A user-side energy storage resource operation method, device, equipment and medium

Abstract

The application discloses a user-side energy storage resource operation method, device, equipment and medium, applied to the electric power field, through acquiring the predicted operation data and energy storage state data of the user-side comprehensive energy system, and determining the degradation cost coefficient according to the preset health stage to which the energy storage battery health state belongs, the energy storage degradation cost can be adjusted with the change of the battery health state, thereby improving the accuracy of the energy storage life cost representation; through constructing a distribution robust opportunity constraint according to the historical operation data, and combining the distribution robust opportunity constraint with the energy storage degradation cost and the source-load-storage constraint to construct a day-ahead economic dispatching model, the adaptability of the day-ahead dispatching plan to uncertain operation conditions can be improved while considering the operation cost; in the intra-day operation stage, the intra-day correction model is solved by taking the day-ahead dispatching plan as a reference track, the day-ahead plan can be corrected according to the actual operation condition, and the power deviation penalty and the renewable energy abandonment penalty are reduced.

Description

Technical Field

[0001] This invention relates to the field of power, and in particular to a method, apparatus, equipment and medium for operating user-side energy storage resources. Background Technology

[0002] In the operation of integrated energy systems on the user side, energy storage resources typically face the combined impact of factors such as fluctuations in user energy demand, changes in renewable energy output, energy price fluctuations, performance degradation of energy storage devices, and the coordinated operation of multiple types of energy-consuming devices. Existing energy storage operation methods struggle to balance system economics, energy supply reliability, energy storage utilization efficiency, and equipment lifespan under complex operating environments. This can easily lead to problems such as mismatches between energy storage charging and discharging schedules and actual energy demand, increased system operating costs, insufficient renewable energy absorption capacity, and long-term performance degradation of energy storage devices. Therefore, improving the overall operational optimization effect of user-side energy storage resources in complex integrated energy operation scenarios has become an urgent technical problem to be solved. Summary of the Invention

[0003] In view of this, the purpose of the present invention is to provide a method, apparatus, equipment and medium for operating user-side energy storage resources, which solves the problem of poor overall operating performance of user-side energy storage resources in complex integrated energy operation scenarios in the prior art.

[0004] To address the aforementioned technical problems, this invention provides a user-side energy storage resource operation method, comprising: Acquire predictive operating data and energy storage status data of the user-side integrated energy system; wherein, the energy storage status data includes the health status of the energy storage battery; Based on the preset health stage to which the health status belongs, the corresponding degradation cost coefficient is determined, and the energy storage degradation cost is determined based on the degradation cost coefficient. Construct a multi-bar opportunity constraint based on historical operating data of the user-side integrated energy system; Based on the energy storage degradation cost, the opportunity constraint of the distributed energy storage rod, and the source-load-storage constraint of the user-side integrated energy system, a day-ahead economic dispatch model with the goal of minimizing operating costs is constructed, and the day-ahead economic dispatch model is solved based on the predicted operating data to obtain the day-ahead dispatch plan. During the intraday operation phase, with the day-ahead scheduling plan as the reference trajectory and minimizing the power deviation penalty and renewable energy curtailment penalty, the intraday correction model is solved in a rolling manner under the conditions of satisfying the source-load-storage constraints and the distributed bar opportunity constraints to obtain the intraday execution plan. The operation of energy storage resources and dispatchable resources is controlled according to the intraday execution plan.

[0005] Optionally, based on the preset health stage to which the health status belongs, a corresponding degradation cost coefficient is determined, including: The entire life cycle of the energy storage battery is set into multiple health state ranges in descending order of health state values, and a degradation cost coefficient is configured for each of the health state ranges. The health status of the energy storage battery is matched with the multiple health status intervals to determine the target health stage of the energy storage battery. Read the target degradation cost coefficient corresponding to the target health stage.

[0006] Optionally, determining the energy storage degradation cost based on the degradation cost coefficient includes: Obtain the energy storage charging power, energy storage discharging power, and scheduling time interval within the current scheduling period; The energy storage charging power is added to the energy storage discharging power to obtain the energy storage power throughput during the current scheduling period; Multiply the energy storage power throughput, the scheduling time interval, and the degradation cost coefficient to obtain the energy storage degradation cost for the current scheduling period; The energy storage degradation cost for each scheduling period within the target scheduling cycle is accumulated to obtain the energy storage degradation cost for the target scheduling cycle.

[0007] Optionally, based on the energy storage degradation cost, the distributed energy storage opportunity constraint, and the source-load-storage constraint of the user-side integrated energy system, a day-ahead economic dispatch model is constructed with the goal of minimizing operating costs, including: Based on the energy storage charging power, energy storage discharging power, charging efficiency, discharging efficiency, self-discharge rate, and scheduling time interval, calculate the change in state of charge between adjacent scheduling periods. Based on the changes in state of charge, the upper limit of energy storage charging power, the upper limit of energy storage discharging power, the upper limit of state of charge, the lower limit of state of charge, and the mutual exclusion relationship between charging and discharging, energy storage operation constraints are established. Based on the input energy, output energy, and energy conversion efficiency of the schedulable resources, establish the operational constraints of the schedulable resources of electrical energy; Establish an electrical energy balance relationship based on electrical load, wind power output, photovoltaic power output, energy storage charging and discharging power, grid interaction power, and dispatchable resource output; Construct corresponding sub-Bruker opportunity constraints based on the aforementioned electrical energy balance relationship; Based on the energy storage degradation cost, an objective function is established with the goal of minimizing operating costs; the energy storage operation constraints, the schedulable resource operation constraints, the power balance relationship, and the corresponding distributed bar opportunity constraints are used as constraints; and the day-ahead economic dispatch model is constructed according to the objective function and the constraints.

[0008] Optionally, based on historical operating data of the user-side integrated energy system, a sub-Bluergy opportunity constraint is constructed, including: Obtain historical load forecasts, renewable energy output forecasts, and electricity price forecasts for multiple historical dispatch periods; Obtain the historical measured values ​​of load, renewable energy output, and electricity price corresponding to the historical predicted load value, the historical predicted renewable energy output value, and the historical predicted electricity price value; By subtracting the historical measured values ​​from the historical predicted values ​​within the same historical dispatch period, we can obtain load forecasting error samples, renewable energy output forecasting error samples, and electricity price forecasting error samples. Calculate the first moment information based on the load forecasting error sample, the renewable energy output forecasting error sample, and the electricity price forecasting error sample; Construct a fuzzy set of prediction error based on the first-order moment information and the range of values ​​of the prediction error; Based on the fuzzy set of prediction errors and the preset risk tolerance, the energy balance relationship in the day-ahead economic dispatch model is constructed as a sub-Bruker chance constraint.

[0009] Optionally, during the intraday operation phase, using the day-ahead scheduling plan as a reference trajectory and aiming to minimize power deviation penalties and renewable energy curtailment penalties, the intraday correction model is solved on a rolling basis under the conditions of satisfying the source-load-storage constraints and the distributed bar opportunity constraints to obtain the intraday execution plan, including: Within the current intraday rolling cycle, obtain real-time load, real-time renewable energy output, real-time electricity price, and real-time energy storage status; The operating deviation of the current intraday rolling cycle is calculated based on the real-time load, the real-time renewable energy output, the real-time electricity price, the real-time energy storage status, and the day-ahead dispatch plan. When the operating deviation meets the preset correction conditions, the energy storage charging and discharging power, grid interaction power and dispatchable resource output in the current intraday rolling cycle and subsequent rolling cycles are set as variables to be corrected. Based on the variable to be corrected, the source load storage constraint, and the sub-Blule bar opportunity constraint, the intraday correction model is solved in a rolling manner, and the intraday execution plan is generated based on the rolling solution results.

[0010] Optionally, the day-ahead economic scheduling model is solved based on the predicted operational data to obtain the day-ahead scheduling plan, including: When the user-side integrated energy system includes multiple user-side energy subsystems and shared energy storage resources, a shared energy storage two-layer optimization model is constructed; wherein, the upper layer model of the two-layer optimization model uses the available capacity variable of the shared energy storage resources as the decision variable, and the lower layer model of the two-layer optimization model uses the operating variables of multiple user-side energy subsystems as the decision variable. The optimality conditions of the lower-level model are converted into the constraints of the upper-level model based on the KKT conditions. The Big-M method is used to linearize the complementary terms in the transformed constraints, resulting in a single-level mixed-integer linear programming model. Based on the predicted operating data, the single-layer mixed integer linear programming model is solved to obtain the shared energy storage capacity configuration scheme, the shared energy storage charging and discharging plan, and the collaborative operation scheme of multiple user-side energy subsystems.

[0011] The present invention also provides a user-side energy storage resource operation device, comprising: The data acquisition module is used to acquire the predicted operation data and energy storage status data of the user-side integrated energy system; wherein, the energy storage status data includes the health status of the energy storage battery; The cost calculation module is used to determine the corresponding degradation cost coefficient according to the preset health stage to which the health status belongs, and to determine the energy storage degradation cost based on the degradation cost coefficient. The constraint construction module is used to construct sub-Bruker opportunity constraints based on historical operating data of the user-side integrated energy system. The model building module is used to construct a day-ahead economic dispatch model with the goal of minimizing operating costs based on the energy storage degradation cost, the opportunity constraints of the distributed energy rod, and the source-load-storage constraints of the user-side integrated energy system, and to solve the day-ahead economic dispatch model based on the predicted operating data to obtain the day-ahead dispatch plan. The rolling correction module is used during the intraday operation phase to solve the intraday correction model on a rolling basis, with the day-ahead scheduling plan as the reference trajectory and the goal of minimizing the power deviation penalty and renewable energy curtailment penalty, while satisfying the source-load-storage constraints and the sub-blob opportunity constraints, to obtain the intraday execution plan. The execution module is used to control the operation of energy storage resources and dispatchable resources according to the intraday execution plan.

[0012] The present invention also provides a user-side energy storage resource operation device, comprising: Memory, used to store computer programs; A processor is used to execute the computer program to implement the user-side energy storage resource operation method described above.

[0013] The present invention also provides a computer-readable storage medium storing computer-executable instructions, which, when loaded and executed by a processor, implement the user-side energy storage resource operation method described above.

[0014] The present invention also provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps of the user-side energy storage resource operation method described above.

[0015] As can be seen from the above technical solution, the present invention acquires the predicted operation data and energy storage status data of the user-side integrated energy system; wherein, the energy storage status data includes the health status of the energy storage battery; according to the preset health stage to which the health status belongs, the corresponding degradation cost coefficient is determined, and the energy storage degradation cost is determined based on the degradation cost coefficient; a sub-Bluer rod opportunity constraint is constructed based on the historical operation data of the user-side integrated energy system; based on the energy storage degradation cost, the sub-Bluer rod opportunity constraint, and the source-load-storage constraint of the user-side integrated energy system, a day-ahead economic dispatch model with the goal of minimizing operating costs is constructed, and the day-ahead economic dispatch model is solved based on the predicted operation data to obtain the day-ahead dispatch plan; during the intraday operation phase, with the day-ahead dispatch plan as the reference trajectory, and with the goal of minimizing the power deviation penalty and renewable energy curtailment penalty, the intraday correction model is solved on a rolling basis under the conditions of satisfying the source-load-storage constraint and the sub-Bluer rod opportunity constraint to obtain the intraday execution plan; the operation of energy storage resources and dispatchable resources is controlled according to the intraday execution plan. The beneficial effects of this invention are as follows: By acquiring predicted operating data and energy storage status data of the user-side integrated energy system, and determining the degradation cost coefficient based on the preset health stage to which the energy storage battery health status belongs, the energy storage degradation cost can be adjusted according to changes in battery health status, thereby improving the accuracy of energy storage lifetime cost characterization. By constructing a multi-bar opportunity constraint based on historical operating data, and using it together with energy storage degradation cost and source-load-storage constraints to construct a day-ahead economic dispatch model, the adaptability of the day-ahead dispatch plan to uncertain operating conditions can be improved while considering operating costs. During the intraday operation phase, the intraday correction model is solved continuously using the day-ahead dispatch plan as a reference trajectory, which can correct the day-ahead plan according to the actual operating conditions, reducing power deviation penalties and renewable energy curtailment penalties. Therefore, the economic efficiency, reliability, and execution adaptability of user-side energy storage resource operation can be improved.

[0016] In addition, the present invention also provides a user-side energy storage resource operation device, equipment and medium, which also have the above-mentioned beneficial effects. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0018] Figure 1 A flowchart of a user-side energy storage resource operation method provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of the structure of a user-side energy storage resource operation device provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of a user-side energy storage resource operation device provided in an embodiment of the present invention. Detailed Implementation

[0019] 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 only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Please refer to Figure 1 , Figure 1 A flowchart illustrating a user-side energy storage resource operation method provided in an embodiment of the present invention. The method may include: S101: Obtain the predicted operation data and energy storage status data of the user-side integrated energy system; among which, the energy storage status data includes the health status of the energy storage battery.

[0021] It should be noted that this method can be executed by a user-side energy management server, edge controller, energy storage controller, or other electronic devices with data acquisition, model solving, and command issuance capabilities. The predicted operational data can include predicted electricity load, renewable energy output, and electricity price for multiple scheduling periods within the target scheduling cycle. The predicted renewable energy output can include at least one of wind power output and photovoltaic power output. The energy storage status data can include one or more of the following: state of charge (SOC), state of health (SOH), rated capacity, charging efficiency, discharging efficiency, self-discharge rate, upper limit of charging power, and upper limit of discharging power. The target scheduling cycle can be one day or other operational planning cycles of the user-side integrated energy system; the length of each scheduling period can be set according to control precision, for example, 15 minutes, 30 minutes, or 1 hour.

[0022] To enable data from different sources to be used in the same optimization model, various forecast values, energy storage status data, and equipment operating parameters can be time-series aligned according to preset time intervals. If some data have different sampling periods, they can be converted to a unified scheduling period through interpolation, aggregation, or by retaining the previous valid value. The above data acquisition methods are not limited to direct collection from local sensors; they can also be obtained through energy management systems, electricity meters, energy storage management systems, weather forecasting platforms, or electricity price information platforms.

[0023] S102: Determine the corresponding degradation cost coefficient based on the preset health stage to which the health status belongs, and determine the energy storage degradation cost based on the degradation cost coefficient.

[0024] In this embodiment, the State of Health (SOH) characterizes the degree to which the current available capacity or performance of the energy storage battery is maintained relative to its initial state. This embodiment can determine the degradation cost coefficient used in the current scheduling cycle based on the matching result between the state of health and a preset health stage, and calculate the energy storage degradation cost accordingly, so that the energy storage degradation cost can change with the health stage of the energy storage battery.

[0025] Furthermore, the aforementioned determination of the corresponding degradation cost coefficient based on the preset health stage to which the health status belongs, and the determination of the energy storage degradation cost based on the degradation cost coefficient, can specifically include: setting multiple health status intervals for the entire life cycle of the energy storage battery according to the health status values ​​from high to low, and configuring a degradation cost coefficient for each health status interval; matching the health status of the energy storage battery with the multiple health status intervals to determine the target health stage of the energy storage battery; and reading the target degradation cost coefficient corresponding to the target health stage.

[0026] For example, SOH can be divided into five health stages: [100%, 80%), [80%, 60%), [60%, 40%), [40%, 20%), and SOH ≤ 20%, with degradation cost coefficients θ1, θ2, θ3, θ4, and θ5 configured for each. It should be noted that θ here is merely a stage label or a generic symbol for that stage coefficient, and does not have an actual numerical value. The value of N and the boundaries of each health state interval can be adjusted based on battery type, operational experience, or historical test data; this embodiment does not limit this. The relationship between maximum depth of discharge and SOH is discussed below. d_max , i =SOH iThis approach clearly distinguishes the degradation characteristics of batteries at different stages of their life cycle. In the initial healthy stage (high State of Health), the battery capacity is large, the deep discharge potential is high, and the degradation cost coefficient is relatively high. In the later healthy stage (low State of Health), the capacity decreases, the deep discharge potential is small, and the degradation cost coefficient decreases accordingly. This method avoids using the same degradation cost coefficient throughout the entire life cycle of the energy storage battery, thereby improving the alignment between the degradation cost coefficient and the battery's health state.

[0027] It should be noted that the degradation cost coefficients corresponding to different health stages can be pre-calibrated based on the relationship model between battery cycle life and depth of discharge, as well as investment. The relationship model between battery cycle life and depth of discharge is as follows: .

[0028] Among them, C cyc The actual cycle life (cycles) of the energy storage battery; L r Under standard cycle conditions (D) r Depth, P r Cycle life (times) of power); d d D represents the actual depth of discharge. r U is the standard cycle depth of discharge; u1 and u2 are fitting coefficients; P avg E represents the average charge / discharge power. bat Let be the rated capacity of the energy storage battery (kWh); 'a' be the battery capacity coefficient; and K(·) be the current correction function, used to correct for the impact of high current on the energy storage cycle life. This model can calculate the actual cycle life of the battery under non-standard operating conditions based on actual usage conditions (depth of discharge, power).

[0029] Based on the above actual cycle life C cyc The battery lifecycle is divided into N stages based on the State of Health (SOH). Since the degradation rate differs in each stage, the remaining cycle life N for each stage varies. cyc,i Compared with the total actual cycle life C cyc The ratio is allocated based on the degradation curve or historical operating data provided by the battery manufacturer. The remaining cycle life N for the current stage i is obtained. cyc,i Then, the degradation cost coefficient per kWh of throughput in the i-th health stage can be calculated using the following formula: .

[0030] Among them, C bat The unit capacity investment cost of energy storage batteries (RMB / kWh); N cyc,i E represents the remaining cycle life (cycles) of the i-th health stage at the rated depth of discharge. batThe above formula is for the rated capacity (kWh) of the energy storage battery. In other implementations, the degradation cost coefficient for each health stage can be determined based on the lifespan curves, historical operating data, or test calibration data provided by the battery manufacturer.

[0031] Furthermore, the determination of energy storage degradation cost based on the degradation cost coefficient can specifically include: obtaining the energy storage charging power, energy storage discharging power, and scheduling time interval within the current scheduling period; adding the energy storage charging power and energy storage discharging power to obtain the energy storage power throughput within the current scheduling period; multiplying the energy storage power throughput, scheduling time interval, and degradation cost coefficient to obtain the energy storage degradation cost for the current scheduling period; and accumulating the energy storage degradation costs for each scheduling period within the target scheduling cycle to obtain the energy storage degradation cost for the target scheduling cycle.

[0032] Specifically, the energy storage degradation cost during the current scheduling period can be expressed as: .

[0033] =P ch (t)+ P dch (t).

[0034] in, This represents the energy storage degradation cost during the t-th scheduling period; This represents the degradation cost coefficient at the current stage; P represents the charging and discharging power, i.e., the energy storage power throughput; ch (t) represents the energy storage charging power during the t-th scheduling period; P dch (t) represents the energy storage discharge power during the t-th scheduling period; Δt represents the scheduling time interval. The target health stage can be determined based on the current state of health (SOH) of the energy storage battery.

[0035] S103: Construct a partial blue bar opportunity constraint based on the historical operation data of the user-side integrated energy system.

[0036] In this embodiment, historical operating data can reflect the deviations between predicted and actual quantities such as load, renewable energy output, and electricity prices. A split-bar chance constraint describing the uncertainty of prediction errors can be constructed based on historical operating data to constrain the operating model without relying on a precise probability distribution.

[0037] Furthermore, the aforementioned construction of sub-Bruker opportunity constraints based on historical operating data of the user-side integrated energy system can specifically include: obtaining historical load forecasts, historical renewable energy output forecasts, and historical electricity price forecasts for multiple historical dispatch periods; obtaining historical measured load values, historical measured renewable energy output values, and historical measured electricity price values ​​corresponding to the historical load forecasts, historical measured renewable energy output values, and historical measured electricity price values; subtracting the historical measured values ​​from the historical forecasts within the same historical dispatch period to obtain load forecast error samples, renewable energy output forecast error samples, and electricity price forecast error samples; calculating first-order moment information based on the load forecast error samples, renewable energy output forecast error samples, and electricity price forecast error samples; constructing a fuzzy set of forecast errors based on the first-order moment information and the range of forecast error values; and constructing the energy balance relationship in the day-ahead economic dispatch model as a sub-Bruker opportunity constraint based on the fuzzy set of forecast errors and a preset risk tolerance.

[0038] Specifically, to address the multiple uncertainties of wind, solar, and load, this embodiment employs a two-stage distributed braided chance-constrained programming (DRCCP) method for modeling and solving. Load demand and renewable energy output are considered as uncertain variables, and the probability distribution of these uncertainties is described using fuzzy sets based on first-order moment information. The prediction error fuzzy set is defined as follows: Where ξ is the uncertainty vector (electricity price, load, wind and solar); μ is the empirical mean; P0 is the set of all probability distributions; and ε is the risk tolerance. Let represent the support set; P is the set of possible probability distributions. This fuzzy set can be constructed using only the first moment information of the uncertain variables, achieving a balance between information utilization efficiency and conservatism compared to stochastic optimization, which requires precise probability distributions, and robust optimization, which has no distribution information at all.

[0039] Correspondingly, the day-ahead economic dispatch model in S104 and the intraday correction model in S105 correspond to a two-stage DRCCP model: The first stage is day-ahead decision-making: under the condition that the predicted values ​​of uncertain variables are known but the actual values ​​are unknown, the day-ahead dispatch plan for each device is determined. The second stage is intraday correction: after the actual values ​​of uncertain variables are gradually observed during intraday operation, the deviation is compensated by adjusting the charging and discharging power of energy storage and the power interaction with the grid, and it is allowed to actively reduce the output of renewable energy to maintain power balance when necessary. The partial blue bar chance constraint requires that the power balance relationship holds with a probability of not less than 1-ε under the fuzzy set P. In this way, it is not necessary to know the complete probability distribution of the prediction error precisely; only the first-order moment information of historical operating data is needed to construct a fuzzy set describing the uncertainty of the prediction error, and the fuzzy set is embedded in the chance constraint, thereby achieving a balance between the utilization efficiency of uncertainty information and the conservatism of decision-making.

[0040] It should also be noted that, in the uncertainty modeling stage, in addition to using distributed bar chance constraints to describe prediction errors, stochastic programming models can be constructed based on actual energy storage application scenarios such as the power source side, grid side, and user side. For example, multiple typical scenarios of load, renewable resource output, and electricity price can be generated based on historical operating data, and scenario reduction techniques can be used to reduce redundant scenarios, thereby reducing the model size. Reinforcement learning methods (such as deep Q-networks and near-end policy optimization) can also be used to learn scheduling strategies based on the changing patterns of historical electricity prices, load, and renewable energy output, thereby outputting operational decisions for energy storage resources and dispatchable resources without explicitly establishing a probability distribution model.

[0041] S104: Based on the energy storage degradation cost, the opportunity constraint of the distributed energy storage system and the source-load-storage constraint of the user-side integrated energy system, a day-ahead economic dispatch model with the goal of minimizing operating costs is constructed, and the day-ahead economic dispatch model is solved based on the predicted operating data to obtain the day-ahead dispatch plan.

[0042] In this embodiment, source-load-storage constraints may include source-side output constraints, load-side energy consumption constraints, energy storage operation constraints, dispatchable resource operation constraints, and electrical energy balance relationships. Source-side output constraints may correspond to wind power output, photovoltaic power output, and grid interaction power; load-side energy consumption constraints may correspond to electrical load, thermal load, and cooling load; energy storage operation constraints may correspond to the charging and discharging power, state of charge, and charge / discharge mutual exclusion relationships of energy storage batteries; and dispatchable resource operation constraints may correspond to the input electrical power, output electrical power, and energy conversion efficiency of dispatchable resources. The day-ahead dispatch plan may include one or more of the following: energy storage charging and discharging plan, grid interaction power plan, dispatchable resource output plan, renewable energy consumption plan, and electrical, thermal, and cooling load satisfaction plan.

[0043] Furthermore, based on the energy storage degradation cost, the opportunity constraint of distributed energy storage, and the source-load-storage constraint of the integrated energy system on the user side, the above-mentioned day-ahead economic dispatch model with the objective of minimizing operating costs can specifically include: Step 41: Calculate the change in state of charge between adjacent scheduling periods based on the energy storage charging power, energy storage discharging power, charging efficiency, discharging efficiency, self-discharge rate, and scheduling time interval.

[0044] Specifically, the change in the state of charge of an energy storage battery (dynamic equation of state of charge) can be calculated as follows: .

[0045] Where SOC(t) represents the state of charge during the t-th scheduling period; σ represents the self-discharge rate; η ch Indicates charging efficiency; η dch P represents discharge efficiency; ch(t) represents the energy storage charging power; P dch (t) represents the energy storage discharge power; Δt represents the scheduling time interval; E bat This refers to the rated capacity (kWh) of the energy storage battery.

[0046] Step 42: Establish energy storage operation constraints based on the change in state of charge, the upper limit of energy storage charging power, the upper limit of energy storage discharging power, the upper limit of state of charge, the lower limit of state of charge, and the mutual exclusion relationship between charging and discharging.

[0047] Specifically, energy storage operation constraints may include the following constraints: State of charge constraints: ; Charge and discharge power constraints: ; Charge-discharge mutual exclusion constraint: ; Periodic equilibrium constraints: ; in, Indicates the upper limit of energy storage charging power; Indicates the upper limit of energy storage discharge power; and These represent the lower limit of the state of charge and the upper limit of the state of charge, respectively. and These represent the charging state variable and the discharging state variable, respectively; T represents the end scheduling period of the target scheduling cycle. The charging and discharging mutual exclusion constraint is used to avoid simultaneous charging and discharging within the same time period, the cycle balance constraint is used to ensure that the energy storage state at the beginning and end of the target scheduling cycle meets the operational balance requirements, and the cycle balance constraint is used to ensure that the energy storage state is consistent at the beginning and end of the operating cycle.

[0048] Step 43: Establish operational constraints for schedulable resources based on their input energy, output energy, and energy conversion efficiency.

[0049] The amount of electricity consumed and produced by a schedulable device must satisfy an equation: input power multiplied by conversion efficiency equals output power. This constraint ensures that the optimization model does not violate the physical conversion capabilities of the device during scheduling; that is, the device cannot generate electricity out of thin air, but must have a corresponding input source.

[0050] Step 44: Establish an electrical energy balance relationship based on electrical load, wind power output, photovoltaic power output, energy storage charging and discharging power, grid interaction power, and dispatchable resource output.

[0051] In a specific example, the electrical energy balance relationship is expressed as: Electrical energy balance relationship: ; in, It represents the power exchange between the grid and the user-side integrated energy system, used to characterize the exchange between the user-side integrated energy system and the grid; These represent wind power output and photovoltaic power output, respectively, and are used to characterize the power output on the source side. This represents the user's electrical load, used to characterize load-side demand; These represent the energy storage and charging power, used to characterize the electrical power of energy storage resources to the system. When the value is negative, it indicates the discharge power.

[0052] Step 45: Construct the corresponding partial bar chance constraints based on the electrical energy balance relationship.

[0053] Since wind and solar power output and load fluctuations mainly affect the power system, this embodiment can convert the power balance relationship into an opportunity constraint with probability guarantees. This allows for exceeding the limit in a few extreme scenarios, provided that the probability of exceeding the limit does not exceed the preset risk tolerance, thereby dealing with uncertainty without being overly conservative.

[0054] Step 46: Establish an objective function with the goal of minimizing operating costs; use energy storage operation constraints, dispatchable resource operation constraints, power balance relationship and corresponding distributed bar opportunity constraints as constraints; construct a day-ahead economic dispatch model based on the objective function and constraints.

[0055] Specifically, the day-ahead dispatching optimization process can use day-ahead forecasting information (load forecasting, wind and solar forecasting, and day-ahead electricity prices) as input to the day-ahead economic dispatching model. Under the aforementioned constraints, minimizing the expected operating costs (including grid interaction costs and phased degradation costs of energy storage) is used as the optimization objective of the day-ahead economic dispatching model to optimize the day-ahead dispatching plans for each device. The day-ahead dispatching decision variables (i.e., the day-ahead dispatching plan) can include energy storage charging and discharging power plans and grid interaction power plans. The objective function is as follows: .

[0056] The power grid interaction cost is expressed by the following formula: .

[0057] The formula for representing the energy storage degradation cost is as follows: .

[0058] in, Indicates the power exchange between power grids; Indicates the electricity purchase price; Indicates the electricity sales price; Represents the energy storage degradation cost coefficient for the i-th health stage; This indicates the charging and discharging power.

[0059] It should also be noted that when the current economic dispatch model considers multiple objectives such as operating costs, renewable resource abandonment, and energy storage degradation costs, a weighted summation method can be used to transform multiple objectives into a single objective function, allowing decision-makers to select a more appropriate operating strategy based on actual system operating experience.

[0060] Furthermore, the above-mentioned solution to the day-ahead economic dispatch model based on predicted operating data to obtain the day-ahead dispatch plan can specifically include: when the user-side integrated energy system includes multiple user-side energy subsystems and shared energy storage resources, constructing a shared energy storage two-layer optimization model; wherein, the upper-layer model of the two-layer optimization model uses the available capacity variable of the shared energy storage resources as the decision variable, and the lower-layer model of the two-layer optimization model uses the operating variables of multiple user-side energy subsystems as the decision variables; converting the optimality conditions of the lower-layer model into the constraints of the upper-layer model according to the KKT conditions; using the Big-M method to linearize the complementary terms in the transformed constraints to obtain a single-layer mixed-integer linear programming model; solving the single-layer mixed-integer linear programming model based on predicted operating data to obtain the shared energy storage capacity configuration scheme, the shared energy storage charging and discharging plan, and the collaborative operation scheme of multiple user-side energy subsystems.

[0061] For example, in the above-mentioned shared energy storage two-layer optimization model, the lower-layer model involves power balance constraints of each user-side subsystem. These constraints are affected by the uncertainty of wind and solar power output and load. Therefore, before the KKT condition transformation, an indicator function is introduced to transform the chance constraint into a deterministic constraint, and the split-bar chance constraint programming is reconstructed into a mixed-integer linear programming problem so that it can be handled by the standard solver. When the two-layer optimization involves the shared energy storage station participating in multi-user collaborative operation, the optimality conditions of the lower-layer operation optimization model are transformed into the constraints of the upper-layer capacity configuration model using the KKT (Karush-Kuhn-Tucker) complementary relaxation conditions. Then, the Big-M method is used to linearize the nonlinear complementary terms in the transformed single-layer nonlinear model to form a solvable single-layer mixed-integer linear programming model. Finally, the Gurobi or CPLEX commercial solver is called to solve the problem and obtain the shared energy storage capacity configuration, the charging and discharging plan for each time period, and the collaborative operation scheme of each user subsystem. It should also be noted that, in the model solving stage, in addition to calling commercial solvers such as Gurobi or CPLEX, distributed optimization algorithms such as the Alternating Direction Multiplier Method (ADMM) can also be used for decomposition and solution. When multiple user-side energy subsystems participate in collaborative optimization, each subsystem can retain its own private data such as load, output, and cost locally, and only exchange necessary coordination variables or multiplier information, thereby reducing the risk of privacy data exposure while achieving collaborative scheduling.

[0062] S105: During the intraday operation phase, with the day-ahead scheduling plan as the reference trajectory and the goal of minimizing power deviation penalties and renewable energy curtailment penalties, the intraday correction model is solved in a rolling manner under the conditions of satisfying source-load-storage constraints and distributed bar opportunity constraints to obtain the intraday execution plan.

[0063] In this embodiment, during the intraday operation phase, real-time operation data can be acquired according to a preset rolling cycle, and the energy storage charging and discharging power, grid interaction power, and dispatchable resource output for subsequent rolling cycles can be corrected using the day-ahead scheduling plan as a reference trajectory. The current intraday rolling cycle can be 15 minutes, 30 minutes, or 1 hour, and can also be set according to the sampling capability and scheduling requirements of the control system.

[0064] Furthermore, during the intraday operation phase, using the day-ahead dispatch plan as a reference trajectory and aiming to minimize power deviation penalties and renewable energy curtailment penalties, the intraday correction model is solved on a rolling basis under the conditions of satisfying source-load-storage constraints and distributed bar opportunity constraints to obtain the intraday execution plan. Specifically, this may include: obtaining real-time load, real-time renewable energy output, real-time electricity price, and real-time energy storage status within the current intraday rolling cycle; calculating the operation deviation of the current intraday rolling cycle based on the real-time load, real-time renewable energy output, real-time electricity price, real-time energy storage status, and day-ahead dispatch plan; when the operation deviation meets the preset correction conditions, setting the energy storage charging and discharging power, grid interaction power, and dispatchable resource output within the current intraday rolling cycle and subsequent rolling cycles as variables to be corrected; and solving the intraday correction model on a rolling basis based on the variables to be corrected, source-load-storage constraints, and distributed bar opportunity constraints, and generating the intraday execution plan based on the rolling solution results.

[0065] Specifically, using the day-ahead scheduling plan as a reference, rolling optimization and corrections are performed at short intervals (e.g., 15 minutes to 1 hour) within the day, correcting deviations from the day-ahead plan based on real-time information. Intraday correction decision variables can include corrections to energy storage charging and discharging power and grid interaction power. Through intraday rolling corrections, the rapid response characteristics of energy storage resources are utilized to effectively balance the power deviation between the day-ahead plan and the actual situation. Furthermore, execution instructions can be issued only for the current rolling cycle, and the solution can be recalculated after acquiring new real-time operating data in the next rolling cycle, thus forming a closed-loop correction between the day-ahead plan and intraday execution.

[0066] It should be noted that the objective function of the above intraday correction model is: ; The power deviation penalty is expressed by the following formula: ; The formula for representing the penalty for wind and solar curtailment (i.e., one type of penalty for renewable energy curtailment) is: ; Where T represents the number of rolling window periods; α is the power deviation penalty coefficient, and β is the wind and solar curtailment penalty coefficient. Indicates the power exchange between power grids; This represents the day-ahead reference value for grid power. Indicates the amount of wind power curtailed; This indicates the power of abandoned light.

[0067] S106: Control the operation of energy storage resources and dispatchable resources according to the intraday execution plan.

[0068] After receiving the intraday execution plan, the energy storage charging and discharging power command can be sent to the energy storage converter or energy storage management system, the grid interaction power plan can be sent to the energy management system, and the dispatchable resource output command can be sent to the corresponding equipment controller. For user-side integrated energy systems that do not include a certain type of equipment, the corresponding control commands can be omitted.

[0069] Furthermore, after the target scheduling cycle ends, energy storage charging and discharging data, energy storage health status, and deviations between the day-ahead scheduling plan and the intraday execution plan can be recorded, and the recorded results can be used as part of subsequent historical operating data. This provides a data foundation for updating the opportunity constraints of the distributed bar and optimizing the operation of the next target scheduling cycle.

[0070] The user-side energy storage resource operation method provided by this invention includes the following steps: S101: Obtaining predicted operation data and energy storage status data of the user-side integrated energy system; wherein, the energy storage status data includes the health status of the energy storage battery; S102: Determining the corresponding degradation cost coefficient according to the preset health stage to which the health status belongs, and determining the energy storage degradation cost based on the degradation cost coefficient; S103: Constructing a sub-Bluer rod opportunity constraint based on the historical operation data of the user-side integrated energy system; S104: Constructing a day-ahead economic dispatch model with the goal of minimizing operating costs based on the energy storage degradation cost, the sub-Bluer rod opportunity constraint, and the source-load-storage constraint of the user-side integrated energy system, and solving the day-ahead economic dispatch model based on the predicted operation data to obtain the day-ahead dispatch plan; S105: During the intraday operation phase, using the day-ahead dispatch plan as a reference trajectory, and aiming to minimize the power deviation penalty and renewable energy abandonment penalty, while satisfying the source-load-storage constraint and the sub-Bluer rod opportunity constraint, continuously solving the intraday correction model to obtain the intraday execution plan; S106: Controlling the operation of energy storage resources and dispatchable resources according to the intraday execution plan. This method acquires predicted operational data and energy storage status data from the user-side integrated energy system and determines the degradation cost coefficient based on the preset health stage of the energy storage battery's health status. This allows the energy storage degradation cost to adjust with changes in battery health status, thereby improving the accuracy of energy storage lifetime cost representation. By constructing a multi-bar opportunity constraint based on historical operational data and using it in conjunction with energy storage degradation costs and source-load-storage constraints to build a day-ahead economic dispatch model, the method can improve the adaptability of the day-ahead dispatch plan to uncertain operating conditions while considering operating costs. During the intraday operation phase, the intraday correction model is solved continuously using the day-ahead dispatch plan as a reference trajectory. This allows for adjustments to the day-ahead plan based on actual operating conditions, reducing power deviation penalties and renewable energy curtailment penalties. Therefore, this method improves the economy, reliability, and execution adaptability of user-side energy storage resource operation.

[0071] Furthermore, the energy storage degradation cost is accurately quantified: the entire battery life cycle is divided into five stages according to the health state, and mathematical models of discharge depth-cycle life-degradation cost coefficient are established for each stage. This overcomes the problem that traditional fixed degradation parameter models cannot accurately reflect the differences in the degree of battery degradation at different life cycle stages, and achieves accurate quantification of energy storage operating costs, which can effectively improve the project's internal rate of return and shorten the dynamic investment payback period.

[0072] Furthermore, the scheduling across multiple time scales is closely integrated: the day-ahead level formulates a basic scheduling plan with the goal of minimizing expected operating costs, while the intraday level makes rolling corrections with the goal of minimizing power deviation penalties and wind and solar curtailment penalties. The two-level framework achieves efficient integration through the rapid response characteristics of energy storage, enabling energy storage resources to participate in system optimization at both long and short time scales, effectively reducing power deviation penalties and wind and solar curtailment levels.

[0073] Furthermore, it achieves a balanced ability to cope with uncertainty: by adopting the sparse robust chance-constrained programming method, it uses fuzzy sets based on first-order moment information to describe multiple uncertainties, achieving a balance between utilizing distribution information and conservatism. Through chance constraints, it satisfies power balance under a pre-set confidence level, avoiding the excessive conservatism of traditional robust optimization and the dependence of stochastic optimization on precise distribution, thus achieving a unity of economy and robustness.

[0074] Furthermore, it boasts high computational efficiency: for multi-user collaborative scenarios in shared energy storage, the upper layer addresses the capacity configuration problem over a long time scale, while the lower layer addresses the multi-user operation optimization problem over a short time scale. By using KKT conditions and the Big-M method, the two-layer model is transformed into a single-layer mixed-integer linear programming model, which can be directly solved efficiently by commercial solvers, demonstrating good engineering practicality.

[0075] The user-side energy storage resource operation device provided in the embodiments of the present invention is described below. The user-side energy storage resource operation device described below and the user-side energy storage resource operation method described above can be referred to each other.

[0076] Please refer to the details. Figure 2 , Figure 2 A schematic diagram of a user-side energy storage resource operation device provided in an embodiment of the present invention may include: The data acquisition module 100 is used to acquire the predicted operation data and energy storage status data of the user-side integrated energy system; wherein, the energy storage status data includes the health status of the energy storage battery; The cost calculation module 200 is used to determine the corresponding degradation cost coefficient according to the preset health stage to which the health status belongs, and to determine the energy storage degradation cost based on the degradation cost coefficient. Constraint construction module 300 is used to construct sub-Bruker opportunity constraints based on historical operating data of the user-side integrated energy system; The model building module 400 is used to construct a day-ahead economic dispatch model with the goal of minimizing operating costs based on the energy storage degradation cost, the distributed rod opportunity constraint, and the source-load-storage constraint of the user-side integrated energy system, and to solve the day-ahead economic dispatch model based on the predicted operating data to obtain the day-ahead dispatch plan. The rolling correction module 500 is used to solve the intraday correction model in the intraday operation phase, with the day-ahead scheduling plan as the reference trajectory and the goal of minimizing the power deviation penalty and renewable energy curtailment penalty, under the conditions of satisfying the source-load-storage constraints and the sub-blob opportunity constraints, to obtain the intraday execution plan. The execution module 600 is used to control the operation of energy storage resources and schedulable resources according to the intraday execution plan.

[0077] Based on the above embodiments, the cost calculation module 200 may include: The coefficient setting unit is used to set multiple health state intervals for the entire life cycle of the energy storage battery in descending order of health state values, and to configure a degradation cost coefficient for each of the health state intervals. A matching unit is used to match the health status of the energy storage battery with the multiple health status intervals to determine the target health stage of the energy storage battery. The reading unit is used to read the target degradation cost coefficient corresponding to the target health stage.

[0078] Based on the above embodiments, the cost calculation module 200 may include: The acquisition unit is used to acquire the energy storage charging power, energy storage discharging power, and scheduling time interval during the current scheduling period. The first calculation unit is used to add the energy storage charging power and the energy storage discharging power to obtain the energy storage power throughput during the current scheduling period. The second calculation unit is used to multiply the energy storage power throughput, the scheduling time interval and the degradation cost coefficient to obtain the energy storage degradation cost for the current scheduling period; The third calculation unit is used to accumulate the energy storage degradation cost for each scheduling period within the target scheduling period to obtain the energy storage degradation cost for the target scheduling period.

[0079] Based on the above embodiments, the model building module 400 may include: The fourth calculation unit is used to calculate the change in state of charge between adjacent scheduling periods based on the energy storage charging power, energy storage discharging power, charging efficiency, discharging efficiency, self-discharge rate, and scheduling time interval. The first constraint establishment unit is used to establish energy storage operation constraints based on the change in state of charge, the upper limit of energy storage charging power, the upper limit of energy storage discharging power, the upper limit of state of charge, the lower limit of state of charge, and the mutual exclusion relationship between charging and discharging. The second constraint establishment unit is used to establish operational constraints for the schedulable resources of electrical energy based on the input energy, output energy, and energy conversion efficiency of the schedulable resources. The relationship establishment unit is used to establish an electrical energy balance relationship based on electrical load, wind power output, photovoltaic power output, energy storage charging and discharging power, grid interaction power, and dispatchable resource output. The third constraint establishment unit is used to construct corresponding sub-Bruker chance constraints based on the electric energy balance relationship; The model building unit is used to establish an objective function with the goal of minimizing operating costs based on the energy storage degradation cost; and to use the energy storage operation constraints, the schedulable resource operation constraints, the power balance relationship, and the corresponding distributed bar opportunity constraints as constraints; and to construct the day-ahead economic dispatch model according to the objective function and the constraints.

[0080] Based on the above embodiments, the constraint construction module 300 may include: The forecast acquisition unit is used to acquire historical load forecasts, historical renewable energy output forecasts, and historical electricity price forecasts for multiple historical dispatch periods. The measured value acquisition unit is used to acquire the historical measured values ​​of load, renewable energy output, and electricity price corresponding to the historical predicted load value, the historical predicted renewable energy output value, and the historical predicted electricity price value. The error calculation unit is used to subtract the historical measured values ​​and historical predicted values ​​within the same historical dispatch period to obtain load prediction error samples, renewable energy output prediction error samples, and electricity price prediction error samples. A first-order moment information calculation unit is used to calculate first-order moment information based on the load forecasting error sample, the renewable energy output forecasting error sample, and the electricity price forecasting error sample. A prediction error fuzzy set construction unit is used to construct a prediction error fuzzy set based on the first-order moment information and the value range of the prediction error. The sub-Bruker chance constraint construction unit is used to construct the electrical energy balance relationship in the day-ahead economic dispatch model into sub-Bruker chance constraints based on the prediction error fuzzy set and the preset risk tolerance.

[0081] Based on any of the above embodiments, the rolling correction module 500 may include: The data acquisition unit is used to acquire real-time load, real-time renewable energy output, real-time electricity price, and real-time energy storage status within the current intraday rolling cycle. The deviation calculation unit is used to calculate the operating deviation of the current intraday rolling cycle based on the real-time load, the real-time renewable energy output, the real-time electricity price, the real-time energy storage status, and the day-ahead dispatch plan. The correction unit is used to set the energy storage charging and discharging power, grid interaction power and dispatchable resource output in the current intraday rolling cycle and subsequent rolling cycles as variables to be corrected when the operating deviation meets the preset correction conditions. The rolling solver unit is used to solve the intraday correction model in a rolling manner based on the variable to be corrected, the source load storage constraint, and the sub-Blule bar opportunity constraint, and generate the intraday execution plan based on the rolling solver results.

[0082] Based on any of the above embodiments, the model building module 400 may include: A shared energy storage two-layer optimization model construction unit is used to construct a shared energy storage two-layer optimization model when the user-side integrated energy system includes multiple user-side energy subsystems and shared energy storage resources; wherein, the upper-layer model of the two-layer optimization model uses the available capacity variable of the shared energy storage resources as the decision variable, and the lower-layer model of the two-layer optimization model uses the operating variables of multiple user-side energy subsystems as the decision variables. A conversion unit is used to convert the optimality conditions of the lower-level model into the constraints of the upper-level model according to the KKT conditions. The linear processing unit is used to linearize the complementary terms in the transformed constraints using the Big-M method to obtain a single-level mixed integer linear programming model. The collaborative scheme determination unit is used to solve the single-layer mixed integer linear programming model based on the predicted operation data to obtain the shared energy storage capacity configuration scheme, the shared energy storage charging and discharging plan, and the collaborative operation scheme of multiple user-side energy subsystems.

[0083] It should be noted that the order of the modules and units in the aforementioned user-side energy storage resource operation device can be changed without affecting the logic.

[0084] An application of the user-side energy storage resource operation device provided in this embodiment of the invention includes a data acquisition module 100 for acquiring predicted operation data and energy storage status data of the user-side integrated energy system; wherein, the energy storage status data includes the health status of the energy storage battery; a cost calculation module 200 for determining the corresponding degradation cost coefficient according to the preset health stage to which the health status belongs, and determining the energy storage degradation cost based on the degradation cost coefficient; a constraint construction module 300 for constructing partial Bruker opportunity constraints based on the historical operation data of the user-side integrated energy system; and a model construction module 400 for constructing model constraints based on the energy storage degradation cost and the partial Bruker opportunity constraints. Based on the opportunities and resource constraints of the user-side integrated energy system, a day-ahead economic dispatch model is constructed with the goal of minimizing operating costs. The model is then solved using the predicted operating data to obtain the day-ahead dispatch plan. A rolling correction module 500, during the intraday operation phase, uses the day-ahead dispatch plan as a reference trajectory and aims to minimize power deviation penalties and renewable energy curtailment penalties. Under the conditions of satisfying the resource constraints and the distributed energy storage constraints, the intraday correction model is solved on a rolling basis to obtain the intraday execution plan. An execution module 600 controls the operation of energy storage resources and dispatchable resources according to the intraday execution plan. This device acquires predicted operating data and energy storage status data of the user-side integrated energy system and determines the degradation cost coefficient based on the preset health stage of the energy storage battery health status. This allows the energy storage degradation cost to adjust with changes in battery health status, thereby improving the accuracy of energy storage lifespan cost characterization. By constructing a multi-bar opportunity constraint based on historical operating data and combining it with energy storage degradation costs and source-load-storage constraints to build a day-ahead economic dispatch model, the adaptability of the day-ahead dispatch plan to uncertain operating conditions can be improved while considering operating costs. During the intraday operation phase, the intraday correction model is solved continuously using the day-ahead dispatch plan as a reference trajectory, which can adjust the day-ahead plan according to actual operating conditions, reducing power deviation penalties and renewable energy curtailment penalties. This improves the economy, reliability, and execution adaptability of user-side energy storage resource operation.

[0085] The user-side energy storage resource operation equipment provided in the embodiments of the present invention will be described below. The user-side energy storage resource operation equipment described below and the user-side energy storage resource operation method described above can be referred to in correspondence with each other.

[0086] Please refer to Figure 3 , Figure 3 A schematic diagram of a user-side energy storage resource operation device provided in an embodiment of the present invention may include: Memory 10 is used to store computer programs; The processor 20 is used to execute computer programs to implement the above-described user-side energy storage resource operation method.

[0087] The memory 10, processor 20, and communication interface 31 all communicate with each other through the communication bus 32.

[0088] In this embodiment of the invention, the memory 10 is used to store one or more programs. The programs may include program code, which includes computer operation instructions. In this embodiment of the invention, the memory 10 may store programs for implementing the following functions: Acquire predictive operation data and energy storage status data of the user-side integrated energy system; among which, the energy storage status data includes the health status of the energy storage batteries; Based on the preset health stage to which the health status belongs, the corresponding degradation cost coefficient is determined, and the energy storage degradation cost is determined based on the degradation cost coefficient. Construct a multi-bar opportunity constraint based on historical operating data of the user-side integrated energy system; Based on the energy storage degradation cost, the opportunity constraint of the distributed energy storage system, and the source-load-storage constraint of the user-side integrated energy system, a day-ahead economic dispatch model with the goal of minimizing operating costs is constructed. The day-ahead economic dispatch model is solved based on the predicted operating data to obtain the day-ahead dispatch plan. During the intraday operation phase, with the day-ahead scheduling plan as the reference trajectory and the goal of minimizing power deviation penalties and renewable energy curtailment penalties, the intraday correction model is solved in a rolling manner under the conditions of satisfying source-load-storage constraints and distributed bar opportunity constraints to obtain the intraday execution plan. Control the operation of energy storage resources and dispatchable resources according to the intraday execution plan.

[0089] In one possible implementation, the memory 10 may include a program storage area and a data storage area, wherein the program storage area may store the operating system and applications required for at least one function; and the data storage area may store data created during use.

[0090] Furthermore, memory 10 may include read-only memory and random access memory, providing instructions and data to the processor. A portion of the memory may also include NVRAM. The memory stores operating systems and operating instructions, executable modules, or data structures, or subsets thereof, or extended sets thereof, wherein the operating instructions may include various operating instructions for implementing various operations. The operating system may include various system programs for implementing various basic tasks and handling hardware-based tasks.

[0091] Processor 20 can be a central processing unit (CPU), an application-specific integrated circuit, a digital signal processor, a field-programmable gate array, or other programmable logic device. Processor 20 can be a microprocessor or any conventional processor. Processor 20 can call programs stored in memory 10.

[0092] Communication interface 31 can be an interface for the communication module, used to connect with other devices or systems.

[0093] Of course, it should be noted that, Figure 3 The structure shown does not constitute a limitation on the user-side energy storage resource operation equipment in the embodiments of the present invention. In practical applications, the user-side energy storage resource operation equipment may include more than Figure 3 More or fewer components as shown, or combinations of certain components.

[0094] It is understood that if the user-side energy storage resource operation method in the above embodiments is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the current technology, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and executes all or part of the steps of the methods in the various embodiments of the present invention. The aforementioned storage medium includes: USB flash drive, mobile hard drive, read-only memory (ROM), random access memory (RAM), electrically erasable programmable ROM, register, hard disk, removable disk, CD-ROM, magnetic disk or optical disk, and other media capable of storing program code.

[0095] Based on this, embodiments of the present invention also provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the user-side energy storage resource operation method described above.

[0096] The following describes a computer program product provided by an embodiment of this application. The computer program product described below can be referred to in conjunction with other embodiments described herein.

[0097] A computer program product includes a computer program / instructions that, when executed by a processor, implement the steps of the aforementioned disclosed user-side energy storage resource operation method.

[0098] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to in the method section.

[0099] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0100] Finally, it should be noted that in this document, relationships such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0101] The above provides a detailed description of the user-side energy storage resource operation method, apparatus, equipment, and medium provided by the present invention. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for operating user-side energy storage resources, characterized in that, include: Acquire predictive operating data and energy storage status data of the user-side integrated energy system; wherein, the energy storage status data includes the health status of the energy storage battery; Based on the preset health stage to which the health status belongs, the corresponding degradation cost coefficient is determined, and the energy storage degradation cost is determined based on the degradation cost coefficient. Construct a multi-bar opportunity constraint based on historical operating data of the user-side integrated energy system; Based on the energy storage degradation cost, the opportunity constraint of the distributed energy storage rod, and the source-load-storage constraint of the user-side integrated energy system, a day-ahead economic dispatch model with the goal of minimizing operating costs is constructed, and the day-ahead economic dispatch model is solved based on the predicted operating data to obtain the day-ahead dispatch plan. During the intraday operation phase, with the day-ahead scheduling plan as the reference trajectory and minimizing the power deviation penalty and renewable energy curtailment penalty, the intraday correction model is solved in a rolling manner under the conditions of satisfying the source-load-storage constraints and the distributed bar opportunity constraints to obtain the intraday execution plan. The operation of energy storage resources and dispatchable resources is controlled according to the intraday execution plan.

2. The user-side energy storage resource operation method according to claim 1, characterized in that, Based on the preset health stage to which the health status belongs, determine the corresponding degradation cost coefficient, including: The entire life cycle of the energy storage battery is set into multiple health state ranges in descending order of health state values, and a degradation cost coefficient is configured for each of the health state ranges. The health status of the energy storage battery is matched with the multiple health status intervals to determine the target health stage of the energy storage battery. Read the target degradation cost coefficient corresponding to the target health stage.

3. The user-side energy storage resource operation method according to claim 1, characterized in that, Determining the energy storage degradation cost based on the aforementioned degradation cost coefficient includes: Obtain the energy storage charging power, energy storage discharging power, and scheduling time interval within the current scheduling period; The energy storage charging power is added to the energy storage discharging power to obtain the energy storage power throughput during the current scheduling period; Multiply the energy storage power throughput, the scheduling time interval, and the degradation cost coefficient to obtain the energy storage degradation cost for the current scheduling period; The energy storage degradation cost for each scheduling period within the target scheduling cycle is accumulated to obtain the energy storage degradation cost for the target scheduling cycle.

4. The user-side energy storage resource operation method according to claim 1, characterized in that, Based on the energy storage degradation cost, the distributed bar opportunity constraint, and the source-load-storage constraint of the user-side integrated energy system, a day-ahead economic dispatch model is constructed with the goal of minimizing operating costs, including: Based on the energy storage charging power, energy storage discharging power, charging efficiency, discharging efficiency, self-discharge rate, and scheduling time interval, calculate the change in state of charge between adjacent scheduling periods. Based on the changes in state of charge, the upper limit of energy storage charging power, the upper limit of energy storage discharging power, the upper limit of state of charge, the lower limit of state of charge, and the mutual exclusion relationship between charging and discharging, energy storage operation constraints are established. Establish operational constraints for schedulable resources based on their input energy, output energy, and energy conversion efficiency; Establish an electrical energy balance relationship based on electrical load, wind power output, photovoltaic power output, energy storage charging and discharging power, grid interaction power, and dispatchable resource output; Construct corresponding sub-Bruker opportunity constraints based on the aforementioned electrical energy balance relationship; Based on the energy storage degradation cost, an objective function is established with the goal of minimizing operating costs; the energy storage operation constraints, the schedulable resource operation constraints, the power balance relationship, and the corresponding distributed bar opportunity constraints are used as constraints; and the day-ahead economic dispatch model is constructed according to the objective function and the constraints.

5. The user-side energy storage resource operation method according to claim 1, characterized in that, Based on historical operating data of the user-side integrated energy system, a multi-bar opportunity constraint is constructed, including: Obtain historical load forecasts, renewable energy output forecasts, and electricity price forecasts for multiple historical dispatch periods; Obtain the historical measured values ​​of load, renewable energy output, and electricity price corresponding to the historical predicted load value, the historical predicted renewable energy output value, and the historical predicted electricity price value; By subtracting the historical measured values ​​from the historical predicted values ​​within the same historical dispatch period, we can obtain load forecasting error samples, renewable energy output forecasting error samples, and electricity price forecasting error samples. Calculate the first moment information based on the load forecasting error sample, the renewable energy output forecasting error sample, and the electricity price forecasting error sample; Construct a fuzzy set of prediction error based on the first-order moment information and the range of values ​​of the prediction error; Based on the fuzzy set of prediction errors and the preset risk tolerance, the energy balance relationship in the day-ahead economic dispatch model is constructed as a sub-Bruker chance constraint.

6. The user-side energy storage resource operation method according to any one of claims 1 to 5, characterized in that, During the intraday operation phase, using the day-ahead scheduling plan as a reference trajectory and aiming to minimize power deviation penalties and renewable energy curtailment penalties, the intraday correction model is solved on a rolling basis under the conditions of satisfying the source-load-storage constraints and the distributed bar opportunity constraints to obtain the intraday execution plan, including: Within the current intraday rolling cycle, obtain real-time load, real-time renewable energy output, real-time electricity price, and real-time energy storage status; The operating deviation of the current intraday rolling cycle is calculated based on the real-time load, the real-time renewable energy output, the real-time electricity price, the real-time energy storage status, and the day-ahead dispatch plan. When the operating deviation meets the preset correction conditions, the energy storage charging and discharging power, grid interaction power and dispatchable resource output in the current intraday rolling cycle and subsequent rolling cycles are set as variables to be corrected. Based on the variable to be corrected, the source load storage constraint, and the sub-Blule bar opportunity constraint, the intraday correction model is solved in a rolling manner, and the intraday execution plan is generated based on the rolling solution results.

7. The user-side energy storage resource operation method according to claim 1, characterized in that, Based on the predicted operational data, the day-ahead economic scheduling model is solved to obtain the day-ahead scheduling plan, including: When the user-side integrated energy system includes multiple user-side energy subsystems and shared energy storage resources, a shared energy storage two-layer optimization model is constructed; wherein, the upper layer model of the two-layer optimization model uses the available capacity variable of the shared energy storage resources as the decision variable, and the lower layer model of the two-layer optimization model uses the operating variables of multiple user-side energy subsystems as the decision variable. The optimality conditions of the lower-level model are converted into the constraints of the upper-level model based on the KKT conditions. The Big-M method is used to linearize the complementary terms in the transformed constraints, resulting in a single-level mixed-integer linear programming model. Based on the predicted operating data, the single-layer mixed integer linear programming model is solved to obtain the shared energy storage capacity configuration scheme, the shared energy storage charging and discharging plan, and the collaborative operation scheme of multiple user-side energy subsystems.

8. A user-side energy storage resource operation device, characterized in that, include: The data acquisition module is used to acquire the predicted operation data and energy storage status data of the user-side integrated energy system; wherein, the energy storage status data includes the health status of the energy storage battery; The cost calculation module is used to determine the corresponding degradation cost coefficient according to the preset health stage to which the health status belongs, and to determine the energy storage degradation cost based on the degradation cost coefficient. The constraint construction module is used to construct sub-Bruker opportunity constraints based on historical operating data of the user-side integrated energy system. The model building module is used to construct a day-ahead economic dispatch model with the goal of minimizing operating costs based on the energy storage degradation cost, the opportunity constraints of the distributed energy rod, and the source-load-storage constraints of the user-side integrated energy system, and to solve the day-ahead economic dispatch model based on the predicted operating data to obtain the day-ahead dispatch plan. The rolling correction module is used during the intraday operation phase to solve the intraday correction model on a rolling basis, with the day-ahead scheduling plan as the reference trajectory and the goal of minimizing the power deviation penalty and renewable energy curtailment penalty, while satisfying the source-load-storage constraints and the sub-blob opportunity constraints, to obtain the intraday execution plan. The execution module is used to control the operation of energy storage resources and dispatchable resources according to the intraday execution plan.

9. A user-side energy storage resource operation device, characterized in that, include: Memory, used to store computer programs; A processor is configured to implement the user-side energy storage resource operation method as described in any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when loaded and executed by a processor, implement the user-side energy storage resource operation method as described in any one of claims 1 to 7.

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