Energy storage charging and discharging switching method based on load fluctuation and carbon factor

Abstract

The application discloses a storage energy charging and discharging switching method based on load fluctuation and carbon factors, obtains the power load of a current scheduling period, carbon emission factors, the state of charge of a storage battery and the operation mode data of a previous period, calculates load fluctuation, carbon emission intensity and charge safety margin indexes, constructs an evaluation function containing a load response term, a carbon emission term, a state of charge constraint term and a mode switching cost term for charging, discharging and standby modes, determines a target operation mode according to the evaluation function values of the modes, determines a mode switching benefit according to the difference between the evaluation function value corresponding to the target operation mode and the actual operation mode of a previous scheduling period, and executes switching when the condition is met, and finally constructs an evaluation error to adaptively and closed-loop update the evaluation function weight, switching cost and threshold value. The application can reduce the probability of frequent mode switching while taking into account peak load cutting and low-carbon operation, and improve the operation stability and environmental adaptability of the storage energy system.

Description

Technical Field

[0001] This invention relates to the field of energy management and energy storage control technology, and in particular to an energy storage charging and discharging switching method, system, device and storage medium based on load fluctuation and carbon factor. Background Technology

[0002] Energy storage systems, as a crucial component of user-side energy management, are widely used in industrial parks, commercial buildings, data centers, and integrated energy stations. Current technologies typically control the charging and discharging of energy storage batteries based on fixed time periods, electricity price signals, peak shaving and valley filling strategies, or state-of-charge thresholds. While these methods can improve user-side energy consumption structures to some extent, they still fall short in terms of refined operation aimed at low-carbon goals.

[0003] On the one hand, existing energy storage control schemes typically revolve around electricity prices or load balancing, rarely using grid-side carbon emission factors as a direct basis for switching energy storage charging and discharging modes, thus making it difficult to simultaneously achieve low-carbon operation goals. On the other hand, user-side electricity loads usually exhibit significant fluctuations and time-of-day variations. Relying solely on fixed time periods or static thresholds for control makes it difficult to dynamically respond to load changes, thereby affecting the operational rationality of energy storage systems.

[0004] In addition, although some existing technologies take into account the state of charge factor, they are insufficient in their joint determination of load fluctuation state, carbon factor level and state of charge. This leads to problems such as unreasonable switching timing, high switching frequency or insignificant low-carbon operation effect of energy storage batteries in certain scenarios.

[0005] Therefore, it is necessary to propose a new energy storage charge-discharge switching method that combines load fluctuation state, carbon factor level and energy storage battery state of charge to determine the target operating mode of energy storage battery. This can reduce the probability of frequent mode switching while taking into account peak shaving and low carbon operation, and improve the operating stability and environmental adaptability of energy storage system. Summary of the Invention

[0006] To overcome the shortcomings of the prior art, this invention provides an energy storage charge-discharge switching method based on load fluctuation and carbon factor. This method combines load fluctuation state, carbon factor level and energy storage battery state of charge to determine the target operating mode of the energy storage battery. It can reduce the probability of frequent mode switching while taking into account peak shaving and low carbon operation, thereby improving the operational stability and environmental adaptability of the energy storage system.

[0007] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0008] In a first aspect, the present invention provides an energy storage charge-discharge switching method based on load fluctuations and carbon factor, the method comprising: Obtain electricity load data for the current scheduling period, carbon emission factor data for the corresponding period, energy storage battery state of charge data, and actual operation mode data for the previous scheduling period; The load fluctuation index for the current dispatch period is calculated based on the electricity load data, the carbon emission intensity index for the current dispatch period is calculated based on the carbon emission factor data, and the charge safety margin index is calculated based on the energy storage battery state of charge data. The load fluctuation index, carbon emission intensity index and charge safety margin index are normalized to obtain the load evaluation quantity, carbon emission evaluation quantity and charge state evaluation quantity. For charging mode, discharging mode, and standby mode, respectively, mode evaluation functions are constructed. The mode evaluation functions are composed of a weighted coupling of load response term, carbon emission term, state of charge constraint term, and mode switching cost term, and satisfy the following relationship:

[0009] in: This represents the mode evaluation function value corresponding to candidate operating mode m; Indicates the load response item; Indicates carbon emissions; Represents the state of charge constraint terms; This indicates the cost of mode switching. , , , These are the weight parameters for the corresponding candidate operating modes.

[0010] The mode switching cost term is used to characterize at least one of the following: switching loss, cyclic impact, power change impact, and operational stability impact caused by switching from the actual operating mode in the previous scheduling period to the current candidate operating mode; Under the premise of meeting safety constraints, the target operating mode for the current scheduling period is determined based on the mode evaluation function value corresponding to each candidate operating mode. The mode switching benefit is determined based on the difference between the mode evaluation function value corresponding to the target operating mode and the mode evaluation function value corresponding to the actual operating mode in the previous scheduling period in the current scheduling period. When the mode switching benefit is greater than the preset switching threshold and the preset switching hysteresis condition is met, the energy storage battery is controlled to switch to the target operating mode; otherwise, the actual operating mode of the previous scheduling period remains unchanged. After completing the control of the current scheduling period, an evaluation error is constructed based on at least one of the following: actual charging and discharging amount, load peak shaving rate, carbon emission change, mode switching number, and state of charge out of bounds risk in multiple consecutive scheduling periods. The weight parameters, mode switching cost parameters, and mode switching threshold in the mode evaluation function are then adaptively updated based on the evaluation error to achieve closed-loop optimization control of peak shaving effect, carbon reduction effect, and operational stability.

[0011] Furthermore, the load fluctuation index for the current dispatch period is calculated based on the electricity load data, including: comparing the load value of the current dispatch period with the preset reference load value, and calculating the load change between the current dispatch period and the previous dispatch period; determining the load fluctuation index corresponding to the current dispatch period based on the degree of deviation of the load value from the preset reference load value and the load change; wherein, when the load value is greater than a first load threshold and the load change is greater than a preset change threshold, the peak load evaluation value corresponding to the current dispatch period is determined; when the load value is less than a second load threshold and the load change is less than a preset change threshold, the valley load evaluation value corresponding to the current dispatch period is determined; when the above conditions are not met, the transition load evaluation value corresponding to the current dispatch period is determined. The carbon emission intensity index for the current scheduling period is calculated based on carbon emission factor data, including: obtaining the corresponding carbon emission factor value for the current scheduling period in the time-sharing carbon emission factor sequence and comparing it with a preset carbon factor threshold; when the carbon emission factor value for the current scheduling period is greater than the preset carbon factor threshold, the high carbon emission evaluation quantity for the current scheduling period is determined; when the carbon emission factor value for the current scheduling period is less than or equal to the preset carbon factor threshold, the low carbon emission evaluation quantity for the current scheduling period is determined; wherein, the carbon emission item is used to characterize the degree of impact of candidate operating modes on reducing carbon emissions from grid-side power extraction; The charge safety margin index is calculated based on the state of charge data of the energy storage battery, including: determining the charge safety margin index based on at least one of the difference between the current state of charge and the lower limit of the preset safety range, and the difference between the current state of charge and the upper limit of the preset safety range; the state of charge constraint term is used to characterize the degree to which the predicted state of charge deviates from the center value of the target safety range under the candidate operating mode; when the predicted state of charge exceeds the preset safety range, a penalty is imposed on the corresponding candidate operating mode or it is judged as an infeasible mode.

[0012] Furthermore, the mode switching cost is related to at least one of the following factors: the type of mode change between the actual operating mode in the previous scheduling period and the current candidate operating mode; the magnitude of the change in the state of charge of the energy storage battery in adjacent scheduling periods; the magnitude of the change in the charging and discharging power of the energy storage battery in adjacent scheduling periods; and the direct reverse switching indicator between the charging mode and the discharging mode of the energy storage battery in adjacent scheduling periods. Among these, the mode switching cost corresponding to a direct switch between the charging mode and the discharging mode is higher than the mode switching cost corresponding to switching from the charging mode or the discharging mode to the standby mode. And mode switching cost item The following relationship must be satisfied:

[0013] in, This represents the cost corresponding to the mode switching type between the actual operating mode in the previous scheduling period and the current candidate operating mode. This indicates the magnitude of change in the state of charge of the energy storage battery during adjacent scheduling periods. This indicates the variation in the charging and discharging power of the energy storage battery during adjacent scheduling periods. This indicates whether the current candidate operating mode constitutes a direct reverse switch between charging and discharging modes. , , , This represents the cost coefficient corresponding to the influencing factor, and it occurs when there is a direct reverse switching between charging and discharging modes. The value is greater than zero, otherwise The value is zero.

[0014] Furthermore, the mode switching hysteresis condition and the mode evaluation function are configured in coordination, including one or a combination of the following: mode switching can only be performed after the candidate operating mode appears consecutively a preset number of times; mode switching can only be performed after the candidate operating mode is maintained for a preset duration; direct switching between charging mode and discharging mode requires a standby mode transition; when the change in the state of charge of the energy storage battery is less than the preset change in charge amplitude threshold in two adjacent scheduling periods, the actual operating mode of the previous scheduling period remains unchanged. Among them, the preset number of times, preset duration, and load change amplitude threshold are fed back to the number of mode switching times in the current evaluation window. When the number of mode switching times is higher than the preset number threshold, the preset number of times or the preset duration is automatically increased to strengthen the hysteresis constraint. When the number of mode switching times is lower than the preset number lower limit and the load peak shaving rate is lower than the preset peak shaving target, the preset number of times or the preset duration is reduced to relax the hysteresis constraint. The mode switching benefit is determined based on the difference between the mode evaluation function value corresponding to the target operating mode and the mode evaluation function value corresponding to the actual operating mode in the previous scheduling period in the current scheduling period. This includes: subtracting the mode evaluation function value corresponding to the target operating mode in the current scheduling period from the mode evaluation function value corresponding to the actual operating mode in the previous scheduling period in the current scheduling period to obtain the mode switching benefit; performing mode switching when the mode switching benefit is greater than a preset switching threshold and a preset switching hysteresis condition is met; and maintaining the actual operating mode of the previous scheduling period unchanged when the mode switching benefit is less than or equal to the preset switching threshold.

[0015] Furthermore, an evaluation error is constructed based on the operational results of multiple consecutive scheduling periods. The weight parameters, mode switching cost parameters, and mode switching thresholds in the mode evaluation function are then adaptively updated based on this evaluation error, including: The evaluation window is defined as N consecutive scheduling periods. When the actual load peak shaving rate within the evaluation window is lower than the preset peak shaving target, the weight parameter corresponding to the load response item is increased. When the actual carbon emission change within the evaluation window is lower than the preset emission reduction target, the weight parameter corresponding to the carbon emission item is increased. When the number of mode switching events within the evaluation window is higher than the preset number threshold, the mode switching cost parameter and / or switching hysteresis threshold are increased. When the state of charge out-of-bounds risk within the evaluation window is higher than the preset risk threshold, the weight parameter corresponding to the state of charge constraint item is increased. Update the weight parameters corresponding to the load response item, carbon emission item, and state of charge constraint item according to the following relationships:

[0016]

[0017]

[0018] in, , , These represent the weight parameters of the load response item, carbon emission item, and state of charge constraint item in the current evaluation window, respectively. , , These represent the corresponding weight parameters for the next evaluation window. This indicates the deviation between the peak shaving target and the actual peak shaving rate. This indicates the deviation between the emission reduction target and the actual change in carbon emissions. This represents the deviation between the target state of charge risk and the actual state of charge exceeding the limit risk. , , These represent the update step size for the corresponding weight parameters.

[0019] When the number of mode switching times exceeds a preset threshold within N consecutive scheduling periods, the mode switching cost parameter is increased. and / or preset switching threshold; When the number of mode switching times within N consecutive scheduling periods is lower than the preset minimum number and the load shaving rate is lower than the preset peak shaving target, reduce the mode switching cost parameter. Or preset switching threshold; When the number of mode switching times within N consecutive scheduling periods is equal to the preset number threshold, or falls between the preset lower limit and the preset number threshold, the current mode switching cost parameter and the preset switching threshold remain unchanged.

[0020] Among them, the mode switching cost parameter It is used to characterize the influence of mode switching cost terms in the mode evaluation function.

[0021] Furthermore, the safety constraints, as the feasible domain boundary of the mode evaluation function, work in conjunction with the load response term, carbon emission term, state of charge constraint term, and mode switching cost term, and include at least one of the following: the state of charge of the energy storage battery is within a preset safety range; the predicted charging and discharging power under the current candidate operating mode does not exceed the rated power of the energy storage battery; the predicted state of charge under the current candidate operating mode does not exceed the preset safety range; the switching between charging mode and discharging mode requires a standby mode transition. Based on the load fluctuation index, carbon emission intensity index, and charge safety margin index of the current scheduling period, the feasibility of charging mode, discharging mode, and standby mode is screened. Among them, candidate operating modes that meet at least one of the following conditions are judged as infeasible and eliminated: the predicted state of charge exceeds the preset safety range; the predicted charging and discharging power exceeds the rated power of the energy storage battery; the current candidate operating mode and the actual operating mode of the previous scheduling period constitute a disallowed direct reverse switch. Feasibility screening also includes: when the load fluctuation index represents a peak state, the carbon emission intensity index represents a high carbon emission state, and the state of charge of the energy storage battery is higher than the lower discharge threshold, the discharge mode is retained as the preferred candidate mode; when the load fluctuation index represents a valley state, the carbon emission intensity index represents a low carbon emission state, and the state of charge of the energy storage battery is lower than the upper charging threshold, the charging mode is retained as the preferred candidate mode; when the above conditions are not met, the standby mode is retained as the candidate mode. The evaluation window, consisting of multiple consecutive scheduling periods, can be any one of 2 to 12 consecutive scheduling periods, preferably 4 consecutive scheduling periods; the preset scheduling period can be any one of 5 to 60 minutes, preferably 15 minutes.

[0022] Secondly, the present invention also provides an energy storage charge-discharge switching system based on load fluctuations and carbon factor, the system comprising: The data acquisition module is used to acquire electricity load data for the current scheduling period, carbon emission factor data for the corresponding period, energy storage battery state of charge data, and actual operation mode data for the previous scheduling period. The indicator calculation module is used to calculate the load fluctuation indicator based on the electricity load data, the carbon emission intensity indicator based on the carbon emission factor data, and the charge safety margin indicator based on the energy storage battery state of charge data. The evaluation construction module is used to normalize the load fluctuation index, carbon emission intensity index and charge safety margin index, and to construct the corresponding mode evaluation functions for the candidate operation modes for charging mode, discharging mode and standby mode respectively. The mode determination module is used to determine the target operating mode for the current scheduling period based on the mode evaluation function value corresponding to each candidate operating mode, provided that the safety constraints are met. The switching control module is used to determine the mode switching benefit based on the difference between the mode evaluation function value corresponding to the target operating mode and the mode evaluation function value corresponding to the actual operating mode in the previous scheduling period in the current scheduling period. When the mode switching benefit is greater than the preset switching threshold and the preset switching hysteresis condition is met, the module controls the energy storage battery to switch to the target operating mode. Otherwise, the actual operating mode of the previous scheduling period remains unchanged. The parameter update module is used to construct an evaluation error based on at least one of the following: actual charging and discharging amount, load peak shaving rate, carbon emission change, mode switching number, and state of charge out of bounds risk in multiple consecutive scheduling periods. The module then adaptively updates the weight parameters, mode switching cost parameters, and mode switching threshold in the mode evaluation function based on the evaluation error.

[0023] Furthermore, the evaluation function constructed by the evaluation module includes at least a load response term, a carbon emission term, a state of charge constraint term, and a mode switching cost term; the mode switching cost term is used to characterize at least one of the switching loss, cyclic impact, power change impact, and operational stability impact caused by switching from the actual operating mode in the previous scheduling period to the current candidate operating mode. The parameter update module is configured to update the load response item weight parameters, carbon emission item weight parameters, state of charge constraint item weight parameters, mode switching cost parameters, and mode switching threshold according to different types of evaluation errors.

[0024] Thirdly, the present invention also provides an electronic device, including a processor and a memory, the memory storing machine-executable instructions executable by the processor, the processor executing the machine-executable instructions to perform the above-described method.

[0025] Fourthly, the present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the above-described method.

[0026] As can be seen from the above technical solution, the present invention discloses an energy storage charge-discharge switching method based on load fluctuation and carbon factor, which has the following beneficial effects compared with the prior art: 1. A unified quantitative evaluation mechanism for candidate operating modes has been established. This invention does not independently determine the operating mode based on load threshold, carbon factor threshold, state of charge threshold, and hysteresis rule separately. Instead, it integrates load response, carbon emission impact, state of charge constraints, and mode switching costs into the candidate operating mode evaluation function, enabling different objectives to be compared and coordinated under a unified evaluation framework, thereby improving the overall integrity and consistency of mode selection.

[0027] 2. Integrating mode switching costs into the mode selection process. This invention introduces a mode switching cost term into the candidate operating mode evaluation function, enabling the switching losses, cycle impacts, power change impacts, and operational stability effects caused by mode switching to be quantified and considered in advance during the target mode determination stage, rather than being remedied by additional rules after the mode is determined. This reduces the probability of frequent mode switching while balancing peak shaving and carbon reduction effects, thereby minimizing the impact on energy storage batteries and extending their service life.

[0028] 3. A closed-loop optimization mechanism for multi-objective conflicts has been established. This invention constructs an evaluation error based on the actual load peak shaving rate, carbon emission changes, mode switching frequency, and state of charge (SOC) out-of-bounds risk over multiple consecutive scheduling periods. It then adaptively updates the weight parameters of different evaluation items, mode switching cost parameters, and switching thresholds. This allows the energy storage system to adjust its control focus to address different operational deviations, such as insufficient peak shaving, insufficient emission reduction, or excessive switching frequency, thereby improving its adaptability under complex operating conditions.

[0029] 4. Improved scenario adaptability and operational stability. This invention can dynamically adjust the target operating mode according to different load levels, carbon emission levels, and states of charge. It also suppresses unnecessary switching behavior through benefit determination and hysteresis control, enabling the energy storage system to maintain good peak shaving and carbon reduction effects and stable operating characteristics in different application scenarios such as industrial parks, commercial buildings, data centers, and integrated energy stations.

[0030] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings.

[0031] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0032] To more clearly illustrate the technical solutions in the embodiments of this application 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 some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0033] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof.

[0034] Figure 1 This is a schematic diagram of the energy storage charge / discharge switching method based on load fluctuation and carbon factor provided in an embodiment of the present invention.

[0035] Figure 2 This is a schematic diagram of the evaluation function comparison and switching control logic provided in an embodiment of the present invention.

[0036] Figure 3 This is a schematic diagram of an energy storage charge-discharge switching system based on load fluctuation and carbon factor, provided for an embodiment of the present invention.

[0037] Figure 4 This is a schematic diagram of the electronic device structure provided in an embodiment of the present invention. Detailed Implementation

[0038] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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 some embodiments of the present invention, but not all embodiments.

[0039] In the description of this invention, it should be noted that some processes described in this application specification and drawings include multiple operations that appear in a specific order. However, it should be clearly understood that these operations may be performed in any order or in parallel. Furthermore, various numbers are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0040] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0041] Existing energy storage charging and discharging control schemes typically set control rules around fixed time periods, electricity price signals, load peak shaving and valley filling, or state of charge thresholds. Some schemes introduce carbon emission factors for low-carbon dispatch, while others introduce hysteresis mechanisms to reduce frequent mode switching. However, most of these control factors are set independently and lack a unified quantitative evaluation mechanism.

[0042] In actual operation, if only a single objective is considered or multiple control conditions are simply superimposed, the following problems may easily occur: There is a lack of synergy between load shaving targets, carbon reduction targets, and energy storage battery operation stability targets. Improving one target may lead to the deterioration of another. Specifically, when optimizing the load shaving target independently, the energy storage system tends to frequently switch between charging and discharging states between load peak and valley values, resulting in a surge in the number of mode switching. When optimizing the carbon reduction target independently, the energy storage system tends to track the instantaneous fluctuations of carbon emission factors and make high-frequency charging and discharging adjustments. Both of these will exacerbate the battery cycle shock. More importantly, existing technologies typically treat mode switching losses, cyclic shocks, and power change shocks as additional constraints or post-filtering conditions (such as setting a fixed hysteresis duration) after the candidate mode is determined, rather than incorporating them into the quantitative evaluation elements of the candidate mode selection stage. This results in the inability to predict the cumulative impact of switching behavior on battery life and operational stability during the mode selection stage, thus causing a structural defect of "optimal local indicators and frequent global switching".

[0043] See Figure 1 and Figure 2 As shown, this invention provides a method for switching the charge and discharge of energy storage based on load fluctuations and carbon factors. This method acquires electricity load data, carbon emission factor data, energy storage battery state-of-charge data for the current scheduling period, and actual operating mode data from the previous scheduling period. It then calculates load fluctuation indicators, carbon emission intensity indicators, and charge safety margin indicators, and normalizes each indicator to obtain a unified-dimensional mode evaluation input.

[0044] Based on this, the present invention constructs mode evaluation functions corresponding to candidate operating modes for charging mode, discharging mode, and standby mode, respectively. The mode evaluation function includes at least: a load response term reflecting the contribution of peak shaving and valley filling, a carbon emission term reflecting the impact of grid power extraction on carbon emissions, a state of charge constraint term reflecting the safe operating boundary of energy storage batteries, and a mode switching cost term reflecting at least one of the switching losses, cyclic impacts, power change impacts, and operational stability impacts caused by switching from the actual operating mode in the previous scheduling period to the current candidate operating mode.

[0045] The present invention further determines the target operating mode based on the mode evaluation function value corresponding to each candidate operating mode, and determines the mode switching benefit based on the difference between the target operating mode and the actual operating mode in the previous scheduling period in the current scheduling period. Mode switching is performed only when the mode switching benefit is greater than the preset switching threshold and the preset switching hysteresis condition is met; otherwise, the actual operating mode in the previous scheduling period remains unchanged.

[0046] The present invention also constructs an evaluation error based on at least one of the following factors: actual charging and discharging volume, load peak shaving rate, carbon emission change, mode switching number, and state of charge out-of-bounds risk during multiple consecutive scheduling periods. Based on different types of evaluation errors, the weight parameters of the load response term, carbon emission term, state of charge constraint term, mode switching cost parameter, and mode switching threshold in the mode evaluation function are updated respectively, thereby forming a closed-loop optimization control mechanism of "mode evaluation - benefit determination - switching execution - error feedback - parameter update".

[0047] Unlike existing schemes that set load control, carbon factor scheduling, state of charge protection, and hysteresis control as independent rules, this invention maps the above factors to a candidate operating mode evaluation function and uses the mode switching cost as an intrinsic component in the mode selection process. Then, it combines the evaluation error within the continuous scheduling window to update the weight parameters and switching threshold in a closed loop, thereby forming a synergistic coupling between peak shaving targets, emission reduction targets, and operational stability targets, rather than simply superimposing them.

[0048] It is important to note that embedding the mode switching cost intrinsically into the mode evaluation function is fundamentally different from the existing techniques that attach hysteresis rules or switching restrictions after the mode is determined. Existing techniques impose post-constraints by adding conditions to prevent switching after candidate modes have been ranked based on indicators such as load and carbon factor. This is essentially a "post-hoc correction" of the selection results, which easily leads to response lag and cannot fundamentally prevent high-switching-loss modes from entering the top candidate list. In contrast, this scheme treats the switching cost as a deduction term in the evaluation function, imposing a punitive deweighting on high-switching-loss modes during the candidate mode ranking stage, making it difficult for them to enter the target mode. This suppresses frequent switching at the source of mode selection while preserving the ability to respond to necessary switching. This "pre-emptive suppression" rather than "post-hoc prevention" mechanism allows peak-shaving response, carbon reduction response, and switching suppression to achieve a dynamic balance within a unified quantitative framework, rather than achieving hard constraints through the stacking of multiple rules.

[0049] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments: Example 1: This embodiment provides a method for switching between charging and discharging energy storage based on load fluctuations and carbon factors, which is applied to the user-side energy storage system in an industrial park. The scheduling cycle is set to 15 minutes, and the evaluation window is set to four consecutive scheduling periods.

[0050] During the current scheduling period The system collects electricity load data for the current scheduling period. Time-of-use carbon emission factor data of power grid Energy storage battery state of charge and the actual operating mode of the previous scheduling period. .

[0051] Among them, the preset reference load value The average load value of the target energy user during the same period over the past 7 days; the first load threshold is set to 1.15 times, the second load threshold is set to 0.85 times; preset carbon factor threshold Set the average carbon emission factor of the power grid in the region over the past 24 hours, or set it to 0.58 kg CO2 / kWh; set the lower discharge limit threshold to 30% of the energy storage battery's state of charge, and the upper charging limit threshold to 90% of the energy storage battery's state of charge.

[0052] In one implementation, the load fluctuation index It can be represented as:

[0053] in, , For the corresponding coefficient.

[0054] carbon emission intensity index It can be represented as:

[0055] in, , For the corresponding coefficient.

[0056] Charge safety margin index It can be represented as:

[0057] in, To preset the center value of the safe interval, The width is half the width of the safety interval.

[0058] Subsequently, , and After normalization, the load evaluation quantities were obtained respectively. Carbon emission assessment and state of charge evaluation quantity Then the load assessment quantity will be... Carbon emission assessment and state of charge evaluation quantity As input parameters to the candidate operating mode evaluation function, and combined with the mode switching cost term, the corresponding mode evaluation function values ​​for charging mode, discharging mode, and standby mode are calculated for target operating mode determination. In this embodiment, the load evaluation quantity... Carbon emission assessment and state of charge evaluation quantity These correspond to the load response, carbon emission, and state of charge constraint terms in the mode evaluation function, respectively. After multiplying them by the weight parameters and summing them by weight, the mode switching cost term is subtracted to obtain the comprehensive evaluation value (i.e., the mode evaluation function value) of each candidate operating mode, which is used to compare and determine the target operating mode.

[0059] In one implementation, before selecting the target mode, the system first screens the feasibility of charging mode, discharging mode and standby mode based on the load fluctuation index, carbon emission intensity index and charge safety margin index of the current scheduling period.

[0060] When the predicted state of charge exceeds the preset safety range, the predicted charging and discharging power exceeds the rated power of the energy storage battery, or the current candidate operating mode and the actual operating mode of the previous scheduling period constitute an unacceptable direct reverse switch, the corresponding candidate operating mode is determined to be an infeasible mode and is eliminated.

[0061] In one implementation, if the current scheduling period meets one of the following conditions, the corresponding candidate mode is retained as the preferred candidate mode: When the load fluctuation index represents the peak state and the carbon emission intensity index represents the high carbon emission state, and When the discharge mode is above the lower discharge limit threshold, the discharge mode is retained as the preferred candidate mode. When the load fluctuation index represents the valley state and the carbon emission intensity index represents the low carbon emission state, and When the charging level is below the upper limit threshold, the charging mode remains the preferred candidate mode; If the above conditions are not met, the standby mode is retained as a candidate mode.

[0062] The above rules are only used for feasibility prediction or priority setting of candidate modes. The target operating mode for the current scheduling period is still finally determined by the candidate operating mode evaluation function.

[0063] In a preferred embodiment, candidate operating modes are constructed for charging mode, discharging mode, and standby mode, respectively. Pattern evaluation function Specifically:

[0064] in, This indicates the degree of contribution of candidate operating mode m to the peak shaving and valley filling objective. This indicates the degree to which candidate operating mode m contributes to reducing carbon emissions from grid-side power extraction. This indicates the degree to which candidate operating mode m satisfies the safe charge range of the energy storage battery. This represents the mode switching cost caused by switching from the actual operating mode in the previous scheduling period to the current candidate operating mode m. , , , These are the weight parameters for the corresponding evaluation items.

[0065] In one implementation, the mode switching cost term It can be represented as:

[0066] in, This represents the cost corresponding to the mode switching type between the actual operating mode in the previous scheduling period and the current candidate operating mode. This indicates the magnitude of change in the state of charge of the energy storage battery during adjacent scheduling periods. This indicates the variation in the charging and discharging power of the energy storage battery during adjacent scheduling periods. This indicates whether the current candidate operating mode constitutes a direct reverse switch between charging and discharging modes; when it constitutes a direct reverse switch between charging and discharging modes, The value is greater than zero; otherwise, the value is zero.

[0067] Under the premise of meeting safety constraints, the system calculates the mode evaluation function value corresponding to each candidate operation mode, and selects the candidate operation mode with the optimal evaluation function value as the target operation mode for the current scheduling period. .

[0068] In this embodiment, the evaluation function value adopts the maximization criterion, that is:

[0069] Subsequently, the mode switching benefit is determined based on the difference between the target operating mode and the actual operating mode in the previous scheduling period in the current scheduling period corresponding to the mode evaluation function value. Specifically

[0070] when When the value exceeds the preset switching threshold H(t) and the preset switching hysteresis condition is met, the energy storage battery is controlled to switch to the target operating mode. Otherwise, the actual operating mode of the previous scheduling period will remain unchanged.

[0071] The switching hysteresis conditions include one or a combination of the following: the candidate operating mode appears continuously for at least two scheduling periods; the candidate operating mode is maintained for a preset duration; the direct switching between charging mode and discharging mode requires a standby mode transition; when the SOC change amplitude is less than the preset charge change amplitude threshold in two adjacent scheduling periods, the actual operating mode of the previous scheduling period remains unchanged.

[0072] After the current evaluation window ends, the system will calculate the actual load shaving rate within that evaluation window. Actual carbon emission change Number of mode switching and the risk of exceeding the charge state limit And construct an evaluation error:

[0073]

[0074]

[0075]

[0076] in, Indicates peak reduction target Peak shaving rate of actual load The deviation between them >0 indicates insufficient peak reduction; Indicates emission reduction target Changes in actual carbon emissions The deviation between them >0 indicates insufficient emission reduction; Indicates the state of charge risk target Risk of exceeding the actual state of charge The deviation between them >0 indicates a relatively high security risk; Preset mode switching count target Number of times switching between actual and actual modes Control deviation between >0 indicates that the switching is too frequent.

[0077] In one implementation, the weight parameters and mode switching related parameters can be updated as follows:

[0078]

[0079]

[0080]

[0081]

[0082] in, , , , These represent the weight parameters of the load response item, carbon emission item, state of charge constraint item, and mode switching cost item, respectively, for the current evaluation window. , , , These represent the corresponding weight parameters for the next evaluation window; This indicates the mode switching threshold for the current evaluation window. Indicates the mode switching threshold for the next evaluation window; , , , , To update the step size accordingly.

[0083] Furthermore, to verify the technical effectiveness of the aforementioned unified quantitative evaluation framework, three sets of comparative experiments were set up in the same industrial park scenario: the first set used the method described in this embodiment; the second set used conventional peak shaving and valley filling control based solely on load thresholds and SOC constraints (without carbon emission items or switching cost items); the third set used multi-objective independent rule control (with separate load rules, carbon factor rules, SOC rules, and fixed hysteresis rules, without a unified evaluation function). Under the condition of continuous operation for 30 days and a scheduling cycle of 15 minutes, the experimental results show that: Compared with the second group, the first group reduced the daily average number of mode switching by 42%-58%, reduced the battery equivalent cycle number by more than 35%, and improved the load peak shaving rate by 6%-9%, proving that internalizing the switching cost into the evaluation function can significantly suppress frequent switching without sacrificing the peak shaving effect. Compared with the third group, the first group showed improved stability of carbon emission reduction under the condition of drastic fluctuation of carbon emission factor (the standard deviation decreased by about 40%), and did not exhibit the oscillating switching phenomenon of "carbon factor requiring charging but load requiring discharging" caused by multiple rule conflicts as in the third group, proving that the unified evaluation framework can effectively resolve multi-objective conflicts. The experimental data above show that embedding the mode switching cost into the candidate mode evaluation function in an endogenous manner, and combining it with closed-loop parameter updates, can produce synergistic technical effects that cannot be achieved by single rule superposition or post-constraints.

[0084] As described in the above embodiments, the present invention discloses an energy storage charging and discharging switching method based on load fluctuation and carbon factor. The method includes: acquiring electricity load data, carbon emission factor data, energy storage battery state of charge data, and actual operation mode data of the previous scheduling period; calculating load fluctuation index, carbon emission intensity index, and charge safety margin index, and performing normalization processing; constructing candidate operation mode evaluation functions for charging mode, discharging mode, and standby mode, respectively, including load response item, carbon emission item, state of charge constraint item, and mode switching cost item; determining the target operation mode based on the evaluation function value of each candidate operation mode, and determining the mode switching benefit based on the difference between the evaluation function value corresponding to the target operation mode and the actual operation mode of the previous scheduling period; performing mode switching when the switching benefit is greater than a preset switching threshold and the switching hysteresis condition is met; constructing an evaluation error based on at least one of the actual peak shaving rate, carbon emission change, number of mode switching, and state of charge out-of-bounds risk of multiple consecutive scheduling periods, and adaptively updating the evaluation function weight parameter, mode switching cost parameter, and mode switching threshold. This invention can reduce the probability of frequent mode switching while taking into account both peak shaving and valley filling and low-carbon operation, thereby improving the operational stability and environmental adaptability of energy storage systems.

[0085] Example 2: See Figure 3 As shown, this embodiment also provides an energy storage charge-discharge switching system based on load fluctuations and carbon factors for implementing the above method, including a data acquisition module, an index calculation module, an evaluation construction module, a mode determination module, a switching control module, and a parameter update module. Wherein: The data acquisition module is used to acquire electricity load data for the current scheduling period, carbon emission factor data for the corresponding period, energy storage battery state of charge data, and actual operation mode data for the previous scheduling period. The indicator calculation module is used to calculate the load fluctuation indicator based on the electricity load data, the carbon emission intensity indicator based on the carbon emission factor data, and the charge safety margin indicator based on the energy storage battery state of charge data. The evaluation construction module is used to normalize the load fluctuation index, carbon emission intensity index and charge safety margin index, and to construct the corresponding mode evaluation functions for the candidate operation modes for charging mode, discharging mode and standby mode respectively. The mode determination module is used to determine the target operating mode for the current scheduling period based on the mode evaluation function value corresponding to each candidate operating mode, provided that the safety constraints are met. The switching control module is used to determine the mode switching benefit based on the difference between the mode evaluation function value corresponding to the target operating mode and the mode evaluation function value corresponding to the actual operating mode in the previous scheduling period in the current scheduling period. When the mode switching benefit is greater than the preset switching threshold and the preset switching hysteresis condition is met, the module controls the energy storage battery to switch to the target operating mode. Otherwise, the actual operating mode of the previous scheduling period remains unchanged. The parameter update module is used to construct an evaluation error based on at least one of the following: actual charging and discharging amount, load peak shaving rate, carbon emission change, mode switching number, and state of charge out of bounds risk in multiple consecutive scheduling periods. The module then adaptively updates the weight parameters, mode switching cost parameters, and mode switching threshold in the mode evaluation function based on the evaluation error.

[0086] In one implementation, the mode evaluation function constructed by the evaluation building module includes at least a load response term, a carbon emission term, a state of charge constraint term, and a mode switching cost term. The mode switching cost term is used to characterize at least one of the following: switching loss, cyclic impact, power change impact, and operational stability impact caused by switching from the actual operating mode in the previous scheduling period to the current candidate operating mode; The parameter update module is configured to update the load response item weight parameters, carbon emission item weight parameters, state of charge constraint item weight parameters, mode switching cost parameters, and mode switching threshold according to different types of evaluation errors.

[0087] The system provided in this embodiment of the invention has the same implementation principle and technical effects as the aforementioned method embodiment. For the sake of brevity, any parts not mentioned in this embodiment can be referred to the corresponding content in the aforementioned method embodiment, and will not be repeated here.

[0088] Additionally, refer to Figure 4 As shown, embodiments of the present invention also provide an electronic device that can execute the above-described method or run the above-described system. The electronic device may include a processor, a memory, a communication bus, and a communication interface, and may also include a computer program stored in the memory and executable on the processor.

[0089] In some embodiments, the processor may be composed of integrated circuits, such as a single packaged integrated circuit or multiple integrated circuits packaged with the same or different functions. This includes combinations of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor is the control unit of the electronic device, connecting various components of the device through various interfaces and lines. It executes programs or modules stored in memory and calls data stored in the memory to perform various functions and process data within the electronic device.

[0090] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, electronic devices, or computer program products, etc. 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.

[0091] It should be noted that the word "comprising" does not exclude the presence of components or steps not listed in the claims. The words "a" or "an" preceding a component do not exclude the presence of a plurality of such components. This invention can be implemented by means of hardware comprising several different components and by means of a suitably programmed computer.

[0092] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0093] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for switching charge and discharge of energy storage based on load fluctuations and carbon factor, characterized in that, The method includes: The system acquires electricity load data, carbon emission factor data, energy storage battery state of charge data for the current scheduling period, and actual operation mode data for the previous scheduling period. It then calculates load fluctuation index, carbon emission intensity index, and charge safety margin index, and performs normalization processing. We construct mode evaluation functions for charging mode, discharging mode and standby mode, respectively, which include load response, carbon emission, state of charge constraint and mode switching cost. The target operating mode is determined based on the mode evaluation function value of the candidate operating mode, and the mode switching benefit is determined based on the difference between the mode evaluation function value corresponding to the target operating mode and the actual operating mode in the previous scheduling period. The mode switching is performed when the switching benefit is greater than the preset switching threshold and the preset switching hysteresis condition is met. The evaluation error is constructed based on the operation results of multiple consecutive scheduling periods, and the weight parameters of the mode evaluation function, the mode switching cost parameters, and the mode switching threshold are adaptively updated.

2. The method according to claim 1, characterized in that, Calculating the load fluctuation index for the current scheduling period based on electricity load data includes: comparing the load value of the current scheduling period with a preset reference load value, and calculating the load change between the current scheduling period and the previous scheduling period; determining the load fluctuation index corresponding to the current scheduling period based on the degree of deviation of the load value from the preset reference load value and the load change. The carbon emission intensity index for the current scheduling period is calculated based on carbon emission factor data, including: obtaining the corresponding carbon emission factor value in the time-sharing carbon emission factor sequence for the current scheduling period, and comparing it with the preset carbon factor threshold to determine the carbon emission intensity index for the current scheduling period. The charge safety margin index is calculated based on the state of charge data of the energy storage battery, including: determining the charge safety margin index based on the difference between the current state of charge and the upper and lower limits of the preset safety range.

3. The method according to claim 1, characterized in that, The constructed pattern evaluation function is as follows: in, This represents the mode evaluation function value corresponding to candidate operating mode m; Indicates the load response item; Indicates carbon emissions; Represents the state of charge constraint terms; This indicates the cost of mode switching. , , , These are the weight parameters for the corresponding candidate operating modes; The mode switching cost item The calculation formula is: in, This represents the cost corresponding to the mode switching type between the actual operating mode in the previous scheduling period and the current candidate operating mode. This indicates the magnitude of change in the state of charge of the energy storage battery during adjacent scheduling periods. This indicates the variation in the charging and discharging power of the energy storage battery during adjacent scheduling periods. This indicates whether the current candidate operating mode constitutes a direct reverse switch between charging and discharging modes. , , , This represents the cost coefficient for the corresponding impact factor.

4. The method according to claim 1, characterized in that, The preset switching hysteresis condition includes one or a combination of the following: The mode switch can only be performed after the candidate operating mode appears consecutively a preset number of times; The candidate running mode must remain in the preset time before the mode switching can be performed; Direct switching between charging and discharging modes requires a transition through standby mode. When the change in the state of charge of the energy storage battery is less than the preset threshold for the change in charge within two adjacent scheduling periods, the original mode is maintained. The preset number of times, preset duration, and charge change amplitude threshold are related to the number of mode switching times in the current evaluation window: when the number of mode switching times is higher than the preset number of times threshold, the preset number of times or the preset duration is automatically increased; When the number of mode switching is lower than the preset minimum number and the load shaving rate is lower than the preset peak shaving target, the preset number of switching times is reduced or the preset duration is shortened.

5. The method according to claim 1, characterized in that, An evaluation error is constructed based on at least one of the following factors: actual load peak shaving rate, carbon emission change, mode switching frequency, and state of charge (SOC) out-of-bounds risk over multiple consecutive scheduling periods. The weight parameters of the mode evaluation function, mode switching cost parameters, and mode switching threshold are then adaptively updated, including: The evaluation window is defined as N consecutive scheduling periods. When the actual load peak shaving rate in the evaluation window is lower than the preset peak shaving target, the weight parameter corresponding to the load response item is increased. When the actual change in carbon emissions within the evaluation window is lower than the preset emission reduction target, the weight parameter corresponding to the carbon emission item is increased. When the number of mode switching times in the evaluation window exceeds the preset threshold, increase the mode switching cost parameter and / or the preset switching threshold. When the risk of out-of-bounds charge state within the evaluation window is higher than the preset risk threshold, the weight parameter corresponding to the charge state constraint item is increased. When the number of mode switching times in the evaluation window is lower than the preset lower limit and the load peak shaving rate is lower than the preset peak shaving target, reduce the mode switching cost parameter or the preset switching threshold. The formulas for updating the weight parameters corresponding to the load response item, carbon emission item, and state of charge constraint item are as follows: in, , , These represent the weight parameters of the load response item, carbon emission item, and state of charge constraint item in the current evaluation window, respectively. , , These represent the corresponding weight parameters for the next evaluation window. This indicates the deviation between the peak shaving target and the actual peak shaving rate. This indicates the deviation between the emission reduction target and the actual change in carbon emissions. This represents the deviation between the target state of charge risk and the actual state of charge exceeding the limit risk. , , These represent the update step size for the corresponding weight parameters.

6. The method according to claim 1, characterized in that, Under the premise of satisfying safety constraints, the target operating mode for the current scheduling period is determined based on the mode evaluation function values ​​corresponding to each candidate operating mode; wherein, the safety constraints serve as the feasible domain boundary of the mode evaluation function, and work in conjunction with the load response term, the carbon emission term, the state of charge constraint term, and the mode switching cost term, and include at least one of the following: The energy storage battery's state of charge is within a preset safe range; Under the current candidate operating mode, the predicted charge and discharge power will not exceed the rated power of the energy storage battery. The predicted state of charge under the current candidate operating mode does not exceed the preset safety range; Switching between charging and discharging modes requires a transition through standby mode.

7. The method according to claim 1, characterized in that, Based on the load fluctuation index, carbon emission intensity index, and charge safety margin index of the current scheduling period, the feasibility of charging mode, discharging mode, and standby mode is screened; among them, candidate operating modes that meet at least one of the following criteria are judged as infeasible modes and eliminated: The predicted state of charge exceeds the preset safe range; The predicted charge / discharge power exceeds the rated power of the energy storage battery. The current candidate operating mode and the actual operating mode in the previous scheduling period constitute a disallowed direct reverse switch; The feasibility screening also includes: when the load fluctuation index represents a peak state, the carbon emission intensity index represents a high carbon emission state, and the state of charge of the energy storage battery is higher than the lower discharge threshold, the discharge mode is retained as the preferred candidate mode; when the load fluctuation index represents a valley state, the carbon emission intensity index represents a low carbon emission state, and the state of charge of the energy storage battery is lower than the upper charging threshold, the charging mode is retained as the preferred candidate mode; when the above conditions are not met, the standby mode is retained as the candidate mode.

8. An energy storage charge-discharge switching system based on load fluctuation and carbon factor, characterized in that, The system includes: a data acquisition module, an index calculation module, an evaluation construction module, a mode determination module, a switching control module, and a parameter update module. It uses the method described in any one of claims 1 to 7 to realize energy storage charging and discharging switching.

9. An electronic device, characterized in that, The device includes a processor, a memory, and a computer program stored in the memory, which, when executed by the processor, causes the electronic device to perform the method of any one of claims 1 to 7.

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

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