operating constraints, output the capacity scheme; otherwise, proceed to step S5. Step S5: Further utilize the mutual exclusion coefficient and small disturbance power flow results to guide capacity rollback, obtain the updated new capacity allocation scheme, and resolve the model and input it for power flow verification until the operating constraints are met, and output the capacity scheme; otherwise, repeat step S5.

[0011] Furthermore, in step S3, for a system with N distributed power access nodes, any pair of distributed power access nodes (i,j) has the following: Using the installed capacity of each node as a decision variable, and with the goal of maximizing the total installed capacity of the system, a linear programming model is established under the upper bound constraints of the installed capacity of any node and the upper bound constraints of a single node, thereby obtaining the maximum carrying capacity and capacity allocation of the distribution network.

[0012] Furthermore, in step S5, for distributed power access node i, its backoff priority D is calculated. i As shown in the following formula: In the formula: C iD represents the distributed power capacity of the nodes under the current scheme. i It also reflects the node's own capacity utilization and its mutual exclusion relationship with other nodes; According to D i The specific method for further verifying the small-disturbance power flow of candidate nodes by selecting several candidate nodes from largest to smallest is as follows: For candidate node i, its DG capacity is reduced slightly, and then the over-limit voltage difference ΔV before and after is calculated. Combining the mutual exclusion backoff priority and the small-disturbance power flow results, the backoff weight w of node i is defined. i : In the formula: K represents the set of candidate backoff nodes; By multiplying the backoff weight of each node by the backoff step size, the backoff capacity can be obtained and substituted into the power flow verification.

[0013] The third objective of this invention is achieved through the following technical solution: This rapid assessment method for the carrying capacity of distributed generation access in distribution networks, including energy storage, includes: The node pair mutual exclusion coefficient R is obtained based on the quantitative calculation method of node mutual exclusion coefficient described above. i,j Construct a mutual exclusion coefficient matrix for scenarios without energy storage; Based on the mutual exclusion coefficient matrix, the single-node bearing capacity after energy storage is recalculated; Update a small number of potentially changing node pairs based on the single-point bearing capacity gain; Based on the previously obtained single-node carrying capacity with energy storage and the updated mutual exclusion coefficient matrix, a capacity constraint model with energy storage is established. The model is solved to obtain an initial solution, which is then used for power flow verification. If the capacity scheme meets the operational constraints, the capacity scheme is output. If not, the mutual exclusion coefficient and small disturbance power flow results are used to guide capacity back-off to obtain an updated new capacity allocation scheme. The model is then solved again and used for power flow verification until the operational constraints are met, and the capacity scheme is output.

[0014] Furthermore, for each node i, let the single-node bearing capacity in the state without energy storage be C. i The node bearing capacity with energy storage is C. Ei Therefore, the screening index H is defined. ij : H ij The larger the value, the stronger the original mutual exclusion of the node pair, and the more significant the change in the bearing capacity of the related nodes after energy storage. Therefore, it is necessary to recalculate the bearing capacity of the node pair.

[0015] Furthermore, for the selected node pairs, the node pair carrying capacity and mutual exclusion coefficient are recalculated, while the unselected pairs retain their original mutual exclusion coefficients. Then, the carrying capacity of the distributed power generation in the distribution network with energy storage is calculated using the updated mutual exclusion coefficients and node carrying capacity.

[0016] The beneficial effects of this invention are: (1) This invention proposes an innovative method for calculating mutual exclusion coefficients, which can quantify the capacity constraint relationship between different distributed power access nodes. Through the mutual exclusion coefficient matrix, strong mutually exclusive node pairs and relatively independent nodes can be intuitively identified, providing a basis for capacity allocation. (2) The capacity allocation model based on the mutual exclusion coefficient can quickly obtain the initial value of capacity allocation. In view of the problem that the initial solution may not be feasible for power flow, this invention uses the mutual exclusion coefficient and the power flow result of small disturbance to guide the capacity rollback, so that the capacity scheme can be gradually corrected to the feasible range. (3) Compared with PSO, the method proposed in this invention does not rely on a large number of random searches, but uses node carrying capacity, mutual exclusion relationship and power flow verification results to determine the direction of capacity adjustment. Therefore, it can significantly reduce the number of power flow calls while maintaining a small error and improve the interpretability of the results. (4) In scenarios involving energy storage, energy storage can alter the carrying capacity of a single node, but the mutually exclusive structure between nodes has a certain degree of inheritance. The fast calculation method for carrying capacity involving energy storage proposed in this invention avoids recalculating all node pairs by selecting a small number of potentially changing node pairs for correction, thus achieving rapid estimation of the carrying capacity of distributed power sources in distribution networks with energy storage.

[0017] In summary, the method proposed in this application can balance computational efficiency and physical interpretability, and is based on the quantitative calculation of the mutual exclusion coefficient of access nodes, thereby enabling rapid assessment of the carrying capacity of the distribution network under the access of distributed power sources and energy storage configuration.

[0018] Other advantages, objectives, and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination, or may be learned from practice of the invention. The objectives and other advantages of the invention can be realized and obtained through the following description. Attached Figure Description

[0019] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will now be described in further detail with reference to the accompanying drawings, wherein: Figure 1 A flowchart for the capacity assessment method for distributed generation access in distribution networks; Figure 2 This is a diagram showing the mutual exclusion coefficients of each node without energy storage in a specific embodiment of the present invention. Figure 3 This is a selection index diagram for candidate node pairs in a scenario involving energy storage, as shown in a specific embodiment. Detailed Implementation

[0020] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0021] The preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be understood that the preferred embodiments are for illustrative purposes only and are not intended to limit the scope of protection of the present invention.

[0022] This invention first proposes a quantitative calculation method for node mutual exclusion coefficients for distributed generation (DG) access in distribution networks, which can quantify the degree of mutual influence between two access nodes. Based on this, it proposes a capacity assessment method for DG access in distribution networks, applicable to scenarios with or without energy storage (for the case of adding energy storage to distribution lines with existing assessment results, this invention further proposes a rapid capacity assessment method based on mutual exclusion structure inheritance, detailed later). This method first calculates the capacity of a single node and the capacity of node pairs, and constructs a mutual exclusion coefficient from the capacity loss of node pairs to characterize the capacity coupling relationship between different access nodes. Then, a capacity allocation model is established based on the mutual exclusion coefficient to obtain initial capacity allocation values. To address the issue that the initial solution may not be feasible for power flow, the mutual exclusion coefficient and small-disturbance power flow results are further used to guide capacity rollback, ensuring that the capacity scheme meets operational constraints. Finally, for DG in distribution networks with energy storage, this invention also proposes a rapid capacity assessment method, because in scenarios with energy storage, the charging and discharging of energy storage affects the DG capacity; recalculating the capacity of all node pairs would result in a large amount of power flow calculation. To improve the efficiency of carrying capacity calculation for distributed generation in power distribution networks with energy storage, this method first recalculates the single-node carrying capacity after energy storage based on the mutual exclusion coefficient matrix in the scenario without energy storage. Then, it updates a small number of node pairs that may change based on the single-point carrying capacity gain. Finally, it establishes a capacity constraint model with energy storage and performs power flow verification and capacity rollback. This rapid evaluation method is suitable for adding energy storage based on the original line / original calculation results without energy storage. It does not require recalculating all the mutual exclusion coefficients of all node pairs. It only requires recalculating the single-node carrying capacity after energy storage, and then selecting a small number of node pairs that may change significantly for recalculation. The remaining node pairs retain the original mutual exclusion coefficients, which can greatly improve the evaluation speed.

[0023] The algorithms and theoretical modeling involved in the invention include the following: (1) Modeling of load and distributed power output in typical days with multiple time periods A typical day is discretized into time periods t=1,...,T (T=24), with a step size Δt=1h. Let the baseline load of node i be (P0L,i,Q0L,i), and the typical daily load factor be α. L (t), then: Let the installed capacity of the k-th DG access node be C. k The typical daily photovoltaic power output multiplier is α. DG (t)∈[0,1] (as in the photovoltaic curve), Its active power injection is then: If power factor constraints are considered, reactive power injection can be written as: Where φ is the phase angle corresponding to the DG operating power factor. This invention only discusses active power coupling and does not consider the inverter's reactive power support capability.

[0024] (2) Multi-period model of energy storage power The charging and discharging power of the m-th energy storage unit during time period t is P. ES，m (t), whose power constraint is: Let the charge and discharge efficiencies be η and η, respectively. c and η d The rated capacity of the energy storage is Emaxm. Then, the evolution relationship between adjacent SOC states over a time interval Δt is: (3) Operational constraints and feasibility criteria At each time period t, the node voltage magnitude |V is obtained through power flow calculation of the distribution network. i (t)|and branch current amplitude|I j (t)∣。 Then the node voltage constraint: Line current constraints: SOC constraints: To facilitate standardized judgment, a time-period feasibility indicator is defined: If F(t) = 1 holds true in all time periods, the typical daily operation is considered feasible. If F(t) = 0 for any candidate distributed power supply capacity configuration in any time period, the capacity is deemed infeasible.

[0025] It should be noted that the above-mentioned typical daily load and distributed generation output modeling, energy storage power multi-period model, and energy storage SOC model are applicable to generating power flow calculations for each time period. During the carrying capacity assessment, typical daily multi-period load curves and distributed generation output curves are used to construct power injection for each time period; for scenarios involving energy storage, energy storage charging and discharging power constraints and SOC constraints are further considered. The above-mentioned load, distributed generation output, and energy storage models are used to generate power flow calculation inputs for each time period and to determine whether candidate capacity schemes meet operational constraints throughout the entire time period. The power flow calculation can be implemented using the forward-backward substitution method or other conventional distribution network power flow calculation methods.

[0026] The specific steps of the above method will be further elaborated below. The present invention provides a quantitative calculation method for node mutual exclusion coefficients in distributed generation access for distribution networks, comprising the following steps: Step S1: Set the installed capacity of the i-th distributed power access node in the system to C. i Set the installed capacity of all distributed power sources connected to the node (except for i) to 0, and find the node C that satisfies the node voltage, current, and SOC constraints in all time periods. i The maximum value of is taken as the maximum bearing capacity of point i, denoted as . ; Step S2: For any distributed power supply access node pair (i,j) within the system, record the total installed capacity of the node pair as S. i,j Set the installed capacity of all DG nodes except i and j to 0, and find the S that satisfies the node voltage, current, and SOC constraints in all time periods. i,j The maximum value is taken as the sum of the maximum bearing capacities of node pairs i and j, denoted as . ; Step S3: If node i and node j do not affect each other, then there should be ≈ When the installed capacity of two nodes is mutually constrained, the sum of the capacities of both nodes connected simultaneously will be less than the sum of the capacities of a single node connected individually. Therefore, a mutual exclusion coefficient R is defined for node pairs. i,j for: A large mutual exclusion coefficient indicates that the two nodes "reject each other"; a small mutual exclusion coefficient indicates that the two nodes have little mutual influence.

[0027] Among them, the node voltage, current and SOC constraints are referred to in the above "(3) Operational Constraints and Feasibility Criteria", which are as follows: At each time period t, the node voltage magnitude |V is obtained through power flow calculation of the distribution network. i (t)|and branch current amplitude|I j (t)∣, then the node voltage constraint is: The line current constraint is: The SOC constraint is: .

[0028] like Figure 1 As shown, based on the quantitative calculation method of node mutual exclusion coefficients described above, this invention also proposes a carrying capacity assessment method for distributed generation access in distribution networks, including the following steps: Step S1: Obtain distribution network parameters and set relevant constraints and distributed power generation nodes; Step S2: Calculate the maximum bearing capacity of a single node and the bearing capacity of a node pair one by one; Step S3: Based on the quantitative calculation method of node mutual exclusion coefficient, construct mutual exclusion coefficients from the capacity loss of node pairs to characterize the capacity coupling relationship between different access nodes, and establish a linear programming model; for a system with N distributed power access nodes, any pair of distributed power access nodes (i,j) has: Using the installed capacity of each node as a decision variable, and with the goal of maximizing the total installed capacity of the system, a linear programming model is established under the upper bound constraints of the installed capacity of any node and the upper bound constraints of a single node, thereby obtaining the maximum carrying capacity and capacity allocation of the distribution network.

[0029] Step S4: Since the power system is not a simple linear system, the maximum carrying capacity calculated by linear programming is an optimistic upper bound. Therefore, the carrying capacity calculation result needs to be substituted into the power flow for verification. If the power flow verification is not feasible, the capacity scheme is backed up and corrected. That is, the initial solution is obtained by solving the model and substituted into the power flow verification. If the capacity scheme meets the operating constraints, the capacity scheme is output; otherwise, proceed to step S5. Step S5: Further utilize the mutual exclusion coefficient and small disturbance power flow results to guide capacity backoff, obtain the updated new capacity allocation scheme, and resolve the model and substitute it into the power flow verification until the operating constraints are met, and output the capacity scheme; otherwise, repeat step S5. For distributed power source access node i, calculate its backoff priority D. i As shown in the following formula: In the formula: C i D represents the distributed power capacity of the nodes under the current scheme. iIt also reflects the node's own capacity utilization and its mutual exclusion relationship with other nodes; if it is large, it means that the node's current capacity accounts for a high proportion of its own upper limit, or that it is strongly mutually exclusive with other nodes with high utilization, so it is more likely to be the node causing capacity conflict.

[0030] According to D i The specific method for selecting several candidate nodes from largest to smallest and performing small-disturbance power flow verification on these candidate nodes is as follows: For candidate node i, its DG capacity is slightly reduced, and then the over-limit voltage difference ΔV before and after is calculated. Combining the mutual exclusion backoff priority and the small disturbance power flow results, the backoff weight w of node i is defined. i : In the formula: K represents the set of candidate backoff nodes; By multiplying the backoff weight of each node by the backoff step size, the backoff capacity can be obtained and substituted into the power flow verification.

[0031] In scenarios involving energy storage, the charging and discharging of energy storage can affect the distributed generation (DG) carrying capacity. Recalculating the carrying capacity of all node pairs would result in a significant computational burden for power flow calculations. Therefore, based on the quantitative calculation method for node mutual exclusion coefficients described above, this invention also provides a rapid assessment method for the carrying capacity of distributed generation (DG) networks with energy storage, including: The mutual exclusion coefficient R of node pairs is obtained based on the quantitative calculation method of node mutual exclusion coefficient. i,j Construct a mutual exclusion coefficient matrix for scenarios without energy storage; Based on the mutual exclusion coefficient matrix, the single-node bearing capacity after energy storage is recalculated; where, for each node i, the single-node bearing capacity without energy storage is set to C. i The node bearing capacity with energy storage is C. Ei Therefore, the screening index H is defined. ij : H ij The larger the value, the stronger the original mutual exclusion of the node pair, and the more significant the change in the bearing capacity of the related nodes after energy storage. Therefore, it is necessary to recalculate the bearing capacity of the node pair. A small number of potentially changing node pairs are selected for updating based on the single-point carrying capacity gain. For the selected node pairs, the carrying capacity and mutual exclusion coefficient are recalculated, while the original mutual exclusion coefficients are retained for the unselected pairs. Then, the carrying capacity of distributed power sources in the distribution network with energy storage is calculated using the updated mutual exclusion coefficients and node carrying capacity.

[0032] Specifically, based on the previously obtained single-node carrying capacity with energy storage and the updated mutual exclusion coefficient matrix, a capacity constraint model with energy storage is established. The model is solved to obtain an initial solution, which is then used for power flow verification. If the capacity scheme meets the operational constraints, the capacity scheme is output; otherwise, the mutual exclusion coefficient and small disturbance power flow results are used to guide capacity backoff to obtain an updated new capacity allocation scheme. The model is then solved again and used for power flow verification until the operational constraints are met, and the capacity scheme is output.

[0033] The above method will be further verified through a specific embodiment below.

[0034] I. Test System and Parameter Settings To verify the effectiveness of the proposed method, an improved IEEE 33-node distribution network was selected as the test system. The system baseline capacity was 10 MVA, the baseline voltage was 12.66 kV, and node 1 was the slack node. Candidate distributed generation (DG) nodes were selected as: 17, 18, 25, 32, and 33. The DG output was measured using a typical daily photovoltaic (PV) curve. Operating constraints included node voltage constraints and line current constraints; the voltage constraint was set at 0.95-1.05 pu, and the current constraint at 1.1 pu.

[0035] Two scenarios were set up: one without energy storage and one with energy storage. The scenario without energy storage was used to verify the effectiveness of the method proposed in this invention; the scenario with energy storage was used to verify that after energy storage is connected, the carrying capacity of distributed power sources in the distribution network can be quickly estimated using a small number of mutual exclusion coefficient updates.

[0036] II. Calculation of Mutual Exclusion Coefficient in Scenarios Without Energy Storage First, the maximum carrying capacity of each candidate DG node when accessed individually was calculated, and the results are shown in Table 1.

[0037] Table 1. Single-node carrying capacity in scenarios without energy storage As shown in Table 1, node 25 has the largest single-node carrying capacity, indicating that its impact on system constraints is relatively small when it is connected to a distributed power source alone; the single-node carrying capacity of nodes 17, 18, 32, and 33 is relatively low, indicating that these nodes are more susceptible to voltage constraints.

[0038] Further calculations of the node pair bearing capacity and mutual exclusivity coefficient yielded the following results: Figure 2 As shown in the figure, the horizontal and vertical axes represent the node numbers of the candidate distributed power sources, and the color values ​​of the blocks represent the mutual exclusion coefficients of the corresponding node pairs. Since the matrix is ​​symmetric about the main diagonal, the off-diagonal color blocks represent the mutual exclusion relationships of the corresponding node pairs.

[0039] As shown in the figure, the mutual exclusion coefficients of 17-18 and 32-33 are 0.483 and 0.488, respectively, which are significantly higher than those of other node pairs, indicating that there is strong capacity competition between adjacent end nodes; the mutual exclusion coefficient of node 25 with other nodes is low, indicating that its capacity coupling with other candidate nodes is weak.

[0040] III. Mutual Exclusion Backoff Correction Results in Energy Storage-Free Scenarios According to Table 1 and Figure 2 Based on the single-node carrying capacity and mutual exclusion coefficient, a capacity allocation model for distributed generation in the distribution network is established. This model aims to maximize the total connected capacity while considering both the upper limit of single-node capacity and the upper limit of node-pair capacity. After solving the linear programming model, the initial capacity allocation scheme is obtained, as shown in Table 2: Table 2. Initial capacity allocation results for LPs in scenarios without energy storage The result satisfies the single-node capacity constraint and node pair mutual exclusion constraint. However, when it is substituted into the 24-hour power flow calculation, it is found that the maximum voltage of 1.0866 pu does not meet the voltage constraint, so capacity rollback is required.

[0041] Table 3. Capacity backoff process guided by mutual exclusion coefficients The final capacity scheme is as follows: the access capacities (MW) of nodes 17, 18, 25, 32, and 33 are 0.4631, 0.2699, 3.0852, 0.7188, and 0.6951, respectively. The total capacity is 5.2320MW.

[0042] To further verify the accuracy and efficiency of the method, this method was compared with the PSO method, and the results are shown in Table 4: Table 4. Comparison of different methods in scenarios without energy storage As shown in Table 4, the distributed generation capacity obtained by the method of this invention is 5.2320 MW, while the result obtained by PSO is 5.2571 MW, with a relative error of approximately 0.48%. In terms of computational efficiency, the method of this invention calls 621 power flow calculations, while PSO requires 3601. The number of power flow calculations for PSO is approximately 5.8 times that of this method. This demonstrates that this method can obtain a capacity result close to that of PSO with fewer power flow calculations.

[0043] The reason is that PSO requires repeated power flow evaluation of a large number of particle positions, while this method first constructs an initial capacity value using single-node carrying capacity and mutual exclusion coefficients, and then performs backtracking corrections only on a few key nodes based on mutual exclusion relationships and small disturbance power flow results, avoiding random searches in the multidimensional capacity space. Therefore, the method of this invention can significantly reduce the number of power flow calls while maintaining a small capacity error.

[0044] IV. Changes in Single-Node Bearing Capacity and Candidate Node Pair Selection in Energy Storage Scenarios To further analyze the changes in distributed power generation capacity after the integration of energy storage, one energy storage unit was connected to each of nodes 32 and 33. Each energy storage unit has a maximum power output of 0.2MW and a capacity of 0.8MWh. The energy storage adopts a fixed charge-discharge strategy based on the photovoltaic curve: charging during periods of high photovoltaic output and discharging during periods of low output or no photovoltaic output, while satisfying the 24h SOC regression constraint.

[0045] After the energy storage is connected, the single-node carrying capacity of each candidate distributed power node is recalculated, and the results are shown in Table 5.

[0046] Table 5. Comparison of single-node bearing capacity before and after energy storage In scenarios involving energy storage, recalculating the carrying capacity of all node pairs would significantly increase power flow calls. The method of this invention filters out a small number of node pairs more likely to change based on the mutual exclusion coefficient without energy storage and the single-point carrying capacity gain after energy storage.

[0047] Calculate Hij for each node pair separately, and the results are as follows: Figure 3 As shown, by Figure 3 It can be seen that H ij The top 5 node pairs are 32-33, 17-18, 17-32, 18-32, and 17-33. In this embodiment, only the carrying capacity of the node pairs containing energy storage is recalculated for these 5 node pairs; the remaining node pairs directly inherit the original mutual exclusion coefficients.

[0048] V. Load-bearing capacity calculation results in scenarios including energy storage Based on the previously obtained single-node carrying capacity including energy storage and the updated mutual exclusion coefficient matrix, a distributed generation capacity allocation model for the distribution network is established. The initial linear programming solution has a large total capacity, but power flow verification is not feasible; therefore, capacity backoff correction is still required. The final capacity allocation scheme is obtained as follows: the access capacities (MW) of nodes 17, 18, 25, 32, and 33 are 0.3300, 0.4190, 3.0042, 0.8396, and 0.7288, respectively. The total capacity is 5.3216 MW.

[0049] To further verify the accuracy and efficiency of the method, the method presented in this paper is compared with the PSO method, and the results are shown in Table 6.

[0050] Table 6. Comparison of results from different methods in scenarios involving energy storage As shown in Table 6, with an error of 2.93%, the number of PSO calls is approximately 6.45 times that of the method of the present invention.

[0051] Further comparison of the results with and without energy storage shows that the total system capacity under PSO increased from 5.2571MW to 5.4824MW, an increase of 0.2253MW; while the total system capacity under the method of this invention increased from 5.2320MW to 5.3216MW, an increase of 0.0896MW. The improvement achieved by this method is lower than that of PSO, indicating that the small number of mutually exclusive edge update strategies has a certain degree of conservatism; however, it can significantly reduce the number of power flow calls while reflecting the role of energy storage in improving the capacity of multi-node distributed power sources.

[0052] In summary, the case study including energy storage shows that after energy storage is integrated, the carrying capacity of a single node needs to be recalculated, while the mutual exclusion relationships between nodes do not need to be fully recalculated. Using H... ij By selecting and correcting a small number of potentially changing node pairs, the power flow call frequency can be significantly reduced while enabling rapid calculation of the distribution network's DG carrying capacity in scenarios involving energy storage.

[0053] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A quantitative calculation method for node mutual exclusion coefficients for distributed generation access in distribution networks, characterized by: The method includes the following steps: Step S1: Set the installed capacity of the i-th distributed power access node in the system to C. i Set the installed capacity of all distributed power sources connected to the node (except for i) to 0, and find the node C that satisfies the node voltage, current, and SOC constraints in all time periods. i The maximum value of is taken as the maximum bearing capacity of point i, denoted as . ; Step S2: For any distributed power supply access node pair (i,j) within the system, record the total installed capacity of the node pair as S. i,j Set the installed capacity of all DG nodes except i and j to 0, and find the S that satisfies the node voltage, current, and SOC constraints in all time periods. i,j The maximum value is taken as the sum of the maximum bearing capacities of node pairs i and j, denoted as . ; Step S3: If node i and node j do not affect each other, then there should be ≈ + When the installed capacity of two nodes is mutually constrained, the sum of the capacities of both nodes connected simultaneously will be less than the sum of the capacities of a single node connected individually. Therefore, a mutual exclusion coefficient R is defined for node pairs. i,j for: A large mutual exclusion coefficient indicates that the two nodes "reject each other"; a small mutual exclusion coefficient indicates that the two nodes have little mutual influence.

2. The quantitative calculation method for node mutual exclusion coefficients for distributed generation access in distribution networks according to claim 1, characterized in that: The node voltage, current, and SOC constraints are as follows: At each time period t, the node voltage magnitude |V is obtained through power flow calculation of the distribution network. i (t)|and branch current amplitude|I j (t)∣, then the node voltage constraint is: The line current constraint is: The SOC constraint is: 。 3. A carrying capacity assessment method for distributed generation access in distribution networks, characterized in that: The method includes the following steps: Step S1: Obtain distribution network parameters and set relevant constraints and distributed power generation nodes; Step S2: Calculate the maximum bearing capacity of a single node and the bearing capacity of a node pair one by one; Step S3: According to the method of any one of claims 1 or 2, a mutual exclusion coefficient is constructed by the node to characterize the capacity coupling relationship between different access nodes, and a linear programming model is established; Step S4: Solve the model to obtain the initial solution and substitute it into the power flow verification. If the capacity scheme meets the operating constraints, output the capacity scheme; otherwise, proceed to step S5. Step S5: Further utilize the mutual exclusion coefficient and small disturbance power flow results to guide capacity rollback, obtain the updated new capacity allocation scheme, and resolve the model and input it for power flow verification until the operating constraints are met, and output the capacity scheme; otherwise, repeat step S5.

4. The carrying capacity assessment method for distributed generation access in distribution networks according to claim 3, characterized in that: In step S3, for a system with N distributed power access nodes, any pair of distributed power access nodes (i,j) has the following: Using the installed capacity of each node as a decision variable, and with the goal of maximizing the total installed capacity of the system, a linear programming model is established under the upper bound constraints of the installed capacity of any node and the upper bound constraints of a single node, thereby obtaining the maximum carrying capacity and capacity allocation of the distribution network.

5. The carrying capacity assessment method for distributed generation access in distribution networks according to claim 4, characterized in that: In step S5, the backoff priority D of the distributed power access node i is calculated. i As shown in the following formula: In the formula: C i D represents the distributed power capacity of the nodes under the current scheme. i It also reflects the node's own capacity utilization and its mutual exclusion relationship with other nodes; According to D i The specific method for further verifying the small-disturbance power flow of candidate nodes by selecting several candidate nodes from largest to smallest is as follows: For candidate node i, its DG capacity is reduced slightly, and then the over-limit voltage difference ΔV before and after is calculated. Combining the mutual exclusion backoff priority and the small-disturbance power flow results, the backoff weight w of node i is defined. i : In the formula: K represents the set of candidate backoff nodes; By multiplying the backoff weight of each node by the backoff step size, the backoff capacity can be obtained and substituted into the power flow verification.

6. A rapid assessment method for the carrying capacity of distributed generation access in distribution networks, including energy storage, characterized in that: The method includes: The method described in any one of claims 1-2 obtains the node pair mutual exclusion coefficient R. i,j Construct a mutual exclusion coefficient matrix for scenarios without energy storage; Based on the mutual exclusion coefficient matrix, the single-node bearing capacity after energy storage is recalculated; Update a small number of potentially changing node pairs based on the single-point bearing capacity gain; Based on the previously obtained single-node carrying capacity with energy storage and the updated mutual exclusion coefficient matrix, a capacity constraint model with energy storage is established. The model is solved to obtain an initial solution, which is then used for power flow verification. If the capacity scheme meets the operational constraints, the capacity scheme is output. If not, the mutual exclusion coefficient and small disturbance power flow results are used to guide capacity back-off to obtain an updated new capacity allocation scheme. The model is then solved again and used for power flow verification until the operational constraints are met, and the capacity scheme is output.

7. The rapid assessment method for the carrying capacity of distributed power generation access in distribution networks, including energy storage, as described in claim 6, is characterized in that: For each node i, let the single-node bearing capacity in the state without energy storage be C. i The node bearing capacity with energy storage is C. Ei Therefore, the screening index H is defined. ij : H ij The larger the value, the stronger the original mutual exclusion of the node pair, and the more significant the change in the bearing capacity of the related nodes after energy storage. Therefore, it is necessary to recalculate the bearing capacity of the node pair.

8. The rapid assessment method for the carrying capacity of distributed power generation access in distribution networks, including energy storage, as described in claim 7, is characterized in that: For selected node pairs, the node pair carrying capacity and mutual exclusion coefficient are recalculated. For unselected pairs, the original mutual exclusion coefficient is retained. Then, the carrying capacity of distributed power sources in the distribution network with energy storage is calculated using the updated mutual exclusion coefficient and node carrying capacity.

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