Grid-side energy storage capacity configuration method based on voltage timing characteristics

Abstract

The application relates to the technical field of power grid side energy storage capacity configuration, and provides a power grid side energy storage capacity configuration method based on voltage time sequence characteristics, which comprises the following steps: extracting power data of a power distribution network transformer substation in a historical quarter; constructing a double-layer optimization model, setting a constraint condition, solving an initial energy storage capacity configuration scheme, and determining voltage effective values of each load node; determining the importance degree weight of each load node, calculating the discrete fluctuation characteristic value, voltage goodness and voltage stability of the load node, and calculating the voltage stability of the power distribution network on the day; substituting the voltage stability on the day into the double-layer optimization model, using an optimization algorithm to solve the double-layer optimization model, obtaining a final energy storage capacity configuration scheme, and realizing power grid side energy storage capacity configuration. The application can improve the power quality stability of the power grid side energy storage capacity configuration.

Description

Technical Field

[0001] This application relates to the field of grid-side energy storage capacity configuration technology, specifically to a grid-side energy storage capacity configuration method based on voltage timing characteristics. Background Technology

[0002] The frigid Northeast region is a core base for wind and solar power generation in my country. The distribution network substations in this area inherently suffer from high load rates and large peak-to-valley differences. Furthermore, wind and solar power output exhibits significant volatility, randomness, and intermittency. Large-scale grid integration of new energy sources further exacerbates the difficulty of distribution network operation and control. Existing grid-side energy storage capacity configurations are insufficiently adaptable, making it difficult to effectively maintain stable substation supply voltage, significantly increasing wind and solar power curtailment, resulting in severe waste of new energy resources, and hindering the regional absorption of new energy and the safe and stable operation of the distribution network.

[0003] Current research on the optimization of grid-side energy storage capacity configuration focuses primarily on grid planning. It generally takes energy storage investment cost and overall operational benefits as the sole optimization objectives, neglecting the voltage and power quality stability of each load node and failing to conduct collaborative optimization based on voltage dynamics. The resulting optimal energy storage configuration can only alleviate substation overload problems to a certain extent and cannot guarantee voltage stability across all load nodes of the transmission line. It is prone to problems such as voltage deviation and excessive fluctuations at the user end, which seriously affect the power quality of the terminal power supply and makes it difficult to meet the multiple needs of distribution network operation stability, renewable energy utilization rate and user power supply quality. Summary of the Invention

[0004] This application provides a grid-side energy storage capacity configuration method based on voltage timing characteristics to address the problem that neglecting the voltage power quality stability of each load node during grid-side energy storage capacity configuration affects the power quality of the terminal supply. The specific technical solution adopted is as follows:

[0005] One embodiment of this application provides a grid-side energy storage capacity configuration method based on voltage timing characteristics, the method comprising the following steps:

[0006] Extract power data from the distribution network substations for one historical quarter;

[0007] A two-layer optimization model is constructed and constraints are set. The initial energy storage capacity configuration scheme is solved by optimization algorithm. Combined with power data, the effective voltage value of each load node in each hour of a natural day is determined.

[0008] Based on the power data of all load nodes in the distribution network, the importance weight of each load node is determined. Based on the difference of the effective voltage value of the same load node in all hours of a natural day, the discrete fluctuation characteristic value of the load node is calculated. Based on the difference of the effective voltage value of the same load node in all hours of a natural day and the preset standard voltage, the voltage improvement of the load node is determined. Combined with the discrete fluctuation characteristic value, the voltage stability of the load node is calculated. Combined with the importance weight, the voltage stability of the distribution network on the corresponding natural day is calculated.

[0009] The daily voltage stability is substituted into the two-layer optimization model, and the optimization algorithm is used to solve the two-layer optimization model to obtain the final energy storage capacity configuration scheme and realize the grid-side energy storage capacity configuration.

[0010] Furthermore, the two-layer optimization model includes an upper-layer objective optimization function and a lower-layer objective optimization function, wherein the lower-layer objective optimization function is equal to the daily voltage stability of the distribution network.

[0011] Furthermore, the constraints include:

[0012] The capacity of the energy storage battery is greater than or equal to the lower limit of the capacity of the energy storage battery allowed to be installed on the grid side, and less than or equal to the upper limit of the capacity of the energy storage battery allowed to be installed on the grid side.

[0013] The power of the energy storage battery is greater than or equal to the lower limit of the power allowed to be installed on the grid side, and less than or equal to the upper limit of the power allowed to be installed on the grid side.

[0014] New energy power stations within a natural day The theoretical output of a new energy power station in one hour under certain constraints is equal to: the output of the new energy power station in one natural day. Actual output per hour, energy storage battery within a natural day The sum of the charging and discharging power per hour, and the power loss from new energy sources, of which, Represents all integers greater than or equal to 1 and less than or equal to the number of hours in a calendar day;

[0015] Energy storage battery within a calendar day The state of charge for one hour is greater than or equal to the preset minimum state of charge and less than or equal to the preset maximum state of charge.

[0016] Furthermore, the power data includes: the load and active power of the power supply end of the distribution network substation for each hour within a natural day in a historical quarter; the average load of each load node on each transmission line segment for each hour within a natural day; the charging and discharging power and state of charge of the energy storage device; and the impedance of each transmission line segment. The load, active power, charging and discharging power, and state of charge are all average values ​​within each hour.

[0017] Furthermore, the specific method for determining the importance weight of the load nodes is as follows:

[0018] The sum of the average load of a load node over all hours of all natural days within a historical quarter is recorded as the historical load accumulation of the load node. The ratio of the historical load accumulation of a load node to the sum of the historical load accumulations of all load nodes is recorded as the importance weight of the load node.

[0019] Furthermore, the specific calculation method for the discrete fluctuation characteristic value of the load node is as follows:

[0020] The normalized value of the coefficient of variation of the effective voltage value of the same load node for all hours within a natural day is denoted as the first stability of the same load node.

[0021] The ratio of the range of the effective voltage values ​​of the same load node over all hours within a natural day to a preset standard voltage is denoted as the second stability of the same load node.

[0022] The weighted sum of the first and second stability of the load node is denoted as the discrete fluctuation characteristic value of the load node.

[0023] Furthermore, the specific method for determining the voltage improvement of the load node is as follows:

[0024] The ratio of the effective voltage value of a load node to a preset standard voltage is denoted as the per-unit voltage value. The absolute value of the difference between the per-unit voltage value and the number 1 is denoted as the per-unit voltage stability value. Based on all the per-unit voltage stability values ​​corresponding to the same load node, a per-unit voltage stability value sequence is established. The ratio of the sum of the absolute values ​​of all negative values ​​in the first-order difference sequence of the per-unit voltage stability value sequence to the sum of the absolute values ​​of all values ​​in the first-order difference sequence of the per-unit voltage stability value sequence is denoted as the voltage improvement degree of the corresponding load node.

[0025] Furthermore, the specific calculation method for the voltage stability of the load node is as follows:

[0026] The difference between the number 1 and the discrete fluctuation characteristic value of the load node is calculated and denoted as the discrete stability characteristic value of the load node. The weighted sum of the discrete stability characteristic value of the load node and the voltage stability is denoted as the voltage stability of the load node.

[0027] Furthermore, the specific calculation method for the daily voltage stability is as follows: using the importance weight of the load nodes as the weight, the voltage stability of all load nodes in the distribution network on the same natural day is weighted and summed.

[0028] Furthermore, the final energy storage capacity configuration scheme includes: the optimal values ​​of the capacity and power of the energy storage battery for each hour of a natural day.

[0029] The beneficial effects of this application are:

[0030] This application constructs a two-layer optimization model and sets constraints. An optimization algorithm is used to solve for the initial energy storage capacity configuration scheme, and combined with power data, the effective voltage value of each load node is determined. Then, based on the difference in the effective voltage value of the same load node across all hours within a natural day, the voltage stability of all load nodes in the distribution network under the initial energy storage capacity configuration scheme is evaluated, and the voltage stability of the load nodes is obtained. Simultaneously, based on the impact of the voltage stability of the load nodes on the overall power supply quality of the distribution network, the importance weight of the load nodes is calculated. Based on the voltage stability and importance weight of the load nodes, the daily voltage stability of the distribution network on the corresponding natural day is calculated. Finally, the daily voltage stability is substituted into the two-layer optimization model. Considering the voltage and power quality stability of the load nodes, the optimization algorithm is used to solve the two-layer optimization model to obtain the final energy storage capacity configuration scheme. This achieves grid-side energy storage capacity configuration and solves the problem of neglecting the voltage and power quality stability of each load node during grid-side energy storage capacity configuration, which affects the power quality of the terminal power supply. Attached Figure Description

[0031] Figure 1 This is a schematic flowchart of a grid-side energy storage capacity configuration method based on voltage timing characteristics, provided in one embodiment of this application. Detailed Implementation

[0032] Please see Figure 1 The diagram illustrates a flowchart of a grid-side energy storage capacity configuration method based on voltage timing characteristics, according to an embodiment of this application. The method includes the following steps:

[0033] Step S001: Extract the power data of the distribution network substations for a historical quarter.

[0034] The power data of the distribution network substations for a historical quarter is retrieved from the distribution network system database. The power data includes the hourly load and active power of the power supply end of the distribution network substations for each natural day within the historical quarter, the hourly average load of each load node on each transmission line segment for each natural day, the charging and discharging power and state of charge of the energy storage device, and the impedance of each transmission line segment. The load, active power, charging and discharging power and state of charge are all hourly average values.

[0035] At this point, the power data of the distribution network substation has been obtained.

[0036] Step S002: Construct a two-layer optimization model and set constraints. Solve the initial energy storage capacity configuration scheme through optimization algorithm. Combine with power data, determine the effective voltage value of each load node in each hour of a natural day.

[0037] Because existing grid-side energy storage capacity configuration methods mainly consider grid-side energy storage investment costs and energy storage operation benefits, they neglect the consideration of load node voltage and power quality during the energy storage capacity configuration optimization process. As a result, although the optimal energy storage capacity configuration scheme obtained by the grid-side intelligent optimization algorithm can reduce the load on the distribution network substation, it cannot guarantee the stability of the voltage of all load nodes on the transmission line, thus affecting the voltage and power quality delivered to the user end. Therefore, this application optimizes the grid-side energy storage capacity configuration based on the voltage timing characteristics of all load nodes on each transmission line.

[0038] Specifically, based on a quarterly historical power data from distribution network substations, a two-layer optimization model is constructed, incorporating the voltage time-series characteristics of all load nodes on the distribution network transmission lines. The upper-layer model considers the investment cost of energy storage batteries and the cost of new energy losses, while the lower-layer model considers the voltage stability of all load nodes on the distribution network transmission lines after energy storage capacity configuration. In this model, the upper-layer model passes the solved energy storage capacity configuration optimization scheme to the lower-layer model, which then solves for the optimal energy storage capacity configuration scheme based on the upper-layer optimization scheme.

[0039] Specifically, the upper-level model of the two-level optimization model, i.e., the upper-level objective function, is expressed as:

[0040]

[0041]

[0042]

[0043] in, Let represent the upper-level objective function of the two-level optimization model. The optimization objectives of the upper-level objective function are to minimize the investment cost of energy storage batteries and to minimize the cost of new energy losses. This indicates the investment cost of energy storage batteries; This indicates the cost of electricity lost by new energy sources; These represent the first preset weighting coefficient and the second preset weighting coefficient, respectively. The values ​​of the first preset weighting coefficient and the second preset weighting coefficient are both greater than or equal to 0 and less than or equal to 1, and the sum of the first preset weighting coefficient and the second preset weighting coefficient is 1. Since the two-layer optimization model is used to solve the optimal scheme for energy storage battery capacity configuration, the unit capacity cost of energy storage battery should be greater than the unit power cost. In this embodiment, the values ​​of the first preset weighting coefficient and the second preset weighting coefficient are 0.6 and 0.4, respectively.

[0044] in, These represent the unit capacity cost and unit power cost of the energy storage battery, respectively. In this embodiment, the unit capacity cost and unit power cost of the energy storage battery are set to 1700 yuan / kW. h and 500 yuan / kW; These represent the capacity and power of the energy storage battery in the energy storage capacity configuration scheme, respectively. The capacity of the energy storage battery is equal to the product of the number of battery packs configured on the grid side and the unit capacity of the battery. In this embodiment, the capacity of the energy storage battery is taken as 5MW. h, where power is measured in MW.

[0045] in, This represents the number of hours within a natural day when energy storage capacity configuration is optimized, with a value of 24. This indicates that the new energy power station is in operation for the first time within a natural day. The theoretical output per hour under the given constraints, expressed in kW. h; This indicates that the new energy power station is in operation for the first time within a natural day. Actual output per hour; This indicates the number of times the energy storage battery is used within a calendar day. The charging and discharging power of the energy storage battery in one natural day is [amount missing]. When charging for 1 hour, the corresponding charge / discharge power is greater than 0. When the energy storage battery is charged for the first 1 hour within a natural day... When discharging for 24 hours, the corresponding charge / discharge power is less than 0. Indicates the number of days within a natural day The hourly unit price of renewable energy is calculated in this embodiment, with the hourly unit price of renewable energy for each natural day within a historical quarter being greater than or equal to 0.113 yuan / kW. h and less than or equal to 0.349 yuan / kW h, the specific value is based on the actual unit new energy electricity price.

[0046] Specifically, the lower-level model of the two-level optimization model, i.e., the lower-level objective function, is expressed as:

[0047]

[0048] in, This represents the lower-level objective function of the two-level optimization model. The objective of the lower-level objective function is to maximize the voltage stability of all load nodes in the power distribution network transmission lines. This represents the daily voltage stability of the distribution network in the grid-side energy storage capacity configuration scheme obtained from solving the upper-level objective optimization function.

[0049] The constraints of the two-level optimization model include four conditions, specifically:

[0050] 1. The capacity of the energy storage battery should be greater than or equal to the lower limit of the allowed capacity of energy storage batteries installed on the grid side, and less than or equal to the upper limit of the allowed capacity of energy storage batteries installed on the grid side. That is, there is a constraint on the number of battery packs connected to the energy storage battery on the grid side; the fewer the number of battery packs connected, the smaller the energy storage battery capacity.

[0051] 2. The power of the energy storage battery should be greater than or equal to the lower limit of the power allowed to be installed on the grid side, and less than or equal to the upper limit of the power allowed to be installed on the grid side.

[0052] 3. New energy power stations within a natural day The theoretical output of a new energy power station in one hour under certain constraints is equal to: the output of the new energy power station in one natural day. Actual output per hour, energy storage battery within a natural day The charging and discharging power per hour, plus the sum of the power loss from new energy sources.

[0053] 4. The energy storage battery within a natural day The state of charge for one hour is greater than or equal to a preset minimum state of charge and less than or equal to a preset maximum state of charge. In this embodiment, the preset minimum state of charge and the preset maximum state of charge are set to 0.2 and 0.9, respectively.

[0054] The upper-level objective function and the lower-level objective function of the two-level optimization model are solved using an intelligent optimization algorithm. During the algorithm operation, each particle corresponds to a set of candidate schemes for grid-side energy storage capacity configuration for a natural day. Each set of candidate schemes includes energy storage capacity configuration schemes for energy storage batteries corresponding to 24 hours within a natural day. The solution result is recorded as the initial energy storage capacity configuration scheme, which includes the optimal values ​​of capacity and power of energy storage batteries for each hour within a natural day.

[0055] In this embodiment, the particle swarm optimization algorithm is selected to solve the upper-level and lower-level objective functions of the two-layer optimization model. The initial population size is set to 50, the maximum and minimum initial inertia weights are set to 0.9 and 0.4 respectively, both initial learning factors are set to 1.5, and the number of iterations is set to 100. The particle swarm optimization algorithm is a well-known technique and will not be described in detail here.

[0056] To clarify the coupling influence and constraint effect of the upper-level objective optimization function on the lower-level objective optimization function in the two-level optimization model, a detailed explanation is given using the single iteration process of the particle swarm optimization algorithm as an example. During the algorithm iteration process, the upper-level objective optimization function is used to solve the problem with the goal of minimizing the comprehensive economic cost. After completing the single iteration, a better candidate scheme for daily energy storage capacity configuration that is suitable for the economic operation of the distribution network is obtained. This better candidate scheme for daily energy storage capacity configuration is then imported into the lower-level objective optimization function. The lower-level objective optimization function extracts the capacity and power of the energy storage batteries for each hour of the day in the better candidate scheme for daily energy storage capacity configuration. At the same time, the hourly load and active power of the power supply substations of the distribution network, the average load of each load node on each transmission line segment within a natural day over 24 hours, and the impedance of each transmission line segment are introduced. All of the above data are used as input conditions for power flow calculation. The effective voltage values ​​of each load node on all transmission line segments in the distribution network within a natural day are obtained through power flow calculation.

[0057] Among them, power flow calculation is a well-known technology and will not be elaborated further.

[0058] At this point, the effective voltage values ​​of all load nodes in the distribution network are obtained hourly on a single day.

[0059] Step S003: Based on the power data of all load nodes in the distribution network, determine the importance weight of each load node; based on the difference in the effective voltage value of the same load node in all hours of a natural day, calculate the discrete fluctuation characteristic value of the load node; based on the difference between the effective voltage value of the same load node in all hours of a natural day and the preset standard voltage, determine the voltage improvement of the load node; combined with the discrete fluctuation characteristic value, calculate the voltage stability of the load node; and combined with the importance weight, calculate the voltage stability of the distribution network on the corresponding natural day.

[0060] Within a distribution network, the load conditions and operational importance of each load node differ significantly within the same calendar day. Directly using the daily average voltage stability of all load nodes to evaluate the voltage stability of all load nodes under the initial energy storage capacity configuration scheme for that calendar day would smooth out the load and priority differences between different load nodes, resulting in insufficient accuracy of the evaluation results and failing to truly reflect the overall voltage operating status of the distribution network. To achieve differentiated and accurate voltage stability evaluation, a load node importance weighting mechanism is introduced.

[0061] Specifically, for any load node in the transmission line of the distribution network, the sum of the average load of the load node in all natural days and all hours within a historical quarter is recorded as the historical load accumulation of the load node. The ratio of the historical load accumulation of the load node to the sum of the historical load accumulations of all load nodes is recorded as the importance weight of the load node.

[0062] The greater the importance weight, the larger the load volume of the corresponding load node in a historical quarter, the greater the importance of the corresponding load node to the grid operation, the greater the impact of the voltage stability state of the corresponding load node on the overall power supply quality of the distribution network, and the more accurately the corresponding load node can reflect the actual adaptation effect of the energy storage capacity configuration scheme.

[0063] Furthermore, the stability of the effective voltage values ​​of each load node under the initial energy storage capacity configuration scheme is evaluated.

[0064] The normalized value of the coefficient of variation of the effective voltage values ​​of the same load node for all hours within a natural day is denoted as the first stability of the same load node; the ratio of the range of the effective voltage values ​​of the same load node for all hours within a natural day to the preset standard voltage is denoted as the second stability of the same load node; the weighted sum of the first stability and the second stability of the load node is denoted as the discrete fluctuation characteristic value of the load node, wherein the weights of the first stability and the second stability of the load node are both greater than or equal to 0 and less than or equal to 1, and the weighted sum of the first stability and the second stability of the load node is 1.

[0065] In this embodiment, the weights for the first and second stability of the load node are both 0.5, and the standard voltage is 10kV.

[0066] The normalized value of the coefficient of variation is determined based on all coefficients of variation of all load nodes on a single natural day, and the maximum-minimum normalization algorithm. In the maximum-minimum normalization method, when the difference between the maximum and minimum values ​​is 0, the sum of the difference between the maximum and minimum values ​​and a preset non-zero small constant is used as the denominator to calculate the normalized value. In this embodiment, the preset non-zero small constant is set to a value of [value missing]. In practical applications, implementers may use other methods from existing technologies, such as the tanh function or the sigmoid function, to calculate the normalized value; no restrictions are imposed here.

[0067] The larger the discrete fluctuation characteristic value of the load node, the greater the difference in the effective voltage value of the load node across all hours of the corresponding natural day, and the more significant the fluctuation of the effective voltage value.

[0068] Furthermore, when the effective voltage value of the load node tends to the preset standard voltage, the trend of the effective voltage value of the load node is positive. Therefore, we analyze the trend of the effective voltage value of each load node under the initial energy storage capacity configuration scheme.

[0069] The ratio of the effective voltage value of a load node to a preset standard voltage is denoted as the per-unit voltage value. The absolute value of the difference between the per-unit voltage value and the number 1 is denoted as the per-unit voltage stability value. All per-unit voltage stability values ​​corresponding to the same load node are arranged in chronological order according to the hours of the corresponding natural days to obtain the per-unit voltage stability value sequence of the same load node. The ratio of the sum of the absolute values ​​of all negative values ​​in the first-order difference sequence of the per-unit voltage stability value sequence to the sum of the absolute values ​​of all values ​​in the first-order difference sequence of the per-unit voltage stability value sequence is denoted as the voltage improvement degree of the corresponding load node.

[0070] It should be noted that when all values ​​in the first-order difference sequence of the voltage per-unit stability value sequence are 0, the voltage improvement of the corresponding load node is assigned the value 1.

[0071] The closer the effective voltage value is to the preset standard voltage, the closer the corresponding voltage per unit stability value is to 0; the voltage improvement of the load node is used to evaluate the degree to which the effective voltage value of the load node tends to the standard voltage within a natural day.

[0072] The difference between the number 1 and the discrete fluctuation characteristic value of the load node is denoted as the discrete stability characteristic value of the load node. The weighted sum of the discrete stability characteristic value of the load node and the voltage improvement is denoted as the voltage stability of the load node. The weights of the discrete stability characteristic value and the voltage improvement of the load node are both greater than or equal to 0 and less than or equal to 1, and the weighted sum of the discrete stability characteristic value and the voltage improvement of the load node is 1.

[0073] In this embodiment, the weights for the discrete stability eigenvalues ​​of the load node and the voltage improvement are both set to 0.5.

[0074] This allows us to obtain the voltage stability of all load nodes in the distribution network on the same natural day.

[0075] The importance weight of load nodes is used as the weight of voltage stability. The voltage stability of all load nodes in the distribution network on the same natural day is weighted and summed. The result of the weighted sum is recorded as the voltage stability of the distribution network on the corresponding natural day.

[0076] Thus, the voltage stability of the distribution network on a given day is obtained.

[0077] Step S004: Substitute the daily voltage stability into the two-layer optimization model, use the optimization algorithm to solve the two-layer optimization model, obtain the final energy storage capacity configuration scheme, and realize the grid-side energy storage capacity configuration.

[0078] The two-layer optimization model features hierarchical iterative optimization. The core function of the lower-layer model is stability assessment. For each set of energy storage capacity configuration candidate schemes output by the upper-layer model, it performs time-series voltage stability performance evaluation, with the core optimization objective being to select the energy storage capacity configuration scheme with superior voltage stability performance. After completing all iterative calculations using the particle swarm optimization algorithm, the entire two-layer optimization model can output the optimal energy storage capacity configuration scheme adapted to the operation of the distribution network. This optimal energy storage capacity configuration scheme can serve as a standardized energy storage capacity configuration instruction, specifically including the optimal values ​​of two core configuration parameters: energy storage battery capacity and power. This ensures that the voltage of all load nodes within the distribution network transmission lines remains stable while minimizing energy storage battery investment costs and renewable energy loss costs.

[0079] The daily voltage stability of the distribution network is substituted into the lower objective function of the two-level optimization model. The two-level optimization model is solved using the particle swarm optimization algorithm. The solution is recorded as the final energy storage capacity configuration scheme, which includes the optimal values ​​of the capacity and power of the energy storage batteries for each hour of the natural day.

[0080] Precise grid connection is achieved based on the configuration instructions corresponding to the final energy storage capacity configuration scheme. The number of energy storage battery groups connected is determined by the ratio of the target energy storage capacity to the unit capacity of the battery, combined with the configuration rules for energy storage battery investment costs mentioned earlier. It is also necessary to ensure that the number of energy storage battery groups connected is an integer to achieve a rational configuration of the battery groups. The optimal rated output power of the energy storage is simultaneously matched and set to achieve precise configuration of the grid-side energy storage capacity and output power, effectively optimizing the power supply voltage characteristics of the distribution network substations and significantly improving the overall power quality of the power grid.

[0081] Thus, based on voltage timing characteristics, the grid-side energy storage capacity configuration is realized.

Claims

1. A grid-side energy storage capacity configuration method based on voltage timing characteristics, characterized in that, The method includes the following steps: Extract power data from the distribution network substations for one historical quarter; A two-layer optimization model is constructed and constraints are set. The initial energy storage capacity configuration scheme is solved by optimization algorithm. Combined with power data, the effective voltage value of each load node in each hour of a natural day is determined. Based on the power data of all load nodes in the distribution network, the importance weight of each load node is determined. Based on the difference of the effective voltage value of the same load node in all hours of a natural day, the discrete fluctuation characteristic value of the load node is calculated. Based on the difference of the effective voltage value of the same load node in all hours of a natural day and the preset standard voltage, the voltage improvement of the load node is determined. Combined with the discrete fluctuation characteristic value, the voltage stability of the load node is calculated. Combined with the importance weight, the voltage stability of the distribution network on the corresponding natural day is calculated. The daily voltage stability is substituted into the two-layer optimization model, and the optimization algorithm is used to solve the two-layer optimization model to obtain the final energy storage capacity configuration scheme and realize the grid-side energy storage capacity configuration.

2. The grid-side energy storage capacity configuration method based on voltage timing characteristics according to claim 1, characterized in that, The two-layer optimization model includes an upper-layer objective optimization function and a lower-layer objective optimization function, wherein the lower-layer objective optimization function is equal to the daily voltage stability of the distribution network. The upper-level objective optimization function The expression is: ； ； ； in, This indicates the investment cost of energy storage batteries; This indicates the cost of electricity lost by new energy sources; These represent the first preset weighting coefficient and the second preset weighting coefficient, respectively. These represent the unit capacity cost and unit power cost of energy storage batteries, respectively. These represent the capacity and power of the energy storage battery in the energy storage capacity configuration scheme, respectively; This indicates the number of hours within a calendar day when energy storage capacity configuration is optimized. This indicates that the new energy power station is in operation for the first time within a natural day. The theoretical output per hour under the constraint conditions; This indicates that the new energy power station is in operation for the first time within a natural day. Actual output per hour; This indicates the number of days a storage battery is in operation within a calendar day. The charging and discharging power of the energy storage battery in one natural day is [amount missing]. When charging for 1 hour, the corresponding charge / discharge power is greater than 0. When the energy storage battery is charged for the first 1 hour within a natural day... When discharging for 24 hours, the corresponding charge / discharge power is less than 0. Indicates the number of days within a natural day The unit price of new energy electricity per hour.

3. The grid-side energy storage capacity configuration method based on voltage timing characteristics according to claim 1, characterized in that, The constraints include: The capacity of the energy storage battery is greater than or equal to the lower limit of the capacity of the energy storage battery allowed to be installed on the grid side, and less than or equal to the upper limit of the capacity of the energy storage battery allowed to be installed on the grid side. The power of the energy storage battery is greater than or equal to the lower limit of the power allowed to be installed on the grid side, and less than or equal to the upper limit of the power allowed to be installed on the grid side. New energy power stations within a natural day The theoretical output of a new energy power station in one hour under certain constraints is equal to: the output of the new energy power station in one natural day. Actual output per hour, energy storage battery within a natural day The sum of the charging and discharging power per hour, and the power loss from new energy sources, of which, Represents all integers greater than or equal to 1 and less than or equal to the number of hours in a calendar day; Energy storage battery within a calendar day The state of charge for one hour is greater than or equal to the preset minimum state of charge and less than or equal to the preset maximum state of charge.

4. The grid-side energy storage capacity configuration method based on voltage timing characteristics according to claim 1, characterized in that, The power data includes: the hourly load and active power of the power supply end of the distribution network substation within a natural day in a historical quarter; the hourly average load of each load node on each transmission line segment within a natural day; the charging and discharging power and state of charge of the energy storage device; and the impedance of each transmission line segment. The load, active power, charging and discharging power, and state of charge are all hourly averages.

5. The grid-side energy storage capacity configuration method based on voltage timing characteristics according to claim 4, characterized in that, The specific method for determining the importance weight of the load node is as follows: The sum of the average load of a load node over all hours of all natural days within a historical quarter is recorded as the historical load accumulation of the load node. The ratio of the historical load accumulation of a load node to the sum of the historical load accumulations of all load nodes is recorded as the importance weight of the load node.

6. The grid-side energy storage capacity configuration method based on voltage timing characteristics according to claim 1, characterized in that, The specific calculation method for the discrete fluctuation characteristic value of the load node is as follows: The normalized value of the coefficient of variation of the effective voltage value of the same load node for all hours within a natural day is denoted as the first stability of the same load node. The ratio of the range of the effective voltage values ​​of the same load node over all hours within a natural day to a preset standard voltage is denoted as the second stability of the same load node. The weighted sum of the first and second stability of the load node is denoted as the discrete fluctuation characteristic value of the load node.

7. The grid-side energy storage capacity configuration method based on voltage timing characteristics according to claim 1, characterized in that, The specific method for determining the voltage improvement of the load node is as follows: The ratio of the effective voltage value of a load node to a preset standard voltage is denoted as the per-unit voltage value. The absolute value of the difference between the per-unit voltage value and the number 1 is denoted as the per-unit voltage stability value. Based on all the per-unit voltage stability values ​​corresponding to the same load node, a per-unit voltage stability value sequence is established. The ratio of the sum of the absolute values ​​of all negative values ​​in the first-order difference sequence of the per-unit voltage stability value sequence to the sum of the absolute values ​​of all values ​​in the first-order difference sequence of the per-unit voltage stability value sequence is denoted as the voltage improvement degree of the corresponding load node.

8. The grid-side energy storage capacity configuration method based on voltage timing characteristics according to claim 1, characterized in that, The specific calculation method for the voltage stability of the load node is as follows: The difference between the number 1 and the discrete fluctuation characteristic value of the load node is calculated and denoted as the discrete stability characteristic value of the load node. The weighted sum of the discrete stability characteristic value of the load node and the voltage stability is denoted as the voltage stability of the load node.

9. The grid-side energy storage capacity configuration method based on voltage timing characteristics according to claim 1, characterized in that, The specific calculation method for the daily voltage stability is as follows: using the importance weight of the load nodes as the weight, the voltage stability of all load nodes in the distribution network on the same natural day is weighted and summed.

10. The grid-side energy storage capacity configuration method based on voltage timing characteristics according to claim 1, characterized in that, The final energy storage capacity configuration scheme includes: the optimal values ​​of the capacity and power of the energy storage batteries for each hour of a natural day.

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