An optical storage system asymmetric scheduling method, system, terminal and storage medium

Abstract

The application discloses the technical field of a kind of optical storage system asymmetric scheduling method, and a kind of optical storage system asymmetric scheduling method, system, terminal and storage medium of method, to solve the problem that existing technology prediction dependence is too strong, scheduling behavior homogenization and high computational complexity, there is insufficient robustness, difficult to meet the problem of minute-level real-time control demand.It includes obtaining historical time-of-use electricity price data, and constructing standard daily electricity price waveform template according to the historical time-of-use electricity price data;Real-time acquisition of current time electricity price, according to the standard daily electricity price waveform template, the phase angle of the current time electricity price in the standard daily electricity price waveform template is obtained;The application can get rid of prediction dependence, and the charging and discharging instruction can be generated by the current time electricity price and the recommended action, which greatly reduces the system deployment threshold and operation and maintenance cost;And the amount of calculation is very small, without high-performance edge computing device, applicable to user-side energy storage and commercial optical storage system lightweight, low-cost, high-robustness scheduling control.

Description

Technical Field

[0001] This invention relates to an asymmetric scheduling method, system, terminal, and storage medium for a photovoltaic-storage system, belonging to the field of energy storage system technology. Background Technology

[0002] With the rapid growth of distributed photovoltaic (PV) installed capacity, energy storage systems have become a key means to enhance the absorption capacity of new energy sources and realize peak-valley arbitrage. Existing PV-energy storage dispatch technologies are mainly divided into three categories: fixed-rule dispatch, prediction-based optimization dispatch, and robust optimization dispatch. However, in practical applications, they still face the following common technical bottlenecks: excessive reliance on prediction leads to insufficient robustness; inherent errors in PV and load forecasting cause actual dispatch commands to deviate significantly from the optimal trajectory; homogenized dispatch behavior exacerbates grid impact, with numerous energy storage systems synchronously responding to time-of-use pricing to create new load peaks, while simultaneously compressing unit arbitrage space; lack of user-grid value synergy, where users provide peak-shaving and valley-filling services to the grid through energy storage arbitrage but cannot share the resulting grid-side benefits; difficulty in dynamically balancing multiple objectives, with maximizing economic benefits contradicting extending battery life, and existing fixed-weight or static optimization methods struggling to achieve refined trade-offs; and high computational complexity of traditional optimization methods, making it difficult to meet minute-level real-time control requirements.

[0003] As can be seen from the above, existing photovoltaic and energy storage systems rely on photovoltaic and load forecasting for scheduling. The forecasting dependence is too strong, the scheduling behavior is homogeneous and the computational complexity is high, resulting in insufficient robustness and difficulty in meeting the requirements of minute-level real-time control. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies and provide an asymmetric scheduling method, system, terminal, and storage medium for photovoltaic-storage systems. This method eliminates the dependence on prediction and does not require any load forecasting or photovoltaic prediction models. It can generate charging and discharging instructions based on the current electricity price and recommended actions, significantly reducing the system deployment threshold and operation and maintenance costs. It also solves the problem of inaccurate scheduling due to unreliable predictions in certain scenarios. Furthermore, this invention requires minimal computation, does not require high-performance edge computing equipment, and can run in real time on a low-cost MCU (microcontroller unit). It is suitable for computing-constrained scenarios such as user-side energy storage and small and medium-sized industrial and commercial energy storage. It is applicable to lightweight, low-cost, and highly robust scheduling control of user-side energy storage and industrial and commercial photovoltaic-storage systems.

[0005] To solve the above-mentioned technical problems, the present invention is implemented using the following technical solution:

[0006] In a first aspect, the present invention provides an asymmetric scheduling method for a photovoltaic-storage system, comprising:

[0007] Step a: Obtain historical time-of-use electricity price data, and construct a standard daily electricity price waveform template based on the historical time-of-use electricity price data;

[0008] Step b: Obtain the current electricity price in real time, and based on the standard daily electricity price waveform template, obtain the phase angle of the current electricity price in the standard daily electricity price waveform template;

[0009] Step c: Obtain the battery SOC, and calculate the charging price threshold and discharging price threshold based on the battery SOC and the standard daily electricity price waveform template;

[0010] Step d: Based on the phase angle and the preset action mapping rule library, obtain recommended actions. Based on the recommended actions, compare the current electricity price with the charging electricity price threshold or the discharging electricity price threshold, obtain the comparison result, and generate charging and discharging instructions based on the recommended actions and the comparison result to complete the scheduling of the photovoltaic energy storage system.

[0011] Furthermore, obtaining the phase angle of the current electricity price in the standard daily electricity price waveform template according to the standard daily electricity price waveform template includes:

[0012] Based on the standard daily electricity price waveform template, map 24 hours to Phase interval: Obtain the phase angle of the current electricity price within the phase interval.

[0013] Furthermore, based on the battery SOC and the standard daily electricity price waveform template, the charging price threshold and discharging price threshold are calculated, and the specific expressions are as follows:

[0014]

[0015]

[0016] In the formula: The threshold for charging electricity price; The discharge electricity price threshold; , , These represent the mean, minimum, and maximum values ​​of the electricity price cycle for the standard daily electricity price waveform template. , All are preset asymmetric coefficients; , All are preset SOC correction factors; SOC is the remaining power.

[0017] Furthermore, the recommended actions include charging, discharging, and standby;

[0018] The step of comparing the current electricity price with a charging or discharging electricity price threshold based on the recommended action, obtaining the comparison result, and generating charging or discharging instructions based on the recommended action and the comparison result includes:

[0019] If the recommended action is charging, the current electricity price is compared with the charging electricity price threshold. If the current electricity price is not greater than the charging electricity price threshold, a charging-related instruction is generated, and steps b, c, and d are repeated.

[0020] If the recommended action is to discharge, the current electricity price is compared with the discharge price threshold. If the current electricity price is not less than the discharge price threshold, a discharge-related instruction is generated, and steps b, c, and d are repeated.

[0021] If the recommended action is standby, the task ends and steps b, c, and d are repeated.

[0022] Furthermore, if the recommended action is charging, the current electricity price is compared with the charging electricity price threshold. If the current electricity price is greater than the charging electricity price threshold, the process ends, and steps b, c, and d are repeated.

[0023] If the recommended action is to discharge, the current electricity price is compared with the discharge price threshold. If the current electricity price is less than the discharge price threshold, the process ends, and steps b, c, and d are repeated.

[0024] Furthermore, a simplified game theory model is used for strategy optimization, specifically including:

[0025] Based on the scheduled photovoltaic-storage system, the relationship between the actual user response contribution and the grid's expected response is obtained, as shown in the following expression:

[0026]

[0027] In the formula: Contribution to actual user response This represents the expected response of the power grid. For user response sensitivity coefficient, This is the incentive coefficient for the electricity price difference. To maximize user responsiveness, This refers to the peak-hour electricity price. This refers to off-peak electricity pricing. This refers to the average daily electricity price.

[0028] Obtain the user's historical response reliability score; based on the user's historical response reliability score, obtain the electricity price discount factor; and based on the electricity price discount factor, calculate the user's actual electricity price. The specific expression is as follows:

[0029]

[0030]

[0031] In the formula: The actual electricity price implemented for users, Based on time-of-use pricing, This is the electricity price discount factor. Assess the user's historical response reliability score. The number of statistical periods is given, where t is the day index. The actual contribution of user responses on day t. Let be the expected power grid response on day t.

[0032] Furthermore, the strategy optimization also includes iterative updates of the game equilibrium, specifically expressed as follows:

[0033]

[0034]

[0035] In the formula: The updated user history response reliability score, The reliability score for user historical responses before the update. Historical weight decay factor The updated electricity price will be applied to users.

[0036] In a second aspect, the present invention provides an asymmetric scheduling system for a photovoltaic energy storage system, comprising:

[0037] Processing module: Acquires historical time-of-use electricity price data and constructs a standard daily electricity price waveform template based on the historical time-of-use electricity price data;

[0038] First calculation module: Real-time acquisition of the current electricity price, and acquisition of the phase angle of the current electricity price in the standard daily electricity price waveform template;

[0039] The second calculation module: obtains the battery SOC, and calculates the charging price threshold and discharging price threshold based on the battery SOC and the standard daily electricity price waveform template;

[0040] Instruction module: Based on the phase angle and the preset action mapping rule library, it obtains recommended actions, compares the current electricity price with the charging electricity price threshold or the discharging electricity price threshold according to the recommended actions, obtains the comparison results, and generates charging and discharging instructions according to the recommended actions and comparison results to complete the scheduling of the photovoltaic energy storage system.

[0041] Thirdly, the present invention provides a terminal, including a processor and a storage medium;

[0042] The storage medium is used to store instructions;

[0043] The processor is configured to operate according to the instructions to perform the steps of the method according to the first aspect.

[0044] Fourthly, a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the steps of the method described in the first aspect.

[0045] Compared with the prior art, the beneficial effects achieved by the present invention are as follows:

[0046] This asymmetric scheduling method for photovoltaic-storage systems eliminates the dependence on prediction, requiring no load forecasting or photovoltaic prediction models. It generates charging and discharging commands based on the current electricity price and recommended actions, significantly reducing the system deployment threshold and operation and maintenance costs. It also solves the problem of inaccurate scheduling due to unreliable predictions in certain scenarios. Furthermore, this invention requires minimal computation, does not require high-performance edge computing equipment, and can run in real time on a low-cost MCU. It is suitable for computing-constrained scenarios such as user-side energy storage and small and medium-sized industrial and commercial energy storage, and is applicable to lightweight, low-cost, and highly robust scheduling control of user-side energy storage and industrial and commercial photovoltaic-storage systems. Attached Figure Description

[0047] Figure 1 This is a flowchart illustrating an asymmetric scheduling method for a photovoltaic energy storage system according to an embodiment of the present invention. Detailed Implementation

[0048] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations thereof. In the absence of conflict, the embodiments of the present invention and the technical features in the embodiments can be combined with each other.

[0049] The term "and / or" simply describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. Additionally, the character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0050] Example 1:

[0051] like Figure 1 As shown, the present invention provides an asymmetric scheduling method for a photovoltaic-storage system, comprising:

[0052] Step a: Obtain historical time-of-use electricity price data, and construct a standard daily electricity price waveform template based on the historical time-of-use electricity price data;

[0053] Step b: Obtain the current electricity price in real time, and based on the standard daily electricity price waveform template, obtain the phase angle of the current electricity price in the standard daily electricity price waveform template;

[0054] Step c: Obtain the battery SOC, and calculate the charging price threshold and discharging price threshold based on the battery SOC and the standard daily electricity price waveform template;

[0055] Step d: Based on the phase angle and the preset action mapping rule library, obtain recommended actions. Based on the recommended actions, compare the current electricity price with the charging electricity price threshold or the discharging electricity price threshold, obtain the comparison result, and generate charging and discharging instructions based on the recommended actions and the comparison result to complete the scheduling of the photovoltaic energy storage system.

[0056] Specifically, historical time-of-use electricity price data should include at least the historical time-of-use electricity price data for the past 7 days.

[0057] This invention eliminates the reliance on forecasting, requiring no load forecasting or photovoltaic forecasting models. It generates charging and discharging commands based on the current electricity price and recommended actions, significantly reducing the system deployment threshold and operation and maintenance costs. It also solves the problem of inaccurate scheduling caused by unreliable forecasts in certain scenarios. Furthermore, this invention requires minimal computation, does not require high-performance edge computing equipment, and can run in real time on a low-cost MCU. It is suitable for computing-constrained scenarios such as user-side energy storage and small and medium-sized industrial and commercial energy storage. It is applicable to lightweight, low-cost, and highly robust scheduling control of user-side energy storage and industrial and commercial photovoltaic energy storage systems.

[0058] In this embodiment, obtaining the phase angle of the current electricity price in the standard daily electricity price waveform template according to the standard daily electricity price waveform template includes: mapping 24 hours to the standard daily electricity price waveform template. Phase interval: Obtain the phase angle of the current electricity price within the phase interval; simultaneously, based on the standard daily electricity price waveform template, obtain key phase points, including the valley phase, peak phase, rising segment inflection point, and falling segment inflection point.

[0059] In this embodiment, the charging price threshold and discharging price threshold are calculated based on the battery SOC and the standard daily electricity price waveform template. The specific expressions are as follows:

[0060]

[0061]

[0062] In the formula: The threshold for charging electricity price; The discharge electricity price threshold; , , These represent the mean, minimum, and maximum values ​​of the electricity price cycle for the standard daily electricity price waveform template. , All are preset asymmetry coefficients, allowing charging and discharging decisions to reference different benchmark electricity prices; , All are preset SOC correction coefficients, which cause the threshold to dynamically drift with the battery's state of charge; SOC is the remaining power.

[0063] Specifically, the asymmetric core: and Independent settings allow for a conservative charging approach (e.g., waiting for lower electricity prices) and an aggressive discharging approach (e.g., releasing electricity early), achieving an asymmetric response. Thresholds are updated in real time, relying solely on historical statistics and the current state, without depending on any future predictions. Traditional "valley charging and peak discharging" is a symmetric strategy, with both charging and discharging decisions based on the same electricity price benchmark. This invention allows for the decoupling of charging and discharging price thresholds, enabling charging to be stopped earlier during periods of rising electricity prices and discharging to be delayed during periods of falling electricity prices, further improving the arbitrage return per unit of electricity.

[0064] In this embodiment, the recommended actions include charging, discharging, and standby; wherein, charging includes forced charging, priority charging, decelerated charging, and preparing to charge; discharging includes preparing to discharge, priority discharging, accelerated discharging, and decelerated discharging; and standby includes stopping charging, stopping discharging, and maintenance.

[0065] The step of comparing the current electricity price with a charging or discharging electricity price threshold based on the recommended action, obtaining the comparison result, and generating charging or discharging instructions based on the recommended action and the comparison result includes:

[0066] If the recommended action is charging, the current electricity price is compared with the charging electricity price threshold. If the current electricity price is not greater than the charging electricity price threshold, a charging-related instruction is generated, and steps b, c, and d are repeated. Optionally, the charging-related instructions include a forced charging instruction, a priority charging instruction, a decelerated charging instruction, and a ready-to-charge instruction.

[0067] If the recommended action is to discharge, the current electricity price is compared with the discharge price threshold. If the current electricity price is not less than the discharge price threshold, a discharge-related instruction is generated, and steps b, c, and d are repeated. Optionally, the discharge-related instructions include a prepare discharge instruction, a priority discharge instruction, an accelerated discharge instruction, and a decelerated discharge instruction.

[0068] If the recommended action is standby, the task ends and steps b, c, and d are repeated.

[0069] Specifically, the preset action mapping rule base is shown in Table 1:

[0070] Table 1: Action Mapping Rule Base

[0071]

[0072] In this embodiment, if the recommended action is charging, the current electricity price is compared with the charging electricity price threshold. If the current electricity price is greater than the charging electricity price threshold, the work ends, and steps b, c, and d are repeated. If the recommended action is discharging, the current electricity price is compared with the discharging electricity price threshold. If the current electricity price is less than the discharging electricity price threshold, the work ends, and steps b, c, and d are repeated.

[0073] In this embodiment, to meet real-time control requirements, this application further includes optimizing the strategy based on the scheduled optical-storage system using a simplified game theory model, specifically including:

[0074] Based on the scheduled photovoltaic-storage system, the relationship between the actual user response contribution and the grid's expected response is obtained, as shown in the following expression:

[0075]

[0076] In the formula: Contribution to actual user response This represents the expected response of the power grid. For user response sensitivity coefficient, This is the incentive coefficient for the electricity price difference. To maximize user responsiveness, This refers to the peak-hour electricity price. This refers to off-peak electricity pricing. This refers to the average daily electricity price.

[0077] Obtain the user's historical response reliability score; based on the user's historical response reliability score, obtain the electricity price discount factor; and based on the electricity price discount factor, calculate the user's actual electricity price. The specific expression is as follows:

[0078]

[0079]

[0080] In the formula: The actual electricity price implemented for users, Based on time-of-use pricing, This is the electricity price discount factor. Assess the user's historical response reliability score. The number of statistical periods is given, where t is the day index. The actual contribution of user responses on day t. Let be the expected power grid response on day t.

[0081] The strategy optimization also includes iterative updates of the game equilibrium, as shown in the following expression:

[0082]

[0083]

[0084] In the formula: The updated user history response reliability score, The reliability score for user historical responses before the update. Historical weight decay factor The updated electricity price will be applied to users.

[0085] Specifically, it also includes rolling execution and adaptive learning: phase detection and threshold update are performed every 15 minutes; historical electricity price waveform template is updated every 24 hours; asymmetric coefficients are updated every 30 days, and Bayesian optimization is performed based on historical revenue data; users can set the game participation willingness coefficient to control the level of enthusiasm for reporting response contributions, achieve a balance between privacy protection and revenue acquisition, and form a positive incentive closed loop of "the better the response, the better the electricity price".

[0086] This invention uses a game-theoretic mechanism to enable the grid to recognize and incentivize users' active participation in peak shaving and valley filling, thus transforming the social value of energy storage into economic benefits for users. Because different users have different SOC states, historical electricity price statistics, and adaptive threshold parameters, even when facing the same real-time electricity price, different users will have different scheduling instructions, thus avoiding transformer overload problems caused by simultaneous charging and discharging of photovoltaic and energy storage clusters.

[0087] In some possible implementations, the scenario is as follows: A small industrial and commercial user configures a 50kW / 100kWh energy storage system and implements a general industrial and commercial time-of-use electricity price; the initial state is: SOC=40%, the time is 10:00 am, the real-time electricity price is 0.85 yuan / kWh, and it is in the middle and late stage of the rising phase of the electricity price waveform, and the corresponding phase angle is obtained according to the action rule mapping library.

[0088] For the above scenario, traditional strategies (prediction-dependent) need to predict the afternoon photovoltaic output and evening load. If the evening load is predicted to be high, the system may not discharge at that time and reserve power for use. If the daily electricity price is predicted to be higher the next day, the system may not discharge at that time and gamble on the next day. If the prediction accuracy is poor, the strategy will fail.

[0089] The strategy of this invention includes: Phase identification: the current phase angle belongs to the priority discharge phase interval; Threshold calculation: the current electricity price is 0.85 yuan, and the discharge price threshold (based on historical statistics + dynamic correction of SOC) is 0.82 yuan, which meets the discharge conditions; Rule triggering: the rule base determines "peak area + exceeding threshold" → generates discharge-related instructions and sends them to the energy storage converter for execution, so that the photovoltaic-storage system discharges at a rate of 0.5C; Game theory preparation: the system records the peak shaving contribution of this discharge behavior to the power grid and prepares to report it in the demand-side response reporting window.

[0090] In summary, this method allows the system to automatically release electricity when electricity prices are high without any prediction, thus achieving arbitrage; at the same time, it obtains additional response compensation through a game theory mechanism.

[0091] This invention does not rely on load and photovoltaic forecasting. Instead, it uses real-time electricity price phase characteristics to generate an economically optimized dispatching strategy. It improves the arbitrage space per unit of electricity through asymmetric threshold design and transforms the social value of energy storage into economic benefits for users through a game-theoretic interaction mechanism. It effectively solves the technical defects of traditional methods, such as strong forecasting dependence, homogeneous dispatching behavior, and mismatch between user and grid value. It is suitable for lightweight, low-cost, and highly robust dispatching control of user-side energy storage and industrial and commercial photovoltaic-storage systems.

[0092] Example 2:

[0093] Based on the same inventive concept as Embodiment 1, the present invention provides an asymmetric scheduling system for a photovoltaic storage system, comprising:

[0094] Processing module: Acquires historical time-of-use electricity price data and constructs a standard daily electricity price waveform template based on the historical time-of-use electricity price data;

[0095] First calculation module: Real-time acquisition of the current electricity price, and acquisition of the phase angle of the current electricity price in the standard daily electricity price waveform template;

[0096] The second calculation module: obtains the battery SOC, and calculates the charging price threshold and discharging price threshold based on the battery SOC and the standard daily electricity price waveform template;

[0097] Instruction module: Based on the phase angle and the preset action mapping rule library, it obtains recommended actions, compares the current electricity price with the charging electricity price threshold or the discharging electricity price threshold according to the recommended actions, obtains the comparison results, and generates charging and discharging instructions according to the recommended actions and comparison results to complete the scheduling of the photovoltaic energy storage system.

[0098] The specific functions of each module described above are explained in the relevant content of the method in Embodiment 1, and will not be repeated here.

[0099] Example 3:

[0100] This invention also provides a terminal, including a processor and a storage medium;

[0101] The storage medium is used to store instructions;

[0102] The processor is configured to operate according to the instructions to execute the steps of the method described in Embodiment 1.

[0103] Example 4:

[0104] This invention also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the method described in Embodiment 1.

[0105] Since the storage medium provided in this embodiment of the invention can execute the method provided in embodiment 1 of the invention, it has the corresponding functional modules and beneficial effects for executing the method.

[0106] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application 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.

[0107] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. 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, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0108] 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, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0109] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0110] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. An asymmetric scheduling method for a photovoltaic-storage system, characterized in that, include: Step a: Obtain historical time-of-use electricity price data, and construct a standard daily electricity price waveform template based on the historical time-of-use electricity price data; Step b: Obtain the current electricity price in real time, and based on the standard daily electricity price waveform template, obtain the phase angle of the current electricity price in the standard daily electricity price waveform template; Step c: Obtain the battery SOC, and calculate the charging price threshold and discharging price threshold based on the battery SOC and the standard daily electricity price waveform template; Step d: Based on the phase angle and the preset action mapping rule library, obtain recommended actions. Based on the recommended actions, compare the current electricity price with the charging electricity price threshold or the discharging electricity price threshold, obtain the comparison result, and generate charging and discharging instructions based on the recommended actions and the comparison result to complete the scheduling of the photovoltaic energy storage system.

2. The asymmetric scheduling method for a photovoltaic-storage system according to claim 1, characterized in that, The step of obtaining the phase angle of the current electricity price in the standard daily electricity price waveform template, based on the standard daily electricity price waveform template, includes: Based on the standard daily electricity price waveform template, map 24 hours to Phase interval: Obtain the phase angle of the current electricity price within the phase interval.

3. The asymmetric scheduling method for a photovoltaic-storage system according to claim 1, characterized in that, The charging and discharging price thresholds are calculated based on the battery SOC and standard daily electricity price waveform template, and the specific expressions are as follows: In the formula: The threshold for charging electricity price; The discharge price threshold; , , These represent the mean, minimum, and maximum values ​​of the electricity price cycle for the standard daily electricity price waveform template. , All are preset asymmetric coefficients; , All are preset SOC correction factors; SOC is the remaining power.

4. The asymmetric scheduling method for a photovoltaic-storage system according to claim 1, characterized in that, The recommended actions include charging, discharging, and standby; The step of comparing the current electricity price with a charging or discharging electricity price threshold based on the recommended action, obtaining the comparison result, and generating charging or discharging instructions based on the recommended action and the comparison result includes: If the recommended action is charging, the current electricity price is compared with the charging electricity price threshold. If the current electricity price is not greater than the charging electricity price threshold, a charging-related instruction is generated, and steps b, c, and d are repeated. If the recommended action is to discharge, the current electricity price is compared with the discharge price threshold. If the current electricity price is not less than the discharge price threshold, a discharge-related instruction is generated, and steps b, c, and d are repeated. If the recommended action is standby, the task ends and steps b, c, and d are repeated.

5. The asymmetric scheduling method for a photovoltaic-storage system according to claim 4, characterized in that, If the recommended action is charging, then the current electricity price is compared with the charging electricity price threshold. If the current electricity price is greater than the charging electricity price threshold, the work ends and steps b, c and d are repeated. If the recommended action is to discharge, the current electricity price is compared with the discharge price threshold. If the current electricity price is less than the discharge price threshold, the process ends, and steps b, c, and d are repeated.

6. The asymmetric scheduling method for a photovoltaic-storage system according to claim 1, characterized in that, It also includes optimizing strategies using a simplified game theory model based on the scheduled optical-storage system, specifically including: Based on the scheduled photovoltaic-storage system, the relationship between the actual user response contribution and the grid's expected response is obtained, as shown in the following expression: In the formula: Contribution to actual user response This represents the expected response of the power grid. For user response sensitivity coefficient, This is the incentive coefficient for the electricity price difference. To maximize user responsiveness, This refers to the peak-hour electricity price. This refers to off-peak electricity pricing. This refers to the average daily electricity price. Obtain the user's historical response reliability score; based on the user's historical response reliability score, obtain the electricity price discount factor; and based on the electricity price discount factor, calculate the user's actual electricity price. The specific expression is as follows: In the formula: The actual electricity price implemented for users, Based on time-of-use pricing, This is the electricity price discount factor. Assess the user's historical response reliability score. The number of statistical periods is given, where t is the day index. The actual contribution of user responses on day t. Let be the expected power grid response on day t.

7. The asymmetric scheduling method for a photovoltaic-storage system according to claim 6, characterized in that, The strategy optimization also includes iterative updates of the game equilibrium, as shown in the following expression: In the formula: The updated user history response reliability score, The reliability score for user historical responses before the update. Historical weight decay factor The updated electricity price will be applied to users.

8. An asymmetric scheduling system for a photovoltaic-storage system, characterized in that, include: Processing module: Acquires historical time-of-use electricity price data and constructs a standard daily electricity price waveform template based on the historical time-of-use electricity price data; First calculation module: Real-time acquisition of the current electricity price, and acquisition of the phase angle of the current electricity price in the standard daily electricity price waveform template; The second calculation module: obtains the battery SOC, and calculates the charging price threshold and discharging price threshold based on the battery SOC and the standard daily electricity price waveform template; Instruction module: Based on the phase angle and the preset action mapping rule library, it obtains recommended actions, compares the current electricity price with the charging electricity price threshold or the discharging electricity price threshold according to the recommended actions, obtains the comparison results, and generates charging and discharging instructions according to the recommended actions and comparison results to complete the scheduling of the photovoltaic energy storage system.

9. A terminal, characterized in that, Including processor and storage media; The storage medium is used to store instructions; The processor is configured to operate according to the instructions to perform the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the steps of the method according to any one of claims 1 to 7.

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