Industrial and commercial energy storage average price period intelligent charging and discharging control method and system

Abstract

The invention discloses an intelligent charging and discharging control method and system for an industrial and commercial energy storage average price period. The method comprises the steps of 1, obtaining an electricity price template, historical load data and equipment constraint parameters; step 2, obtaining the number N of all-day peak segments according to the electricity price template, dividing the charging and discharging processes into N groups, and enabling each group to correspond to the charging and discharging period of one peak segment; 3, in each charging and discharging period, the stage state of the flat segment is judged, if peak segment discharging is not completed, a flat segment charging decision is executed, and if part of peak segment discharging is completed, a flat segment discharging decision is executed, and special scene processing is carried out. According to the method, automatic decision-making of charging and discharging in a flat period is realized, the labor cost and the error rate are reduced, power load changes can be dynamically adapted, the energy storage utilization rate is improved, and investment recovery is accelerated.

Description

Technical Field

[0001] This invention belongs to the field of new energy storage control technology, specifically relating to a method and system for intelligent charge and discharge control of energy storage systems in industrial and commercial scenarios. Background Technology

[0002] With the widespread adoption of industrial and commercial energy storage, as a core device for electricity demand-side management, its main profit model is based on generating revenue through peak-valley electricity price differences. However, current technology suffers from the following pain points: 1. Limited profitability due to time-of-use pricing: In some regions (such as some southern provinces), there is no midday off-peak period in the time-of-use pricing policy. Relying solely on the traditional "off-peak charging and peak discharging" strategy cannot achieve "two charging and two discharging" (i.e., charging during off-peak hours, discharging during peak hours, recharging during flat hours, and re-discharging during peak hours within a day). The system utilization rate is low, the rate of return is low, and the investment value is not fully explored.

[0003] 2. The contradiction between extended equipment lifespan and revenue demand: Long-life energy storage systems with a cycle life of 10,000 or even 15,000 cycles have appeared on the market, and equipment costs are gradually decreasing. However, traditional strategies rely solely on fixed peak-valley price differences and fail to fully utilize the advantages of long lifespan to explore potential revenue during flat periods. As a result, revenue in scenarios with small electricity price differences is not effectively developed, and the system investment payback period is extended.

[0004] 3. Poor adaptability to dynamic electricity demand: The electricity load of industrial and commercial users is greatly affected by changes in operating status and production capacity (such as order fluctuations and production line adjustments). The traditional method of manually configuring charging and discharging time periods and power requires frequent manual intervention, which is inefficient and prone to errors (such as misjudging electricity price periods or load power, leading to transformer overload or backfeeding of electricity to the grid), and cannot dynamically optimize electricity costs.

[0005] 4. Investment recovery pressure: The investment recovery period of energy storage systems is relatively long (usually 5-8 years). Users have an urgent need to accelerate short-term returns, but existing strategies lack the ability to make dynamic decisions in a flat period and it is difficult to improve short-term returns through flexible charging and discharging.

[0006] In summary, the industry urgently needs an intelligent charging and discharging control method that can automatically adapt to fluctuations in flat-rate electricity prices and combine historical load forecasts with equipment constraints to solve the problems of low efficiency, insufficient revenue mining, and poor dynamic adaptability caused by manual configuration. Summary of the Invention

[0007] The purpose of this invention is to provide a smart charging and discharging control method and system for commercial and industrial energy storage during grid parity periods, aiming to solve the following problems of existing commercial and industrial energy storage systems during grid parity periods: The lack of midday off-peak hours and low utilization rates during peak periods make it impossible to fully exploit the price difference between peak and off-peak periods; manual configuration of charging and discharging strategies is inefficient, error-prone, and difficult to dynamically adapt to changes in user electricity load; long-life energy storage equipment is not fully utilized, and revenue in scenarios with small electricity price differences has not been effectively developed; and it cannot meet users' short-term revenue needs for accelerated investment recovery.

[0008] In view of the above problems, this application provides a smart charging and discharging control method and system for commercial and industrial energy storage during grid parity periods. The core of this invention is to run a second-level response smart algorithm in the energy storage station controller, which is triggered to execute each time the time falls into the grid parity period. By dynamically analyzing the electricity price template, historical load data and equipment constraints, the charging and discharging mode of the grid parity period is automatically determined.

[0009] The first aspect disclosed in this application provides a smart charging and discharging control method for commercial and industrial energy storage during grid parity periods, the method comprising: Step 1: Obtain the electricity price template, historical load data, and equipment constraint parameters. The electricity price template is the electricity price and time range for each period of the day. The historical load data is the load power for each period of the past D days. The equipment constraint parameters are the maximum available capacity of the transformer, the rated charging power of the energy storage system, the rated discharging power of the energy storage system, the available capacity of the energy storage battery, the SOC of the energy storage battery, the preset peak-off price difference threshold, the preset valley-off price difference threshold, and the insurance margin. Each period of the day includes peak period, peak period, flat period, valley period, and deep valley period. D is a preset value. Step 2: Based on the electricity price template, obtain the number of peak periods N throughout the day, and divide the charging and discharging process into N groups, with each group corresponding to the charging and discharging cycle of one peak period; Step 3: In each charge and discharge cycle, determine the stage state of the flat segment. If the peak segment discharge has not been completed, execute the flat segment charging decision; if the peak segment discharge has been partially completed, execute the flat segment discharge decision. Specifically, when the flat period is followed by a valley period and immediately follows a peak period, it is determined whether the sum of the predicted valley period charging amount and the current available energy storage battery amount can meet the demand of the subsequent peak period: If the conditions are not met, charging will be performed in advance during the flat period. The charging amount is the predicted discharge demand during the peak period minus the predicted charging amount during the valley period minus the current available amount of the energy storage battery. The charging amount shall not exceed the maximum allowable charging amount during the flat period, where the maximum allowable charging amount during the flat period is the maximum available capacity of the transformer. If the conditions are met, the system will either remain in standby mode or make a regular charging / discharging decision.

[0010] Preferably, the step of making a flat-section charging decision specifically includes the following steps: When the price difference between the flat and peak periods is greater than or equal to the preset flat-peak price difference threshold, the current SOC of the energy storage battery is less than 95%, and the predicted future peak discharge demand is greater than the current available energy storage battery capacity, execute: The average load power of the flat period of the past D days or the weighted average load power of the flat periods of some dates in the past D days is taken as the flat period load power of the day. The maximum allowable charging power is obtained by subtracting the average daily load power from the maximum available capacity of the transformer. The amount of electricity that needs to be replenished is calculated by subtracting the current available amount of energy storage batteries from the predicted future peak discharge demand. If both the amount of electricity to be replenished and the maximum allowable charging power are greater than 0, then the minimum value between the maximum allowable charging power and the rated charging power of the energy storage system will be used as the charging power for charging until the available amount of the energy storage battery reaches the sum of the current available amount of the energy storage battery and the amount of electricity to be replenished, or the phase ends. The predicted peak discharge demand is calculated by multiplying the average load power of the peak period over the past D days by the corresponding peak period duration, or by multiplying the weighted average load power of the peak periods on some dates within the past D days by the corresponding peak period duration.

[0011] Preferably, the execution of the flat-segment discharge decision specifically includes the following steps: If the price difference between the off-peak and peak periods is greater than or equal to a preset off-peak price difference threshold, and the predicted future peak discharge demand is less than the current available energy storage battery capacity, then execute: The discharge power is limited to be less than or equal to the average load power of the day. The current available energy storage battery capacity minus the predicted future peak discharge demand is used as the redundancy capacity. Use the redundancy charge × (1 - safety margin) as the calculation of the actual discharge capacity; Discharge is performed using the minimum value between the discharge power and the rated discharge power of the energy storage until the released power reaches the actual dischargeable amount or the discharge phase ends.

[0012] The second aspect disclosed in this application provides an intelligent charging and discharging control system for commercial and industrial energy storage during grid parity periods, the system comprising: The data module is used to acquire electricity price templates, historical load data, and equipment constraint parameters. The electricity price template is the electricity price and time range for each period of the day. The historical load data is the average load power for each period of the past D days. The equipment constraint parameters are the maximum available capacity of the transformer, the rated charging power of the energy storage system, the rated discharging power of the energy storage system, the available capacity of the energy storage battery, the SOC of the energy storage battery, the preset peak-off price difference threshold, the preset valley-off price difference threshold, and the insurance margin. The periods of the day include peak periods, peak periods, flat periods, and valley periods. The grouping module is used to obtain the number of peak periods N throughout the day based on the electricity price template, and divide the charging and discharging process into N groups, with each group corresponding to the charging and discharging cycle of one peak period. The decision module is used to determine the stage state of the flat segment in each charge and discharge cycle. If the peak segment discharge has not been completed, the flat segment charging decision is executed; if the peak segment discharge has been partially completed, the flat segment discharge decision is executed. Specifically, when the flat period is followed by a valley period and immediately follows a peak period, it is determined whether the sum of the predicted valley period charging amount and the current available energy storage battery amount can meet the demand of the subsequent peak period: If the conditions are not met, charging will be performed in advance during the flat period. The charging amount is the predicted discharge demand during the peak period minus the predicted charging amount during the valley period minus the current available amount of the energy storage battery. The charging amount shall not exceed the maximum allowable charging amount during the flat period, where the maximum allowable charging amount during the flat period is the maximum available capacity of the transformer. If the conditions are met, the system will either remain in standby mode or make a regular charging / discharging decision.

[0013] The third aspect disclosed in this application provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described intelligent charging and discharging control method for commercial and industrial energy storage during grid parity periods.

[0014] The fourth aspect disclosed in this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described intelligent charging and discharging control method for commercial and industrial energy storage during periods of grid parity.

[0015] The fifth aspect disclosed in this application provides a computer program product, including a computer program or instructions, which, when executed by a processor, implement the steps of the above-described intelligent charging and discharging control method for commercial and industrial energy storage during periods of grid parity.

[0016] The beneficial effects of this invention are as follows: (1) Automated decision-making reduces labor costs and error rates: The algorithm is triggered and executed in seconds during the flat section, automatically judging the charging and discharging mode. There is no need for manual configuration of time period and power, avoiding losses such as transformer overload and power backflow caused by human misjudgment, and greatly improving operation efficiency.

[0017] (2) Improve energy storage utilization and accelerate investment recovery: By tapping the price difference between flat and peak periods (especially in the case of long-life energy storage equipment), we can achieve multi-mode operation of "valley charging-flat charging-peak discharging" or "valley charging-peak discharging-flat discharging", increase the number of daily charging and discharging of the system (such as "one charging and two discharging" when there is no midday valley period), significantly increase revenue and shorten the investment recovery period.

[0018] (3) Dynamically adapt to changes in electricity load: Based on historical load data, predict the daily load and dynamically adjust the charging and discharging amount in combination with transformer capacity and discharge power constraints to adapt to changes in user operation or production capacity (such as load fluctuations caused by order fluctuations) and ensure that the strategy matches the actual electricity demand. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is an overall flowchart of a smart charging and discharging control method for commercial and industrial energy storage during the grid parity period.

[0021] Figure 2 A schematic diagram of the decision-making process for charging on flat sections.

[0022] Figure 3 This is a schematic diagram of the decision-making process for flat-section discharge. Detailed Implementation

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] Example 1: like Figure 1 As shown in the figure, this application provides a smart charging and discharging control method for commercial and industrial energy storage during grid parity periods. The method includes: Step 1: Obtain the electricity price template, historical load data, and equipment constraint parameters. The electricity price template is the electricity price and time range for each period of the day. The historical load data is the load power for each period of the past D days. The equipment constraint parameters are the maximum available capacity of the transformer, the rated charging power of the energy storage system, the rated discharging power of the energy storage system, the available capacity of the energy storage battery, the SOC of the energy storage battery, the preset peak-off price difference threshold, the preset valley-off price difference threshold, and the insurance margin. Each period of the day includes peak segment, peak segment, flat segment, valley segment, and deep valley segment, and D is a preset value. The load power for each period of the day mentioned here and thereafter can be a power curve composed of 24 load power values, with each load power value corresponding to the load power of one hour in the 24 hours of the day. Step 2: Based on the electricity price template, obtain the number of peak periods N throughout the day, and divide the charging and discharging process into N groups, with each group corresponding to the charging and discharging cycle of one peak period.

[0025] Step 3: In each charge and discharge cycle, determine the stage state of the flat segment. If the peak segment discharge has not been completed, execute the flat segment charging decision. If the peak segment discharge has been partially completed, execute the flat segment discharge decision.

[0026] Specifically, when the flat period is followed by a valley period and immediately follows a peak period, it is determined whether the sum of the predicted valley period charging amount and the current available energy storage battery amount can meet the demand of the subsequent peak period: If the conditions are not met, charging will be performed in advance during the flat period. The charging amount is the predicted discharge demand during the peak period minus the predicted charging amount during the valley period minus the current available amount of the energy storage battery. The charging amount shall not exceed the maximum allowable charging amount during the flat period, where the maximum allowable charging amount during the flat period is the maximum available capacity of the transformer. If the conditions are met, the system will either remain in standby mode or make a regular charging / discharging decision.

[0027] like Figure 2 As shown, when making a flat-section charging decision, the following steps are specifically executed: When the price difference between the flat and peak periods is greater than or equal to the preset flat-peak price difference threshold, the current SOC of the energy storage battery is less than 95%, and the predicted future peak discharge demand is greater than the current available energy storage battery capacity, execute: The average load power of the flat period of the past D days or the weighted average load power of the flat period of some dates in the past D days is taken as the flat period load power of the day. Specifically, the weighted average load power of the flat period of some dates in the past D days can be calculated by taking the weighted average load power of the flat period of the previous 1 day, 7 days, 14 days, 21 days and 28 days before the current day, with the weights set to preset values. The maximum allowable charging power is obtained by subtracting the average daily load power from the maximum available capacity of the transformer. The amount of electricity that needs to be replenished is calculated by subtracting the current available amount of energy storage batteries from the predicted future peak discharge demand. If both the amount of electricity to be replenished and the maximum allowable charging power are greater than 0, then the minimum value between the maximum allowable charging power and the rated charging power of the energy storage system will be used as the charging power for charging until the available amount of the energy storage battery reaches the sum of the current available amount of the energy storage battery and the amount of electricity to be replenished, or the phase ends. The predicted peak discharge demand is calculated by multiplying the average load power of the peak period over the past D days by the corresponding peak period duration, or by multiplying the weighted average load power of the peak periods on some dates within the past D days by the corresponding peak period duration.

[0028] like Figure 3 As shown, when making a flat-section discharge decision, the following steps are specifically executed: If the price difference between the off-peak and peak periods is greater than or equal to a preset off-peak price difference threshold, and the predicted future peak discharge demand is less than the current available energy storage battery capacity, then execute: The discharge power is limited to be less than or equal to the average load power of the day. The current available energy storage battery capacity minus the predicted future peak discharge demand is used as the redundancy capacity. Use the redundancy charge × (1 - safety margin) as the calculation of the actual discharge capacity; Discharge is performed using the minimum value between the discharge power and the rated discharge power of the energy storage until the released power reaches the actual dischargeable amount or the discharge phase ends.

[0029] For example, taking an industrial and commercial energy storage system in an industrial park as an example, the specific parameters are as follows: Energy storage system: The transformer has a maximum usable capacity of 1000kWh, the energy storage system has a rated charge / discharge power of 200kW, and a cycle life of 15,000 cycles; Electricity price template (a certain day): Off-peak period (0:00-8:00, 0.3 yuan / kWh), Flat period (10:00-12:00, 0.6 yuan / kWh), Peak period (14:00-18:00, 1.2 yuan / kWh), High-peak period (19:00-21:00, 1.5 yuan / kWh); Historical load: The average load power of the D-day flat section is 150kW, and the total transformer capacity is 500kW (current load power is 200kW, available capacity is 300kW). Constraint parameters: Preset peak-off price difference threshold ΔP1 = 0.3 yuan / kWh (peak-off price difference = 1.2 - 0.6 = 0.6 ≥ ΔP1), preset valley-off price difference threshold ΔP2 = 0.2 yuan / kWh (valley-off price difference = 0.6 - 0.3 = 0.3 ≥ ΔP2), insurance margin 5%.

[0030] Scenario 1: Flat-segment charging decision (peak-segment discharge not yet completed) After the valley period ends, the energy storage battery SOC is 90% (900kWh). Execution is triggered at the start of the flat period (10:00-12:00). Analyzing the electricity price template, there are two peak periods throughout the day (14:00-18:00 and 19:00-21:00), requiring two separate charging and discharging cycles. Currently in Group 1 (charged during off-peak hours but not discharged during peak hours), predicted peak discharge demand: Peak 1 load power 200kW (historical average), discharge duration 4 hours, requiring 800kWh; Peak 2 load power 180kW, discharge duration 2 hours, requiring 360kWh; Total demand 1160kWh. The current available energy storage battery capacity is 900kWh (855kWh after deducting a 5% insurance margin) < 1160kWh, and the off-peak price difference is 0.6 ≥ ΔP1; The transformer has a usable capacity of 300kW, and the charging power P_charge_max = 300kW - 150kW for flat load = 150kW, which is less than the rated charging power of the energy storage of 200kW. The required electricity supply ΔE_need = 1160kWh - 855kWh = 305kWh; The average duration is 2 hours (10:00-12:00), and the maximum chargeable capacity is 150kW × 2h = 300kWh (close to ΔE_need). Performing flat-segment charging: Charging at 150kW for 2 hours increases the SOC of the energy storage battery from 90% to 90% + 300kWh / 1000kWh = 120% (but due to battery capacity limitations, it is actually charged to 100%, i.e., 100kWh is added). The remaining demand of 205kWh will be supplemented by subsequent off-peak charging.

[0031] Scenario 2: Flat-segment discharge decision (partial peak segment discharge has been completed) After peak discharge (14:00-18:00), the energy storage battery SOC=30% (300kWh), and then enters the flat period (assuming another flat period is 16:00-17:00). Predicted discharge demand for the remaining peak period 2 (19:00-21:00): Load power 180kW × 2h = 360kWh; The current available energy storage battery capacity is 300kWh (285kWh after deducting a 5% safety margin) < 360kWh, so discharge will not be triggered. If the SOC of the energy storage battery is 50% (500kWh), the usable capacity of the energy storage battery is 475kWh > 360kWh, and the redundant capacity is 475kWh - 360kWh = 115kWh. The price difference between the valley and the flat price = 0.6 - 0.3 = 0.3 ≥ ΔP2, triggering the discharge of the flat segment; Discharge power = Flat load power 150kW is less than the rated discharge power of energy storage 200kW; Dischargeable capacity = 115kWh × (1-5%) = 109kWh; The peak period lasts for 1 hour (16:00-17:00), and the discharge capacity is 150kW×1h=150kWh>109kWh. After discharging 109kWh, the SOC of the energy storage battery drops to 50%-109kWh / 1000kWh=39.1%, and the remaining power meets the requirements of peak period 2 (360kWh≤391kWh).

[0032] In summary, the intelligent charging and discharging control method for commercial and industrial energy storage during grid parity periods provided in this application has the following technical effects: (1) Automated decision-making reduces labor costs and error rates: The algorithm is triggered and executed in seconds during the flat section, automatically judging the charging and discharging mode. There is no need for manual configuration of time period and power, avoiding losses such as transformer overload and power backflow caused by human misjudgment, and greatly improving operation efficiency.

[0033] (2) Improve energy storage utilization and accelerate investment recovery: By tapping the price difference between flat and peak periods (especially in the case of long-life energy storage equipment), we can achieve multi-mode operation of "valley charging-flat charging-peak discharging" or "valley charging-peak discharging-flat discharging", increase the number of daily charging and discharging of the system (such as "one charging and two discharging" when there is no midday valley period), significantly increase revenue and shorten the investment recovery period.

[0034] (3) Dynamically adapt to changes in electricity load: Based on historical load data, predict the daily load and dynamically adjust the charging and discharging amount in combination with transformer capacity and discharge power constraints to adapt to changes in user operation or production capacity (such as load fluctuations caused by order fluctuations) and ensure that the strategy matches the actual electricity demand.

[0035] Example 2: Based on the same inventive concept as the intelligent charging and discharging control method for commercial and industrial energy storage during grid parity periods in Embodiment 1, this application provides an intelligent charging and discharging control system for commercial and industrial energy storage during grid parity periods, the system comprising: The data module is used to acquire electricity price templates, historical load data, and equipment constraint parameters. The electricity price template is the electricity price and time range for each period of the day. The historical load data is the average load power for each period of the past D days. The equipment constraint parameters are the maximum available capacity of the transformer, the rated charging power of the energy storage system, the rated discharging power of the energy storage system, the available capacity of the energy storage battery, the SOC of the energy storage battery, the preset peak-off price difference threshold, the preset valley-off price difference threshold, and the insurance margin. The periods of the day include peak periods, peak periods, flat periods, and valley periods. The grouping module is used to obtain the number of peak periods N throughout the day based on the electricity price template, and divide the charging and discharging process into N groups, with each group corresponding to the charging and discharging cycle of one peak period. The decision module is used to determine the stage state of the flat segment in each charge and discharge cycle. If the peak segment discharge has not been completed, the flat segment charging decision is executed; if the peak segment discharge has been partially completed, the flat segment discharge decision is executed. Specifically, when the flat period is followed by a valley period and immediately follows a peak period, it is determined whether the sum of the predicted valley period charging amount and the current available energy storage battery amount can meet the demand of the subsequent peak period: If the conditions are not met, charging will be performed in advance during the flat period. The charging amount is the predicted discharge demand during the peak period minus the predicted charging amount during the valley period minus the current available amount of the energy storage battery. The charging amount shall not exceed the maximum allowable charging amount during the flat period, where the maximum allowable charging amount during the flat period is the maximum available capacity of the transformer. If the conditions are met, the system will either remain in standby mode or make a regular charging / discharging decision.

[0036] Through the foregoing detailed description of a smart charging and discharging control method for commercial and industrial energy storage during grid parity periods, those skilled in the art can clearly understand the smart charging and discharging control system for commercial and industrial energy storage during grid parity periods in this embodiment. Since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and relevant parts can be referred to in the method section.

[0037] Example 3: In Embodiment 3, a computer device is provided, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the steps of the above-described intelligent charging and discharging control method for commercial and industrial energy storage during the grid parity period.

[0038] Example 4: In Embodiment 4, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, it implements the steps of the above-described intelligent charging and discharging control method for commercial and industrial energy storage during the grid parity period.

[0039] Example 5: In Embodiment 5, a computer program product is provided, including a computer program or instructions, which, when executed by a processor, implement the steps of the above-described intelligent charging and discharging control method for commercial and industrial energy storage during periods of grid parity.

[0040] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0041] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A smart charging and discharging control method for commercial and industrial energy storage during periods of grid parity, characterized in that, The method includes: Step 1: Obtain the electricity price template, historical load data, and equipment constraint parameters. The electricity price template is the electricity price and time range for each period of the day. The historical load data is the load power for each period of the past D days. The equipment constraint parameters are the maximum available capacity of the transformer, the rated charging power of the energy storage system, the rated discharging power of the energy storage system, the available capacity of the energy storage battery, the SOC of the energy storage battery, the preset peak-off price difference threshold, the preset valley-off price difference threshold, and the insurance margin. Each period of the day includes peak period, peak period, flat period, valley period, and deep valley period. D is a preset value. Step 2: Based on the electricity price template, obtain the number of peak periods N throughout the day, and divide the charging and discharging process into N groups, with each group corresponding to the charging and discharging cycle of one peak period; Step 3: In each charge and discharge cycle, determine the stage state of the flat segment. If the peak segment discharge has not been completed, execute the flat segment charging decision; if the peak segment discharge has been partially completed, execute the flat segment discharge decision. Specifically, when the flat period is followed by a valley period and immediately follows a peak period, it is determined whether the sum of the predicted valley period charging amount and the current available energy storage battery amount can meet the demand of the subsequent peak period: If the conditions are not met, charging will be performed in advance during the flat period. The charging amount is the predicted discharge demand during the peak period minus the predicted charging amount during the valley period minus the current available amount of the energy storage battery. The charging amount shall not exceed the maximum allowable charging amount during the flat period, where the maximum allowable charging amount during the flat period is the maximum available capacity of the transformer. If the conditions are met, the system will either remain in standby mode or make a regular charging / discharging decision.

2. The intelligent charging and discharging control method for commercial and industrial energy storage during grid parity periods as described in claim 1, characterized in that, The execution of the flat-section charging decision specifically includes the following steps: When the price difference between the flat and peak periods is greater than or equal to the preset flat-peak price difference threshold, the current SOC of the energy storage battery is less than 95%, and the predicted future peak discharge demand is greater than the current available energy storage battery capacity, execute: The average load power of the flat period of the past D days or the weighted average load power of the flat periods of some dates in the past D days is taken as the flat period load power of the day. The maximum allowable charging power is obtained by subtracting the average daily load power from the maximum available capacity of the transformer. The amount of electricity that needs to be replenished is calculated by subtracting the current available amount of energy storage batteries from the predicted future peak discharge demand. If both the amount of electricity to be replenished and the maximum allowable charging power are greater than 0, then the minimum value between the maximum allowable charging power and the rated charging power of the energy storage system will be used as the charging power for charging until the available amount of the energy storage battery reaches the sum of the current available amount of the energy storage battery and the amount of electricity to be replenished, or the phase ends. The predicted peak discharge demand is calculated by multiplying the average load power of the peak period over the past D days by the corresponding peak period duration, or by multiplying the weighted average load power of the peak periods on some dates within the past D days by the corresponding peak period duration.

3. The intelligent charging and discharging control method for commercial and industrial energy storage during grid parity periods as described in claim 2, characterized in that, The execution of the flat-segment discharge decision specifically includes the following steps: If the price difference between the off-peak and peak periods is greater than or equal to a preset off-peak price difference threshold, and the predicted future peak discharge demand is less than the current available energy storage battery capacity, then execute: The discharge power is limited to be less than or equal to the average load power of the day. The current available energy storage battery capacity minus the predicted future peak discharge demand is used as the redundancy capacity. Use the redundancy charge × (1 - safety margin) as the calculation of the actual discharge capacity; Discharge is performed using the minimum value between the discharge power and the rated discharge power of the energy storage until the released power reaches the actual dischargeable amount or the discharge phase ends.

4. A smart charging and discharging control system for commercial and industrial energy storage during periods of grid parity, characterized in that, The system includes: The data module is used to acquire electricity price templates, historical load data, and equipment constraint parameters. The electricity price template is the electricity price and time range for each time period of the day. The historical load data is the load power for each time period of the past D days. The equipment constraint parameters are the maximum available capacity of the transformer, the rated charging power of the energy storage system, the rated discharging power of the energy storage system, the available capacity of the energy storage battery, the SOC of the energy storage battery, the preset peak-off price difference threshold, the preset valley-off price difference threshold, and the insurance margin. The time periods of the day include peak periods, peak periods, flat periods, valley periods, and deep valley periods, and D is a preset value. The grouping module is used to obtain the number of peak periods N throughout the day based on the electricity price template, and divide the charging and discharging process into N groups, with each group corresponding to the charging and discharging cycle of one peak period. The decision module is used to determine the stage state of the flat segment in each charge and discharge cycle. If the peak segment discharge has not been completed, the flat segment charging decision is executed; if the peak segment discharge has been partially completed, the flat segment discharge decision is executed. Specifically, when the flat period is followed by a valley period and immediately follows a peak period, it is determined whether the sum of the predicted valley period charging amount and the current available energy storage battery amount can meet the demand of the subsequent peak period: If the conditions are not met, charging will be performed in advance during the flat period. The charging amount is the predicted discharge demand during the peak period minus the predicted charging amount during the valley period minus the current available amount of the energy storage battery. The charging amount shall not exceed the maximum allowable charging amount during the flat period, where the maximum allowable charging amount during the flat period is the maximum available capacity of the transformer. If the conditions are met, the system will either remain in standby mode or make a regular charging / discharging decision.

5. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the intelligent charging and discharging control method for commercial and industrial energy storage during the parity period as described in any one of claims 1 to 3.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the intelligent charging and discharging control method for commercial and industrial energy storage during the parity period as described in any one of claims 1 to 3.

7. A computer program product, comprising a computer program or instructions, characterized in that, When the computer program or instructions are executed by the processor, they implement the steps of the intelligent charging and discharging control method for commercial and industrial energy storage during the parity period as described in any one of claims 1 to 3.

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