Optical storage capacity configuration method and device and medium

Abstract

The invention discloses an optical storage capacity configuration method and device and a medium, relates to the technical field of new energy and energy storage, and is used for solving the capacity configuration problem of an optical storage system. In order to solve the problem that a traditional configuration strategy still needs to be optimized in terms of user economic benefits and cost control, the invention provides an optical storage capacity configuration method, and the whole life cycle of a photovoltaic energy storage system is used as a calculation range to determine the system operation cost. The service life of photovoltaic equipment and the service life of energy storage equipment are not necessarily the same, and the photovoltaic equipment and the energy storage equipment need to be replaced more than once in the whole life cycle of the photovoltaic energy storage system. Therefore, the investment cost of the photovoltaic energy storage system is lengthened to the full life cycle, so that the system cost can be determined more accurately. And capacity configuration of the photovoltaic energy storage system is carried out by using the optical storage capacity configuration scheme, so that a photovoltaic energy storage system construction planning scheme with globally optimal economic benefits can be obtained.

Description

Technical Field

[0001] The present application relates to the technical field of new energy and energy storage technologies, and particularly to a method, device, and medium for configuring the capacity of a photovoltaic energy storage system. Background Art

[0002] Photovoltaic power generation, as a clean and pollution-free energy form, has been widely applied in various fields. However, due to the intermittent and unstable characteristics of photovoltaic power generation, an energy storage system is currently used in conjunction with a photovoltaic power generation system to balance power supply and demand.

[0003] Currently, the mainstream method for configuring the capacity of a photovoltaic energy storage system mainly constructs a model based on a set of typical daily photovoltaic output scenarios. The photovoltaic capacity is planned according to the power demand, and the corresponding energy storage capacity is planned based on the photovoltaic capacity to meet the need for balancing the supply and demand of photovoltaic power. However, this configuration scheme for the capacity of a photovoltaic energy storage system does not consider the economic benefits of users and the requirements for cost control, and there is still room for further optimization in the configuration of the capacity of a photovoltaic energy storage system.

[0004] Therefore, those skilled in the art now urgently need a method for configuring the capacity of a photovoltaic energy storage system to ensure that, based on the power demand and budget of users themselves, a configuration of the photovoltaic energy storage capacity that comprehensively considers cost and performance can be provided, thereby maximizing energy utilization, minimizing costs, and meeting the economic and sustainable requirements for long-term operation. Summary of the Invention

[0005] The purpose of the present application is to provide a method, device, and medium for configuring the capacity of a photovoltaic energy storage system, which can provide an optimal configuration scheme for the capacity of a photovoltaic energy storage system with the best economic benefits on the premise of meeting the economic and sustainable requirements for long-term operation.

[0006] To solve the above technical problems, the present application provides a method for configuring the capacity of a photovoltaic energy storage system, including:

[0007] Taking the photovoltaic planned capacity and the energy storage planned capacity as variables and the total investment cost as the output, a total investment cost model is established; wherein, the total investment cost is the sum of the total construction cost and the total maintenance cost of the photovoltaic equipment and the energy storage equipment in the complete life cycle of the photovoltaic energy storage system;

[0008] According to the charging and discharging power of the photovoltaic energy storage system and the real-time electricity price, a model for the electricity cost savings of the photovoltaic energy storage system in the complete life cycle is established;

[0009] The system operation cost is determined according to the total investment cost and the electricity cost savings;

[0010] Taking the minimum of the system operation cost as the optimization goal, a configuration scheme for the capacity of a photovoltaic energy storage system is determined according to the electricity cost savings model and the total investment cost model;

[0011] Configure the photovoltaic capacity and energy storage capacity of the photovoltaic energy storage system according to the described photovoltaic and energy storage capacity configuration scheme.

[0012] In a possible embodiment, taking the lowest system operation cost as the optimization goal, determining the photovoltaic and energy storage capacity configuration scheme according to the electricity cost savings model and the total investment cost model includes:

[0013] Taking the lowest system operation cost as the optimization goal, solving the electricity cost savings model and the total investment cost model based on preset constraint conditions to determine the photovoltaic and energy storage capacity configuration scheme;

[0014] Among them, the constraint conditions include: the planned energy storage capacity is greater than or equal to the product of the planned photovoltaic capacity, the continuous discharge duration of the energy storage device, and a preset photovoltaic and energy storage configuration ratio.

[0015] In a possible embodiment, determining the system operation cost according to the total investment cost and the electricity cost savings includes:

[0016] Determine a first intermediate result according to the total investment cost and a preset first weighting coefficient;

[0017] Determine a second intermediate result according to the electricity cost savings and a preset second weighting coefficient;

[0018] Take the difference between the first intermediate result and the second intermediate result as the system operation cost;

[0019] Then, taking the lowest system operation cost as the optimization goal, determining the photovoltaic and energy storage capacity configuration scheme according to the electricity cost savings model and the total investment cost model includes:

[0020] Based on N groups of different first weighting coefficients and second weighting coefficients, taking the lowest system operation cost as the optimization goal, determine N different photovoltaic and energy storage capacity configuration schemes;

[0021] Among them, N is a positive integer and N≥2.

[0022] In a possible embodiment, based on N groups of different first weighting coefficients and second weighting coefficients, taking the lowest system operation cost as the optimization goal, determining N different photovoltaic and energy storage capacity configuration schemes includes:

[0023] Based on the first weighting coefficient and the second weighting coefficient in the short-term cost priority scenario, determine a first photovoltaic and energy storage capacity configuration scheme; among them, the first weighting coefficient in the short-term cost priority scenario is greater than the second weighting coefficient;

[0024] Based on the first weighting coefficient and the second weighting coefficient in the scenario of giving priority to long-term benefits, determine the second configuration plan for the optical storage capacity; wherein, the first weighting coefficient in the scenario of giving priority to long-term benefits is less than the second weighting coefficient;

[0025] Based on the first weighting coefficient and the second weighting coefficient in the scenario of giving priority to comprehensive balance, determine the third configuration plan for the optical storage capacity; wherein, the first weighting coefficient in the scenario of giving priority to comprehensive balance is equal to the second weighting coefficient.

[0026] In a possible embodiment, after determining N different configuration plans for the optical storage capacity based on N groups of different first weighting coefficients and second weighting coefficients, with the lowest system operation cost as the optimization goal, the method further includes:

[0027] Calculate the net present value and internal rate of return of each configuration plan for the optical storage capacity;

[0028] Before configuring the photovoltaic capacity and energy storage capacity of the photovoltaic energy storage system according to the configuration plan for the optical storage capacity, the method further includes:

[0029] Select one configuration plan for the optical storage capacity as the target configuration plan for the photovoltaic capacity and energy storage capacity of the photovoltaic energy storage system according to the net present value, the internal rate of return, and the system operation cost.

[0030] In a possible embodiment, the method further includes:

[0031] Round down the quotient of the life cycle duration of the photovoltaic energy storage system and the service life of the photovoltaic device to obtain the number of times of photovoltaic device reconstruction; wherein, the number of times of photovoltaic device reconstruction is at least 1;

[0032] Round down the quotient of the life cycle duration of the photovoltaic energy storage system and the service life of the energy storage device to obtain the number of times of energy storage device reconstruction; wherein, the number of times of energy storage device reconstruction is at least 1;

[0033] Determine the total construction cost of the photovoltaic device according to the product of the number of times of photovoltaic device reconstruction and the construction cost of the photovoltaic device;

[0034] Determine the total construction cost of the energy storage device according to the product of the number of times of energy storage device reconstruction and the construction cost of the energy storage device;

[0035] Determine the total construction cost according to the sum of the total construction cost of the photovoltaic device and the total construction cost of the energy storage device.

[0036] In a possible embodiment, with the lowest operating cost of the system as the optimization goal, determining the optical storage capacity configuration scheme according to the electricity cost savings model and the total investment cost model includes:

[0037] Generate initial particles with the planned photovoltaic capacity and the planned energy storage capacity as coordinates, and set the number of particles, the maximum number of iterations, and the learning factor;

[0038] Calculate the fitness value of each particle with the lowest operating cost of the system as the optimization goal, determine the global optimal solution of the initial generation particles, and perform optimization evolution;

[0039] Repeat the steps of calculating the fitness value of each particle with the lowest operating cost of the system as the optimization goal, determining the global optimal solution of the initial generation particles, and performing optimization evolution until the number of times of optimization evolution reaches the maximum number of iterations;

[0040] Output the global optimal solution as the optical storage capacity configuration scheme.

[0041] To solve the above technical problems, the present application also provides an optical storage capacity configuration device, including:

[0042] A cost module, which is used to establish a total investment cost model with the planned photovoltaic capacity and the planned energy storage capacity as variables and the total investment cost as the output; wherein, the total investment cost is the sum of the total construction cost and the total maintenance cost of the photovoltaic equipment and the energy storage equipment in the complete life cycle of the photovoltaic energy storage system;

[0043] A revenue module, which is used to establish a model for the electricity cost savings of the photovoltaic energy storage system in the entire life cycle according to the charge and discharge power of the photovoltaic energy storage system and the real-time electricity price;

[0044] A target module, which is used to determine the system operating cost according to the total investment cost and the electricity cost savings;

[0045] A solution module, which is used to determine the optical storage capacity configuration scheme with the lowest operating cost of the system as the optimization goal according to the electricity cost savings model and the total investment cost model;

[0046] A configuration module, which is used to configure the photovoltaic capacity and the energy storage capacity of the photovoltaic energy storage system according to the optical storage capacity configuration scheme.

[0047] To solve the above technical problems, the present application also provides an optical storage capacity configuration device, including:

[0048] A memory, which is used to store a computer program;

[0049] A processor, which is used to implement the steps of the above-mentioned optical storage capacity configuration method when executing the computer program.

[0050] To solve the above technical problems, the present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned optical storage capacity configuration method are implemented.

[0051] For the optical storage capacity configuration method provided by the present application, by modeling the total investment cost and the saved electricity cost of the photovoltaic energy storage system in its complete life cycle, the difference between the cost and the benefit of the photovoltaic energy storage system in its entire life cycle can be determined, that is, the system operation cost. Among them, since the photovoltaic energy storage system usually brings benefits continuously for a long time and requires maintenance and equipment replacement, this method determines the system operation cost with the entire life cycle of the photovoltaic energy storage system as the calculation scope. Since the service lives of the photovoltaic equipment and the energy storage equipment are not necessarily the same, and the photovoltaic equipment and the energy storage equipment may need to be replaced more than once in the entire life cycle of the photovoltaic energy storage system. Therefore, stretching the investment cost of the photovoltaic energy storage system to the entire life cycle is conducive to more accurately determining the system cost. Based on the above accurate modeling of the system cost and benefit, this method solves the model with the lowest system operation cost as the goal, and can obtain an optical storage capacity configuration scheme with the optimal global benefit in the entire life cycle of the system. Configuring the capacity of the photovoltaic energy storage system according to this optical storage capacity configuration scheme can obtain a construction planning scheme for the photovoltaic energy storage system with the optimal global economic benefit.

[0052] The optical storage capacity configuration device and the computer-readable storage medium provided by the present application correspond to the above method and have the same effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the embodiments of the present application, the drawings required for the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0054] Figure 1 It is a flowchart of an optical storage capacity configuration method provided by the present invention;

[0055] Figure 2 It is a flowchart of solving an optical storage capacity configuration scheme provided by the present invention;

[0056] Figure 3 It is a structural diagram of an optical storage capacity configuration device provided by the present invention;

[0057] Figure 4 It is a structural diagram of another optical storage capacity configuration device provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0058] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the protection scope of the present application.

[0059] The core of the present application is to provide a method, device, and medium for configuring the capacity of a photovoltaic energy storage system.

[0060] In order to enable those skilled in the art to better understand the solution of the present application, the present application will be further described in detail below in conjunction with the accompanying drawings and specific implementation manners.

[0061] Currently, most photovoltaic energy storage systems are based on on-site power supply requirements and the photovoltaic power generation characteristic curves of the region to establish photovoltaic devices whose capacity and power generation can meet the needs, and then configure energy storage devices with corresponding capacities according to the power supply and demand balance requirements of the photovoltaic devices. Although some capacity configuration schemes also consider factors such as initial investment costs, electricity purchase costs, and electricity price mechanisms. However, since the photovoltaic energy storage system is a system with high construction investment costs and a long return period. This capacity configuration scheme that considers short-term costs and benefits still cannot meet the economic needs of customers for capacity configuration planning before building a photovoltaic energy storage system.

[0062] Therefore, to solve the above problems, the present application provides a method for configuring the capacity of a photovoltaic energy storage system, as Figure 1 shown, including:

[0063] S11: Establish a total investment cost model with the photovoltaic planned capacity and the energy storage planned capacity as variables and the total investment cost as the output.

[0064] Among them, the total investment cost is the sum of the total construction cost and the total maintenance cost of the photovoltaic device and the energy storage device in the complete life cycle of the photovoltaic energy storage system.

[0065] S12: Establish a model for the cost of saving electricity in the complete life cycle of the photovoltaic energy storage system according to the charge and discharge power and the real-time electricity price of the photovoltaic energy storage system.

[0066] S13: Determine the system operation cost according to the total investment cost and the cost of saving electricity.

[0067] S14: Take the lowest system operation cost as the optimization goal, and determine the photovoltaic energy storage capacity configuration scheme according to the model for the cost of saving electricity and the total investment cost model.

[0068] S15: Configure the photovoltaic capacity and the energy storage capacity of the photovoltaic energy storage system according to the photovoltaic energy storage capacity configuration scheme.

[0069] First, for step S11, it is the step of establishing an investment cost model required for the construction of a photovoltaic energy storage system within the time range of the entire life cycle of the photovoltaic energy storage system. It is easy to know that since this method is a capacity configuration planning scheme before the construction of the photovoltaic energy storage system, the life cycle here is the expected project operation cycle of the photovoltaic energy storage system.

[0070] Furthermore, when the investment cost is extended to the entire life cycle of the photovoltaic energy storage system, the total investment cost can be divided into two parts: construction cost and maintenance cost. The construction cost includes the construction expenses of all photovoltaic devices and energy storage devices. In addition, when a photovoltaic device or an energy storage device needs to be replaced due to damage during the operation of the photovoltaic energy storage system, the reconstruction cost required for replacing the new photovoltaic device and energy storage device is also included in the construction cost. The maintenance cost is the total maintenance cost required to maintain the photovoltaic devices and energy storage devices throughout the life cycle of the photovoltaic energy storage system.

[0071] Thus, it can be seen that the total investment cost can be expressed by the following formula:

[0072] (1);

[0073] In the formula, represents the total construction cost, represents the total maintenance cost.

[0074] Based on the total investment cost, it can characterize all the costs required for investment in the photovoltaic energy storage system throughout its life cycle.

[0075] Furthermore, in order to take into account the different reconstruction times of photovoltaic devices and energy storage devices due to their different service lives in the entire life cycle of the photovoltaic energy storage system. That is, the total construction cost in the above step S11 is the construction cost of the entire life cycle of the photovoltaic energy storage system, rather than the initial construction cost at the beginning of the system construction (i.e., the construction cost of the primary photovoltaic and energy storage devices), so as to make the modeling of the total investment cost of the photovoltaic energy storage system more accurate. This embodiment also provides a possible implementation scheme. The determination process of the total construction cost includes:

[0076] S21: Round down the quotient of the life cycle duration of the photovoltaic energy storage system and the service life of the photovoltaic device to obtain the reconstruction times of the photovoltaic device.

[0077] Among them, the reconstruction times of the photovoltaic device are at least 1;

[0078] S22: Round down the quotient of the life cycle duration of the photovoltaic energy storage system and the service life of the energy storage device to obtain the reconstruction times of the energy storage device.

[0079] Among them, the reconstruction times of the energy storage device are at least 1.

[0080] S23: Determine the total construction cost of the photovoltaic equipment according to the product of the number of reconstructions of the photovoltaic equipment and the construction cost of the photovoltaic equipment.

[0081] S24: Determine the total construction cost of the energy storage equipment according to the product of the number of reconstructions of the energy storage equipment and the construction cost of the energy storage equipment.

[0082] S25: Determine the total construction cost according to the sum of the total construction cost of the photovoltaic equipment and the total construction cost of the energy storage equipment.

[0083] That is, the total construction cost in this embodiment is:

[0084] (2);

[0085] In the formula, max(·) represents taking the maximum value for calculation; represents performing a floor calculation; and correspond to steps S21 and S22 above. T n represents the project operation period of the photovoltaic energy storage system, that is, the duration of the entire life cycle; T pv 、T ess respectively represent the service lives of the photovoltaic equipment and the energy storage equipment; 、 respectively represent the unit costs of investing in the photovoltaic and energy storage equipment; 、 respectively represent the planned capacities of the photovoltaic equipment and the energy storage equipment.

[0086] The total maintenance cost is:

[0087] (3);

[0088] In the formula, T n is in years, t n represents that 1 year has 8760 hours; 、 respectively represent the unit costs of maintaining the photovoltaic and energy storage equipment; represents the photovoltaic power generation; represents the energy storage charge and discharge power. When is positive, it represents the discharge power of the energy storage. When is negative, it represents the charge power of the energy storage.

[0089] After that, for step S12, it is the step of calculating the benefits of the photovoltaic energy storage system during the entire life cycle. The most direct economic benefit that the photovoltaic energy storage system can generate is the savings in electricity costs. Therefore, this step is also to model and calculate the savings in electricity costs.

[0090] It should also be noted that the electricity cost savings in this embodiment also take into account the characteristics of unstable photovoltaic power generation and changing actual electricity loads. Based on the typical photovoltaic power generation curve and the future electricity load curve, the power supply and demand relationship of the photovoltaic energy storage system throughout its life cycle is predicted. Furthermore, a mechanism for buying electricity, selling electricity, and real-time electricity prices can be introduced to perform more accurate revenue calculations.

[0091] For example, the electricity cost savings in step S12 can be expressed by the following formula:

[0092] (4);

[0093] In the formula, represents the revenue decay coefficient of the photovoltaic energy storage system; is the electricity purchase price corresponding to time t; is the curve function of the future electricity load curve. When t has a specific value, represents the electricity load of the electricity-consuming enterprise at time t; is the power taken from the grid by the electricity-consuming enterprise at time t; e is the electricity selling price for feeding back to the power grid; is the power fed back to the power grid by the electricity-consuming enterprise at time t; represents the calculation step size. Among them, buying electricity from the mains means charging the photovoltaic energy storage system, and selling electricity to the mains means discharging the photovoltaic energy storage system to the mains.

[0094] It should also be noted that step S11 and step S12 are respectively used to model the cost and revenue of the photovoltaic energy storage system. There is no sequence relationship between them, and they can also be executed in parallel. Figure 1 in is only one possible execution sequence. And after both step S11 and step S12 are completed, simply based on the total investment cost and the electricity cost savings the total economic benefit of the photovoltaic energy storage system throughout its life cycle can be evaluated by taking the difference between them, that is, the system operation cost in step S13. The simplest system operation cost can be . At this time, when the system operation cost is positive, it means that the photovoltaic energy storage system operates at a loss; when the system operation cost is negative, it means that the photovoltaic energy storage system operates at a profit. And the smaller the value of the system operation cost (considering the positive and negative signs), the better the economy of the photovoltaic energy storage system.

[0095] Therefore, step S14 can take the minimum system operation cost as the optimization goal, solve the total investment cost model and the electricity cost savings model obtained in steps S11 and S12 above, and obtain a set of PV planning capacity and energy storage planning capacity with the minimum system operation cost as the final PV and energy storage capacity configuration plan. Then, step S15 can plan and construct the PV energy storage system based on this PV and energy storage capacity configuration plan.

[0096] It should also be noted that corresponding constraint conditions can be introduced in the above model solving process, such as system power constraints, energy storage system constraints, anti-backflow constraints, etc. The constraint conditions can be freely set according to actual needs, and this embodiment does not limit this. However, further, this embodiment provides a possible constraint condition setting scheme, and the constraint conditions include:

[0097] 1. System power constraints;

[0098] A) Power balance constraint:

[0099] ;

[0100] B) Transformer capacity constraint:

[0101] ;

[0102] ;

[0103] In the formula, is the maximum capacity of the transformer. , are the signs of purchasing electricity from and selling electricity to the power grid respectively; when the user purchases electricity from the power grid, is 1, is 0; when the user sells electricity to the power grid, is 0, is 1.

[0104] C) Power purchase and sale status constraint:

[0105] ;

[0106] ;

[0107] 2. Energy storage system constraints;

[0108] A) Energy storage charge and discharge power constraint:

[0109] ;

[0110] ;

[0111] In the formula, , The electricity of the energy storage system at time t and the next moment of time t respectively; is the charge-discharge efficiency of the energy storage; and are the depth of discharge and the health factor of the energy storage system respectively.

[0112] B) Energy storage charge-discharge power constraint:

[0113] ;

[0114] ;

[0115] In the formula, is the maximum charge-discharge power of the energy storage system; C is the charge-discharge rate of the energy storage system.

[0116] 3. Anti-backflow constraint (preventing energy storage from discharging into the grid):

[0117] ;

[0118] 4. Photovoltaic power constraint:

[0119] ;

[0120] ;

[0121] In the formula, is the normalized value of the local photovoltaic output at time t; is the planned photovoltaic capacity; is the maximum value of the photovoltaic installed capacity; R is the maximum area where photovoltaic panels can be installed; is the installation area of a single photovoltaic panel; is the capacity of a single photovoltaic panel.

[0122] It should be noted that the above-provided constraint conditions in this embodiment are only a possible solution that can be adopted during the capacity configuration planning of the photovoltaic energy storage system. In actual applications, more or fewer constraint conditions than those provided in this embodiment can be adopted, or other constraint conditions can be adopted based on other needs. This embodiment does not limit this.

[0123] In summary, this application provides a method for configuring the capacity of a photovoltaic energy storage system. By modeling the total investment cost and the saved electricity cost of the photovoltaic energy storage system throughout its entire life cycle, the photovoltaic planned capacity and the energy storage planned capacity are solved with the goal of optimizing the overall life cycle benefit (i.e., minimizing the system operation cost) to obtain a photovoltaic energy storage capacity configuration plan with the globally optimal economic benefit. Based on this plan, the maximization of energy utilization, the minimization of costs, and the requirements of long-term operation economy and sustainability can be achieved.

[0124] On the other hand, as can be seen from the above embodiments, the present application does not limit the constraint conditions based on which the solution model is solved, and corresponding constraint conditions can be set according to different requirements of the actual application scenarios. However, the currently common constraint conditions are all constraints from aspects such as the electrical parameters and safety protection of the photovoltaic energy storage system itself, such as the above-mentioned capacity constraint, charge-discharge power constraint, anti-backflow constraint, and so on.

[0125] This embodiment provides another possible implementation of the constraint condition. Specifically, step S14 is as follows:

[0126] Taking the lowest system operation cost as the optimization goal, solving the electricity cost saving model and the total investment cost model based on the preset constraint conditions to determine the photovoltaic energy storage capacity configuration plan.

[0127] Among them, the constraint conditions include: the planned capacity of the energy storage is greater than or equal to the product of the planned capacity of the photovoltaic and the continuous discharge duration of the energy storage device and the preset photovoltaic energy storage configuration ratio.

[0128] It should be noted that this embodiment proposes a new constraint condition, but it does not mean that other constraint conditions mentioned in the above embodiments cannot be included. The constraint conditions proposed in this embodiment and other constraint conditions mentioned in the above embodiments can be implemented together without interference.

[0129] In addition, a constraint condition proposed in this embodiment is a constraint based on policy factors. In the actual construction of the photovoltaic energy storage system, due to different construction regions, there may be different requirements for the photovoltaic energy storage construction ratio. That is, in addition to power supply and demand and economic benefits, there are also constraints brought by other factors, that is, restricting the minimum ratio of the energy storage device capacity to the photovoltaic device capacity. This constraint condition is:

[0130] 5. Energy storage capacity constraint:

[0131] ;

[0132] In the formula, represents the photovoltaic energy storage configuration ratio; h represents the continuous discharge duration of the energy storage device, generally not less than 2 hours.

[0133] It can be seen that this embodiment introduces constraint conditions brought by other factors in addition to power supply and demand and system parameters. Especially in some regions, there are requirements for the ratio of the photovoltaic device capacity to the energy storage device capacity. This embodiment can include the photovoltaic energy storage configuration ratio in the scope of constraints, so that the determined photovoltaic energy storage capacity configuration plan can meet the requirements of the photovoltaic energy storage configuration ratio, broadening the range of user requirements that this method can meet.

[0134] On the other hand, this embodiment also provides a further implementation. Step S13 further includes:

[0135] S131: Determine a first intermediate result based on the total investment cost and a preset first weighting coefficient.

[0136] S132: Determine a second intermediate result based on the electricity cost savings and a preset second weighting coefficient.

[0137] S133: Use the difference between the first intermediate result and the second intermediate result as the system operation cost.

[0138] Then step S14 specifically includes:

[0139] Based on N groups of different first weighting coefficients and second weighting coefficients, with the lowest system operation cost as the optimization goal, determine N different configurations of optical storage capacity.

[0140] Wherein, N is a positive integer and N≥2.

[0141] That is to say, in this embodiment, it no longer simply uses the difference between the total investment cost and the electricity cost savings as the system operation cost; instead, corresponding weighting coefficients are added before the total investment cost and the electricity cost savings, so that different scenarios can be defined according to actual different needs; different weighting coefficient configurations are available in different scenarios, and the corresponding weighting coefficient configurations can be set in advance according to the different importance of the total investment cost (cost) and the electricity cost savings (benefit), so as to provide the optimal optical storage capacity configuration schemes corresponding to multiple scenarios and bring richer optical storage capacity configuration choices to customers.

[0142] In addition, based on the above embodiment, this embodiment further provides a further implementation scheme. Step S14 of the above embodiment: Based on N groups of different first weighting coefficients and second weighting coefficients, with the lowest system operation cost as the optimization goal, determining N different configurations of optical storage capacity specifically includes:

[0143] S141-A: Determine a first optical storage capacity configuration scheme based on the first weighting coefficient and the second weighting coefficient in the short-term cost priority scenario.

[0144] Wherein, the first weighting coefficient in the short-term cost priority scenario is greater than the second weighting coefficient.

[0145] S142-A: Determine a second optical storage capacity configuration scheme based on the first weighting coefficient and the second weighting coefficient in the long-term benefit priority scenario.

[0146] Wherein, the first weighting coefficient in the long-term benefit priority scenario is less than the second weighting coefficient.

[0147] S143 - A: Determine the third photovoltaic - energy - storage capacity configuration plan based on the first weighting coefficient and the second weighting coefficient under the scenario of comprehensive balance priority; wherein, the first weighting coefficient under the scenario of comprehensive balance priority is equal to the second weighting coefficient.

[0148] It should be noted that this embodiment only restricts the magnitude relationship between the first weighting coefficient and the second weighting coefficient under different scenarios, but does not limit their specific values, which can be freely selected according to actual needs. Generally speaking, the more the long - term benefits are emphasized, the smaller the ratio value of the first weighting coefficient to the second weighting coefficient. On the contrary, the more the short - term costs are emphasized, the larger the ratio value of the first weighting coefficient to the second weighting coefficient.

[0149] In a possible embodiment, the first weighting coefficient and the second weighting coefficient satisfy:

[0150] (5);

[0151] In the formula, represents the first weighting coefficient, represents the second weighting coefficient.

[0152] At this time, based on the optimization objective of the system operation cost is:

[0153] (6);

[0154] Under this condition, the first weighting coefficient in the short - term cost - priority scenario can be taken as 0.8, and the corresponding second weighting coefficient = 0.2. Similarly, the second weighting coefficient in the long - term benefit - priority scenario can be taken as 0.8, and the corresponding first weighting coefficient = 0.2. In addition, the first weighting coefficient in the comprehensive balance - priority scenario = the first weighting coefficient = 0.5.

[0155] It can be seen that this embodiment divides the customer's demand for the economic benefits of photovoltaic energy storage in the actual application scenario into three major scenarios: short - term cost - priority scenario, long - term benefit - priority scenario, and comprehensive balance - priority scenario. Among them, the short - term cost - priority scenario indicates that the customer is more sensitive to the total investment cost and hopes to obtain considerable benefits on the premise of reducing the total investment cost. Therefore, there is . The long - term benefit - priority scenario indicates that the customer is more concerned about the long - term benefits brought by investing in and constructing a photovoltaic energy storage system. Without considering the total investment cost, the customer hopes to obtain as much benefit as possible. Therefore, there is . The comprehensive balance - priority scenario indicates that the customer has no preference between the short - term input cost and the long - term obtained benefits, and balances the influence of the total investment cost and the electricity cost savings on the optimization configuration of photovoltaic energy storage. Therefore, there is .

[0156] In summary, in this embodiment, different preferences that customers may have in actual applications are divided into three typical scenarios, and corresponding weight coefficients are set for three possible demand scenarios where customers are sensitive to short-term costs, concerned about long-term benefits, and balance both costs and benefits. Therefore, when determining the optical storage capacity configuration plan, three alternative plans are obtained based on the three scenarios to meet the diverse capacity configuration optimization needs of customers.

[0157] Further, after providing multiple different optical storage capacity configuration plans for customers based on the solution provided in the above embodiment, to more intuitively show the advantages and disadvantages of the optical storage capacity configuration plans in different scenarios, this embodiment also provides a possible implementation plan. After step S14 of the above method, it further includes:

[0158] S21: Calculate the net present value and internal rate of return of each optical storage capacity configuration plan.

[0159] Before step S15, the method further includes:

[0160] S22: Select an optical storage capacity configuration plan as the target optical storage capacity configuration plan for configuring the photovoltaic capacity and energy storage capacity of the photovoltaic energy storage system according to the net present value, internal rate of return, and system operation cost.

[0161] Among them, the net present value The calculation formula is as follows:

[0162] (7);

[0163] In the formula, is the discount rate.

[0164] The internal rate of return The calculation formula is as follows:

[0165] (8);

[0166] Exemplarily, combined with the three application scenarios provided in the above embodiment, there are three different optical storage capacity configuration plans after steps S14 and steps S21 and S22 are executed. And as shown in Table 1 below, each optical storage capacity configuration plan also has its own net present value , internal rate of return and system operation cost as indicators for users to refer to and select.

[0167] Table 1 Output result table of optical storage capacity configuration plan

[0168]

[0169] Among them, the net present value or the internal rate of return The higher it is, the stronger the profitability of the configuration plan of the energy storage capacity characterized by light is. The system operation cost comprehensively reflects the investment cost required by the plan in each scenario. Based on this, when customers choose a suitable investment plan for the energy storage capacity of light, they can weigh these indicators according to their own preferences and goals. For example, if they seek to maximize long-term benefits, they can choose the plan with the highest net present value and internal rate of return . On the contrary, if customers are sensitive to costs, they can give priority to the plan with the lowest system operation cost.

[0170] As can be seen from the above, in addition to the system operation cost that directly reflects the cost and benefits of the photovoltaic energy storage system, this embodiment also provides the net present value and internal rate of return of two parameters to evaluate the profitability of different configuration plans of the energy storage capacity of light. Thus, it brings a more comprehensive plan evaluation criterion for customers, facilitating customers to choose the most suitable configuration plan of the energy storage capacity of light according to their own needs.

[0171] On the other hand, for how to solve the model in the above step S14 to obtain the configuration plan of the energy storage capacity of light, it can be achieved by means of a direct solver or the like. However, this embodiment also provides another possible solution. The above step S14 specifically further includes:

[0172] S141-B: Generate initial particles with the planned capacity of photovoltaic and the planned capacity of energy storage in the coordinates, and set the number of particles, the maximum number of iterations, and the learning factor.

[0173] S142-B: Calculate the fitness value of each particle with the lowest system operation cost as the optimization goal, determine the global optimal solution of the initial generation of particles, and perform optimization evolution.

[0174] S143-B: Repeat step S142-B until the number of times of performing optimization evolution reaches the maximum number of iterations, and output the global optimal solution as the configuration plan of the energy storage capacity of light.

[0175] It is easy to know that this method solves the above model through the particle swarm optimization algorithm to obtain the configuration plan of the energy storage capacity of light. The advantage of this plan is that the iterative solution process is dynamically traceable, which is convenient for researchers to analyze the iterative process and improve the model to further optimize the configuration plan of the energy storage capacity of the photovoltaic energy storage system.

[0176] Furthermore, for the solution of the above model by this embodiment through the particle swarm optimization algorithm, a possible complete solution process is given below in combination with an example, as Figure 2 shown:

[0177] 1. Data preparation: Obtain data such as the typical power of photovoltaic for 8760 hours in a year, the prediction of electricity load, time-of-use electricity price, and on-grid electricity price, and establish a total investment cost model and a model for saving electricity costs.

[0178] 2. Initialization of the particle swarm optimization algorithm: Generate initial particles with the planned capacity of photovoltaic and the planned capacity of energy storage, and set parameters such as the number of particles, the maximum number of iterations, and the learning factor.

[0179] Among them, the coordinates and the velocity of the i-th particle are generally expressed as:

[0180] ;

[0181] ;

[0182] In the formula, represents the d-th coordinate value of the particle; represents the d-th velocity value of the particle. Since the variables to be solved in this method are only the planned capacity of photovoltaic and the planned capacity of energy storage, the dimension of the particle is 2, corresponding to the planned capacity of photovoltaic and the planned capacity of energy storage respectively.

[0183] 3. Calculate the objective value of the fitness function: Solve the fitness value of each particle with the lowest operating cost of the park system as the optimization goal, determine the global optimal solution of the initial generation of particles, and start the optimization evolution.

[0184] 4. Particle update: The particle adjusts and updates its velocity and position according to its own historical best position and the best position in the group, and then judges each particle to determine whether the value of the particle satisfies the constraint conditions. If it satisfies, normal fitness calculation is performed. If it does not satisfy, the fitness of the particle is directly assigned a sufficiently large constant, and the fitness value is calculated and the global optimum of the particle is updated:

[0185] ;

[0186] ;

[0187] In the formula, is the inertia factor; , are the learning factors, generally taking ; represents a random number in the interval [0,1]; represents the -th dimension of the individual extreme value of the -th particle; represents the -th dimension of the global optimal solution.

[0188] 5. Iterative optimization: Determine whether the maximum number of iterations is reached. If not, repeat step 4; when the maximum number of iterations is reached, the algorithm stops and outputs the global optimal solution, that is, the optimal photovoltaic and energy storage capacity configuration plan.

[0189] In the above embodiments, a method for configuring the photovoltaic and energy storage capacity is described in detail. The present application also provides an embodiment corresponding to a device for configuring the photovoltaic and energy storage capacity. It should be noted that the present application describes the embodiments of the device part from two perspectives, one is from the perspective of functional modules, and the other is from the perspective of hardware.

[0190] From the perspective of functional modules, as Figure 3 shown, the present embodiment provides a device for configuring the photovoltaic and energy storage capacity, including:

[0191] A cost module 11, configured to establish a total investment cost model with the photovoltaic planned capacity and the energy storage planned capacity as variables and the total investment cost as the output; wherein, the total investment cost is the sum of the total construction cost and the total maintenance cost of the photovoltaic equipment and the energy storage equipment in the complete life cycle of the photovoltaic energy storage system;

[0192] A revenue module 12, configured to establish a model for saving electricity costs of the photovoltaic energy storage system in the entire life cycle according to the charge and discharge power of the photovoltaic energy storage system and the real-time electricity price;

[0193] A target module 13, configured to determine the system operation cost according to the total investment cost and the saved electricity cost;

[0194] A solution module 14, configured to take the lowest system operation cost as the optimization goal, and determine the photovoltaic and energy storage capacity configuration plan according to the saved electricity cost model and the total investment cost model;

[0195] A configuration module 15, configured to configure the photovoltaic capacity and the energy storage capacity of the photovoltaic energy storage system according to the photovoltaic and energy storage capacity configuration plan.

[0196] Since the embodiments of the device part correspond to the embodiments of the method part, please refer to the description of the embodiments of the method part for the embodiments of the device part, and will not be elaborated here.

[0197] Figure 4 The following is a structural diagram of a device for configuring the photovoltaic and energy storage capacity provided in another embodiment of the present application. As Figure 4 shown, a device for configuring the photovoltaic and energy storage capacity includes: a memory 20, configured to store a computer program;

[0198] A processor 21, configured to implement the steps of a method for configuring the photovoltaic and energy storage capacity as described in the above embodiments when executing the computer program.

[0199] A light storage capacity configuration device provided in this embodiment may include, but is not limited to, a mobile terminal, a personal computer, a workstation, etc.

[0200] Among them, the processor 21 may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 21 may be implemented in at least one hardware form of a digital signal processor (DSP), a field-programmable gate array (FPGA), or a programmable logic array (PLA). The processor 21 may also include a main processor and a coprocessor. The main processor is a processor used to process data in the wake state, also known as the central processing unit (CPU); the coprocessor is a low-power processor used to process data in the standby state. In some embodiments, the processor 21 may be integrated with a graphics processing unit (GPU), and the GPU is responsible for rendering and drawing the content to be displayed on the display screen. In some embodiments, the processor 21 may also include an artificial intelligence (AI) processor, and the AI processor is used to process computational operations related to machine learning.

[0201] The memory 20 may include one or more computer-readable storage media, and the computer-readable storage media may be non-transitory. The memory 20 may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices and flash storage devices. In this embodiment, the memory 20 is at least used to store the following computer program 201. After the computer program is loaded and executed by the processor 21, it can implement the relevant steps of a light storage capacity configuration method disclosed in any of the foregoing embodiments. In addition, the resources stored in the memory 20 may also include an operating system 202 and data 203, etc., and the storage method may be temporary storage or permanent storage. Among them, the operating system 202 may include Windows, Unix, Linux, etc. The data 203 may include, but is not limited to, a light storage capacity configuration method, etc.

[0202] In some embodiments, a light storage capacity configuration device may further include a display screen 22, an input / output interface 23, a communication interface 24, a power supply 25, and a communication bus 26.

[0203] Those skilled in the art can understand that Figure 4 the structure shown in does not constitute a limitation on a light storage capacity configuration device, and it may include more or fewer components than shown in the figure.

[0204] An optical storage capacity configuration device provided by an embodiment of the present application includes a memory and a processor. When the processor executes the program stored in the memory, the following method can be implemented: An optical storage capacity configuration method.

[0205] Finally, the present application also provides an embodiment corresponding to a computer-readable storage medium. A computer program is stored on the computer-readable storage medium, and when the computer program is executed by the processor, the steps recorded in the above method embodiment are implemented.

[0206] It can be understood that if the method in the above embodiment is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and executes all or part of the steps of the methods described in various embodiments of the present application. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0207] The above has introduced in detail an optical storage capacity configuration method, device, and medium provided by the present application. The various embodiments in the specification are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. For the same or similar parts between the various embodiments, reference can be made to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple. For the relevant parts, reference can be made to the description of the method part. It should be noted that for those of ordinary skill in the art in this technical field, without departing from the principle of the present application, several improvements and modifications can be made to the present application, and these improvements and modifications also fall within the protection scope of the present application.

[0208] It should also be noted that in this specification, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or device comprising the said element.

Claims

1. A method for configuring optical storage capacity, characterized in that: include: A total investment cost model is established with the planned photovoltaic capacity and the planned energy storage capacity as variables and the total investment cost as the output; wherein the total investment cost is the sum of the total construction cost and the total maintenance cost of the photovoltaic equipment and the energy storage equipment in the complete life cycle of the photovoltaic energy storage system; According to the charging and discharging power of the photovoltaic energy storage system and the real-time electricity price, a model for saving electricity costs of the photovoltaic energy storage system in its complete life cycle is established; Determine the system operating cost based on the total investment cost and the electricity cost savings; Taking the lowest system operating cost as the optimization goal, determining the photovoltaic storage capacity configuration plan according to the electricity cost saving model and the total investment cost model; The photovoltaic capacity and energy storage capacity of the photovoltaic energy storage system are configured according to the photovoltaic storage capacity configuration scheme.

2. The optical storage capacity configuration method according to claim 1, characterized in that: Taking the lowest system operating cost as the optimization goal, determining the photovoltaic storage capacity configuration plan according to the electricity cost saving model and the total investment cost model includes: Taking the lowest system operating cost as the optimization goal, solving the electricity cost saving model and the total investment cost model based on preset constraints to determine the photovoltaic storage capacity configuration plan; Among them, the constraint conditions include: the planned energy storage capacity is greater than or equal to the product of the planned photovoltaic capacity, the continuous discharge time of the energy storage equipment and the preset photovoltaic storage configuration ratio.

3. The method for configuring optical storage capacity according to claim 1, characterized in that: Based on the total investment cost and electricity cost savings, the system operating costs include: Determining a first intermediate result according to the total investment cost and a preset first weighting coefficient; Determining a second intermediate result according to the electricity cost saving and a preset second weighting coefficient; using the difference between the first intermediate result and the second intermediate result as the system operation cost; Taking the lowest system operating cost as the optimization goal, the photovoltaic storage capacity configuration scheme is determined according to the electricity cost saving model and the total investment cost model, including: Based on N groups of different first weighting coefficients and second weighting coefficients, and taking the lowest system operating cost as an optimization goal, determining N different solar storage capacity configuration schemes; Wherein, N is a positive integer and N≥2.

4. The method for configuring optical storage capacity according to claim 3, characterized in that: Based on N groups of different first weighting coefficients and second weighting coefficients, and taking the lowest system operation cost as the optimization goal, determining N different photovoltaic storage capacity configuration schemes includes: Determine a first optical storage capacity configuration scheme based on the first weighting coefficient and the second weighting coefficient in a short-term cost priority scenario; wherein the first weighting coefficient in the short-term cost priority scenario is greater than the second weighting coefficient; Determine a second optical storage capacity configuration scheme based on the first weighting coefficient and the second weighting coefficient in the long-term benefit priority scenario; wherein the first weighting coefficient in the long-term benefit priority scenario is less than the second weighting coefficient; Based on the first weighting coefficient and the second weighting coefficient in the comprehensive balance priority scenario, a third optical storage capacity configuration scheme is determined; wherein the first weighting coefficient in the comprehensive balance priority scenario is equal to the second weighting coefficient.

5. The optical storage capacity configuration method according to claim 3, characterized in that: After determining N different optical storage capacity configuration schemes based on N different sets of the first weighting coefficients and the second weighting coefficients and taking the lowest system operation cost as the optimization goal, the method further includes: Calculating the net present value and internal rate of return of each of the optical storage capacity configuration schemes; Before configuring the photovoltaic capacity and energy storage capacity of the photovoltaic energy storage system according to the photovoltaic energy storage capacity configuration scheme, the method further includes: A photovoltaic storage capacity configuration scheme is selected according to the net present value, the internal rate of return and the system operating cost as a target photovoltaic storage capacity configuration scheme for configuring the photovoltaic capacity and energy storage capacity of the photovoltaic energy storage system.

6. The optical storage capacity configuration method according to claim 1, characterized in that: The method also includes: The quotient of the life cycle of the photovoltaic energy storage system and the service life of the photovoltaic device is rounded down to an integer to obtain the number of photovoltaic device reconstructions; wherein the number of photovoltaic device reconstructions is at least 1; The quotient of the life cycle of the photovoltaic energy storage system and the service life of the energy storage device is rounded down to an integer to obtain the number of energy storage device reconstructions; wherein the number of energy storage device reconstructions is at least 1; Determining the total cost of photovoltaic equipment construction according to the product of the number of times the photovoltaic equipment is rebuilt and the cost of photovoltaic equipment construction; Determining the total cost of energy storage equipment construction according to the product of the number of reconstructions of the energy storage equipment and the cost of energy storage equipment construction; The total construction cost is determined according to the sum of the total construction cost of the photovoltaic equipment and the total construction cost of the energy storage equipment.

7. The method for configuring optical storage capacity according to any one of claims 1 to 6, characterized in that: Taking the lowest system operating cost as the optimization goal, determining the photovoltaic storage capacity configuration plan according to the electricity cost saving model and the total investment cost model includes: Generate initialization particles whose coordinates carry the planned photovoltaic capacity and the planned energy storage capacity, and set the number of particles, the maximum number of iterations, and the learning factor; The fitness value of each particle is calculated with the lowest operating cost of the system as the optimization goal, the global optimal solution of the first generation of particles is determined, and optimal evolution is performed; Repeatedly calculating the fitness value of each particle with the lowest system operation cost as the optimization goal, determining the global optimal solution of the first generation of particles, and performing the optimization evolution step until the number of optimization evolutions reaches the maximum number of iterations; The global optimal solution is output as the optical storage capacity configuration scheme.

8. A device for configuring optical storage capacity, characterized in that: include: A cost module is used to establish a total investment cost model with the planned photovoltaic capacity and the planned energy storage capacity as variables and the total investment cost as output; wherein the total investment cost is the sum of the total construction cost and the total maintenance cost of the photovoltaic equipment and the energy storage equipment in the complete life cycle of the photovoltaic energy storage system; A revenue module, used to establish a model for saving electricity costs of the photovoltaic energy storage system in its entire life cycle according to the charging and discharging power of the photovoltaic energy storage system and the real-time electricity price; A target module, for determining system operation costs based on the total investment cost and electricity cost savings; A solution module, used for taking the lowest system operation cost as the optimization target, and determining the photovoltaic storage capacity configuration plan according to the electricity cost saving model and the total investment cost model; A configuration module is used to configure the photovoltaic capacity and energy storage capacity of the photovoltaic energy storage system according to the photovoltaic storage capacity configuration scheme.

9. A device for configuring optical storage capacity, characterized in that: include: Memory for storing computer programs; A processor, configured to implement the steps of the optical storage capacity configuration method as described in any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the optical storage capacity configuration method according to any one of claims 1 to 7 are implemented.

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