Multi-constraint optical storage capacity configuration and economic calculation method adapting to different life cycles

Abstract

The invention discloses a multi-constraint optical storage capacity configuration and economic calculation method suitable for different life cycles, and relates to the field of photovoltaic energy storage planning, and the method comprises the steps: constructing a mathematical model with the maximum cumulative net income as a target; the accumulated net earnings are accumulated net earnings of respective full life cycles based on capacity planning of the optical storage system; constructing a power balance constraint condition, a demand control constraint condition and a photovoltaic installed capacity constraint condition of the mathematical model according to the power utilization data information of the industrial and commercial users; setting an energy storage constraint condition of an energy storage system and an operation constraint condition of a light storage system in the mathematical model; setting economic benefit constraint conditions of the energy storage system and the light storage system in respective full life cycles; and solving the mathematical model by using a preset mathematical operation optimization algorithm so as to determine an optical storage capacity configuration scheme and an economic calculation result of the industrial and commercial users based on a solving result. According to the scheme, the optical storage capacity optimal configuration scheme and the economic measurement and calculation result of industrial and commercial users can be quickly and accurately obtained.

Description

Technical Field

[0001] The present invention relates to the field of photovoltaic energy storage planning, and in particular to a photovoltaic storage capacity configuration and economic calculation method that is adaptable to different life cycles with multiple constraints. Background Art

[0002] At present, with the continuous growth of installed capacity of new energy, the peak photovoltaic power generation period in the daytime is the valley period of electricity price for industry and commerce in various regions, which seriously affects the power generation income of distributed photovoltaic power stations. The relevant technologies calculate the investment income by planning photovoltaic and energy storage separately or planning the photovoltaic and energy storage coordinated configuration scheme by group intelligent algorithm, which cannot accurately evaluate the overall investment income of photovoltaic and energy storage. If the photovoltaic and energy storage construction capacity is not reasonably planned, the project investment recovery period will be greatly extended. At the same time, distributed photovoltaic grid connection is restricted in many places across the country. Some counties in many provinces have become photovoltaic grid connection red line areas, which do not allow photovoltaic grid connection or photovoltaic power generation must be fully absorbed; some regions have clearly put forward energy storage configuration requirements to deal with the problem of insufficient grid carrying capacity and ensure the safe and stable operation of the grid.

[0003] At present, the configuration of industrial and commercial photovoltaic and energy storage capacity is mainly based on separate investment calculations, which cannot take into account the coordinated operation and scheduling strategies between photovoltaic and energy storage and the overall consideration of benefit calculation; and the algorithm for planning the coordinated configuration capacity of photovoltaic and energy storage by swarm intelligence algorithm is prone to fall into the local optimal solution, the solution process is highly random, the results cannot be fully reproduced, the interpretability is not strong, and the benefit model of photovoltaic and energy storage investment projects in different life cycles is not considered. Therefore, through the coordinated operation and scheduling strategy of photovoltaic and energy storage, the problems of distributed photovoltaic consumption, photovoltaic installation red line, and photovoltaic reverse transmission are solved, the carrying capacity of the power grid is improved, and the overall economic benefits of photovoltaic and energy storage in different life cycles are considered at the same time. Scientific and reasonable planning of the optimal configuration plan of photovoltaic and energy storage is still a technical problem that needs to be solved urgently. Summary of the invention

[0004] In view of this, the purpose of the present invention is to provide a multi-constrained photovoltaic storage capacity configuration and economic calculation method that adapts to different life cycles, which can obtain the optimal photovoltaic storage capacity configuration plan to maximize economic benefits. The specific plan is as follows:

[0005] In the first aspect, the present application discloses a method for configuring and calculating the capacity of a photovoltaic storage system with multiple constraints and adapting to different life cycles, including:

[0006] Constructing a mathematical model with the goal of maximizing the cumulative net benefit; the cumulative net benefit is the cumulative net benefit of the entire life cycle based on the capacity planning of the photovoltaic storage system;

[0007] Constructing the power balance constraint conditions, demand control constraint conditions, and photovoltaic installed capacity constraint conditions of the mathematical model based on the electricity consumption data information of industrial and commercial users;

[0008] Setting energy storage constraints of the energy storage system and operation constraints of the photovoltaic storage system in the mathematical model; wherein the energy storage constraints include charge and discharge state constraints, charge and discharge quantity constraints, and charge and discharge power constraints;

[0009] Setting economic benefit constraints for the energy storage system and the photovoltaic storage system in the mathematical model over their respective life cycles;

[0010] The mathematical model is solved using a preset mathematical operations optimization algorithm to determine the photovoltaic storage capacity configuration plan and economic calculation results of industrial and commercial users based on the solution results.

[0011] Optionally, the power balance constraint condition, demand control constraint condition, and photovoltaic installed capacity constraint condition of the mathematical model constructed according to the electricity consumption data information of industrial and commercial users include:

[0012] Constructing the power balance constraint conditions of the mathematical model according to the power load power of industrial and commercial users, the output value of photovoltaic power generation, energy storage charging power, energy storage discharging power, and grid interaction power;

[0013] The industrial and commercial users are set to take power from the grid to meet the demand control constraint; the industrial and commercial users are set to send power back to the grid to meet the transformer capacity constraint;

[0014] The photovoltaic installed capacity constraints are set according to the installable area of ​​photovoltaic panels for industrial and commercial users, the installation area of ​​a single photovoltaic panel, and the capacity information of a single photovoltaic panel.

[0015] Optionally, before setting the industrial and commercial user to obtain power from the power grid to meet the demand control constraint condition; setting the industrial and commercial user to reversely feed power to the power grid to meet the transformer capacity constraint condition, the method further includes:

[0016] Construct demand control constraints that the power taken from the power grid by industrial and commercial users at the current moment is less than or equal to the maximum demand of the corresponding historical month at the current moment;

[0017] Construct a transformer capacity constraint condition that the power sent back to the power grid by industrial and commercial users is less than or equal to the active power conversion limit of the transformer capacity; wherein the active power conversion limit of the transformer capacity is the limit value of the transformer capacity converted into active power according to a preset power factor.

[0018] Optionally, the step of setting energy storage constraints of the energy storage system in the mathematical model includes:

[0019] Set the charging state flag corresponding to the energy storage system when charging and the discharging state flag corresponding to the energy storage system when discharging to obtain the charging and discharging state constraint conditions; wherein, when the charging state flag is set to 1 at any time, the discharging state flag is set to 0; when the discharging state flag is 1 at any time, the charging state flag is 0;

[0020] Constructing charging and discharging power constraints according to the system power change relationship of the energy storage system at adjacent moments, energy storage charging efficiency, energy storage discharging efficiency, the duration between adjacent moments, the discharge depth and the configuration capacity of the energy storage system;

[0021] The charging and discharging power constraints are constructed based on the relationship between the energy storage charging power and the maximum energy storage charging power, and the relationship between the energy storage discharging power and the maximum energy storage discharging power.

[0022] Optionally, setting the operating constraints of the photovoltaic storage system in the mathematical model includes:

[0023] The operation constraint conditions of the photovoltaic storage system are set according to the relationship between the photovoltaic power generation output value of the photovoltaic storage system and the power load of industrial and commercial users.

[0024] Optionally, the setting of economic benefit constraints of the energy storage system and the photovoltaic storage system in the mathematical model over their respective life cycles includes:

[0025] Economic benefit constraints are constructed based on the first-year economic benefit information of the photovoltaic storage system, the photovoltaic energy storage benefit attenuation factor corresponding to the photovoltaic energy storage in the first stage of the photovoltaic storage system's entire life cycle, and the photovoltaic energy storage benefit attenuation factor corresponding to the photovoltaic life cycle after the second stage of the photovoltaic storage system's entire life cycle is retired.

[0026] Optionally, solving the mathematical model using a preset mathematical operations optimization algorithm to determine the photovoltaic storage capacity configuration scheme and economic calculation results of industrial and commercial users based on the solution results includes:

[0027] Use the preset mathematical operations optimization algorithm to solve the mathematical model in sections to obtain the solution that satisfies the conditions for maximizing the cumulative net profit;

[0028] Determine the photovoltaic system and the photovoltaic storage configuration capacity of the energy storage system of the industrial and commercial user and the operation scheduling strategy of the energy storage system based on the solution result;

[0029] Calculate the economic benefits generated after executing the operation scheduling strategy to obtain corresponding economic measurement results.

[0030] In the second aspect, the present application discloses a multi-constraint optical storage capacity configuration and economic calculation device adapted to different life cycles, including:

[0031] A model building module, used to build a mathematical model with the goal of maximizing the cumulative net benefit; the cumulative net benefit is the cumulative net benefit of the entire life cycle based on the capacity planning of the photovoltaic storage system;

[0032] A first condition setting module is used to construct the power balance constraint condition, demand control constraint condition, and photovoltaic installed capacity constraint condition of the mathematical model according to the power consumption data information of industrial and commercial users;

[0033] A second condition setting module is used to set energy storage constraints of the energy storage system and operation constraints of the photovoltaic storage system in the mathematical model; wherein the energy storage constraints include charge and discharge state constraints, charge and discharge quantity constraints, and charge and discharge power constraints;

[0034] A third condition setting module is used to set economic benefit constraints of the energy storage system and the photovoltaic storage system in the mathematical model during their respective life cycles;

[0035] The solution module is used to solve the mathematical model using a preset mathematical operations optimization algorithm to determine the photovoltaic storage capacity configuration plan and economic calculation results of industrial and commercial users based on the solution results.

[0036] In a third aspect, the present application discloses an electronic device, comprising:

[0037] Memory, used to store computer programs;

[0038] A processor is used to execute the computer program to implement the steps of the aforementioned disclosed method for configuring and calculating the economic value of photovoltaic storage capacity with multiple constraints and adapting to different life cycles.

[0039] In a fourth aspect, the present application discloses a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, the steps of the aforementioned disclosed multi-constraint photovoltaic storage capacity configuration and economic calculation method that adapts to different life cycles are implemented.

[0040] It can be seen that the present invention provides a method for configuring photovoltaic storage capacity and economic calculation with multiple constraints that adapt to different life cycles, including: constructing a mathematical model with the goal of maximizing cumulative net benefits; the cumulative net benefits are the cumulative net benefits of the entire life cycle based on the capacity planning of the photovoltaic storage system; constructing the power balance constraints, demand control constraints, and photovoltaic installed capacity constraints of the mathematical model according to the electricity consumption data information of industrial and commercial users; setting the energy storage constraints of the energy storage system and the operation constraints of the photovoltaic storage system in the mathematical model; wherein the energy storage constraints include charging and discharging state constraints, charging and discharging quantity constraints, and charging and discharging power constraints; setting the economic benefit constraints of the energy storage system and the photovoltaic storage system in the mathematical model under their respective life cycles; solving the mathematical model using a preset mathematical operations optimization algorithm to determine the photovoltaic storage capacity configuration plan and economic calculation results of industrial and commercial users based on the solution results. It can be seen that the mathematical model is constructed with the goal of maximizing the cumulative net benefits of the entire life cycle of the capacity planning of the photovoltaic storage system, and the various income and expenditures from the initial stage of system construction to the aging and retirement of equipment are comprehensively considered to ensure that industrial and commercial users obtain the best economic returns in the long-term investment in the photovoltaic storage system and avoid the loss of income caused by short-sighted decisions. In addition, a variety of constraints are constructed based on the electricity consumption data information of industrial and commercial users, so that the mathematical model closely fits the actual electricity consumption scenarios and site conditions of users. Finally, the carefully constructed mathematical model is solved using the preset mathematical operations optimization algorithm, which can quickly and accurately obtain the photovoltaic storage capacity configuration plan and economic calculation results of industrial and commercial users, providing users with scientific and quantitative decision-making basis. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0042] Figure 1 A flow chart of a method for configuring and calculating the capacity of a photovoltaic storage system and economic calculations with multiple constraints and adapting to different life cycles disclosed in this application;

[0043] Figure 2 A specific flow chart of a method for configuring and calculating the capacity of photovoltaic storage with multiple constraints and adapting to different life cycles disclosed in this application;

[0044] Figure 3 This is a schematic diagram of the structure of a multi-constraint optical storage capacity configuration and economic calculation device adapted to different life cycles disclosed in this application;

[0045] Figure 4This is a structural diagram of an electronic device disclosed in this application. DETAILED DESCRIPTION

[0046] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0047] At present, the configuration of industrial and commercial photovoltaic and energy storage capacity is mainly based on separate investment calculations, which cannot take into account the coordinated operation and scheduling strategies between photovoltaic and energy storage and the overall consideration of benefit calculation; and the algorithm for planning the coordinated configuration capacity of photovoltaic and energy storage by swarm intelligence algorithm is prone to fall into the local optimal solution, the solution process is highly random, the results cannot be fully reproduced, the interpretability is not strong, and the benefit model of photovoltaic and energy storage investment projects in different life cycles is not considered. Therefore, through the coordinated operation and scheduling strategy of photovoltaic and energy storage, the problems of distributed photovoltaic consumption, photovoltaic installation red line, and photovoltaic reverse transmission are solved, the carrying capacity of the power grid is improved, and the overall economic benefits of photovoltaic and energy storage in different life cycles are considered at the same time. Scientific and reasonable planning of the optimal configuration plan of photovoltaic and energy storage is still a technical problem that needs to be solved urgently.

[0048] To this end, the present invention provides a multi-constrained photovoltaic storage capacity configuration and economic calculation scheme that adapts to different life cycles, which can obtain the optimal photovoltaic storage capacity configuration scheme to maximize economic benefits.

[0049] Reference Figure 1 As shown, the embodiment of the present invention discloses a method for configuring and calculating the capacity of a photovoltaic storage system with multiple constraints and adapting to different life cycles, including:

[0050] Step S11: constructing a mathematical model with the goal of maximizing the cumulative net benefit; the cumulative net benefit is the cumulative net benefit of the entire life cycle based on the capacity planning of the photovoltaic storage system.

[0051] In this embodiment, a mixed integer linear programming mathematical model covering 8760 hours throughout the year is constructed, guided by the peak-valley arbitrage strategy of energy time shifting, with distributed photovoltaic power generation and self-use as the main method for industrial and commercial users, and energy storage taking into account the consumption of distributed photovoltaic surplus power for grid connection. The optimization goal of the model is to maximize the cumulative net profit of the entire life cycle of photovoltaic storage capacity planning.

[0052] Step S12: constructing the power balance constraint conditions, demand control constraint conditions, and photovoltaic installed capacity constraint conditions of the mathematical model according to the electricity consumption data information of industrial and commercial users.

[0053] In this embodiment, the power balance constraint of the mathematical model is constructed according to the power load of industrial and commercial users, the output value of photovoltaic power generation, energy storage charging power, energy storage discharging power, and grid interaction power; it can be understood that when industrial and commercial users install photovoltaics and energy storage, the power balance constraints between the industrial and commercial users' power load, photovoltaic power generation, energy storage charging and discharging power (i.e., the operation scheduling strategy of energy storage) and the power interaction power with the grid are as follows:

[0054] ;

[0055] In the formula, The first hour of the year is 8760 hours. Hour; Full-year historical data for commercial and industrial users The power load at the time; In order to characterize the full-year normalized typical output curve of the industrial and commercial user after pre-installing photovoltaic Output value at the moment; The installed capacity planned for the PV system; , They are energy storage charging power and energy storage discharging power respectively; , For industrial and commercial users The grid interaction power at any moment includes the power fed back to the grid and the power taken from the grid.

[0056] In this way, it is ensured that at any time, the photovoltaic power generation power in the photovoltaic storage system, the charging and discharging power of the energy storage system, the power of the power load of industrial and commercial users, and the power interacting with the power grid (power purchase or sales power) are balanced, avoiding excess or insufficient power. Moreover, under the framework of power balance, the energy storage system can perform charging and discharging operations according to the real-time power situation to achieve time shifting of energy. For example, during the period of low electricity prices, the energy storage system is charged with low-priced electricity from the grid, and discharged during the peak electricity price period, which not only meets the power balance, but also realizes peak-valley arbitrage, improves the economy and efficiency of the energy storage system, and makes it play a greater value in the entire photovoltaic storage system.

[0057] In this embodiment, the industrial and commercial users are set to draw electricity from the power grid to meet the demand control constraint condition; the industrial and commercial users are set to send back the power to the power grid to meet the transformer capacity constraint condition; wherein, before setting the industrial and commercial users to draw electricity from the power grid to meet the demand control constraint condition; setting the industrial and commercial users to send back the power to the power grid to meet the transformer capacity constraint condition, it also includes: constructing a demand control constraint condition that the industrial and commercial users draw electricity from the power grid at the current moment is less than or equal to the maximum demand of the historical month corresponding to the current moment; constructing a transformer capacity constraint condition that the industrial and commercial users send back the power to the power grid is less than or equal to the transformer capacity converted active power limit value; wherein, the transformer capacity converted active power limit value is the limit value of the transformer capacity converted into active power according to the preset power factor.

[0058] It is understandable that, considering the mathematical model needs to meet the demand control requirements when industrial and commercial users draw power from the grid and when the surplus power of photovoltaic power is connected to the grid, the power sent back to the grid and the power drawn from the grid by industrial and commercial users cannot exceed the transformer capacity and demand, respectively, that is:

[0059] ;

[0060] in, for The month to which the time belongs, For the whole year Maximum demand per month; The active power limit is calculated based on the transformer capacity according to a certain power factor; , They are respectively the signs for taking electricity from the power grid and supplying electricity back to the power grid.

[0061] It should be noted that industrial and commercial users The power drawn from the grid and the power fed back to the grid cannot occur at the same time, that is, when the user draws power from the grid, is 1, is 0; when the user feeds electricity back to the grid, is 0, is 1, and the corresponding formula is as follows:

[0062] ;

[0063] In this way, industrial and commercial users are usually charged according to demand, that is, the unit price of electricity is determined according to the maximum demand during peak hours. Demand control constraints can optimize the operation of the photovoltaic storage system and limit the maximum demand during peak hours so that it does not exceed the demand value agreed in the contract or minimizes the demand value, thereby avoiding high fines or higher electricity unit prices due to excessive demand, directly reducing the user's electricity cost, especially for industrial and commercial users with large fluctuations in electricity load, which can bring significant economic benefits. In addition, demand control of industrial and commercial users helps to stabilize the load curve of the power grid and reduce the power supply pressure and fluctuations during peak hours. Moreover, in the planning and design stage of the photovoltaic storage system, demand control constraints provide an important basis for determining the capacity of the energy storage system, photovoltaic installed capacity, etc.

[0064] In this embodiment, photovoltaic installed capacity constraints are set according to the installable area of ​​photovoltaic panels of industrial and commercial users, the installation area of ​​a single photovoltaic panel, and the capacity information of a single photovoltaic panel. It can be understood that considering the need for photovoltaic installed capacity constraints in the mathematical model, the maximum photovoltaic installed capacity can be determined according to the area of ​​the roof, carport, etc. of the industrial and commercial users, where the photovoltaic installed capacity constraints are as follows:

[0065] ;

[0066] in, is the maximum area where photovoltaic panels can be installed, is the installation area of ​​a single photovoltaic panel, The capacity of a single photovoltaic panel.

[0067] It can be seen that since the access capacity of the power grid for industrial and commercial users is limited to a certain extent, the photovoltaic installed capacity constraint can ensure that the installed capacity of the photovoltaic system matches the grid access capacity, avoiding the situation where the photovoltaic installed capacity is too large and the power generation power exceeds the grid access allowable value, resulting in the inability to fully connect to the grid.

[0068] Step S13: setting energy storage constraints of the energy storage system and operation constraints of the photovoltaic storage system in the mathematical model; wherein the energy storage constraints include charge and discharge state constraints, charge and discharge quantity constraints and charge and discharge power constraints.

[0069] In this embodiment, a charging state flag corresponding to the energy storage system when charging and a discharging state flag corresponding to the energy storage system when discharging are set to obtain a charging and discharging state constraint condition; wherein, when the charging state flag is set to 1 at any time, the discharging state flag is set to 0; when the discharging state flag is 1 at any time, the charging state flag is 0; it can be understood that the energy storage charging and discharging state constraint condition formula is as follows:

[0070] ;

[0071] in, , They are The energy storage system is charged and discharged at all times. is 1, is 0; when the energy storage is discharged, is 0, is 1.

[0072] In this embodiment, the charge and discharge power constraint condition is constructed according to the system power change relationship of the energy storage system at adjacent moments, the energy storage charging efficiency, the energy storage discharging efficiency, the duration between adjacent moments, the discharge depth and the configuration capacity of the energy storage system; it can be understood that the charge and discharge power constraint condition is expressed by constructing an equation through the following constraint condition:

[0073] ;

[0074] ;

[0075] in, , They are Moment and The energy storage system power at the moment; for Moment and the length of time between moments; , They are energy storage charging efficiency and discharging efficiency respectively; is the discharge depth of the energy storage system; The energy storage system is configured with capacity so that the battery is not overcharged or over-discharged based on the battery's capacity limitations.

[0076] In this embodiment, the charging and discharging power constraint condition is constructed based on the magnitude relationship between the energy storage charging power and the maximum energy storage charging power, and the magnitude relationship between the energy storage discharging power and the maximum energy storage discharging power. It can be understood that the energy storage charging and discharging power constraint condition is expressed by the following equation:

[0077] ;

[0078] ;

[0079] in, For maximum energy storage charging power, is the maximum energy storage discharge power; It is the ratio of the energy storage system charging and discharging power to the energy storage capacity. It should be noted that the energy storage charging and discharging power constraint is a nonlinear constraint and needs to be linearized. The linearization process is as follows:

[0080] make , , then the above charge and discharge power constraints change to:

[0081] ;

[0082] ;

[0083] The following constraints are further added to the above charging and discharging power constraints:

[0084] ;

[0085] ;

[0086] in, for Always consider the maximum allowable charging capacity of the energy storage system’s charging state, for Always consider the maximum allowable discharge capacity of the energy storage system in the discharge state. is the constraint parameter, specifically, The value of is much larger than other variables with smaller conventional value ranges in the above formula, and is generally a larger number that can be set.

[0087] In this way, through conditions such as power constraints, it is ensured that the energy storage system can fully utilize its capacity and avoid the ineffective use of part of the capacity due to unreasonable charging and discharging operations, thereby improving the energy storage and release efficiency of the energy storage system and enabling it to better play the role of peak shaving and valley filling and smoothing power fluctuations in the photovoltaic storage system.

[0088] In this embodiment, the operation constraint of the photovoltaic storage system is set according to the relationship between the photovoltaic power generation output of the photovoltaic storage system and the power load of industrial and commercial users. It can be understood that the operation constraint is expressed as follows:

[0089] make Characterization The relationship between photovoltaic output and load at any moment. for The photovoltaic output is greater than or equal to the load at all times; for The photovoltaic output is less than or equal to the load at all times.

[0090] Add the following constraints on the relationship between photovoltaic output and load output:

[0091] ;

[0092] When the photovoltaic output is greater than or equal to the load, the storage energy cannot be discharged:

[0093] ;

[0094] When the photovoltaic output is less than or equal to the load, the energy storage discharge power cannot be fed back to the grid:

[0095] ;

[0096] In the formula, A larger number that can be set.

[0097] In the distributed photovoltaic installation red line area, it is mandatory to allocate storage, and photovoltaic surplus power is not allowed to be connected to the grid. The following anti-backflow constraints are added:

[0098] ;

[0099] It can be seen that the operating constraints can coordinate the operating relationship between photovoltaic power generation, energy storage system and user load in the photovoltaic storage system, so that the entire system can automatically adjust the operating status of each part according to different working conditions and needs, such as changes in light intensity, load fluctuations, electricity price time periods, etc., to achieve optimal operation of the system.

[0100] Step S14: setting economic benefit constraints of the energy storage system and the photovoltaic storage system in the mathematical model over their respective life cycles.

[0101] In this embodiment, economic benefit constraints are constructed based on the economic benefit information of the first year of the photovoltaic storage system, the photovoltaic energy storage benefit attenuation factor corresponding to the photovoltaic energy storage in the first stage of the photovoltaic storage system during the entire life cycle, and the photovoltaic benefit attenuation factor corresponding to the photovoltaic life cycle after the second stage energy storage system is retired during the entire life cycle of the photovoltaic storage system. It can be understood that in order to ensure that the operating income of the photovoltaic storage system meets the requirements of the corresponding investment indicators, the overall income of photovoltaic storage under different life cycles of photovoltaic and energy storage meets the internal rate of return requirements, so it is necessary to configure the economic benefit constraints of the photovoltaic storage capacity, considering that the total income of the photovoltaic storage system in the first year (first year) is:

[0102] ;

[0103] in, For commercial and industrial users Time-of-use electricity prices of the power grid or retail packages; The benchmark coal-fired electricity price in the province where the industrial and commercial user is located; The first year's revenue of the solar energy storage system; Represents the total revenue of the PV storage system in the first year.

[0104] Since the discharge of industrial and commercial distributed energy storage is not allowed to be fed back to the grid, the annual revenue of energy storage charging and discharging is for:

[0105] ;

[0106] According to the above two formulas, the economic benefit information of the photovoltaic system in the first year can be obtained: for:

[0107] ;

[0108] Due to the different life cycles of photovoltaic and energy storage (generally, the life cycle of photovoltaic is about twice that of energy storage), when both photovoltaic and energy storage systems are within their life cycles, after considering the annual operation and maintenance costs of photovoltaic and energy storage, the overall net income of the photovoltaic and energy storage system is:

[0109] ;

[0110] in, This is the first time that the solar energy storage system has been put into operation. Year; For the life cycle of energy storage system; , are the annual revenue attenuation coefficients of photovoltaic and energy storage respectively; , are the electricity fee discount coefficients for photovoltaic and energy storage users respectively. If photovoltaic energy storage is invested by the owner, then , All are 1; , They are the annual operation and maintenance costs per unit capacity of photovoltaic and energy storage respectively; It is the overall net income of the photovoltaic and energy storage systems during their life cycle.

[0111] After the energy storage system reaches the end of its life cycle, the photovoltaic income is only equal to the photovoltaic installed capacity. The net income is:

[0112] ;

[0113] ;

[0114] in, For the photovoltaic system life cycle; is the photovoltaic income per unit time; It is the overall net benefit of the photovoltaic system during the remaining life cycle after the energy storage system reaches the end of its life cycle.

[0115] The above formula Piecewise function linearization:

[0116] ;

[0117] ;

[0118] Therefore, the overall revenue of the photovoltaic storage system meets the internal rate of return requirement constraint:

[0119] ;

[0120] in, Calculate internal rate of return requirements for investments; , They are the unit capacity construction costs of photovoltaic and energy storage respectively.

[0121] In this way, the economic benefit constraint can help industrial and commercial users make accurate plans in the early stages of their investment in PV storage systems. By considering the first-year revenue of the PV storage system and combining it with the revenue over the entire life cycle, including factors such as operation and maintenance costs, revenue decay, and electricity discount coefficients, users can accurately assess the scale of funds required to invest in the PV storage system, as well as the expected returns under different capacity configurations, thereby avoiding blind investment.

[0122] When planning the configuration of photovoltaic storage capacity, it is usually necessary to maximize the benefits as much as possible while satisfying the constraints of equipment operation and system operation. Therefore, from the above steps, it can be seen that the maximum cumulative net benefit target of the photovoltaic storage capacity planning over the entire life cycle considered by the present invention is:

[0123] ;

[0124] Step S15: Solve the mathematical model using a preset mathematical operations optimization algorithm to determine the photovoltaic storage capacity configuration plan and economic calculation results for industrial and commercial users based on the solution results.

[0125] In this embodiment, the mathematical model is solved in sections using a preset mathematical operations optimization algorithm to obtain a solution result that satisfies the cumulative net profit maximization condition; based on the solution result, the photovoltaic storage configuration capacity of the photovoltaic system and the energy storage system of the industrial and commercial users and the operation scheduling strategy of the energy storage system are determined; the economic benefits generated after the execution of the operation scheduling strategy are calculated to obtain the corresponding economic calculation results. It can be understood that the optimal solution of the mathematical model can be achieved through mathematical operations optimization algorithms such as the branch and bound method and the cutting plane method, and the optimal configuration capacity of photovoltaic and energy storage and the operation scheduling strategy of the energy storage system can be obtained. Under the conditions of satisfying relevant constraints, the benefits can be maximized to ensure the investment return of the project.

[0126] like Figure 2 As shown, a specific method for multi-constraint photovoltaic storage capacity configuration and economic calculation that adapts to different life cycles is disclosed, which is divided into the following three stages:

[0127] Data acquisition phase: Obtain the user's historical load power data: This is one of the basic data, which is used to understand the changes in the user's power load in the past period of time, and provide a reference for the actual power load for subsequent model establishment and analysis. Obtain the user's electricity price and demand information: Collect relevant information such as the electricity price structure faced by the user (such as time-of-use electricity price, etc.) and demand billing. These data are crucial for calculating economic benefits and formulating operating strategies, because different electricity price periods and demand restrictions will affect the economic benefits and operation of the photovoltaic storage system. Obtain a typical photovoltaic output curve: Obtain typical photovoltaic output data for local or similar areas. This curve reflects the changes in the power generation of the photovoltaic system at different times and under different weather conditions. It is an important reference for establishing a photovoltaic storage system model and helps to analyze the role and impact of the photovoltaic system in the entire system.

[0128] Model building phase: Establish a collaborative operation scheduling configuration model that considers the safe operation boundary of the system: Based on the data obtained previously, build a model to ensure the safe and stable operation of the photovoltaic storage system. The model will take into account various constraints, such as power balance constraints, demand control constraints, photovoltaic installed capacity constraints, energy storage system energy storage constraints (including charging and discharging state constraints, charging and discharging power constraints) and photovoltaic storage system operation constraints, etc., to ensure that the system will not have power imbalance, equipment overload and other safety problems during operation, while achieving collaborative operation and optimized scheduling between photovoltaic, energy storage and user loads. Establish a photovoltaic storage system profit model considering different life cycles: Consider the income of the photovoltaic storage system throughout its life cycle from construction to decommissioning, including initial investment cost, operation and maintenance cost, income attenuation, electricity discount coefficient and other factors, and establish a comprehensive economic profit model. The model aims to maximize the cumulative net income of the photovoltaic storage system over the entire life cycle of capacity planning, and provide an economic objective function for subsequent optimization solutions.

[0129] Model processing and solution stage: Since the established model may contain nonlinear constraints or objective functions, in order to facilitate the solution, the model needs to be linearized and converted into a form that can be solved using mathematical operations optimization algorithms. Then, the model is solved using the preset mathematical operations optimization algorithms (such as linear programming, mixed integer programming, etc.) to obtain the optimal photovoltaic storage capacity configuration plan and economic calculation results.

[0130] Result output stage: Output the photovoltaic storage capacity configuration results: Output the configuration information such as photovoltaic installed capacity and energy storage system capacity obtained by solving, and provide users with specific references for the construction scale of photovoltaic storage systems and equipment selection. Output economic calculation results: Give the economic benefit calculation results of the photovoltaic storage system over the entire life cycle, including economic indicators such as cumulative net income, investment payback period, and internal rate of return, to help users evaluate the economic benefits and investment value of the photovoltaic storage system.

[0131] It can provide scientific and reasonable photovoltaic storage system construction plans and economic evaluations for industrial and commercial users, helping users to maximize economic benefits and achieve efficient use of energy while meeting electricity needs.

[0132] It can be seen that the present invention provides a method for configuring photovoltaic storage capacity and economic calculation with multiple constraints that adapt to different life cycles, including: constructing a mathematical model with the goal of maximizing cumulative net benefits; the cumulative net benefits are the cumulative net benefits of the entire life cycle based on the capacity planning of the photovoltaic storage system; constructing the power balance constraints, demand control constraints, and photovoltaic installed capacity constraints of the mathematical model according to the electricity consumption data information of industrial and commercial users; setting the energy storage constraints of the energy storage system and the operation constraints of the photovoltaic storage system in the mathematical model; wherein the energy storage constraints include charging and discharging state constraints, charging and discharging quantity constraints, and charging and discharging power constraints; setting the economic benefit constraints of the energy storage system and the photovoltaic storage system in the mathematical model under their respective life cycles; solving the mathematical model using a preset mathematical operations optimization algorithm to determine the photovoltaic storage capacity configuration plan and economic calculation results of industrial and commercial users based on the solution results. It can be seen that the mathematical model is constructed with the goal of maximizing the cumulative net benefits of the entire life cycle of the capacity planning of the photovoltaic storage system, and the various income and expenditures from the initial stage of system construction to the aging and retirement of equipment are comprehensively considered to ensure that industrial and commercial users obtain the best economic returns in the long-term investment in the photovoltaic storage system and avoid the loss of income caused by short-sighted decisions. In addition, a variety of constraints are constructed based on the electricity consumption data information of industrial and commercial users, so that the mathematical model closely fits the actual electricity consumption scenarios and site conditions of users. Finally, the carefully constructed mathematical model is solved using the preset mathematical operations optimization algorithm, which can quickly and accurately obtain the photovoltaic storage capacity configuration plan and economic calculation results of industrial and commercial users, providing users with scientific and quantitative decision-making basis.

[0133] Reference Figure 3 As shown, the embodiment of the present invention also discloses a specific multi-constraint optical storage capacity configuration and economic calculation device adapted to different life cycles, including:

[0134] A model building module 11 is used to build a mathematical model with the goal of maximizing the cumulative net benefit; the cumulative net benefit is the cumulative net benefit of the entire life cycle based on the capacity planning of the photovoltaic storage system;

[0135] A first condition setting module 12 is used to construct power balance constraint conditions, demand control constraint conditions, and photovoltaic installed capacity constraint conditions of the mathematical model according to the power consumption data information of industrial and commercial users;

[0136] The second condition setting module 13 is used to set the energy storage constraint conditions of the energy storage system and the operation constraint conditions of the photovoltaic storage system in the mathematical model; wherein the energy storage constraint conditions include the charge and discharge state constraint conditions, the charge and discharge quantity constraint conditions and the charge and discharge power constraint conditions;

[0137] A third condition setting module 14 is used to set economic benefit constraints of the energy storage system and the photovoltaic storage system in the mathematical model during their respective life cycles;

[0138] The solution module 15 is used to solve the mathematical model using a preset mathematical operations optimization algorithm to determine the photovoltaic storage capacity configuration plan and economic calculation results of industrial and commercial users based on the solution results.

[0139] It can be seen that the present invention provides a mathematical model for constructing a cumulative net benefit maximization as the goal; the cumulative net benefit is the cumulative net benefit of the entire life cycle based on the capacity planning of the photovoltaic storage system; the power balance constraint, demand control constraint, and photovoltaic installed capacity constraint of the mathematical model are constructed according to the electricity consumption data information of industrial and commercial users; the energy storage constraint of the energy storage system in the mathematical model and the operation constraint of the photovoltaic storage system are set; wherein the energy storage constraint includes the charge and discharge state constraint, the charge and discharge quantity constraint, and the charge and discharge power constraint; the economic benefit constraint of the energy storage system and the photovoltaic storage system in the mathematical model under their respective life cycles is set; the mathematical model is solved using a preset mathematical operations optimization algorithm to determine the photovoltaic storage capacity configuration plan and economic calculation results of industrial and commercial users based on the solution results. It can be seen that the mathematical model is constructed with the goal of maximizing the cumulative net benefit of the capacity planning of the photovoltaic storage system throughout the life cycle, and the various income and expenditures from the initial stage of system construction to the aging and retirement of equipment are comprehensively considered to ensure that industrial and commercial users obtain the best economic return in the long-term investment process of photovoltaic storage systems and avoid the loss of income caused by short-sighted decisions. Moreover, a variety of constraints are constructed based on the electricity consumption data of industrial and commercial users, so that the mathematical model closely fits the users' actual electricity consumption scenarios and site conditions. Finally, the carefully constructed mathematical model is solved using a preset mathematical operations optimization algorithm, which can quickly and accurately obtain the photovoltaic storage capacity configuration plan and economic calculation results of industrial and commercial users, providing users with a scientific and quantitative decision-making basis.

[0140] Furthermore, the present application also discloses an electronic device. Figure 4 This is a structural diagram of an electronic device 20 according to an exemplary embodiment. The content in the diagram cannot be regarded as any limitation on the scope of use of the present application.

[0141] Figure 4A schematic diagram of the structure of an electronic device 20 provided in an embodiment of the present application. The electronic device 20 may specifically include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. The memory 22 is used to store a computer program, which is loaded and executed by the processor 21 to implement the relevant steps in the multi-constraint photovoltaic storage capacity configuration and economic calculation method for different life cycles disclosed in any of the aforementioned embodiments. In addition, the electronic device 20 in this embodiment may specifically be an electronic computer.

[0142] In this embodiment, the power supply 23 is used to provide working voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and the external device, and the communication protocol it follows is any communication protocol that can be applied to the technical solution of the present application, and is not specifically limited here; the input and output interface 25 is used to obtain external input data or output data to the outside world, and its specific interface type can be selected according to specific application needs and is not specifically limited here.

[0143] Among them, the processor 21 may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 21 can be implemented in at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array). The processor 21 may also include a main processor and a coprocessor. The main processor is a processor for processing data in the awake state, also known as a CPU (Central Processing Unit); the coprocessor is a low-power processor for processing data in the standby state. In some embodiments, the processor 21 may be integrated with a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the display screen. In some embodiments, the processor 21 may also include an AI (Artificial Intelligence) processor, which is used to process computing operations related to machine learning.

[0144] In addition, the memory 22, as a carrier for storing resources, can be a read-only memory, a random access memory, a disk or an optical disk, etc. The resources stored thereon can include an operating system 221, a computer program 222, etc., and the storage method can be temporary storage or permanent storage.

[0145] Among them, the operating system 221 is used to manage and control the hardware devices and computer programs 222 on the electronic device 20, so as to realize the operation and processing of the massive data 223 in the memory 22 by the processor 21, which can be Windows Server, Netware, Unix, Linux, etc. In addition to including computer programs that can be used to complete the multi-constraint optical storage capacity configuration and economic calculation method for different life cycles performed by the electronic device 20 disclosed in any of the aforementioned embodiments, the computer program 222 can also further include computer programs that can be used to complete other specific tasks. In addition to data transmitted from an external device received by the electronic device, the data 223 can also include data collected by its own input and output interface 25, etc.

[0146] Furthermore, the present application also discloses a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, the aforementioned disclosed multi-constraint photovoltaic storage capacity configuration and economic calculation method adapted to different life cycles is implemented. The specific steps of the method can be referred to the corresponding contents disclosed in the aforementioned embodiments, and will not be repeated here.

[0147] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0148] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented with electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel may use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application. The steps of the method or algorithm described in conjunction with the embodiments disclosed herein can be implemented directly with hardware, a software module executed by a processor, or a combination of the two. The software module can be placed in a random access memory RAM (Random Access Memory), memory, read-only memory ROM (Read Only Memory), electrically programmable EPROM (Electrically Programmable Read Only Memory), electrically erasable programmable EEPROM (ElectricErasable Programmable Read Only Memory), register, hard disk, removable disk, CD-ROM (CompactDisc-Read Only Memory), or any other form of storage medium known in the technical field.

[0149] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0150] The scheme provided by the present invention is introduced in detail above. Specific examples are used in this article to illustrate the principle and implementation mode of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation mode and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A method for configuring and calculating the capacity of photovoltaic storage with multiple constraints and adapting to different life cycles, characterized in that: include: Constructing a mathematical model with the goal of maximizing the cumulative net benefit; the cumulative net benefit is the cumulative net benefit of the entire life cycle based on the capacity planning of the photovoltaic storage system; Constructing the power balance constraint conditions, demand control constraint conditions, and photovoltaic installed capacity constraint conditions of the mathematical model based on the electricity consumption data information of industrial and commercial users; Setting energy storage constraints of the energy storage system and operation constraints of the photovoltaic storage system in the mathematical model; wherein the energy storage constraints include charge and discharge state constraints, charge and discharge quantity constraints, and charge and discharge power constraints; Setting economic benefit constraints for the energy storage system and the photovoltaic storage system in the mathematical model over their respective life cycles; The mathematical model is solved using a preset mathematical operations optimization algorithm to determine the photovoltaic storage capacity configuration plan and economic calculation results of industrial and commercial users based on the solution results.

2. The method for configuring and calculating the optical storage capacity with multiple constraints and adapting to different life cycles according to claim 1 is characterized in that: The power balance constraint conditions, demand control constraint conditions, and photovoltaic installed capacity constraint conditions of the mathematical model constructed according to the electricity consumption data information of industrial and commercial users include: Constructing the power balance constraint conditions of the mathematical model according to the power load power of industrial and commercial users, the output value of photovoltaic power generation, energy storage charging power, energy storage discharging power, and grid interaction power; The industrial and commercial users are set to take power from the grid to meet the demand control constraint; the industrial and commercial users are set to send power back to the grid to meet the transformer capacity constraint; The photovoltaic installed capacity constraints are set according to the installable area of ​​photovoltaic panels for industrial and commercial users, the installation area of ​​a single photovoltaic panel, and the capacity information of a single photovoltaic panel.

3. The method for configuring and calculating the optical storage capacity with multiple constraints and adapting to different life cycles according to claim 2 is characterized in that: Before setting the industrial and commercial user to obtain power from the power grid to meet the demand control constraint condition and setting the industrial and commercial user to send power back to the power grid to meet the transformer capacity constraint condition, the method further includes: Construct demand control constraints that the power taken from the power grid by industrial and commercial users at the current moment is less than or equal to the maximum demand of the corresponding historical month at the current moment; Construct a transformer capacity constraint condition that the power sent back to the power grid by industrial and commercial users is less than or equal to the active power conversion limit of the transformer capacity; wherein the active power conversion limit of the transformer capacity is the limit value of the transformer capacity converted into active power according to a preset power factor.

4. The method for configuring and calculating the optical storage capacity with multiple constraints and adapting to different life cycles according to claim 1 is characterized in that: The energy storage constraint conditions of the energy storage system in the mathematical model are set, including: Set the charging state flag corresponding to the energy storage system when charging and the discharging state flag corresponding to the energy storage system when discharging to obtain the charging and discharging state constraint conditions; wherein, when the charging state flag is set to 1 at any time, the discharging state flag is set to 0; when the discharging state flag is 1 at any time, the charging state flag is 0; Constructing charging and discharging power constraints according to the system power change relationship of the energy storage system at adjacent moments, energy storage charging efficiency, energy storage discharging efficiency, the duration between adjacent moments, the discharge depth and the configuration capacity of the energy storage system; The charging and discharging power constraints are constructed based on the relationship between the energy storage charging power and the maximum energy storage charging power, and the relationship between the energy storage discharging power and the maximum energy storage discharging power.

5. The method for configuring and calculating the photovoltaic storage capacity with multiple constraints and adapting to different life cycles according to claim 1 is characterized in that: Setting the operating constraints of the photovoltaic storage system in the mathematical model includes: The operation constraint conditions of the photovoltaic storage system are set according to the relationship between the photovoltaic power generation output value of the photovoltaic storage system and the power load of industrial and commercial users.

6. The method for configuring and calculating the optical storage capacity and economic calculation with multiple constraints and adapting to different life cycles according to claim 1 is characterized in that: The setting of economic benefit constraints of the energy storage system and the photovoltaic storage system in the mathematical model during their respective life cycles includes: Economic benefit constraints are constructed based on the first-year economic benefit information of the photovoltaic storage system, the photovoltaic energy storage benefit attenuation factor corresponding to the photovoltaic energy storage in the first stage of the photovoltaic storage system's entire life cycle, and the photovoltaic benefit attenuation factor corresponding to the photovoltaic life cycle after the second stage energy storage system is retired in the photovoltaic storage system's entire life cycle.

7. The method for configuring and calculating the optical storage capacity with multiple constraints and adapting to different life cycles and economic calculation according to any one of claims 1 to 6, characterized in that: The method of solving the mathematical model by using a preset mathematical operations optimization algorithm to determine the photovoltaic storage capacity configuration scheme and economic calculation results of industrial and commercial users based on the solution results includes: Use the preset mathematical operations optimization algorithm to solve the mathematical model in sections to obtain the solution that satisfies the conditions for maximizing the cumulative net profit; Determine the photovoltaic system and the photovoltaic storage configuration capacity of the energy storage system of the industrial and commercial user and the operation scheduling strategy of the energy storage system based on the solution result; Calculate the economic benefits generated after executing the operation scheduling strategy to obtain corresponding economic measurement results.

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