Energy storage power station optimization control method and device, electronic equipment and storage medium

Abstract

The invention provides an energy storage power station optimization control method and apparatus, an electronic device and a storage medium. The method comprises the steps of establishing a charging and discharging power strategy set of a target energy storage power station and a demand electric charge formula of the target energy storage power station; calculating the demand electric charge corresponding to each charging and discharging power strategy in the charging and discharging power strategy set based on a demand electric charge formula; calculating a fitness value, and optimizing the charging and discharging power strategies of the charging and discharging power strategy set based on the fitness value through a genetic optimization algorithm; and repeating the above steps until an optimization ending condition is met, determining an optimal charging and discharging power strategy, and controlling the target energy storage power station based on the optimal charging and discharging power strategy. Through the genetic algorithm, an optimal solution can be searched in a global range, a better overall solution can be found, the energy-saving potential of the optical storage and charging system can be fully excavated, energy storage resources can be better managed, loss caused by overcharging or discharging can be avoided, a charging and discharging strategy can be optimized, the electricity charge demand can be effectively reduced, and the operation cost can be saved.

Description

Technical Field

[0001] The present disclosure relates to the technical field of energy storage power station control, and in particular to an energy storage power station optimization control method, device, electronic equipment and storage medium. Background Art

[0002] In current technology, electric vehicle charging stations mainly use distribution network power supply, and the power supply form is relatively simple. In order to alleviate the problem of the single source of power for electric vehicle charging systems, renewable energy generators such as photovoltaics can be introduced into the charging system. The integrated photovoltaic storage and charging power station combines photovoltaic power generation, energy storage and charging stations, uses photovoltaic power generation as the main power supply to charge electric vehicles, and uses the energy storage system to digest the surplus photovoltaic power generation, supplement the insufficient energy supply during the peak demand period of charging piles, reduce the peak and fill the valley to reduce the cost of purchasing electricity from the power grid, thereby achieving the purpose of optimizing the operation mode and improving the economic efficiency of operation.

[0003] In the problem of optimizing the operation and scheduling of electric vehicle photovoltaic storage charging stations, the current control method is relatively mechanical, which gives priority to using photovoltaic power generation and energy storage to meet the charging station load. This leads to a large amount of electricity purchased by the power grid during peak electricity consumption periods (high electricity prices), which increases the cost of charging stations. Summary of the invention

[0004] The present disclosure aims to solve one of the technical problems in the related art at least to some extent.

[0005] To this end, an object of the present disclosure is to propose an energy storage power station optimization control method.

[0006] The second objective of the present disclosure is to provide an energy storage power station optimization control device.

[0007] A third objective of the present disclosure is to provide an electronic device.

[0008] A fourth object of the present disclosure is to provide a non-transitory computer-readable storage medium.

[0009] A fifth object of the present disclosure is to provide a computer program product.

[0010] To achieve the above-mentioned purpose, the first aspect of the present disclosure proposes an energy storage power station optimization control method, including: establishing a charging and discharging power strategy set of a target energy storage power station and a demand electricity fee formula of the target energy storage power station, wherein the charging and discharging power strategy set includes at least one charging and discharging power strategy, and the charging and discharging power strategy at least includes the charging strategy of the target energy storage power station in each time period within a control cycle; calculating the demand electricity fee corresponding to each charging and discharging power strategy in the charging and discharging power strategy set based on the demand electricity fee; calculating the fitness value corresponding to each charging and discharging power strategy in the charging and discharging power strategy set based on the demand electricity fee, and optimizing the charging and discharging power strategies of the charging and discharging power strategy set based on the fitness value through a genetic optimization algorithm; repeating the above-mentioned steps of calculating the demand electricity fee corresponding to each charging and discharging power strategy in the charging and discharging power strategy set based on the demand electricity fee formula and subsequent steps until the optimization end condition is met, obtaining the target charging and discharging power strategy set, and determining the optimal charging and discharging power strategy based on the target charging and discharging power strategy set, and controlling the target energy storage power station based on the optimal charging and discharging power strategy.

[0011] According to one embodiment of the present disclosure, the demand electricity fee formula is: g (t) = P load (t)+P b (t)-P v (t) wherein said P load (t) is the load of the target energy storage power station in the tth period, and P v (t) is the average power of photovoltaic power generation in the target energy storage power station in the tth period, and P b (t) is the average charge and discharge power of the charge and discharge power strategy set in the tth period.

[0012] According to one embodiment of the present disclosure, the calculation of the fitness value corresponding to each charging and discharging power strategy in the charging and discharging power strategy set based on the demand electricity charge includes: obtaining a preset constant coefficient; and taking the difference between the constant coefficient and the demand electricity charge as the fitness value.

[0013] According to an embodiment of the present disclosure, the formula for calculating the fitness value corresponding to each charging and discharging power strategy in the charging and discharging power strategy set based on the demand electricity fee is: Wherein, G is the adaptation function, M is the constant coefficient, and F is the demand electricity fee.

[0014] According to one embodiment of the present disclosure, judging whether a charging and discharging power strategy set satisfies an optimization termination condition includes: for this optimization, obtaining the average charging and discharging power of a preceding charging and discharging power strategy set optimized for this time; calculating the rate of change between the average charging and discharging power of the preceding charging and discharging power strategy set and the average charging and discharging power of the charging and discharging power strategy set optimized for this time; and determining whether the optimization termination condition is met based on the rate of change.

[0015] According to one embodiment of the present disclosure, determining whether the optimization end condition is met based on the change rate includes: comparing the change rate with a change rate threshold; and determining that the optimization end condition is met in response to the change rate being less than the change rate threshold.

[0016] According to one embodiment of the present disclosure, the method further includes: converting the average charge and discharge power into a charge and discharge power coefficient, and optimizing the average charge and discharge power by optimizing the charge and discharge power coefficient.

[0017] To achieve the above-mentioned purpose, the second embodiment of the present disclosure proposes an energy storage power station optimization control device, including: an establishment module, used to establish a charging and discharging power strategy set of a target energy storage power station and a demand electricity fee formula of the target energy storage power station, wherein the charging and discharging power strategy set includes at least one charging and discharging power strategy, and the charging and discharging power strategy at least includes the charging strategy of the target energy storage power station in each time period within a control cycle; a calculation module, used to calculate the demand electricity fee corresponding to each charging and discharging power strategy in the charging and discharging power strategy set based on the demand electricity fee formula; a determination module, used to calculate the demand electricity fee based on the demand electricity fee The fitness value corresponding to each charge and discharge power strategy in the charge and discharge power strategy set is optimized based on the fitness value by a genetic optimization algorithm; a control module is used to repeat the above-mentioned calculation of the demand electricity fee corresponding to each charge and discharge power strategy in the charge and discharge power strategy set based on the demand electricity fee formula and subsequent steps until the optimization end condition is met, obtain the target charge and discharge power strategy set, determine the optimal charge and discharge power strategy based on the target charge and discharge power strategy set, and control the target energy storage power station based on the optimal charge and discharge power strategy.

[0018] To achieve the above-mentioned purpose, the third aspect embodiment of the present disclosure proposes an 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, and the instructions are executed by the at least one processor to implement the energy storage power station optimization control method as described in the first aspect embodiment of the present disclosure.

[0019] To achieve the above-mentioned purpose, the fourth aspect embodiment of the present disclosure proposes a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to implement the energy storage power station optimization control method as described in the first aspect embodiment of the present disclosure.

[0020] To achieve the above-mentioned purpose, the fifth aspect of the present disclosure proposes a computer program product, including a computer program, which, when executed by a processor, is used to implement the energy storage power station optimization control method as described in the first aspect of the present disclosure.

[0021] Therefore, genetic algorithms can search for the optimal solution on a global scale and discover better overall solutions, fully tapping the energy-saving potential of the photovoltaic storage and charging system. This helps to better manage energy storage resources, avoid losses caused by overcharging or discharging, optimize charging and discharging strategies, and effectively reduce demand electricity charges, thereby saving operating costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 is a schematic diagram of an energy storage power station optimization control method according to an embodiment of the present disclosure;

[0023] Figure 2 It is a structural schematic diagram of an energy storage power station according to one embodiment of the present disclosure;

[0024] Figure 3 is a schematic diagram of another energy storage power station optimization control method according to an embodiment of the present disclosure;

[0025] Figure 4 is a schematic diagram of another control method of an energy storage power station according to an embodiment of the present disclosure;

[0026] Figure 5 is a flow chart of a genetic algorithm according to one embodiment of the present disclosure;

[0027] Figure 6 is a schematic diagram of an energy storage power station optimization control device according to one embodiment of the present disclosure;

[0028] Figure 7 is a schematic diagram of an electronic device according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0029] Embodiments of the present disclosure are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present disclosure, and should not be construed as limiting the present disclosure.

[0030] The acquisition, storage, use, and processing of data in the technical solution disclosed in this disclosure are in compliance with the relevant provisions of relevant laws and regulations.

[0031] It should be noted that in the embodiments of the present application, certain software, components, models and other existing solutions in the industry may be mentioned, and they should be considered as exemplary. Their purpose is only to illustrate the feasibility of implementing the technical solution of the present application, but it does not mean that the applicant has or will necessarily use the solution.

[0032] Figure 1 is a schematic diagram of an energy storage power station optimization control method according to an embodiment of the present disclosure, such as Figure 1 As shown, the energy storage power station optimization control method includes the following steps:

[0033] S101, establishing a charging and discharging power strategy set of a target energy storage power station and a demand electricity fee formula of the target energy storage power station, wherein the charging and discharging power strategy set includes at least one charging and discharging power strategy, and the charging and discharging power strategy at least includes a charging strategy of the target energy storage power station in each time period within a control cycle.

[0034] It should be noted that the energy storage equipment of the target energy storage power station system is a controllable device. Establishing a model of the energy storage equipment is crucial to the optimization method and can affect the working efficiency of the entire system. Figure 2 This is a schematic diagram of the structure of an energy storage power station disclosed in the present invention. Figure 2 As shown, the energy storage power station includes: mains power supply, transformer, energy storage battery, AC charging pile, DC charging pile, photovoltaic power generation output, daily load, DC bus, etc. In other words, the target energy storage power station involves the selection of mains power and photovoltaic power, as well as the charge / discharge control of the energy storage equipment.

[0035] The energy storage power station optimization control method of the embodiment of the present application can be applied to the energy storage power station control scenario. The executor of the energy storage power station optimization control of the embodiment of the present application can be the energy storage power station optimization control device of the embodiment of the present application, and the energy storage power station optimization control device can be set on an electronic device.

[0036] It should be noted that the charging and discharging power strategy in the embodiment of the present disclosure is a charging strategy for each time period of the target energy storage power station in each control cycle. The charging and discharging power strategy may include a variety of information, which is not limited here. For example, the charging and discharging power strategy may include an adjustment strategy for the output power of the target energy storage power station, an adjustment strategy for the power generation power, etc.

[0037] In the embodiment of the present disclosure, the control period is a cycle for controlling the energy storage power station. The control period may be one year, three months, or one week, etc., and may be specifically limited according to actual design requirements.

[0038] It should be noted that the charge / discharge parameters in the embodiments of the present disclosure can be changed according to the time period, so as to adapt to the power demand in different time periods. For example, when the control cycle is 1 day and the time period is divided into 1 hour, the charge / discharge parameters included in the element are a set of 24 different charge / discharge parameters.

[0039] In the embodiment of the present disclosure, the demand electricity fee is the fee for purchasing electricity from the power grid during a control period.

[0040] It should be noted that since the power generation costs are different in different time periods, the corresponding demand electricity charges in different time periods are variable.

[0041] The demand charge formula can be established based on the battery parameters of the target energy storage power station. It should be noted that the battery parameters in the embodiments of the present disclosure may be multiple, and no limitation is made here. For example, the battery parameters of the energy storage battery may be one or more of capacity, SOC curve, DOD maximum discharge depth, battery loss cost, battery life attenuation coefficient and efficiency factor. Quantify the life loss caused by each charge or discharge of the energy storage battery. If the energy storage battery is to meet the requirements of service life or maximum number of uses, the total life loss within one day will be limited. Therefore, in order to meet the requirements of the service life and maximum number of uses of the energy storage battery, it is necessary to calculate the maximum loss degree of the energy storage battery in each day. For example, based on the principle of no more than two cycles in one day, the requirements of service life and maximum number of uses can be met.

[0042] It should be noted that the charging and discharging power strategy set may be manually established or randomly established, and no limitation is made here.

[0043] S102, calculating the demand electricity fee corresponding to each charging and discharging power strategy in the charging and discharging power strategy set based on the demand electricity fee formula.

[0044] In the embodiment of the present disclosure, since the charging and discharging power strategy may include multiple power strategies for each time period, when calculating the demand electricity charge corresponding to each charging and discharging power strategy, the demand electricity charge for each time period of each charging and discharging power strategy may be averaged to calculate and determine the demand electricity charge.

[0045] S103, calculating the fitness value corresponding to each charging and discharging power strategy in the charging and discharging power strategy set based on the demand electricity fee, and optimizing the charging and discharging power strategies in the charging and discharging power strategy set based on the fitness value by using a genetic optimization algorithm.

[0046] It should be noted that the genetic algorithm is an optimization algorithm inspired by Darwin's theory of evolution. Its design imitates the evolution law of organisms in nature, establishes a corresponding relationship between biological evolution and optimization problems, uses fitness functions to correspond to the environment, and determines the survival probability based on the size of the fitness value. The survival of the fittest is selected according to the survival probability, which corresponds to the "survival of the fittest" in the evolutionary process. The solution to the problem corresponds to the biological individual, and the encoding of the solution corresponds to the individual chromosome. Through selection operations, crossover operations, and mutation operations, the optimal solution is solved according to the evolutionary idea.

[0047] S104, repeat the above-mentioned calculation of the demand electricity fee corresponding to each charge and discharge power strategy in the charge and discharge power strategy set based on the demand electricity fee formula and subsequent steps until the optimization end condition is met, obtain the target charge and discharge power strategy set, and determine the optimal charge and discharge power strategy based on the target charge and discharge power strategy set, and control the target energy storage power station based on the optimal charge and discharge power strategy.

[0048] In the embodiment of the present disclosure, after determining the target optimal element, a control instruction can be automatically generated based on the charge / discharge parameters corresponding to the target optimal element and sent to the control device of the target energy storage power station, and the parameters of the target energy storage power station can be adjusted by the control device to achieve automatic control of the target energy storage power station.

[0049] In an embodiment of the present disclosure, a charging and discharging power strategy set of a target energy storage power station and a demand electricity charge formula of the target energy storage power station are first established, wherein the charging and discharging power strategy set includes at least one charging and discharging power strategy, and then the demand electricity charge corresponding to each charging and discharging power strategy in the charging and discharging power strategy set is calculated based on the demand electricity charge formula, and then the fitness value corresponding to each charging and discharging power strategy in the charging and discharging power strategy set is calculated based on the demand electricity charge, and the charging and discharging power strategies of the charging and discharging power strategy set are optimized based on the fitness value through a genetic optimization algorithm, and finally the above-mentioned calculation of the demand electricity charge corresponding to each charging and discharging power strategy in the charging and discharging power strategy set based on the demand electricity charge formula and subsequent steps are repeated until the optimization end condition is met, the target charging and discharging power strategy set is obtained, and the optimal charging and discharging power strategy is determined based on the target charging and discharging power strategy set, and the target energy storage power station is controlled based on the optimal charging and discharging power strategy. Therefore, genetic algorithms can search for the optimal solution on a global scale and discover better overall solutions, fully tapping the energy-saving potential of the photovoltaic storage and charging system. This helps to better manage energy storage resources, avoid losses caused by overcharging or discharging, optimize charging and discharging strategies, and effectively reduce demand electricity charges, thereby saving operating costs.

[0050] In the embodiment of the present disclosure, the average charge and discharge power is converted into a charge and discharge power coefficient, and the average charge and discharge power is optimized by optimizing the charge and discharge power coefficient.

[0051] In the embodiment of the present disclosure, the demand electricity fee formula is:

[0052] P g (t) = P load (t)+P b (t)-P v (t)

[0053] Among them, P load (t) is the load of the target energy storage power station in the tth period, P v (t) is the average power of photovoltaic power generation in the target energy storage power station in the tth period, P b (t) is the average charge and discharge power of the charge and discharge power strategy set in the tth period.

[0054] In the above embodiment, the fitness value corresponding to each charging and discharging power strategy in the charging and discharging power strategy set is calculated based on the demand electricity fee, and the fitness value corresponding to each charging and discharging power strategy in the charging and discharging power strategy set can also be calculated by Figure 3 Explaining further, the method includes:

[0055] S301, obtaining a preset constant coefficient.

[0056] S302: Taking the difference between the constant coefficient and the demand electricity fee as the fitness value.

[0057] In the embodiment of the present disclosure, the formula for calculating the fitness value corresponding to each charging and discharging power strategy in the charging and discharging power strategy set based on the demand electricity fee is:

[0058]

[0059] Among them, G is the adaptation function, M is the constant coefficient, and F is the demand electricity fee. It should be noted that M is a sufficiently large constant, and M is generally much larger than F.

[0060] The calculation formula for demand electricity charges is:

[0061]

[0062] Where t is the time period sequence number in the control sampling time cycle, N is the total number of sampling periods, f(t) is the average electricity price purchased by the charging station from the power grid in the tth period, P g (t) is the average power purchased by the charging station from the grid in period t, and Δt is the duration of the sampling period.

[0063] Where P g The calculation method of (t) is:

[0064] P g (t) = P load (t)+P b (t)-P v (t)

[0065] Among them, P load (t) is the load of the charging station in the tth period, which is a known quantity, P v (t) is the average power of photovoltaic power generation in the tth period, which is a known quantity, P b (t) is the average charging and discharging power of the energy storage unit in the tth period, which is the variable to be optimized. Its positive value represents charging and its negative value represents discharging. The numerical value represents the size of the charging and discharging power.

[0066] At the same time, it can be seen that when the demand electricity fee is smaller, the corresponding fitness value is larger. Therefore, when determining the optimal charging and discharging power strategy based on the fitness value in the future, the optimal solution with relatively low demand electricity fee can be selected.

[0067] It should be noted that, in the present disclosure, optimizing the average charge and discharge power by using a genetic algorithm with minimizing the demand electricity charge as the adaptation function includes: converting the average charge and discharge power into a charge and discharge power coefficient, and optimizing the average charge and discharge power by optimizing the charge and discharge power coefficient.

[0068] Specifically, the specific charge and discharge power can be described by the maximum charge and discharge power of the energy storage unit combined with the charge and discharge coefficient, as shown in the following formula:

[0069]

[0070] Among them, P b,max is the maximum charge and discharge power of the energy storage unit, k t represents the charging and discharging power coefficient of the energy storage unit in the tth period, satisfying k t ∈[-1, 1], thereby optimizing the variable P b (t) is equivalent to optimizing k t , then, the average charge and discharge power is converted into the charge and discharge power coefficient including:

[0071]

[0072] In addition, during the data processing, the following constraints need to be met:

[0073] (1) Power balance constraint.

[0074] Each power in the charging station should meet the balance conditions:

[0075] P g (t)+P v (t) = P load (t)+P b (t)

[0076] That is, during period t, the charging and discharging power of the energy storage unit and the charging load of the charging station are balanced with the sum of the photovoltaic output power and the power purchased by the power grid during that period.

[0077] (2) Energy storage unit constraints.

[0078] The energy storage unit cannot be charged and discharged at the same time, and its charge and discharge power cannot exceed the upper and lower limits allowed:

[0079] 0≤|P b (t)|≤P b,max

[0080] Among them, P b，max It is the maximum value of the charging and discharging power of the energy storage unit.

[0081] (3) Battery SOC protection constraint.

[0082] The batteries in the energy storage unit cannot be overcharged or over-discharged during charging and discharging, that is, there are constraints:

[0083] SOC min ≤SOC(t)≤SOC max

[0084] In the formula, SOC min , SOC max They are the lower and upper limits of the battery power SOC respectively.

[0085] The present invention utilizes a genetic algorithm to solve the optimal solution for the average power of energy storage battery charging and discharging every 15min / 20min under the electric load demand within a control period.

[0086] In the above embodiment, judging whether the charging and discharging power strategy set meets the optimization end condition includes:

[0087] S401, for this optimization, obtaining the average charge and discharge power of the preceding charge and discharge power strategy set for this optimization.

[0088] It should be noted that the preceding charging and discharging power strategy set is the charging and discharging power strategy set optimized the last time before the current optimization.

[0089] S402, calculating a change rate of the charge and discharge average power of the previous charge and discharge power strategy set and the charge and discharge average power of the currently optimized charge and discharge power strategy set.

[0090] S403: Determine whether an optimization end condition is met based on the change rate.

[0091] In a possible implementation, the change rate may be compared with a change rate threshold, and then, in response to the change rate being less than the change rate threshold, it is determined that the optimization end condition is met.

[0092] In the disclosed embodiment, firstly, for this optimization, the average charge and discharge power of the previous charge and discharge power strategy set for this optimization is obtained, and then the change rate is calculated between the average charge and discharge power of the previous charge and discharge power strategy set and the average charge and discharge power of the current optimized charge and discharge power strategy set, and finally, based on the change rate, it is determined whether the optimization end condition is met. Thus, the direction and accuracy of the target optimal element finally generated can be controlled by controlling the end condition, so as to achieve the purpose of controllability.

[0093] like Figure 5 This is a genetic algorithm flow chart. When the genetic algorithm solves the charging station optimization control problem, one individual represents an energy scheduling scheme, the group size δ represents the number of schemes, and the chromosome X of individual i i As shown below:

[0094]

[0095] Among them, represents the charging and discharging power coefficient of the energy storage unit of individual i in period t, which corresponds to k t ; N represents the number of time periods in the control cycle. When the control cycle is 24 hours and each time period is 15 minutes, the number of time periods is 96; when the control cycle is 24 hours and each time period is 20 minutes, the number of time periods is 72.

[0096] The genetic algorithm also includes the following operations:

[0097] Selection operation: The “roulette wheel” selection method is used to randomly select from the charging and discharging power strategy set of size δ according to the size of the fitness value until the next generation population of size δ is formed.

[0098] Crossover operation: Use the random number function random(0,1) to generate a random number uniformly distributed between 0 and 1. When the random number is less than the preset probability P c If , a crossover operation occurs, otherwise no crossover operation occurs.

[0099] Mutation operation: Use the random number function random(0,1) to generate a random number uniformly distributed between 0 and 1. When the random number is less than the preset probability P m , then a mutation operation is performed.

[0100] The present disclosure sets the genetic algorithm to judge the termination condition of the iterative process of the charging station optimization control scheme as the fitness change of the optimal individual is less than the preset threshold, that is, the fitness function of the gene string (energy scheduling scheme) no longer changes beyond the set threshold range as the number of iterations increases. According to the above settings, if the termination condition is not met, return to the gene string (energy scheduling scheme) selection stage to continue iteration; if the termination condition is met, the optimal individual in the current population is output as the optimal load distribution scheme.

[0101] By introducing genetic algorithms, a powerful optimization tool, efficient, intelligent and flexible operation of photovoltaic storage and charging stations can be achieved to maximize the use of renewable energy, reduce dependence on traditional power grids, and improve the economy and environmental friendliness of the system.

[0102] In addition, this method is insensitive to the selection of the initial population and has a certain fault tolerance. Even if the quality of individuals in the initial population is not high, the algorithm can gradually approach the optimal solution through a continuous evolution process. This feature makes the genetic algorithm more robust when dealing with uncertain factors in solar storage and charging stations (such as load demand fluctuations, changes in photovoltaic power generation, etc.). The solution rate can be processed simultaneously through parallel computing to evaluate and evolve multiple candidate solutions, thereby significantly shortening the optimization time.

[0103] In a specific example, the control period to be determined is 24 hours, and the prediction is performed with 1 hour as a period. It is known that the power grid electricity price (yuan / kwh) for each period of the day is predicted: price = [0.23656, 0.23656, 0.23656, 0.23656, 0.23656, 0.23656, 0.23656, 0.75, 0.75, 0.75, 1.23, 1.23, 1.23, 0.75, 0.75, 0.75, 1.16, 1.16, 1.16, 1.16, 0.75, 0.75, 0.23656].

[0104] It is known that the predicted photovoltaic power generation for the day (kwh) is: pv_power = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0070984, 0.4450712, 0.92489055, 1.0267305500000001, 2.1220966999999997, 2.86353654999999996, 1.5090085, 1.17472155, 1.16495745, 0.73915795, 0.70671355, 0.49109585, 0.20680835, 0.0005110999999999999, 0.0, 0.0, 0.0, 0.0].

[0105] Energy storage battery maximum discharge depth: dod = 0.9; Energy storage battery capacity: capacity = 233kwh; Energy storage battery maximum charge / discharge capacity: battery_maxstate = capacity × dod = 209.7kw; Energy storage battery efficiency: {0.1: (0.9733, 0.96), 0.2: (0.9669, 0.97), 0.3: (0.961, 0.98), 0.4: (0.9553, 0.99), 0.5: (0.9499, 1)}, where (charge / discharge efficiency, loss efficiency). Calculation of actual SOC change of energy storage battery:

[0106] In the prediction process, the genetic algorithm parameters are designed as follows: maximum number of iterations max_epochs: 1000000; population size δ: 100; crossover probability: P c =80%; mutation probability: P m =1%; time period length: Δt=1h.

[0107] A set of charging and discharging power strategies of size 100 is randomly generated, where the first charging and discharging power strategy X1 = [0.2, 0.34, 0.36, 0.02, 0.0, 0.06, 0.0, 0.02, -0.42, 0.36, -0.44, -0.24, -0.02, -0.22, 0.38, 0.24, -0.32, -0.22, 0.0, -0.08, 0.44, -0.18, -0.16, 0.44].

[0108] The corresponding purchase amount buy_eles of this energy scheduling scheme is: [423.19573689109524, 443.6514672216441, 489.3643902439024, 417.56905167985207, 411.74, 372.0471550395562, 224.6329016, 198.02398047985204, 88.87801725, 249.9176596939024, 82.2342829, 122.054320 , 25000002, 169.58897130000003, 131.35831385, 258.7196766963415, 231.55172631931433, 136.48634245, 178.28193955, 246.70319165, 231.87140809999997, 349.22536585365856, 216.14339259999997, 238.7985712, 488.12536585365854].

[0109] At the first iteration (epoch=1):

[0110] 1) For each charging and discharging power strategy X in the charging and discharging power strategy set i Calculate their fitness values ​​respectively, and randomly select 100 charging and discharging power strategies from the charging and discharging power strategy set according to the selection probability to form a population;

[0111] 2) According to the crossover probability, two charging and discharging power strategies are randomly selected from the population as parents for crossover operation, and their two offspring enter the new charging and discharging power strategy set. The charging and discharging power strategy set that has not been crossovered in the population is directly copied to the new charging and discharging power strategy set;

[0112] 3) Select a charge-discharge power strategy from the new charge-discharge power strategy set for mutation operation according to the mutation probability, and replace the original charge-discharge power strategy in the new charge-discharge power strategy set with the mutated charge-discharge power strategy.

[0113] Follow the above steps to perform a second iteration until the maximum number of iterations is reached. At this time, the optimal energy storage action found by the genetic algorithm is output: [0.22, 0.14, 0.14, 0.14, 0.1, 0.12, 0.0, 0.0, 0.0, -0.34, -0.32, -0.34, 0.32, 0.34, 0.34, -0.22, -0.22, -0.24, -0.18, -0.14, 0.0, 0.0, 0.0].

[0114] Corresponding to the energy storage power station optimization control method provided in the above-mentioned embodiments, an embodiment of the present disclosure further provides an energy storage power station optimization control device. Since the energy storage power station optimization control device provided in the embodiment of the present disclosure corresponds to the energy storage power station optimization control method provided in the above-mentioned embodiments, the implementation method of the above-mentioned energy storage power station optimization control method is also applicable to the energy storage power station optimization control device provided in the embodiment of the present disclosure, and will not be described in detail in the following embodiments.

[0115] Figure 6 is a schematic diagram of an energy storage power station optimization control device according to one embodiment of the present disclosure. Figure 6 As shown, the energy storage power station optimization control device 600 includes: an establishment module 610, a calculation module 620, a determination module 630 and a control module 640.

[0116] Establishing module 610, used to establish a charging and discharging power strategy set of a target energy storage power station and a demand electricity fee formula of the target energy storage power station, wherein the charging and discharging power strategy set includes at least one charging and discharging power strategy, and the charging and discharging power strategy at least includes the charging strategy of the target energy storage power station in each time period within the control cycle.

[0117] The calculation module 620 is used to calculate the demand electricity fee corresponding to each charging and discharging power strategy in the charging and discharging power strategy set based on the demand electricity fee formula.

[0118] The determination module 630 is used to calculate the fitness value corresponding to each charging and discharging power strategy in the charging and discharging power strategy set based on the demand electricity fee, and optimize the charging and discharging power strategies in the charging and discharging power strategy set based on the fitness value by using a genetic optimization algorithm.

[0119] The control module 640 is used to repeat the above-mentioned calculation of the demand electricity fee corresponding to each charge and discharge power strategy in the charge and discharge power strategy set based on the demand electricity fee formula and subsequent steps until the optimization end condition is met, obtain the target charge and discharge power strategy set, and determine the optimal charge and discharge power strategy based on the target charge and discharge power strategy set, and control the target energy storage power station based on the optimal charge and discharge power strategy.

[0120] Therefore, genetic algorithms can search for the optimal solution on a global scale and discover better overall solutions, fully tapping the energy-saving potential of the photovoltaic storage and charging system. This helps to better manage energy storage resources, avoid losses caused by overcharging or discharging, optimize charging and discharging strategies, and effectively reduce demand electricity charges, thereby saving operating costs.

[0121] In order to implement the above embodiment, the present disclosure further provides an electronic device 700, Figure 7 is a schematic diagram of an electronic device according to an embodiment of the present disclosure, such as Figure 7 As shown, the electronic device 700 includes: a processor 701 and a memory 702 that is communicatively connected to the processor, the memory 702 stores instructions that can be executed by at least one processor, and the instructions are executed by at least one processor 701 to implement the present disclosure. Figure 1-Figure 5 An energy storage power station optimization control method according to an embodiment.

[0122] In order to implement the above embodiments, the present disclosure also provides a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to enable the computer to implement the above embodiments. Figure 1-Figure 5 An energy storage power station optimization control method according to an embodiment.

[0123] In order to implement the above embodiments, the present disclosure also provides a computer program product, including a computer program. When the computer program is executed by a processor, the computer program implements the above embodiments. Figure 1-Figure 5 An energy storage power station optimization control method according to an embodiment.

[0124] It should be noted that personal information from users should be collected for legitimate and reasonable purposes and should not be shared or sold outside of these legitimate uses. In addition, such collection / sharing should be carried out after receiving the user's informed consent, including but not limited to notifying the user to read the user agreement / user notice and sign the agreement / authorization including authorization of relevant user information before the user uses the function. In addition, any necessary steps should be taken to protect and safeguard access to such personal information data and ensure that others who have access to personal information data comply with its privacy policy and procedures.

[0125] The present application is expected to provide an implementation scheme for users to selectively block the use or access of personal information data. That is, the present disclosure is expected to provide hardware and / or software to prevent or block access to such personal information data. Once the personal information data is no longer needed, the risk can be minimized by limiting data collection and deleting the data. In addition, when applicable, such personal information is de-identified to protect the privacy of the user.

[0126] In the description of the aforementioned embodiments, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0127] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0128] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0129] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that contains, stores, communicates, propagates or transmits a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0130] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0131] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0132] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0133] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for optimizing and controlling an energy storage power station, characterized in that: include: Establishing a charging and discharging power strategy set of a target energy storage power station and a demand electricity fee formula of the target energy storage power station, wherein the charging and discharging power strategy set includes at least one charging and discharging power strategy, and the charging and discharging power strategy at least includes a charging strategy of the target energy storage power station in each time period within a control cycle; Calculate the demand electricity fee corresponding to each charging and discharging power strategy in the charging and discharging power strategy set based on the demand electricity fee formula; Calculating the fitness value corresponding to each charging and discharging power strategy in the charging and discharging power strategy set based on the demand electricity fee, and optimizing the charging and discharging power strategies of the charging and discharging power strategy set based on the fitness value by using a genetic optimization algorithm; Repeat the above steps of calculating the demand electricity charge corresponding to each charging and discharging power strategy in the charging and discharging power strategy set based on the demand electricity charge formula and subsequent steps until the optimization end condition is met, obtain the target charging and discharging power strategy set, and determine the optimal charging and discharging power strategy based on the target charging and discharging power strategy set, and control the target energy storage power station based on the optimal charging and discharging power strategy.

2. The method according to claim 1, characterized in that: The demand charge formula is: P g (t)=P load (t)+P b (t)-P v (t) Among them, the P load (t) is the load of the target energy storage power station in the tth period, and P v (t) is the average power of photovoltaic power generation in the target energy storage power station in the tth period, and P b (t) is the average charge and discharge power of the charge and discharge power strategy set in the tth period.

3. The method according to claim 2, characterized in that The calculating, based on the demand electricity fee, the fitness value corresponding to each charging and discharging power strategy in the charging and discharging power strategy set includes: Get the preset constant coefficient; The difference between the constant coefficient and the demand electricity fee is used as the fitness value.

4. The method according to claim 3, characterized in that The formula for calculating the fitness value corresponding to each charging and discharging power strategy in the charging and discharging power strategy set based on the demand electricity fee is: Wherein, G is the adaptation function, M is the constant coefficient, and F is the demand electricity fee.

5. The method according to any one of claims 1 to 4, characterized in that Determine whether the charging and discharging power strategy set meets the optimization end conditions, including: For this optimization, the average charging and discharging power of the preceding charging and discharging power strategy set for this optimization is obtained; Calculate the change rate of the charge and discharge average power of the previous charge and discharge power strategy set and the charge and discharge average power of the currently optimized charge and discharge power strategy set; It is determined whether an optimization end condition is satisfied based on the change rate.

6. The method according to claim 5, characterized in that The determining whether the optimization end condition is met based on the change rate includes: comparing the rate of change to a rate of change threshold; In response to the conversion rate being less than the change rate threshold, it is determined that an optimization end condition is satisfied.

7. The method according to claim 1, characterized in that The method further comprises: The average charge and discharge power is converted into a charge and discharge power coefficient, and the average charge and discharge power is optimized by optimizing the charge and discharge power coefficient.

8. An energy storage power station optimization control device, characterized in that: include: An establishment module is used to establish a charging and discharging power strategy set of a target energy storage power station and a demand electricity fee formula of the target energy storage power station, wherein the charging and discharging power strategy set includes at least one charging and discharging power strategy, and the charging and discharging power strategy at least includes a charging strategy of the target energy storage power station in each time period within a control cycle; A calculation module, used for calculating the demand electricity fee corresponding to each charging and discharging power strategy in the charging and discharging power strategy set based on the demand electricity fee formula; A determination module, configured to calculate a fitness value corresponding to each charging and discharging power strategy in the charging and discharging power strategy set based on the demand electricity fee, and optimize the charging and discharging power strategies of the charging and discharging power strategy set based on the fitness value by a genetic optimization algorithm; A control module is used to repeat the above-mentioned steps of calculating the demand electricity fee corresponding to each charge and discharge power strategy in the charge and discharge power strategy set based on the demand electricity fee formula and subsequent steps until the optimization end condition is met, obtain the target charge and discharge power strategy set, and determine the optimal charge and discharge power strategy based on the target charge and discharge power strategy set, and control the target energy storage power station based on the optimal charge and discharge power strategy.

9. An electronic device, characterized in that: Including memory. processor; The processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory, so as to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 7 when executed by a processor.

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