Control method and device of energy storage power station, electronic equipment and storage medium

Abstract

The invention provides a control method and device of an energy storage power station, electronic equipment and a storage medium. The control method comprises the steps that a charging / discharging parameter set and battery parameters of a target energy storage power station are acquired; establishing a loss cost equation based on the battery parameters; performing iterative calculation on the charge / discharge parameter set to obtain candidate charge / discharge parameters, and determining loss cost; and obtaining candidate optimal elements output by each iteration, determining a target optimal element, and controlling the target energy storage power station based on the charging / discharging parameters corresponding to the target optimal element. The solution space of the operation state of the energy storage equipment of the target energy storage power station is explored through the advantages of the self-adaptive update position function, the relatively optimal operation state solution set is found in combination with the current update frequency, self-adaptive adjustment is carried out according to the environment change, the operation flexibility and adaptability of the target energy storage power station are improved, and the energy storage power station performance is improved. A reliable and real-time optimization control strategy is provided, the energy efficiency and the control precision of the target energy storage power station are improved, and the control cost is saved.

Description

Technical Field

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

[0002] Faced with increasingly severe energy and environmental pressures, replacing fossil energy with new energy has become an inevitable trend for sustainable development, among which wind and solar resources are clean, safe and inexhaustible, and are particularly valued. Electric vehicles are gradually attracting attention from the reform of the automobile industry due to their low energy consumption and intelligent features. Their large-scale application can effectively reduce automobile carbon emissions and dependence on oil resources. Photovoltaic charging stations are new energy charging stations that use photovoltaics to charge electric vehicles. Their daytime power generation can effectively supply charging for electric vehicles in the station, realize local energy consumption and storage regulation, improve the consumption rate of photovoltaics, play a role in peak shaving and valley filling, and improve the utilization rate of clean energy. It is an important means of energy conservation and emission reduction.

[0003] There are many research methods for optimizing the operation and scheduling strategy of photovoltaic storage charging stations, which usually use optimization algorithms such as differential evolution algorithm and simulated annealing algorithm to solve complex mathematical models. These methods involve tedious modeling and iterative calculation processes. However, these methods have certain limitations in terms of solution speed and there will be some problems in the existing controller online application. 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 provide a control method for an energy storage power station.

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

[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 a control method for an energy storage power station, comprising: obtaining a charge / discharge parameter set of a target energy storage power station and battery parameters of the target energy storage power station, wherein the charge / discharge parameter set includes multiple elements, and each element includes at least the charge / discharge parameters of the target energy storage power station in different time periods within a control cycle; establishing a loss cost equation based on the battery parameters; iteratively calculating the charge / discharge parameter set based on a particle swarm algorithm to obtain candidate charge / discharge parameters of particles output in each iteration, and determining the loss cost based on the loss cost equation and the candidate charge / discharge parameters; until the iteration reaches the maximum number of iterations, obtaining the candidate optimal element output in each iteration, determining the target optimal element based on the loss costs of all the candidate optimal elements, and controlling the target energy storage power station based on the charge / discharge parameters corresponding to the target optimal element.

[0011] According to one embodiment of the present disclosure, the iterative calculation of the charge / discharge parameter set based on the particle swarm algorithm includes: obtaining the current number of iterations; updating the charge / discharge parameters and power change rate of each element in the charge / discharge parameter set based on the number of iterations; and processing each element in the updated charge / discharge parameter set through the particle swarm algorithm to determine the candidate optimal particle for this iteration.

[0012] According to one embodiment of the present disclosure, updating the charge / discharge parameters and power change rate of each element in the charge / discharge parameter set based on the number of iterations includes: in response to the number of iterations being 1, randomly initializing the power change rate of each element; in response to the number of iterations being greater than 1, updating the charge / discharge parameter set based on the number of iterations and the maximum number of iterations.

[0013] According to one embodiment of the present disclosure, a formula for updating the charge / discharge parameter of each element of the charge / discharge parameter set based on the number of iterations and the maximum number of iterations is: Among them, the x t is the charge / discharge parameter of the element after iteration, x t-1 represents the charge / discharge parameter of the element after t-1 iterations, v t-1 represents the power change rate of the element after t-1 iterations, MaxD represents the maximum number of iterations, rand is a random number in the range of [0, 1], X min and X max are the maximum and minimum change values ​​of the SOC of the energy storage battery respectively, and a and b are constant coefficients.

[0014] According to one embodiment of the present disclosure, the formula for updating the power change rate of each element of the charge / discharge parameter set based on the number of iterations and the maximum number of iterations is: t =w×v t-1 +c1×r1×(p best,t -x t )+c1×r1×(g best,t -x t ) where v t represents the power change rate obtained at the tth iteration, w represents the inertia weight, v t-1 represents the power change rate obtained at the t-1th iteration, c1 and c2 represent two acceleration constants, r1 and r2 are two independent random numbers in the range of [0, 1], and p best,t Indicates the best position obtained in the tth iteration, g best,t represents the global optimal position obtained at the tth iteration.

[0015] According to one embodiment of the present disclosure, the loss cost equation is: C=city electricity purchase cost+battery loss cost-photovoltaic grid-connected electricity amount*photovoltaic grid-connected electricity fee, wherein C is the loss cost.

[0016] According to an embodiment of the present disclosure, the obtaining of the candidate optimal element outputted in each iteration includes: taking the element with the smallest loss cost outputted in each iteration as the candidate optimal element.

[0017] To achieve the above-mentioned purpose, a second aspect of the present disclosure provides a control device for an energy storage power station, including:

[0018] According to one embodiment of the present disclosure, the control device of the energy storage power station is also used for: an acquisition module, used to acquire a charge / discharge parameter set of a target energy storage power station and battery parameters of the target energy storage power station, wherein the charge / discharge parameter set includes multiple elements, and each element includes at least the charge / discharge parameters of the target energy storage power station in different time periods within a control cycle; an establishment module, used to establish a loss cost equation based on the battery parameters; a calculation module, used to iteratively calculate the charge / discharge parameter set based on a particle swarm algorithm to obtain candidate charge / discharge parameters of particles output in each iteration, and determine the loss cost based on the loss cost equation and the candidate charge / discharge parameters; a control module, used to obtain the candidate optimal element output in each iteration in response to the iteration reaching the maximum number of iterations, determine the target optimal element based on the loss costs of all the candidate optimal elements, and control the target energy storage power station based on the charge / discharge parameters corresponding to the target optimal element.

[0019] 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 control method of the energy storage power station as described in the first aspect embodiment of the present disclosure.

[0020] 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 control method of the energy storage power station as described in the first aspect embodiment of the present disclosure.

[0021] 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 control method of the energy storage power station as described in the first aspect of the present disclosure.

[0022] Therefore, the present disclosure, through the advantage of adaptively updating the position function, can continuously explore the solution space of the operating state of the energy storage equipment of the target energy storage power station through the objective function and constraint conditions in the iterative solution process, and find the relatively optimal operating state solution set in combination with the current number of updates, and make adaptive adjustments according to environmental changes, thereby improving the flexibility and adaptability of the operation of the target energy storage power station. At the same time, the rate and time of the solution are well controlled, providing a reliable and real-time optimization control strategy, improving the energy efficiency and control accuracy of the target energy storage power station, and fully tapping the energy-saving potential of the photovoltaic storage and charging system, reducing energy consumption, and saving control costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 is a schematic diagram of a control method of an energy storage power station according to an embodiment of the present disclosure;

[0024] Figure 2 It is a structural schematic diagram of an energy storage power station according to one embodiment of the present disclosure;

[0025] Figure 3 It is a flow chart of a particle swarm algorithm according to one embodiment of the present disclosure;

[0026] Figure 4 is a schematic diagram of another control method of an energy storage power station according to an embodiment of the present disclosure;

[0027] Figure 5 is a schematic diagram of a control device of an energy storage power station according to an embodiment of the present disclosure;

[0028] Figure 6 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 a control method for an energy storage power station according to an embodiment of the present disclosure, such as Figure 1 As shown, the control method of the energy storage power station includes the following steps:

[0033] S101, obtaining a charge / discharge parameter set of a target energy storage power station and battery parameters of the target energy storage power station, wherein the charge / discharge parameter set includes multiple elements, and each element includes at least a charge / discharge parameter of the target energy storage power station in different time periods 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 station includes: mains power supply, transformer, energy storage battery, alternating current (AC) charging pile, direct current (DC) charging pile, photovoltaic power generation output, daily load, DC bus, etc. In other words, the target energy storage station involves the selection of mains and photovoltaic power, as well as the charge / discharge control of energy storage equipment.

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

[0036] 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. To quantify the life loss caused by each charge or discharge of the energy storage battery, in order for the energy storage battery 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.

[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 period 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] S102, establishing a loss cost equation based on battery parameters.

[0040] In the embodiment of the present disclosure, the loss cost equation is a calculation formula for calculating the loss cost of the energy storage battery.

[0041] It should be noted that in the present disclosure, the target energy storage power station can first be system modeled based on battery parameters. A mathematical model of the target energy storage power station is established, including a photovoltaic power generation model, an energy storage system model, and a load demand model, so as to clarify the objective function and constraints.

[0042] S103, iteratively calculating the charge / discharge parameter set based on the particle swarm algorithm to obtain candidate charge / discharge parameters of the output particles in each iteration, and determining the loss cost based on the loss cost equation and the candidate charge / discharge parameters.

[0043] S104, in response to the iteration reaching the maximum number of iterations, obtaining the candidate optimal elements outputted by each iteration, determining the target optimal element based on the loss costs of all the candidate optimal elements, and controlling the target energy storage power station based on the charge / discharge parameters corresponding to the target optimal element.

[0044] 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.

[0045] In an embodiment of the present disclosure, a charge / discharge parameter set of a target energy storage power station and battery parameters of the target energy storage power station are first obtained, wherein the charge / discharge parameter set includes a plurality of elements, each element at least including charge / discharge parameters of the target energy storage power station in different time periods within a control cycle, and then a loss cost equation is established based on the battery parameters, and then the charge / discharge parameter set is iteratively calculated based on a particle swarm algorithm to obtain candidate charge / discharge parameters of particles output in each iteration, and the loss cost is determined based on the loss cost equation and the candidate charge / discharge parameters, and finally, in response to the iteration reaching a maximum number of iterations, the candidate optimal element output in each iteration is obtained, the target optimal element is determined based on the loss costs of all candidate optimal elements, and the target energy storage power station is controlled based on the charge / discharge parameters corresponding to the target optimal element. Therefore, the present disclosure, through the advantage of adaptively updating the position function, can continuously explore the solution space of the operating state of the energy storage equipment of the target energy storage power station through the objective function and constraint conditions in the iterative solution process, and find the relatively optimal operating state solution set in combination with the current number of updates, and make adaptive adjustments according to environmental changes, thereby improving the flexibility and adaptability of the operation of the target energy storage power station. At the same time, the rate and time of the solution are well controlled, providing a reliable and real-time optimization control strategy, improving the energy efficiency and control accuracy of the target energy storage power station, and fully tapping the energy-saving potential of the photovoltaic storage and charging system, reducing energy consumption, and saving control costs.

[0046] like Figure 3 As shown in the figure, it is a flow chart of the improved particle swarm algorithm. The improved particle swarm algorithm disclosed in the present invention is specifically divided into the following steps:

[0047] Step 1: Initialize algorithm parameters. Initialize two uniform acceleration constants c1 and c2, and set the position boundary range (i.e., the charge / discharge power boundary range) and the speed boundary range (i.e., the charge / discharge power change boundary range).

[0048] In the embodiment of the present disclosure, the number of particles in the particle swarm in the algorithm is used as the number of elements in the initial solution set, and the particle swarm is initialized, that is, the elements in each particle are assigned values. In this process, the key parameters of the energy storage battery are used as constraints, so that the initial value of each element in each particle can be obtained, wherein each element represents a solution for a time period, the solution represents the charging / discharging power of the energy storage battery, and each particle represents a solution within a control period.

[0049] Exemplarily, N particles form the initial particle group, each particle is a one-dimensional vector, and this data represents the charge / discharge power of the energy storage battery, and the value range is [-1, -0.98, -0.96, ..., 0.96, 0.98, 1]. In other words, the charge / discharge power in this embodiment is actually a coefficient, which can reduce the complexity of subsequent calculations. After the coefficient is obtained, it is converted through the energy storage battery parameters. For example, the position of the i-th particle can be represented by x i , i = 1, 2, ..., N, the velocity of the i-th particle is v i , the particle positions and velocity means are randomly generated within a given range.

[0050] For example, in the present disclosure, 1h is used as a period for solving, so as to obtain the working state of the energy storage battery within the control period of the target energy storage power station system and reduce the operating cost of the entire target energy storage power station. Regarding the control period, this embodiment can use days as a period according to the working state of the target energy storage power station. Thus, the energy storage battery to be optimized or solved has 24 working periods.

[0051] It should be noted that when using the improved particle swarm algorithm to calculate the operation strategy of the energy storage battery, the constraints that need to be considered are the transformer capacity constraint, the upper and lower limit constraints of the battery hourly power, and the battery capacity constraint.

[0052] Step 2: Calculate the fitness value according to the fitness function and record the individual best position p best (The optimal solution of the current iteration) and the global optimal position g best (optimal solution for all iterations).

[0053] Among them, the individual best position is the particle corresponding to the minimum fitness value calculated, and the global best position is the particle corresponding to the minimum fitness value calculated from the beginning of the calculation to the current state.

[0054] In the present disclosure, a fitness function can be established according to demand, such as minimizing the mains power consumption, maximizing the photovoltaic utilization rate, and so on.

[0055] Exemplarily, the optimization goal in this embodiment is to minimize the system operation cost, so the fitness function (ie, the objective function) uses C=city electricity purchase cost+battery loss cost-photovoltaic grid-connected power*photovoltaic grid-connected electricity fee.

[0056] That is to say, it involves floating electricity charges, battery usage costs, and electricity purchase costs. The designed objective function quantitatively evaluates the solutions obtained in each round of iterative exploration, as well as other cost indicators that need to be considered. The results of the quantitative evaluation are reflected in each decision step of generating this set of solutions through the position and speed update formula. With such multiple rounds of solution iterations, the exploration solution given by the algorithm will converge to the possible optimal solution with a high probability.

[0057] Step 3: Update the position of the particle swarm and constrain the out-of-bounds position.

[0058] In this embodiment, the position of the particle is updated adaptively according to the current number of iterations during the iterative update process, making the algorithm's search space more flexible, enhancing the local search capability of the particle swarm algorithm, and solving the problem that the traditional particle swarm algorithm is prone to fall into the local optimal solution during the solution process, thereby improving the optimization performance of the algorithm. Therefore, for the problem of optimizing the operation of target energy storage power stations considering uncertain factors and complex nonlinear models, the improved particle swarm algorithm has great application potential in solving the charging and discharging strategies of energy storage devices.

[0059] Specifically, in this embodiment, the charge / discharge power of the energy storage battery is updated according to the current number of iterations, thereby obtaining a new charge / discharge power of the energy storage battery.

[0060] In another embodiment, the charge / discharge power of the energy storage battery can be updated in combination with the current number of iterations and the total number of iterations, that is, several sets of charge / discharge power parameter sets are updated based on the current number of updates and the maximum number of updates to obtain a new charge / discharge power of the energy storage battery.

[0061] Step 4: Use the speed update formula to update the speed of the particle group and constrain the speed that exceeds the limit. In other words, update the corresponding charge / discharge power change after updating the charge / discharge power parameter set.

[0062] Step 5: Calculate the fitness value according to the fitness function; for each particle, compare its fitness value with the fitness value of its individual best position. If it is less than the fitness value of the individual best position, then the position at this time is taken as the individual best position p. best For each particle, compare its fitness value with the fitness value of the global optimal position. If it is smaller than the fitness value of the global optimal position, it will be taken as the global optimal value g best .

[0063] Next, determine whether the iteration end condition is met (i.e., the maximum number of iterations is reached). If so, output the optimal position corresponding to the global optimal value. Otherwise, return to step 3 to continue iterative updating until the maximum number of iterations is reached to obtain the final result.

[0064] In the above embodiment, the charge / discharge parameter set is iteratively calculated based on the particle swarm algorithm, and the Figure 4 Explaining further, the method includes:

[0065] S401, obtaining the current number of iterations.

[0066] S402, updating the charge / discharge parameter and power change rate of each element in the charge / discharge parameter set based on the number of iterations.

[0067] In the disclosed embodiment, in response to the number of iterations being 1, the power change rate of each element is randomly initialized. In response to the number of iterations being greater than 1, the charge / discharge parameter set is updated based on the number of iterations and the maximum number of iterations.

[0068] In the embodiment of the present disclosure, the formula for updating the charge / discharge parameter of each element of the charge / discharge parameter set based on the number of iterations and the maximum number of iterations is:

[0069] x t =(x t-1 +v t-1 )+(a×b(0.92×(MaxD-t) / MaxD))×rand×(X max +rand×(X min -X max ))

[0070] Among them, x t is the charge / discharge parameter of the element after iteration, x t-1 represents the charge / discharge parameter of the element after t-1 iterations, v t-1 represents the power change rate of the element after t-1 iterations, MaxD represents the maximum number of iterations, rand is a random number in the range of [0, 1], X min and X max are the maximum and minimum change values ​​of the SOC of the energy storage battery respectively, and a and b are constant coefficients.

[0071] The formula for updating the power change rate of each element of the charge / discharge parameter set based on the number of iterations and the maximum number of iterations is:

[0072] v t =w×v t-1 +c1×r1×(p best,t -x t )+c1×r1×(g best,t -x t )

[0073] Among them, v t represents the power change rate obtained at the tth iteration, w represents the inertia weight, v t-1 represents the power change rate obtained at the t-1th iteration, c1 and c2 represent two acceleration constants, r1 and r2 are two independent random numbers in the range of [0, 1], and p best,t Indicates the best position obtained in the tth iteration, g best,trepresents the global optimal position obtained at the tth iteration.

[0074] It should be noted that the charge / discharge power parameter set corresponding to the extreme value of the current updated objective function value is selected as the optimal position. At the same time, when the objective function value corresponding to the optimal position is better than the objective function value corresponding to the global optimal position, the optimal position is taken as the global optimal position.

[0075] S403, processing each element in the updated charge / discharge parameter set by using a particle swarm algorithm to determine the candidate optimal particle for this iteration.

[0076] In the disclosed embodiment, the current number of iterations is first obtained, and then the charge / discharge parameters and power change rate of each element in the charge / discharge parameter set are updated based on the number of iterations, and finally the particle swarm algorithm is used to process each element in the updated charge / discharge parameter set to determine the candidate optimal particle for this iteration. Thus, by updating the charge / discharge parameters and power change rate in each iteration, a timely response can be made according to the actual situation to achieve dynamic optimization, and at the same time, a good balance can be achieved between global search and local search, which is helpful to find the global optimal solution or approximate optimal solution in a complex multi-dimensional space.

[0077] In the embodiment of the present disclosure, the loss cost equation is:

[0078] C = cost of purchasing electricity from the city + cost of battery loss - photovoltaic grid-connected electricity * photovoltaic grid-connected electricity fee

[0079] Among them, C is the loss cost.

[0080] The loss cost formula is:

[0081]

[0082] Among them, C b is the cost per kWh of battery loss; E is the battery capacity; I is the cost per kWh; N is the maximum number of times the battery is used; η is the efficiency factor of the battery; μ is the number of times the battery is used μ Decay lifespan.

[0083] The parameters of the particle swarm algorithm are: maximum number of iterations max_epochs: 10000; population size pops: 200.

[0084] First iteration:

[0085] 1) Set the inertia weight w = 0.9, the individual learning factor c1 = 1.49445, the group learning factor c2 = 2.09, and randomly generate a number between 0 and 1, r1 = 0.4, r2 = 0.7.

[0086] 2) Randomly generate 200 populations, each particle has x (position) and v (velocity), ( represents the position of the i-th particle in the t-th iteration, and the value represents the change in the SOC of the energy storage device at that moment, with negative values ​​representing charging and positive values ​​representing discharging). ( represents the speed of the ith particle in the tth iteration, and the value represents the change in the SOC change value at that moment), then

[0087] 3) Calculate the fitness of the 200 particles with the current iteration number of 1 = total electricity purchase cost + battery loss cost - photovoltaic grid-connected electricity * photovoltaic grid-connected electricity fee. For example, the fitness value of the first particle is -3951.2433750805735. Calculate the fitness values ​​of 200 particles and record the particle with the smallest fitness in the first iteration, that is, p best,1 and g best,1 .

[0088] 4) Update position and velocity according to the update formula.

[0089] 5) 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 improved particle swarm algorithm is output: [-0.26,-0.18,-0.17,-0.17,-0.17,-0.05,-0.1,0.0,0.0,0.0,0.37,0.35,0.34,-0.34,-0.32,-0.32,0.18,0.22,0.2,0.15,0.12,0.0,0.0,0.0].

[0090] In one possible method, 24 hours is used as the control cycle and 1 hour is used as the time period.

[0091] Given the predicted grid electricity price for the day (yuan / kwh): price = [0.23656,0.23656,0.23656,0.23656,0.23656,0.23656,0.23656,0.75,0.75,0.75,1.23,1.23,1.23,0.75,0.75,0.75,1.16,1.16,1.16,1.16,1.16,0.75,0.75,0.23656].

[0092] Photovoltaic power generation on the day (kwh): pv_power = [0.0, 0.0, 0.0, 0.0, 0.0, 0.010697529710769281, 1.7410381556415584, 1.124066561045884, 2.0798468388907914, 3.959269543171545, 4.186959701570339, 2.8957320893490293, 1.4638259578239634, 1.7337356801630464,2.5681755619892797,2.7142610024079223, 0.585574259948416,0.8078183702111318,0.6968567787320027,0.0,0.0,0.0,0.0].

[0093] The maximum discharge depth of the energy storage battery dod = 0.9, the energy storage battery capacity capacity = 200kwh, the maximum charge / discharge amount of the energy storage battery battery_maxstate = capacity × dod = 180kw, the 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 the actual SOC change of the energy storage battery:

[0094] Photovoltaic use cost = photovoltaic installation cost / use period / number of days used per year, cost_pv = 3000 / 20 / 315.

[0095] Corresponding to the control methods of the energy storage power stations provided in the above-mentioned embodiments, an embodiment of the present disclosure further provides a control device of an energy storage power station. Since the control device of the energy storage power station provided in the embodiment of the present disclosure corresponds to the control methods of the energy storage power stations provided in the above-mentioned embodiments, the implementation methods of the control methods of the above-mentioned energy storage power stations are also applicable to the control devices of the energy storage power stations provided in the embodiments of the present disclosure, and will not be described in detail in the following embodiments.

[0096] Figure 5 is a schematic diagram of a control device of an energy storage power station according to an embodiment of the present disclosure. Figure 5 As shown, the control device 500 of the energy storage power station includes: an acquisition module 510, an establishment module 520, a calculation module 530 and a control module 540.

[0097] Among them, the acquisition module 510 is used to obtain the charge / discharge parameter set of the target energy storage power station and the battery parameters of the target energy storage power station, wherein the charge / discharge parameter set includes multiple elements, and each element at least includes the charge / discharge parameters of the target energy storage power station in different time periods within the control cycle.

[0098] The module 520 is used to establish a loss cost equation based on the battery parameters.

[0099] The calculation module 530 is used to iteratively calculate the charge / discharge parameter set based on the particle swarm algorithm to obtain candidate charge / discharge parameters of the output particles in each iteration, and determine the loss cost based on the loss cost equation and the candidate charge / discharge parameters.

[0100] The control module 540 is used to obtain the candidate optimal elements output by each iteration in response to the iteration reaching the maximum number of iterations, determine the target optimal element based on the loss costs of all candidate optimal elements, and control the target energy storage power station based on the charging / discharging parameters corresponding to the target optimal element.

[0101] Therefore, the present disclosure, through the advantage of adaptively updating the position function, can continuously explore the solution space of the operating state of the energy storage equipment of the target energy storage power station through the objective function and constraint conditions in the iterative solution process, and find the relatively optimal operating state solution set in combination with the current number of updates, and make adaptive adjustments according to environmental changes, thereby improving the flexibility and adaptability of the operation of the target energy storage power station. At the same time, the rate and time of the solution are well controlled, providing a reliable and real-time optimization control strategy, improving the energy efficiency and control accuracy of the target energy storage power station, and fully tapping the energy-saving potential of the photovoltaic storage and charging system, reducing energy consumption, and saving control costs.

[0102] In order to implement the above embodiment, the present disclosure further provides an electronic device 600, Figure 6 is a schematic diagram of an electronic device according to an embodiment of the present disclosure, such as Figure 6 As shown, the electronic device 600 includes: a processor 601 and a memory 602 that is communicatively connected to the processor, the memory 602 stores instructions that can be executed by at least one processor, and the instructions are executed by at least one processor 601 to implement the present disclosure. Figure 1-Figure 4 A control method for an energy storage power station according to an embodiment.

[0103] 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 4 A control method for an energy storage power station according to an embodiment.

[0104] 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 4 A control method for an energy storage power station according to an embodiment.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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 control method for an energy storage power station, characterized in that: include: Acquire a charge / discharge parameter set of a target energy storage power station and battery parameters of the target energy storage power station, wherein the charge / discharge parameter set includes a plurality of elements, each element including at least a charge / discharge parameter of the target energy storage power station in different time periods within a control cycle; Establishing a loss cost equation based on the battery parameters; Iteratively calculating the charge / discharge parameter set based on a particle swarm algorithm to obtain candidate charge / discharge parameters of the output particles in each iteration, and determining the loss cost based on the loss cost equation and the candidate charge / discharge parameters; In response to the iteration reaching the maximum number of iterations, the candidate optimal elements outputted in each iteration are obtained, a target optimal element is determined based on the loss costs of all the candidate optimal elements, and the target energy storage power station is controlled based on the charge / discharge parameters corresponding to the target optimal element.

2. The method according to claim 1, characterized in that The iterative calculation of the charge / discharge parameter set based on the particle swarm algorithm includes: Get the current number of iterations; updating the charge / discharge parameter and the power change rate of each element in the charge / discharge parameter set based on the number of iterations; Each element in the updated charge / discharge parameter set is processed by the particle swarm algorithm to determine the candidate optimal particle for this iteration.

3. The method according to claim 2, characterized in that The updating of the charge / discharge parameter and the power change rate of each element in the charge / discharge parameter set based on the number of iterations includes: In response to the number of iterations being 1, randomly initializing the power change rate of each element; In response to the number of iterations being greater than 1, a charge / discharge parameter set is updated based on the number of iterations and the maximum number of iterations.

4. The method according to claim 3, characterized in that The formula for updating the charge / discharge parameter of each element of the charge / discharge parameter set based on the number of iterations and the maximum number of iterations is: xt=(xt-1+vt-1)+(a×b(0.92×(MaxD-t) / MaxD)) ×rand×(X max +rand×(X min -X max )) Among them, the x t is the charge / discharge parameter of the element after iteration, x t-1 represents the charge / discharge parameter of the element after t-1 iterations, v t-1 represents the power change rate of the element after t-1 iterations, MaxD represents the maximum number of iterations, rand is a random number in the range of [0, 1], X min and X max are the maximum and minimum change values ​​of the SOC of the energy storage battery respectively, and a and b are constant coefficients.

5. The method according to claim 4, characterized in that The formula for updating the power change rate of each element of the charge / discharge parameter set based on the number of iterations and the maximum number of iterations is: v t =w×v t-1 +c1×r1×(p best,t -x t )+c1×r1×(g best,t -x t ) Among them, v t represents the power change rate obtained at the tth iteration, w represents the inertia weight, v t-1 represents the power change rate obtained at the t-1th iteration, c1 and c2 represent two acceleration constants, r1 and r2 are two independent random numbers in the range of [0, 1], and p best,t Indicates the best position obtained in the tth iteration, g best,t represents the global optimal position obtained at the tth iteration.

6. The method according to claim 1, characterized in that The loss cost equation is: C = cost of purchasing electricity from the city + cost of battery loss - photovoltaic grid-connected electricity * photovoltaic grid-connected electricity fee Wherein, C is the loss cost.

7. The method according to claim 1, characterized in that The step of obtaining the optimal candidate element outputted in each iteration includes: The element with the smallest loss cost output each time is taken as the candidate optimal element.

8. A control device for an energy storage power station, characterized in that: include: An acquisition module, used to acquire a charge / discharge parameter set of a target energy storage power station and battery parameters of the target energy storage power station, wherein the charge / discharge parameter set includes a plurality of elements, each element including at least a charge / discharge parameter of the target energy storage power station in different time periods within a control cycle; A building module for building a loss cost equation based on the battery parameters; A calculation module, configured to iteratively calculate the charge / discharge parameter set based on a particle swarm algorithm to obtain candidate charge / discharge parameters of the output particles in each iteration, and determine the loss cost based on the loss cost equation and the candidate charge / discharge parameters; A control module is used to obtain the candidate optimal element output by each iteration in response to the iteration reaching the maximum number of iterations, determine the target optimal element based on the loss cost of all the candidate optimal elements, and control the target energy storage power station based on the charging / discharging parameters corresponding to the target optimal element.

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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