Method and device for determining day-ahead charging and discharging power of energy storage system, and storage medium

Abstract

The application discloses a kind of energy storage system day before current charging and discharging power determination method, device, storage medium.Therein, the method includes: obtaining the first time sequence corresponding to minimum charging price element in day-ahead forecast node price time sequence, and the second time sequence corresponding to maximum discharging price element;With the second time sequence as benchmark, the first time sequence is traversed, and the preliminary charging and discharging plan curve is obtained;With the state of charge of energy storage system as constraint condition, the optimal preliminary charging and discharging plan curve is obtained by maximizing the target, and the target charging and discharging plan is obtained, wherein the target charging and discharging plan is used to indicate the charging and discharging power of energy storage system at each time point in predetermined period.The application solves the technical problems that the energy storage system control mode in the related art is relatively single and fixed, cannot dynamically determine the energy storage operation mode, the actual effect of peak load shifting and valley filling to power grid is poor, and the energy storage system cannot be maximized.

Description

Technical Field

[0001] This application relates to the field of energy storage regulation, and more specifically, to a method, apparatus, and storage medium for determining the day-ahead charge and discharge power of an energy storage system. Background Technology

[0002] With the continuous development of energy storage technology, under the current peak-valley electricity pricing mechanism, energy storage can minimize the cost of purchasing electricity through peak-valley price difference operation and capacity management. The existing energy storage management system is based on a fixed peak-valley electricity price, with clear electricity prices and their time characteristics, and the judgment logic is simple and mechanical, without the need for dynamic determination of the energy storage operation mode.

[0003] With the advancement of electricity market reform, users have begun to sign long-term power purchase agreements with electricity sales companies, and electricity prices have begun to fluctuate in a market-driven manner. Based on the spot market, electricity prices will fluctuate within a shorter time scale. However, existing energy storage management systems cannot dynamically determine the energy storage operation mode, making it difficult to meet actual needs. The actual effect of peak shaving and valley filling on the power grid is poor, and the use value of energy storage systems cannot be maximized.

[0004] There is currently no effective solution to the above problems. Summary of the Invention

[0005] This application provides a method, apparatus, and storage medium for determining the day-ahead charge and discharge power of an energy storage system, in order to at least solve the technical problem that the control mode of energy storage systems in related technologies is relatively simple and fixed, making it impossible to dynamically determine the energy storage operation mode, resulting in poor actual peak shaving and valley filling effects on the power grid and failure to maximize the utilization of the energy storage system.

[0006] According to one aspect of the embodiments of this application, a method for determining the day-ahead charging and discharging power of an energy storage system is provided, comprising: obtaining a first time series corresponding to the minimum charging price element and a second time series corresponding to the maximum discharging price element in the day-ahead predicted node electricity price time series; traversing the first time series based on the second time series to obtain a preliminary charging and discharging plan curve; optimizing the preliminary charging and discharging plan curve with the energy storage system's state of charge as a constraint and maximizing revenue as the objective to obtain a target charging and discharging plan, wherein the target charging and discharging plan is used to indicate the charging and discharging power of the energy storage system at each time point within a predetermined period.

[0007] Optionally, using the second time series as a reference, the first time series is traversed to obtain a preliminary charge-discharge plan curve, including: taking each time point in the second time series as a reference, traversing the first time series to find all element combinations earlier than the time points, and determining all element combinations as target charging time combinations; determining the minimum electricity value corresponding to the target charging time combination and the electricity price at the time point, judging whether the time point is a charging time or a discharging time based on the relationship between the electricity price at the time point and the minimum electricity value, and obtaining the preliminary charge-discharge plan curve based on the judgment result.

[0008] Optionally, the time point is determined as the charging time or the discharging time based on the relationship between the electricity price at a given time and the minimum electricity value, including: when the electricity price at a given time is greater than the minimum electricity value, the time point is determined as the discharging time, and charging is performed at the time corresponding to the minimum electricity value.

[0009] Optionally, determining the time point as the discharge time includes: obtaining the minimum arbitrage price difference, wherein the minimum arbitrage price difference is used to indicate the arbitrage threshold for whether the energy storage system should charge or discharge; determining the time point as the discharge time if the arbitrage at the determined time point is greater than the minimum arbitrage price difference, and determining the time point as neither charging nor discharging if the arbitrage at the determined time point is less than the minimum arbitrage price difference, and continuing to match it with the next time point.

[0010] Optionally, the initial charge-discharge plan curve is optimized with state of charge as a constraint and maximum benefit as the objective to obtain the target charge-discharge plan. This includes: dividing the initial charge-discharge plan curve to generate a continuous first time period combination for charging and a second time period combination for discharging; optimizing the first time period combination based on state of charge and electricity price to obtain an optimized charging curve; optimizing the second time period combination based on state of charge and electricity price to obtain an optimized discharging curve; and combining the optimized charging curve and the optimized discharging curve to obtain the target charge-discharge plan.

[0011] Optionally, the first time period combination is optimized based on the state of charge and electricity price to obtain a charging optimization curve, including: obtaining the number of time points in the first predetermined time period that needs to be charged; if the number of time points in the first time period of the first time period combination is less than the number of time points in the first predetermined time period, determining that the entire first time period combination is used for charging; if the number of time points in the first time period of the first time period combination is greater than the number of time points in the first predetermined time period, marking each time point corresponding to each time period in the first time period combination according to the electricity price, and obtaining the charging optimization curve based on the marking results.

[0012] Optionally, the time points corresponding to each time period in the first time period combination are marked according to the electricity price, and the charging optimization curve is obtained according to the marking results. This includes: obtaining the electricity price corresponding to each time point in each time period in the first time period combination, sorting the electricity price in ascending order, marking the nodes with the highest number of first predetermined time periods in the sorting results as charging time points, and marking the other time points in the first time period combination as no charging and no discharging time points to obtain the charging optimization curve.

[0013] Optionally, the second time period combination is optimized based on the state of charge and electricity price to obtain a discharge optimization curve, including: obtaining the number of time points in the second predetermined time period to be discharged; if the number of time points in the second time period of the second time period combination is less than the number of time points in the second predetermined time period, the second time period is determined to be used entirely for discharge; if the number of time points in the second time period of the second time period combination is greater than the number of time points in the second predetermined time period, each time point corresponding to each time period in the second time period combination is marked according to the electricity price, and the discharge optimization curve is obtained based on the marking results.

[0014] Optionally, the time points corresponding to each time period in the second time period combination are marked according to the electricity price, and the discharge optimization curve is obtained according to the marking results. This includes: obtaining the electricity price corresponding to each time point in each time period in the second time period combination, sorting the electricity price in descending order, marking the nodes with the highest number of second predetermined time periods in the sorting results as discharge time points, and marking other time points in the second time period as no-charging and no-discharging time points to obtain the discharge optimization curve.

[0015] Optionally, obtaining the first time series corresponding to the minimum charging price element and the second time series corresponding to the maximum discharging price element includes: determining the total length LEN of the day-ahead forecast node price time series, wherein the total length LEN is used to indicate the total number of time points in the day-ahead forecast node price time series, obtaining the daily charging and discharging frequency, capacity, and rated power of the energy storage system; selecting the number N of time points to participate in charging and discharging from the total length LEN of the day-ahead forecast node price time series based on the daily charging and discharging frequency, capacity, and rated power; obtaining the first time series by obtaining the first N minimum charging price elements from the total length LEN of the day-ahead forecast node price time series; and obtaining the second time series by obtaining the last N maximum discharging price elements from the total length LEN of the day-ahead forecast node price time series.

[0016] Optionally, after obtaining the target charging and discharging plan, the method further includes: obtaining the rated charging amount and rated discharging amount of the target charging and discharging plan within a predetermined period; calculating the sum of the rated charging amount and rated discharging amount; determining the ratio of the sum to the rated capacity of the energy storage system as the number of charging and discharging cycles of the energy storage system within the predetermined period; and performing an iterative optimization calculation on the target charging plan with the goal of maximizing the benefit, using the number of charging and discharging cycles as a constraint.

[0017] Optionally, with the number of charge-discharge cycles as a constraint, a traversal optimization operation is performed on the target charging plan to maximize the benefit. This includes: taking N maximum electricity prices from the target charging plan, traversing them sequentially in increments of N+1 until reaching LEN, to obtain all permutations and combinations; determining the target combination with the maximum benefit among all permutations and combinations under the constraint of the number of charge-discharge cycles; wherein, the target combination is used to indicate the optimal combination of charge-discharge cycles within a predetermined period; and updating the target charging plan based on the target combination to obtain the optimal charging plan, wherein, the optimal charging plan is used to indicate the optimal number of charge-discharge cycles within a predetermined period and the charge-discharge power at each time point during charge-discharge.

[0018] Optionally, the number of time points N to participate in charging and discharging can be selected from the total length LEN of the daily predicted nodal electricity price time series based on the daily charging and discharging frequency, capacity, and rated power. This includes: determining the product of the daily charging and discharging frequency and capacity, calculating the ratio of the product to the rated power, and using the ratio as the number of elements N.

[0019] According to another aspect of the embodiments of this application, a device for determining the day-ahead charge and discharge power of an energy storage system is also provided, comprising: an acquisition module, configured to acquire a first time series corresponding to the minimum charging price element and a second time series corresponding to the maximum discharging price element in the day-ahead predicted node electricity price time series; a traversal module, configured to traverse the first time series based on the second time series to obtain a preliminary charge and discharge plan curve; and an optimization module, configured to optimize the preliminary charge and discharge plan curve with the state of charge as a constraint and the goal of maximizing revenue to obtain a target charge and discharge plan, wherein the target charge and discharge plan is used to indicate the charge and discharge power of the energy storage system at each time point within a predetermined period.

[0020] According to another aspect of the embodiments of this application, a non-volatile storage medium is also provided, the storage medium including a stored program, wherein, when the program is running, it controls the device where the storage medium is located to execute any method for determining the day-ahead charge and discharge power of an energy storage system.

[0021] According to another aspect of the embodiments of this application, an electronic device is also provided, including: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to execute instructions to implement a method for determining the day-ahead charge and discharge power of any energy storage system.

[0022] In this embodiment, an energy storage system operation curve is generated based on the day-ahead forecast node electricity price. This involves obtaining a first time series corresponding to the minimum charging price element and a second time series corresponding to the maximum discharging price element from the day-ahead forecast node electricity price time series. Using the second time series as a benchmark, the first time series is iterated to obtain a preliminary charging and discharging plan curve. Then, with the energy storage system's state of charge as a constraint and maximizing revenue as the objective, the preliminary charging and discharging plan curve is optimized to obtain the charging and discharging power of the energy storage system at each time point within a predetermined period. This achieves flexible and dynamic adjustment of the energy storage system's charging and discharging power based on a shorter time scale, enabling more effective peak shaving and valley filling, further ensuring real-time power balance and grid safety. This solves the technical problem that the energy storage system control methods in related technologies are relatively simple and fixed, unable to dynamically determine the energy storage operation mode, resulting in poor actual peak shaving and valley filling effects on the grid and an inability to maximize the utilization of the energy storage system. Attached Figure Description

[0023] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments of this application and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0024] Figure 1 This is a flowchart illustrating a method for determining the day-ahead charge / discharge power of an energy storage system according to an embodiment of this application.

[0025] Figure 2 This is a diagram showing the physical module structure of an energy storage system that can be used in some embodiments of this application;

[0026] Figure 3 This is a schematic diagram illustrating the overall process of the energy storage system participating in the spot market in the embodiments of this application;

[0027] Figure 4 This is a schematic diagram of the preliminary charge / discharge plan curves generated during the iteration process;

[0028] Figure 5 This is a schematic diagram of the charge / discharge plan curve after optimization based on SOC constraints during the iteration process;

[0029] Figure 6 This is a schematic diagram of the optimal planned operating power curve based on 5-minute granularity;

[0030] Figure 7 This is a schematic diagram of a device for determining the day-ahead charge and discharge power of an energy storage system according to an embodiment of this application;

[0031] Figure 8A schematic block diagram of an example electronic device 800 that can be used to implement embodiments of this application is shown. Detailed Implementation

[0032] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0033] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0034] Among related technologies, new energy storage technologies participating in electricity spot trading have explored a completely new business model for energy storage development, and energy storage power stations will change the current situation of difficulty in achieving profitability. Electricity spot trading refers to transactions conducted in the electricity spot market. The electricity spot market mainly includes day-ahead, intraday, and real-time electricity and ancillary service trading markets such as reserves. The spot market, medium- and long-term direct trading market, and futures electricity derivatives market constitute the modern electricity market system. Domestic spot trading, as a supplement to the market-based electricity balance mechanism, plays a role in price discovery, improving trading varieties, and fostering full competition.

[0035] The current market is the main trading platform for the spot market. By determining the start-up combination and power generation period of the generating units one day in advance, and adjusting the deviations of the required power generation and consumption curves and medium- and long-term contracts, the balance of power generation and consumption for the next day can be basically achieved, and the grid safety constraints can be met.

[0036] It should be noted that the intraday market refers to a trading platform that provides market participants with a way to fine-tune their power generation and consumption plans after the day-ahead market closes, in order to cope with various forecast deviations and unplanned situations. The real-time market refers to a market that is organized and implemented hourly in advance, which is close to the real-time operation of the system and truly reflects the short-term resource scarcity and system congestion of the system, so as to achieve real-time power balance and safe operation of the power grid.

[0037] To meet the demands of the intraday market, this application provides an embodiment of a method for the day-ahead charge and discharge power of an energy storage system participating in the spot market. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0038] Figure 1 This is a method for determining the day-ahead charge / discharge power of an energy storage system according to an embodiment of this application, such as... Figure 1 As shown, the method includes the following steps:

[0039] Step S102: Obtain the first time series corresponding to the minimum charging price element and the second time series corresponding to the maximum discharging price element in the day-ahead predicted node electricity price time series;

[0040] Step S104: Using the second time series as a reference, traverse the first time series to obtain a preliminary charge and discharge plan curve;

[0041] Step S106: Using the state of charge of the energy storage system as a constraint and maximizing the benefit as the objective, optimize the preliminary charge and discharge plan curve to obtain the target charge and discharge plan. The target charge and discharge plan is used to indicate the charge and discharge power of the energy storage system at each time point within a predetermined period.

[0042] It should be noted that the electricity price elements in the day-ahead forecast node electricity price time series are the electricity prices corresponding to each point in time. In other words, the aforementioned electricity price elements refer to the electricity prices corresponding to each point in time in the day-ahead forecast node electricity price time series.

[0043] Figure 2 This is a diagram of the physical module structure of an energy storage system that can be used in some embodiments of this application, such as... Figure 2 As shown, the energy storage system includes multiple energy storage subsystems, each of which includes a bidirectional energy storage converter and an energy storage battery.

[0044] In the aforementioned method for determining the day-ahead charging and discharging power of the energy storage system, the first time series corresponding to the minimum charging price element and the second time series corresponding to the maximum discharging price element in the day-ahead predicted node electricity price time series are obtained. Using the second time series as a benchmark, the first time series is traversed to obtain a preliminary charging and discharging plan curve. Then, the preliminary charging and discharging plan curve is optimized with the state of charge of the energy storage system as a constraint and the goal of maximizing benefits, to obtain the charging and discharging power of the energy storage system at each time point within a predetermined period. This achieves the technical effect of flexibly and dynamically adjusting the charging and discharging power of the energy storage system based on a shorter time scale, realizing more effective peak shaving and valley filling, and further ensuring real-time power balance and safe grid operation. This solves the technical problem that the energy storage system control mode in related technologies is relatively simple and fixed, making it impossible to dynamically determine the energy storage operation mode, resulting in poor actual peak shaving and valley filling effects on the grid and failure to maximize the utilization of the energy storage system.

[0045] Specifically, using the second time series as a reference, the first time series is traversed to obtain a preliminary charge-discharge plan curve. This can be achieved by sequentially using each time point in the second time series as a reference, traversing the first time series to find all element combinations earlier than the time points, and determining all element combinations as target charging time combinations. The minimum electricity value corresponding to the target charging time combination and the electricity price at the corresponding time point are then determined. Based on the relationship between the electricity price at the time point and the minimum electricity value, it is determined whether the time point is a charging time or a discharging time. The preliminary charge-discharge plan curve is obtained based on the determination result.

[0046] In some embodiments of this application, the time point is determined as the charging time or the discharging time based on the relationship between the current electricity price and the minimum electricity value. This can be achieved by determining the time point as the discharging time when the current electricity price is greater than the minimum electricity value, and charging is performed at the time corresponding to the minimum electricity value.

[0047] Since energy storage systems incur certain operating costs when discharging, determining a discharge point can also help to obtain the minimum arbitrage price difference. This minimum arbitrage price difference serves as an arbitrage threshold indicating whether the energy storage system should charge or discharge. If the arbitrage opportunity at the determined discharge point is greater than the minimum arbitrage price difference, then the discharge point is determined. Conversely, if the arbitrage opportunity at the determined discharge point is less than the minimum arbitrage price difference, then neither charging nor discharging is performed, and the system continues to match this with the next possible discharge point. In short, discharge is only initiated when the energy storage system can generate profit, thus avoiding the waste of its stored energy resources.

[0048] In some optional embodiments of this application, the initial charge-discharge plan curve is optimized with the state of charge as a constraint and the goal of maximizing the benefit to obtain the target charge-discharge plan. This can be achieved through the following steps, specifically;

[0049] The initial charge / discharge plan curve is divided to generate a continuous first time period combination for charging and a second time period combination for discharging. The first time period combination is optimized based on the state of charge and electricity price to obtain the charging optimization curve. The second time period combination is optimized based on the state of charge and electricity price to obtain the discharging optimization curve. The charging optimization curve and the discharging optimization curve are combined to obtain the target charge / discharge plan.

[0050] Optionally, the first time period combination is optimized based on the state of charge and electricity price to obtain a charging optimization curve, including: obtaining the number of time points in the first predetermined time period that needs to be charged; if the number of time points in the first time period of the first time period combination is less than the number of time points in the first predetermined time period, determining that the entire first time period combination is used for charging; if the number of time points in the first time period of the first time period combination is greater than the number of time points in the first predetermined time period, marking each time point corresponding to each time period in the first time period combination according to the electricity price, and obtaining the charging optimization curve based on the marking results.

[0051] Specifically, the process involves marking each time point in the first time period combination according to the electricity price, and obtaining the charging optimization curve based on the marking results. This includes: obtaining the electricity price corresponding to each time point in the first time period combination, sorting the electricity prices in ascending order, marking the nodes with the highest number of first predetermined time periods in the sorting results as charging time points, and marking the other time points in the first time period combination as no-charging and no-discharging time points to obtain the charging optimization curve.

[0052] Optionally, the second time period combination is optimized based on the state of charge and electricity price to obtain a discharge optimization curve, including: obtaining the number of time points in the second predetermined time period to be discharged; if the number of time points in the second time period of the second time period combination is less than the number of time points in the second predetermined time period, the second time period is determined to be used entirely for discharge; if the number of time points in the second time period of the second time period combination is greater than the number of time points in the second predetermined time period, each time point corresponding to each time period in the second time period combination is marked according to the electricity price, and the discharge optimization curve is obtained based on the marking results.

[0053] Specifically, based on the electricity price, each time point corresponding to each time period in the second time period combination is marked. The discharge optimization curve is obtained based on the marking results. This can be done by: obtaining the electricity price corresponding to each time point in each time period in the second time period combination, sorting the electricity price in descending order, marking the nodes with the highest number of second predetermined time periods in the sorting results as discharge time points, and marking the other time points in the second time period as no-charge and no-discharge time points to obtain the discharge optimization curve.

[0054] As an optional implementation, obtaining the first time series corresponding to the minimum charging price element and the second time series corresponding to the maximum discharging price element can be achieved as follows: Specifically, determine the total length LEN of the day-ahead forecast node price time series, where the total length LEN indicates the total number of time points in the day-ahead forecast node price time series, and obtain the daily charging and discharging frequency, capacity, and rated power of the energy storage system; select the number N of time points to participate in charging and discharging from the total length LEN of the day-ahead forecast node price time series based on the daily charging and discharging frequency, capacity, and rated power; obtain the first time series by obtaining the first N minimum charging price elements from the total length LEN of the day-ahead forecast node price time series; and obtain the second time series by obtaining the last N maximum discharging price elements from the total length LEN of the day-ahead forecast node price time series.

[0055] In some embodiments of this application, after obtaining the target charge and discharge plan, the rated charge amount and rated discharge amount of the target charge and discharge plan within a predetermined period can also be obtained. Then, the sum of the rated charge amount and rated discharge amount is calculated, and the ratio of the sum to the rated capacity of the energy storage system is determined as the number of charge and discharge cycles of the energy storage system within the predetermined period. With the number of charge and discharge cycles as a constraint, the target charge plan is traversed and optimized to maximize the benefit.

[0056] Optionally, with the number of charge-discharge cycles as a constraint, a traversal optimization operation is performed on the target charging plan to maximize the benefit. This includes: taking N maximum electricity prices from the target charging plan, traversing them sequentially in increments of N+1 until reaching LEN, to obtain all permutations and combinations; determining the target combination with the maximum benefit among all permutations and combinations under the constraint of the number of charge-discharge cycles; wherein, the target combination is used to indicate the optimal combination of charge-discharge cycles within a predetermined period; and updating the target charging plan based on the target combination to obtain the optimal charging plan, wherein, the optimal charging plan is used to indicate the optimal number of charge-discharge cycles within a predetermined period and the charge-discharge power at each time point during charge-discharge.

[0057] It should be noted that the above-mentioned charging and discharging power can be the rated charging and discharging power of the energy storage system, that is, the maximum value of the charging and discharging power. However, considering that charging and discharging based on the rated power for a long time would place high demands on the performance of the energy storage system, and that charging and discharging based on the rated power for a long time could easily lead to overcharging or over-discharging of the energy storage system, affecting the electrical performance of the energy storage system, such as service life and cycle life, etc. Therefore, in some embodiments of this application, a power value less than the rated charging and discharging power is generally selected for charging and discharging, for example, charging and discharging based on the power value corresponding to 80% of the rated charging and discharging power.

[0058] It should be noted that the number of time points N to participate in charging and discharging is selected from the total length LEN of the daily predicted nodal electricity price time series based on the daily charging and discharging frequency, capacity, and rated power. This is achieved through the following process: Specifically, the product of the daily charging and discharging frequency and the capacity is determined, and the ratio of the product to the rated power is calculated. This ratio is then used as the number of elements N.

[0059] Figure 3 This is a schematic diagram illustrating the overall process of the energy storage system participating in the spot market in the embodiments of this application, such as... Figure 3 As shown, the process mainly includes:

[0060] With the objectives of maximizing net present value (NPV), shortest payback period, and internal maximum return (IRR) as the criteria, the minimum daily return target is calculated, the daily limit on the number of charge / discharge cycles is set, and information such as the initial system state of charge (SOC), capacity, day-ahead forecast node electricity price, and price spread is obtained. Then, the charge / discharge combination iterative optimization model is entered, generating preliminary optimization results for each iteration. Based on the SOC limit to maximize return, further optimization is performed, outputting and analyzing multiple sets of returns until the day-ahead planned power curve corresponding to the maximum return is selected. It should be noted that the day-ahead planned power can be represented in various formats, including but not limited to curves, tables, and text.

[0061] The above technical solution will now be described with reference to a specific embodiment.

[0062] 1. The objective functions for calculating the maximum internal net present value (NPV), the minimum dynamic payback period (Pt), and the maximum internal rate of return (IRR) are as follows:

[0063]

[0064]

[0065]

[0066] Pt*Dt*365*L≤TC

[0067] In the formula, I represents the annual revenue of the energy storage system (assuming equal annual revenue); n represents the lifespan of the system; r represents the discount rate of funds; C represents the investment and construction cost of the energy storage system; Dt is the number of daily charge-discharge cycles; L is the number of effective operating days per year; and TC is the total number of battery charge-discharge cycles.

[0068] The minimum daily revenue limit (I) and the maximum daily charge / discharge count (Dt) are determined through internal analysis of NPV / Pt / IRR weights.

[0069] 2. Obtain initial SOC, rated_capacity, day-ahead predicted node price elec_price, historical settlement price his_elec_price, rated_power, minimum and maximum SOC limits soc_min and soc_max, initial SOC init_soc, system charging and discharging efficiency, minimum price difference limit dis_elec_price, and predicted price time granularity time_gap (15 minutes per hour, value 0.25), etc.

[0070] 3. The charging / discharging combination optimization iterative algorithm model outputs the revenue and planning curves for different combinations. The specific steps are as follows:

[0071] 1) Sort the predicted electricity prices elec_price in ascending order, initialize N, with the initial value N = Dt * rate_capcity / rated_power, and N is at most the length LEN of the elec_price sequence;

[0072] 2) Obtain the initial combination formed by the first N minimum charging price elements: down_array, and the corresponding time series combination: down_array_time;

[0073] 3) Obtain the initial combination formed by the last N maximum discharge price elements: up_array, and the corresponding time series combination: up_array_time;

[0074] 4) Sort down_array_time and up_array_time in ascending order of time, and arrange down_array and up_array following down_array_time, and up_array_time in order to ensure that the time and electricity price corresponding to the array index are consistent;

[0075] 5) Initialize a charge / discharge time sequence init_charge_discharge with a length of the same as elec_price, with all values ​​being 0 by default (0: no charging or discharging, 1: charging, -1: discharging);

[0076] 6) Using the upper limit combination up_array_time as a benchmark, traverse each element and search for the element corresponding to the down_array_time combination. Finally, generate the preliminary charge and discharge plan power init_charge_discharge for each point. The optimization method is as follows:

[0077] 1. With the current element time currTime of up_array_time as the benchmark, search for all element combinations tmp_down_array_time in the down_array_time combination that are earlier than currTime;

[0078] 2. Then judge whether the length of tmp_down_array_time is 0. If it is 0, it means that the discharge time point of currTime is relatively early and there is no charging time before, so init_charge_discharge[currTime] = 0 corresponding to the current currTime time point; enter 1 to iterate the next element of up_array_time again. If it is not 0, enter 3;

[0079] 3. Search for the charging time combination tmp_down_array_time earlier than the currTime time. Immediately afterwards, search for the minimum value MIN_VALUE of the electricity price corresponding to tmp_down_array_time. Then record the time min_ele_index of the current minimum value, generate the charging time point matching the currTime moment, and write it into init_charge_discharge. The specific implementation is as follows:

[0080] a) If [electricity price at currTime moment] - MIN_VALUE >= dis_elec_price, generate a discharge at the currTime moment and a charge at the min_ele_index moment, that is, init_charge_discharge[currTime] = -1, init_charge_discharge[min_ele_index] = -1. At the same time, delete the electricity price and time corresponding to the min_ele_index time in the initial charging electricity price combination down_array and the time combination down_array_time, because the element at the min_ele_index moment in the charging combination has been matched by the currTime moment in the discharge combination at this time;

[0081] b) If [electricity price at currTime moment] - MIN_VALUE < dis_elec_price, it means that the price difference between charging and discharging is less than the initial value, and the charging and discharging matching condition is not satisfied. Generate no charge and no discharge at the currTime moment and no charge and no discharge at the min_ele_index moment; continue to iterate the next element of up_array_time until the iteration ends directly. Figure 4 It is the preliminary charge-discharge plan curve generated during this iteration process.

[0082] 7) In step 6, a preliminary charge and discharge plan curve init_charge_discharge will be output. At this time, no SOC charge and discharge limit is applied to the curve. Since the battery cannot be charged and discharged continuously, SOC limit needs to be considered.

[0083] Next, we move on to the SOC optimization curve. We can use permutation and combination techniques to maximize the returns and ultimately output the planned power curve. The specific SOC-constrained optimization method is as follows:

[0084] 1. First, the initial charge / discharge plan curve `init_charge_discharge` is divided into continuous charge / discharge sequences. This is then processed by the continuous charge / discharge sequence division module, which generates continuous charging and discharging combinations and records them separately: `charge_list` (a set of continuous charging time periods) and `discharge_list` (a set of continuous charging time periods). For easier understanding, please refer to [link to documentation / documentation]. Figure 4 There are two charging combinations and two discharging combinations, so the length of the charge_list and discharge_list combinations is 2, and each combination has multiple charging and discharging times.

[0085] 2. Continuing to use charge_list and discharge_list as inputs to the SOC optimization algorithm, considering that the number of charging combinations and discharging combinations is the same, the specific optimization can be implemented through the following steps:

[0086] a) First, use the combined charge / discharge size as the iteration condition;

[0087] b) First, determine the current charging combination charge_list[i] (representing the i-th combination), and determine the number of time periods required for charging count = (soc_max*rate_capcity - soc*rate_capcity) / power / time_gap (when the predicted time interval is 1 hour, time_gap = 1); generate the optimal charging time sequence for the current combination, as implemented below:

[0088] 1. If count >= the length of the current combination charge_list[i], it means that charge_list[i] can be entirely filled with charges;

[0089] 2. If count < length of the current combination charge_list[i], it means that there are unreasonable charging points in the charge_list[i] combination. We need to find all the time points of charge_list[i], search for the corresponding electricity price, sort them in ascending order according to the electricity price, take the time point corresponding to the first count electricity prices and mark it as charging, and mark other time points as 0 so that they neither charge nor discharge.

[0090] c) Then determine the current discharge combination discharge_list[i] (representing the i-th combination), and determine the number of time periods required for discharge count = ((soc-soc_min)*rate_capacity) / power / time_gap (when the predicted time interval is 1 hour, time_gap = 1); generate the optimal charging time sequence for the current combination, as specifically implemented below:

[0091] 1. If count >= the length of the current combination discharge_list[i], it means that discharge_list[i] can be entirely empty;

[0092] 2. If count < the length of the current combination discharge_list[i], it means that discharge_list[i] cannot be fully discharged because the energy storage has no electricity to discharge. In this case, the count time points with the largest electricity price corresponding to the time series of discharge_list[i] are selected for discharge, and the remaining points are neither charged nor discharged.

[0093] d) Re-enter a until the iteration ends. Figure 5 This is the charge / discharge plan curve after optimization based on SOC constraints during the iteration process, such as... Figure 5 As shown.

[0094] 8) For the planned power curve output in 7), judge it according to the time sequence, and traverse it from the beginning:

[0095] When charging, the charging cost is:

[0096] tmp_cost=tmp_cost+elec_price[i]*rated_power*time_gap;

[0097] The charging amount is: charge = elec_price[i] * rated_power * time_gap;

[0098] When discharging, the discharge benefit is:

[0099] tmp_profit=tmp_profit+elec_price[i]*rated_power*efficiency*time_gap;

[0100] The discharge amount is: discharge = elec_price[i] * rated_power * efficiency * time_gap;

[0101] After the traversal is complete:

[0102] The output profit is profit = tmp_profit - tmp_cost;

[0103] The revenue per kilowatt-hour is per_price = profit / [(charge + discharge) / 2];

[0104] The daily charge / discharge frequency is Dtt = (charge + discharge) / rate_capacity;

[0105] 9) Start iterating from N to LEN, outputting LEN-N profit, per_price, and Dtt data. With Dt as the constraint, aim to maximize the profit, search for the optimal combination value of N-Len, result_indiex, and search for the planned power generated by the corresponding result_indiex. This period is the optimal generated planned power.

[0106] It is worth noting that the above technical solutions are not limited to the time gap of the electrical sequence, and can be 1 hour, 0.5 hours, 0.25 hours, etc. Figure 6 This is a schematic diagram of the optimal planned operating power curve based on 5-minute granularity. It can be understood that the above technical solution is not limited to one day and can be optimized based on data from multiple days.

[0107] Figure 7 This is a device for determining the day-ahead charge / discharge power of an energy storage system according to an embodiment of this application, such as... Figure 7 As shown, the device includes:

[0108] The acquisition module 70 is used to acquire the first time series corresponding to the minimum charging price element and the second time series corresponding to the maximum discharging price element in the day-ahead predicted node electricity price time series.

[0109] Traversal module 72 is used to traverse the first time series based on the second time series to obtain a preliminary charge and discharge plan curve;

[0110] The optimization module 74 is used to optimize the initial charge and discharge plan curve with the state of charge as a constraint and the goal of maximizing the benefit, so as to obtain the target charge and discharge plan. The target charge and discharge plan is used to indicate the charge and discharge power of the energy storage system at each time point within a predetermined period.

[0111] In this determining device, the acquisition module 70 is used to acquire the first time series corresponding to the minimum charging price element and the second time series corresponding to the maximum discharging price element in the day-ahead predicted node electricity price time series; the traversal module 72 is used to traverse the first time series based on the second time series to obtain a preliminary charging and discharging plan curve; the optimization module 74 is used to optimize the preliminary charging and discharging plan curve with the state of charge as a constraint and the goal of maximizing benefits to obtain a target charging and discharging plan. The target charging and discharging plan is used to indicate the charging and discharging power of the energy storage system at each time point within a predetermined period. This achieves the technical effect of flexibly and dynamically adjusting the charging and discharging power of the energy storage system based on a shorter time scale, realizing more effective peak shaving and valley filling, and further ensuring real-time power balance and safe grid operation. This solves the technical problem that the energy storage system control method in related technologies is relatively simple and fixed, and cannot dynamically determine the energy storage operation mode, resulting in poor actual peak shaving and valley filling effect on the grid and failure to maximize the utilization of the energy storage system.

[0112] According to another aspect of the embodiments of this application, a non-volatile storage medium is also provided, the storage medium including a stored program, wherein, when the program is running, it controls the device where the storage medium is located to execute any method for determining the day-ahead charge and discharge power of an energy storage system.

[0113] Specifically, the aforementioned storage medium is used to store program instructions for the following functions, thereby implementing the following functions:

[0114] Obtain the first time series corresponding to the minimum charging price element and the second time series corresponding to the maximum discharging price element in the current day-ahead forecast node electricity price time series; use the second time series as a benchmark to traverse the first time series to obtain the preliminary charging and discharging plan curve; optimize the preliminary charging and discharging plan curve with the energy storage system's state of charge as a constraint and the goal of maximizing revenue to obtain the target charging and discharging plan, wherein the target charging and discharging plan is used to indicate the charging and discharging power of the energy storage system at each time point within a predetermined period.

[0115] Optionally, in this embodiment, the storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. More specific examples of the storage medium include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0116] In an exemplary embodiment of this application, a computer program product is also provided, including a computer program that, when executed by a processor, implements the method for determining the day-ahead charge and discharge power of an energy storage system as described above.

[0117] Optionally, when executed by a processor, the computer program may perform the following steps:

[0118] Obtain the first time series corresponding to the minimum charging price element and the second time series corresponding to the maximum discharging price element in the current day-ahead forecast node electricity price time series; use the second time series as a benchmark to traverse the first time series to obtain the preliminary charging and discharging plan curve; optimize the preliminary charging and discharging plan curve with the energy storage system's state of charge as a constraint and the goal of maximizing revenue to obtain the target charging and discharging plan, wherein the target charging and discharging plan is used to indicate the charging and discharging power of the energy storage system at each time point within a predetermined period.

[0119] An electronic device is provided according to an embodiment of the present application, the electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the method for determining the day-ahead charge and discharge power of an energy storage system as described above.

[0120] Optionally, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0121] Figure 8 A schematic block diagram of an example electronic device 800 that can be used to implement embodiments of this application is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the application described and / or claimed herein.

[0122] like Figure 8As shown, device 800 includes a computing unit 801, which can perform various appropriate actions and processes based on a computer program stored in read-only memory (ROM) 802 or a computer program loaded from storage unit 808 into random access memory (RAM) 803. RAM 803 may also store various programs and data required for the operation of device 800. The computing unit 801, ROM 802, and RAM 803 are interconnected via bus 804. Input / output (I / O) interface 805 is also connected to bus 804.

[0123] Multiple components in device 800 are connected to I / O interface 805, including: input unit 806, such as keyboard, mouse, etc.; output unit 807, such as various types of monitors, speakers, etc.; storage unit 808, such as disk, optical disk, etc.; and communication unit 809, such as network card, modem, wireless transceiver, etc. Communication unit 809 allows device 800 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0124] The computing unit 801 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 801 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 801 performs the various methods and processes described above, such as the method for determining the day-ahead charge and discharge power of an energy storage system. For example, in some embodiments, the method for determining the day-ahead charge and discharge power of an energy storage system can be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 808. In some embodiments, part or all of the computer program can be loaded and / or installed on device 800 via ROM 802 and / or communication unit 809. When the computer program is loaded into RAM 803 and executed by the computing unit 801, one or more steps of the method for determining the day-ahead charge and discharge power of an energy storage system described above can be performed. Alternatively, in other embodiments, the computing unit 801 may be configured by any other suitable means (e.g., by means of firmware) to perform a method for determining the day-ahead charge and discharge power of the energy storage system.

[0125] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip systems, payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0126] The program code used to implement the methods of this application may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0127] In the context of this application, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0128] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0129] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with embodiments of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0130] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, servers in distributed systems, or servers incorporating blockchain technology.

[0131] In the relevant embodiments of this application, an energy storage system operation curve is generated based on the day-ahead forecast node electricity price. This involves obtaining a first time series corresponding to the minimum charging price element and a second time series corresponding to the maximum discharging price element from the day-ahead forecast node electricity price time series. Using the second time series as a reference, the first time series is traversed to obtain a preliminary charging and discharging plan curve. Then, with the state of charge of the energy storage system as a constraint and maximizing revenue as the objective, the preliminary charging and discharging plan curve is optimized to obtain the charging and discharging power of the energy storage system at each time point within a predetermined period. This achieves flexible and dynamic adjustment of the energy storage system's charging and discharging power based on a shorter time scale, enabling more effective peak shaving and valley filling, further ensuring real-time power balance and safe grid operation. This solves the technical problem that the energy storage system control methods in related technologies are relatively simple and fixed, making it impossible to dynamically determine the energy storage operation mode, resulting in poor actual peak shaving and valley filling effects on the grid and an inability to maximize the utilization of the energy storage system.

[0132] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0133] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0134] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0135] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0136] Furthermore, the functional units in the various embodiments of this application 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. The integrated unit can be implemented in hardware or as a software functional unit.

[0137] 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 computer-readable storage medium. Based on this understanding, the technical solution of this application, 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, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.

[0138] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for determining the day-ahead charge / discharge power of an energy storage system, characterized in that, include: Obtain the first time series corresponding to the minimum charging price element and the second time series corresponding to the maximum discharging price element in the current day-ahead forecast node electricity price time series; Using the second time series as a reference, the first time series is traversed to obtain a preliminary charge and discharge plan curve; Using the state of charge of the energy storage system as a constraint and maximizing revenue as the objective, the initial charge-discharge plan curve is optimized to obtain the target charge-discharge plan. The initial charge-discharge plan curve is divided to generate a first time period combination for charging and a second time period combination for discharging. The first time period combination is optimized based on the state of charge and electricity price to obtain the optimized charging curve. The second time period combination is optimized based on the state of charge and electricity price to obtain the discharge optimization curve; the charging optimization curve and the discharge optimization curve are combined to obtain the target charge and discharge plan; the target charge and discharge plan is used to indicate the charge and discharge power of the energy storage system at each time point within a predetermined period.

2. The method according to claim 1, characterized in that, Using the second time series as a reference, the first time series is traversed to obtain a preliminary charge-discharge plan curve, including: Using each time point in the second time series as a reference, traverse the first time series to find all element combinations earlier than the time points, and determine all element combinations as the target charging time combination; Determine the minimum electricity value corresponding to the target charging time combination and the electricity price at the time point. Based on the relationship between the electricity price at the time point and the minimum electricity value, determine whether the time point is a charging time or a discharging time. Based on the determination result, obtain the preliminary charging and discharging plan curve.

3. The method according to claim 2, characterized in that, Determining the time point as the charging or discharging time based on the relationship between the current electricity price and the minimum electricity value includes: If the electricity price at the specified time is greater than the minimum electricity value, the specified time point is determined as the discharge time, and charging is performed at the time corresponding to the minimum electricity value.

4. The method according to claim 3, characterized in that, Determining the time point as the discharge moment includes: Obtain the minimum arbitrage spread, wherein the minimum arbitrage spread is used to indicate the arbitrage threshold for whether the energy storage system is charging or discharging; If the arbitrage opportunity at the time of discharge is greater than the minimum arbitrage spread, then the time point is determined to be the time of discharge. If the arbitrage opportunity at the time of discharge is determined to be less than the minimum arbitrage spread, then the time point is determined to be neither charged nor discharged, and the process continues to match the next time point.

5. The method according to claim 1, characterized in that, Based on the state of charge and electricity price, the first time period combination is optimized to obtain the charging optimization curve, including: Obtain the number of time points in the first predetermined time period that needs to be charged. If the number of time points in the first time period of the first time period combination is less than the number of time points in the first predetermined time period, determine that the entire first time period combination is used for charging. If the number of time points in the first time period of the first time period combination is greater than the number of time points in the first predetermined time period, the time points corresponding to each time period in the first time period combination are marked according to the electricity price, and the charging optimization curve is obtained based on the marking results.

6. The method according to claim 5, characterized in that, The charging optimization curve is obtained by marking each time point corresponding to each time period in the first time period combination according to the electricity price, and the marking results are used to obtain the charging optimization curve, including: Obtain the electricity price corresponding to each time point in each time period of the first time period combination, sort the electricity price in ascending order, mark the nodes with the highest number of first predetermined time periods in the sorting result as charging time points, and mark the other time points in the first time period combination as no charging and no discharging time points to obtain the charging optimization curve.

7. The method according to claim 1, characterized in that, The second time period combination is optimized based on the state of charge and electricity price to obtain the discharge optimization curve, including: Obtain the number of time points in the second predetermined time period to be discharged. If the number of time points in the second time period in the combination of the second time periods is less than the number of time points in the second predetermined time period, determine that the entire second time period is used for discharge. If the number of time points in the second time period of the second time period combination is greater than the number of time points in the second predetermined time period, the time points corresponding to each time period in the second time period combination are marked according to the electricity price, and the discharge optimization curve is obtained based on the marking results.

8. The method according to claim 7, characterized in that, The time points corresponding to each time period in the second time period combination are marked according to the electricity price, and the discharge optimization curve is obtained based on the marking results, including: Obtain the electricity price corresponding to each time point in each time period of the second time period combination, sort the electricity price in descending order, mark the nodes with the highest number of second predetermined time periods in the sorting result as discharge time points, and mark the other time points in the second time period as no charging and no discharging time points to obtain the discharge optimization curve.

9. The method according to claim 1, characterized in that, Obtain the first time series corresponding to the element with the minimum charging price, and the second time series corresponding to the element with the maximum discharging price, including: Determine the total length LEN of the day-ahead forecast node electricity price time series, wherein the total length LEN is used to indicate the total number of time points in the day-ahead forecast node electricity price time series, and obtain the daily charge and discharge times, capacity, and rated power of the energy storage system; The number of time points N to participate in charging and discharging is selected from the total length LEN of the daily charge and discharge frequency, capacity, and rated power. The first time series is obtained by extracting the N minimum charging electricity price elements from the total length LEN of the day-ahead predicted node electricity price time series. The second time series is obtained by taking the N maximum discharge price elements from the total length LEN of the predicted node electricity price time series.

10. The method according to claim 9, characterized in that, After obtaining the target charge / discharge plan, the method further includes: Obtain the rated charge and rated discharge amounts of the target charge / discharge plan within the predetermined period; Calculate the sum of the rated charge amount and the rated discharge amount; determine the ratio of the sum to the rated capacity of the energy storage system as the number of charge and discharge cycles of the energy storage system within the predetermined period; Using the number of charge-discharge cycles as a constraint, the target charge-discharge plan is traversed and optimized to maximize the benefit.

11. The method according to claim 10, characterized in that, Using the number of charge-discharge cycles as a constraint, the target charge-discharge plan is traversed and optimized to maximize the benefit, including: Take N maximum electricity prices from the target charging and discharging plan, and iterate through them in N+1 increments until LEN is reached to obtain all permutations and combinations. Under the constraint of the number of charge-discharge cycles, determine the target combination that yields the greatest benefit among all the possible combinations; wherein the target combination is used to indicate the optimal combination of charge-discharge cycles within the predetermined period; The target charge-discharge plan is updated based on the target combination to obtain the optimal charge-discharge plan, wherein the optimal charge-discharge plan is used to indicate the optimal number of charge-discharge cycles and the charge-discharge power at each time point during the charge-discharge cycle.

12. The method according to claim 9, characterized in that, Based on the daily charge / discharge frequency, capacity, and rated power, the number N of time points N to participate in charging / discharging is selected from the total length LEN of the day-ahead predicted nodal electricity price time series, including: Determine the product of the daily charge / discharge frequency and the capacity, calculate the ratio of the product to the rated power, and use the ratio as the number of time points N.

13. A device for determining the day-ahead charge / discharge power of an energy storage system, characterized in that, include: The acquisition module is used to acquire the first time series corresponding to the minimum charging price element and the second time series corresponding to the maximum discharging price element in the day-ahead predicted node electricity price time series. The traversal module is used to traverse the first time series based on the second time series to obtain a preliminary charge-discharge plan curve; The optimization module is used to optimize the initial charge-discharge plan curve with the state of charge as a constraint and the goal of maximizing the benefit, so as to obtain the target charge-discharge plan. The initial charge-discharge plan curve is divided to generate a continuous first time period combination for charging and a second time period combination for discharging. The first time period combination is optimized based on the state of charge and the electricity price to obtain the charging optimization curve. The second time period combination is optimized based on the state of charge and electricity price to obtain the discharge optimization curve; the charging optimization curve and the discharge optimization curve are combined to obtain the target charge and discharge plan; the target charge and discharge plan is used to indicate the charge and discharge power of the energy storage system at each time point within a predetermined period.

14. A non-volatile storage medium, characterized in that, The storage medium includes a stored program, wherein, when the program is executed, it controls the device containing the storage medium to perform the method for determining the daytime charge and discharge power of the energy storage system according to any one of claims 1 to 12.

15. An electronic device, characterized in that, include: processor; Memory used to store the processor's executable instructions; The processor is configured to execute the instructions to implement the method for determining the daytime charge and discharge power of the energy storage system as described in any one of claims 1 to 12.

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