Green power peak shifting energy storage scheduling method and system based on green power inventory and double-window reward

Abstract

The invention provides a green electricity peak shifting energy storage scheduling method and system based on green electricity inventory and double-window reward, and the method comprises the steps: recognizing a surplus window and a peak section window of renewable energy in a future scheduling period based on collected power system operation data and environment parameters; comprehensively evaluating key parameters of the system based on the operation data and the environment data, calculating a support coefficient of the characterization system for an energy storage adjustment demand degree, and updating a green electricity inventory state in a pre-constructed green electricity inventory account book according to an obtained energy source of the charging and discharging power, and performing energy consistency check to ensure that the green electricity power during discharging does not exceed the available amount of the current green electricity inventory, constructing a multi-objective optimization function based on a surplus window, a peak section window and a support coefficient, and solving the multi-objective optimization function to obtain the optimal charging and discharging power of the energy storage system. And amplitude limiting correction is carried out on the optimal charging and discharging power sequence based on the maximum available power of environmental capacity reduction obtained through environmental parameter calculation, and a final scheduling instruction is generated to be issued and executed.

Description

Green electricity inventory and dual-window incentive-based green electricity peak-shifting and energy storage dispatching methods and systems Technical Field

[0001] This invention relates to the field of power system operation and optimized dispatch, specifically to a green electricity inventory and dual-window incentive method and system for green electricity peak-shifting and energy storage dispatch. Background Technology

[0002] In recent years, with the rapid development of renewable energy sources such as photovoltaics and wind power in high-proportion new energy power grids in Tibet, the problems of power fluctuation, output randomness, and poor predictability of the system have become increasingly prominent. Typical operating characteristics are as follows: high output surplus at midday: photovoltaic power significantly exceeds load demand between 11:00 and 15:00, resulting in a large amount of curtailment; short-term shortage at night: wind and solar power output decreases after sunset, load rises rapidly, system frequency stability decreases, and a large amount of peak-shaving resources are needed; limited support capacity of interconnected systems: the long-chain transmission structure in the plateau region results in low equivalent short-circuit capacity and small interconnection strength K_inter; significant equipment derating in low-pressure and low-temperature environments: the available power P_max of energy storage systems is closely related to the environment, and capacity degradation in high-altitude areas can reach 20%-30%.

[0003] Traditional energy storage dispatch mainly relies on electricity price signals or AGC load tracking, which has the following shortcomings: First, the target deviates from the green attribute: existing methods are mostly aimed at peak shaving and valley filling or economic benefits, without explicitly constraining "green electricity priority use", which means that the discharge source during the evening peak period cannot be guaranteed to be green electricity stored during the day.

[0004] Second, there is a lack of energy consistency constraints: the discharge power of the energy storage system is not mathematically bound to the charging source, making it difficult to verify the green energy closed loop of "storage first, use later".

[0005] Third, weight adjustment is difficult in complex environments: factors such as weak interconnection, low inertia, and high altitude derating have a significant impact on the optimization of weights, and traditional multi-parameter manual tuning is difficult to adapt to dynamic environments.

[0006] Fourth, assessment and traceability are difficult: green certificate settlement and consumption assessment require traceable energy source links, and traditional scheduling methods lack a clear inventory accounting mechanism.

[0007] Therefore, there is an urgent need for an energy storage dispatching method that can achieve midday green energy replenishment and evening peak green energy consumption under complex power grid conditions, enabling traceability of energy sources and adaptive adjustment of optimization weights, thereby maximizing the proportion of green energy consumption and frequency stability under safety constraints. Summary of the Invention

[0008] To address the limitations of existing technologies in achieving midday green energy replenishment and evening peak green energy utilization, as well as the issues of traceability of energy sources and adaptive adjustment of optimization weights, this invention proposes a green energy peak-shifting energy storage scheduling method and system based on green energy inventory and dual-window rewards.

[0009] Firstly, a green electricity peak-shifting and energy storage scheduling method based on green electricity inventory and dual-window rewards is provided, comprising: identifying the surplus window and peak window of renewable energy in the future scheduling cycle based on collected power system operation data and environmental parameters; calculating the support coefficient characterizing the system's demand for energy storage regulation based on the operation data and environmental data, the key parameters including short-term power gap intensity, reciprocal of interconnection strength, reciprocal of equivalent inertia, and environmental derating degree; updating the green electricity inventory status in the pre-constructed green electricity inventory ledger according to the energy source of the acquired charging and discharging power, and performing energy consistency verification to ensure that the green electricity power during discharge does not exceed the current available green electricity inventory; constructing a multi-objective optimization function based on the surplus window, peak window, and support coefficient, solving the multi-objective optimization function to obtain the optimal charging and discharging power of the energy storage system, calculating the maximum available power for environmental derating based on the environmental parameters, using the maximum available power to limit and correct the optimal charging and discharging power sequence, generating a final scheduling command for execution, wherein the constraints of the multi-objective optimization function include green electricity inventory constraints, power constraints, capacity constraints, and ramping constraints.

[0010] Secondly, a green electricity inventory and dual-window incentive-based green electricity peak-shifting and energy storage dispatching system is provided, comprising: an identification module for identifying the surplus window and peak window of renewable energy in the future dispatching cycle based on collected power system operation data and environmental parameters; a calculation module for comprehensively evaluating key system parameters based on the operation data and environmental data to calculate the support coefficient characterizing the system's ability to meet energy storage regulation needs, wherein the key parameters include short-term power deficit intensity, reciprocal of interconnection strength, reciprocal of equivalent inertia, and environmental derating degree; and a verification module for updating the pre-built green electricity inventory account according to the energy source of the acquired charging and discharging power. The system monitors the green energy inventory status and performs energy consistency checks to ensure that the green energy power during discharge does not exceed the current available green energy inventory. The scheduling module is used to construct a multi-objective optimization function based on the surplus window, peak window, and support coefficient. The system solves the multi-objective optimization function to obtain the optimal charging and discharging power of the energy storage system. Based on the environmental parameters, the system calculates the maximum available power for environmental derating. The maximum available power is used to limit and correct the optimal charging and discharging power sequence, and the final scheduling command is generated and executed. The constraints of the multi-objective optimization function include green energy inventory constraints, power constraints, capacity constraints, and ramp constraints.

[0011] In another aspect, this application also provides an electronic device, including: at least one processor and a memory; the memory and the processor are connected via a bus; the memory is used to store one or more programs; when the one or more programs are executed by the at least one processor, a green electricity inventory and dual-window reward green electricity peak-shifting energy storage scheduling method as described above is implemented.

[0012] In another aspect, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed, implements a green electricity inventory and dual-window reward green electricity peak-shifting and energy storage scheduling method as described above.

[0013] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention provides a green electricity inventory and dual-window reward-based green electricity peak-shifting energy storage scheduling method and system. This method identifies the surplus window and peak window of renewable energy within future scheduling cycles based on collected power system operation data and environmental parameters. It calculates the support coefficient representing the system's demand for energy storage regulation based on comprehensive evaluation of key system parameters using operation and environmental data. Then, it updates the green electricity inventory status in a pre-constructed green electricity inventory ledger according to the energy source of the acquired charging and discharging power, and performs energy consistency verification to ensure that the green electricity power during discharge does not exceed the current available green electricity inventory. Furthermore, it constructs a multi-objective optimization function based on the surplus window, peak window, and support coefficient, solves the multi-objective optimization function to obtain the optimal charging and discharging power of the energy storage system, and performs amplitude limiting correction on the optimal charging and discharging power sequence based on the maximum available power for environmental derating calculated from environmental parameters. Finally, it generates and executes the final scheduling command, thereby realizing a midday green electricity charging and evening peak green electricity usage scheduling method under complex grid conditions. This achieves traceability of energy sources and adaptive adjustment of optimization weights, thereby maximizing the green electricity consumption ratio and frequency stability level under safety constraints.

[0014] Compared with the prior art, the present invention has the following beneficial effects: (1) It forms a closed loop of green electricity inventory, realizes the energy traceability and settlement verification of "storage first and use later"; (2) The dual-window incentive mechanism accurately corresponds to the midday surplus and evening peak gap, and improves the green electricity consumption and peak green proportion; (3) The support coefficient α realizes the unified adjustment of multiple factors and avoids multi-parameter tuning; (4) The plateau power grid has strong adaptability: through capacity reduction and inertia factor correction, it ensures stable operation in low temperature and low density environment; (5) Good compatibility: it can be incrementally deployed under the existing EMS / AGC framework; (6) It achieves measurable green dispatch target: the indicators include peak green electricity proportion, curtailment rate reduction rate, inventory consistency deviation, etc. Attached Figure Description

[0015] Figure 1 is a flowchart of the green electricity inventory and dual-window reward green electricity peak-shifting energy storage scheduling method of the present invention; Figure 2 is a structural schematic diagram of the green electricity inventory and dual-window reward green electricity peak-shifting energy storage scheduling system of the present invention; Figure 3 is a structural schematic diagram of an electronic device of the present invention. Detailed Implementation

[0016] The purpose of this invention is to address the shortcomings of existing technologies by providing an energy storage scheduling method based on green electricity inventory and dual-window rewards. Specifically, it includes the following technical means: (1) establishing a green electricity inventory model for the energy storage system to achieve mathematical consistency constraints on "green electricity source and discharge usage"; (2) designing a dual-window reward mechanism to incentivize "charging green electricity" during the midday surplus period and "using green electricity" during the evening peak period; (3) constructing a single-knob support coefficient α to normalize and map multi-dimensional factors (gap, interconnection strength, inertia, temperature, altitude) and automatically adjust the optimization weights; (4) forming a rolling optimization scheduling framework to achieve adaptive peak shifting and stable control of the energy storage system under complex grid conditions; and (5) achieving traceability and quantitative verification of green electricity usage through inventory constraints.

[0017] To better understand the present invention, the following description, in conjunction with the accompanying drawings and embodiments, will further illustrate the content of the present invention.

[0018] Example 1: A green electricity peak-shifting and energy storage dispatching method based on green electricity inventory and dual-window rewards, as shown in Figure 1, includes: Step 1: Based on the collected power system operation data and environmental parameters, identify the surplus window and peak window of renewable energy in the future dispatching cycle; Step 2: Based on the operation data and environmental data, comprehensively evaluate the key system parameters and calculate the support coefficient characterizing the system's demand for energy storage regulation; Step 3: Update the green electricity inventory status in the pre-constructed green electricity inventory ledger according to the energy source of the acquired charging and discharging power, and perform energy consistency verification to ensure that the green electricity power during discharge does not exceed the current available green electricity inventory; Step 4: Construct a multi-objective optimization function based on the surplus window, peak window, and support coefficient, solve the multi-objective optimization function to obtain the optimal charging and discharging power of the energy storage system, calculate the maximum available power for environmental derating based on the environmental parameters, use the maximum available power to limit and correct the optimal charging and discharging power sequence, and generate the final dispatching command for execution.

[0019] Key parameters include short-term power deficit strength, reciprocal of interconnection strength, reciprocal of equivalent inertia, and environmental derating degree; constraints of the multi-objective optimization function include green energy inventory constraints, power constraints, capacity constraints, and ramp-up constraints.

[0020] In this embodiment, during the identification of the surplus window and peak window of renewable energy in the future scheduling cycle based on the collected power system operation data and environmental data in step 1, the surplus window and peak window can be accurately identified by calculating the net surplus power and then using a threshold judgment method. Specifically, this includes: generating a renewable energy output prediction curve based on the solar irradiance prediction data in the environmental parameters combined with the capacity factor model of the photovoltaic power station; generating a load prediction curve using a time series prediction method based on the acquired historical load data and similar day analysis; correcting the renewable energy output prediction curve and the load prediction curve based on the operation data, and obtaining the net surplus power curve by subtracting the corrected load prediction curve from the corrected renewable energy output prediction curve; and identifying the surplus window and peak window using a preset threshold judgment method based on the net surplus power curve.

[0021] Among them, the surplus window is the set of consecutive periods in which the output of renewable energy exceeds the load demand, and the peak window is the set of consecutive periods in which the load demand exceeds the supply capacity of renewable energy.

[0022] In one specific embodiment, data acquisition and window identification include the following: At the beginning of each scheduling cycle, the system first collects necessary operational data from automated systems such as SCADA and EMS, including renewable energy output forecasts, load forecasts, real-time frequency, energy storage SOC status, ambient temperature, and altitude. Then, based on the collected forecast data, the system identifies energy surplus and deficit periods within future scheduling cycles by calculating the net surplus power curve. A surplus window is defined as the set of consecutive periods where renewable energy output exceeds load demand, and a peak window is defined as the set of consecutive periods where load demand exceeds renewable energy supply capacity. Window identification can be implemented using threshold judgment, sliding window methods, or adaptive clustering algorithms to ensure accurate capture of the system's energy surplus and deficit characteristics.

[0023] Furthermore, the dual-window mechanism includes: a spare window. The period during which photovoltaic output exceeds load; peak window During periods when the load exceeds the available capacity of renewable energy sources, the peak SOC target is updated in real time using a time-sliding window algorithm and re-identified and reset in each scheduling cycle. .

[0024] In this embodiment, during step 2, when calculating the support coefficient representing the system's ability to meet energy storage regulation requirements based on the collected operational and environmental data, multiple parameters are introduced as feature factors for normalization mapping to achieve weight self-adjustment optimization. Specifically, this includes: calculating the key parameters of the evaluation system based on the operational and environmental data; using the short-term power gap intensity, the reciprocal of interconnection intensity, the reciprocal of equivalent inertia, and the degree of environmental derating among the key parameters as feature factors; normalizing and weighting each feature factor; and obtaining the support coefficient representing the system's ability to meet energy storage regulation requirements through a saturated mapping function.

[0025] In one specific embodiment, the calculation of the support coefficient includes the following: comprehensively assessing the system's demand for energy storage support based on the current operating status of the system and environmental conditions. Support Coefficient The system is calculated by integrating multiple dimensional feature factors, including: short-term power gap intensity (reflecting system power balance pressure), reciprocal of interconnect strength (reflecting external support capability), reciprocal of equivalent inertia (reflecting frequency stability margin), and environmental derating degree (reflecting the actual usable capacity of the equipment). After normalization, each feature factor is weighted, summed, and processed by a saturated mapping function to obtain a support coefficient with values ​​ranging from [0,1]. This coefficient dynamically reflects the system's dependence on energy storage regulation and is used for adaptive adjustment of subsequent optimization weights.

[0026] Furthermore, the support coefficient It is obtained by normalizing and fusing multiple factors, and its calculation formula is shown below:

[0027] in, The support coefficient is dimensionless and ranges from [0,1], representing the degree of system demand for energy storage support; The short-term power gap intensity characterizes the maximum power imbalance within a short time window in the future, and is dimensionless. It is inversely proportional to the interconnection strength. ,in The interconnection strength coefficient characterizes the strength of the electrical connection with the external power grid; It is inversely proportional to the equivalent inertia. ,in The system's equivalent inertia constant is expressed in seconds (s). The degree of environmental derating characterizes the impact of environmental factors such as high altitude and low temperature on equipment capacity; it is dimensionless. Let be the weight coefficients of each factor, satisfying and ; For saturation mapping functions, such as the Sigmoid function: .

[0028] when When the value is close to 1, it indicates that the system is in a vulnerable state and requires enhanced energy storage support; when... When the value is close to 0, it indicates that the system is stable and the frequency of energy storage operation can be reduced.

[0029] Furthermore, the support coefficient Define the linkage between weights and support coefficients:

[0030] in, Weights are supported by frequency. As the evening peak discharge reward weight, As a reward weight for surplus discharge, For lifespan protection weight, Power smoothing weights.

[0031] When the system is in a vulnerable state (such as low interconnect strength or large frequency fluctuations), A high value increases the weight of frequency preservation and green support; when the system is stable, the frequency of operation is reduced, and the lifespan is extended.

[0032] In this embodiment, in step 3, the green electricity inventory status in the pre-built green electricity inventory ledger is updated according to the energy source of the obtained charging and discharging power, and energy consistency verification is performed to ensure that the green electricity power during discharge does not exceed the current available green electricity inventory. This is mainly achieved through energy conservation constraint verification, capacity boundary constraint verification, and power feasibility constraint verification. If any of the aforementioned verifications fails, a correction mechanism is triggered to adjust the charging and discharging plan or correct the inventory record.

[0033] In one specific embodiment, the green electricity inventory update and consistency verification includes the following: establishing a green electricity inventory ledger for the energy storage system to track the energy stock from renewable energy sources in real time. Within each scheduling step, the green electricity inventory value is updated based on the actual charging and discharging power and efficiency coefficient. During charging, if the energy comes from renewable energy, the inventory is increased after being converted according to the charging efficiency; during discharging, the green electricity inventory is consumed first, and deducted after being converted according to the discharging efficiency. The system simultaneously performs consistency verification to ensure that the green electricity discharge power at any time does not exceed the available inventory, and the total inventory does not exceed the physical capacity of the energy storage, thereby ensuring the physical feasibility of the energy and the traceability of its green attributes. If an inventory deviation or energy imbalance is detected, the system will trigger a correction mechanism or suspend reward items.

[0034] Furthermore, the green electricity inventory state equation is defined at time... Green electricity inventory (Unit: MWh), its evolution satisfies:

[0035] in, For a moment Green electricity inventory refers to the energy stock derived from renewable energy in the energy storage system, measured in MWh. For a moment Green electricity inventory, in MWh; For a moment Charging power from renewable energy sources, measured in MW; For a moment The discharge power released from the green energy inventory, in MW; The value represents the charging efficiency, which is dimensionless and ranges from [0.85 to 0.98], with a typical value of 0.95. Discharge efficiency, dimensionless, with a value range of [0.85, 0.98], and a typical value of 0.95; The scheduling step size is expressed in hours (h), with a typical value of 0.25h (15 minutes).

[0036] Furthermore, to ensure "storage first, use later", the following energy consistency and capacity constraints are imposed:

[0037] in, For a moment The discharge power released from the green energy inventory, in MW; For a moment The green electricity inventory, in MWh; The scheduling step size is expressed in hours (h). For a moment The total energy state of an energy storage system, expressed in MWh; This refers to the rated capacity of the energy storage system, expressed in MWh.

[0038] This constraint ensures that the green power output does not exceed the available inventory, while the inventory does not exceed the system's physical capacity.

[0039] Furthermore, to ensure the availability of green electricity during peak periods, before the start of peak periods... Apply pre-peak SOC constraints:

[0040] in, The energy storage state of charge is dimensionless and ranges from [0,1] at the moment before the start of the peak period. The time before the peak begins, i.e., the peak window. The starting time; The pre-peak SOC target value is dimensionless and ranges from [0.3, 0.9], determined jointly by load forecasting and inventory target.

[0041] This constraint ensures that energy storage has sufficient green electricity reserves before the evening peak, guaranteeing the green attributes of peak-hour power supply.

[0042] In this embodiment, after obtaining the surplus window, peak window and support coefficient based on the aforementioned steps, a multi-objective optimization function can be further constructed and solved to obtain the optimal charging and discharging power of the energy storage system. After further correction, the final scheduling command is obtained and issued for execution.

[0043] In one specific embodiment, the optimization function construction and power command generation include the following: Based on prediction information and the current state, a multi-objective optimization problem is constructed. The optimization objectives comprehensively consider system frequency stability, energy storage lifetime protection, power smoothing, and the peak-shifting effect of green electricity. A dual-window reward mechanism incentivizes energy storage to absorb renewable energy during surplus periods and to release green electricity during peak periods. Optimization weights are determined based on the support coefficient. Dynamic adjustment: When the system is highly vulnerable, increase the weight of frequency support and green energy dispatch; when the system is stable, increase the weight of lifetime protection. The optimization problem, under the conditions of power constraints, capacity constraints, ramp-up constraints, and green energy inventory constraints, is solved using quadratic programming or mixed-integer programming methods to obtain the optimal charging and discharging power sequence for future periods.

[0044] The optimized charging and discharging power reference values ​​are converted into control commands, along with green electricity percentage information. Before issuing the commands, the system adjusts the maximum available power based on the environmental derating model and performs limiting to prevent exceeding equipment capacity. Simultaneously, ramp-up constraints are applied to avoid power surges impacting the system. Upon receiving the commands, the energy storage station controller executes the charging and discharging actions via the PCS (Power Conversion System). When emergency situations such as frequency exceeding limits or voltage anomalies are detected, the system automatically switches to a safety mode, prioritizing frequency support or voltage regulation and suspending routine optimized scheduling.

[0045] At the end of each dispatch cycle, the system automatically compiles and calculates key operational indicators, including: peak-period green electricity ratio (reflecting the utilization rate of green energy during the evening peak period), green electricity absorption rate (reflecting the absorption effect of renewable energy), inventory consistency deviation (reflecting the accuracy of energy traceability), improvement in curtailment rate, and frequency compliance rate. These indicators are stored in a database for green dispatch assessment, green certificate trading settlement, and dispatch strategy optimization. The system regularly generates operational reports, supports energy source traceability queries, and provides quantitative assessment basis for the green transformation of the power grid.

[0046] Furthermore, the peak shifting optimization objective function (i.e., the aforementioned multi-objective optimization function) is:

[0047] in, To comprehensively optimize the objective function value, The frequency-supported weighting function, and the support coefficients Positive correlation The sum of squares of the system frequency deviations. , For the system's real-time frequency, The system's rated frequency, Frequency deviation, in Hz 2 , For lifetime protection weighting function, and support coefficient negative correlation The cost function is the energy storage lifetime loss function, which is related to charge / discharge power and cycle depth. The power smoothing weighting function, and the support coefficient negative correlation The sum of squares of the power change rate, , Let be the charge / discharge power at time t. The charge / discharge power at time t-1 The change in power between adjacent time points, in MW. 2 , The evening peak discharge reward weight, and the support coefficient Positive correlation This is a set of peak time periods, representing the peak load time. For a moment The green electricity discharge power, measured in MW. Weighting of surplus charging rewards, and support coefficient. Positive correlation This is a set of surplus times, representing periods of surplus for renewable energy. For a moment The green electricity charging power is measured in MW.

[0048] The last two negative values ​​represent reward items, used to incentivize "green use during evening peak hours" and "greening during midday hours".

[0049] Furthermore, in the environmental derating model under high altitude and low temperature conditions, the maximum available power of energy storage is... Determined by air density and temperature:

[0050] in, The actual maximum available power under environmental conditions, in MW; Rated power of the energy storage system, in MW; Altitude, in meters (m). This refers to the ambient temperature, expressed in °C. The air density reduction factor is dimensionless and ranges from [0.7, 1.0]. altitude Air density at that location, in kg / m³ 3 ; is the temperature derating factor, dimensionless, with a value range of [0.8, 1.0].

[0051] air density It can be calculated using the International Standard Atmospheric Formula:

[0052] in, The air density at standard sea level, kg / m 3 ; For temperature lapse rate, K / m; Standard sea level temperature, K; It is the acceleration due to gravity. m / s 2 ; The molar mass of dry air, kg / mol; This is the universal gas constant. J / (mol·K).

[0053] Example 2: The green electricity peak shifting and energy storage scheduling method of the present invention is described in detail below with a specific embodiment. This embodiment is applicable to main grid power station scenarios with large-scale photovoltaic output, especially photovoltaic-energy storage integrated power stations in high-altitude areas.

[0054] Step S1: Data Acquisition and Window Identification. Before the start of each scheduling cycle, the main dispatch station collects necessary operational data from each subsystem through standardized interfaces. Data acquisition includes three levels: First, it obtains solar irradiance prediction data for the next 24 hours from the weather forecast system, and combines this data with the photovoltaic power plant's capacity factor model to generate a renewable energy output prediction curve. The prediction model takes into account factors such as temperature correction, shading, and component aging to ensure prediction accuracy.

[0055] Secondly, based on historical load data and similar day analysis, a time series forecasting method is used to generate load forecast curves. The prediction model comprehensively considers seasonal characteristics, temperature effects, and holiday factors to improve prediction accuracy.

[0056] Finally, real-time acquisition of system operating status parameters, including the current frequency. Energy storage SOC status Ambient temperature Altitude wait.

[0057] Based on the above data, the net residual power curve is calculated using the following formula:

[0058] In the formula: For a moment The net surplus power, with positive values ​​indicating surplus and negative values ​​indicating deficit, is expressed in MW. For a moment The projected photovoltaic power output is expressed in MW. For a moment The load forecast is in MW.

[0059] The recognition window is determined by a threshold: when When marked as surplus windows ;when At that time, it is marked as a peak segment window. .

[0060] in and The threshold for window identification can be dynamically adjusted according to system characteristics, with a typical value of 5%-10% of the rated power.

[0061] Step S2: Calculate the support coefficient. The calculation process is divided into three stages: factor extraction, normalization, and fusion.

[0062] In the factor extraction stage, four key features are calculated: short-time power gap intensity. This was obtained by analyzing the maximum power gap within a short future time window:

[0063] In the formula: The short-time power shortage intensity is dimensionless and ranges from [0,1]. The current moment; For short-term evaluation, the typical value is 1-2 hours; For a moment Net surplus power, in MW; This is the system's rated power reference value, in MW.

[0064] Inverse ratio of interconnect strength Reflects the strength of the electrical connection between the system and the external power grid:

[0065] In the formula: It is inversely proportional to the interconnection strength and is dimensionless; The interconnection strength coefficient characterizes the degree of coupling between the system and the external power grid; The system's equivalent impedance is expressed in Ω. This is the impedance of the tie line, in Ω.

[0066] Inverse ratio of equivalent inertia Evaluate the frequency stability margin of the system:

[0067] In the formula: It is the inverse ratio of equivalent inertia, with units of s. -1 ; The system's equivalent inertia constant is expressed in seconds (s). The system's rated frequency, Hz; The rotational kinetic energy of the system is expressed in MJ.

[0068] Environmental degradation Considering the impact of high altitude and low temperature on equipment capacity:

[0069] In the formula: The degree of environmental degradation is dimensionless and ranges from [0, 0.4]. The air density reduction factor; This is the temperature derating factor.

[0070] In the normalization stage, each factor is mapped to the [0,1] interval:

[0071] In the formula: For the normalized first One factor value, dimensionless, with a value range of [0,1]; For the first The original values ​​of each factor; For the first The minimum value of each factor; For the first The maximum value of each factor.

[0072] During the fusion phase, the support coefficients are obtained through weighted summation and saturation mapping:

[0073] In the formula: The support coefficient is dimensionless and ranges from [0,1]. For the first The weights of each factor satisfy the following conditions: ; For the normalized first One factor value; For the Sigmoid saturation mapping function: , For input variables.

[0074] Step S3: Green Energy Inventory Update and Consistency Verification. Green energy inventory management follows the principle of energy conservation and establishes a complete energy tracking mechanism.

[0075] During charging, the system determines the source of the charging energy. If the charging power comes from renewable energy (determined through power balance analysis), the green electricity inventory is updated.

[0076] In the formula: The green electricity inventory after charging, in MWh; Current green electricity inventory, in MWh; Charging efficiency, dimensionless, typical value 0.95; Charging power from renewable energy sources, measured in MW; The scheduling step size is expressed in hours (h).

[0077] During the discharge process, the green energy reserve is consumed first, following the "first-in, first-out" principle:

[0078] In the formula: The green electricity inventory after discharge, in MWh; Current green electricity inventory, in MWh; Green electricity discharge power, measured in MW; Discharge efficiency, dimensionless, typical value 0.95; The scheduling step size is expressed in hours (h).

[0079] Consistency checks ensure physical feasibility, including three layers of constraints: energy conservation constraints;

[0080] In the formula: This is a green electricity inventory, in MWh. This refers to non-green electricity stock, measured in MWh. This represents the total energy of the energy storage system, expressed in MWh.

[0081] Capacity boundary constraints:

[0082] In the formula: This is a green electricity inventory, in MWh. The total energy of the energy storage system is expressed in MWh. This refers to the rated capacity of the energy storage system, expressed in MWh.

[0083] Power feasibility constraints:

[0084] In the formula: Green electricity discharge power, measured in MW; Current green electricity inventory, in MWh; Discharge efficiency, dimensionless; The scheduling step size is expressed in hours (h). Maximum discharge power limit, in MW.

[0085] If a constraint violation is detected, the system triggers a correction mechanism to adjust the charge / discharge plan or correct inventory records.

[0086] Step S4: Rolling optimization solution. The optimization problem is constructed into a multi-objective quadratic programming form, in the prediction time domain. The optimal charging and discharging strategy is sought internally. The objective function consists of five parts, with the weights of each part dynamically adjusted based on the support coefficients: The frequency support term minimizes the system frequency deviation.

[0087] In the formula: To support the target item for frequency; Frequency-supported weights, and support coefficients Positive correlation; To predict the time domain length, a typical step size is 24-96. For a moment The predicted frequency, in Hz; The system's rated frequency, Hz.

[0088] Lifetime protection measures, taking into account depth of charge / discharge and cycle count:

[0089] In the formula: For lifetime loss target items; Lifetime protection weight, and support coefficient Negative correlation; For a moment The charging and discharging power, measured in MW; This represents the change in power, measured in MW. This is the power amplitude loss coefficient, with a typical value of 0.001-0.01; This is the power variation loss coefficient, with a typical value of 0.002-0.02.

[0090] Power smoothing term, reducing power fluctuations:

[0091] In the formula: For power smoothing objective terms; Power smoothing weights, and support coefficients Negative correlation; For a moment The charging and discharging power is expressed in MW.

[0092] Evening peak support reward item, incentivizing peak discharge:

[0093] In the formula: For evening peak support rewards, negative values ​​indicate rewards; Weighting of evening peak rewards, and support coefficient. Positive correlation; For the collection of peak time periods; For a moment The green electricity discharge power is expressed in MW.

[0094] Surplus absorption rewards incentivize learning during spare time:

[0095] In the formula: To absorb the surplus as a reward item, a negative value represents the reward; For excess absorption weight, and support coefficient Positive correlation; To gather during spare time; For a moment The green electricity charging power is measured in MW.

[0096] The constraints include power constraints, capacity constraints, ramp-up constraints, and green energy inventory constraints. The optimization problem is solved using the interior-point method or the efficient set method to obtain the optimal charge-discharge power sequence. .

[0097] Step S5: Power command generation and limiting execution. Based on the optimization results, actual control commands are generated, taking into account equipment physical limitations and safety constraints.

[0098] First, the maximum available power is corrected based on the environmental derating model:

[0099] In the formula: This represents the maximum usable power under actual environmental conditions, expressed in MW. Rated power of the energy storage system, in MW; The air density reduction factor is dimensionless. This is the temperature derating factor, which is dimensionless.

[0100] Then, power limiting is performed:

[0101] In the formula: This is the power command value after limiting, in MW; The optimal power value obtained from the optimization solution is expressed in MW. This represents the actual maximum available power, expressed in MW.

[0102] Apply ramp rate constraints to prevent power surges:

[0103] In the formula: For a moment The power command is in MW; The power command from the previous moment, in MW; The maximum gradeability is expressed in MW / min, with a typical value of 10%-20% of the rated power per minute. The control period is expressed in minutes.

[0104] The generated instructions include power values ​​and green electricity percentage labels. The instruction set is as follows:

[0105] In the formula: This is the power command value, in MW; This is a label for the percentage of green electricity, dimensionless, with a value range of [0,1]. Green electricity discharge power, measured in MW; This represents the total discharge power, expressed in MW.

[0106] The instructions are issued to the energy storage station controller for execution via the IEC60870-5-104 protocol.

[0107] Step S6: Operational Indicator Statistics and Reconciliation. During the operation of the system, key performance indicators are continuously collected to evaluate scheduling effectiveness and support decision optimization.

[0108] The percentage of green electricity generated during peak periods reflects the contribution of green energy during critical periods:

[0109] In the formula: The percentage of green electricity generated during peak periods is dimensionless and ranges from [0,1]. For the collection of peak time periods; For a moment The discharge power released from the green energy inventory, in MW; For a moment Total discharge power, expressed in MW.

[0110] Green electricity absorption rate assesses the effectiveness of renewable energy absorption:

[0111] In the formula: The green energy consumption rate is dimensionless and ranges from [0,1]. For a moment The green electricity absorbed by energy storage, measured in MW; For a moment Available photovoltaic power, in MW; integration interval is the statistical period (e.g., one day or one month).

[0112] Inventory consistency deviation test for energy tracking accuracy:

[0113] In the formula: The inventory consistency deviation rate is dimensionless, and its typical value should be less than 0.05. The green energy inventory value is calculated using the equation of state, in MWh. The actual measured or estimated green energy inventory value is expressed in MWh. This refers to the rated capacity of the energy storage system, expressed in MWh.

[0114] These metrics are stored regularly and reports are generated to support green scheduling assessments and operational optimization.

[0115] Example 3: The following example of the independent operation of a county-level microgrid will be used to describe in detail the green electricity peak shifting and energy storage scheduling method of the present invention. This example is applicable to county-level microgrid scenarios with weak interconnection capabilities and unstable communication.

[0116] Step S1: Local Data Acquisition and Window Recognition. The microgrid controller acquires data based on local measurement devices, without relying on an external dispatch system. A simplified window recognition algorithm is adopted, using a moving average method to smooth the power curve and improve robustness under poor data quality conditions. Window recognition employs an adaptive threshold, dynamically adjusting recognition parameters based on historical statistical characteristics.

[0117] Step S2: Adaptive Support Coefficient Adjustment. When an interruption in communication with the main network is detected, the controller automatically increases the support coefficient. To meet emergency requirements, frequency support is enhanced. The support coefficient adjustment employs piecewise linear mapping for smooth switching between different operating modes. Considering the unique low inertia characteristics of microgrids, a virtual inertia compensation term is added.

[0118] Step S3: Simplify Inventory Management. A simplified inventory accounting method is adopted, with energy statistics performed hourly. Under communication constraints, inventory updates rely on local metering and are periodically synchronized and corrected with the remote system. An inventory fault tolerance mechanism is set up to allow a certain range of deviations.

[0119] Step S4: The local optimization control algorithm employs a rule-based simplification strategy to reduce computational complexity. A charging / discharging rule table is formulated based on the current SOC and prediction window. When computational resources are limited, a piecewise linearization method is used for approximate solution.

[0120] Step S5: Set up multiple security modes, automatically switching based on system status. Normal mode performs full optimized scheduling; degraded mode retains only basic peak-shaving functions; emergency mode prioritizes frequency stability. Mode switching uses hysteresis logic to avoid frequent switching.

[0121] Step S6: Offline data caching and synchronization of critical operational data with local caching, followed by batch uploading after communication is restored. A timestamp mechanism is used to ensure data consistency. Resume interrupted uploads to guarantee data integrity.

[0122] Example 4: The following example of multi-site collaborative aggregation operation will be used to describe in detail the green electricity peak shifting and energy storage scheduling method of the present invention. This example is applicable to regional scheduling scenarios in which multiple energy storage sites participate in collaboration.

[0123] Steps S1-S2: The hierarchical coordination architecture adopts a hierarchical control architecture. The upper-level coordination center is responsible for overall goal decomposition and resource allocation, while each lower-level site performs local optimization. The coordination center formulates differentiated scheduling strategies based on the capacity, location, and network constraints of each site. Information exchange adopts a publish-subscribe model to improve system scalability.

[0124] Steps S3-S4: Distributed Inventory Management and Optimization. Each station maintains an independent green electricity inventory ledger and reports to the coordination center periodically. The coordination center maintains a global inventory view, detecting and correcting inventory inconsistencies. Optimization employs a distributed algorithm, with each station making decisions based on local information and global guidance. A consensus protocol ensures the convergence of distributed optimization.

[0125] Steps S5-S6: The Aggregated Response and Effect Evaluation Coordination Center aggregates the response capabilities of each station to form regional-level regulatory resources. The output allocation of each station is dynamically adjusted based on real-time deviations. A station contribution evaluation mechanism is established for benefit distribution. Overall regional indicators are statistically analyzed to assess the collaborative effect.

[0126] The present invention has the following beneficial effects: The method of the present invention can be directly embedded into the existing power grid dispatch automation system and has the following industrial application characteristics: (1) Strong compatibility: It can be implemented through software expansion on the existing EMS, AGC, and SCADA platforms without adding new hardware equipment; (2) Good scalability: It is suitable for multi-level operation structures such as centralized energy storage, microgrids and regional aggregation; (3) Strong environmental adaptability: Through the derating factor and The linkage mechanism can work stably in high-altitude, low-temperature, and weakly interconnected power grids; (4) Outstanding management value: the green electricity inventory mechanism provides a quantitative basis for green certificate verification and green dispatch assessment; (5) Safe and reliable: it can automatically degrade to safe mode under abnormal communication or prediction conditions to ensure the safe operation of the system.

[0127] Therefore, the present invention has significant engineering feasibility and socio-economic benefits.

[0128] Example 5: Based on the same inventive concept, this invention also provides a green electricity inventory and dual-window incentive-based green electricity peak-shifting and energy storage dispatching system, as shown in Figure 2. It includes: an identification module, used to identify the surplus window and peak window of renewable energy within a future dispatching cycle based on collected power system operation data and environmental parameters; a calculation module, used to comprehensively evaluate key system parameters based on the operation data and environmental data to calculate a support coefficient characterizing the system's ability to meet energy storage regulation needs, the key parameters including short-term power deficit intensity, reciprocal of interconnection strength, reciprocal of equivalent inertia, and environmental derating degree; and a verification module, used to update the energy source based on the acquired charging and discharging power. The system establishes a pre-built green energy inventory ledger with green energy inventory status and performs energy consistency checks to ensure that the green energy power during discharge does not exceed the current available green energy inventory. A scheduling module is used to construct a multi-objective optimization function based on the surplus window, peak window, and support coefficient. The module solves the multi-objective optimization function to obtain the optimal charging and discharging power of the energy storage system. It also calculates the maximum available power for environmental derating based on the environmental parameters, uses the maximum available power to limit and correct the optimal charging and discharging power sequence, and generates a final scheduling command for execution. The constraints of the multi-objective optimization function include green energy inventory constraints, power constraints, capacity constraints, and ramp constraints.

[0129] Preferably, the identification module identifies the surplus window and peak window of renewable energy within a future scheduling cycle, including: generating a renewable energy output prediction curve based on solar irradiance prediction data from the environmental parameters combined with a photovoltaic power plant capacity factor model; generating a load prediction curve using a time series prediction method based on acquired historical load data and similar day analysis; correcting the renewable energy output prediction curve and the load prediction curve based on the operating data, and obtaining a net surplus power curve by subtracting the corrected load prediction curve from the corrected renewable energy output prediction curve; and identifying the surplus window and peak window using a preset threshold judgment method based on the net surplus power curve, wherein the surplus window is a set of continuous periods where renewable energy output exceeds load demand, and the peak window is a set of continuous periods where load demand exceeds renewable energy supply capacity.

[0130] Preferably, the calculation module is further configured to: calculate key parameters of the evaluation system based on the operating data and the environmental data; use the short-time power gap intensity, the reciprocal of the interconnection intensity, the reciprocal of the equivalent inertia, and the degree of environmental derating among the key parameters as characteristic factors; normalize and weight the characteristic factors, and obtain the support coefficient characterizing the system's ability to meet energy storage regulation requirements through a saturated mapping function.

[0131] Preferably, the short-time power gap intensity, a key parameter in the calculation module, is obtained through the following calculation formula:

[0132] in, This represents the short-time power shortage strength. The current moment; For short-term evaluation window length; For a moment Net residual power; The system rated power reference value; the interconnection strength inverse ratio, a key parameter in the calculation module, is obtained through the following calculation formula:

[0133] in, It is inversely proportional to the interconnection strength; Interconnection strength coefficient; System equivalent impedance; The tie line impedance; the equivalent inertia ratio in the key parameters of the calculation module is obtained through the following formula:

[0134] in, It is inversely proportional to the equivalent inertia; The system's equivalent inertia constant; The system's rated frequency; The system's rotational kinetic energy; the environmental degradation degree among the key parameters in the calculation module is obtained through the following calculation formula:

[0135] in, To reduce the degree of environmental degradation; Air density reduction factor; Temperature derating factor.

[0136] Preferably, the verification module updates the green electricity inventory after determining that the charging power comes from renewable energy based on the energy source of the acquired charging and discharging power:

[0137] in, For the storage of green electricity after charging; Current green electricity inventory; For charging efficiency; Charging power from renewable energy sources; The scheduling step size; the discharge consumption of the green electricity inventory status in the green electricity inventory ledger in the verification module follows the following calculation formula:

[0138] in, For storing green electricity after discharge; For green electricity inventory; For green electricity discharge power; For discharge efficiency; Scheduling step size.

[0139] Preferably, the energy consistency check performed in the verification module includes energy conservation constraints, capacity boundary constraints, and power feasibility constraints. If any one of the checks fails, a correction mechanism is triggered to adjust the charge / discharge plan or correct inventory records. The energy conservation constraints follow the following calculation formula:

[0140] in, For green electricity inventory; Non-green energy reserves; The total energy of the energy storage system; the capacity boundary constraints follow the following calculation formula:

[0141] in, The rated capacity of the energy storage system; the power feasibility constraint satisfies the following calculation formula:

[0142] in, For green electricity discharge power; Discharge efficiency; Scheduling step size; This is the maximum discharge power limit.

[0143] Preferably, the objective optimization function in the scheduling module is obtained by the following formula:

[0144] in, To comprehensively optimize the objective function value; Frequency-supported weighting function; This is the sum of squares of the system frequency deviations; For lifetime protection weighting function; The energy storage lifetime loss cost function; The power smoothing weighting function; This is the sum of squares of the power change rates; Weighting for evening peak discharge rewards; For the collection of peak time periods; For a moment The green electricity discharge power; Weighting of rewards for surplus charging; To gather during spare time; For a moment The green electricity charging power.

[0145] As shown in Figure 3, Embodiment 6 of the present invention also provides an electronic device, which may be a computer device, a microcontroller device, a smart mobile device, etc. The electronic device in this embodiment may include a processor, a memory, a transceiver component, etc. The memory, processor, and transceiver component are connected via a bus; the memory can be used to store executable programs, and an exemplary executable program may include instructions; the processor is used to execute the instructions stored in the memory. The memory can also be used to store data, which can be accessed and / or modified when instructions are executed.

[0146] The processor may be a central processing unit (CPU), or it may 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. It is the computing core and control core of the terminal, and it is suitable for implementing one or more instructions. Specifically, it is suitable for loading and executing one or more instructions in the storage medium to realize the corresponding method flow or corresponding function, so as to realize the steps of the green electricity inventory and dual-window reward green electricity peak shifting and energy storage scheduling method in the above embodiment.

[0147] Example 7, based on the same inventive concept, also provides a readable storage medium, specifically an electronic device readable storage medium (Memory). This readable storage medium is a memory device within an electronic device, used to store programs and data. It is understood that the storage medium here can include both built-in storage media within the electronic device and extended storage media supported by the electronic device. The storage medium provides storage space, which stores the terminal's operating system. Furthermore, this storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more executable programs (including program code). It should be noted that the storage medium here can be high-speed RAM or non-volatile memory, such as at least one disk storage device. Loading and executing one or more instructions stored in the storage medium by the processor can implement the steps of the green energy inventory and dual-window reward green energy peak-shifting and storage scheduling method described in the above embodiments.

[0148] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0149] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in one or more blocks of the flowchart illustrations and / or one or more blocks of the block diagrams.

[0150] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means that implement the functions specified in one or more flowcharts and / or one or more block diagrams.

[0151] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, such that the instructions, which execute on the computer or other programmable apparatus, provide steps for implementing the functions specified in one or more flowcharts and / or one or more block diagrams.

[0152] The above are merely embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention are included within the scope of the claims of the present invention pending approval.

Claims

1. A method for scheduling green electricity peak-shifting and energy storage based on green electricity inventory and dual-window rewards, characterized in that, include: Based on the collected power system operation data and environmental parameters, the surplus window and peak window of renewable energy in the future dispatch cycle are identified. Based on the operational data and environmental data, a comprehensive evaluation of key system parameters is performed to calculate the support coefficient characterizing the system's ability to meet energy storage regulation needs. These key parameters include short-term power deficit intensity, reciprocal of interconnection strength, reciprocal of equivalent inertia, and environmental derating. The green energy inventory status in the pre-built green energy inventory ledger is updated according to the energy source of the obtained charging and discharging power, and an energy consistency check is performed to ensure that the green energy power during discharge does not exceed the current available green energy inventory. A multi-objective optimization function is constructed based on the surplus window, peak window, and support coefficient. The optimal charging and discharging power of the energy storage system is obtained by solving the multi-objective optimization function. The maximum available power for environmental derating is calculated based on the environmental parameters. The maximum available power is used to limit and correct the optimal charging and discharging power sequence, and the final scheduling command is generated and executed. The constraints of the multi-objective optimization function include green energy inventory constraints, power constraints, capacity constraints, and ramping constraints.

2. The method according to claim 1, characterized in that, The process of identifying the surplus window and peak window of renewable energy within future scheduling cycles includes: generating a renewable energy output prediction curve based on solar irradiance prediction data from the environmental parameters combined with a photovoltaic power plant capacity factor model; generating a load prediction curve using a time series forecasting method based on acquired historical load data and similar day analysis; correcting the renewable energy output prediction curve and the load prediction curve based on the operating data, and obtaining a net surplus power curve by subtracting the corrected load prediction curve from the corrected renewable energy output prediction curve; and identifying the surplus window and peak window using a preset threshold judgment method based on the net surplus power curve. The surplus window is a set of continuous periods where renewable energy output exceeds load demand, and the peak window is a set of continuous periods where load demand exceeds renewable energy supply capacity.

3. The method according to claim 1, characterized in that, The step of calculating the support coefficient of the system to meet the energy storage regulation demand based on the operational data and environmental data includes: calculating the key parameters of the evaluation system based on the operational data and environmental data; using the short-term power gap intensity, the reciprocal of the interconnection intensity, the reciprocal of the equivalent inertia, and the degree of environmental derating among the key parameters as feature factors; normalizing and weighting each feature factor, and obtaining the support coefficient of the system to meet the energy storage regulation demand through a saturated mapping function.

4. The method according to claim 3, characterized in that, The short-time power gap strength in the key parameter is obtained by the following calculation formula: in, This represents the short-term power shortage strength. The current moment; For short-term evaluation window length; For a moment Net residual power; The system rated power reference value; the inverse ratio of interconnection strength in the key parameters is obtained by the following calculation formula: in, It is inversely proportional to the interconnection strength; Interconnection strength coefficient; System equivalent impedance; The tie line impedance; the equivalent inertia ratio in the key parameters is obtained through the following calculation formula: in, It is inversely proportional to the equivalent inertia; The system's equivalent inertia constant; The system's rated frequency; The system's rotational kinetic energy; the environmental derating factor among the key parameters is obtained through the following calculation formula: in, To reduce the degree of environmental degradation; Air density reduction factor; Temperature derating factor.

5. The method according to claim 1, characterized in that, After determining that the charging power comes from renewable energy sources based on the energy source of the acquired charging and discharging power, the green electricity inventory is updated using the following formula: in, For the storage of green electricity after charging; Current green electricity inventory; For charging efficiency; Charging power from renewable energy sources; The scheduling step size; the discharge consumption of the green electricity inventory status in the green electricity inventory ledger follows the following calculation formula: in, For storing green electricity after discharge; For green electricity inventory; For green electricity discharge power; For discharge efficiency; Scheduling step size.

6. The method according to claim 1, characterized in that, The energy consistency check includes energy conservation constraints, capacity boundary constraints, and power feasibility constraints. If any check fails, a correction mechanism is triggered to adjust the charge / discharge plan or correct inventory records. The energy conservation constraints follow the following calculation formula: in, For green electricity inventory; Non-green energy reserves; The total energy of the energy storage system; the capacity boundary constraints follow the following calculation formula: in, The rated capacity of the energy storage system; the power feasibility constraint satisfies the following calculation formula: in, For green electricity discharge power; Discharge efficiency; Scheduling step size; This is the maximum discharge power limit.

7. The method according to claim 1, characterized in that, The objective optimization function is obtained through the following formula: in, To comprehensively optimize the objective function value, The frequency-supported weighting function, The sum of squares of the system frequency deviations. For lifetime protection weighting function, The energy storage lifetime loss cost function, For power smoothing weighting function, The sum of squares of the power change rate, As the evening peak discharge reward weight, For the collection of peak time periods, For a moment The green electricity discharge power, Weighting of surplus charging rewards To gather during spare time, For a moment The green electricity charging power.

8. A green electricity inventory and dual-window reward green electricity peak-shifting and energy storage dispatching system, characterized in that, include: The identification module is used to identify the surplus window and peak window of renewable energy in future dispatch cycles based on the collected power system operation data and environmental parameters. The calculation module is used to comprehensively evaluate the key parameters of the system based on the operating data and the environmental data, calculate the support coefficient of the system to meet the energy storage regulation demand, and the key parameters include the short-term power gap intensity, the reciprocal of the interconnection intensity, the reciprocal of the equivalent inertia, and the degree of environmental derating; the verification module is used to update the green electricity inventory status in the pre-built green electricity inventory ledger according to the energy source of the obtained charging and discharging power, and perform energy consistency verification to ensure that the green electricity power during discharge does not exceed the current available green electricity inventory. The scheduling module is used to construct a multi-objective optimization function based on the surplus window, peak window and support coefficient, solve the multi-objective optimization function to obtain the optimal charging and discharging power of the energy storage system, calculate the maximum available power for environmental derating based on the environmental parameters, use the maximum available power to limit and correct the optimal charging and discharging power sequence, generate the final scheduling command and issue it for execution. The constraints of the multi-objective optimization function include green energy inventory constraints, power constraints, capacity constraints and ramping constraints.

9. An electronic device, characterized in that, include: At least one processor and memory; The memory and the processor are connected via a bus; the memory is used to store one or more programs. When the one or more programs are executed by the at least one processor, the green electricity peak-shifting and energy storage scheduling method of any one of claims 1 to 7 is implemented.

10. A readable storage medium, characterized in that, It contains an execution program, which, when executed, implements the green electricity inventory and dual-window reward green electricity peak-shifting energy storage scheduling method as described in any one of claims 1 to 7.

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