Energy storage power station locating and sizing method and device

Abstract

The invention provides an energy storage power station locating and sizing method and device. The method comprises the steps that based on a preset power grid basic data input mode, power grid basic data of a preset area are collected, and the power grid basic data comprise nodes of a power grid and sensitivity coefficients corresponding to the nodes; according to the nodes of the power grid and the sensitivity coefficients corresponding to the nodes, voltage weak nodes are identified based on a preset threshold value, and candidate nodes are generated; an evaluation index system is established, comprehensive scoring calculation and sorting are performed on the candidate nodes, and energy storage access optimal nodes are obtained; and based on a preset energy storage capacity calculation method, calculating the energy storage capacity of the energy storage access optimal node and carrying out energy storage power matching. According to the method, a dynamic corresponding mechanism of capacity calculation and system requirements is established by analyzing the matching relation between the node power vacancy and the duration, and it is ensured that the energy storage system can meet the voltage support requirement and has good economical efficiency.

Description

Technical Field

[0001] This disclosure relates to the fields of new energy technology and energy storage technology, and more specifically, to a method and apparatus for site selection and capacity determination of energy storage power stations. Background Technology

[0002] With the large-scale integration of renewable energy into the power system, the operating characteristics of the power grid have changed significantly. The output of new energy sources, represented by wind and solar power, exhibits strong volatility and randomness, making voltage fluctuations, reactive power imbalances, and system stability issues increasingly prominent. Especially in regions with high renewable energy utilization rates, where renewable energy capacity accounts for a large proportion and wind and solar resources are concentrated in the grid, traditional voltage control and reactive power regulation methods face new challenges.

[0003] In traditional power systems, voltage control primarily relies on transformer tap changers and reactive power compensation devices (such as capacitor banks, static var compensators (SVCs), and static synchronous compensators (STATCOMs). While these devices can improve node voltage levels to some extent, their adjustment speed is relatively slow, and their control strategies are fixed, making it difficult to cope with dynamic voltage disturbances caused by rapid fluctuations in renewable energy output. Furthermore, traditional devices are mostly passive, lacking energy storage capacity and unable to perform peak shaving and valley filling or smooth power output, thus limiting their support for power grids with a high proportion of renewable energy.

[0004] In recent years, energy storage systems (ESS) have been increasingly applied in power systems. ESS systems can absorb energy when there is a power surplus and release energy when there is a power shortage, thereby balancing power, stabilizing voltage, and improving power quality. In areas with a high proportion of wind and solar power integration, the proper configuration of energy storage systems can not only improve voltage stability but also, to some extent, enhance the absorption capacity of renewable energy, reduce wind and solar curtailment, and promote the safe and economical operation of the power grid.

[0005] Against this backdrop, energy storage site selection and capacity determination have become key issues in the operation planning of new energy power grids. The location and capacity of energy storage devices directly affect voltage regulation effectiveness, system operating economy, and return on investment for energy storage equipment. By comprehensively analyzing the voltage characteristics of grid nodes, line impedance characteristics, power flow distribution patterns, and the output characteristics of new energy sources, energy storage configuration schemes can be determined more scientifically, thereby improving the overall system performance.

[0006] In energy storage system configuration research, node voltage sensitivity analysis is a commonly used theoretical tool. This method calculates the sensitivity of node voltage to changes in reactive power, determines the sensitivity of each node to voltage fluctuations, and thus identifies voltage-vulnerable nodes. This quantitative analysis method provides a theoretical basis for energy storage deployment. Furthermore, comprehensive evaluation methods based on multiple indicators (such as the Analytic Hierarchy Process, AHP) can comprehensively consider factors such as voltage stability, line losses, investment costs, and renewable energy output characteristics, achieving multi-objective decision optimization for energy storage site selection.

[0007] In determining energy storage capacity, calculations are typically based on nodal power deficit models. By analyzing the power balance of the system under typical operating scenarios, the active or reactive power deficit of each node over a certain period can be determined, thereby identifying the required energy capacity and power level of the energy storage system. For example, when a node experiences low voltage, the energy storage device can discharge at its rated power for a certain period to support the voltage; when the voltage is high or there is excess output, the energy storage can reverse charge to absorb energy, achieving dynamic balance.

[0008] To verify the effectiveness of energy storage site selection and capacity determination, system simulations are often performed using the MATLAB platform. By establishing a distribution network model that includes wind farms, photovoltaic power plants, loads, and energy storage systems, the impact of energy storage systems on node voltage, system losses, and power balance can be analyzed under typical operating scenarios. By comparing simulation results before and after energy storage commissioning, the role of energy storage configuration in voltage support and economical operation can be quantified.

[0009] In summary, as the proportion of renewable energy continues to increase, energy storage technology plays an increasingly important role in voltage control and system regulation. Analysis methods based on node voltage sensitivity and multi-objective optimization strategies for energy storage configuration provide new technical pathways for achieving voltage stability and the friendly integration of renewable energy. Through reasonable site selection and capacity determination of energy storage power stations, the operating characteristics of the distribution network can be significantly improved, ensuring the safe and efficient operation of the power grid under high renewable energy integration.

[0010] With the widespread integration of new energy sources such as wind and solar power, problems such as frequent voltage fluctuations, local voltage exceeding limits, and severe wind and solar curtailment have emerged in power grid operation. Existing energy storage configuration methods mostly rely on experience or static analysis, lacking systematic and quantitative basis, making it impossible to accurately determine the connection location and capacity of energy storage power stations. Furthermore, the lack of verification of the operational effectiveness of configuration schemes leads to low utilization and economic efficiency of energy storage systems.

[0011] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0012] The purpose of this disclosure is to provide a method and apparatus for site selection and capacity determination of energy storage power stations, thereby overcoming, to at least to some extent, one or more problems caused by the limitations and defects of related technologies.

[0013] According to one aspect of this disclosure, a method for site selection and capacity determination of an energy storage power station is provided, comprising:

[0014] Based on a preset power grid basic data input method, power grid basic data for a preset area is collected. The power grid basic data includes the nodes of the power grid and the sensitivity coefficients corresponding to the nodes.

[0015] Based on the nodes of the power grid and the sensitivity coefficients corresponding to those nodes, weak voltage nodes are identified and candidate nodes are generated based on a preset threshold.

[0016] An evaluation index system is established, and the candidate nodes are comprehensively scored and ranked to obtain the preferred nodes for energy storage access.

[0017] Based on the preset energy storage capacity calculation method, the energy storage capacity of the preferred energy storage access node is calculated and energy storage power matching is performed.

[0018] In one exemplary embodiment of this disclosure, the power grid basic data input of the method further includes:

[0019] Based on the pre-defined expected power grid architecture, a grid structure model containing a pre-defined number of nodes is constructed, and voltage-power relationship expressions are generated.

[0020] In the model, a new energy output matrix load characteristic input is established to generate a system net load expression;

[0021] The nodes are evaluated, and a node sensitivity coefficient table containing the correspondence between the nodes of the power grid and their sensitivity coefficients is established.

[0022] In one exemplary embodiment of this disclosure, the voltage-power relationship expression in the method is:

[0023]

[0024] Where, ΔV i For voltage, ΔQ j For power, S ij N represents the voltage sensitivity of node i to changes in reactive power at node j. node It represents the total number of nodes participating in the optimization.

[0025] In one exemplary embodiment of this disclosure, the preset number of nodes in the method further includes a first preset number of core nodes and a second preset number of joint nodes surrounding the core nodes.

[0026] In one exemplary embodiment of this disclosure, the method for generating candidate nodes further includes:

[0027] Generate a voltage sensitivity matrix based on the principle of voltage sensitivity.

[0028] Weak nodes are identified based on preset thresholds;

[0029] Define and calculate the node voltage vulnerability index to generate candidate nodes.

[0030] In one exemplary embodiment of this disclosure, the method for generating preferred energy storage access nodes further includes:

[0031] Establish an evaluation index system and construct raw data to generate a node evaluation index matrix;

[0032] The indicators of the nodes are normalized and directionally processed.

[0033] A judgment matrix is ​​constructed using the analytic hierarchy process (AHP) to generate normalized node feature vectors.

[0034] Based on the weighted synthesis method, a comprehensive score is calculated for the normalized results of the node feature vectors.

[0035] The comprehensive scores are calculated and ranked, and the preferred nodes for energy storage access are selected.

[0036] In one exemplary embodiment of this disclosure, the evaluation metrics in the method further include:

[0037] The voltage improvement degree represents the percentage improvement in node voltage deviation after the energy storage system is connected.

[0038] The renewable energy consumption rate represents the increase in the utilization rate of wind and solar power output in the region after the energy storage system is put into operation.

[0039] Access costs include the purchase of energy storage equipment, line access, and substation renovation.

[0040] In one exemplary embodiment of this disclosure, the energy storage capacity calculation of the method further includes:

[0041] Calculate the node power deficit of the preferred energy storage access node;

[0042] Calculate energy storage capacity based on the energy storage capacity calculation formula;

[0043] Energy storage power matching is performed based on a preset power coefficient.

[0044] In one exemplary embodiment of this disclosure, in the method, if the energy storage system outputs ΔP at node i with a constant power... i The duration is T iThe system round-trip efficiency is η i The energy storage capacity formula can then be expressed as:

[0045]

[0046] E i It is the energy storage capacity (MWh); ΔP i It is the power deficit (MW); T i It is the duration of support (h); η i It is the round-trip efficiency of the energy storage system, taking into account energy loss.

[0047] In one aspect of this disclosure, a site selection and capacity determination device for an energy storage power station is provided, comprising:

[0048] The basic data acquisition module is used to acquire basic power grid data for a preset area based on a preset power grid basic data input method. The basic power grid data includes the nodes of the power grid and the sensitivity coefficients corresponding to the nodes.

[0049] The candidate node generation module is used to identify weak voltage nodes based on the nodes of the power grid and the sensitivity coefficients corresponding to the nodes, and generate candidate nodes based on a preset threshold.

[0050] The preferred node generation module is used to establish an evaluation index system, perform comprehensive scoring calculation and sorting on the candidate nodes, and obtain the preferred energy storage access nodes;

[0051] The energy storage power matching module is used to calculate the energy storage capacity of the preferred energy storage access node and perform energy storage power matching based on a preset energy storage capacity calculation method.

[0052] An exemplary embodiment of this disclosure provides a method for site selection and capacity determination of an energy storage power station. The method includes: collecting basic grid data for a preset area based on a preset grid basic data input method, the basic grid data including grid nodes and their corresponding sensitivity coefficients; identifying voltage-weak nodes based on a preset threshold according to the grid nodes and their corresponding sensitivity coefficients, generating candidate nodes; establishing an evaluation index system, calculating and ranking the candidate nodes through comprehensive scoring, and obtaining preferred energy storage access nodes; and calculating the energy storage capacity of the preferred energy storage access nodes and performing energy storage power matching based on a preset energy storage capacity calculation method. This disclosure establishes a dynamic correspondence mechanism between capacity calculation and system demand by analyzing the matching relationship between node power deficit and duration, ensuring that the energy storage system can meet voltage support requirements while also possessing good economic efficiency.

[0053] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0054] The above and other features and advantages of this disclosure will become more apparent from the detailed description of exemplary embodiments thereof with reference to the accompanying drawings.

[0055] Figure 1 A flowchart of a method for site selection and capacity determination of an energy storage power station according to an exemplary embodiment of the present disclosure is shown;

[0056] Figure 2 A schematic block diagram of an energy storage power station site selection and capacity determination device according to an exemplary embodiment of the present disclosure is shown. Detailed Implementation

[0057] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the embodiments set forth herein; rather, they are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted.

[0058] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a thorough understanding of embodiments of this disclosure. However, those skilled in the art will recognize that the technical solutions of this disclosure can be practiced without one or more of the specific details described, or other methods, components, materials, apparatuses, steps, etc., can be employed. In other instances, well-known structures, methods, apparatuses, implementations, materials, or operations are not shown or described in detail to avoid obscuring various aspects of this disclosure.

[0059] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, or in one or more software-hardened modules, or in different network and / or processor devices and / or microcontroller devices.

[0060] In this example embodiment, a method for site selection and capacity determination of an energy storage power station is first provided; refer to Figure 1 As shown, the method for site selection and capacity determination of an energy storage power station may include the following steps:

[0061] Step S110: Based on the preset power grid basic data input method, collect the power grid basic data of the preset area. The power grid basic data includes the nodes of the power grid and the sensitivity coefficients corresponding to the nodes.

[0062] Step S120: Based on the nodes of the power grid and the sensitivity coefficients corresponding to the nodes, identify weak voltage nodes based on a preset threshold and generate candidate nodes;

[0063] Step S130: Establish an evaluation index system, calculate and rank the candidate nodes based on comprehensive scores, and obtain the preferred nodes for energy storage access.

[0064] Step S140: Based on the preset energy storage capacity calculation method, calculate the energy storage capacity of the preferred energy storage access node and perform energy storage power matching.

[0065] An exemplary embodiment of this disclosure provides a method for site selection and capacity determination of an energy storage power station. The method includes: collecting basic grid data for a preset area based on a preset grid basic data input method, the basic grid data including grid nodes and their corresponding sensitivity coefficients; identifying voltage-weak nodes based on a preset threshold according to the grid nodes and their corresponding sensitivity coefficients, generating candidate nodes; establishing an evaluation index system, calculating and ranking the candidate nodes through comprehensive scoring, and obtaining preferred energy storage access nodes; and calculating the energy storage capacity of the preferred energy storage access nodes and performing energy storage power matching based on a preset energy storage capacity calculation method. This disclosure establishes a dynamic correspondence mechanism between capacity calculation and system demand by analyzing the matching relationship between node power deficit and duration, ensuring that the energy storage system can meet voltage support requirements while also possessing good economic efficiency.

[0066] The following will further explain a method for site selection and capacity determination of an energy storage power station in this example embodiment.

[0067] Example 1:

[0068] Explanation of technical terms and abbreviations:

[0069] Energy Storage System (ESS): refers to a device or system that can store and release energy in a power system.

[0070] Voltage Sensitivity Coefficient (VSC): refers to the degree to which node voltage responds to changes in reactive power.

[0071] Voltage-weak node (VWN): refers to the node in the power grid that is most sensitive to reactive power disturbances and is prone to voltage overruns.

[0072] Analytic Hierarchy Process (AHP): A multi-indicator comprehensive evaluation method that calculates node priority by constructing a judgment matrix, determining weight coefficients, and integrating different indicators.

[0073] Voltage Improvement Rate (VIR): This refers to the percentage reduction in node voltage deviation after an energy storage system is connected, and is used to measure the degree to which energy storage improves voltage quality.

[0074] Renewable Energy Utilization Rate (REUR): This represents the proportion of renewable energy effectively utilized in a system, reflecting the system's ability to absorb new energy.

[0075] In step S110, basic power grid data for a preset area can be collected based on a preset power grid basic data input method. The basic power grid data includes the nodes of the power grid and the sensitivity coefficients corresponding to the nodes.

[0076] In this example embodiment, the power grid basic data input for the method further includes:

[0077] Based on the pre-defined expected power grid architecture, a grid structure model containing a pre-defined number of nodes is constructed, and voltage-power relationship expressions are generated.

[0078] In the model, a new energy output matrix load characteristic input is established to generate a system net load expression;

[0079] The nodes are evaluated, and a node sensitivity coefficient table containing the correspondence between the nodes of the power grid and their sensitivity coefficients is established.

[0080] In this example embodiment, the voltage-power relationship expression in the method is:

[0081]

[0082] Where, ΔV i For voltage, ΔQ j For power, S ij N represents the voltage sensitivity of node i to changes in reactive power at node j. node It represents the total number of nodes participating in the optimization.

[0083] In this example embodiment, the preset number of nodes in the method further includes a first preset number of core nodes and a second preset number of joint nodes surrounding the core nodes.

[0084] In step S120, weak voltage nodes can be identified based on the nodes of the power grid and the sensitivity coefficients corresponding to the nodes, and candidate nodes can be generated.

[0085] In this example embodiment, the method for generating candidate nodes further includes:

[0086] Generate a voltage sensitivity matrix based on the principle of voltage sensitivity.

[0087] Weak nodes are identified based on preset thresholds;

[0088] Define and calculate the node voltage vulnerability index to generate candidate nodes.

[0089] In step S130, an evaluation index system can be established, and the candidate nodes can be comprehensively scored and ranked to obtain the preferred nodes for energy storage access.

[0090] In this example embodiment, the method for generating preferred energy storage access nodes further includes:

[0091] Establish an evaluation index system and construct raw data to generate a node evaluation index matrix;

[0092] The indicators of the nodes are normalized and directionally processed.

[0093] A judgment matrix is ​​constructed using the analytic hierarchy process (AHP) to generate normalized node feature vectors.

[0094] Based on the weighted synthesis method, a comprehensive score is calculated for the normalized results of the node feature vectors.

[0095] The comprehensive scores are calculated and ranked, and the preferred nodes for energy storage access are selected.

[0096] In this example embodiment, the evaluation metrics in the method further include:

[0097] The voltage improvement degree represents the percentage improvement in node voltage deviation after the energy storage system is connected.

[0098] The renewable energy consumption rate represents the increase in the utilization rate of wind and solar power output in the region after the energy storage system is put into operation.

[0099] Access costs include the purchase of energy storage equipment, line access, and substation renovation.

[0100] In step S140, the energy storage capacity of the preferred energy storage access node can be calculated and energy storage power matching can be performed based on the preset energy storage capacity calculation method.

[0101] In this example embodiment, the energy storage capacity calculation of the method further includes:

[0102] Calculate the node power deficit of the preferred energy storage access node;

[0103] Calculate energy storage capacity based on the energy storage capacity calculation formula;

[0104] Energy storage power matching is performed based on a preset power coefficient.

[0105] In this example embodiment, the method is such that if the energy storage system outputs ΔP at node i with a constant power... i The duration is T i The system round-trip efficiency is η i The energy storage capacity formula can then be expressed as:

[0106]

[0107] E i It is the energy storage capacity (MWh); ΔP i It is the power deficit (MW); T i It is the duration of support (h); η i It is the round-trip efficiency of the energy storage system, taking into account energy loss.

[0108] In this example embodiment, the present disclosure presents a method for site selection and capacity determination of energy storage power stations based on voltage sensitivity analysis and power deficit calculation, enabling scientific site selection and rational configuration of energy storage systems. Through a complete process analysis of weak node identification, candidate node scoring, energy storage capacity calculation, and scenario verification, the method can effectively improve grid voltage stability, reduce wind curtailment rate, increase the level of renewable energy absorption, and improve the economic operation performance of the system.

[0109] Example 2:

[0110] In this example embodiment, the power grid basic data input includes:

[0111] This embodiment addresses the practical needs of planning independent microgrids in a pre-defined regional area by establishing a basic data input module based on the characteristics of the regional power grid. This module serves as a prerequisite for the site selection and capacity optimization of energy storage power stations. Through the systematic input of grid node parameters, line impedance, wind and solar power output characteristics, and voltage sensitivity data, high-precision operation of the subsequent model is achieved.

[0112] Network and Node Parameter Modeling: Based on the typical power grid architecture of a preset region, a network structure model containing 30 core nodes is constructed to characterize the backbone network characteristics of the voltage-supported region. The nodes are connected by transmission lines with equivalent impedances varying between 0.02Ω and 0.15Ω to reflect differences in line length, conductor cross-section, and operating conditions. The rated voltage level is set at 110kV, corresponding to the medium- and high-voltage distribution level of the preset region.

[0113]

[0114] in:

[0115] N node Z is the total number of nodes participating in the optimization; line This refers to the branch impedance range, used for subsequent power flow and sensitivity calculations; Urated This is the system's rated voltage; It is a set of node numbers.

[0116] The total number of nodes satisfies:

[0117]

[0118] The fundamental relationship between voltage and power can be expressed as:

[0119]

[0120] Where S ij This represents the voltage sensitivity (kV / Mvar) of node i to the reactive power change of node j, and this formula is the theoretical basis for constructing the sensitivity matrix.

[0121] New Energy and Load Characteristics Input: The preset region is a national-level renewable energy demonstration zone with wind power and photovoltaic installed capacities of 5000MW and 2000MW, respectively. Considering the significant peak-valley difference in regional load (approximately 80MW), this paper parameterizes the renewable energy output fluctuation and load characteristics to facilitate subsequent energy storage capacity configuration.

[0122] Define the new energy output matrix:

[0123] P ren ={P wind P pv} = {5000, 2000}

[0124] Load characteristics are expressed using the peak-to-valley difference parameter ΔP L =80MW, used to calculate the power demand P of the energy storage system during peak shaving and valley filling regulation. es :

[0125] P es ≥α·ΔP L

[0126] Where α is the energy storage regulation proportional coefficient, with a value range of [0.6, 0.9]; the above parameters reflect the characteristics of strong fluctuations in renewable energy output and limited system regulation margin under typical intraday scenarios. System net load P net (t) can be expressed as:

[0127] P net (t)=P load (t)-(P wind (t)+P pv (t))

[0128] When P net When (t) < 0, the phenomenon of "wind and solar power curtailment" occurs, and energy storage is needed to balance the power difference.

[0129] Voltage sensitivity coefficient calculation and input: To achieve the voltage support function of the energy storage station, this embodiment introduces the node voltage sensitivity coefficient S. i This is used to describe the response of node voltage to changes in reactive power injection:

[0130]

[0131] In this model, scalability was evaluated for 30 core nodes and their peripheral key nodes (nodes 32, 35, and 40), and the following sensitivity table was constructed:

[0132] Node number <![CDATA[Sensitivity coefficient S i > 1—9 0.02～0.045 10 0.07 11—14，16—19 0.022～0.042 15 0.055 20—30 0.023～0.043 32 0.060 35 0.048 40 0.021

[0133] Among them, the sensitivity coefficient Si of nodes 10, 15, and 32 is greater than or equal to 0.05 kV / Mvar, indicating that they are more sensitive to reactive power fluctuations and have poor voltage stability, making them potential areas for priority deployment of energy storage.

[0134] This case study introduces additional nodes 32, 35, and 40 to the original 30 nodes. The physical and mathematical significance of these additions is as follows:

[0135] Node 32: The end grid connection point of the concentrated wind and solar power area, where voltage fluctuations are severe, is a highly sensitive node;

[0136] Node 35: The coupling point between the backbone network and the branch network, with moderate sensitivity, representing intermediate layer voltage regulation;

[0137] Node 40: Remote load node, with low sensitivity, used to reflect the voltage response characteristics of non-sensitive areas.

[0138] This design expands the coverage of sensitivity data and ensures the engineering integrity of the voltage support model.

[0139] In this example embodiment, voltage weak point identification includes:

[0140] 1) Principle of voltage sensitivity analysis

[0141] To achieve the voltage support function of the energy storage station, a node voltage sensitivity matrix is ​​introduced as follows:

[0142]

[0143] in:

[0144] U i : Voltage amplitude of the i-th node (unit: kV);

[0145] Q j Reactive power injection at the j-th node (unit: Mvar);

[0146] S: Represents the sensitivity of reactive power change at node j to voltage change at node i (unit: kV / Mvar).

[0147] To simplify the location calculation, only the sensitivity of each node itself (i.e., the diagonal element) is considered. Therefore, the single-node sensitivity coefficient is defined as:

[0148]

[0149] Sensitivity coefficient S i This characterizes the linear response capability of node voltage changes to reactive power injection changes. When S i When the value is large, the node voltage responds more to reactive power disturbances, meaning the voltage stability is poor, and the node is defined as a "voltage weak node".

[0150] Under small perturbation conditions, the sensitivity coefficient can be obtained by linearizing the Jacobian matrix:

[0151]

[0152] Therefore, the voltage sensitivity matrix can be obtained:

[0153]

[0154] J QQ This represents the partial derivative matrix of reactive power with respect to voltage changes. By numerically inverting this matrix, the sensitivity value S of the voltage at each node to reactive power changes can be obtained. i Its physical meaning is: the change in voltage at a node when 1 Mvar of reactive power is injected into it.

[0155] 2) Voltage sensitivity threshold determination and weak node identification

[0156] In this scheme, a fixed threshold is used in engineering:

[0157] S th = 0.05 (kv / Mvar)

[0158] The program will use the sensitivity coefficient S i With preset threshold S th When compared to 0.05 kV / Mvar, the judgment rule is as follows:

[0159] (Voltage weak point)

[0160] (Non-weak node)

[0161] If you input S i ,available

[0162] Ω weak={10, 15, 32}

[0163] Ω non-weak ={1, 2, ..., 30, 35, 40}\Ω weak

[0164] 3) Data Results and Summary

[0165] The node sensitivity data is as follows:

[0166]

[0167]

[0168] Calculations show that S satisfies i Nodes with a sensitivity of ≥0.05 include nodes 10, 15, and 32, which are identified as "weak voltage nodes". Other nodes, such as 35 and 40, although located at the edge of the system branch, do not yet meet the weak voltage criterion requirements due to their low sensitivity values ​​(0.048 and 0.021, respectively).

[0169] To facilitate subsequent multi-objective optimization algorithms, a node voltage vulnerability index W can be defined. i :

[0170]

[0171] Vulnerability indicators quantify the voltage sensitivity of nodes; the higher the value, the more energy storage the node requires. Calculations show that:

[0172]

[0173] It is evident that node 10 has the highest vulnerability and should be given priority in subsequent energy storage site selection.

[0174] To facilitate a comprehensive understanding of the sensitivity distribution, the mean is defined and calculated. With variance and standard deviation σ s The following formula can be obtained:

[0175]

[0176] These statistics are used to identify sensitivity outliers and to help set thresholds.

[0177] Meaning of node projects (corresponding to the introduction of nodes 32, 35, and 40):

[0178] Node 32: At the end of the wind-solar cluster, the accumulation of line impedance and reactive power disturbance causes S 32 =0.060, which is a weak node and is suitable as a priority target for energy storage deployment.

[0179] Node 35: Inter-network coupling / transition point, S 35 =0.048, close to the threshold, can be considered a suboptimal position.

[0180] Node 40: Remote load / observation node, S 40 =0.021, low sensitivity, used as a reference point to test the conductivity of energy storage effect.

[0181] If energy storage is deployed at node i and reactive power injection is provided... The theoretical voltage improvement is:

[0182]

[0183] In subsequent steps (site selection, capacity determination), this formula will be used as the basic linear approximation for measuring the contribution of energy storage to voltage support.

[0184] In this example embodiment, the comprehensive score for candidate nodes includes:

[0185] To further determine the optimal energy storage access point among the identified voltage-weak nodes, this step constructs a comprehensive scoring model to conduct multi-indicator decision analysis on each candidate node from three dimensions: "voltage improvement degree," "new energy absorption rate," and "access cost." The algorithm is based on the Analytic Hierarchy Process (AHP) weighted model, and normalizes and reverse-scores the cost index to achieve comprehensive ranking and priority selection.

[0186] 1) Establishment of evaluation index system and construction of raw data

[0187] Let the set of power grid nodes be:

[0188] Ω = {1, 2, ..., N}

[0189] The set of voltage weak points is as follows:

[0190]

[0191] For any node i∈Ω weak The three evaluation indicators are defined as follows:

[0192] a. Voltage Improvement Ratio I1(i): Represents the percentage improvement in node voltage deviation after the energy storage system is integrated. Defined as:

[0193]

[0194] In the formula, and These represent the mean square error of the node voltage before and after energy storage integration.

[0195] b. Renewable Energy Utilization Rate I²(i): This represents the percentage increase in the utilization rate of wind and solar power output within the region after the energy storage system is put into operation. Defined as:

[0196]

[0197] In the formula, P used (i) represents the renewable energy power near node i that can be absorbed by the grid, P total (i) represents the theoretical maximum power output.

[0198] c. Access Cost I3(i): Includes costs for energy storage equipment purchase, line access, substation upgrades, etc., in ten thousand yuan. This is a reverse indicator; the lower the value, the better.

[0199] By combining simulation calculations with typical wind-solar-storage grid operation data from a pre-defined region, a node evaluation index matrix is ​​obtained:

[0200]

[0201] The table shows the following columns: the first column is the node number; the second column is the voltage improvement rate; the third column is the renewable energy absorption rate; and the fourth column is the grid connection cost. Nodes 10, 15, and 32 are weak voltage nodes, and node 25 serves as a non-weak control node to verify the effectiveness of the evaluation algorithm.

[0202] 2) Indicator normalization and directional processing

[0203] To eliminate the dimensional differences between indicators and unify the evaluation direction, the range standardization method is used to map each indicator to the [0,1] interval.

[0204] a. For positive indicators (the larger the value, the better):

[0205]

[0206] b. For contrarian indicators (smaller values ​​are better):

[0207]

[0208] Where: x ij This represents the original value of node i on index j; x′ ij The normalized value; max(x) j min(x) j ) are the maximum and minimum values ​​of the j-th indicator, respectively.

[0209] 3) Setting indicator weights

[0210] When determining the weights, the balance between voltage support and economic efficiency is comprehensively considered, and the analytic hierarchy process (AHP) is used to construct the judgment matrix:

[0211]

[0212] After passing the consistency test (CR < 0.1), the normalized result of the eigenvector is as follows:

[0213] w=[w1,w2,w3]=[0.5,0.3,0.2]

[0214] Where: w1 = 0.5 — weight of voltage improvement; w2 = 0.3 — weight of renewable energy absorption rate; w3 = 0.2 — weight of grid connection cost. This indicates that this study takes voltage support effect as the main optimization objective, while also considering the system's renewable energy absorption and economic efficiency.

[0215] 4) Comprehensive scoring calculation model

[0216] Based on the weighted comprehensive method, the comprehensive benefit score S of node i is... i Represented as:

[0217] s i =100×(w1I1′(i)+w2I2′(i)+w3I3′(i))

[0218] The coefficient 100 converts the score to a percentage for easier comparison. The calculation yields:

[0219] Node number Voltage improvement absorption rate Cost (ten thousand yuan) Overall Score 10 0.85 0.70 80 84.7 32 0.82 0.68 75 83.2 15 0.78 0.65 90 79.6 25 0.65 0.55 70 65.3

[0220] Comprehensive score ranking and candidate node selection

[0221] Sort by comprehensive score in descending order, as follows:

[0222] Rank(S i ) = sort(S i (descend)

[0223] The first k=2 nodes were selected as the set of candidate access points for energy storage, and the result is as follows:

[0224] Ω candidate ={i|rank(S i )≤2}

[0225] Ω candidate ={10,32}

[0226] Nodes 10 and 32 are the preferred locations for energy storage integration. Comprehensive evaluation and analysis show that: Node 10 is located in the central main line of the pre-defined regional power grid, exhibiting high voltage sensitivity; integrating energy storage will significantly improve system voltage fluctuations. Node 32 is located in a region with concentrated wind and solar power grid connections; energy storage deployment there can effectively smooth fluctuations and enhance the absorption capacity of new energy sources. Both nodes have relatively low integration costs, offering high investment cost-effectiveness. Therefore, nodes 10 and 32 are identified as the optimal candidate nodes for energy storage integration, providing key input conditions for the next step of energy storage capacity optimization.

[0227] In this example embodiment, the energy storage capacity calculation includes:

[0228] 1) Determination of power deficit parameters for candidate nodes

[0229] First, based on the aforementioned voltage sensitivity analysis and node scoring results, nodes 10 and 32, with the highest comprehensive scores, were selected as candidate locations for energy storage access, and node 15 was used as a comparison node to verify the model's rationality. Based on power flow simulation and voltage support condition data, the power deficit ΔP (MW), the required continuous support time T (h), and the energy storage system efficiency η (dimensionless) for each node during peak hours were obtained. The following table shows the node input parameters:

[0230]

[0231] The definition of node power deficit is:

[0232]

[0233] in: Let be the load power of node i; This represents the power that the node can support at the current voltage; when ΔP i When a node experiences insufficient voltage, energy storage injection is required for compensation. This parameter directly reflects the power balance gap of the node during peak hours and is the basis for energy storage capacity calculation.

[0234] 2) Energy storage capacity calculation formula and theoretical basis

[0235] Energy storage capacity E i The calculation is based on the energy relationship between nodal power deficit and support duration. It is assumed that the energy storage system outputs ΔP at node i with a constant power output. i The duration is T i The system round-trip efficiency is η i The energy storage capacity formula can then be expressed as:

[0236]

[0237] E i It is the energy storage capacity (MWh); ΔPi It is the power deficit (MW); T i It is the duration of support (h); η i It is the round-trip efficiency of the energy storage system, taking into account energy loss.

[0238] Substituting the parameters of node 10, node 32, and node 15 into the formula, we get:

[0239]

[0240] This shows that node 10 requires approximately 200 MWh of energy storage capacity; node 32 requires approximately 178 MWh; and node 15 requires only 136 MWh, verifying its non-weak node characteristics. The capacity results reflect the voltage support requirements of different nodes, providing a quantitative basis for subsequent energy storage power matching.

[0241] 3) Energy storage power matching design

[0242] Once the energy storage system capacity is determined, to ensure the system has sufficient transient support capability, the output power needs to be determined based on the capacity and time parameters. Energy storage power P i The theoretical expression is:

[0243]

[0244] And because

[0245]

[0246] It can be deduced that:

[0247] P i =ΔP i

[0248] This indicates that, under ideal support scenarios, the energy storage capacity should equal the node power deficit. To ensure engineering redundancy and safety margin, a power factor of 5%–10% is typically added in system design.

[0249] P i ′=k p ·ΔP i ,k p ∈[1.05,1.10]

[0250] If we take k p =1.1, then the design power of node 10 is approximately:

[0251] P′ 10 =1.1 × 45 = 49.5 MW

[0252] Final configuration result table:

[0253]

[0254] The results led to the final configuration scheme for energy storage site selection and capacity determination, which can serve as a reference for the planning of energy storage supported by the grid voltage in the pre-defined area.

[0255] In the embodiments of this example, the technical effects of this disclosure include: Significantly improved voltage support capability: By accurately identifying voltage-weak nodes through sensitivity analysis, energy storage systems are preferentially deployed in areas with large voltage fluctuations, thereby effectively improving system voltage stability. Significantly improved renewable energy absorption: The energy storage system smooths wind power output fluctuations through peak shaving and valley filling, significantly reducing wind curtailment rates and improving renewable energy utilization efficiency. Scientifically and rationally configured energy storage capacity: The determination of energy storage capacity comprehensively considers factors such as node power deficit, duration, and energy storage system efficiency, ensuring the energy required for voltage support while avoiding over-investment, achieving dynamic matching between capacity and grid demand. Optimized operational economy: Through scientific site selection and reasonable capacity allocation, the total life-cycle investment and operating costs of the system are significantly reduced, resulting in significant economic benefits. Comprehensive improvement in overall system performance: Simulation verification shows that after energy storage configuration, voltage fluctuation rate is controlled within the allowable range of the rated value, wind curtailment rate is significantly reduced, energy storage utilization rate is improved, and the system's operational stability and economy are superior to traditional methods, demonstrating excellent comprehensive performance.

[0256] In this example embodiment, the innovation of this disclosure is as follows: This scheme introduces voltage sensitivity indicators into the energy storage site selection process for the first time, identifying voltage-weak areas using sensitivity thresholds, thus achieving quantitative decision-making for energy storage locations and overcoming the limitations of previous reliance on experience-based judgment. In the evaluation of candidate nodes, multiple factors such as voltage improvement effects, renewable energy absorption levels, and grid connection costs are considered simultaneously, and the weights of each indicator are determined using the analytic hierarchy process (AHP), achieving multi-objective comprehensive optimization of energy storage site selection. This scheme establishes a dynamic correspondence mechanism between capacity calculation and system demand by analyzing the matching relationship between node power deficit and duration, ensuring that the energy storage system can meet both voltage support requirements and good economic efficiency. In the result verification stage, this scheme quantitatively evaluates the energy storage configuration scheme from both voltage stability and economic perspectives, ensuring that the final result not only has technical reliability but also meets economic feasibility requirements. This invention integrates the entire process of voltage sensitivity analysis, candidate node screening, capacity calculation, and system verification into a single framework, realizing an automated, visualized, and reproducible energy storage planning process, possessing good versatility and promotional value.

[0257] It should be noted that although the steps of the method in this disclosure are described in a specific order in the accompanying drawings, this does not require or imply that the steps must be performed in that specific order, or that all the steps shown must be performed to achieve the desired result. Additional or alternative steps may be omitted, multiple steps may be combined into one step, and / or a step may be broken down into multiple steps.

[0258] Furthermore, in this example embodiment, a site selection and capacity determination device for an energy storage power station is also provided. (Refer to...) Figure 2 As shown, the energy storage power station site selection and capacity determination device 200 may include: a basic data acquisition module 210, a candidate node generation module 220, a preferred node generation module 230, and an energy storage power matching module 240. Wherein:

[0259] The basic data acquisition module 210 is used to acquire basic power grid data of a preset area based on a preset power grid basic data input method. The basic power grid data includes the nodes of the power grid and the sensitivity coefficients corresponding to the nodes.

[0260] The candidate node generation module 220 is used to identify weak voltage nodes based on the nodes of the power grid and the sensitivity coefficients corresponding to the nodes, and generate candidate nodes based on a preset threshold.

[0261] The preferred node generation module 230 is used to establish an evaluation index system, perform comprehensive scoring calculation and sorting on the candidate nodes, and obtain the preferred energy storage access nodes.

[0262] The energy storage power matching module 240 is used to calculate the energy storage capacity of the preferred energy storage access node and perform energy storage power matching based on a preset energy storage capacity calculation method.

[0263] The specific details of each of the above-mentioned energy storage power station site selection and capacity determination device modules have been described in detail in the corresponding energy storage power station site selection and capacity determination method, so they will not be repeated here.

[0264] It should be noted that although several modules or units of an energy storage power station site selection and capacity determination device 200 have been mentioned in the detailed description above, this division is not mandatory. In fact, according to embodiments of this disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0265] Furthermore, the above figures are merely illustrative of the processes included in the method according to exemplary embodiments of the present invention, and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Additionally, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0266] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and embodiments are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the claims.

[0267] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. A method for site selection and capacity determination of an energy storage power station, characterized in that, The method includes: Based on a preset power grid basic data input method, power grid basic data for a preset area is collected. The power grid basic data includes the nodes of the power grid and the sensitivity coefficients corresponding to the nodes. Based on the nodes of the power grid and the sensitivity coefficients corresponding to those nodes, weak voltage nodes are identified and candidate nodes are generated based on a preset threshold. An evaluation index system is established, and the candidate nodes are comprehensively scored and ranked to obtain the preferred nodes for energy storage access. Based on the preset energy storage capacity calculation method, the energy storage capacity of the preferred energy storage access node is calculated and energy storage power matching is performed.

2. The method as described in claim 1, characterized in that, The power grid basic data input for the method also includes: Based on the pre-defined expected power grid architecture, a grid structure model containing a pre-defined number of nodes is constructed, and voltage-power relationship expressions are generated. In the model, a new energy output matrix load characteristic input is established to generate a system net load expression; The nodes are evaluated, and a node sensitivity coefficient table containing the correspondence between the nodes of the power grid and their sensitivity coefficients is established.

3. The method as described in claim 2, characterized in that, The voltage-power relationship expression in the method is as follows: where ΔV i is the voltage, ΔQ j is the power, S ij denotes the voltage sensitivity of the change in reactive power from node i to node j, N node is the total number of nodes participating in the optimization.

4. The method as described in claim 1, characterized in that, The preset number of nodes in the method also includes a first preset number of core nodes and a second preset number of joint nodes surrounding the core nodes.

5. The method as described in claim 1, characterized in that, The method for generating candidate nodes also includes: Generate a voltage sensitivity matrix based on the principle of voltage sensitivity. Weak nodes are identified based on preset thresholds; Define and calculate the node voltage vulnerability index to generate candidate nodes.

6. The method as described in claim 1, characterized in that, The method for generating preferred nodes for energy storage access also includes: Establish an evaluation index system and construct raw data to generate a node evaluation index matrix; The indicators of the nodes are normalized and directionally processed. A judgment matrix is ​​constructed using the analytic hierarchy process (AHP) to generate normalized node feature vectors. Based on the weighted synthesis method, a comprehensive score is calculated for the normalized results of the node feature vectors. The comprehensive scores are calculated and ranked, and the preferred nodes for energy storage access are selected.

7. The method as described in claim 6, characterized in that, The evaluation metrics in the method also include: The voltage improvement degree represents the percentage improvement in node voltage deviation after the energy storage system is connected. The renewable energy consumption rate represents the increase in the utilization rate of wind and solar power output in the region after the energy storage system is put into operation. Access costs include the purchase of energy storage equipment, line access, and substation renovation.

8. The method as described in claim 1, characterized in that, The energy storage capacity calculation method further includes: Calculate the node power deficit of the preferred energy storage access node; Calculate energy storage capacity based on the energy storage capacity calculation formula; Energy storage power matching is performed based on a preset power coefficient.

9. The method as described in claim 8, characterized in that, In the method described above, if the energy storage system outputs ΔP at node i with constant power... i The duration is T i The system round-trip efficiency is η i The energy storage capacity formula can then be expressed as: E i It is the energy storage capacity (MWh); ΔP i It is the power deficit (MW); T i It is the duration of support (h); η i It is the round-trip efficiency of the energy storage system, taking into account energy loss.

10. A site selection and capacity determination device for an energy storage power station, characterized in that, The device includes: The basic data acquisition module is used to acquire basic power grid data for a preset area based on a preset power grid basic data input method. The basic power grid data includes the nodes of the power grid and the sensitivity coefficients corresponding to the nodes. The candidate node generation module is used to identify weak voltage nodes based on the nodes of the power grid and the sensitivity coefficients corresponding to the nodes, and generate candidate nodes based on a preset threshold. The preferred node generation module is used to establish an evaluation index system, perform comprehensive scoring calculation and sorting on the candidate nodes, and obtain the preferred energy storage access nodes; The energy storage power matching module is used to calculate the energy storage capacity of the preferred energy storage access node and perform energy storage power matching based on a preset energy storage capacity calculation method.

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