Energy storage system planning curve optimization method and device, storage medium and electronic equipment

Abstract

The present invention provides a method and device for optimizing a plan curve for an energy storage system, a storage medium, and electronic equipment. The method includes: obtaining basic information, an electricity price sequence, and a charge-discharge sequence; setting a charge-discharge optimization model based on the basic information, the electricity price sequence, and the charge-discharge sequence; applying an optimization algorithm to execute an optimization process corresponding to the charge-discharge optimization model; the optimization process includes: generating a contemporary population containing multiple charge-discharge plan curves; updating the cumulative revenue and state of charge at the target time point corresponding to the energy storage state in the charge-discharge plan curve to obtain a total revenue; if the contemporary population is not the last generation, re-executing the optimization process based on the charge-discharge plan curve in the population that meets the first optimization condition; conversely, screening the charge-discharge plan curve with the largest total revenue as the optimal charge-discharge plan curve. Using this method, a charge-discharge plan can be flexibly formulated over a period of time while maximizing revenue, generating an optimal charge-discharge plan curve.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage systems, and in particular to a method and device for optimizing a planning curve of an energy storage system, a storage medium, and electronic equipment. Background Art

[0002] In recent years, my country has achieved remarkable success in developing new energy sources, particularly wind and photovoltaic power generation. With installed capacity consistently ranking among the highest globally, new energy is becoming a crucial component of energy supply. Several provinces have already mandated that new energy be combined with energy storage, making energy storage a crucial component of new energy planning.

[0003] Currently, most energy storage systems employ a simple peak-shaving and valley-filling model, discharging stored energy during peak periods and recharging it during valley periods. However, with market changes and developments, electricity prices may fluctuate over time, and these price fluctuations may affect user electricity demand. The peak-shaving and valley-filling model currently used in energy storage systems utilizes a charging and discharging plan designed for a fixed electricity price. In a market characterized by ever-changing electricity prices, this model fails to maximize revenue and does not allow for flexible adjustment of charging and discharging plans for each time period. Summary of the Invention

[0004] In view of this, the present invention provides a method for optimizing a planning curve of an energy storage system. By this method, a charging and discharging plan within a period of time can be flexibly formulated while maximizing the benefits, thereby generating an optimal charging and discharging planning curve.

[0005] The present invention also provides an energy storage system planning curve optimization device to ensure the implementation and application of the above method in practice.

[0006] A method for optimizing a planning curve of an energy storage system, comprising:

[0007] Obtaining basic information, an electricity price sequence, and a preset charge and discharge sequence for the current time period of the energy storage system; the electricity price sequence includes electricity price information corresponding one-to-one to multiple target time points in the current time period; the charge and discharge sequence includes energy storage states corresponding one-to-one to each target time point, where the energy storage state is a charging state, a discharging state, or a static state;

[0008] Setting a charge-discharge optimization model based on the basic information, electricity price sequence, and charge-discharge sequence;

[0009] Applying a preset optimization algorithm to execute the optimization process corresponding to the charge-discharge optimization model to obtain the optimal charge-discharge plan curve for the current time period;

[0010] The optimization process includes:

[0011] Generate a contemporary population, wherein the contemporary population includes multiple individuals, and the individuals are charge and discharge plan curves; for each of the charge and discharge plan curves, update the cumulative benefit and charge state under the energy storage state corresponding to each target time point in the charge and discharge plan curve to obtain the total benefit of the energy storage system under the charge and discharge plan curve; after obtaining the total benefit of each charge and discharge plan curve in the contemporary population, determine whether the contemporary population is the last generation population; if not, re-execute the optimization process based on the charge and discharge plan curve in the contemporary population that meets the first optimization condition; if so, screen out the charge and discharge plan curve with the largest total benefit from all generated populations as the optimal charge and discharge plan curve; wherein the charge and discharge curve that meets the first optimization condition is the charge and discharge plan curve whose total benefit reaches a preset benefit threshold, or the charge and discharge plan curve with the largest total benefit in the contemporary population.

[0012] Optionally, in the above method, setting a charge-discharge optimization model based on the basic information, the electricity price sequence, and the charge-discharge sequence includes:

[0013] Obtaining the initial state of charge contained in the basic information;

[0014] A charge and discharge optimization model is set based on the initial state of charge, the electricity price sequence, and the charge and discharge sequence.

[0015] In the above method, optionally, generating the contemporary population comprises:

[0016] If it is necessary to generate an initial generation of population, a first generation of charge and discharge plan curves with a preset number of individuals are randomly generated based on the charge and discharge optimization model to form the initial generation of population;

[0017] If a non-first generation population needs to be generated currently, the charge and discharge plan curve of the previous generation that meets the first optimization condition is used as a reference curve to generate a new charge and discharge plan curve of the preset number of individuals to form a new generation population.

[0018] Optionally, the method described above includes updating the cumulative revenue and state of charge corresponding to each target time point in the energy storage state in the charge and discharge plan curve to obtain the total revenue of the energy storage system under the charge and discharge plan curve, including:

[0019] The cumulative revenue and state of charge under the energy storage state corresponding to each target time point are updated in chronological order, and the cumulative revenue corresponding to the last target time point obtained is the total revenue of the energy storage system under the charge and discharge plan curve.

[0020] Optionally, the method of updating the accumulated revenue and state of charge corresponding to each target time point in chronological order includes:

[0021] When the energy storage state corresponding to the target time point is a charging state, determining the charging stage of the energy storage system at the target time point, and updating the accumulated revenue and state of charge of the energy storage system at the target time point according to the charging stage;

[0022] When the energy storage state corresponding to the target time point is a discharging state, determining the discharging stage of the energy storage system at the target time point, and updating the accumulated revenue and state of charge of the energy storage system at the target time point according to the discharging stage;

[0023] When the energy storage state corresponding to the target time point is a static state, the accumulated income and state of charge at the target time point are set to the obtained historical accumulated income and historical state of charge.

[0024] Optionally, in the above method, updating the accumulated revenue and state of charge of the energy storage system at the target time point according to the charging stage includes:

[0025] When the charging stage is the fully charged stage, setting the state of charge at the target time point to the maximum state of charge; and calculating the cumulative profit at the target time point based on a preset penalty item and the historical cumulative profit;

[0026] When the charging stage is a continuous charging stage, calculate the first change value of the state of charge when charging at the rated power within the target time period corresponding to the target time point; determine whether the first change value is less than a preset first threshold; if so, set the first change value to the state of charge at the target time point, and calculate the cumulative profit at the target time point based on the electricity price information corresponding to the target time point, the historical accumulated profit, and the rated power and cost per kilowatt-hour in the basic information; if not, set the state of charge at the target time point to the maximum state of charge, and calculate the cumulative profit at the target time point based on the electricity price information corresponding to the target time point, the historical accumulated profit, and the rated capacity, cost per kilowatt-hour, and initial state of charge in the basic information.

[0027] Optionally, the above method, calculating a first change in the state of charge when charging at rated power within a target time period corresponding to the target time point, includes:

[0028] The rated power, rated capacity, and historical state of charge are used to calculate a first change value of the state of charge when charging at the rated power within a target time period corresponding to the target time point.

[0029] Optionally, in the above method, updating the accumulated revenue and state of charge of the energy storage system at the target time point according to the discharge stage includes:

[0030] When the discharge phase is an empty phase, setting the state of charge at the target time point to a minimum state of charge; and calculating the cumulative profit at the target time point based on the penalty item and the historical cumulative profit;

[0031] When the discharge stage is a continuous discharge stage, calculate the second change value of the state of charge when discharging at the rated power within the target time period; determine whether the second change value is greater than a preset second threshold; if so, set the second change value to the state of charge at the target time point, and calculate the cumulative profit at the target time point based on the electricity price information corresponding to the target time point, the historical accumulated profit, and the rated power and cost per kilowatt-hour in the basic information; if not, set the state of charge at the target time point to the minimum state of charge, and calculate the cumulative profit at the target time point based on the electricity price information corresponding to the target time point, the historical accumulated profit, and the rated capacity, cost per kilowatt-hour, and initial state of charge in the basic information.

[0032] Optionally, in the above method, calculating the second change value of the state of charge when discharging at rated power within the target time period includes:

[0033] The rated power, rated capacity, and historical state of charge are used to calculate a second change value of the state of charge when discharging at the rated power within the target time period.

[0034] The above method is optional.

[0035] When the target time point is the first time point of the current time period, the historical cumulative income and the historical state of charge are respectively the initial cumulative income and the initial state of charge, and the initial cumulative income is zero;

[0036] When the target time point is not the first time point of the current time period, the historical accumulated revenue and historical state of charge are respectively the accumulated revenue and state of charge of the target time point before the target time point.

[0037] A device for optimizing a planning curve of an energy storage system, comprising:

[0038] an acquisition unit, configured to acquire basic information, an electricity price sequence, and a preset charge-discharge sequence of the energy storage system for the current time period; the electricity price sequence comprising electricity price information corresponding one-to-one to a plurality of target time points in the current time period; and the charge-discharge sequence comprising energy storage states corresponding one-to-one to each of the target time points, wherein the energy storage states are a charging state, a discharging state, or a static state.

[0039] A setting unit, configured to set a charge-discharge optimization model based on the basic information, the electricity price sequence, and the charge-discharge sequence;

[0040] An optimization unit, configured to apply a preset optimization algorithm to execute an optimization process corresponding to the charge-discharge optimization model to obtain an optimal charge-discharge plan curve for the current time period;

[0041] The optimization process includes:

[0042] Generate a contemporary population, wherein the contemporary population includes multiple individuals, each of which is a charge-discharge plan curve; for each of the charge-discharge plan curves, update the cumulative benefit and charge state under the energy storage state corresponding to each target time point in the charge-discharge plan curve to obtain the total benefit of the energy storage system under the charge-discharge plan curve; after obtaining the total benefit of each charge-discharge plan curve in the contemporary population, determine whether the contemporary population is the last generation of population; if not, re-execute the optimization process based on the charge-discharge plan curve in the contemporary population that meets the first optimization condition; if so, screen out the charge-discharge plan curve with the largest total benefit from all generated populations as the optimal charge-discharge plan curve.

[0043] A storage medium includes stored instructions, wherein when the instructions are executed, the device where the storage medium is located is controlled to execute the above-mentioned energy storage system planning curve optimization method.

[0044] An electronic device includes a memory and one or more instructions, wherein the one or more instructions are stored in the memory and configured to be executed by one or more processors to execute the above-mentioned energy storage system planning curve optimization method.

[0045] Compared with the prior art, the present invention has the following advantages:

[0046] The present invention provides a method for optimizing a plan curve of an energy storage system, comprising: obtaining basic information, an electricity price sequence, and a charge-discharge sequence; setting a charge-discharge optimization model based on the basic information, the electricity price sequence, and the charge-discharge sequence; applying a preset optimization algorithm to execute an optimization process corresponding to the charge-discharge optimization model to obtain an optimal charge-discharge plan curve for the current time period; wherein the optimization process comprises: generating a contemporary population, the contemporary population comprising a plurality of charge-discharge plan curves; for each charge-discharge plan curve, updating the cumulative benefit and state of charge under the energy storage state corresponding to each target time point in the charge-discharge plan curve to obtain the total benefit of the energy storage system under the charge-discharge plan curve; after obtaining the total benefit of each charge-discharge plan curve in the contemporary population, determining whether the contemporary population is the last generation of population; if not, re-executing the optimization process based on the charge-discharge plan curve in the contemporary population that meets the first optimization condition; if so, screening out the charge-discharge plan curve with the largest total benefit from all generated populations as the optimal charge-discharge plan curve. By applying the method provided by the present invention, a charge and discharge plan within a period of time can be flexibly formulated while ensuring maximum benefits, thereby generating an optimal charge and discharge plan curve. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0048] Figure 1 A method flow chart of an energy storage system planning curve optimization method provided by an embodiment of the present invention;

[0049] Figure 2 An example diagram of an energy storage system planning curve provided by an embodiment of the present invention;

[0050] Figure 3 A flowchart of a method for optimizing a planning curve of an energy storage system in a charging state provided by an embodiment of the present invention;

[0051] Figure 4 A flow chart of a method for optimizing a planning curve of an energy storage system in a discharging state provided by an embodiment of the present invention;

[0052] Figure 5 A structural diagram of a device for optimizing a planning curve for an energy storage system provided by an embodiment of the present invention;

[0053] Figure 6 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0054] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0055] In this application, relational terms such as first and second are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. The terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or apparatus comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article or apparatus comprising the element.

[0056] The present invention can be used in a variety of general or special computing device environments or configurations, such as personal computers, server computers, handheld or portable devices, tablet devices, multi-processor devices, distributed computing environments including any of the above devices or devices, and the like.

[0057] The embodiment of the present invention provides a method for optimizing the planning curve of an energy storage system. The method can be applied to various system platforms, and its execution subject can be a processor of a computer terminal or various mobile devices. The method flow chart of the method is as follows: Figure 1 As shown, specifically including:

[0058] S101: Obtain basic information of the energy storage system in the current time period, electricity price sequence, and preset charge and discharge sequence.

[0059] A time period can be an hour, a day, or a week, for example. The electricity price sequence contains electricity price information corresponding to multiple target time points in the current time period. The charge-discharge sequence contains energy storage states corresponding to each target time point, which can be charging, discharging, or resting. Each energy storage state in the charge-discharge sequence is a state variable, and at any given moment, the energy storage state can be charging, discharging, or resting. The resting state refers to the state in which the energy storage system is neither charging nor discharging.

[0060] It should be noted that the basic information includes the rated power P of the energy storage systemrated , rated capacity C, initial state of charge SOC0, and cost per kilowatt-hour (cpkw). The time interval between each target time point is the same. The initial state of charge can be calculated from the daily electricity price series, or the state of charge at the last target time point of the previous day can be used as the initial state of charge for today.

[0061] Optionally, based on the number of time points at which predicted electricity prices are available, the energy storage system's charge and discharge plan curves for multiple consecutive time periods can be optimized to eliminate the impact of the energy storage system's initial state of charge. Upon entering the next time period, the energy storage system's electricity price sequence is updated, and after the electricity price sequence is updated, the energy storage system's charge and discharge plan curves can be re-updated.

[0062] The electricity price sequence is Prc=[Prc1, Prc2, ..., Prc i 、…、Prc n ], the charge and discharge sequence is Status = [Status1, Status2, ..., Status i ,…,Status n The electricity price sequence can be the predicted electricity price information of the electricity spot market or the confirmed peak, valley and flat electricity price information.

[0063] S102: Setting a charge and discharge optimization model based on basic information, electricity price sequence, and charge and discharge sequence.

[0064] Specifically, the charge-discharge optimization model is composed of the initial state of charge, electricity price sequence and charge-discharge sequence in the basic information.

[0065] Among them, the charge and discharge optimization model bf=f([Prc1, Prc2, ..., Prc i 、…、Prc n ],[Status1,Status2,…,Status i ,…,Status n ],SOC0).

[0066] After the charge-discharge optimization model is constructed, a preset optimization algorithm is applied to execute the optimization process corresponding to the charge-discharge optimization model as described in S103 to S106 to obtain the optimal charge-discharge plan curve for the current time period. The optimization algorithm may be a particle swarm optimization algorithm, an ant colony algorithm, or a simulated annealing algorithm.

[0067] S103: Generate contemporary population.

[0068] Among them, the contemporary population contains multiple individuals, and each individual is a charge and discharge plan curve. Figure 2The horizontal axis of the charge and discharge plan curve is time, and the vertical axis is the energy storage state. Figure 2 During the period, the energy storage system is in a static state from 0:00 to 13:00, and discharges from 13:00 to 15:00. After the discharge is completed at 15:00, it is in a static state from 15:00 to 18:00. It is charged from 18:00 to 20:00, and after being fully charged at 20:00, it is in a static state from 20:00 to a quarter hour.

[0069] Before applying the optimization algorithm, the number of individuals in each generation of the population and the number of iterations of the optimization algorithm are pre-set.

[0070] Specifically, the populations that generate the present include:

[0071] If the initial generation of population is needed, the initial generation of charge and discharge plan curves with a preset number of individuals are randomly generated based on the charge and discharge optimization model to form the initial generation of population;

[0072] If a non-first generation population needs to be generated currently, the charge and discharge plan curve of the previous generation that meets the first optimization condition is used as a reference curve to generate a new charge and discharge plan curve of the preset number of individuals to form a new generation population.

[0073] Understandably, the generation methods for first-generation and non-first-generation populations differ. For first-generation populations, a reference curve is not available. Instead, a charge-discharge plan curve corresponding to a preset number of individuals is randomly generated based on the charge-discharge optimization model. This is the charge-discharge plan curve for the preset number of individuals. Individual optimization is performed based on the first-generation individuals, and the more outstanding individuals are used as the reference curve. The next generation population is generated using this reference curve as a benchmark.

[0074] It should be noted that the first optimization condition can be that the total benefit of the charge-discharge plan curve reaches a preset benefit threshold, or that the total benefit of the charge-discharge plan curve is the maximum total benefit in the contemporary population to which it belongs. Therefore, the charge-discharge plan curve that meets the first optimization condition is the charge-discharge plan curve whose total benefit reaches the preset benefit threshold, or the charge-discharge plan curve with the maximum total benefit in the contemporary population. The charge-discharge plan curve that meets the first optimization condition can be used as a reference curve for the next generation of a new charge-discharge plan curve, and the optimization is performed based on the reference curve.

[0075] Optionally, the energy storage system in the embodiments of the present invention has a limited lifespan, and the number of charge and discharge cycles of the energy storage system may affect the service life of the energy storage system. To prevent excessive charge and discharge cycles from affecting the lifespan of the energy storage system, the number of charge and discharge cycles in the charge and discharge plan curve may be limited during population generation. If the number of charge and discharge cycles in the charge and discharge plan curve exceeds a set value, subsequent charge and discharge operations in the charge and discharge plan curve will no longer be executed after the number of charge and discharge operations executed according to the charge and discharge plan curve reaches the set value.

[0076] S104: For each charge and discharge plan curve, update the cumulative revenue and state of charge under the energy storage state corresponding to each target time point in the charge and discharge plan curve to obtain the total revenue of the energy storage system under the charge and discharge plan curve.

[0077] In this invention, determining the energy storage state corresponding to each target time point in the charge-discharge plan curve requires calculating the revenue at that target time point in the charging or discharging state. The cumulative revenue and state of charge corresponding to each target time point are updated in chronological order. The total revenue corresponding to the charge-discharge plan curve is the cumulative revenue corresponding to the last target time point.

[0078] It should be noted that when updating the cumulative revenue and state of charge corresponding to the target time point, the rated power P in the basic information is applied according to the energy storage state at the target time point. rated , rated capacity C, initial state of charge SOC0, cost per kWh cpkw, and electricity price information Prc corresponding to the target time point in the electricity price sequence i , calculate the cumulative revenue and state of charge corresponding to the target time point.

[0079] It should also be noted that the state of charge (SOC) value range in the energy storage system can be [0, 1]. However, for some battery types, the upper and lower limits of the energy storage system's state of charge (SOC) can also be restricted to be consistent with the actual usage of the energy storage system.

[0080] S105: After obtaining the total benefit of each charge and discharge plan curve in the contemporary population, determine whether the contemporary population is the last generation population.

[0081] If the current population is not the last generation population, then based on the charge and discharge plan curve in the current population that meets the first optimization condition, the process returns to S103 ; if the current population is the last generation population, then S106 is executed.

[0082] S106: From all generated populations, a charge-discharge plan curve with the largest total benefit is selected as the optimal charge-discharge plan curve.

[0083] Specifically, to ensure that the charge and discharge plan curve of the current time period can obtain the maximum benefit, in the process of optimizing through the optimization algorithm, multiple populations are generated after multiple iterations, and the charge and discharge plan curve with the highest total benefit is found in each population as the charge and discharge plan curve of the energy storage system.

[0084] In the method provided in an embodiment of the present invention, basic information such as the rated power, rated capacity, initial state of charge, and cost per kilowatt-hour of the energy storage system is obtained, as well as an electricity price sequence. The electricity price information corresponding to each target time point in the electricity price sequence is the predicted electricity price of the energy storage system at the target time point. A corresponding charge and discharge sequence is set based on the electricity price sequence. Each energy storage state in the charge and discharge sequence can be any of a charging state, a discharging state, and a static state. A charge and discharge optimization model is set based on the initial state of charge, the electricity price sequence, and the charge and discharge sequence. An optimization algorithm is applied to execute an optimization process corresponding to the charge and discharge optimization model to determine the specific energy storage state at each target time point in the charge and discharge sequence of the charge and discharge optimization model. During the optimization process, multiple initial charge and discharge plan curves are randomly generated based on the charge and discharge optimization model and the number of individuals. The cumulative benefits and state of charge corresponding to each target time point in the initial charge and discharge plan curves are then updated in chronological order to obtain the total benefits of the initial charge and discharge plan curves. If the first generation of population is not the last generation of population, then based on the total revenue of each first generation of charge and discharge plan curve, the charge and discharge plan curve that meets the first optimization condition is screened out as the optimization direction to generate a new generation of population, and the optimization process is continued until the total revenue of each charge and discharge plan curve in the last generation of population is obtained. Then, from all the generated populations, the charge and discharge plan curve with the highest total revenue is found as the charge and discharge plan curve for the current time period. During the optimization process of the charge and discharge plan curve of the energy storage system of the present invention, on the basis of ensuring the maximization of the revenue of the current time period, the optimal charge and discharge plan curve can be generated according to the change of electricity price, and the charge and discharge plan of the energy storage system at each time point can be flexibly set according to the charge and discharge plan curve.

[0085] The method provided by the embodiment of the present invention only needs to apply the optimization algorithm on the basis of the charge and discharge optimization model to optimize the target multiple times without adding other restrictions. In each optimization process, the outstanding individuals of the contemporary population are found, and the optimal information of the outstanding individuals is passed to the next generation. After multiple iterations, the optimal target is quickly locked.

[0086] In the method provided by the embodiment of the present invention, the energy storage states corresponding to the target time points in the charge and discharge sequence include charging state, discharging state and static state. Therefore, updating the cumulative revenue and charge state of the energy storage state corresponding to each target time point in chronological order includes updating the cumulative revenue and charge state in the charging state, the cumulative revenue and charge state in the discharging state and the cumulative revenue and charge state in the static state.

[0087] For each target time point under the charge and discharge state curve, the process of updating the cumulative revenue and state of charge under the energy storage state corresponding to the target time point may include the following three cases:

[0088] (1) When the energy storage state corresponding to the target time point is a charging state, determining the charging stage of the energy storage system at the target time point, and updating the cumulative revenue and charge state of the energy storage system at the target time point according to the charging stage. The charging stage includes a fully charged stage and a continuous charging stage.

[0089] refer to Figure 3 In the charging state, the process of updating the cumulative revenue and state of charge of the energy storage system at the target time point according to the charging stage may specifically include:

[0090] S301: When the charging stage is the fully charged stage, the state of charge at the target time point is set to the maximum state of charge; based on a preset penalty item and historical accumulated benefits, the accumulated benefit at the target time point is calculated.

[0091] In the above S301, the fully charged stage indicates that the state of charge of the energy storage system has reached or exceeded the maximum value, and no charging action is performed. At this time, if the value range of the state of charge in the energy storage system is [0,1], the state of charge of the energy storage system at the target time point is the maximum state of charge, that is, SOC = 1. In the fully charged stage, in order to avoid the next target time point still being in the charging state, the penalty term Penalty is introduced to appropriately adjust the profit at the current target time point, and then combined with the historical accumulated profit to obtain the cumulative profit at the current target time point, that is, the accumulated profit bf = bf 历史 -Penalty.

[0092] The historical cumulative profit can be the cumulative profit at the previous target time point or the initial cumulative profit. When the target time point is the first time point of the current time period, the historical cumulative profit is the initial cumulative profit; otherwise, it is the cumulative profit at the previous target time point. The penalty term is a relatively large positive value used to adjust the cumulative profit when the battery is fully charged or discharged. This penalty term can be set based on experience or determined during actual application debugging.

[0093] S302: When the charging stage is a continuous charging stage, a first change value of the state of charge when charging at a rated power within a target time period corresponding to a target time point is calculated.

[0094] In the present invention, the continuous charging stage means that the energy storage system's state of charge has not reached its maximum value during the charging state and the charging action is continuously executed. Using the rated power, rated capacity, and historical state of charge, the calculation formula for calculating the first change in state of charge when charging at the rated power during the target time period corresponding to the target time point is:

[0095] SOC1=SOC 历史 +P rated*t / C;

[0096] Among them, SOC 历史 is the historical state of charge; P rated is the rated power; t is the previous target time period, which is the interval between the previous target time point and the current target time point; and C is the rated capacity of the energy storage system. The historical SOC can be either the SOC at the previous target time point or the initial SOC. When the target time point is the first time point in the current time period, the historical SOC is the initial SOC; otherwise, it is the SOC at the previous target time point.

[0097] S303: Determine whether the first change value is less than a preset first threshold.

[0098] If the first change value is less than the preset first threshold, then S304 is executed; if the first change value is not less than the preset first threshold, then S305 is executed. The first threshold may be 1.

[0099] S304: Setting the first change value as the state of charge at the target time point, and calculating the cumulative revenue at the target time point based on the electricity price information corresponding to the target time point, the historical cumulative revenue, and the rated power and cost per kilowatt-hour in the basic information.

[0100] In the present invention, if the first change value is less than the preset first threshold value, the state of charge at the target time point is: SOC = SOC 历史 +P rated *t / C. Based on the electricity price information corresponding to the target time point, the historical cumulative income, and the rated power and cost per kilowatt-hour in the basic information, the calculation formula for the cumulative income at the target time point is:

[0101] bf i =bf 历史 -P rated *t*(cpkw+Prc i );

[0102] Among them, bf 历史 is the historical cumulative income, cpkw is the cost per kilowatt-hour, Prc i is the electricity price information corresponding to the target time point in the electricity price sequence. When the target time point is the first time point of the current time period, the historical cumulative profit is the initial cumulative profit; otherwise, it is the cumulative profit at the previous target time point.

[0103] S305: Set the state of charge at the target time point to the maximum state of charge, and calculate the cumulative profit at the target time point based on the electricity price information corresponding to the target time point, the historical cumulative profit, and the rated capacity, cost per kilowatt-hour, and initial state of charge in the basic information.

[0104] In the present invention, if the first change value is not less than the preset first threshold value, the state of charge at the target time point is the maximum state of charge, that is, SOC = 1. Based on the electricity price information corresponding to the target time point, the historical accumulated revenue, and the rated capacity, cost per kilowatt-hour, and initial state of charge in the basic information, the calculation formula for calculating the accumulated revenue at the target time point is:

[0105] bf i =bf 历史 -(1-SOC0)*C*(cpkw+Prc i );

[0106] Where SOC0 is the initial state of charge. When the target time point is the first time point of the current time period, the historical cumulative profit is the initial cumulative profit; otherwise, it is the cumulative profit at the previous target time point.

[0107] (2) When the energy storage state corresponding to the target time point is the discharge state, determine the discharge stage of the energy storage system at the target time point, and update the cumulative revenue and charge state of the energy storage system at the target time point according to the discharge stage. The discharge stage includes the emptying stage and the continuous discharge stage.

[0108] refer to Figure 4 In the discharge state, the process of updating the cumulative revenue and state of charge of the energy storage system at the target time point according to the discharge stage may specifically include:

[0109] S401: When the discharge phase is the emptying phase, the state of charge at the target time point is set to the minimum state of charge; based on the penalty item and the historical cumulative benefits, the cumulative benefit at the target time point is calculated.

[0110] In the present invention, the emptying phase means that the energy storage system has been completely discharged in the discharge state, the state of charge is at or below the minimum value, and the energy storage system does not perform a discharge action. The state of charge at the target time point is updated to the minimum state of charge, that is, SOC = 0. The cumulative benefit at the target time point is:

[0111] bf=bf 历史 -Penalty;

[0112] Among them, when the target time point is the first time point of the current time period, the historical cumulative return is the initial cumulative return, otherwise it is the cumulative return of the previous target time point.

[0113] S402: When the discharging stage is a continuous discharging stage, a second change value of the state of charge when discharging at rated power within a target time period is calculated.

[0114] In the present invention, the continuous discharge phase means that the energy storage system's state of charge has not reached the minimum value during the discharge state and the discharge action is continuously executed. Using the rated power, rated capacity, and historical state of charge, the calculation formula for calculating the second change value of the state of charge when discharging at the rated power during the target time period is:

[0115] SOC2=SOC 历史 -P rated *t / C;

[0116] When the target time point is the first time point of the current time period, the historical state of charge is the initial state of charge; otherwise, it is the state of charge at the previous target time point.

[0117] S403: Determine whether the second change value is greater than a preset second threshold.

[0118] If the second change value is greater than a preset second threshold, then S404 is executed; if the second change value is not greater than the preset second threshold, then S405 is executed. The second threshold may be 0.

[0119] S404: Set the second change value as the state of charge at the target time point, and calculate the cumulative revenue at the target time point based on the electricity price information corresponding to the target time point, the historical cumulative revenue, and the rated power and cost per kilowatt-hour in the basic information.

[0120] In the present invention, if the second change value is greater than the second threshold value, the state of charge at the target time point is: SOC = SOC 历史 -P rated *t / C. Based on the electricity price information corresponding to the target time point, the historical cumulative income, and the rated power and cost per kilowatt-hour in the basic information, the calculation formula for calculating the cumulative income at the target time point is: bf i =bf 历史 +P rated *t*(Prc i -cpkw);

[0121] Among them, when the target time point is the first time point of the current time period, the historical cumulative return is the initial cumulative return, otherwise it is the cumulative return of the previous target time point.

[0122] S405: Set the state of charge at the target time point to the minimum state of charge, and calculate the cumulative profit at the target time point based on the electricity price information corresponding to the target time point, the historical cumulative profit, and the rated capacity, cost per kilowatt-hour, and initial state of charge in the basic information.

[0123] In the present invention, if the second change value is not greater than the second threshold value, the state of charge at the target time point is: SOC = 0. Based on the electricity price information corresponding to the target time point, the historical cumulative income, and the rated capacity, cost per kilowatt-hour, and initial state of charge in the basic information, the calculation formula for calculating the cumulative income at the target time point is:

[0124] bf i =bf 历史 -SOC0*C*(Prc i -cpkw);

[0125] Among them, when the target time point is the first time point of the current time period, the historical cumulative return is the initial cumulative return, otherwise it is the cumulative return of the previous target time point.

[0126] It should be noted that the energy storage system is charged and discharged at rated power and rated capacity during the charging and discharging process. The rated power of the energy storage system can be continuously variable within a certain range, for example: rated ,P rated ] is continuously variable, with negative values ​​indicating charging and positive values ​​indicating discharging. When the energy storage system power is continuously variable, the control complexity of the system increases, but higher returns can be achieved.

[0127] (3) When the energy storage state corresponding to the target time point is a static state, the cumulative income and state of charge at the target time point are set to the historical cumulative income and historical state of charge that have been obtained.

[0128] Among them, when the target time point is the first time point of the current time period, the historical cumulative income and the historical state of charge are the initial cumulative income and the initial state of charge respectively, and the initial cumulative income is zero; when the target time point is not the first time point of the current time period, the historical cumulative income and the historical state of charge are the cumulative income and the state of charge of the target time point before the target time point respectively.

[0129] Based on the method provided in the above embodiment, the corresponding cumulative revenue and charge state are updated under different energy storage states, and the total revenue of the charge and discharge plan curve is determined, so as to further screen the optimal charge and discharge plan curve from multiple charge and discharge plan curves to maximize the revenue of the energy storage system in the current time period.

[0130] The specific implementation processes and derivative methods of the above embodiments are all within the protection scope of the present invention.

[0131] and Figure 1 Corresponding to the method described above, the embodiment of the present invention further provides an energy storage system planning curve optimization device for Figure 1The specific implementation of the method, the energy storage system planning curve optimization device provided by the embodiment of the present invention can be applied to a computer terminal or various mobile devices, and its structural diagram is as follows Figure 5 As shown, specifically including:

[0132] An acquisition unit 501 is configured to acquire basic information, an electricity price sequence, and a preset charge-discharge sequence for the current time period of the energy storage system; the electricity price sequence includes electricity price information corresponding to multiple target time points in the current time period, and the charge-discharge sequence includes energy storage states corresponding to each target time point, where the energy storage state is a charging state, a discharging state, or a static state.

[0133] A setting unit 502 is configured to set a charge-discharge optimization model based on the basic information, the electricity price sequence, and the charge-discharge sequence;

[0134] The optimization unit 503 is configured to apply a preset optimization algorithm to execute the optimization process corresponding to the charge-discharge optimization model to obtain the optimal charge-discharge plan curve for the current time period;

[0135] The optimization process includes:

[0136] Generate a contemporary population, wherein the contemporary population includes multiple individuals, each of which is a charge-discharge plan curve; for each of the charge-discharge plan curves, update the cumulative benefit and charge state under the energy storage state corresponding to each target time point in the charge-discharge plan curve to obtain the total benefit of the energy storage system under the charge-discharge plan curve; after obtaining the total benefit of each charge-discharge plan curve in the contemporary population, determine whether the contemporary population is the last generation of population; if not, re-execute the optimization process based on the charge-discharge plan curve in the contemporary population that meets the first optimization condition; if so, screen out the charge-discharge plan curve with the largest total benefit from all generated populations as the optimal charge-discharge plan curve.

[0137] In the device provided by an embodiment of the present invention, basic information such as the rated power, rated capacity, initial state of charge, and cost per kilowatt-hour of an energy storage system is obtained, as well as a sequence of electricity prices. The electricity price information corresponding to each target time point in the sequence represents the specific electricity price of the energy storage system at that target time point. A corresponding charge-discharge sequence is set based on the electricity price sequence. Each energy storage state in the charge-discharge sequence can be any of a charging state, a discharging state, and a static state. A charge-discharge optimization model is set based on the initial state of charge, the electricity price sequence, and the charge-discharge sequence. This charge-discharge optimization model serves as an arbitrage model for the energy storage system. A particle swarm algorithm is applied to execute an optimization process corresponding to the charge-discharge optimization model to determine the specific energy storage state at each target time point in the charge-discharge optimization model. During the optimization process, multiple initial charge-discharge plan curves are randomly generated based on the charge-discharge optimization model and the number of individuals. The cumulative revenue and charge function for each energy storage state corresponding to each target time point in the initial charge-discharge plan curves are then updated sequentially in chronological order to obtain the total revenue of the initial charge-discharge plan curves. If the first generation of populations is not the last generation, the total revenue of each first generation of charge and discharge plan curves is selected to select the charge and discharge plan curve that meets the first optimization condition as the optimization direction to generate a new generation of populations. The optimization process continues until the total revenue of each charge and discharge plan curve in the last generation of populations is obtained. Then, from all generated populations, the charge and discharge plan curve with the highest total revenue is found and used as the charge and discharge plan curve for the current time period. The charging and discharging time of the energy storage system in the current time period can be planned according to this charge and discharge plan curve to obtain the maximum revenue for the current time period.

[0138] By applying the device provided in the embodiment of the present invention, the charge and discharge plan curve of the energy storage system is optimized to help the energy storage system obtain greater benefits.

[0139] The specific working process of each unit and subunit in the energy storage system planning curve optimization device disclosed in the above embodiment of the present invention can be found in the corresponding content of the energy storage system planning curve optimization method disclosed in the above embodiment of the present invention, and will not be repeated here.

[0140] An embodiment of the present invention further provides a storage medium, which includes stored instructions, wherein when the instructions are executed, the device where the storage medium is located is controlled to execute the above-mentioned energy storage system planning curve optimization method.

[0141] The embodiment of the present invention further provides an electronic device, the structural diagram of which is shown in FIG. Figure 6As shown, the system specifically includes a memory 601 and one or more instructions 602, wherein the one or more instructions 602 are stored in the memory 601 and are configured to be executed by one or more processors 603 to perform the following operations:

[0142] Obtaining basic information, an electricity price sequence, and a preset charge and discharge sequence for the current time period of the energy storage system; the electricity price sequence includes electricity price information corresponding to multiple target time points in the current time period, and the charge and discharge sequence includes energy storage states corresponding to each target time point, where the energy storage state is a charging state, a discharging state, or a static state;

[0143] Setting a charge-discharge optimization model based on the basic information, electricity price sequence, and charge-discharge sequence;

[0144] Applying a preset optimization algorithm to execute the optimization process corresponding to the charge-discharge optimization model to obtain the optimal charge-discharge plan curve for the current time period;

[0145] The optimization process includes:

[0146] Generate a contemporary population, wherein the contemporary population includes multiple individuals, each of which is a charge-discharge plan curve; for each of the charge-discharge plan curves, update the cumulative benefit and charge state corresponding to the energy storage state at each target time point in the charge-discharge plan curve to obtain the total benefit of the energy storage system under the charge-discharge plan curve; after obtaining the total benefit of each charge-discharge plan curve in the contemporary population, determine whether the contemporary population is the last generation of population; if not, re-execute the optimization process based on the charge-discharge plan curve in the contemporary population that meets the first optimization condition; if so, screen out the charge-discharge plan curve with the largest total benefit from all generated populations as the optimal charge-discharge plan curve.

[0147] Each embodiment in this specification is described in a progressive manner. The same or similar parts between the embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments. In particular, for system or system embodiments, since they are basically similar to method embodiments, the description is relatively simple. For relevant parts, refer to the partial description of the method embodiment. The system and system embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. A person of ordinary skill in the art can understand and implement it without expending creative work.

[0148] Those skilled in the art may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented by electronic hardware, computer software, or a combination of both.

[0149] To clearly illustrate the interchangeability of hardware and software, the above descriptions have generally described the components and steps of each example by function. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Professionals may use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of the present invention.

[0150] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for optimizing a planning curve of an energy storage system, characterized in that: include: Obtain basic information about the energy storage system's current time period, electricity price sequence, and preset charge and discharge sequence; The electricity price sequence includes electricity price information corresponding to multiple target time points in the current time period, and the charge and discharge sequence includes energy storage states corresponding to each target time point, wherein the energy storage state is a charging state, a discharging state, or a static state; Setting a charge-discharge optimization model based on the basic information, electricity price sequence, and charge-discharge sequence; Applying a preset optimization algorithm to execute the optimization process corresponding to the charge-discharge optimization model to obtain the optimal charge-discharge plan curve for the current time period; The optimization process includes: Generate a contemporary population, wherein the contemporary population includes multiple individuals, and the individuals are charge and discharge plan curves; for each of the charge and discharge plan curves, update the cumulative benefit and charge state under the energy storage state corresponding to each target time point in the charge and discharge plan curve to obtain the total benefit of the energy storage system under the charge and discharge plan curve; after obtaining the total benefit of each charge and discharge plan curve in the contemporary population, determine whether the contemporary population is the last generation population; if not, re-execute the optimization process based on the charge and discharge plan curve in the contemporary population that meets the first optimization condition; if so, screen out the charge and discharge plan curve with the largest total benefit from all generated populations as the optimal charge and discharge plan curve; wherein the charge and discharge curve that meets the first optimization condition is the charge and discharge plan curve whose total benefit reaches a preset benefit threshold, or the charge and discharge plan curve with the largest total benefit in the contemporary population.

2. The method according to claim 1, characterized in that The setting of a charge-discharge optimization model based on the basic information, the electricity price sequence, and the charge-discharge sequence includes: Obtaining the initial state of charge contained in the basic information; A charge and discharge optimization model is set based on the initial state of charge, the electricity price sequence, and the charge and discharge sequence.

3. The method according to claim 2, characterized in that The generation of contemporary populations includes: If it is necessary to generate an initial generation of population, a first generation of charge and discharge plan curves with a preset number of individuals are randomly generated based on the charge and discharge optimization model to form the initial generation of population; If a non-first generation population needs to be generated currently, the charge and discharge plan curve of the previous generation that meets the first optimization condition is used as a reference curve to generate a new charge and discharge plan curve of the preset number of individuals to form a new generation population.

4. The method according to claim 3, characterized in that The updating of the cumulative benefit and state of charge under the energy storage state corresponding to each target time point in the charge and discharge plan curve to obtain the total benefit of the energy storage system under the charge and discharge plan curve includes: The cumulative revenue and state of charge under the energy storage state corresponding to each target time point are updated in chronological order, and the cumulative revenue corresponding to the last target time point obtained is the total revenue of the energy storage system under the charge and discharge plan curve.

5. The method according to claim 4, characterized in that The updating of the accumulated revenue and state of charge under the energy storage state corresponding to each target time point in chronological order includes: When the energy storage state corresponding to the target time point is a charging state, determining the charging stage of the energy storage system at the target time point, and updating the accumulated revenue and state of charge of the energy storage system at the target time point according to the charging stage; When the energy storage state corresponding to the target time point is a discharging state, determining the discharging stage of the energy storage system at the target time point, and updating the accumulated revenue and state of charge of the energy storage system at the target time point according to the discharging stage; When the energy storage state corresponding to the target time point is a static state, the accumulated income and state of charge at the target time point are set to the obtained historical accumulated income and historical state of charge.

6. The method according to claim 5, characterized in that The updating of the accumulated revenue and state of charge of the energy storage system at the target time point according to the charging stage includes: When the charging stage is the fully charged stage, setting the state of charge at the target time point to the maximum state of charge; and calculating the cumulative profit at the target time point based on a preset penalty item and the historical cumulative profit; When the charging stage is a continuous charging stage, calculate the first change value of the state of charge when charging at the rated power within the target time period corresponding to the target time point; determine whether the first change value is less than a preset first threshold; if so, set the first change value to the state of charge at the target time point, and calculate the cumulative profit at the target time point based on the electricity price information corresponding to the target time point, the historical accumulated profit, and the rated power and cost per kilowatt-hour in the basic information; if not, set the state of charge at the target time point to the maximum state of charge, and calculate the cumulative profit at the target time point based on the electricity price information corresponding to the target time point, the historical accumulated profit, and the rated capacity, cost per kilowatt-hour, and initial state of charge in the basic information.

7. The method according to claim 6, characterized in that The calculating a first change value of the state of charge when charging at a rated power within a target time period corresponding to the target time point includes: The rated power, rated capacity, and historical state of charge are used to calculate a first change value of the state of charge when charging at the rated power within a target time period corresponding to the target time point.

8. The method according to claim 6, characterized in that The updating of the accumulated revenue and state of charge of the energy storage system at the target time point according to the discharge stage includes: When the discharge phase is an empty phase, setting the state of charge at the target time point to a minimum state of charge; and calculating the cumulative profit at the target time point based on the penalty item and the historical cumulative profit; When the discharge stage is a continuous discharge stage, calculate the second change value of the state of charge when discharging at the rated power within the target time period; determine whether the second change value is greater than a preset second threshold; if so, set the second change value to the state of charge at the target time point, and calculate the cumulative profit at the target time point based on the electricity price information corresponding to the target time point, the historical accumulated profit, and the rated power and cost per kilowatt-hour in the basic information; if not, set the state of charge at the target time point to the minimum state of charge, and calculate the cumulative profit at the target time point based on the electricity price information corresponding to the target time point, the historical accumulated profit, and the rated capacity, cost per kilowatt-hour, and initial state of charge in the basic information.

9. The method according to claim 7, characterized in that The calculating a second change value of the state of charge when discharging at rated power within the target time period includes: The rated power, rated capacity, and historical state of charge are used to calculate a second change value of the state of charge when discharging at the rated power within the target time period.

10. The method according to any one of claims 5 to 9, characterized in that When the target time point is the first time point of the current time period, the historical cumulative income and the historical state of charge are respectively the initial cumulative income and the initial state of charge, and the initial cumulative income is zero; When the target time point is not the first time point of the current time period, the historical accumulated revenue and historical state of charge are respectively the accumulated revenue and state of charge of the target time point before the target time point.

11. An energy storage system planning curve optimization device, characterized in that: include: An acquisition unit, configured to acquire basic information of the energy storage system in the current time period, a sequence of electricity prices, and a preset charge and discharge sequence; The electricity price sequence includes electricity price information corresponding to multiple target time points in the current time period, and the charge and discharge sequence includes energy storage states corresponding to each target time point, where the energy storage state is a charging state, a discharging state, or a static state. A setting unit, configured to set a charge-discharge optimization model based on the basic information, the electricity price sequence, and the charge-discharge sequence; An optimization unit, configured to apply a preset optimization algorithm to execute an optimization process corresponding to the charge-discharge optimization model to obtain an optimal charge-discharge plan curve for the current time period; The optimization process includes: Generate a contemporary population, wherein the contemporary population includes multiple individuals, each of which is a charge-discharge plan curve; for each of the charge-discharge plan curves, update the cumulative benefit and charge state under the energy storage state corresponding to each target time point in the charge-discharge plan curve to obtain the total benefit of the energy storage system under the charge-discharge plan curve; after obtaining the total benefit of each charge-discharge plan curve in the contemporary population, determine whether the contemporary population is the last generation of population; if not, re-execute the optimization process based on the charge-discharge plan curve in the contemporary population that meets the first optimization condition; if so, screen out the charge-discharge plan curve with the largest total benefit from all generated populations as the optimal charge-discharge plan curve.

12. A storage medium, characterized in that: The storage medium includes stored instructions, wherein when the instructions are executed, the device where the storage medium is located is controlled to execute the energy storage system planning curve optimization method according to any one of claims 1 to 10.

13. An electronic device, characterized in that: The system comprises a memory and one or more instructions, wherein the one or more instructions are stored in the memory and configured to be executed by one or more processors to execute the energy storage system planning curve optimization method according to any one of claims 1 to 10.

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