Energy storage power station economic operation evaluation method and device, computer equipment and storage medium

Abstract

The application discloses an energy storage power station economic operation evaluation method and device, computer equipment and a storage medium. The method comprises the following steps: constructing a user-side distributed energy storage power station economic benefit model; wherein the user-side distributed energy storage power station economic benefit model comprises an energy storage power station basic benefit model and an auxiliary service benefit model; determining the energy storage power station basic benefit; determining the auxiliary service benefit; evaluating the overall benefit of the energy storage power station based on the energy storage power station basic benefit and the auxiliary service benefit; monitoring the enterprise power load in real time, dynamically adjusting the charging and discharging plan to optimize the demand management; participating in the frequency modulation and peak shaving auxiliary service provided by the power grid to increase additional income sources; adjusting the energy storage power station basic benefit model according to the actual operation of the energy storage system, and comparing the comprehensive benefit change. Through the implementation of the method of the application, not only the economic benefit of the energy storage power station itself is improved, but also the overall economy and operation safety of the internal power system of industrial and commercial enterprises are significantly enhanced.

Description

Technical Field

[0001] This invention relates to a method for optimizing electricity load demand, and more specifically to a method, apparatus, computer equipment, and storage medium for evaluating the economic operation of energy storage power stations. Background Technology

[0002] In recent years, with the large-scale deployment of distributed photovoltaic power stations in industrial and commercial enterprises and the increase in investment in energy storage power stations, especially energy storage devices represented by electrochemical energy storage, these enterprises have rapidly penetrated their internal power distribution networks. Because electrochemical energy storage and distributed photovoltaic power generation belong to different industry sectors, they each have unique working principles, operating characteristics, economic benefit models, and maintenance requirements. This difference leads to their typically being invested in and operated by different entities, each pursuing its own optimal operating strategy and economic benefit model.

[0003] However, in practical applications, all the power infrastructure within a company is interconnected and mutually influential. Optimal operation of a single facility does not necessarily mean overall optimization of the company's power system, and may even negatively impact the safe operation of the main power grid. For example, the ideal operating mode for distributed photovoltaic power stations is to generate as much power as possible to maximize their economic benefits; while energy storage power stations tend to charge when electricity prices are low and discharge during peak hours, outputting the stored energy to the company's distribution network to maximize economic benefits. Based on such strategies, most investors and operators in the industry have established corresponding economic benefit assessment models.

[0004] However, a deeper analysis of the specific operational status of the enterprise's internal power distribution network reveals significant flaws and shortcomings in the aforementioned economic benefit assessment model. Specifically, during peak electricity price periods, if photovoltaic power generation exceeds the enterprise's own electricity demand, and the energy storage station also supplies electricity externally, the excess may be fed back to the main grid. However, the price of this fed-back electricity is far lower than the peak electricity price. This means that although the energy storage station and the photovoltaic station each achieve their maximum economic benefits individually, from the perspective of the enterprise's overall power system, this operational strategy does not achieve global optimization and may instead lead to unnecessary energy waste and economic losses.

[0005] Therefore, it is necessary to design a new method that not only improves the economic benefits of energy storage power stations themselves, but also significantly enhances the overall economic efficiency and operational safety of the power systems within industrial and commercial enterprises, thereby achieving a dual benefit of demand-based electricity cost savings and multi-dimensional arbitrage. Summary of the Invention

[0006] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method, device, computer equipment and storage medium for evaluating the economic operation of energy storage power stations.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: an economic operation evaluation method for energy storage power stations, comprising:

[0008] Construct an economic benefit model for user-side distributed energy storage power stations; wherein, the economic benefit model for user-side distributed energy storage power stations includes a basic benefit model for energy storage power stations and an ancillary service benefit model;

[0009] Determine the basic revenue of the energy storage power station based on the economic benefit model of the user-side distributed energy storage power station;

[0010] Ancillary service revenue is determined based on the aforementioned ancillary service revenue model;

[0011] The overall revenue of the energy storage power station is assessed based on its basic revenue and ancillary service revenue.

[0012] Real-time monitoring of enterprise electricity load and dynamic adjustment of charging and discharging plans to optimize demand management;

[0013] Participating in frequency regulation and peak shaving ancillary services provided by the power grid can increase additional revenue streams.

[0014] Based on the actual operation of the energy storage system, the basic revenue model of the energy storage power station is adjusted, and the changes in overall revenue are compared.

[0015] The further technical solution is as follows: the basic revenue model of the energy storage power station is based on the price difference between peak and off-peak charging to calculate the revenue, and the basic revenue of the energy storage power station is calculated by the method of accumulating daily dynamic discharge revenue and deducting the accumulated charging expenditure; the ancillary service revenue model is for the revenue brought about by the optimization of power load demand, and considers that the energy storage system reduces the user's maximum demand value by peak shaving and valley filling.

[0016] Its further technical solution is as follows: the determination of the basic revenue of the energy storage power station based on the economic benefit model of the user-side distributed energy storage power station includes:

[0017] The statistical period is set to the Gregorian month, and the time interval is divided into segments with minute intervals.

[0018] Based on the user-side distributed energy storage power station economic benefit model, the energy storage charging and discharging strategy and price for each minute segment are determined according to the electricity price policy, photovoltaic output and supply and demand balance of the enterprise's electricity load in the user's region, and the basic revenue of the energy storage power station is calculated.

[0019] The further technical solution is as follows: determining the ancillary service revenue based on the ancillary service revenue model includes:

[0020] Two maximum demand values ​​are obtained by using a sliding window statistical method, representing the situation before and after the implementation of demand shaving services by energy storage, and the difference between the two maximum demand values ​​before and after the reduction is calculated.

[0021] The difference before and after the reduction is multiplied by the demand electricity price to obtain the ancillary service revenue.

[0022] The further technical solution is as follows: the assessment of the overall revenue of the energy storage power station based on the basic revenue and ancillary service revenue of the energy storage power station includes:

[0023] The total revenue of the energy storage power station is obtained by summing the basic revenue and ancillary service revenue.

[0024] The further technical solution is as follows: the real-time monitoring of enterprise power load and dynamic adjustment of charging and discharging plans to optimize demand management includes:

[0025] Install and configure a real-time monitoring system to monitor changes in the enterprise's electricity load, and automatically trigger the energy storage system to discharge when the load approaches a preset demand threshold.

[0026] Based on production plans and historical data, predict potential periods of high load in the future and prepare for energy storage charging in advance.

[0027] A distributed charging mode is used during peak hours.

[0028] The further technical solution is as follows: Adjusting the basic revenue model of the energy storage power station based on the actual operating conditions of the energy storage system, and comparing changes in overall revenue, includes:

[0029] The base revenue of the energy storage power station is adjusted to take into account that some of the electricity is used to reduce maximum demand;

[0030] By comparing the monthly energy storage revenue before and after implementation, including the basic revenue and ancillary service revenue of the energy storage power station, the overall economic benefit improvement rate is quantified.

[0031] This invention also provides an economic operation evaluation device for energy storage power stations, comprising:

[0032] A construction unit is used to construct an economic benefit model for a user-side distributed energy storage power station; wherein, the economic benefit model for the user-side distributed energy storage power station includes a basic benefit model for the energy storage power station and an ancillary service benefit model.

[0033] The basic revenue calculation unit is used to determine the basic revenue of the energy storage power station based on the economic revenue model of the user-side distributed energy storage power station.

[0034] An ancillary service revenue calculation unit is used to determine ancillary service revenue based on the ancillary service revenue model.

[0035] An evaluation unit is used to evaluate the overall revenue of the energy storage power station based on its basic revenue and ancillary service revenue.

[0036] The adjustment unit is used to monitor the enterprise's power load in real time and dynamically adjust the charging and discharging plan to optimize demand management;

[0037] Adding units allows for participation in frequency regulation and peak shaving ancillary services provided by the power grid, thereby increasing additional revenue streams.

[0038] The comparison unit is used to adjust the basic revenue model of the energy storage power station based on the actual operation of the energy storage system, and to compare the changes in comprehensive revenue.

[0039] The present invention also provides a computer device, the computer device including a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the above-described method.

[0040] The present invention also provides a storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0041] The beneficial effects of this invention compared to existing technologies are as follows: By constructing an economic benefit model for user-side distributed energy storage power stations that includes both basic revenue and ancillary service revenue, this invention can not only determine the basic revenue of the energy storage power station and the additional revenue sources from participating in grid frequency regulation, peak shaving, and other ancillary services, but also dynamically adjust charging and discharging strategies based on real-time monitoring of enterprise electricity load to optimize demand management, thereby maximizing the overall revenue of the energy storage power station. This method further considers adjusting the model according to the actual operation of the energy storage system to ensure continuous optimization of comprehensive revenue. Therefore, this method not only significantly improves the economic benefits of the energy storage power station itself, but also enhances the overall economy and operational safety of the power system within industrial and commercial enterprises, achieving a dual benefit improvement of demand-based electricity cost savings and multi-dimensional arbitrage, providing enterprises with an effective means to flexibly cope with electricity price fluctuations and load changes.

[0042] The present invention will be further described below with reference to the accompanying drawings and specific embodiments. Attached Figure Description

[0043] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0044] Figure 1 A flowchart illustrating the economic operation evaluation method for energy storage power stations provided in this embodiment of the invention;

[0045] Figure 2 A schematic diagram of the original fixed strategy of the energy storage power station economic operation evaluation method provided in the embodiments of the present invention;

[0046] Figure 3 A schematic diagram of the adjusted energy storage charge-discharge curve provided in an embodiment of the present invention;

[0047] Figure 4 A schematic diagram illustrating the adjustment strategy provided in an embodiment of the present invention;

[0048] Figure 5 A schematic block diagram of an energy storage power station economic operation evaluation device provided in an embodiment of the present invention;

[0049] Figure 6 A schematic block diagram of a computer device provided for an embodiment of the present invention. Detailed Implementation

[0050] 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, not all, of the embodiments of the present invention. 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.

[0051] It should be understood that, when used in this specification and the appended claims, the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0052] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0053] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0054] Please see Figure 1 , Figure 1This is a flowchart illustrating the economic operation evaluation method for energy storage power stations provided in this embodiment of the invention. This method is applied to a server. By constructing a user-side distributed energy storage power station economic benefit model that includes a basic benefit model and an ancillary service benefit model, it not only enhances the economic efficiency of the energy storage power station itself based on the price difference between peak and off-peak charging and participation in ancillary services such as grid frequency regulation and peak shaving, but also optimizes demand management by dynamically adjusting charging and discharging plans based on real-time monitoring of enterprise electricity load, thereby significantly enhancing the overall economy and operational safety of the power system within industrial and commercial enterprises. Specific measures include using the energy storage system to reduce the maximum demand value of users through peak shaving and valley filling to save demand-related electricity costs, and employing a method of daily dynamic revenue accumulation and sliding window statistical calculation of demand reduction benefits to accurately calculate the changes in comprehensive revenue, ultimately achieving a dual benefit improvement of demand-related electricity cost savings and multi-dimensional arbitrage, further quantifying the improvement ratio of overall economic efficiency.

[0055] Figure 1 This is a flowchart illustrating the economic operation evaluation method for energy storage power stations provided in this embodiment of the invention. Figure 1 As shown, the method includes the following steps S110 to S170.

[0056] S110. Construct an economic benefit model for user-side distributed energy storage power stations; wherein, the economic benefit model for user-side distributed energy storage power stations includes a basic benefit model for energy storage power stations and an ancillary service benefit model.

[0057] In this embodiment, the basic revenue model of the energy storage power station is calculated based on the price difference between peak and off-peak hours, and the basic revenue of the energy storage power station is calculated by subtracting the accumulated charging expenses from the daily dynamic discharge revenue. The ancillary service revenue model considers the revenue brought by the optimization of power load demand, and takes into account the reduction of the user's maximum demand value by the energy storage system through peak shaving and valley filling.

[0058] Specifically, the basic revenue model for energy storage power stations relies heavily on electricity price volatility, particularly by charging during off-peak hours (when demand is lowest) and discharging during peak hours (when demand is highest) to profit from the price difference. This method not only effectively utilizes energy price differences within the power grid but also further enhances economic efficiency by optimizing the charging and discharging strategies of the energy storage system. Specifically, the model calculates the basic revenue of the energy storage power station by subtracting the accumulated charging expenses from the daily dynamic accumulation of discharge revenue. This means that for each minute-level time segment, the charging and discharging strategy of the energy storage system is dynamically adjusted based on the prevailing electricity price policy, photovoltaic output, and the supply-demand balance of the enterprise's electricity load. Based on this, the charging and discharging costs and revenues per minute are calculated, and these values ​​are ultimately summed to arrive at the total revenue for a day or month.

[0059] The ancillary service revenue model focuses on how energy storage systems can reduce users' maximum demand through peak shaving and valley filling, thereby saving businesses on demand-based electricity costs. "Peak shaving and valley filling" refers to using energy storage systems to release stored energy during peak electricity load periods and store excess energy during off-peak periods, thus smoothing the overall electricity load curve for businesses. This not only reduces the business's demand for grid electricity during periods of high electricity prices but also avoids additional costs incurred due to excessive peak loads. Furthermore, two maximum demand values ​​are obtained using a 5-minute sliding window statistical method—one before the energy storage implements peak shaving services, and the other afterward. The difference between these two values, multiplied by the local demand-based electricity price, yields the ancillary service revenue provided by the energy storage system.

[0060] In summary, by constructing an economic benefit model for user-side distributed energy storage power stations that incorporates the two models mentioned above, we can not only assess the economic benefits of the energy storage power station itself, but also deeply analyze its impact on the overall economic efficiency and operational safety of the enterprise's internal power system, achieving the dual benefits of demand-based electricity cost savings and multi-dimensional arbitrage. This comprehensive evaluation method overcomes the limitations of previous methods that only considered the economic benefits of the energy storage power station itself, providing users with a more comprehensive and accurate economic operation evaluation tool.

[0061] S120. Determine the basic revenue of the energy storage power station based on the economic benefit model of the user-side distributed energy storage power station.

[0062] In this embodiment, the basic revenue of an energy storage power station refers to the revenue from the price difference in electricity volume achieved within a statistical period (such as one month) by using a strategy of charging when the electricity price is low and discharging when the electricity price is high, taking advantage of the peak-valley electricity price difference.

[0063] In this embodiment, step S120 aims to determine the basic revenue of the energy storage power station based on the constructed economic benefit model of the user-side distributed energy storage power station. This step is one of the core parts of the entire evaluation method because it directly relates to the actual economic benefits obtained by the energy storage power station through electricity price arbitrage. Specifically, the basic revenue of the energy storage power station refers to the revenue achieved within a specific time period (such as one month) by rationally arranging the charging and discharging strategy of the energy storage system and utilizing the peak-valley electricity price difference. The following will describe in detail how to perform this step and break it down into two sub-steps: S121 and S122.

[0064] In one embodiment, step S120 described above may include steps S121 to S122.

[0065] S121. Set the statistical period to the Gregorian calendar month and divide the time interval into segments with minute intervals.

[0066] First, to accurately calculate the basic revenue of an energy storage power station, a clear time frame needs to be established as the statistical period; here, a calendar month is chosen. This choice not only aligns with the financial reporting cycles of most companies but also facilitates electricity bill settlement with power companies. Next, this monthly time interval is divided into minute-by-minute segments. This is done to capture the grid status, photovoltaic output, and changes in the company's electricity load at each moment in greater detail, thereby enabling the development of more precise energy storage charging and discharging strategies.

[0067] S122. Based on the user-side distributed energy storage power station economic benefit model, the energy storage charging and discharging strategy and price for each minute segment are determined according to the electricity price policy, photovoltaic output and supply and demand balance of the enterprise's electricity load in the user's region, and the basic revenue of the energy storage power station is calculated.

[0068] In this step, multiple factors need to be considered to determine the optimal energy storage charging and discharging strategy and its corresponding price for each minute-level segment:

[0069] Electricity pricing policies: Different regions have different peak-valley electricity pricing mechanisms, and understanding and applying this information is crucial to maximizing the basic returns of energy storage power stations.

[0070] Solar power output: If a user has installed a distributed photovoltaic (PV) power generation system, its power generation will directly affect the charging and discharging decisions of the energy storage system. For example, during peak PV output periods in the daytime, charging may be more suitable than discharging.

[0071] Enterprise electricity load supply and demand balance status: Real-time monitoring of changes in internal enterprise electricity demand can help predict when high load periods or low load periods will occur, thereby optimizing the operation mode of energy storage systems.

[0072] Based on the above information, corresponding energy storage charging and discharging strategies can be formulated for each minute-level segment, and the basic revenue of the energy storage power station can be calculated accordingly. Specifically, for each minute, based on the current electricity price, photovoltaic output, and the enterprise's electricity load status, a decision is made whether to charge or discharge, and the corresponding electricity consumption and price are recorded. Then, by accumulating the costs of all charging periods and the revenue of discharging periods each day, the basic revenue of the energy storage power station for the entire month is finally obtained.

[0073] In summary, by executing sub-steps S121 and S122, the advantages of energy storage systems can be effectively utilized to maximize economic benefits while meeting user electricity demands. This method not only improves the operational efficiency of energy storage power stations but also saves users significant electricity costs.

[0074] In this embodiment, with the Gregorian month as the statistical period, the time period interval is cut into slices at minute intervals, and the energy storage charge-discharge strategy is set for each minute-level segment. Based on the peak-valley electricity price policy in the user's area, combined with the supply-demand balance state of the power generation output of the user's distributed photovoltaic power station and the enterprise's electricity consumption load, the energy storage charge-discharge price under the minute-level power supply and consumption transient state is determined. This price is comprehensively affected by the minute-level transient of distributed photovoltaic power generation output, the minute-level transient of enterprise electricity consumption load, the interval electricity price policy, the transient of energy storage SOC, and the setting of charge-discharge power, P 卖电价 、P 购电价 It shows the characteristics of minute-level oscillatory fluctuations during the entire statistical long period. The price acquisition logic is as follows:

[0075] Judge the current power direction (P) of the energy storage system: If P>0, enter the charging state processing flow; judge whether the user's gateway load (G) is greater than zero. If G≥0, all the discharged power of the energy storage is used for the electricity load, and the discharge income = the discharge amount in this period (1 minute) * the corresponding mechanism electricity price in this period (peak-valley electricity price); when G≤P, all the discharged power of the energy storage is for grid connection, and the discharge benefit = the discharge amount in this period (1 minute) * the photovoltaic grid connection electricity price; if P<G<0, part of the discharged power of the energy storage is used for the load and part is used for photovoltaic, then the electricity quantity is divided according to the power ratio. The grid-connected electricity quantity = the discharge amount in this period (1 minute) * |G / P|, and the grid-connected income + the grid-connected electricity quantity * the photovoltaic grid connection electricity price; the electricity quantity for load electricity consumption == the discharge amount in this period (1 minute) * (1 - |G / P|); the load electricity consumption income = the load electricity consumption quantity * the corresponding mechanism electricity price in this period (peak-valley electricity price); if P = 0, the energy storage stands by and the income is 0.

[0076] When P<0 and the energy storage is in the charging state, and the user's gateway load G≤0, then all the charging amount of the energy storage comes from photovoltaic, and the charging cost = the charging amount in this period (1 minute) * the photovoltaic grid connection electricity price; when G≥|P|, all the charging of the energy storage comes from the mains power, and the charging cost = the charging amount in this period (1 minute) * the corresponding mechanism electricity price in this period (peak-valley electricity price); when 0<G<|P|, part of the charging of the energy storage is from photovoltaic and part is from the mains power, then the charging amount is divided according to the power ratio. The mains power charging amount = the charging amount in this period * G / P, and the charging cost = the mains power charging amount * the corresponding mechanism electricity price in this period; the photovoltaic charging amount = the charging amount in this period * (1 - G / P), and the charging cost = the photovoltaic charging amount * the photovoltaic grid connection electricity price. Adding the two together is the charging cost of the electricity quantity in this period (1 minute).

[0077] All branches finally converge at a common node: "P = 0, the energy storage stands by", at this time the energy storage system does not participate in charge-discharge and only serves as a standby resource.

[0078] A revenue of 0 indicates that energy storage did not generate any economic benefits during this period (such as electricity price arbitrage or demand reduction contributions). Where: P is the energy storage charging and discharging power; G is the load at the industrial and commercial enterprise threshold.

[0079] S130. Determine the ancillary service revenue based on the ancillary service revenue model.

[0080] In this embodiment, ancillary service revenue refers to the economic benefits brought about by the energy storage power station reducing users' demand charges by decreasing their maximum demand after performing demand shaving services. Specifically, this involves optimizing the charging and discharging strategy of the energy storage power station so that users reduce their demand for electricity from the grid during periods of high electricity prices, thereby reducing users' maximum demand (i.e., the maximum demand at the threshold load). This optimization not only directly reduces users' demand charges but also indirectly improves the economic operating efficiency of the entire system.

[0081] In one embodiment, step S130 described above may include steps S131 to S132.

[0082] S131. Obtain two maximum demand values ​​through a 5-minute sliding window statistical method, representing the situation before and after the implementation of demand shaving services by energy storage, and calculate the difference between the two maximum demand values ​​before and after the reduction.

[0083] First, a 5-minute sliding window statistical method was used to calculate the daily maximum demand for one month before and after the implementation of demand shaving services at the energy storage power station. For each time point t, all load data collected in the next five minutes were averaged to obtain a value representing the demand level for that time period. Then, the largest value was selected as the maximum demand for the day. Finally, the maximum demand values ​​obtained before and after the implementation of demand shaving services at the energy storage power station were compared, and the difference between the two was calculated.

[0084] S132. Multiply the difference before and after the reduction by the demand electricity price to obtain ancillary service revenue.

[0085] Multiplying the demand reduction difference obtained in step S131 by the demand rate (PR) in the user's region yields the additional economic benefits from the demand shaving service provided by the energy storage power station. This benefit reflects the ability of the energy storage system to help users save on demand charges by optimizing their electricity consumption patterns.

[0086] In this embodiment,

[0087] Where: P meter_i The load at the checkpoint was collected at 15-minute intervals;

[0088] The load demand value is obtained using a 5-minute sliding window statistical method.

[0089] P moni_i For monitoring loads of the enterprise's power distribution outgoing line bay circuits;

[0090] n represents the number of power distribution outgoing line bays and circuits of the enterprise;

[0091] PR represents the demand electricity price in the region where the company is located.

[0092] S140. Evaluate the overall revenue of the energy storage power station based on its basic revenue and ancillary service revenue.

[0093] In this embodiment, the total revenue of an energy storage power station refers to the combination of the basic revenue (such as the revenue from the price difference between off-peak and peak load) and the revenue from ancillary services (such as the reduction in demand charges through demand shaving services), which comprehensively reflects the total value created by the energy storage power station in optimizing the enterprise's electricity costs.

[0094] Specifically, the basic revenue and ancillary service revenue of the energy storage power station are summed to obtain the total revenue of the energy storage power station.

[0095] Specifically, the basic revenue and ancillary service revenue of the energy storage power station are summed, i.e.: REV total =REV service +REV base After energy storage power stations implement demand shaving services, their overall revenue, consisting of basic revenue and ancillary service revenue, shows an increasing trend. Specifically, REV... service This refers to the revenue generated from ancillary services through demand shaving, while REV base This represents the basic revenue achieved by an energy storage power station through buying electricity low and selling it high. This method allows for accurate assessment of the comprehensive economic benefits of energy storage power stations in different application scenarios, providing investors with strong data support and demonstrating the economic feasibility and attractiveness of energy storage projects. Furthermore, this approach helps companies better understand how to leverage energy storage technology to optimize their energy usage structure and further improve overall operational efficiency.

[0096] S150: Real-time monitoring of enterprise power load, dynamic adjustment of charging and discharging plans to optimize demand management.

[0097] In one embodiment, step S150 described above may include steps S151 to S153.

[0098] S151. Install and configure a real-time monitoring system for monitoring changes in the enterprise's electricity load, and automatically trigger the discharge operation of the energy storage system when it approaches a preset demand threshold.

[0099] S152. Based on production plans and historical data, predict potential high-load periods in the future and prepare for energy storage charging in advance.

[0100] S153. A distributed charging mode is adopted during peak hours.

[0101] By optimizing the charging and discharging strategies of energy storage power stations through real-time monitoring and intelligent scheduling, effective management of enterprise electricity load can be achieved. The specific steps are as follows:

[0102] First, deploy a high-precision real-time monitoring system within the enterprise to continuously track changes in electricity load. This system should have data acquisition, analysis, and early warning functions, and should automatically trigger the energy storage system to discharge when the load approaches a preset demand threshold, thus smoothing load spikes and preventing excessive demand. Furthermore, the energy storage system must have a sufficiently fast response time, enabling it to switch from standby to discharge mode in a short period.

[0103] Based on enterprise production plans and historical electricity consumption data, big data analytics and machine learning algorithms are used to predict potential high-load periods. For these periods, it's necessary to plan the energy storage charging window in advance to ensure the system is fully charged before the peak load arrives. This not only helps reduce enterprise electricity demand during peak hours but also effectively reduces demand-based electricity charges.

[0104] During off-peak hours, if a company's current demand is found to be close to a set threshold, centralized charging of the energy storage system should be suspended, and a "fragmented charging" strategy should be adopted instead. This means charging should only occur when the company's actual electricity load falls below a certain safety threshold, thus avoiding a surge in maximum demand due to charging. This flexible charging method can better balance the relationship between the company's electricity consumption and the energy storage system's charging, maximizing the economic benefits of the energy storage system.

[0105] S160. Participate in frequency regulation and peak shaving ancillary services provided by the power grid to increase additional revenue sources.

[0106] Energy storage power stations can not only provide power support for enterprises internally, but also participate in ancillary services such as frequency regulation and peak shaving of the external power grid, further broadening revenue channels. For example, when the power grid faces a supply-demand imbalance, energy storage power stations can transmit or absorb power to the grid through a rapid response mechanism to help maintain grid frequency stability; while during peak electricity consumption periods, they can release stored energy as backup power to alleviate grid pressure.

[0107] S170. Adjust the basic revenue model of the energy storage power station according to the actual operation of the energy storage system, and compare the changes in comprehensive revenue.

[0108] In one embodiment, step S170 described above may include steps S171 to S172.

[0109] S171. Considering that some electricity is used to reduce maximum demand, the basic revenue of the energy storage power station is adjusted;

[0110] S172. Compare the monthly energy storage revenue before and after implementation, including the basic revenue and ancillary service revenue of the energy storage power station, and quantify the overall improvement in economic benefits.

[0111] In one embodiment, to accurately assess the economic performance of energy storage power stations under the new strategy, it is necessary to appropriately adjust the original basic revenue model and compare the changes in energy storage revenue before and after implementation. Specific steps include:

[0112] Since some electricity is used to reduce maximum demand, this portion no longer directly generates traditional price difference revenue. Therefore, when calculating the basic revenue of an energy storage power station, it is necessary to deduct the cost of this portion of electricity and re-estimate the actual revenue that the remaining available capacity can bring.

[0113] Finally, the adjusted base revenue of the energy storage power station is combined with the newly added ancillary service revenue to obtain the total monthly energy storage revenue, which is then compared with the revenue under the original plan. The purpose of this is to clarify the increase in economic benefits brought about by the implementation of the new strategy, providing a strong basis for subsequent investment decisions. For example, through the numerical calculations in the above case, we can see that the monthly revenue increase rate of the energy storage system under the new strategy reached 33.93%, indicating that the optimized charging and discharging strategy significantly improved the overall economic benefits of the energy storage power station.

[0114] In this embodiment, the application of demand charge control and management for industrial and commercial electricity users is described. Demand charge is a capacity fee charged by the power grid company based on the user's maximum power consumption (demand) (e.g., charged in "yuan / kVA·month"). The traditional fixed "two-charge, two-discharge" strategy does not consider the problem of peak load overlap for enterprises—if the energy storage charging period coincides with the peak production load of the enterprise, it will push up the maximum demand, thus increasing demand charge. However, the dynamic high-frequency charging and discharging strategy can accurately smooth out load peaks through "peak shaving and valley filling + demand prediction," controlling the maximum demand within the target threshold and achieving the dual benefits of "demand charge savings + multi-dimensional arbitrage."

[0115] Original curing strategy such as Figure 2As shown, daily charging occurs during off-peak hours (e.g., 2-6 AM, when enterprise load is low and charging does not affect demand) and peak discharge occurs during peak hours (e.g., 6-10 PM). However, random load shocks occur during production (e.g., large machine tool startup, production line switching, causing a sudden increase in demand of 20%-50%), leading to exceeding the monthly maximum demand and additional demand-related electricity costs. The original energy storage charging and discharging (purple line) is shown in the figure below, and is illustrated using the basic energy storage revenue model. The energy storage operation yields revenue with an installed capacity of 650kW / 1300kWh. The first charge occurs during off-peak hours at a price of 0.4219, and the first discharge occurs during normal hours at a price of 0.6941. The second charge also occurs during off-peak hours at a price of 0.4219, and the second discharge occurs during peak hours at a price of 0.9663. Substituting these values ​​into the formula, the daily basic revenue from energy storage is calculated as: 1500*0.9663 + 1500*0.6941 - 1500*0.4219 - 1500*0.4219 = 1227.9 yuan. Figure 3 As shown.

[0116] This embodiment uses dynamic high-frequency charging and discharging combined with demand management optimization. It monitors the demand curve in real time. When the load approaches the preset demand threshold, the energy storage immediately discharges at high frequency (each discharge lasts 5 minutes) to replace the grid power supply, smooth out load peaks, and prevent the demand from exceeding the threshold.

[0117] Anticipate periods of load surges (e.g., based on production plans, anticipate peak machine start-up times at 9 AM and 3 PM) and reserve power 1-2 hours in advance through high-frequency, small-batch charging to ensure sufficient power for peak shaving when the surge occurs.

[0118] Dynamic adjustment of charging periods: If enterprise demand is close to the threshold during off-peak hours, charging will be suspended or switched to "fragmented charging" (charging when demand is below the threshold and stopping when demand is above the threshold) to avoid driving up demand. Strategies include... Figure 4 As shown, a series of judgment conditions are listed on the left. These conditions are based on the real-time operating status parameters of the energy storage system, such as power difference (P energy), remaining time (O time), and battery state of charge (SOC). Specifically, these conditions include:

[0119] When the power difference is 0, it means that there is no net power flow at present, and the system may be in a balanced or standby state.

[0120] If the remaining time is greater than 0, it indicates that the energy storage system still has available time within a specific task cycle.

[0121] When the power difference is less than 0 and the battery charge is not full (i.e., SOC is less than 100%), it indicates that the grid load is low, and the energy storage system can be charged.

[0122] If the power difference is greater than 0 and the battery has power (i.e., SOC > 0%), the grid load is relatively high, and the energy storage system can consider discharging to meet the demand.

[0123] The middle part shows the scheduling logic and execution actions based on the above judgment conditions. This part determines the specific operations that the energy storage system should take through a series of decision points, such as whether to charge, discharge, or remain on standby. For example, if it is detected that the power difference is less than 0 and the battery is not fully charged, the system will start the charging process; conversely, if the power difference is greater than 0 and the battery still has available power to release, a discharge command will be triggered. In addition, this part also includes some special logic processing, such as maintaining an operation for a period of time or delaying judgment, aiming to ensure the stability and efficiency of the system and avoid frequent switching of operation modes.

[0124] The right part provides information on exception handling and call instructions, annotating the subroutines or functional modules involved in the middle part, including call logic, priority order, etc. This part of the content is crucial for understanding how the system responds to special situations. For example, when the demand approaches the upper limit, the system will start the peak shaving mode and give priority to discharging to reduce the maximum demand; while when the energy storage power is too low, even if the demand is high, discharging is prohibited to prevent deep discharge from damaging the battery.

[0125] Specifically, the triggering conditions are as follows:

[0126] P energy = 0 and O time > 0: When there is no net power flow between the grid and the load (i.e., the system is in a balanced state), and the remaining task time is greater than zero.

[0127] P energy < 0 and SOC < 100%: The grid gateway load is negative (excess photovoltaic power generation or insufficient enterprise power consumption), and the energy storage battery is not fully charged.

[0128] P energy > 0 and SOC > 0%: The grid gateway load is positive (rising enterprise power demand), and the energy storage system still has available power.

[0129] P energy = 0 and P energy > P threshold: The current power is zero but the trend is upward, predicting that a load growth signal may appear in the future (there may be a contradiction in the expression, which should be understood as a boundary condition handling).

[0130] P energy = 0 and P energy < P threshold: The current power is zero, and the actual power is lower than the set threshold, indicating that the system is in a low load or stable state.

[0131] Execution logic:

[0132] For each triggering condition, there is a corresponding execution action:

[0133] When the first condition is met, the system enters a "waiting" state, takes no action, and continues to monitor changes.

[0134] If the second condition is met, a charging command is initiated, and demand management strategies are considered.

[0135] The third condition triggers a discharge operation to release stored electrical energy to support high-load periods.

[0136] The fourth condition may trigger predictive scheduling to prepare for an upcoming high load.

[0137] The last condition causes the system to enter standby mode, maintaining basic monitoring functions.

[0138] Strategy Explanation:

[0139] Multiple safety protection mechanisms, including but not limited to SOC limiting (preventing overcharging and over-discharging), demand management (avoiding the maximum demand from being pushed up by charging), and anomaly detection (taking emergency shutdown or safe standby measures in case of communication interruption, voltage change, etc.).

[0140] Intelligent dynamic response: The system can collect P energy data in real time and automatically adjust the charging and discharging strategy according to different triggering conditions to achieve minute-level dynamic response and adapt to electricity price fluctuations and load changes.

[0141] Balancing economy and safety: While pursuing profit margins, we also emphasize the safety of equipment to ensure both economy and safety during operation.

[0142] Closed-loop feedback control: By monitoring key parameters (such as P energy, O time, SOC, etc.) in real time, a closed-loop feedback control system is formed to ensure the efficient and stable operation of the system.

[0143] This design not only solves the problem of the rigidity of the traditional "two-charge, two-discharge" strategy, but also realizes the transformation of energy storage systems from passive following to active optimization, providing strong technical support for the efficient, economical and safe operation of user-side energy storage power stations.

[0144] The adjusted energy storage charge-discharge curve is as follows: Figure 3 As shown, the revenue model has changed to: revenue from new ancillary services + basic energy storage revenue, where the revenue from new ancillary services follows the... Calculations are performed, assuming PR is 42.3 yuan / kVA / month. It is 3400. If the capacity is 3700, then the additional ancillary service revenue is 42.3 * (3700 - 3400) = 12690 yuan. However, considering that energy storage will reduce maximum demand during off-peak periods, some of the discharge volume in the original energy storage revenue will not generate revenue. Assuming 3% of the discharge volume, the actual usable energy storage capacity is 1500% (1 - 3%) = 1455. Substituting these values ​​into the calculation, the result is: 1455 * 0.9663 + 1455 * 0.6941 - 1455 * 0.4219 - 1455 * 0.4219 = 1191.063 yuan. The original profit and loss from energy storage is then 1227.9 - 1191.063 = 36.837 yuan / day. Assuming a 30-day month, the monthly profit and loss is 30 * 36.837 = 1105.11 yuan. Under the new strategy, the increased revenue from energy storage is 12690 - 1191.063 = 12498.937. Under the original basic model, the monthly revenue from energy storage was 36837 yuan. The monthly revenue increase rate is 33.93%.

[0145] This embodiment addresses the limitations of the economic evaluation of user-side distributed energy storage power stations, which is one-sided and unreasonable and fails to consider the impact of their operation on the overall economic efficiency of the user's power system.

[0146] Definitions:

[0147] Maximum electrical load demand: The maximum load demand value generated by the actual electrical equipment of the electricity-consuming enterprise only reflects the maximum demand value generated by the actual electrical load of all electrical equipment within the enterprise;

[0148] Maximum demand at the checkpoint: The maximum demand value after adjusting the user's electricity load through load shaving by utilizing regulating resources such as energy storage power stations;

[0149] Maximum demand reduction contribution: The difference between the maximum demand at the threshold and the maximum electricity demand reflects the economic value contribution of demand optimization technology services.

[0150] Maximum demand reduction benefit: Based on the demand-based electricity pricing scheme of the province (city) where the user is located, calculate the amount of basic electricity fee reduction resulting from the maximum demand reduction in the current month;

[0151] Energy storage charging and discharging revenue: The monthly cumulative value of the difference between the cost of charging energy and the revenue of discharging energy, based on the grid-mandated electricity price. The charging and discharging electricity price varies depending on the photovoltaic (PV) grid integration status. If the load on the consumer's grid connection is positive, the charging and discharging prices are based on the grid-mandated electricity price. If the load on the consumer's grid connection is negative, the charging and discharging prices are based on the grid-connected electricity price for PV systems participating in market-based transactions.

[0152] Energy storage charging and discharging profit and loss: The difference between the cumulative monthly revenue of the original fixed charging and discharging strategy of energy storage and the "energy storage charging and discharging revenue" represents the increase or decrease in the economic operating revenue of energy storage itself from receiving demand optimization technology services.

[0153] The aforementioned method for evaluating the economic operation of energy storage power stations constructs an economic benefit model for user-side distributed energy storage power stations, including both basic and ancillary service revenue. This model not only determines the basic revenue of the energy storage power station and the additional revenue sources from participating in grid frequency regulation and peak shaving services, but also dynamically adjusts charging and discharging strategies based on real-time monitoring of enterprise electricity load to optimize demand management, thereby maximizing the overall revenue of the energy storage power station. This method further considers model adjustments based on the actual operating conditions of the energy storage system to ensure continuous optimization of comprehensive revenue. Therefore, this method not only significantly improves the economic efficiency of the energy storage power station itself but also enhances the overall economic efficiency and operational security of the power system within industrial and commercial enterprises, achieving a dual benefit of demand-based electricity cost savings and multi-dimensional arbitrage, providing enterprises with an effective means to flexibly cope with electricity price fluctuations and load changes.

[0154] Figure 5 This is a schematic block diagram of an energy storage power station economic operation evaluation device 300 provided in an embodiment of the present invention. Figure 5 As shown, corresponding to the above-described economic operation evaluation method for energy storage power stations, the present invention also provides an economic operation evaluation device 300 for energy storage power stations. This energy storage power station economic operation evaluation device 300 includes a unit for executing the above-described economic operation evaluation method for energy storage power stations, and the device can be configured in a server. Specifically, please refer to... Figure 5 The energy storage power station economic operation evaluation device 300 includes a construction unit 301, a basic revenue calculation unit 302, an ancillary service revenue calculation unit 303, an evaluation unit 304, an adjustment unit 305, an addition unit 306, and a comparison unit 307.

[0155] The system comprises: a construction unit 301 for constructing an economic benefit model for a user-side distributed energy storage power station; wherein the economic benefit model includes a basic benefit model and an ancillary service benefit model; a basic benefit calculation unit 302 for determining the basic benefit of the energy storage power station based on the economic benefit model; an ancillary service benefit calculation unit 303 for determining the ancillary service benefit based on the ancillary service benefit model; an evaluation unit 304 for evaluating the overall benefit of the energy storage power station based on the basic benefit and ancillary service benefit; an adjustment unit 305 for real-time monitoring of enterprise electricity load and dynamically adjusting charging and discharging plans to optimize demand management; an addition unit 306 for participating in frequency regulation and peak shaving ancillary services provided by the power grid to increase additional revenue sources; and a comparison unit 307 for adjusting the basic benefit model of the energy storage power station according to the actual operation of the energy storage system and comparing changes in overall benefit.

[0156] In one embodiment, the basic revenue calculation unit 302 includes: a time division subunit, used to set the statistical period to a calendar month and divide the time interval into segments by minute intervals; and a first calculation subunit, used to determine the energy storage charging and discharging strategy and price for each minute segment based on the user-side distributed energy storage power station economic revenue model, according to the electricity price policy, photovoltaic output and supply and demand balance of the enterprise's electricity load in the user's region, and to calculate the basic revenue of the energy storage power station.

[0157] In one embodiment, the ancillary service revenue calculation unit 303 includes:

[0158] The difference calculation subunit is used to obtain two maximum demand values ​​through a 5-minute sliding window statistical method, representing the situation before and after the implementation of demand shaving services by energy storage, and to calculate the difference between the two maximum demand values ​​before and after the reduction; the revenue calculation subunit is used to multiply the difference before and after the reduction by the demand electricity price to obtain the ancillary service revenue.

[0159] In one embodiment, the evaluation unit 304 is used to sum the basic revenue and ancillary service revenue of the energy storage power station to obtain the total revenue of the energy storage power station.

[0160] In one embodiment, the adjustment unit 305 includes:

[0161] The monitoring subunit is used to install and configure a real-time monitoring system for monitoring changes in the enterprise's electricity load, and to automatically trigger the energy storage system to discharge when it approaches a preset demand threshold; the prediction subunit is used to predict future high-load periods based on production plans and historical data, and to prepare for energy storage charging in advance; the adoption subunit is used to adopt a distributed charging mode during peak periods.

[0162] In one embodiment, the comparison unit 307 includes:

[0163] The revenue adjustment subunit is used to adjust the basic revenue of the energy storage power station, taking into account that some electricity is used to reduce maximum demand; the comparison subunit is used to compare the monthly energy storage revenue before and after implementation, including the basic revenue of the energy storage power station and ancillary service revenue, to quantify the overall improvement in economic benefits.

[0164] It should be noted that those skilled in the art can clearly understand that the specific implementation process of the above-mentioned energy storage power station economic operation evaluation device 300 and each unit can be referred to the corresponding description in the aforementioned method embodiments. For the sake of convenience and brevity, it will not be repeated here.

[0165] The aforementioned energy storage power station economic operation evaluation device 300 can be implemented as a computer program, which can be used in, for example... Figure 6 It runs on the computer device shown.

[0166] Please see Figure 6 , Figure 6 This is a schematic block diagram of a computer device provided in an embodiment of this application. The computer device 500 is a server, wherein the server can be a standalone server or a server cluster composed of multiple servers.

[0167] See Figure 6 The computer device 500 includes a processor 502, a memory, and a network interface 505 connected via a system bus 501. The memory may include a non-volatile storage medium 503 and internal memory 504.

[0168] The non-volatile storage medium 503 may store an operating system 5031 and a computer program 5032. The computer program 5032 includes program instructions that, when executed, cause the processor 502 to execute an economic operation evaluation method for an energy storage power station.

[0169] The processor 502 provides computing and control capabilities to support the operation of the entire computer device 500.

[0170] The internal memory 504 provides an environment for the operation of the computer program 5032 in the non-volatile storage medium 503. When the computer program 5032 is executed by the processor 502, the processor 502 can execute an economic operation evaluation method for an energy storage power station.

[0171] This network interface 505 is used for network communication with other devices. Those skilled in the art will understand that... Figure 6The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device 500 to which the present application is applied. The specific computer device 500 may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0172] The processor 502 is used to run the computer program 5032 stored in the memory to implement all the steps of the energy storage power station economic operation evaluation method.

[0173] It should be understood that in the embodiments of this application, the processor 502 may be a central processing unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0174] It will be understood by those skilled in the art that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program includes program instructions and can be stored in a storage medium, which is a computer-readable storage medium. The program instructions are executed by at least one processor in the computer system to implement the process steps of the embodiments of the above methods.

[0175] Therefore, the present invention also provides a storage medium. This storage medium can be a computer-readable storage medium. The storage medium stores a computer program, wherein when executed by a processor, the computer program causes the processor to perform all the steps of the energy storage power station economic operation evaluation method.

[0176] The storage medium can be any computer-readable storage medium capable of storing program code, such as a USB flash drive, portable hard drive, read-only memory (ROM), magnetic disk, or optical disk.

[0177] Those skilled in the art will 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.

[0178] In the several embodiments provided by this invention, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For example, the division of each unit is merely a logical functional division, and there may be other division methods in actual implementation. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed.

[0179] The steps in the method of this invention can be adjusted, merged, or reduced in order according to actual needs. The units in the device of this invention can be merged, divided, or reduced according to actual needs. Furthermore, the functional units in the various embodiments of this invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0180] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, 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 includes several instructions to cause a computer device (which may be a personal computer, a terminal, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention.

[0181] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. An economic operation evaluation method for energy storage power stations, characterized in that, include: Construct an economic benefit model for user-side distributed energy storage power stations; wherein, the economic benefit model for user-side distributed energy storage power stations includes a basic benefit model for energy storage power stations and an ancillary service benefit model; Determine the basic revenue of the energy storage power station based on the economic benefit model of the user-side distributed energy storage power station; Ancillary service revenue is determined based on the aforementioned ancillary service revenue model; The overall revenue of the energy storage power station is assessed based on its basic revenue and ancillary service revenue. Real-time monitoring of enterprise electricity load and dynamic adjustment of charging and discharging plans to optimize demand management; Participating in frequency regulation and peak shaving ancillary services provided by the power grid can increase additional revenue streams. Based on the actual operation of the energy storage system, the basic revenue model of the energy storage power station is adjusted, and the changes in overall revenue are compared.

2. The method for evaluating the economic operation of an energy storage power station according to claim 1, characterized in that, The basic revenue model for the energy storage power station is based on the price difference between peak and off-peak charging to calculate revenue. It uses a method of accumulating daily dynamic discharge revenue and deducting the accumulated charging expenditure to calculate the basic revenue of the energy storage power station. The ancillary service revenue model is for the revenue brought about by power load demand optimization. It considers that the energy storage system reduces the user's maximum demand value by peak shaving and valley filling.

3. The method for evaluating the economic operation of an energy storage power station according to claim 2, characterized in that, The determination of the basic revenue of the energy storage power station based on the user-side distributed energy storage power station economic benefit model includes: The statistical period is set to the Gregorian month, and the time interval is divided into segments with minute intervals. Based on the user-side distributed energy storage power station economic benefit model, the energy storage charging and discharging strategy and price for each minute segment are determined according to the electricity price policy, photovoltaic output and supply and demand balance of the enterprise's electricity load in the user's region, and the basic revenue of the energy storage power station is calculated.

4. The method for evaluating the economic operation of an energy storage power station according to claim 3, characterized in that, Determining ancillary service revenue based on the ancillary service revenue model includes: Two maximum demand values ​​are obtained by using a sliding window statistical method, representing the situation before and after the implementation of demand shaving services by energy storage, and the difference between the two maximum demand values ​​before and after the reduction is calculated. The difference before and after the reduction is multiplied by the demand electricity price to obtain the ancillary service revenue.

5. The method for evaluating the economic operation of an energy storage power station according to claim 1, characterized in that, The assessment of the overall revenue of the energy storage power station based on its basic revenue and ancillary service revenue includes: The total revenue of the energy storage power station is obtained by summing the basic revenue and ancillary service revenue.

6. The method for evaluating the economic operation of an energy storage power station according to claim 1, characterized in that, The real-time monitoring of enterprise electricity load and dynamic adjustment of charging and discharging plans to optimize demand management include: Install and configure a real-time monitoring system to monitor changes in the enterprise's electricity load, and automatically trigger the energy storage system to discharge when the load approaches a preset demand threshold. Based on production plans and historical data, predict potential periods of high load in the future and prepare for energy storage charging in advance. A distributed charging mode is used during peak hours.

7. The method for evaluating the economic operation of an energy storage power station according to claim 1, characterized in that, The adjustment of the basic revenue model of the energy storage power station based on the actual operation of the energy storage system, and the comparison of changes in comprehensive revenue, include: The base revenue of the energy storage power station is adjusted to take into account that some of the electricity is used to reduce maximum demand; By comparing the monthly energy storage revenue before and after implementation, including the basic revenue and ancillary service revenue of the energy storage power station, the overall economic benefit improvement rate is quantified.

8. An economic operation evaluation device for energy storage power stations, characterized in that, include: A construction unit is used to construct an economic benefit model for a user-side distributed energy storage power station; wherein, the economic benefit model for the user-side distributed energy storage power station includes a basic benefit model for the energy storage power station and an ancillary service benefit model. The basic revenue calculation unit is used to determine the basic revenue of the energy storage power station based on the economic revenue model of the user-side distributed energy storage power station. An ancillary service revenue calculation unit is used to determine ancillary service revenue based on the ancillary service revenue model. An evaluation unit is used to evaluate the overall revenue of the energy storage power station based on its basic revenue and ancillary service revenue. The adjustment unit is used to monitor the enterprise's power load in real time and dynamically adjust the charging and discharging plan to optimize demand management; Adding units allows for participation in frequency regulation and peak shaving ancillary services provided by the power grid, thereby increasing additional revenue streams. The comparison unit is used to adjust the basic revenue model of the energy storage power station based on the actual operation of the energy storage system, and to compare the changes in comprehensive revenue.

9. A computer device, characterized in that, The computer device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method as described in any one of claims 1 to 7.

10. A storage medium, characterized by The storage medium stores a computer program that, when executed by a processor, implements the method as described in any one of claims 1 to 7.

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