Photovoltaic capacity configuration optimization method, device, equipment, medium and product

Abstract

The present application relates to the technical field of energy storage optimization configuration, and discloses a photovoltaic storage capacity configuration optimization method, device, equipment, medium and product, the method comprising: obtaining typical daily operation data sets of photovoltaic power stations under multiple statistical scenarios and historical electricity price data; performing copy splicing processing on the typical daily operation data sets of each statistical scenario to obtain continuous time axis data of a target time length under the corresponding statistical scenario, the continuous time axis data being composed of operation data of a settlement period and a warm-up period; solving a pre-constructed double-layer collaborative optimization model based on the continuous time axis data of the target time length under each statistical scenario and the historical electricity price data to obtain target energy storage configuration data of the photovoltaic power station, the double-layer collaborative optimization model comprising an inner layer objective function and an outer layer objective function, the inner layer objective function being constructed with the maximum net income of each settlement period as a target, and the outer layer objective function being constructed with the maximum net present value of the whole life cycle of the energy storage as a target.

Description

Technical Field

[0001] This invention relates to the field of energy storage optimization configuration technology, specifically to photovoltaic energy storage capacity configuration optimization methods, devices, equipment, media, and products. Background Technology

[0002] With the continuous expansion of photovoltaic (PV) installations, the problem of midday curtailment caused by insufficient grid absorption capacity is becoming increasingly prominent. Configuring energy storage systems has become a core technical means to mitigate curtailment and realize arbitrage opportunities based on peak-valley price differences. Among related technologies, there are two typical paths for energy storage configuration and operation optimization: one is to first preset a fixed operating strategy, such as timed charging and discharging rules, and then conduct capacity optimization; the other is to first determine the energy storage capacity and then optimize the operating strategy accordingly. Both paths generally have significant drawbacks: First, the planning and operation stages are disconnected, failing to form a two-layer coupled optimization mechanism, and thus failing to fully tap the revenue potential of energy storage throughout its entire lifecycle; second, daily simulations often adopt boundary assumptions of daily zero-state start-up or zero-energy discharge at the end of the day, violating the physical characteristics of continuous energy storage operation, leading to a severe underestimation of the economic value of cross-day energy time shifts. Summary of the Invention

[0003] This invention provides a method, apparatus, equipment, medium, and product for optimizing photovoltaic power generation and storage capacity configuration, in order to solve the problem of inaccurate photovoltaic power generation and storage capacity configuration in related technologies.

[0004] In a first aspect, the present invention provides a method for optimizing photovoltaic (PV) power storage capacity configuration. This method includes: acquiring typical daily operation datasets and historical electricity price data for PV power plants under multiple statistical scenarios; copying and splicing the typical daily operation datasets for each statistical scenario to obtain continuous time axis data for the target duration under the corresponding statistical scenario. The continuous time axis data consists of operation data for the settlement period and the preheating period. The operation data for the preheating period is used for energy storage state of charge evolution, and the operation data for the settlement period is used for revenue statistics. Based on the continuous time axis data and historical electricity price data for the target duration under each statistical scenario, a pre-constructed two-layer collaborative optimization model is solved to obtain the target energy storage configuration data for the PV power plant. The two-layer collaborative optimization model includes an inner objective function and an outer objective function. The inner objective function is constructed with the goal of maximizing the net revenue for each settlement period, which is determined by energy storage configuration parameters and electricity price data. The outer objective function is constructed with the goal of maximizing the net present value (NPV) of the energy storage over its entire lifecycle, which is calculated based on the net revenue and energy storage configuration parameters for each settlement period.

[0005] The photovoltaic energy storage capacity configuration optimization method provided by this invention constructs a two-layer collaborative optimization model that includes maximizing inner-layer net revenue and maximizing outer-layer net present value over the entire life cycle. This deeply couples energy storage configuration parameters with operating strategies. While matching the optimal operating strategy for each group of candidate energy storage configurations, it locks the optimal capacity configuration with the net present value over the entire life cycle as the target. This fully explores the full life cycle revenue potential of energy storage in curtailment and peak-valley arbitrage. At the same time, by copying and splicing typical daily operation datasets into continuous time axis data including the preheating period and the settlement period, the steady-state evolution of the energy storage charge state is completed during the preheating period. This eliminates the boundary effects of traditional daily simulations, realistically restores the physical scenario of continuous cross-day operation of energy storage, accurately calculates the economic value of cross-day energy time shift, and improves the accuracy and reliability of revenue calculation. This provides photovoltaic power plants with an energy storage capacity configuration scheme that is more in line with actual operating rules and has better revenue potential.

[0006] In one optional implementation, the step of solving a pre-constructed two-layer collaborative optimization model based on continuous time axis data of the target duration under each statistical scenario and historical electricity price data to obtain the target energy storage configuration data for the photovoltaic power station includes: obtaining the value space of energy storage configuration parameters; performing discretization processing based on the value space to obtain multiple candidate configuration data; solving the two-layer collaborative optimization model based on each candidate configuration data, continuous time axis data of the target duration under each statistical scenario, and historical electricity price data to obtain the net present value of the energy storage throughout its entire life cycle for the corresponding candidate configuration data; and determining the target energy storage configuration data from the multiple candidate configuration data based on the net present value of the energy storage throughout its entire life cycle for each candidate configuration data.

[0007] The method provided in this optional implementation discretizes the value space of energy storage configuration parameters to generate multiple sets of candidate configuration data, achieving a systematic traversal of all feasible energy storage capacity schemes and avoiding the omission of potential optimal solutions. For each set of candidate configurations, a two-layer collaborative optimization solution is carried out in combination with continuous time axis data. This not only matches a dedicated optimal operation strategy for each configuration, achieving deep coupling between capacity planning and operation strategy, but also eliminates the boundary effects of traditional daily simulation through a warm-up period, accurately calculating the net present value of each configuration throughout its entire life cycle. Finally, the target configuration is selected based on the comparison of net present value, ensuring that the obtained energy storage capacity scheme is the configuration result with the best life cycle return and conforms to the actual operation law, significantly improving the scientificity and reliability of photovoltaic energy storage capacity configuration decision-making.

[0008] In one optional implementation, the two-layer collaborative optimization model is solved based on candidate configuration data, continuous time axis data of target duration under each statistical scenario, and historical electricity price data to obtain the net present value of the energy storage lifecycle for the corresponding candidate configuration data. This includes: determining the data on the space for preventing low-priced grid-purchased electricity from crowding out zero-cost curtailment in each settlement period based on continuous time axis data of target duration under each statistical scenario and preset forward-looking resource prediction rules; determining the range of electricity price thresholds for each settlement period based on historical electricity price data; solving the inner-layer objective function based on preset multi-priority charge-discharge control logic, curtailment space data for each settlement period, candidate configuration data, and the range of electricity price values ​​for each settlement period to obtain the optimal electricity price threshold data for each statistical scenario under the corresponding candidate configuration and the net revenue for each settlement period; the preset multi-priority charge-discharge control logic determines the charge-discharge rules for energy storage through the curtailment space data and electricity price thresholds for each settlement period; and solving the outer-layer objective function based on the net revenue for each settlement period under each candidate configuration and the candidate configuration data to obtain the net present value of the energy storage lifecycle for the corresponding candidate configuration data.

[0009] The method provided in this optional embodiment determines the curtailment space data for each settlement period by pre-setting forward-looking resource prediction rules, thereby eliminating the resource mismatch problem of low-priced grid-purchased electricity crowding out zero-cost curtailment and ensuring the priority consumption of photovoltaic curtailment. It defines the range of electricity price thresholds based on historical electricity price data, limiting the reasonable search boundary for inner-layer optimization and effectively improving optimization efficiency. By solving the inner-layer objective function based on pre-set multi-priority charging and discharging control logic, it deeply couples the curtailment space constraint with the electricity price threshold, matching a dedicated optimal charging and discharging strategy for each candidate configuration, achieving deep binding between capacity configuration and operation strategy. Simultaneously, it eliminates the boundary effects of traditional daily simulations by relying on continuous time axis data, accurately calculating the net income for each settlement period. Finally, based on the net income and candidate configurations, it solves the outer-layer objective function to obtain the net present value of each configuration throughout its entire lifecycle, providing a scientific and reliable basis for income assessment for capacity configuration decisions.

[0010] In one optional implementation, the inner objective function is solved based on preset multi-priority charging and discharging control logic, the curtailment space data for each settlement period, the candidate configuration data, and the electricity price range for each settlement period to obtain the optimal electricity price threshold under the corresponding candidate configuration and the net revenue for each settlement period. This includes: determining multiple candidate electricity price thresholds for the corresponding settlement period based on the electricity price range for each settlement period; and iteratively solving the inner objective function using the operating data for multiple settlement periods, the candidate configuration data, and the curtailment space data for multiple settlement periods based on the differential evolution algorithm until convergence accuracy is achieved to obtain the optimal electricity price threshold under the corresponding candidate configuration and the net revenue for each settlement period.

[0011] The method provided in this optional embodiment determines the candidate electricity price threshold based on the range of electricity prices in each settlement period, which limits the reasonable search boundary for the inner layer optimization, effectively avoids invalid searches, and improves optimization efficiency. It uses a differential evolution algorithm to iteratively solve the inner layer objective function to convergence accuracy, which can efficiently traverse potential optimal solutions within the feasible region, avoid getting trapped in local optima, and ensure the stability and optimality of the final solution. This accurately matches the optimal electricity price threshold for each group of candidate energy storage configurations, achieving a deep binding between energy storage configuration parameters and operating strategies, fundamentally solving the problem of the separation between planning and operation in related technologies. Simultaneously, iterative optimization based on multi-settlement period operating data and curtailment space data can fully adapt to the operating patterns of different statistical scenarios, accurately calculate the net income in each settlement period, further improving the accuracy and reliability of income calculation, and providing solid data support for the outer layer's full life-cycle net present value assessment.

[0012] In one optional implementation, the optimal electricity price threshold data includes the optimal charging electricity price threshold and the optimal discharging electricity price threshold. The preset multi-priority charging and discharging control logic is as follows: At the current control time t, read the time-series data of the abandoned electricity space for all time periods after time t within the settlement period, and calculate the abandoned electricity space data that needs to be reserved for future photovoltaic abandoned electricity at the current time by combining the rated charging power of energy storage, the power constraint of the grid connection point, and the charging efficiency of energy storage; if the first priority control condition is met, then execute the abandoned electricity charging action, and the charging power is taken as the minimum value of the current photovoltaic abandoned electricity power, the rated charging power of energy storage, and the power corresponding to the remaining rechargeable capacity of energy storage. After charging is completed, update the state of charge of energy storage. The first priority control condition is that there is photovoltaic abandoned electricity that can be absorbed at the current time, and the current state of charge of energy storage is lower than the preset safe upper limit of state of charge; when the second priority control condition is met, calculate the allowable grid connection space data at the current time based on the current remaining rechargeable capacity of energy storage, the amount of abandoned electricity absorbed at the current time, and the abandoned electricity space data. The maximum amount of electricity purchased for charging is determined when the maximum amount of electricity purchased for charging is greater than 0. The charging action is then executed, with the charging power being the minimum of the energy storage's rated charging power, the power corresponding to the maximum allowed amount of electricity, and the power corresponding to the remaining chargeable capacity of the energy storage. After charging is completed, the energy storage's state of charge (SOC) and charging cost are updated, and subsequent priority control is terminated. The second priority control condition is that no electricity wasted for charging, the current grid electricity price is less than or equal to the optimal charging price threshold, and the current SOC of the energy storage is lower than the preset safe upper limit. When the third priority control condition is met, a discharging action is executed, with the discharging power being the minimum of the energy storage's rated discharging power, the remaining connectable power at the grid connection point, and the power corresponding to the remaining dischargeable capacity of the energy storage. After discharging is completed, the energy storage's SOC and discharging revenue are updated. The third priority control condition is that no charging action is executed, the current grid electricity price is greater than or equal to the optimal discharging price threshold, and the current SOC of the energy storage is higher than the preset safe lower limit.

[0013] The method provided in this optional embodiment, through forward-looking calculation to reserve curtailment space and combined with a three-level progressive charge-discharge control logic, prioritizes the absorption of photovoltaic curtailment, avoiding the crowding out of curtailment space by low-priced electricity purchases. It uses the optimal charging and discharging price thresholds as the basis for triggering charge-discharge decisions, orderly conducting market-based electricity price arbitrage. Simultaneously, it combines energy storage power, capacity, grid connection limitations, and state-of-charge safety boundary constraints to regulate energy storage operating conditions. This control logic achieves refined and orderly management of energy storage charging and discharging actions, with operating rules deeply adapted to the optimized electricity price thresholds. While ensuring the safe and stable operation of the equipment, it fully explores the value of curtailment utilization and peak-valley price differences, effectively improving the overall economic benefits of energy storage operation in different scenarios.

[0014] Secondly, this invention provides a photovoltaic (PV) power storage capacity configuration optimization device, comprising: an acquisition module for acquiring typical daily operation datasets and historical electricity price data of PV power plants under multiple statistical scenarios; a processing module for copying and splicing the typical daily operation datasets of each statistical scenario to obtain continuous time axis data of the target duration under the corresponding statistical scenario, wherein the continuous time axis data consists of operation data of the settlement period and the preheating period, the operation data of the preheating period is used for energy storage state of charge evolution, and the operation data of the settlement period is used for revenue statistics; and a solution module for solving based on each statistical scenario. The continuous time axis data and historical electricity price data of the target duration in the scenario are used to solve the pre-constructed two-layer collaborative optimization model to obtain the target energy storage configuration data of the photovoltaic power station. The two-layer collaborative optimization model includes an inner objective function and an outer objective function. The inner objective function is constructed with the goal of maximizing the net income of each settlement period. The net income of each settlement period is determined by the energy storage configuration parameters and electricity price data. The outer objective function is constructed with the goal of maximizing the net present value of energy storage over its entire life cycle. The net present value of energy storage over its entire life cycle is calculated based on the net income of each settlement period and the energy storage configuration parameters.

[0015] Thirdly, the present invention provides an electronic device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the photovoltaic power storage capacity configuration optimization method of the first aspect or any corresponding embodiment described above.

[0016] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute the photovoltaic power generation and storage capacity configuration optimization method of the first aspect or any corresponding embodiment thereof.

[0017] Fifthly, the present invention provides a computer program product, including computer instructions, which are used to cause a computer to execute the photovoltaic power generation and storage capacity configuration optimization method of the first aspect or any corresponding embodiment described above. Attached Figure Description

[0018] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0019] Figure 1 This is a schematic diagram of an application scenario according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the first process of the photovoltaic power generation and storage capacity configuration optimization method according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the second process of the photovoltaic power generation and storage capacity configuration optimization method according to an embodiment of the present invention; Figure 4 This is a schematic diagram of the third process of the photovoltaic power storage capacity configuration optimization method according to an embodiment of the present invention; Figure 5 This is a schematic diagram showing the comparative analysis results of the photovoltaic power storage capacity configuration optimization method in the embodiments of this application; Figure 6 This is a structural block diagram of a photovoltaic power storage capacity configuration optimization device according to an embodiment of the present invention; Figure 7 This is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] It is understood that before using the technical solutions disclosed in the various embodiments of the present invention, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in the present invention and their authorization should be obtained in accordance with relevant laws and regulations through appropriate means.

[0022] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0023] As an optional application scenario of this invention, considering the specific application environment architecture or specific hardware architecture upon which the photovoltaic energy storage capacity configuration optimization method depends, the specific application environment architecture or specific hardware architecture is described here. For example... Figure 1 As shown, the architecture system may include at least one terminal device and at least one server. Figure 1 The system is illustrated in the example, which includes a computer 101, a mobile terminal 102, and a server 103, and the terminal devices such as the computer 101 and the mobile terminal 102 are connected to the server 103 through a network 110.

[0024] Specifically, the terminal device can be a smartphone, tablet, laptop, PDA, desktop computer, game console, smart TV, smart wearable device, in-vehicle terminal, VR (Virtual Reality) device, AR (Augmented Reality) device, etc. Server 103 can be a standalone physical server, a server cluster, a distributed system, or a cloud server providing cloud services. Network 110 can be a wired or wireless network, examples of which include, but are not limited to, the Internet, corporate intranet, local area network, wide area network, mobile communication network, and combinations thereof.

[0025] In related technologies, there are two main typical paths for energy storage configuration and operation optimization: one is to first preset a fixed operation strategy, such as timed charging and discharging rules, and then carry out capacity optimization; the other is to first determine the energy storage capacity and then optimize the operation strategy accordingly. Both paths generally have significant drawbacks: First, the planning and operation stages are isolated, failing to form a two-layer coupled optimization mechanism, and thus failing to fully explore the revenue potential of energy storage throughout its entire lifecycle; second, daily simulations often adopt boundary assumptions of daily zero-state start-up or zero-energy discharge at the end of the day, violating the physical characteristics of continuous energy storage operation, leading to a severe underestimation of the economic value of cross-day energy time-shifting.

[0026] In view of this, this application provides a method for optimizing photovoltaic (PV) energy storage capacity configuration, which can be applied to a single server to optimize the configuration of PV energy storage capacity. The method provided in this application constructs a two-layer collaborative optimization model that includes maximizing inner-layer net revenue and maximizing outer-layer net present value (NPV) over the entire lifecycle. This deeply couples energy storage configuration parameters with operating strategies. While matching the optimal operating strategy for each group of candidate energy storage configurations, it locks in the optimal capacity configuration with NPV over the entire lifecycle as the target. This fully explores the lifecycle revenue potential of energy storage in curtailment and peak-valley arbitrage. Furthermore, by replicating and splicing typical daily operating datasets into continuous timeline data including the preheating period and settlement period, the steady-state evolution of the energy storage's state of charge is completed during the preheating period. This eliminates the boundary effects of traditional daily simulations, realistically recreates the physical scenario of continuous cross-day energy storage operation, accurately calculates the economic value of cross-day energy time shifts, and improves the accuracy and reliability of revenue calculation. This provides PV power plants with an energy storage capacity configuration scheme that is more in line with actual operating patterns and has better revenue potential.

[0027] According to an embodiment of the present invention, a method for optimizing photovoltaic storage capacity configuration is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0028] This embodiment provides a photovoltaic power generation and energy storage capacity configuration optimization method, which can be used in the aforementioned server. Figure 2 This is a flowchart of a photovoltaic power generation and storage capacity configuration optimization method according to an embodiment of the present invention, such as... Figure 2 As shown, the process includes the following steps: Step S201: Obtain typical daily operation datasets and historical electricity price data of photovoltaic power plants under multiple statistical scenarios.

[0029] For example, the photovoltaic power station is a photovoltaic power generation site for which energy storage capacity optimization is to be performed; multiple statistical scenarios can be various operating conditions classified according to month, season, sunlight conditions, and power generation characteristics, used to cover the differentiated power generation and consumption patterns throughout the year. In this embodiment, multiple statistical scenarios can be multiple months of the year. The typical daily operation dataset is extracted from the long-term measured data of the corresponding statistical scenario, representing the representative daily time-series operation data that can represent the overall power generation, curtailment, and output change characteristics of the scenario. Historical electricity price data represents the time-series data of the purchase price and grid connection price of the power grid in the target area in various periods in the past, providing a basis for electricity price threshold optimization, charging and discharging determination, and revenue calculation. The target area refers to the power supply jurisdiction and administrative scope where the photovoltaic power station is located. The area uniformly implements the local time-of-use electricity price policy, grid connection rules, and power consumption standards. The electricity price level and grid operating conditions in the area are the limiting range for calculating the power station's purchase and sale costs and revenues.

[0030] Step S202: Copy and splice the typical daily operation datasets of each statistical scenario to obtain continuous time axis data for the target duration under the corresponding statistical scenario. The continuous time axis data consists of operation data during the settlement period and the preheating period. The operation data during the preheating period is used for the evolution of energy storage charge state, and the operation data during the settlement period is used for revenue statistics.

[0031] For example, the target duration may include, but is not limited to, 24 hours in a day. In this embodiment, a typical daily operation dataset under a single statistical scenario is extracted, containing fixed time-series data such as photovoltaic output, curtailment, and load for each time period of 24 hours. According to the set target duration, this typical daily data is repeatedly copied and seamlessly spliced ​​together in chronological order to generate 48 hours of continuous time-series data, forming a complete continuous time axis. The spliced ​​continuous data is fixedly divided into two parts: a preheating period and a settlement period. The first half of the continuous data (the first 24 hours) is used only to simulate the continuous evolution of the energy storage state of charge (SOC), without calculating revenue, allowing the SOC to escape the initial zero value limitation and reach a steady state of true continuous operation. The second half of the continuous data (the last 24 hours) is used as the core revenue calculation interval. Based on the stable SOC after preheating, the net revenue of energy storage operation is accurately calculated. Finally, the complete continuous time axis data including the preheating period and the settlement period under this statistical scenario is obtained.

[0032] Step S203: Solve the pre-constructed two-layer collaborative optimization model based on the continuous time axis data and historical electricity price data of the target duration under each statistical scenario to obtain the target energy storage configuration data of the photovoltaic power station. The two-layer collaborative optimization model includes an inner objective function and an outer objective function. The inner objective function is constructed with the goal of maximizing the net income of each settlement period. The net income of each settlement period is determined by the energy storage configuration parameters and electricity price data. The outer objective function is constructed with the goal of maximizing the net present value of the energy storage throughout its entire life cycle. The net present value of the energy storage throughout its entire life cycle is calculated based on the net income of each settlement period and the energy storage configuration parameters.

[0033] For example, based on continuous time-axis operating data and historical electricity price data as input, a two-layer collaborative optimization model is invoked for coupled solution. The inner layer combines energy storage configuration parameters and electricity price data to optimize the operating strategy and maximize net income in each settlement period; the outer layer introduces the net income and energy storage configuration parameters output by the inner layer for each period, calculates the net present value over the entire life cycle, and completes the optimization. Through the iterative calculation of the two-layer model, the optimal energy storage configuration data adapted to the photovoltaic power station is finally output. In this embodiment, the optimization variable of the inner layer objective function is the charging electricity price threshold. Discharge price threshold The inner objective function is shown in the following equation:

[0034] in, This indicates the net operating income for a single day. Revenue from electricity sales in time period t refers to the operating income obtained by energy storage from selling electricity to the grid during that time period. It is calculated from the on-grid electricity price in time period t and the amount (or power) of electricity sold during that time period. The cost of electricity purchase in time period t refers to the cost paid by energy storage to purchase electricity from the grid during that time period. It is calculated from the grid price (electricity purchase price) of electricity in time period t and the amount of electricity (or power) purchased during that time period.

[0035] The constraints are ,in, The price is set at 1.0 yuan / MWh to prevent frequent start-ups and shutdowns that are not profitable.

[0036] The second objective function is:

[0037] in, Represents the net present value of energy storage over its entire lifecycle. Indicates the initial investment cost; Indicates the first Total annual revenue is obtained by summing up the net operating revenue of each day. This indicates the total lifecycle accounting period of the energy storage system; Indicates the project's benchmark discount rate; Indicates the first Total annual operating cost.

[0038] It can be calculated using the following formula:

[0039] in, This represents the investment cost per unit power of energy storage (RMB / MW). The unit capacity investment cost of energy storage (RMB / MWh) is taken from the pre-processed economic parameters of energy storage equipment; This indicates the rated power (MW) of the energy storage system. This indicates the rated capacity (MWh) of the energy storage system.

[0040] The photovoltaic energy storage capacity configuration optimization method provided in this embodiment constructs a two-layer collaborative optimization model that includes maximizing inner-layer net revenue and maximizing outer-layer net present value over the entire life cycle. This deeply couples energy storage configuration parameters with operation strategies. While matching the optimal operation strategy for each group of candidate energy storage configurations, it locks the optimal capacity configuration with the net present value over the entire life cycle as the target. This fully explores the full life cycle revenue potential of energy storage in curtailment and peak-valley arbitrage. At the same time, by copying and splicing typical daily operation datasets into continuous time axis data including the preheating period and the settlement period, the steady-state evolution of the energy storage charge state is completed during the preheating period. This eliminates the boundary effects of traditional daily simulations, realistically restores the physical scenario of continuous cross-day operation of energy storage, accurately calculates the economic value of cross-day energy time shift, and improves the accuracy and reliability of revenue calculation. This provides photovoltaic power plants with an energy storage capacity configuration scheme that is more in line with actual operating rules and has better revenue potential.

[0041] This embodiment provides a photovoltaic power generation and energy storage capacity configuration optimization method, which can be used in the aforementioned server. Figure 3 This is a flowchart of a photovoltaic power generation and storage capacity configuration optimization method according to an embodiment of the present invention, such as... Figure 3 As shown, the process includes the following steps: Step S301: Obtain typical daily operation datasets and historical electricity price data for photovoltaic power plants under multiple statistical scenarios. For details, please refer to [link to relevant documentation]. Figure 2 Step S201 of the illustrated embodiment will not be described again here.

[0042] Step S302: Copy and splice the typical daily operation datasets of each statistical scenario to obtain continuous time axis data for the target duration under the corresponding statistical scenario. The continuous time axis data consists of operation data during the settlement period and the preheating period. The operation data during the preheating period is used for the evolution of energy storage charge state, and the operation data during the settlement period is used for revenue statistics.

[0043] Step S303: Solve the pre-constructed two-layer collaborative optimization model based on the continuous time axis data and historical electricity price data of the target duration under each statistical scenario to obtain the target energy storage configuration data of the photovoltaic power station. The two-layer collaborative optimization model includes an inner objective function and an outer objective function. The inner objective function is constructed with the goal of maximizing the net income of each settlement period. The net income of each settlement period is determined by the energy storage configuration parameters and electricity price data. The outer objective function is constructed with the goal of maximizing the net present value of the energy storage throughout its entire life cycle. The net present value of the energy storage throughout its entire life cycle is calculated based on the net income of each settlement period and the energy storage configuration parameters.

[0044] Specifically, step S303 includes: Step S3031: Obtain the value space of energy storage configuration parameters.

[0045] For example, energy storage configuration parameters may include, but are not limited to, installed capacity and rated power. The value range of energy storage configuration parameters includes the range of rated power and the range of installed capacity.

[0046] Step S3032: Discretize the data based on the value space to obtain multiple candidate configuration data.

[0047] For example, considering the construction conditions of photovoltaic power plants, grid access restrictions, equipment specifications, and site conditions, upper and lower limits of the rated power and installed capacity for permissible operation of energy storage are defined. This limits the feasible range for optimization calculations, providing boundary constraints for subsequent discrete value selection and candidate configuration generation. Within the established reasonable range of power and capacity, values ​​are uniformly selected at fixed steps, dividing the continuous numerical range into several independent and fixed discrete values. This yields multiple sets of energy storage power and capacity candidate parameters for selection, narrowing the optimization solution range. All the discrete power and capacity values ​​obtained are freely cross-combined and paired to form multiple sets of one-to-one power and capacity parameter combinations. Each combination constitutes a complete energy storage candidate configuration scheme.

[0048] Step S3033: Solve the two-layer collaborative optimization model based on the candidate configuration data, the continuous time axis data of the target duration under each statistical scenario, and the historical electricity price data to obtain the net present value of the energy storage life cycle of the corresponding candidate configuration data.

[0049] In some optional implementations, step S3033 above includes: Step a1: Based on the continuous time axis data of the target duration under each statistical scenario and the preset forward-looking resource prediction rules, determine the data of the space for preventing low-priced online electricity purchases from crowding out zero-cost abandoned electricity in each settlement period.

[0050] For example, in this embodiment of the application, when the simulation steps to time... At that time, calculate the potential abandoned power resources within the future horizon. To prevent low-priced online electricity purchases from crowding out zero-cost abandoned electricity space, the calculation formula is as follows:

[0051] in, Indicates at simulation time t At the same time, it reserves potential storage space for abandoned electricity in the future to constrain current grid-purchased electricity charging behavior and prevent low-priced electricity purchases from crowding out the zero-cost photovoltaic abandoned electricity consumption space; Indicates the first k The amount of photovoltaic power curtailment in a future time period, Indicates the rated power of energy storage; Indicates the first k The remaining grid connection capacity for a future time period, i.e., the total grid connection capacity and the current photovoltaic power generation. The difference represents the maximum additional power allowed to be connected to the grid, and is a representation of the grid connection constraint for power curtailment and absorption. Specifically, ; The charging efficiency of an energy storage system is used to convert the abandoned power that can be absorbed into the actual storable power. This efficiency must be multiplied by the charging efficiency to account for energy loss.

[0052] Step a2: Determine the range of electricity price thresholds for each settlement period based on historical electricity price data.

[0053] For example, historical time-of-use electricity price data for the target area are grouped by settlement period, and the minimum, maximum, and quantile distribution of the off-grid electricity price (purchase price) and the on-grid electricity price for each period are statistically analyzed. Based on the range of historical off-grid electricity prices, the range of the optimal charging electricity price threshold is set, usually from the minimum to the middle-high quantile of the historical purchase price, to avoid the threshold being too low to trigger arbitrage or too high to lose economic viability. Based on the range of historical on-grid electricity prices, the range of the optimal discharging electricity price threshold is set, usually from the middle-low quantile to the maximum of the historical on-grid electricity price, to ensure that the discharging action can cover the high-price range during peak periods. Based on actual needs, the above range is appropriately narrowed to form the final electricity price threshold search boundary used for inner-layer optimization.

[0054] Step a3: Based on the preset multi-priority charging and discharging control logic, the curtailment space data of each settlement period, the candidate configuration data, and the electricity price range of each settlement period, the inner objective function is solved to obtain the optimal electricity price threshold data for each statistical scenario under the corresponding candidate configuration and the net revenue of each settlement period. The preset multi-priority charging and discharging control logic determines the charging and discharging rules of energy storage through the curtailment space data and electricity price threshold of each settlement period.

[0055] Specifically, the optimal electricity price threshold data includes the optimal charging electricity price threshold and the optimal discharging electricity price threshold, and the preset multi-priority charging and discharging control logic is as follows: First, at the current control time t, read the time series data of the curtailment space for all time periods after time t within the settlement period. Combined with the rated charging power of energy storage, the power constraint of the grid connection point, and the charging efficiency of energy storage, calculate the curtailment space data that needs to be reserved for future photovoltaic curtailment at the current time.

[0056] For example, at the current control time t, firstly, read the time series data of the curtailment space for all time periods after time t within the settlement period to obtain the photovoltaic curtailment power for each future time period; then, for each future time period, take the minimum value of the photovoltaic curtailment power, the rated charging power of energy storage, and the remaining power constraint of the grid connection point to obtain the upper limit of the actual curtailment power that can be absorbed in that time period; after summing the absorbable curtailment power of all future time periods, multiply by the energy storage charging efficiency to finally obtain the total curtailment space electricity that needs to be reserved for future photovoltaic curtailment at the current time. This electricity will be used to constrain the maximum allowable capacity of grid-purchased electricity charging at the current time, so as to avoid low-priced electricity purchases crowding out the absorption space of zero-cost curtailment.

[0057] Second, if the first priority control condition is met, the abandoned power charging action is executed. The charging power is the minimum value of the current photovoltaic abandoned power, the rated charging power of the energy storage, and the power corresponding to the remaining chargeable capacity of the energy storage. After charging is completed, the state of charge of the energy storage is updated. The first priority control condition is that there is photovoltaic abandoned power that can be absorbed at the current time, and the current state of charge of the energy storage is lower than the preset safe upper limit of the state of charge.

[0058] For example, at any given moment, there is available photovoltaic (PV) power curtailment (curtailment power greater than 0), and the current state of charge (SOC) of the energy storage is below a preset safety limit. If both conditions are met, the charging power is calculated, and the minimum value among the current PV curtailment power, the rated charging power of the energy storage, and the power corresponding to the remaining chargeable capacity of the energy storage is taken. This power is used to perform the curtailment charging action, prioritizing the storage of zero-cost PV curtailment into the energy storage. After charging is complete, the energy storage SOC is updated based on the charging amount. If the conditions are not met, the process directly proceeds to the next priority control step. In this embodiment, if curtailment resources exist, charging is prioritized at maximum capacity and included in the calculation. .

[0059] Third, when the second priority control condition is met, based on the current remaining rechargeable capacity of the energy storage, the amount of abandoned electricity consumed at the current moment, and the abandoned electricity space data, the maximum amount of electricity allowed for grid-purchased electricity charging at the current moment is calculated. When the maximum amount of electricity allowed for grid-purchased electricity charging is greater than 0, the grid-purchased electricity charging action is executed. The charging power is taken as the minimum value of the rated charging power of the energy storage, the power corresponding to the maximum allowed amount of electricity, and the power corresponding to the remaining rechargeable capacity of the energy storage. After charging is completed, the energy storage state of charge and charging cost are updated, and the subsequent priority control at the current moment is terminated. The second priority control condition is that the abandoned electricity charging action has not been executed, the current grid electricity price is less than or equal to the optimal charging electricity price threshold, and the current state of charge of the energy storage is lower than the preset safe upper limit of the state of charge.

[0060] For example, it is confirmed that no power curtailment charging action has been performed, the current grid electricity price is not higher than the optimal charging price threshold, and the current state of charge (SOC) of the energy storage is lower than the preset safety limit. If all conditions are met, the maximum allowable amount of electricity for grid-purchased charging is calculated based on the current remaining rechargeable capacity of the energy storage, the current amount of power curtailed, and the reserved future power curtailment space (the core is to release the remaining rechargeable capacity for grid-purchased charging while ensuring the future curtailment consumption). When the maximum allowable amount of electricity is greater than 0, the minimum value of the rated charging power of the energy storage, the power corresponding to the time period of the maximum allowable amount of electricity, and the power corresponding to the remaining rechargeable capacity of the energy storage is taken as the charging power, and the grid-purchased electricity charging action is performed; after charging is completed, the energy storage SOC and electricity purchase cost are updated, and the subsequent priority control at the current moment is terminated. If the conditions are not met or the maximum allowable amount of electricity is ≤0, the next priority control process is directly entered. In this embodiment of the application, the current remaining battery space is calculated. Calculate the allowed electricity consumption for online shopping: .like ,and To implement online shopping for charging, among which, This indicates the maximum amount of electricity that can be charged via online shopping at the current moment. This represents the grid-purchased electricity price at the time t before control. This represents the optimal charging price threshold.

[0061] Fourth, when the third priority control condition is met, the discharge action is executed. The discharge power is the minimum value of the rated discharge power of the energy storage, the remaining connectable power at the grid connection point, and the power corresponding to the remaining dischargeable capacity of the energy storage. After the discharge is completed, the energy storage state of charge and discharge revenue are updated. The third priority control condition is that no charging action is performed, the current grid-connected electricity price is greater than or equal to the optimal discharge electricity price threshold, and the current state of charge of the energy storage is higher than the preset safe lower limit of the state of charge.

[0062] For example, if no charging action is performed at the current moment, the grid-connected electricity price is not lower than the optimal discharge price threshold, and the energy storage state of charge is higher than the preset safety lower limit, the minimum value of the energy storage rated discharge power, the remaining connectable power at the grid connection point, and the power corresponding to the remaining dischargeable capacity of the energy storage is taken as the discharge power, and the discharge action is performed; after the discharge is completed, the energy storage state of charge is updated and the cumulative discharge revenue is calculated. If the conditions are not met, the energy storage does not perform any operation at the current moment. In this embodiment of the application, if the current state is not charging, and The discharge operation is performed. The discharge power is constrained by the grid connection point as follows:

[0063] in, This represents the rated discharge power of the energy storage, which is the maximum discharge power allowed for safe operation of the equipment and is a hardware constraint. Indicates the maximum total power capacity of the grid connection point; This indicates the remaining connectable power at the grid connection point, which is the upper limit of the additional power that the grid can currently receive, and serves as a grid connection constraint. This means taking the minimum value between the rated discharge power of the energy storage and the remaining connectable power at the grid connection point as the safe upper limit of the discharge power, ensuring that the discharge does not exceed the capacity of the energy storage device or the grid connection capacity.

[0064] In some alternative implementations, step a3 above includes: Step a31: Determine multiple candidate electricity price thresholds for the corresponding settlement period based on the range of electricity price values ​​for each settlement period.

[0065] For example, in this embodiment of the application, for each settlement period, the value ranges of the charging price threshold and the discharging price threshold are first determined. Then, within the corresponding range, multiple candidate combinations containing the charging threshold and the discharging threshold are generated by means of uniform sampling, random sampling or quantile division. At the same time, it is ensured that the candidate combinations meet the economic logic that the charging threshold is lower than the discharging threshold. After eliminating invalid combinations, the set of valid candidate price thresholds for the settlement period is obtained, which provides an initial solution for subsequent inner-layer optimization iterations.

[0066] Step a32: Based on the differential evolution algorithm, the inner objective function is iteratively solved using the running data of multiple settlement periods, the data of each candidate configuration, and the curtailment space data of multiple settlement periods until convergence accuracy is achieved, so as to obtain the optimal electricity price threshold under the corresponding candidate configuration and the net revenue of each settlement period.

[0067] For example, in this embodiment of the application, the candidate electricity price threshold combination for each settlement period is used as the initial population individuals of the differential evolution algorithm. For each individual, the operation data of multiple settlement periods, the current candidate energy storage configuration data, and the curtailment space data of each settlement period are combined. The preset multi-priority charging and discharging control logic is called to simulate the charging and discharging behavior of energy storage in each settlement period, and the inner objective function value (i.e. the sum of net income in each settlement period) corresponding to the individual is calculated. Then, new candidate individuals are generated through mutation and crossover operations of the differential evolution algorithm. Based on the merits of the objective function values, the better individuals are retained to update the population. The above process is iterated until the algorithm reaches the preset convergence accuracy. Finally, the optimal electricity price threshold that maximizes the net income in each settlement period under the candidate energy storage configuration and the corresponding net income in each settlement period are output.

[0068] Step a4: Based on the net income of each settlement period under each candidate configuration and the data of each candidate configuration, solve the outer objective function to obtain the net present value of the energy storage life cycle of the corresponding candidate configuration data.

[0069] For example, in this embodiment of the application, based on the parameters of each candidate energy storage configuration and the net income of each settlement period, according to the calculation framework of net present value of the whole life cycle, the net income of each settlement period is first discounted to the base year at a preset discount rate and accumulated to obtain the total discounted income. Then, the total discounted cost is calculated by combining the initial investment, full life cycle operation and maintenance costs corresponding to the candidate configuration. The net present value of the energy storage of the whole life cycle under the candidate configuration is obtained by the difference between the total discounted income and the total discounted cost, thus completing the solution of the outer objective function.

[0070] This embodiment provides a photovoltaic power generation and energy storage capacity configuration optimization method, which can be used in the aforementioned server. Figure 4 This is a flowchart of a photovoltaic power generation and storage capacity configuration optimization method according to an embodiment of the present invention, such as... Figure 4 As shown, the process includes the following steps: Step S401: Obtain typical daily operation datasets and historical electricity price data of photovoltaic power plants under multiple statistical scenarios.

[0071] Specifically, step S401 includes: Step S4011: Obtain historical operation data and historical electricity price data of the photovoltaic power station.

[0072] For example, in this embodiment of the application, historical operating data and historical electricity price data of photovoltaic power plants are collected. The data sources include: photovoltaic output data: actual power generation and curtailment data of the power plants in the past year, with an original sampling frequency of 15 minutes / point; electricity price data: time-of-use electricity price of photovoltaic power plants and spot market price prediction coefficients; physical constraints: transformer capacity limit at the grid connection point and maximum allowable reverse power.

[0073] Step S4012 involves cleaning and multi-scale aggregation of the historical operating data of the photovoltaic power station to obtain hourly operating data.

[0074] For example, in this embodiment of the application, the original photovoltaic data is subjected to integrity verification, zero or dead values ​​caused by communication failures are removed, and missing time periods are filled in using linear interpolation. In order to match the simulation step size and retain instantaneous power characteristics, the power generation data at the 15-minute granularity is aggregated into hourly data. Specifically, a "summation aggregation" strategy is adopted, which adds up the power generation of the four 15-minute intervals within each hour to obtain the hourly power generation. At the same time, the amount of electricity wasted during that hour is retained. and maximum power limiting .

[0075] Step S4013: Process the hourly running data based on the weighted clustering algorithm to obtain typical daily running datasets for multiple statistical scenarios.

[0076] For example, in this embodiment of the application, based on a weighted clustering algorithm, the data for 365 days of the year is compressed into 12 typical monthly scenarios. For each month... Construct a typical daily dataset containing the following feature vectors. Specifically, this includes: 24-hour on-grid electricity price vector 24-hour off-grid electricity price vector Distribution of abandoned power resources 24 hours a day .

[0077] Step S402 involves copying and concatenating the typical daily operation datasets for each statistical scenario to obtain continuous timeline data for the target duration under the corresponding statistical scenario. The continuous timeline data consists of operation data from the settlement period and the preheating period. The operation data from the preheating period is used for energy storage state of charge evolution, and the operation data from the settlement period is used for revenue statistics. For details, please refer to [link to relevant documentation]. Figure 3 Step S302 of the illustrated embodiment will not be described again here.

[0078] Step S403: Based on continuous time axis data and historical electricity price data for the target duration under each statistical scenario, the pre-constructed two-layer collaborative optimization model is solved to obtain the target energy storage configuration data for the photovoltaic power station. The two-layer collaborative optimization model includes an inner objective function and an outer objective function. The inner objective function is constructed with the goal of maximizing the net income in each settlement period. The net income in each settlement period is determined by the energy storage configuration parameters and electricity price data. The outer objective function is constructed with the goal of maximizing the net present value (NPV) of the energy storage over its entire life cycle. The NPV of the energy storage over its entire life cycle is calculated based on the net income in each settlement period and the energy storage configuration parameters. For details, please refer to [link to details]. Figure 3 Step S303 of the illustrated embodiment will not be described again here.

[0079] Example: The following specific example illustrates the method for optimizing photovoltaic power generation and energy storage capacity configuration.

[0080] After two-layer collaborative optimization and full lifecycle calculation, the final configuration results and comparative analysis are output. The comparative analysis mainly includes: initial investment, net present value, and IRR under different rated power levels; comparative demonstration of schemes with different durations; and optimization results of operational strategies. A schematic diagram of the comparative analysis results is shown below. Figure 5 As shown. (Through) Figure 5 It can be determined that as the energy storage capacity increases, the project's NPV exhibits an inverted U-shaped trend of first rising and then falling. When the capacity is less than 44MWh, increasing the capacity can significantly capture more curtailment and price difference revenue, and the NPV rises rapidly. At 22MW / 44MWh, the NPV reaches a peak of RMB 8.381 million, corresponding to an internal rate of return (IRR) of 41.36%. As the capacity continues to increase (e.g., exceeding 50MWh), the marginal returns decrease, and the high initial investment and battery replacement costs cause the NPV to fall back.

[0081] Conclusion: This algorithm successfully locked the globally optimal solution – the 22MW / 44MWh (2 hours) configuration scheme.

[0082] To verify the superiority of this invention, the optimal solution output by the algorithm (2-hour scheme) is compared with common long-term energy storage (4-hour scheme) as shown in Table 1 below: Table 1

[0083] As shown in Table 1 above, although the 4-hour scheme (comparison scheme) has a slightly larger capacity, the 2-hour scheme can more accurately achieve "rapid throughput" during periods of power curtailment, resulting in higher utilization, due to the forward-looking control strategy employed in this embodiment. The 4-hour scheme suffers from significantly lower battery utilization and higher replacement costs (13.8 million yuan vs. 13.2 million yuan), leading to a substantial decline in economic viability. This demonstrates that the "dual-layer collaboration + forward-looking control" method proposed in this embodiment can effectively avoid the investment risks associated with blindly configuring large capacities.

[0084] After applying the operating strategy of this invention, the annual curtailment rate of the photovoltaic power station increased by approximately 15.4%. Under the spot market rules of the Shandong power grid, by accurately capturing the peak electricity price (approximately RMB 1000 / MWh) between 17:00 and 19:00 in the evening for discharge, a win-win situation of photovoltaic power consumption and ancillary service revenue was achieved.

[0085] The method provided in this application, through a two-layer collaborative architecture, can uncover the maximum regulation potential of energy storage systems in complex electricity markets (especially those with midday valleys and evening peaks). Compared to traditional fixed strategies, the optimal capacity and operating threshold combination determined by this method can more accurately capture peak-valley price arbitrage opportunities. Experiments show that the internal rate of return (IRR) of the energy storage system configured using this method can be significantly improved (reaching 41.36% in the example), effectively avoiding resource waste due to over-configuration or limited returns due to under-configuration. It improves the realism and credibility of simulation calculations and eliminates "boundary effect" errors. The 48-hour rolling time-domain simulation mechanism successfully solves the errors caused by forced SOC reset in traditional single-day simulations. This method can realistically simulate the physical process of battery energy transfer across days, making the simulation results closer to the continuous operating state of the power station after actual commissioning. This is crucial for long-term (e.g., 20 years) full life-cycle financial calculations, providing high-fidelity data support for investment decisions. By introducing a forward-looking resource prediction and capacity reservation mechanism, this invention effectively addresses the pain point that energy storage systems in high-proportion photovoltaic (PV) grid connection scenarios are prone to missing out on subsequent zero-cost curtailment of PV power due to prioritizing current low-priced grid electricity. This strategy significantly improves the curtailment absorption capacity of PV power plants while ensuring energy storage revenue (the absorption rate is increased by approximately 15% in this example), achieving a dual optimization of economic benefits (reduced charging costs) and social benefits (increased green electricity share). The inner-layer optimization employs a differential evolution algorithm, trained based on typical daily scenarios after clustering. This makes the method applicable not only to current market rules, but also to changes in future electricity price curves (such as peak-valley shifts) or PV output characteristics. Simply updating the input data allows the algorithm to automatically evolve new optimal threshold parameters without requiring manual rewriting of the control logic, demonstrating strong engineering applicability and scalability.

[0086] This embodiment also provides a photovoltaic power generation and storage capacity configuration optimization device, which is used to implement the above embodiments and preferred embodiments, and will not be repeated as already described. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0087] This embodiment provides a photovoltaic power generation and energy storage capacity configuration optimization device, such as... Figure 6 As shown, it includes: The acquisition module 601 is used to acquire typical daily operation datasets and historical electricity price data of photovoltaic power plants under multiple statistical scenarios. The processing module 602 is used to copy and splice the typical daily operation datasets of each statistical scenario to obtain continuous time axis data of the target duration under the corresponding statistical scenario. The continuous time axis data consists of operation data of the settlement period and the preheating period. The operation data of the preheating period is used for the evolution of energy storage charge state, and the operation data of the settlement period is used for revenue statistics. The solver module 603 is used to solve a pre-built two-layer collaborative optimization model based on continuous time axis data and historical electricity price data for the target duration under various statistical scenarios, so as to obtain the target energy storage configuration data of the photovoltaic power station. The two-layer collaborative optimization model includes an inner objective function and an outer objective function. The inner objective function is constructed with the goal of maximizing the net income in each settlement period. The net income in each settlement period is determined by the energy storage configuration parameters and electricity price data. The outer objective function is constructed with the goal of maximizing the net present value of the energy storage over its entire life cycle. The net present value of the energy storage over its entire life cycle is calculated based on the net income in each settlement period and the energy storage configuration parameters.

[0088] In some alternative implementations, the solver module 603 includes: The first acquisition submodule is used to acquire the value space of energy storage configuration parameters; The first processing submodule is used to perform discretization based on the value space to obtain multiple candidate configuration data; The solution submodule is used to solve the two-layer collaborative optimization model based on each candidate configuration data, continuous time axis data of the target duration under each statistical scenario, and historical electricity price data, so as to obtain the net present value of the energy storage life cycle of the corresponding candidate configuration data. The determination submodule is used to determine the target energy storage configuration data from multiple candidate configuration data based on the net present value of the entire life cycle of energy storage.

[0089] In some alternative implementations, the solver submodule includes: The first determining unit is used to determine the data on the space for curtailment of electricity in each settlement period to prevent low-priced online electricity purchases from crowding out zero-cost electricity, based on continuous time axis data of the target duration under each statistical scenario and preset forward-looking resource prediction rules. The second determining unit is used to determine the range of electricity price threshold values ​​for each settlement period based on historical electricity price data; The first solution unit is used to solve the inner objective function based on the preset multi-priority charging and discharging control logic, the abandoned power space data of each settlement period, the candidate configuration data, and the range of electricity price values ​​of each settlement period. It obtains the optimal electricity price threshold data for each statistical scenario under the corresponding candidate configuration and the net income of each settlement period. The preset multi-priority charging and discharging control logic determines the charging and discharging rules of energy storage through the abandoned power space data and electricity price thresholds of each settlement period. The second solution unit is used to solve the outer objective function based on the net income of each settlement period under each candidate configuration and the data of each candidate configuration, so as to obtain the net present value of the energy storage life cycle of the corresponding candidate configuration data.

[0090] In some alternative implementations, the first solving unit includes: The sub-unit is determined to identify multiple candidate electricity price thresholds for the corresponding settlement period based on the range of electricity price values ​​for each settlement period. The solution sub-unit is used to iteratively solve the inner objective function based on the differential evolution algorithm, using running data from multiple settlement periods, candidate configuration data, and abandoned power space data from multiple settlement periods, until convergence accuracy is achieved, and to obtain the optimal electricity price threshold under the corresponding candidate configuration and the net revenue for each settlement period.

[0091] In some optional implementations, the optimal electricity price threshold data includes the optimal charging electricity price threshold and the optimal discharging electricity price threshold, and the preset multi-priority charging and discharging control logic is as follows: At the current control time t, read the time series data of the curtailment space for all time periods after time t within the settlement period, and combine the rated charging power of energy storage, the power constraint of the grid connection point, and the charging efficiency of energy storage to calculate the curtailment space data that needs to be reserved for future photovoltaic curtailment at the current time. If the first priority control condition is met, the abandoned power charging action is executed. The charging power is the minimum value of the current photovoltaic abandoned power, the rated charging power of the energy storage, and the power corresponding to the remaining chargeable capacity of the energy storage. After charging is completed, the state of charge of the energy storage is updated. The first priority control condition is that there is photovoltaic abandoned power that can be absorbed at the current time, and the current state of charge of the energy storage is lower than the preset safe upper limit of the state of charge. When the second priority control condition is met, based on the current remaining rechargeable capacity of the energy storage, the amount of abandoned electricity consumed at the current moment, and the abandoned electricity space data, the maximum amount of electricity allowed for grid-purchased electricity charging at the current moment is calculated. When the maximum amount of electricity allowed for grid-purchased electricity charging is greater than 0, the grid-purchased electricity charging action is executed. The charging power is taken as the minimum value of the rated charging power of the energy storage, the power corresponding to the maximum allowed amount of electricity, and the power corresponding to the remaining rechargeable capacity of the energy storage. After charging is completed, the energy storage state of charge and charging cost are updated, and the subsequent priority control at the current moment is terminated. The second priority control condition is that the abandoned electricity charging action has not been executed, the current grid electricity price is less than or equal to the optimal charging electricity price threshold, and the current state of charge of the energy storage is lower than the preset safe upper limit of the state of charge. When the third priority control condition is met, the discharge action is executed. The discharge power is the minimum value of the rated discharge power of the energy storage, the remaining connectable power at the grid connection point, and the power corresponding to the remaining dischargeable capacity of the energy storage. After the discharge is completed, the energy storage state of charge and discharge revenue are updated. The third priority control condition is that no charging action is performed, the current grid-connected electricity price is greater than or equal to the optimal discharge electricity price threshold, and the current state of charge of the energy storage is higher than the preset safe lower limit of the state of charge.

[0092] In some alternative implementations, the acquisition module 601 includes: The second acquisition module is used to acquire historical operating data and historical electricity price data of photovoltaic power plants; The second processing submodule is used to perform data cleaning and multi-scale aggregation processing on the historical operation data of photovoltaic power stations to obtain hourly operation data. The third processing submodule is used to process hourly running data based on a weighted clustering algorithm to obtain typical daily running datasets for multiple statistical scenarios.

[0093] The photovoltaic power generation and storage capacity configuration optimization device provided in this embodiment of the invention can execute the photovoltaic power generation and storage capacity configuration optimization method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects for executing the method. Further functional descriptions of the above modules and units are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0094] Figure 7 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0095] The following is a detailed reference. Figure 7 This diagram illustrates a suitable structural schematic for implementing an electronic device according to embodiments of the present invention. The electronic device may include a processor (e.g., a central processing unit, graphics processor, etc.) 701, which can perform various appropriate actions and processes based on a program stored in read-only memory (ROM) 702 or a program loaded from memory 708 into random access memory (RAM) 703. The RAM 703 also stores various programs and data required for the operation of the electronic device. The processor 701, ROM 702, and RAM 703 are interconnected via a bus 704. An input / output (I / O) interface 705 is also connected to the bus 704.

[0096] Typically, the following devices can be connected to I / O interface 705: input devices 706 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 707 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; memory devices 708 including, for example, magnetic tapes, hard disks, etc.; and communication devices 709. Communication device 709 allows electronic devices to exchange data via wireless or wired communication with other devices. Although Figure 7 Electronic devices with various devices are shown, but it should be understood that it is not required to implement or have all of the devices shown, and more or fewer devices may be implemented or have instead.

[0097] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 709, or installed from a memory 708, or installed from a ROM 702. When the computer program is executed by the processor 701, it performs the functions defined in the photovoltaic power generation and storage capacity configuration optimization method of the embodiments of the present invention.

[0098] Figure 7 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0099] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, the photovoltaic energy storage capacity configuration optimization method shown in the above embodiments is implemented.

[0100] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0101] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for optimizing photovoltaic power generation and energy storage capacity configuration, characterized in that, The method includes: Acquire typical daily operation datasets and historical electricity price data for photovoltaic power plants under multiple statistical scenarios; The typical daily operation datasets of each statistical scenario are copied and spliced ​​to obtain continuous time axis data for the target duration under the corresponding statistical scenario. The continuous time axis data consists of operation data during the settlement period and the preheating period. The operation data during the preheating period is used for energy storage charge state evolution, and the operation data during the settlement period is used for revenue statistics. Based on continuous time axis data and historical electricity price data for target durations under various statistical scenarios, a pre-constructed two-layer collaborative optimization model is solved to obtain the target energy storage configuration data for photovoltaic power plants. The two-layer collaborative optimization model includes an inner objective function and an outer objective function. The inner objective function is constructed with the goal of maximizing the net income in each settlement period. The net income in each settlement period is determined by energy storage configuration parameters and electricity price data. The outer objective function is constructed with the goal of maximizing the net present value of energy storage over its entire life cycle. The net present value of energy storage over its entire life cycle is calculated based on the net income in each settlement period and the energy storage configuration parameters.

2. The method according to claim 1, characterized in that, The step of solving the pre-constructed two-layer collaborative optimization model based on continuous time axis data and historical electricity price data under various statistical scenarios to obtain the target energy storage configuration data for photovoltaic power plants includes: Obtain the value range of energy storage configuration parameters; Based on the value space, discretization is performed to obtain multiple candidate configuration data; Based on the candidate configuration data, the continuous time axis data of the target duration under each statistical scenario, and the historical electricity price data, the two-layer collaborative optimization model is solved to obtain the net present value of the energy storage life cycle of the corresponding candidate configuration data. Based on the net present value of the entire life cycle of energy storage for each candidate configuration data, the target energy storage configuration data is determined from multiple candidate configuration data.

3. The method according to claim 2, characterized in that, The two-layer collaborative optimization model is solved based on candidate configuration data, continuous time axis data of target duration under each statistical scenario, and historical electricity price data to obtain the net present value of the energy storage life cycle for the corresponding candidate configuration data, including: Based on continuous time axis data of target duration under various statistical scenarios and preset forward-looking resource prediction rules, data is determined for each settlement period to prevent low-priced online electricity purchases from squeezing out zero-cost abandoned electricity space. The range of electricity price threshold values ​​for each settlement period is determined based on the historical electricity price data. Based on the preset multi-priority charging and discharging control logic, the abandoned power space data of each settlement period, the candidate configuration data, and the range of electricity price values ​​of each settlement period, the inner objective function is solved to obtain the optimal electricity price threshold data for each statistical scenario under the corresponding candidate configuration and the net revenue of each settlement period. The preset multi-priority charging and discharging control logic determines the charging and discharging rules of energy storage through the abandoned power space data and electricity price thresholds of each settlement period. Based on the net income of each settlement period under each candidate configuration and the solution of the outer objective function of each candidate configuration data, the net present value of the energy storage life cycle of the corresponding candidate configuration data is obtained.

4. The method according to claim 3, characterized in that, The process of solving the inner objective function based on preset multi-priority charging and discharging control logic, the curtailment space data for each settlement period, the candidate configuration data, and the electricity price range for each settlement period yields the optimal electricity price threshold under the corresponding candidate configuration and the net revenue for each settlement period, including: Multiple candidate electricity price thresholds for each settlement period are determined based on the range of electricity price values ​​for each settlement period. Based on the differential evolution algorithm, the inner objective function is iteratively solved using the running data of multiple settlement periods, the data of each candidate configuration, and the curtailment space data of multiple settlement periods until convergence accuracy is achieved, so as to obtain the optimal electricity price threshold under the corresponding candidate configuration and the net revenue of each settlement period.

5. The method according to claim 3 or 4, characterized in that, The optimal electricity price threshold data includes the optimal charging electricity price threshold and the optimal discharging electricity price threshold. The preset multi-priority charging and discharging control logic is as follows: At the current control time t, read the time series data of the curtailment space for all time periods after time t within the settlement period, and combine the rated charging power of energy storage, the power constraint of the grid connection point, and the charging efficiency of energy storage to calculate the curtailment space data that needs to be reserved for future photovoltaic curtailment at the current time. If the first priority control condition is met, the abandoned power charging action is executed. The charging power is the minimum value of the current photovoltaic abandoned power, the rated charging power of the energy storage, and the power corresponding to the remaining chargeable capacity of the energy storage. After charging is completed, the state of charge of the energy storage is updated. The first priority control condition is that there is photovoltaic abandoned power that can be absorbed at the current time, and the current state of charge of the energy storage is lower than the preset safe upper limit of the state of charge. When the second priority control condition is met, based on the current remaining rechargeable capacity of the energy storage, the amount of abandoned electricity consumed at the current moment, and the abandoned electricity space data, the maximum amount of electricity allowed for grid-purchased electricity charging at the current moment is calculated. When the maximum amount of electricity allowed for grid-purchased electricity charging is greater than 0, the grid-purchased electricity charging action is executed. The charging power is taken as the minimum value of the rated charging power of the energy storage, the power corresponding to the maximum allowed electricity, and the power corresponding to the remaining rechargeable capacity of the energy storage. After charging is completed, the energy storage state of charge and charging cost are updated, and the subsequent priority control at the current moment is terminated. The second priority control condition is that no abandoned electricity charging action is executed, the current grid electricity price is less than or equal to the optimal charging electricity price threshold, and the current state of charge of the energy storage is lower than the preset safe upper limit value of the state of charge. When the third priority control condition is met, the discharge action is executed. The discharge power is the minimum value of the rated discharge power of the energy storage, the remaining connectable power at the grid connection point, and the power corresponding to the remaining dischargeable capacity of the energy storage. After the discharge is completed, the energy storage state of charge and discharge revenue are updated. The third priority control condition is that no charging action is performed, the current grid-connected electricity price is greater than or equal to the optimal discharge electricity price threshold, and the current state of charge of the energy storage is higher than the preset safe lower limit of the state of charge.

6. The method according to any one of claims 1 to 4, characterized in that, The steps for obtaining typical daily operation datasets and historical electricity price data of photovoltaic power plants under multiple statistical scenarios include: Obtain historical operation data and historical electricity price data of photovoltaic power plants; Historical operational data of photovoltaic power plants were cleaned and aggregated at multiple scales to obtain hourly operational data. The hourly operational data is processed using a weighted clustering algorithm to obtain typical daily operational datasets for multiple statistical scenarios.

7. A photovoltaic power generation and energy storage capacity configuration optimization device, characterized in that, The device includes: The acquisition module is used to acquire typical daily operation datasets and historical electricity price data of photovoltaic power plants under multiple statistical scenarios; The processing module is used to copy and splice typical daily operation datasets of various statistical scenarios to obtain continuous time axis data of the target duration under the corresponding statistical scenario. The continuous time axis data consists of operation data of the settlement period and the preheating period. The operation data of the preheating period is used for energy storage charge state evolution, and the operation data of the settlement period is used for revenue statistics. The solution module is used to solve a pre-constructed two-layer collaborative optimization model based on continuous time axis data and historical electricity price data for the target duration under various statistical scenarios, to obtain the target energy storage configuration data for photovoltaic power plants. The two-layer collaborative optimization model includes an inner objective function and an outer objective function. The inner objective function is constructed with the goal of maximizing the net income in each settlement period. The net income in each settlement period is determined by energy storage configuration parameters and electricity price data. The outer objective function is constructed with the goal of maximizing the net present value of energy storage over its entire life cycle. The net present value of energy storage over its entire life cycle is calculated based on the net income in each settlement period and the energy storage configuration parameters.

8. An electronic device, characterized in that, include: The system includes a memory and a processor, which are interconnected. The memory stores computer instructions, and the processor executes the computer instructions to perform the photovoltaic power generation and storage capacity configuration optimization method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the photovoltaic power generation and storage capacity configuration optimization method according to any one of claims 1 to 6.

10. A computer program product, characterized in that, It includes computer instructions for causing a computer to execute the photovoltaic power generation and storage capacity configuration optimization method according to any one of claims 1 to 6.

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