Energy storage optimal configuration method and system for power grid voltage sag and carbon emission coordination constraints

Abstract

The present application belongs to the technical field of power grid optimization configuration, and particularly relates to a kind of energy storage optimization configuration method and system for power grid voltage sag and carbon emission collaborative constraint, method includes based on power grid voltage sag event monitoring data, constructs the joint probability distribution of voltage sag amplitude and duration;Establish the economic loss function of user node under voltage sag event, and the incremental carbon emission calculation model considering voltage sag compensation operation and conventional energy regulation two kinds of energy storage operation mode;Build the energy storage capacity configuration optimization model with reducing voltage sag risk exposure loss cost and total carbon emission cost as target, solve the model to obtain optimal capacity configuration scheme, risk exposure loss cost is calculated based on joint probability distribution, risk exposure coefficient and economic loss, total carbon emission cost is calculated based on incremental carbon emission.The present application can realize the collaborative promotion of voltage sag management effect and carbon emission, and improve the power quality guarantee precision of key user load.

Description

Technical Field

[0001] This invention belongs to the field of power grid optimization configuration technology, specifically relating to an energy storage optimization configuration method and system oriented towards the coordinated constraints of power grid voltage sag and carbon emissions. Background Technology

[0002] In industrial production, numerous precision equipment and automated production lines are extremely sensitive to power quality. Power quality disturbances such as voltage sags in the power system can easily lead to equipment downtime, production interruptions, and even product scrapping, resulting in economic losses for users. Energy storage systems can not only provide rapid power support during voltage sags, thus mitigating their impact on users, but also, under normal operating conditions, can transfer electrical energy across different time scales through charge and discharge regulation, improving the utilization of renewable energy sources. Energy storage alters the temporal distribution of electrical energy during charging and discharging, and its operational behavior may have a promoting or inhibiting effect on the overall carbon emissions of the power system.

[0003] However, existing energy storage configuration methods typically focus on single-objective optimization, such as peak shaving and valley filling, renewable energy integration, or power quality management, lacking a comprehensive consideration of voltage sag mitigation needs and carbon emission constraints. Furthermore, the analysis of voltage sag events often uses deterministic indicators, which fail to reflect the stochastic nature of these events and quantify the differentiated sensitivity of different user types to voltage sags. This is particularly true for sensitive users such as high-end manufacturing, semiconductors, and data centers, where the economic losses from a single severe voltage sag event often far exceed those from multiple minor voltage sags. Simply using expected economic losses cannot reflect the degree of voltage sag risk exposure faced by sensitive users. Therefore, in the face of voltage sags, how to rationally configure energy storage capacity and formulate appropriate operating strategies to ensure a low-carbon power structure while meeting voltage sag mitigation needs and effectively improving power quality has become a crucial issue in power system planning. Summary of the Invention

[0004] The purpose of this invention is to address the aforementioned problems in the existing technology by providing an energy storage optimization configuration method and system that can meet the requirements of voltage sag mitigation while ensuring the decarbonization of power sources, and effectively improve power quality, while addressing the coordinated constraints of grid voltage sag and carbon emissions.

[0005] To achieve the above objectives, the technical solution of the present invention is as follows:

[0006] In a first aspect, the present invention provides an energy storage optimization configuration method oriented towards the combined constraints of grid voltage sag and carbon emissions, the energy storage optimization configuration method comprising:

[0007] S1. Based on the monitoring data of grid voltage sag events, construct the joint probability distribution of voltage sag amplitude and duration, establish the economic loss function of user nodes under voltage sag events, and a carbon emission increment calculation model considering energy storage operation mode, wherein the energy storage operation mode includes voltage sag compensation operation mode and conventional energy regulation operation mode.

[0008] S2. Construct an energy storage capacity configuration optimization model. The energy storage capacity configuration optimization model aims to reduce the risk exposure loss cost and total carbon emission cost of user nodes under voltage sag events. Solve the energy storage capacity configuration optimization model to obtain the optimal energy storage capacity configuration scheme.

[0009] The risk exposure loss cost of the user node under voltage sag event is calculated based on the joint probability distribution of voltage sag amplitude and duration, the risk exposure coefficient of voltage sag event, and the economic loss of the user node under voltage sag event. The total carbon emission cost is calculated based on the carbon emission increment considering the energy storage operation mode.

[0010] Furthermore, the expression for the carbon emission increment calculation model considering the energy storage operation mode is as follows:

[0011] ;

[0012] ; ;

[0013] ; ;

[0014] In the above formula, The incremental carbon emissions resulting from energy storage, taking into account energy storage operation modes; The increase in carbon emissions generated by energy storage in voltage sag compensation mode; The increase in carbon emissions generated by energy storage operating in conventional energy regulation mode; Electricity stored for conventional energy regulation; The average carbon emission factor during low-carbon periods; The average carbon emission factor during high-carbon periods; This represents the average number of charge-discharge cycles. This refers to the rated capacity of the energy storage. The total discharge capacity used for energy storage to compensate for voltage sags; To improve the overall charging and discharging efficiency of the energy storage system; This represents the average carbon emission level of the power grid. For voltage dip events during the observation time The joint probability distribution of voltage sag amplitude and duration, voltage sag events This refers to the voltage sag amplitude. Duration is Voltage sag events; This represents the total number of all voltage dips during the observation period. This represents the maximum temporary decrease. This is the sensitivity coefficient of node voltage to power injection; , These represent the total number of amplitude intervals and the total number of duration intervals for voltage dips, respectively.

[0015] Furthermore, the formula for calculating the risk exposure loss cost of the user node under a voltage sag event is as follows:

[0016] ;

[0017] In the above formula, For user nodes For voltage sag events Risk exposure loss cost, voltage sag event This refers to the voltage sag amplitude. Duration is Voltage sag events; For user nodes voltage sag event The resulting economic losses; For voltage sag events The risk exposure coefficient; For user nodes The number of voltage dips that occurred; For voltage sag events The joint probability distribution of voltage sag amplitude and duration; , These represent the total number of amplitude intervals and the total number of duration intervals, respectively.

[0018] Furthermore, the economic loss function expression for the user node under a voltage sag event is as follows:

[0019] ;

[0020] In the above formula, For user nodes voltage sag event Economic losses due to voltage dips This refers to the voltage sag amplitude. Duration is Voltage sag events; Losses due to production interruption; Costs related to equipment damage or maintenance; For product quality losses; User load capacity; Value per unit of electricity output; For voltage sag events Downtime caused; For voltage sag events The probability of causing equipment damage; Costs related to equipment damage; Value per unit of product; For voltage sag events The resulting increase in the defect rate.

[0021] Furthermore, the formula for calculating the risk exposure factor of the voltage sag event is as follows:

[0022] ;

[0023] In the above formula, For voltage sag events Risk exposure coefficient, voltage sag event This refers to the voltage sag amplitude. Duration is Voltage sag events; This is the voltage risk amplification factor; This is a risk amplification factor based on duration. , These represent the maximum temporary drop and the maximum duration, respectively.

[0024] Furthermore, the objective function of the energy storage capacity configuration optimization model is:

[0025] ;

[0026] ;

[0027] In the above formula, The objective function is... For user nodes For voltage sag events Risk exposure loss cost, voltage sag event This refers to the voltage sag amplitude after energy storage compensation, which is reduced from... Become Duration is determined by Become Voltage sag events, , ; , These are the sensitivity coefficient of node voltage to power injection and the influence coefficient of power injection on voltage recovery speed, respectively. This refers to the energy storage discharge power; This refers to the rated capacity of the energy storage. The total discharge capacity used for energy storage to compensate for voltage sags; For energy storage The state of charge at any given moment; Total carbon emissions; To account for the carbon emission increment of energy storage operation mode; This represents the baseline carbon emissions without energy storage. The carbon emission increment generated by energy storage in conventional energy regulation operation mode, obtained from the carbon emission increment model based on energy storage operation mode; To improve the overall charging and discharging efficiency of the energy storage system; This represents the average carbon emission level of the power grid. For voltage dip events during the observation time The joint probability distribution of voltage sag amplitude and duration. This represents the total number of all voltage dips during the observation period. The average carbon emission factor during low-carbon periods; The average carbon emission factor during high-carbon periods; Electricity stored for conventional energy regulation; This represents the average number of charge-discharge cycles. The number of user nodes connected to energy storage; This is the carbon emission cost coefficient; This refers to the annualized investment cost of the energy storage system. , , , These are the unit capacity investment cost, unit power investment cost, unit operation and maintenance cost, and rated power of energy storage, respectively. , These are the discount rate and the energy storage life, respectively.

[0028] Furthermore, the constraints of the energy storage capacity configuration optimization model include energy storage energy state and power constraints, user power quality constraints, energy storage support voltage sag constraints, energy storage investment constraints, and grid power balance constraints.

[0029] The energy storage support voltage sag constraint includes:

[0030] ;

[0031] ;

[0032] ;

[0033] ;

[0034] In the above formula, , These are the maximum transient drop and the maximum duration, respectively. For energy storage The state of charge at any given moment; , These are energy storage charging efficiency and discharging efficiency, respectively. Power for energy storage charging; , These are the energy storage charging time and the discharging time, respectively. For time intervals.

[0035] Furthermore, the steps for constructing the joint probability distribution of the voltage sag amplitude and duration include:

[0036] Define the amplitude range of voltage sag and duration range ,in , These represent the total number of amplitude intervals and the total number of duration intervals, respectively. Let the observation time be the time within which the interval falls. The number of voltage sag events is Then the voltage sag event is obtained. The joint probability distribution of voltage sag amplitude and duration is as follows: ,in This represents the total number of all voltage dips during the observation period.

[0037] Furthermore, a voltage sag event is identified when the voltage at a user node in the power grid meets the following conditions: ,in for User node voltage at time , This is the voltage sag judgment coefficient. This is the rated voltage; for user nodes The For each voltage sag event, its sag magnitude and duration are extracted using the following formula:

[0038] ; ;

[0039] In the above formula, For user nodes The The lowest voltage of the secondary voltage dip event; , User nodes The The end time and start time of the voltage sag event.

[0040] Secondly, the present invention provides an energy storage optimization configuration system oriented towards the combined constraints of grid voltage sag and carbon emissions. The energy storage optimization configuration system is based on the aforementioned energy storage optimization configuration method oriented towards the combined constraints of grid voltage sag and carbon emissions. The energy storage optimization configuration system includes:

[0041] The pre-calculation module is used to construct a joint probability distribution of voltage sag amplitude and duration based on grid voltage sag event monitoring data, establish an economic loss function for user nodes under voltage sag events, and a carbon emission increment calculation model considering energy storage operation modes, including voltage sag compensation operation mode and conventional energy regulation operation mode.

[0042] The capacity optimization configuration module is used to construct an energy storage capacity configuration optimization model. The optimization model aims to reduce the risk exposure loss cost and total carbon emission cost of user nodes under voltage sag events. Solving the energy storage capacity configuration optimization model yields the optimal energy storage capacity configuration scheme. The risk exposure loss cost of user nodes under voltage sag events is calculated based on the joint probability distribution of voltage sag amplitude and duration, the risk exposure coefficient of voltage sag events, and the economic loss of user nodes under voltage sag events. The total carbon emission cost is calculated based on the carbon emission increment considering the energy storage operation mode.

[0043] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0044] The method described in this invention firstly constructs a joint probability distribution of voltage sag amplitude and duration based on grid voltage sag event monitoring data. This joint probability distribution is then incorporated into the calculation of risk exposure loss costs, enabling cost calculations to cover voltage sag scenarios under different amplitude-duration combinations. This enhances the adaptability and robustness of energy storage configuration schemes to random disturbances caused by actual grid voltage sags. Secondly, a risk exposure coefficient is introduced when calculating the economic loss from voltage sags at user nodes. This amplifies the losses from severe voltage sag events faced by sensitive users in a differentiated manner, avoiding the problem that simply using expected economic losses cannot reflect the degree of risk exposure for sensitive users. Finally, an energy storage capacity configuration optimization model is constructed with the dual objectives of reducing the risk exposure loss cost of voltage sags at user nodes and the total carbon emission cost. This model integrates the voltage sag compensation operation mode and the conventional energy regulation operation mode of energy storage into the calculation of carbon emission increments, allowing the energy storage configuration scheme to simultaneously consider the needs of power quality management and low-carbon operation. The above design can achieve a synergistic improvement in voltage sag management benefits and carbon emissions during the energy storage capacity configuration stage, improving the accuracy of power quality assurance for key user loads and the rationality of investment decisions. Attached Figure Description

[0045] Figure 1 This is a flowchart of the method described in this invention.

[0046] Figure 2 This is a structural block diagram of the system described in this invention. Detailed Implementation

[0047] The present invention will now be described in further detail with reference to specific embodiments and accompanying drawings.

[0048] Example 1:

[0049] See Figure 1 An energy storage optimization configuration method oriented towards the combined constraints of grid voltage sag and carbon emissions is carried out in the following steps:

[0050] S1. Based on the monitoring data of grid voltage sag events, construct the joint probability distribution of voltage sag amplitude and duration, establish the economic loss function of user nodes under voltage sag events, and calculate the carbon emission increment considering energy storage operation mode, wherein the energy storage operation mode includes voltage sag compensation operation mode and conventional energy regulation operation mode.

[0051] Specifically, firstly, voltage sag monitoring data from power grid user nodes is collected. Based on preset voltage sag criteria, the voltage monitoring data is used for event identification to extract voltage sag events. A voltage sag event is identified when the voltage at a user node in the power grid meets the following criteria: ,in for User node voltage at time , This is the voltage sag judgment coefficient. The rated voltage is used. Then, for each voltage sag event, its sag amplitude and duration characteristics are extracted. For user nodes... The For each voltage sag event, its sag magnitude and duration are extracted using the following formula:

[0052] ; ;

[0053] In the above formula, For user nodes The The lowest voltage of the secondary voltage dip event; , User nodes The The end time and start time of the voltage sag event.

[0054] Specifically, the amplitude and duration of voltage sag events are divided into intervals, the occurrence frequency of each type of sag event is counted, and its proportion in all voltage sag events is calculated to obtain the joint probability distribution of voltage sag amplitude and duration, which is used to characterize the distribution pattern of voltage sag events of different severity. The steps for constructing the joint probability distribution of voltage sag amplitude and duration include:

[0055] Define the amplitude range of voltage sag and duration range ,in , These represent the total number of amplitude intervals and the total number of duration intervals, respectively. Let the observation time be the time within which the interval falls. The number of voltage sag events is Then the voltage sag event is obtained. The joint probability distribution of voltage sag amplitude and duration is as follows: ,in This represents the total number of all voltage dips during the observation period.

[0056] Specifically, energy storage operation modes are divided into voltage sag compensation mode and conventional energy regulation mode, and the incremental impact of energy storage systems on electricity carbon emissions is calculated for each of these two modes. Under conventional operation, energy storage reduces overall carbon emissions by transferring electrical energy between different time periods through charging during low-carbon emission periods and discharging during high-carbon emission periods. When a voltage sag event occurs... To boost node voltage, energy storage is needed to inject power into the nodes. However, due to energy losses during the charging and discharging process of energy storage, voltage sag compensation operation will result in a certain degree of carbon emissions. Therefore, the following carbon emission increment calculation model considering energy storage operation mode is constructed:

[0057] ;

[0058] ; ;

[0059] ; ;

[0060] In the above formula, The incremental carbon emissions resulting from energy storage, taking into account energy storage operation modes; The increase in carbon emissions generated by energy storage in voltage sag compensation mode; The increase in carbon emissions generated by energy storage operating in conventional energy regulation mode; Electricity stored for conventional energy regulation; The average carbon emission factor during low-carbon periods; The average carbon emission factor during high-carbon periods. ; This represents the average number of charge-discharge cycles. This refers to the rated capacity of the energy storage. The total discharge capacity used for energy storage to compensate for voltage sags; To improve the overall charging and discharging efficiency of the energy storage system; This represents the average carbon emission level of the power grid. For voltage dip events during the observation time The joint probability distribution of voltage sag amplitude and duration, voltage sag events This refers to the voltage sag amplitude. Duration is Voltage sag events; This represents the total number of all voltage dips during the observation period. This represents the maximum temporary decrease. This is the sensitivity coefficient of node voltage to power injection; , These represent the total number of amplitude intervals and the total number of duration intervals for voltage dips, respectively.

[0061] Specifically, the economic loss function expression for the user node under a voltage sag event is as follows:

[0062] ;

[0063] In the above formula, For user nodes voltage sag event Economic losses due to voltage dips This refers to the voltage sag amplitude. Duration is Voltage sag events; Losses due to production interruption; Costs related to equipment damage or maintenance; For product quality losses; User load capacity; Value per unit of electricity output; For voltage sag events Downtime caused; For voltage sag events The probability of causing equipment damage; Costs related to equipment damage; Value per unit of product; For voltage sag events The resulting increase in the defect rate.

[0064] S2. Construct an energy storage capacity configuration optimization model. The energy storage capacity configuration optimization model aims to reduce the risk exposure loss cost and total carbon emission cost of user nodes under voltage sag events. Solve the energy storage capacity configuration optimization model to obtain the optimal energy storage capacity configuration scheme.

[0065] Specifically, the risk exposure loss cost of the user node under a voltage sag event is calculated based on the joint probability distribution of the voltage sag amplitude and duration, the risk exposure coefficient of the voltage sag event, and the economic loss of the user node under the voltage sag event. The specific calculation formula is as follows:

[0066] ;

[0067] In the above formula, For user nodes For voltage sag events Risk exposure loss cost, voltage sag event This refers to the voltage sag amplitude. Duration is Voltage sag events; For user nodes voltage sag event The resulting economic losses; For voltage sag events The risk exposure coefficient; For user nodes The number of voltage dips that occurred; For voltage sag events The joint probability distribution of voltage sag amplitude and duration; , These represent the total number of amplitude intervals and the total number of duration intervals, respectively.

[0068] Specifically, in the risk exposure loss cost, a risk exposure coefficient is introduced to amplify the risk of voltage sag events that approach or exceed the user's tolerance boundary. Users with a higher proportion of severe voltage sag events will receive a higher risk exposure value, thus receiving priority in compensation resource allocation during subsequent configuration optimization. The formula for calculating the risk exposure coefficient of the voltage sag event is as follows:

[0069] ;

[0070] In the above formula, For voltage sag events Risk exposure factor, voltage sag event This refers to the voltage sag amplitude. Duration is Voltage sag events; This is the voltage risk amplification factor; This is a risk amplification factor based on duration. , These represent the maximum temporary drop and the maximum duration, respectively.

[0071] Specifically, the objective function of the energy storage capacity configuration optimization model is:

[0072] ;

[0073] ;

[0074] In the above formula, The objective function is... For user nodes For voltage sag events Risk exposure loss cost, voltage sag event This refers to the voltage sag after energy storage compensation, which is reduced from... Become Duration is determined by Become Voltage sag events, , ; , These are the sensitivity coefficient of node voltage to power injection and the influence coefficient of power injection on voltage recovery speed, respectively. This refers to the energy storage discharge power; This refers to the rated capacity of the energy storage. The total discharge capacity used for energy storage to compensate for voltage sags; For energy storage The state of charge at any given moment; Total carbon emission cost, which is based on the baseline carbon emissions without energy storage. And considering the incremental carbon emissions from energy storage operation modes Calculated; The carbon emission increment generated by energy storage in conventional energy regulation operation mode, obtained from the carbon emission increment model based on energy storage operation mode; For the overall efficiency of energy storage charging and discharging; This represents the average carbon emission level of the power grid. For voltage dip events during the observation time The joint probability distribution of voltage sag amplitude and duration. This represents the total number of all voltage dips during the observation period. The average carbon emission factor during low-carbon periods; The average carbon emission factor during high-carbon periods; Electricity stored for conventional energy regulation; This represents the average number of charge-discharge cycles. The number of user nodes connected to energy storage; This is the carbon emission cost coefficient; This refers to the annualized investment cost of the energy storage system. , , , These are the unit capacity investment cost, unit power investment cost, unit operation and maintenance cost, and rated power of energy storage, respectively. , These are the discount rate and the energy storage life, respectively.

[0075] Specifically, the constraints of the energy storage capacity configuration optimization model include energy storage state of being and power constraints, user power quality constraints, energy storage support voltage sag constraints, energy storage investment constraints, and grid power balance constraints; the energy storage state of being and power constraints include:

[0076] ;

[0077] In the above formula, , They are respectively The energy storage charging and discharging power at any given time;

[0078] To limit the voltage sag and its duration after energy storage compensation to within the user's acceptable range, user power quality constraints are set as follows:

[0079] ;

[0080] To ensure that energy storage has sufficient capacity to support voltage sag events, energy storage voltage sag support constraints are set, including:

[0081] ;

[0082] ;

[0083] ;

[0084] ;

[0085] In the above formula, , These are the maximum temporary drop and the maximum duration, respectively. For energy storage The state of charge at any given moment; , These are energy storage charging efficiency and discharging efficiency, respectively. Power for energy storage charging; , These are the energy storage charging time and the discharging time, respectively. For time intervals;

[0086] The energy storage investment constraints include:

[0087] ;

[0088] In the above formula, Budget for energy storage investment;

[0089] The power grid power balance constraints include:

[0090] ;

[0091] In the above formula, , , They represent The grid power supply, energy storage power, and user load power at any given time are displayed. A positive value for energy storage power indicates energy discharge, while a negative value indicates energy charging.

[0092] Example 2:

[0093] See Figure 2 A power storage optimization configuration system for grid voltage sag and carbon emission co-constraints includes a pre-calculation module and a capacity optimization configuration module. The pre-calculation module is used to construct a joint probability distribution of voltage sag amplitude and duration based on grid voltage sag event monitoring data, establish an economic loss function for user nodes under voltage sag events, and calculate a carbon emission increment model considering energy storage operation modes, including a voltage sag compensation operation mode and a conventional energy regulation operation mode. Specifically, the pre-calculation module executes S1 in Example 1, which will not be elaborated here. The capacity optimization configuration module is used to construct an energy storage capacity configuration optimization model. The energy storage capacity configuration optimization model aims to reduce the risk exposure loss cost and total carbon emission cost of user nodes under voltage sag events. Solving the energy storage capacity configuration optimization model yields the optimal energy storage capacity configuration scheme. Specifically, the risk exposure loss cost of user nodes under voltage sag events is calculated based on the joint probability distribution of voltage sag amplitude and duration, the risk exposure coefficient of voltage sag events, and the economic loss of user nodes under voltage sag events. The total carbon emission cost is calculated based on the carbon emission increment considering the energy storage operation mode. In detail, the capacity optimization configuration module executes S2 in Example 1, which will not be elaborated further here.

[0094] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program goods. 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, this application can take the form of a computer program goods 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.

[0095] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, as well as combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0096] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0097] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0098] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. An energy storage optimization configuration method oriented towards the combined constraints of grid voltage sag and carbon emissions, characterized in that: The energy storage optimization configuration method includes: S1. Based on the monitoring data of grid voltage sag events, construct a joint probability distribution of voltage sag amplitude and duration; establish an economic loss function for user nodes under voltage sag events, and a carbon emission increment calculation model considering energy storage operation mode, wherein the energy storage operation mode includes voltage sag compensation operation mode and conventional energy regulation operation mode. S2. Construct an energy storage capacity configuration optimization model. The energy storage capacity configuration optimization model aims to reduce the risk exposure loss cost and total carbon emission cost of user nodes under voltage sag events. Solve the energy storage capacity configuration optimization model to obtain the optimal energy storage capacity configuration scheme. The risk exposure loss cost of the user node under voltage sag event is calculated based on the joint probability distribution of voltage sag amplitude and duration, the risk exposure coefficient of voltage sag event, and the economic loss of the user node under voltage sag event. The total carbon emission cost is calculated based on the carbon emission increment considering the energy storage operation mode.

2. The energy storage optimization configuration method for the combined constraints of grid voltage sag and carbon emissions as described in claim 1, characterized in that: The expression for the carbon emission increment calculation model considering energy storage operation mode is as follows: ； ； ； ； ； In the above formula, The incremental carbon emissions resulting from energy storage, taking into account energy storage operation modes; The increase in carbon emissions generated by energy storage in voltage sag compensation mode; The increase in carbon emissions generated by energy storage operating in conventional energy regulation mode; Electricity stored for conventional energy regulation; The average carbon emission factor during low-carbon periods; The average carbon emission factor during high-carbon periods; This represents the average number of charge-discharge cycles. This refers to the rated capacity of the energy storage. The total discharge capacity used for energy storage to compensate for voltage sags; To improve the overall charging and discharging efficiency of the energy storage system; This represents the average carbon emission level of the power grid. For voltage dip events during the observation time The joint probability distribution of voltage sag amplitude and duration, voltage sag events This refers to the voltage sag amplitude. Duration is Voltage sag events; This represents the total number of all voltage dips during the observation period. This represents the maximum temporary decrease. This is the sensitivity coefficient of node voltage to power injection; , These represent the total number of amplitude intervals and the total number of duration intervals for voltage dips, respectively.

3. The energy storage optimization configuration method for the coordinated constraints of grid voltage sag and carbon emissions as described in claim 1 or 2, characterized in that: The formula for calculating the risk exposure loss cost of the user node under a voltage sag event is as follows: ； In the above formula, For user nodes For voltage sag events Risk exposure loss cost, voltage sag event This refers to the voltage sag amplitude. Duration is Voltage sag events; For user nodes voltage sag event The resulting economic losses; For voltage sag events The risk exposure coefficient; For user nodes The number of voltage dips that occurred; For voltage sag events The joint probability distribution of voltage sag amplitude and duration; , These represent the total number of amplitude intervals and the total number of duration intervals, respectively.

4. The energy storage optimization configuration method for the coordinated constraints of grid voltage sag and carbon emissions as described in claim 1 or 2, characterized in that: The economic loss function expression for the user node under a voltage sag event is as follows: ； In the above formula, For user nodes voltage sag event Economic losses due to voltage dips This refers to the voltage sag amplitude. Duration is Voltage sag events; Losses due to production interruption; Costs related to equipment damage or maintenance; For product quality losses; User load capacity; Value per unit of electricity output; For voltage sag events Downtime caused; For voltage sag events The probability of causing equipment damage; Costs related to equipment damage; Value per unit of product; For voltage sag events The resulting increase in the defect rate.

5. The energy storage optimization configuration method for the coordinated constraints of grid voltage sag and carbon emissions as described in claim 1 or 2, characterized in that: The formula for calculating the risk exposure factor of the voltage sag event is as follows: ； In the above formula, For voltage sag events Risk exposure coefficient, voltage sag event This refers to the voltage sag amplitude. Duration is Voltage sag events; This is the voltage risk amplification factor; This is a risk amplification factor based on duration. , These represent the maximum temporary drop and the maximum duration, respectively.

6. The energy storage optimization configuration method for the coordinated constraints of grid voltage sag and carbon emissions as described in claim 1 or 2, characterized in that: The objective function of the energy storage capacity configuration optimization model is: ； ； In the above formula, The objective function is... For user nodes For voltage sag events Risk exposure loss cost, voltage sag event This refers to the voltage sag amplitude after energy storage compensation, which is reduced from... Become Duration is determined by Become Voltage sag events, , ; , These are the sensitivity coefficient of node voltage to power injection and the influence coefficient of power injection on voltage recovery speed, respectively. This refers to the energy storage discharge power; This refers to the rated capacity of the energy storage. The total discharge capacity used for energy storage to compensate for voltage sags; For energy storage The state of charge at any given moment; Total carbon emissions; To account for the carbon emission increment of energy storage operation mode; This represents the baseline carbon emissions without energy storage. The carbon emission increment generated by energy storage in conventional energy regulation operation mode, obtained from the carbon emission increment model based on energy storage operation mode; To improve the overall charging and discharging efficiency of the energy storage system; This represents the average carbon emission level of the power grid. For voltage dip events during the observation time The joint probability distribution of voltage sag amplitude and duration. This represents the total number of all voltage dips during the observation period. The average carbon emission factor during low-carbon periods; The average carbon emission factor during high-carbon periods; Electricity stored for conventional energy regulation; This represents the average number of charge-discharge cycles. The number of user nodes connected to energy storage; This is the carbon emission cost coefficient; This refers to the annualized investment cost of the energy storage system. , , , These are the unit capacity investment cost, unit power investment cost, unit operation and maintenance cost, and rated power of energy storage, respectively. , These are the discount rate and the energy storage life, respectively.

7. The energy storage optimization configuration method for the combined constraints of grid voltage sag and carbon emissions as described in claim 6, characterized in that: The constraints of the energy storage capacity configuration optimization model include energy storage energy state and power constraints, user power quality constraints, energy storage support voltage sag constraints, energy storage investment constraints, and grid power balance constraints. The energy storage support voltage sag constraint includes: ； ； ； ； In the above formula, , These are the maximum transient drop and the maximum duration, respectively. For energy storage The state of charge at any given moment; , These are energy storage charging efficiency and discharging efficiency, respectively. Power for energy storage charging; , These are the energy storage charging time and the discharging time, respectively. For time intervals.

8. The energy storage optimization configuration method for the coordinated constraints of grid voltage sag and carbon emissions as described in claim 1 or 2, characterized in that: The steps for constructing the joint probability distribution of the voltage sag amplitude and duration include: Define the amplitude range of voltage sag and duration range ,in , These represent the total number of amplitude intervals and the total number of duration intervals, respectively. Let the observation time be the time within which the interval falls. The number of voltage sag events is Then the voltage sag event is obtained. The joint probability distribution of voltage sag amplitude and duration is as follows: ,in This represents the total number of all voltage dips during the observation period.

9. The energy storage optimization configuration method for the coordinated constraints of grid voltage sag and carbon emissions as described in claim 1 or 2, characterized in that: A voltage sag event is identified when the voltage at a user node in the power grid meets the following conditions: ,in for User node voltage at time , This is the voltage sag judgment coefficient. This is the rated voltage; for user nodes The For each voltage sag event, its sag magnitude and duration are extracted using the following formula: ； ； In the above formula, For user nodes The The lowest voltage of the secondary voltage dip event; , User nodes The The end time and start time of the voltage sag event.

10. An energy storage optimization configuration system oriented towards the combined constraints of grid voltage sag and carbon emissions, wherein the energy storage optimization configuration system is based on the energy storage optimization configuration method oriented towards the combined constraints of grid voltage sag and carbon emissions as described in any one of claims 1-9, characterized in that: The energy storage optimization configuration system includes: The pre-calculation module is used to construct a joint probability distribution of voltage sag amplitude and duration based on grid voltage sag event monitoring data, establish an economic loss function for user nodes under voltage sag events, and a carbon emission increment calculation model considering energy storage operation modes, including voltage sag compensation operation mode and conventional energy regulation operation mode. The capacity optimization configuration module is used to construct an energy storage capacity configuration optimization model. The optimization model aims to reduce the risk exposure loss cost and total carbon emission cost of user nodes under voltage sag events. Solving the energy storage capacity configuration optimization model yields the optimal energy storage capacity configuration scheme. The risk exposure loss cost of user nodes under voltage sag events is calculated based on the joint probability distribution of voltage sag amplitude and duration, the risk exposure coefficient of voltage sag events, and the economic loss of user nodes under voltage sag events. The total carbon emission cost is calculated based on the carbon emission increment considering the energy storage operation mode.

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