Optimization method and system for composite energy storage facing new energy access

Abstract

The present application relates to a kind of composite energy storage optimization method and system for new energy access, belong to energy field, in the method, new energy access point is obtained by evaluating access point vulnerability, the vulnerability index of new energy access point, vulnerability index is used to characterize the dangerous degree of new energy access point to power grid in the condition of grid connection;New energy access point is based on the resource allocation of hash mapping, determine L from the candidate composite energy storage unit set Matching composite energy storage unit of new energy access point, L is the integer greater than 1, the value of L is positively correlated with vulnerability index, L composite energy storage unit is used to electrically balance the net power fluctuation of new energy access point to power grid;New energy access point requests power grid to configure L for new energy access point Composite energy storage unit.

Description

Technical Field

[0001] This invention belongs to the energy field and relates to a composite energy storage optimization method and system for new energy access. Background Technology

[0002] With the continuous expansion of new energy power generation such as wind power and photovoltaics, their decentralized grid connection has become the norm. New energy output is significantly affected by weather, exhibiting intermittent and random fluctuations, leading to frequent changes in power at the grid connection point and adversely impacting grid frequency stability and voltage quality. To suppress these power fluctuations, composite energy storage systems composed of different types of energy storage units (such as lithium batteries and supercapacitors) are typically connected to the grid. Through coordinated control of the charging and discharging of each energy storage unit, the power fluctuations of new energy sources are mitigated and compensated.

[0003] However, in the task of power smoothing for multiple new energy access points in a composite energy storage system, the allocation of its energy storage resources is not flexible enough. Summary of the Invention

[0004] In view of this, in order to solve the above problems, the present invention provides a composite energy storage optimization method and system for new energy access.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] Firstly, a composite energy storage optimization method for renewable energy access is provided, applied to renewable energy access points. The method includes: the renewable energy access point assesses its vulnerability to obtain a vulnerability index, which characterizes the degree of danger posed by the renewable energy access point to the power grid under grid connection conditions; the renewable energy access point allocates resources based on hash mapping, determining L composite energy storage units matching the renewable energy access point from a set of candidate composite energy storage units, where L is an integer greater than 1, and the value of L is positively correlated with the vulnerability index; the L composite energy storage units are used to electrically balance the net power fluctuations of the renewable energy access point to the power grid; and the renewable energy access point requests the power grid to configure L composite energy storage units for the renewable energy access point.

[0007] Therefore, the above scheme constructs a three-step core architecture with access points as the main body, vulnerability assessment as the driving force, and hash mapping as the allocation method. The degree of danger posed by an access point to the power grid is quantified into a vulnerability index, which determines the required number of energy storage units, L. Hash mapping is used to locate L consecutive units from the candidate set, and finally, the access point initiates a configuration request to the power grid. This architecture decouples traditional centralized optimization into autonomous assessment and independent calculation by each access point. The computational complexity is reduced from being strongly correlated with the number of access points to constant-level operations for each access point, solving the real-time bottleneck of existing technologies in multi-access-point scenarios. Simultaneously, the positive correlation between L and vulnerability automatically tilts energy storage resources towards high-risk access points, achieving flexible and adaptive resource allocation.

[0008] Optionally, the renewable energy access point (NEA) obtains a vulnerability index by assessing its vulnerability, including: determining the volatility component, short-circuit capacity ratio component, and voltage sensitivity component of the NEA within a preset time window; a larger volatility component indicates a greater impact on the grid when the NEA is connected to the grid; a larger short-circuit capacity ratio component indicates a weaker ability of the grid to withstand impacts; and a larger voltage sensitivity component indicates a higher likelihood of voltage exceedance due to power fluctuations at the NEA's grid connection point. The NEA determines its vulnerability index based on the volatility component, short-circuit capacity ratio component, and voltage sensitivity component.

[0009] Therefore, by concretizing the abstract risk level of the access point into three quantifiable components—volatility, short-circuit capacity ratio, and voltage sensitivity—a comprehensive characterization of the access point's risk features can be achieved from three dimensions: "impact source strength," "grid withstand capability," and "response sensitivity." Volatility reflects the degree of disorder in the renewable energy output itself, short-circuit capacity ratio reveals the grid's tolerance at that node, and voltage sensitivity characterizes the tightness of power-voltage coupling. The weighted synthesis of these three factors allows vulnerability assessment to simultaneously incorporate renewable energy-side behavior and grid-side characteristics, forming a comprehensive measure of risk level. This enables the allocation of energy storage resources to be based on the actual risk level of the access point rather than pre-set fixed zones, making allocation more rational and flexible.

[0010] Optionally, the volatility components satisfy the following relationship:

[0011] ;

[0012] Where Ifluc represents the volatility component, K is the K adoption times within the preset time window, p1 is the power generated by the renewable energy access point at the first adoption time among the K adoption times, p2 is the power generated by the renewable energy access point at the second adoption time among the K adoption times, and so on, pK is the power generated by the renewable energy access point at the Kth adoption time among the K adoption times, and Δp is the average power generated by the renewable energy access point at each of the K adoption times;

[0013] The short-circuit capacity ratio component satisfies the following relationship: Iscr = Pinst / Ssc;

[0014] Wherein, Pinst is the installed capacity of the new energy access point, and Ssc is the short-circuit capacity of the grid connection point where the new energy access point is connected to the grid.

[0015] The voltage sensitivity components satisfy the following relationship: Ivs=|∂Vi / ∂Pi|;

[0016] Where Vi represents the voltage amplitude at the grid connection point, ∂Vi represents the change in voltage at the grid connection point when subjected to disturbance, Pi represents the electrical power generated by the renewable energy access point, and ∂Pi represents the disturbance in the electrical power generated by the renewable energy access point.

[0017] Therefore, the above scheme clearly defines the three components: volatility, short-circuit capacity ratio, and voltage sensitivity. Volatility is expressed as the variance of power samples within a sliding window to capture short-term fluctuations in energy; the short-circuit capacity ratio, as the ratio of installed capacity to short-circuit capacity, characterizes the relative strength of the grid; and voltage sensitivity quantifies local response characteristics using the voltage-power partial derivative. All three utilize measurable physical quantities, ensuring that the assessment results reflect the current grid status in real time.

[0018] Optionally, the method further includes: the new energy access point determines a set of candidate composite energy storage units that match the vulnerability index based on the profile of each energy storage unit.

[0019] Therefore, before hash-based location, profiling and matching can pre-exclude energy storage units that do not meet basic requirements or are in poor condition, forming a valid candidate set. This reduces the search space for hash-based location, ensuring that subsequent matching targets only eligible resources, thus improving computational efficiency.

[0020] Optionally, the renewable energy access point determines a set of candidate composite energy storage units matching the vulnerability index based on the profile of each energy storage unit. This includes: the renewable energy access point determines N as the number of candidate composite energy storage units matching the vulnerability index, where N is an integer greater than 1, and the number of candidate composite energy storage units is negatively correlated with the vulnerability index; the renewable energy access point determines the number of candidate composite energy storage units as the length of the sliding window; the renewable energy access point slides the sliding window with a preset step size, and during the sliding window process, the starting position of the sliding window is the first candidate composite energy storage unit among all candidate composite energy storage units in the power grid, and the sliding window... The ending position is the last candidate composite energy storage unit among all candidate composite energy storage units in the power grid. During the sliding window process, the sliding window is located at T positions. For the j-th position among the T positions, the new energy access point determines the matching degree between the profile of the N candidate composite energy storage units covered by the sliding window at the j-th position and the new energy access point. When j traverses from 1 to T, T matching degrees are obtained. The new energy access point determines the highest matching degree from the T matching degrees and determines the candidate composite energy storage units covered by the sliding window at the position corresponding to the highest matching degree as the candidate composite energy storage unit set.

[0021] Therefore, the above scheme evaluates the matching degree of each of the N consecutive candidate energy storage units covered within the window with the access point requirements in each profile dimension. The overall matching degree of a single unit is obtained by weighted summation, and then the overall matching degrees of the N units are accumulated to represent the overall matching degree of the window. Its core lies in using the window as the evaluation unit, treating the N consecutive units as a whole for adaptability assessment, rather than selecting and splicing individual units. This ensures the continuity of the final selected candidate set, making the subsequent hash formula's operation of "taking L consecutive energy storage units from the starting index" structurally adaptable. The candidate set with continuous indices allows for direct sequential extraction after hash positioning, eliminating the need to handle index fragmentation or skip addressing, thus ensuring a smooth and efficient resource allocation process.

[0022] Optionally, the new energy access point determines the matching degree between the profiles of the N candidate composite energy storage units covered by the sliding window at the j-th position and the new energy access point, including: the new energy access point obtains the response capability profiles of each of the N candidate composite energy storage units, the response capability profiles including at least the following profile dimensions: response speed level, capacity margin, and health status; for each candidate composite energy storage unit among the N candidate composite energy storage units: the new energy access point determines the single-dimensional matching degree of the candidate composite energy storage unit based on the difference between the actual value of each profile dimension of the candidate composite energy storage unit and the new energy access point's demand value for each profile dimension, and finally obtains multiple single-dimensional matching degrees corresponding to multiple profile dimensions of the candidate composite energy storage unit; and the new energy access point performs a weighted summation of the multiple single-dimensional matching degrees to obtain the comprehensive matching degree of the candidate composite energy storage unit, wherein the weight of each of the multiple profile dimensions in the weighted summation is determined according to the vulnerability index; the new energy access point determines the matching degree as the sum of the comprehensive matching degrees of the N candidate composite energy storage units.

[0023] Therefore, for each energy storage unit within the window, its single-dimensional matching degree across multiple profile dimensions, such as response speed level, capacity margin, and health status, is calculated. The overall matching degree of the unit is then obtained by weighting and summing these dimensions. The weights of each profile dimension are dynamically determined based on the vulnerability index of the access point. This dynamic weight adjustment mechanism allows the same matching process to generate differentiated screening tendencies for access points with different risk levels, improving the targeting and rationality of the candidate set determination.

[0024] Optionally, the L composite energy storage units and the candidate set of composite energy storage units satisfy the following relationship:

[0025] ;

[0026] Wherein, StartIdx indicates the first composite energy storage unit among L composite energy storage units, L of which are consecutively selected from the first composite energy storage unit in the candidate set, HashID is the dynamic hash seed of the renewable energy access point, m is the sequence number of the renewable energy access point among at least one renewable energy access point in the same vulnerability level, the vulnerability index belongs to the vulnerability level, N is the total number of composite energy storage units in the candidate set, M is the maximum number of renewable energy access points allowed under the vulnerability level, Aid is the mutual assistance offset, and mod is the modulo operation. This is for rounding down.

[0027] Therefore, by using parameters such as the dynamic hash seed HashID, the same-level sequence number m, and the mutual offset Aid, a unique starting position in the candidate set is calculated for each access point. HashID is generated from the access point identifier and time-varying parameters, allowing the starting position of the same access point to change dynamically in different periods, achieving resource rotation allocation and avoiding device lifespan differentiation caused by fixed allocation. The m parameter ensures that access points of the same level receive different offsets due to different sequence numbers, effectively resolving resource contention conflicts. This formula transforms the complex allocation problem that originally required global optimization into a deterministic mapping that each access point can independently calculate, resulting in extremely low computational complexity and solving the problem of insufficient flexibility in allocation methods.

[0028] Optionally, HashID satisfies the following relationship: HashID = (A × ID + B) mod 65537;

[0029] Where A is the preset first disturbance coefficient, B is the preset second disturbance coefficient, ID is the identifier of the new energy access point, and 65537 is the hash modulus in prime number form.

[0030] Therefore, a dynamic hash seed is generated by multiplying the access point identifier ID by the perturbation coefficient A, adding the perturbation coefficient B, and then taking the modulo of the prime number 65537. The prime number modulo 65537 ensures a uniform distribution of hash values, making the starting positions of each access point statistically random and avoiding clustering effects. The introduction of perturbation coefficients A and B makes the hash highly sensitive to input parameters; even small differences can produce significantly different outputs, enhancing the unpredictability and fairness of the allocation. This formula only involves multiplication and modulo operations, with minimal computational overhead, making it suitable for real-time execution at the access point and ensuring the feasibility of allocation flexibility from a computational mechanism perspective.

[0031] Optionally, when no additional composite energy storage unit is required at the new energy access point, Aid is set to 0; when an additional composite energy storage unit is required at the new energy access point, Aid is set to a preset value that is neither 0 nor 0.

[0032] Therefore, when resources are plentiful in the area where the access point is located, Aid is set to 0, and the starting index is located within the resource pool of this area. When resources are insufficient in this area and cross-area borrowing is required, Aid takes a non-zero preset value, causing the starting index to jump to the logical address space of the resource pool in the adjacent area. This design seamlessly embeds cross-area cooperation into a unified hash allocation framework, without changing the core algorithm structure; cross-area resource scheduling can be achieved simply by adjusting the Aid parameter. This parameterized cooperation method avoids the complexity of establishing an independent cross-area negotiation protocol required by traditional methods, greatly simplifying system design and giving resource allocation sufficient flexibility in the spatial dimension.

[0033] Secondly, a composite energy storage optimization system for new energy access is provided. The system includes a new energy access point, which is configured as follows: the new energy access point obtains a vulnerability index by assessing its vulnerability, which characterizes the degree of danger to the power grid when the new energy access point is connected to the grid; the new energy access point determines L composite energy storage units matching the new energy access point from a set of candidate composite energy storage units based on hash mapping resource allocation, where L is an integer greater than 1 and the value of L is positively correlated with the vulnerability index; the L composite energy storage units are used to electrically balance the net power fluctuation of the new energy access point to the power grid; the new energy access point requests the power grid to configure L composite energy storage units for the new energy access point.

[0034] The objectives and other advantages of this invention can be realized and obtained through the following description. Attached Figure Description

[0035] To make the objectives, technical solutions, and advantages of the present invention clearer, the preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings, wherein:

[0036] Figure 1 This invention provides a schematic diagram of the architecture of a composite energy storage optimization system for new energy access.

[0037] Figure 2 A flowchart of a composite energy storage optimization method for new energy access provided by the present invention;

[0038] Figure 3 This is a schematic diagram of the structure of an electronic device provided by the present invention. Detailed Implementation

[0039] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other. The accompanying drawings are for illustrative purposes only, representing only schematic diagrams and not actual physical objects, and should not be construed as limiting the present invention. To better illustrate the embodiments of the present invention, some parts in the drawings may be omitted, enlarged, or reduced, and do not represent the actual product form.

[0040] like Figure 1 As shown in the figure, this application provides a composite energy storage optimization system for new energy access, which includes a new energy access point and a composite energy storage unit.

[0041] Renewable Energy Generation Connection Point (REGCP):

[0042] A renewable energy access point refers to the physical interface that electrically connects a renewable energy power generation system to the public power grid. In specific engineering implementations, the form of the renewable energy access point varies depending on the type of renewable energy and its voltage level. For wind farms, the access point is typically the wind farm's grid-connected step-up substation. This substation collects the collected power lines from each wind turbine unit after being stepped up by a box-type transformer, and further steps up the voltage to the grid voltage level (such as 35kV, 110kV, or 220kV) via a main transformer, before connecting to the public power grid via high-voltage circuit breakers, disconnectors, and other switching equipment. For photovoltaic power plants, the access point is typically the grid-connected cabinet after the AC side of the photovoltaic inverter has merged, or the high-voltage side output terminal of the step-up transformer. The DC power output from the photovoltaic modules is converted to AC power by a string inverter or a centralized inverter, collected through an AC combiner box, and then connected to the grid via a step-up transformer. For distributed renewable energy, the access point is typically the grid-connected switchgear in the user's distribution room, connected to the user's low-voltage busbar or the public distribution network via a dedicated grid-connected circuit breaker.

[0043] The deployment method of renewable energy access points is determined based on the scale and geographical location of the renewable energy power station. Centralized renewable energy power stations (such as large wind farms and ground-mounted photovoltaic power stations) are typically deployed as independent step-up substations, equipped with complete primary and secondary electrical equipment, including main transformers, high-voltage switchgear, protection and control devices, remote communication devices, etc., and connected to the power grid substation via dedicated grid connection lines. Distributed renewable energy (such as rooftop photovoltaics and decentralized wind power) is connected to the user's distribution system or public distribution network nearby. Its access point is usually located in the user's distribution room or at the distribution line tower, equipped with necessary equipment such as grid connection switches, metering devices, and anti-islanding protection devices.

[0044] The connection between the renewable energy access point and the power grid is achieved through the Point of Common Coupling (PCC). This point is the legally defined boundary for power exchange and settlement between the renewable energy generation system and the power grid, typically marked by the outgoing terminals of the grid-connected switchgear or the power grid ownership boundary. A data channel is established between the access point and the power grid dispatch center via fiber optic, wireless public network, or power line carrier communication methods to achieve real-time uploading of operational data and reception of dispatch commands. The remote terminal units (RTUs) or intelligent monitoring terminals configured at the access point interact with the power grid dispatch automation system through standard communication protocols such as IEC60870-5-104 and IEC61850, uploading operating parameters such as active power, reactive power, voltage, and frequency, and receiving active power adjustment commands, reactive power and voltage adjustment commands, and grid connection / disconnection control commands.

[0045] Hybrid Energy Storage Unit (HESU):

[0046] A composite energy storage unit refers to an energy storage system composed of two or more different types of energy storage devices, capable of collaboratively performing power smoothing and energy regulation tasks. In specific engineering implementations, composite energy storage units typically include a combination of energy-type energy storage devices and power-type energy storage devices. Energy-type energy storage devices are represented by lithium-ion batteries, typically in the form of battery compartments or cabinets, composed of several battery modules connected in series or parallel. The capacity of a single battery compartment is usually in the range of 1MW / 2MWh to 5MW / 10MWh, and a battery management system (BMS) is used to monitor and protect the voltage, temperature, and state of charge of each cell in real time. Power-type energy storage devices are represented by supercapacitors, which are modules composed of double-layer capacitor cells connected in series and parallel. Typical power levels range from hundreds of kilowatts to megawatts, with response times down to the millisecond level.

[0047] The deployment of composite energy storage units can be divided into two types: centralized deployment and distributed deployment. Centralized deployment involves arranging multiple types of energy storage devices within a single energy storage power station. This station typically has independent step-up transformers and high-voltage switchgear, connecting to the grid through a single point of connection. For example, a typical centralized composite energy storage power station might be configured with a 60MW / 120MWh lithium iron phosphate battery pack and a 32MW supercapacitor energy storage system. All devices are connected to the station's AC bus via their respective PowerConversion Systems (PCS), and then stepped up to 35kV or 110kV by the main transformer before being connected to the grid. Distributed deployment, on the other hand, disperses different types of energy storage devices near multiple new energy access points. Each energy storage unit connects to different nodes of the distribution network via its own PCS, achieving coordinated control through a communication network.

[0048] Each energy storage unit is equipped with an independent energy storage converter. The PCS is the core power electronic device that enables bidirectional power flow between the energy storage device and the grid. Its DC side is connected to the DC bus of the energy storage device (for lithium batteries and supercapacitors), and its AC side is connected to the grid via a grid-connected switch. The PCS is connected to the station-level energy management system (EMS) via fiber optic or Ethernet, receives power commands, and executes charge and discharge control. As the upper-level controller of the composite energy storage unit, the EMS is responsible for collecting the real-time status of each energy storage unit (including state of charge (SOC), state of health (SOH), available power, etc.), executing the optimized operation method described in this application, generating power allocation commands, and issuing them to each PCS for execution. The BMS and PCS communicate via a controller area network (CAN) bus or RS485 bus to achieve real-time interaction of battery status data and safety interlock protection.

[0049] The renewable energy access point and the composite energy storage unit are electrically coupled through the power grid, rather than being physically directly electrically connected. Specifically, the composite energy storage unit is connected to the power grid through its grid connection point, and the renewable energy access point is also connected to the power grid through its PCC (Power Control Center). The two are electrically interconnected through the power grid's transmission lines and distribution network. When the output of the renewable energy access point fluctuates, this fluctuation manifests as a change in the power injected into the power grid by the access point. This power change causes voltage and frequency fluctuations at nodes electrically close to each other in the power grid. The composite energy storage unit obtains power fluctuation information of the target renewable energy access point by real-time monitoring of the electrical quantities at its grid connection point (usually accomplished by the measurement unit built into the PCS or an independent power quality monitoring device). This information can be obtained through power commands generated by the power grid dispatch center or estimated based on local measurements. Based on the power commands generated by the method described in this application, the composite energy storage unit is controlled by the PCS to charge or discharge, thereby offsetting the net power fluctuations of the renewable energy access point to the power grid and thus achieving "virtual support" for the renewable energy access point electrically. This coupling method eliminates the need for dedicated transmission lines between renewable energy access points and energy storage units. Instead, it utilizes the existing power grid as the power transmission carrier, significantly reducing the engineering complexity and investment costs of system deployment. Simultaneously, the system achieves binding and coordination of the renewable energy access points and composite energy storage units in control logic through a communication link between the EMS and the power grid dispatch center. This allows energy storage resources to be dynamically configured based on the real-time vulnerability of each access point.

[0050] like Figure 3 As shown in the figure, this application provides a composite energy storage optimization method for new energy access, which is applied to the system described in the above embodiment.

[0051] The specific process of this method is as follows:

[0052] S201, the vulnerability index of the new energy access point is obtained by assessing the vulnerability of the access point. The vulnerability index is used to characterize the degree of danger that the new energy access point poses to the power grid when connected to the grid.

[0053] Specifically, the new energy access point determines its volatility component, short-circuit capacity ratio component, and voltage sensitivity component within a preset time window.

[0054] The volatility component reflects the degree of fluctuation in the power output of the connection point over a short period of time. A larger value for the volatility component indicates a greater impact on the power grid when the connection point is connected to the grid. The specific calculation method for the volatility component is as follows:

[0055] First, the renewable energy access point acquires its power values ​​at each sampling moment within a preset time window. The preset time window can be set according to the grid dispatch cycle and the fluctuation characteristics of the renewable energy source. For example, for wind power access points, where wind speed changes rapidly, the preset time window can be 10 to 15 minutes; for photovoltaic access points, affected by cloud movement, the preset time window can be 5 to 10 minutes. The sampling interval can be determined based on the data acquisition capability of the monitoring terminal configured at the access point, with a typical sampling interval of 1 to 5 seconds. Taking a 15-minute window and a 1-second sampling interval as an example, the total number of sampling moments K is 900. The power values ​​at each sampling moment can be directly read from the power measurement device at the access point. This power measurement device is typically the active power signal output by the voltage transformer and current transformer installed at the grid connection point via a power transmitter, or calculated from synchronous phasor data provided by a phasor measurement unit (PMU).

[0056] Let p1 be the electrical power emitted by the access point at the first sampling time, p2 at the second sampling time, and so on, with pK being the Kth sampling time. Each sampled value is in MW and rounded to two decimal places. Calculate the arithmetic mean Δp of the K electrical power values ​​within this window, i.e., Δp = (p1 + p2 + ... + pK) / K. The volatility component Ifluc is then calculated using the following formula:

[0057] ;

[0058] This formula essentially represents the variance of the power sample, reflecting the degree of dispersion of the power value relative to the mean within a window, with units of MW². A larger Ifluc value indicates more severe power fluctuations during that period. For example, at the connection point of a 100MW wind farm, the power fluctuates slightly between 78MW and 84MW within a 15-minute window, with a calculated Ifluc of approximately 3.2MW², indicating relatively gentle fluctuations during this period. However, in another 15-minute window, affected by gusts, the power drops sharply from 82MW to 18MW within 15 minutes before rebounding to 75MW. The deviation between each sampled value and the mean increases significantly, and the calculated Ifluc is approximately 480MW², indicating extremely severe fluctuations during this period and a serious impact on the power grid.

[0059] The short-circuit capacity ratio component reflects the relative strength of the power grid connected to that connection point. A larger short-circuit capacity ratio component indicates a weaker ability of the power grid to withstand shocks at that connection point. The specific calculation method for the short-circuit capacity ratio component is as follows:

[0060] Obtain the installed capacity (Pinst) of the access point and the short-circuit capacity (Ssc) of the grid connection point to which the access point is connected. The installed capacity (Pinst) is the rated active power capacity of the renewable energy power plants connected to the access point, in MW. For wind farms, the installed capacity is the sum of the rated power of all wind turbines within the farm; for photovoltaic power plants, the installed capacity is the sum of the rated output power of all inverters on the AC side or the sum of the nominal power of photovoltaic modules. This data can be directly obtained from the power plant design documents, equipment nameplates, or grid dispatch ledgers, and is either a fixed value or a quasi-fixed value updated as the power plant expands.

[0061] The short-circuit capacity Ssc is the apparent power corresponding to the short-circuit current that the grid can provide when a three-phase metallic short-circuit fault occurs at the grid connection point, and its unit is MVA. Ssc can be obtained by taking the square root of 3 × Un × Isc, where Un is the rated line voltage of the grid connection point (unit: kV), and Isc is the effective value of the three-phase short-circuit current at that point (unit: kA). The short-circuit capacity Ssc can be obtained through one of the following methods: First, by querying the short-circuit capacity value of the grid connection point from the annual short-circuit capacity distribution map provided by the grid dispatch center; Second, by calculating the equivalent impedance of the node according to the grid operation mode report issued by the grid dispatch center, Ssc = Un² / Z, where Z is the modulus of the Thevenin equivalent impedance of the node; Third, for connection points with suitable conditions, it can be measured through on-site short-circuit tests or harmonic injection methods. The short-circuit capacity Ssc changes dynamically with changes in grid topology and different start-up methods, and the typical update cycle is monthly or quarterly, or it is reissued by the dispatch center when the maintenance mode of the large grid changes.

[0062] Based on the above data, the short-circuit capacity ratio component Iscr is calculated using the following formula:

[0063] Iscr=Pinst / Ssc

[0064] For example, a wind farm with an installed capacity of 100MW (Pinst) and a grid connection short-circuit capacity (Ssc) of 400MVA has an Iscr of 100 / 400 = 0.25. Another wind farm with the same installed capacity of 100MW, but located at the end of the grid and connected via a long-distance line, has a grid connection short-circuit capacity (Ssc) of only 120MVA, resulting in an Iscr of 100 / 120 ≈ 0.83. Comparing the two, the Iscr value of the latter is approximately 3.3 times that of the former, indicating that under the same installed capacity, the grid support capacity of the latter is significantly weaker than that of the former, and the same power fluctuation will cause greater voltage and frequency deviations.

[0065] The voltage sensitivity component reflects the responsiveness of the grid connection point's voltage to power changes. A higher voltage sensitivity component value indicates that the grid connection point is more prone to voltage exceedances due to power fluctuations. The specific calculation method for the voltage sensitivity component is as follows:

[0066] Based on the power flow equations under the current grid operation mode, the partial derivative ∂Vi / ∂Pi of the voltage amplitude Vi at the grid connection point with respect to the injected active power Pi is obtained. The methods for obtaining this partial derivative include: first, the monitoring terminal at the connection point obtains the power flow Jacobian matrix data under the current operation mode from the energy management system (EMS) of the grid dispatch center via the power communication protocol, and extracts the sensitivity coefficient corresponding to the grid connection point; second, the power quality monitoring device or PMU device installed locally at the connection point performs online identification using the disturbance observation method, i.e., continuously recording the time series of the voltage amplitude and active power at the grid connection point, and recursively estimating the value of ∂Vi / ∂Pi using the least squares method or Kalman filtering algorithm; third, for connection points without the above communication and monitoring conditions, offline simulation calculation can be used to pre-calculate the sensitivity coefficient of each connection point based on the grid model under typical operation mode and store it as a data table, which is then retrieved during operation based on the current grid topology and load level.

[0067] The voltage sensitivity component Ivs takes the absolute value of this partial derivative:

[0068] Ivs=|∂Vi / ∂Pi|

[0069] The unit of this value is pu / MW or kV / MW. For example, at a certain grid-connected point, ∂Vi / ∂Pi is 0.0015pu / MW, meaning that for every 1MW change in injected active power, the voltage amplitude (in per unit value) at that point changes by 0.0015, or 0.15%. At another grid-connected point, affected by long lines and light loads, this partial derivative is 0.006pu / MW, meaning that the same 1MW power change will cause a 0.6% voltage fluctuation. The latter's Ivs value is four times that of the former, indicating that the voltage at this point is more sensitive to power changes, and the risk of voltage exceeding limits is higher when there are fluctuations in renewable energy output.

[0070] After obtaining the volatility component Ifluc, the short-circuit capacity ratio component Iscr, and the voltage sensitivity component Ivs, the renewable energy access point determines the vulnerability index based on these three components. One specific implementation involves normalizing each component and then weighted summing, as follows:

[0071] The first step is to normalize each component, mapping it to the [0,1] interval to eliminate dimensional differences. For the volatility component Ifluc, the normalization benchmark value can be determined based on the historical operational statistics of the access point. For example, take the calculated Ifluc values ​​for all preset time windows within the past year for the access point, using the maximum value Ifluc_max and the minimum value Ifluc_min as benchmarks, and the normalized volatility index Ifluc_norm = (Ifluc - Ifluc_min) / (Ifluc_max - Ifluc_min). If the current Ifluc exceeds the historical maximum value, then Ifluc_norm is set to 1. For the short-circuit capacity ratio component Iscr, which is already a dimensionless ratio, it can be used directly or normalized according to the maximum short-circuit capacity ratio of the area where the access point is located. For the voltage sensitivity component Ivs, it can be normalized by referring to the voltage sensitivity warning threshold specified in the power grid operation regulations. For example, if Ivs_max is 0.01 pu / MW, then Ivs_norm = Ivs / Ivs_max, with an upper limit of 1.

[0072] The second step is to determine the weighting coefficients for each component. Let w1 be the weight of the volatility component, w2 be the weight of the short-circuit capacity component, and w3 be the weight of the voltage sensitivity component, with the three satisfying w1 + w2 + w3 = 1. The values ​​of the weighting coefficients can be determined based on the actual operating needs of the power grid and the type of renewable energy at the access point. For example, for wind power access points, whose output fluctuates significantly, w1 = 0.5, w2 = 0.3, and w3 = 0.2 can be set; for photovoltaic access points, whose output changes rapidly, w1 = 0.4, w2 = 0.3, and w3 = 0.3 can be set. When the power grid dispatch center issues a warning signal indicating significant frequency regulation pressure, w1 can be temporarily increased; when the power grid enters maintenance mode and short-circuit capacity generally decreases, w2 can be increased; when the power grid voltage stability margin is small, w3 can be increased. The adjustment of the weighting coefficients can be automatically executed by the access point based on the operating condition indicators issued by the dispatch center, or it can be uniformly configured and issued by the dispatch center.

[0073] The third step is to calculate the Grid Vulnerability Index (GVI). GVI = w1 × Ifluc_norm + w2 × Iscr_norm + w3 × Ivs_norm. GVI is a value between 0 and 1; a higher value indicates a greater risk to the grid from the access point. For example, if a wind power access point has calculated Ifluc_norm = 0.85, Iscr_norm = 0.60, and Ivs_norm = 0.40 within a 15-minute window, with weights set as w1 = 0.5, w2 = 0.3, and w3 = 0.2, then GVI = 0.5 × 0.85 + 0.3 × 0.60 + 0.2 × 0.40 = 0.425 + 0.18 + 0.08 = 0.685, indicating that the access point is currently at a medium-to-high risk level. The calculated GVI value is used by the access point in subsequent steps to determine the required number L of composite energy storage units, as well as for the screening and matching of candidate energy storage units.

[0074] S202, resource allocation based on hash mapping for new energy access points, determines L composite energy storage units that match the new energy access points from the candidate set of composite energy storage units, where L is an integer greater than 1, and the value of L is positively correlated with the vulnerability index. The L composite energy storage units are used to electrically balance the net power fluctuations of the new energy access points to the power grid.

[0075] This step specifically includes the following three sub-steps: obtaining and sorting the list of available energy storage units, determining the candidate subset based on a sliding window, and determining the starting index and extracting L consecutive energy storage units based on a hash map. Each step is explained in detail below.

[0076] First sub-step: Obtain and sort the list of available energy storage units:

[0077] The renewable energy access point first obtains information on all currently available composite energy storage units in the power grid, forming a list of available energy storage units sorted by logical index. This list serves directly as the data basis for subsequent sliding window operations without pre-screening.

[0078] Specifically, the renewable energy access point obtains real-time data on all operational and dispatchable composite energy storage units within its electrical reachable range through the power grid dispatch center or regional energy management system. The electrical reachable range is determined as follows: the access point sends a request to the dispatch center, which calculates the electrical distance between the access point's grid connection point and the grid connection points of each energy storage unit based on the power grid topology, and returns information on all energy storage units whose electrical distance is less than or equal to a preset threshold (e.g., 0.2 pu, base power 100 MVA). This information includes:

[0079] (1) Logical index: The available energy storage units are uniformly numbered, starting from 0 and increasing sequentially. For example, if the dispatch center returns 12 energy storage units, their logical indices are 0, 1, 2, ..., 11.

[0080] (2) Response Capability Profile: This profile includes information in at least three dimensions: response speed level, capacity margin, and health status. The response speed level reflects the dynamic response capability of the energy storage unit to power commands, and is divided into "high" (response time ≤ 100ms, corresponding to supercapacitors), "medium" (response time ≤ 1s, corresponding to lithium batteries), and "low" (response time ≥ 1min, corresponding to pumped hydro storage, etc.). Capacity margin represents the remaining energy currently available for charging and discharging, measured in MWh, and is calculated and reported in real time by the local monitoring system of the energy storage unit based on its state of charge and rated capacity. State of Health (SOH) indicates the degree of performance degradation of the energy storage unit, expressed as a percentage, and is provided by the battery management system (BMS) or device diagnostic system.

[0081] (3) Grid connection point location identifier: used to identify the electrical zone to which the energy storage unit belongs when cross-regional mutual assistance is required. The identifier can adopt the node number uniformly assigned by the power grid dispatch center.

[0082] The renewable energy access point will sort all available energy storage units in ascending order of their logical indices, forming a list of length M_total. The energy storage units in the list are consecutive in their logical indices, ensuring the continuity of the subset of units selected by the subsequent sliding window in terms of logical indices.

[0083] Second sub-step: Determine the candidate subset based on the sliding window:

[0084] After obtaining the list of available energy storage units, the renewable energy access point further determines the number N of candidate energy storage units that match its own vulnerability index, where N is an integer greater than 1. The value of N is negatively correlated with the vulnerability index; that is, the higher the vulnerability index, the higher the required precision of the candidate units, and the smaller the value of N; the lower the vulnerability index, the more candidate units can be included for selection. The specific value of N can be determined according to a preset segmented mapping relationship. For example, when GVI ≥ 0.8, N = 4; when 0.5 ≤ GVI < 0.8, N = 6; when GVI < 0.5, N = 8.

[0085] The number N of new energy access points is determined as the length of the sliding window, and the window slides across the list of available energy storage units with a preset step size. The preset step size is usually 1, meaning that the window slides the position of one energy storage unit at a time. The starting position of the sliding window is the first energy storage unit in the list (logical index 0), and the ending position is the last energy storage unit in the list (logical index M_total-1). During the sliding process, the sliding window is located at T positions, where T = M_total-N+1.

[0086] For the j-th position (j=1,2,…,T) of the sliding window, the renewable energy access point determines the degree of matching between the profiles of the N energy storage units covered by that window and its own needs. The calculation process for the degree of matching is as follows:

[0087] (1) Obtain the response capability profile data of each of the N energy storage units in the window.

[0088] (2) For each energy storage unit, calculate the one-dimensional matching degree of each profile dimension:

[0089] For example, response speed level matching: if the energy storage unit's response speed level meets the access point requirements, it is set to 1; if it partially meets the requirements (e.g., the access point requires "high" but the energy storage unit is "medium"), it is set to 0.5; if it does not meet the requirements, it is set to 0. The access point's response speed requirements are determined by the vulnerability index: when GVI ≥ 0.7, "high" or "medium" is required; when GVI < 0.7, all levels are accepted.

[0090] Capacity margin matching degree: Let the required capacity of the access point be C_req, and the available capacity margin of the energy storage unit be C_avail, then the one-dimensional matching degree = min(C_avail / C_req, 1.0). C_req can be determined comprehensively based on GVI and the required number of energy storage units L, for example, C_req = L × basic capacity value.

[0091] Health status matching degree: directly take the normalized value of SOH, i.e., SOH / 100%.

[0092] (3) For each energy storage unit, the weighted sum of its single-dimensional matching degree across each profile dimension is used to obtain the overall matching degree of the energy storage unit. The weights of each profile dimension are dynamically determined based on the vulnerability index of the access point. For example, when GVI≥0.7, the weight of response speed level is 0.5, the weight of capacity margin is 0.3, and the weight of health status is 0.2; when GVI<0.7, the weight of response speed level is 0.3, the weight of capacity margin is 0.4, and the weight of health status is 0.3. The total weight is 1.

[0093] (4) Sum the overall matching degree of the N energy storage units in the window to obtain the overall matching degree of the window position.

[0094] The new energy access point traverses T window positions in the manner described above, obtaining T matching degree values. Then, the window position corresponding to the highest matching degree is selected, and the N energy storage units covered within that window are determined as a candidate subset. This candidate subset contains N logically consecutive energy storage units, which will serve as the resource pool for subsequent hash mapping allocation. Here, N represents the "total number of composite energy storage units in the candidate set of composite energy storage units" in the subsequent hash formula.

[0095] Third sub-step: Determine the starting index and extract L composite energy storage units based on the hash map:

[0096] After determining the candidate subset (containing N consecutive energy storage units), the renewable energy access point determines the number L of composite energy storage units required for matching based on its own vulnerability index. L is an integer greater than 1 and is positively correlated with the vulnerability index. For example, L=4 when GVI≥0.8, L=3 when 0.5≤GVI<0.8, and L=2 when GVI<0.5.

[0097] The new energy access point adopts a hash mapping-based approach to determine the starting index StartIdx from the above N candidate energy storage units, and extracts L energy storage units consecutively from StartIdx as the final L composite energy storage units to be matched.

[0098] The formula for calculating the starting index StartIdx is as follows:

[0099] ;

[0100] The meanings of each parameter and how to obtain them are explained below:

[0101] HashID: The dynamic hash seed for new energy access points, calculated using the formula HashID = (A × ID + B) mod 65537. Where ID is the unique identifier of the access point (e.g., site code); A and B are preset disturbance coefficients, for example, A = 39827, B = 39829; mod 65537 represents taking the modulo of the prime number 65537. B can be associated with the current scheduling cycle number, causing HashID to change dynamically over time.

[0102] m: The sequence number of the renewable energy access point among at least one renewable energy access point within the same vulnerability level. Vulnerability levels are determined based on GVI (Gross Variable Interaction), for example, GVI ≥ 0.7 is high level, 0.4 ≤ GVI < 0.7 is medium level, and GVI < 0.4 is low level. Access points within the same level obtain each other's identifiers through broadcast information from the dispatch center and determine the m value (starting from 0) by sorting them lexicographically. m is used to generate offsets within the same level to avoid resource contention conflicts.

[0103] N: The total number of energy storage units in the candidate subset, i.e., the sliding window length N determined in the second sub-step.

[0104] L: The number of energy storage units required to match.

[0105] M: The maximum number of renewable energy access points allowed under each vulnerability level, which is a preset parameter. For example, M=3 for high-level, M=5 for medium-level, and M=8 for low-level. The smaller the M value, the greater the offset increment when access points compete for resources within the same level, thus prioritizing resource acquisition for high-risk access points.

[0106] Aid: Mutual assistance offset. When the access point has sufficient local resources, Aid=0, and the starting index is located within the local candidate subset. When the access point needs to borrow from another area due to insufficient local available energy storage units, Aid takes a non-zero preset value, which is associated with the identifier of the target area, so that StartIdx points to the logical index range of the resource pool of the target area.

[0107] : Floor down operator.

[0108] mod: Modulo operator.

[0109] Example of calculation process: Let N=8, L=4, M=3, m=1, HashID=12345, Aid=0. The calculation is as follows:

[0110] Modulus = (12345+0+0)mod2 = 1. StartIdx = L×1 = 4. That is, starting from the 4th energy storage unit in the candidate subset (logical index starts from 0), 4 energy storage units are selected consecutively.

[0111] The renewable energy access point extracts L consecutive energy storage units from a candidate subset based on StartIdx, which are then selected as the final L composite energy storage units. These L energy storage units are logically grouped into a virtual aggregate to electrically balance the net power fluctuations of the renewable energy access point on the grid. The power command of the virtual aggregate is generated based on the real-time power output fluctuations of the renewable energy access point and is sent to the energy storage converters of each energy storage unit via the energy management system for charging and discharging, thereby smoothing out the grid-connected power fluctuations of the access point.

[0112] S203, The renewable energy access point requests the power grid to configure L composite energy storage units for the renewable energy access point.

[0113] The renewable energy access point sends a configuration request message to the power grid dispatch center through its communication link. The communication link can be fiber optic or a dedicated power wireless network, and the communication protocol should conform to power industry standards such as IEC60870-5-104 or IEC61850MMS. The configuration request message must include at least: the identifier of the renewable energy access point, the device identifiers of the L composite energy storage units, and the validity period of the configuration. Upon receiving the request, the power grid dispatch center checks the current availability of the L composite energy storage units. After confirming that all are idle and dispatchable, it returns a confirmation response to the renewable energy access point and issues configuration instructions to each of the L composite energy storage units via the energy management system. The configuration instructions include the identifier of the access point it serves and the corresponding power command calculation method, binding each energy storage unit to the access point in its control logic and enabling them to collaboratively perform power smoothing tasks within the validity period.

[0114] In summary, the above scheme constructs a three-step core architecture with access points as the main body, vulnerability assessment as the driving force, and hash mapping as the allocation method. The degree of danger posed by an access point to the power grid is quantified into a vulnerability index, which determines the required number of energy storage units, L. Hash mapping is used to locate L consecutive units from the candidate set, and finally, the access point initiates a configuration request to the power grid. This architecture decouples traditional centralized optimization into autonomous assessment and independent calculation by each access point. The computational complexity is reduced from being strongly correlated with the number of access points to constant-level operations for each access point, solving the real-time bottleneck of existing technologies in multi-access-point scenarios. Simultaneously, the positive correlation between L and vulnerability automatically tilts energy storage resources towards high-risk access points, achieving flexible and adaptive resource allocation.

[0115] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Exemplarily, the electronic device may be a terminal, or a chip (system) or other component or assembly that can be disposed in the terminal. Figure 3 As shown, the electronic device 200 may include a processor 201. Optionally, the electronic device 200 may also include a memory 202 and / or a transceiver 203. The processor 201 is coupled to the memory 202 and the transceiver 203, for example, via a communication bus.

[0116] The following is combined with Figure 3 A detailed description of each component of the electronic device 200 is provided below:

[0117] The processor 201 is the control center of the electronic device 200. It can be a single processor or a collective term for multiple processing elements. For example, the processor 201 can be one or more central processing units (CPUs), application-specific integrated circuits (ASICs), or one or more integrated circuits configured to implement the embodiments of this application, such as one or more digital signal processors (DSPs), or one or more field-programmable gate arrays (FPGAs).

[0118] Optionally, the processor 201 can perform various functions of the electronic device 200 by running or executing software programs stored in the memory 202 and calling data stored in the memory 202, such as performing the aforementioned functions. Figure 2 The method shown.

[0119] In a specific implementation, as one example, the processor 201 may include one or more CPUs, for example... Figure 3 CPU0 and CPU1 are shown in the diagram.

[0120] In a specific implementation, as one example, the electronic device 200 may also include multiple processors, for example... Figure 3 The processor 201 shown is an example. Each of the processors 201 can be a single-core processor or a multi-core processor. Here, "processor" can refer to one or more devices, circuits, and / or processing cores used to process data (e.g., computer program instructions).

[0121] The memory 202 is used to store the software program that executes the solution of this application, and is controlled by the processor 201 to execute it. The specific implementation method can be referred to the above method embodiment, and will not be repeated here.

[0122] Optionally, the memory 202 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. The memory 202 may be integrated with the processor 201 or exist independently, and may be accessed through the interface circuit of the electronic device 200. Figure 3 (Not shown in the image) is coupled to processor 201, but this embodiment does not specifically limit this.

[0123] Transceiver 203 is used for communication with other electronic devices. For example, if electronic device 200 is a terminal, transceiver 203 can be used to communicate with a network device or with another terminal device. As another example, if electronic device 200 is a network device, transceiver 203 can be used to communicate with a terminal or with another network device.

[0124] Optionally, transceiver 203 may include a receiver and a transmitter. Figure 3 (Not shown separately). The receiver is used to implement the receiving function, and the transmitter is used to implement the transmitting function.

[0125] Optionally, the transceiver 203 can be integrated with the processor 201, or it can exist independently and be connected via the interface circuit of the electronic device 200. Figure 3 (Not shown in the image) is coupled to processor 201, but this embodiment does not specifically limit this.

[0126] Understandable, Figure 3 The structure of the electronic device 200 shown does not constitute a limitation on the electronic device. Actual electronic devices may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0127] Furthermore, the technical effects of the electronic device 200 can be referred to the technical effects of the methods described in the above-described method embodiments, and will not be repeated here.

[0128] It should be understood that the processor in the embodiments of this application can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor, etc.

[0129] It should also be understood that the memory in the embodiments of this application can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDR SDRAM), enhanced synchronous DRAM (ESDRAM), synchronous linked DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0130] The above embodiments can be implemented, in whole or in part, by software, hardware (such as circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0131] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0132] In this application, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or multiple items. For example, at least one of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0133] It should be understood that in the various embodiments of this application, the sequence number of each process does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0134] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0135] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0136] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0137] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0138] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0139] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0140] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A composite energy storage optimization method for new energy access, characterized in that, Applied to new energy access points, the method includes: The vulnerability index of the new energy access point is obtained by assessing the vulnerability of the access point. The vulnerability index is used to characterize the degree of danger that the new energy access point poses to the power grid when connected to the grid. The new energy access point uses hash mapping for resource allocation. From the set of candidate composite energy storage units, L composite energy storage units that match the new energy access point are determined. L is an integer greater than 1. The value of L is positively correlated with the vulnerability index. The L composite energy storage units are used to electrically balance the net power fluctuation of the new energy access point to the power grid. The new energy access point requests the power grid to configure the L composite energy storage units for the new energy access point; The L composite energy storage units and the candidate set of composite energy storage units satisfy the following relationship: ； Wherein, StartIdx indicates the first composite energy storage unit among the L composite energy storage units, the L composite energy storage units being the L consecutive composite energy storage units starting from the first composite energy storage unit in the candidate composite energy storage unit set, HashID is the dynamic hash seed of the new energy access point, m is the sequence number of the new energy access point among at least one new energy access point in the same vulnerability level, the vulnerability index belongs to the vulnerability level, N is the total number of composite energy storage units in the candidate composite energy storage unit set, M is the maximum number of new energy access points allowed under the vulnerability level, Aid is the mutual assistance offset, and mod is the modulo operation. This is for rounding down.

2. The method according to claim 1, characterized in that, The vulnerability index of the new energy access point is obtained by assessing the vulnerability of the access point, including: The renewable energy access point determines the volatility component, short-circuit capacity ratio component, and voltage sensitivity component of the renewable energy access point within a preset time window. A larger volatility component indicates a greater impact of the renewable energy access point on the power grid during grid connection. A larger short-circuit capacity ratio indicates a weaker ability of the power grid to withstand impacts. A larger voltage sensitivity component indicates a higher likelihood of voltage exceedance at the grid connection point due to power fluctuations. The new energy access point determines the vulnerability index based on the volatility component, the short-circuit capacity ratio component, and the voltage sensitivity component.

3. The method according to claim 2, characterized in that, The volatility components satisfy the following relationship: ； Where Ifluc represents the volatility component, K is the K adoption times within the preset time window, p1 is the power generated by the new energy access point at the first adoption time among the K adoption times, p2 is the power generated by the new energy access point at the second adoption time among the K adoption times, and so on, pK is the power generated by the new energy access point at the Kth adoption time among the K adoption times, and Δp is the average power generated by the new energy access point at each of the K adoption times; The short-circuit capacity ratio components satisfy the following relationship: Iscr = Pinst / Ssc; Wherein, Pinst is the installed capacity of the new energy access point, and Ssc is the short-circuit capacity of the grid connection point where the new energy access point is connected to the power grid; The voltage sensitivity components satisfy the following relationship: Ivs=|∂Vi / ∂Pi|; Where Vi represents the voltage amplitude of the grid connection point, ∂Vi represents the change in the grid connection point when subjected to disturbance, Pi represents the electrical power generated by the new energy access point, and ∂Pi represents the disturbance in the electrical power generated by the new energy access point.

4. The method according to claim 1, characterized in that, The method further includes: Based on the profile of each energy storage unit, the new energy access point determines a set of candidate composite energy storage units that match the vulnerability index.

5. The method according to claim 4, characterized in that, Based on the profile of each energy storage unit, the new energy access point determines a set of candidate composite energy storage units that match the vulnerability index, including: The number of candidate composite energy storage units that match the vulnerability index for the new energy access point is N, where N is an integer greater than 1, and the number of candidate composite energy storage units is negatively correlated with the vulnerability index. The new energy access point determines the number of candidate composite energy storage units as the length of the sliding window; The new energy access point slides the sliding window with a preset step size. During the sliding process, the starting position of the sliding window is the first candidate composite energy storage unit among all candidate composite energy storage units of the power grid, and the ending position of the sliding window is the last candidate composite energy storage unit among all candidate composite energy storage units of the power grid. During the sliding process, the sliding window is located at T positions respectively. For the j-th position among the T positions, the new energy access point determines the matching degree between the profiles of the N candidate composite energy storage units covered by the sliding window at the j-th position and the new energy access point. When j traverses from 1 to T, T matching degrees are obtained. The new energy access point determines the highest matching degree from the T matching degrees, and determines the candidate composite energy storage unit covered by the sliding window at the position corresponding to the highest matching degree as the candidate composite energy storage unit set.

6. The method according to claim 5, characterized in that, The determination of the matching degree between the profiles of the N candidate composite energy storage units covered by the sliding window at the j-th position and the new energy access point includes: The new energy access point obtains the response capability profiles of each of the N candidate composite energy storage units. The response capability profiles include at least the following profile dimensions: response speed level, capacity margin, and health status. For each of the N candidate composite energy storage units: the new energy access point determines the single-dimensional matching degree of the candidate composite energy storage unit based on the difference between the actual value of each profile dimension of the candidate composite energy storage unit and the demand value of the new energy access point for each profile dimension, and finally obtains multiple single-dimensional matching degrees corresponding to multiple profile dimensions of the candidate composite energy storage unit; and the new energy access point performs a weighted summation of the multiple single-dimensional matching degrees to obtain the comprehensive matching degree of the candidate composite energy storage unit, wherein the weight of each of the multiple profile dimensions in the weighted summation is determined according to the vulnerability index; The new energy access point determines the matching degree by summing the comprehensive matching degrees of the N candidate composite energy storage units.

7. The method according to claim 1, characterized in that, HashID satisfies the following relationship: HashID = (A × ID + B) mod 65537; Where A is the preset first disturbance coefficient, B is the preset second disturbance coefficient, ID is the identifier of the new energy access point, and 65537 is the hash modulus in prime number form.

8. The method according to claim 1, characterized in that, When the new energy access point does not require the assistance of an additional composite energy storage unit, the value of Aid is 0. When the new energy access point requires the assistance of an additional composite energy storage unit, the value of Aid is a preset value that is neither 0 nor 0.

9. A composite energy storage optimization system for new energy access, characterized in that, The system includes a new energy access point, which is configured as follows: The vulnerability index of the new energy access point is obtained by assessing the vulnerability of the access point. The vulnerability index is used to characterize the degree of danger that the new energy access point poses to the power grid when connected to the grid. The new energy access point uses hash mapping for resource allocation. From the set of candidate composite energy storage units, L composite energy storage units that match the new energy access point are determined. L is an integer greater than 1. The value of L is positively correlated with the vulnerability index. The L composite energy storage units are used to electrically balance the net power fluctuation of the new energy access point to the power grid. The new energy access point requests the power grid to configure the L composite energy storage units for the new energy access point; The L composite energy storage units and the candidate set of composite energy storage units satisfy the following relationship: ； Wherein, StartIdx indicates the first composite energy storage unit among the L composite energy storage units, the L composite energy storage units being the L consecutive composite energy storage units starting from the first composite energy storage unit in the candidate composite energy storage unit set, HashID is the dynamic hash seed of the new energy access point, m is the sequence number of the new energy access point among at least one new energy access point in the same vulnerability level, the vulnerability index belongs to the vulnerability level, N is the total number of composite energy storage units in the candidate composite energy storage unit set, M is the maximum number of new energy access points allowed under the vulnerability level, Aid is the mutual assistance offset, and mod is the modulo operation. This is for rounding down.

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