Energy storage planning method and system considering the evolution path of renewable energy proportion

Abstract

The present invention provides an energy storage planning method and system that considers the evolution path of the proportion of new energy sources. The method includes: using a logistic growth model to generate an evolution path for the penetration rate of new energy sources driven by a carbon neutrality goal; constructing a multi-stage collaborative optimization model for thermal power and energy storage that includes the cost of modifying thermal power units and the cost of burning coal for thermal power units, and using lithium-ion batteries as energy storage carriers to construct a dynamic state of charge model for self-discharge rate and efficiency decay; constructing a first objective function with the goal of minimizing the total cost of system operation; setting multi-stage collaborative constraints on the first objective function; constructing a multi-stage energy storage planning model based on the first objective function and the multi-stage coordination constraints, and solving the multi-stage energy storage planning model. The embodiment of the present invention can achieve dynamic adaptation of renewable energy high-penetration systems and energy storage planning, improve the economic efficiency of energy storage configuration, and provide reliable technical support for new power systems.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage planning, and in particular to an energy storage planning method and system that considers the evolution path of the proportion of new energy. Background Art

[0002] In recent years, with the rapid increase in renewable energy penetration, the power system has faced significant challenges, including insufficient peak-shaving capacity, high wind and solar curtailment rates, and limited thermal power flexibility. Driven particularly by the goal of carbon neutrality, the proportion of fluctuating power sources like wind and photovoltaics continues to grow. The regulation capabilities of traditional thermal power units struggle to match the dynamic characteristics of renewable energy output, putting pressure on both system economics and reliability. Energy storage systems, as a key means of smoothing fluctuations and enhancing flexibility, have demonstrated significant advantages in scenarios such as grid peak shaving.

[0003] However, current planning methods are often based on static scenarios or single phases, failing to fully integrate the dynamic evolution of renewable energy penetration and the synergistic benefits of thermal power flexibility transformation and energy storage deployment. This results in high planning costs and insufficient adaptability. Consequently, existing technologies have the following limitations: First, renewable energy penetration forecasts often use linear models, ignoring the nonlinear impact of policy objectives and market saturation, making it difficult to accurately depict long-term evolutionary trends. Second, energy storage planning is often independent of the thermal power transformation process and fails to consider the temporal variations of peaks and valleys, making it difficult to optimize the full lifecycle cost. Summary of the Invention

[0004] The purpose of the present invention is to provide an energy storage planning method and system that takes into account the evolution path of the proportion of new energy. It aims to solve the problems of high planning costs and insufficient adaptability caused by traditional technologies that are based on static scenarios or single stages and fail to fully couple the dynamic evolution path of new energy penetration rate, nor to coordinate the synergistic benefits of thermal power flexibility transformation and energy storage configuration.

[0005] In a first aspect, the present invention provides an energy storage planning method that considers the evolution path of the proportion of new energy, the method comprising:

[0006] Using the logistic growth model, we generate the evolution path of new energy penetration driven by the carbon neutrality goal;

[0007] A multi-stage collaborative optimization model for thermal power and energy storage was constructed, including the cost of thermal power unit transformation and the cost of coal combustion. A dynamic state-of-charge model for self-discharge rate and efficiency decay was constructed using lithium-ion batteries as the energy storage medium.

[0008] A first objective function is constructed with the goal of minimizing the total system operating cost, where the total system operating cost includes the energy storage construction cost, the coal burning cost of the thermal power units, the coal burning cost of the thermal power units, and the peak-valley arbitrage income in each stage;

[0009] Setting multi-stage coordinated constraints on the first objective function, the multi-stage coordinated constraints including thermal power unit output operation constraints, thermal power unit ramping constraints, system power balance constraints, wind farm operation constraints, energy storage charging and discharging power constraints, energy storage operation state constraints, and new energy penetration rate constraints;

[0010] A multi-stage energy storage planning model is constructed according to the first objective function and the multi-stage coordination constraint conditions, and the multi-stage energy storage planning model is solved.

[0011] In a second aspect, the present invention provides an energy storage planning system that considers the evolution path of the proportion of new energy, the system comprising:

[0012] An evolution path generation module, which uses a logistic growth model to generate an evolution path for new energy penetration driven by the carbon neutrality goal;

[0013] An optimization model construction module is used to construct a multi-stage collaborative optimization model for thermal power and energy storage, including the cost of thermal power unit modification and the cost of coal combustion. It also uses lithium-ion batteries as the energy storage medium to construct a dynamic state-of-charge model for self-discharge rate and efficiency decay.

[0014] An objective function construction module is used to construct a first objective function with the goal of minimizing the total system operation cost, wherein the total system operation cost includes the energy storage construction cost, the coal burning cost of the thermal power unit, the coal burning cost of the thermal power unit, and the peak-valley arbitrage income in each stage;

[0015] a constraint construction module, configured to set multi-stage coordinated constraint conditions for the first objective function, wherein the multi-stage coordinated constraint conditions include thermal power unit output operation constraint, thermal power unit ramping constraint, system power balance constraint, wind farm operation constraint, energy storage charge and discharge power constraint, energy storage operation state constraint, and new energy penetration constraint;

[0016] The planning model construction module is used to construct a multi-stage energy storage planning model according to the first objective function and the multi-stage coordination constraint conditions, and solve the multi-stage energy storage planning model.

[0017] In a third aspect, the present invention provides a storage medium storing one or more programs, which, when executed by a processor, implement the above-mentioned energy storage planning method considering the evolution path of the proportion of new energy.

[0018] In a fourth aspect, the present invention provides an electronic device, comprising a memory and a processor, wherein:

[0019] The memory is used to store computer programs;

[0020] When the processor is used to execute the computer program stored in the memory, it implements the above-mentioned energy storage planning method that considers the evolution path of the proportion of new energy.

[0021] Compared with the prior art, the present invention has the following advantages:

[0022] The present invention uses a logistic growth model to generate a new energy penetration rate evolution path that is connected with the dual carbon goals, which serves as input data for subsequent multi-stage energy storage planning. It then establishes a thermal storage multi-stage collaborative optimization model that includes the flexibility transformation cost of thermal power units, coal-burning costs, and a dynamic model of energy storage charge state. Taking the minimization of total system cost as the objective function, the energy storage power / capacity configuration, the number of thermal power unit transformations, and the operation strategy are coordinated and optimized. The constraints include thermal power output limit, ramp rate, system power balance, energy storage SOC dynamic boundary, and mutual exclusion of charge and discharge states. A multi-stage dynamic planning model for energy storage is constructed to solve the multi-stage optimal energy storage configuration plan. This method realizes the dynamic adaptation of renewable energy high penetration system and energy storage planning, can improve the economy of energy storage configuration, and provide reliable technical support for new power systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This is a flow chart of an energy storage planning method considering the evolution path of the proportion of new energy, proposed in one embodiment of the present invention;

[0024] Figure 2 This is a schematic diagram of the structure of an energy storage planning system that considers the evolution path of the proportion of new energy, proposed in one embodiment of the present invention.

[0025] The following specific embodiments will further illustrate the present invention in conjunction with the above-mentioned drawings. DETAILED DESCRIPTION

[0026] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are 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. Unless otherwise defined, the technical terms or scientific terms used herein should be the common meanings understood by people with ordinary skills in the field to which the invention belongs. The words "including" and similar words used in this article mean that the elements or objects appearing before the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects.

[0027] like Figure 1As shown, an embodiment of the present invention provides an energy storage planning method that considers the evolution path of the proportion of new energy. The method includes steps S101 to S105, wherein:

[0028] Step S101: Using the Logistic growth model, generate the new energy penetration rate evolution path driven by the carbon neutrality goal;

[0029] In some embodiments, the new energy penetration rate evolution path is generated specifically according to the following formula:

[0030] ;

[0031] in, is the new energy penetration rate in year t; is the upper limit of the policy target, is the base year penetration rate, r is the comprehensive growth rate, is the base year, is the target value of new energy penetration rate in the corresponding year of the kth stage. Each stage corresponds to ten years. For example, taking 2020 as the starting point and 2050 as the end point, 10 years is considered as a planning stage.

[0032] Specifically, assuming that policy intensity and technical and economic feasibility remain unchanged, a composite growth rate r = 0.12 is set to reflect the steady development path of new energy under existing policies. Substituting the r value into the above equation generates a new energy penetration rate evolution path. This generated logistic new energy penetration rate evolution path provides dynamic boundary conditions for multi-stage energy storage planning. Subsequent energy storage planning stages (k = 1, 2, ...) correspond to different new energy penetration rates. The new energy penetration rate serves as an input parameter of the energy storage planning model and is embedded in the constraints and objective functions of subsequent stages.

[0033] It should also be noted that traditional new energy growth rate forecasts often use fixed growth rates or linear extrapolation methods, which cannot reflect the saturation characteristics of technology diffusion and the dynamic impact of policy intervention, and can easily cause planning schemes to be out of line with long-term goals. This step uses the logistic growth model to characterize the S-shaped curve characteristics of technology maturity and market penetration by introducing the upper limit of policy targets and the comprehensive growth rate r. The value of r=0.12 is based on historical data fitting, reflecting the development inertia under the current policy intensity. The generated nonlinear penetration rate path not only avoids the waste of resources caused by early aggressive investment, but also ensures that accelerated deployment in the later stage meets the carbon neutrality goal, providing a time coupling benchmark for multi-stage planning.

[0034] Step S102: Constructing a multi-stage collaborative optimization model for thermal power generation and energy storage that includes the cost of thermal power unit transformation and the cost of coal combustion for thermal power units, and constructing a state-of-charge dynamic model for self-discharge rate and efficiency decay using lithium-ion batteries as energy storage carriers;

[0035] It's important to note that traditional thermal power cost models only consider fuel consumption, ignoring flexibility modification costs and the dynamic improvement of unit regulation capabilities, leading to planning results that deviate from actual operating economics. This step establishes a dual-cost correlation model, embedding modification costs as decision variables into the objective function. A 0-1 variable is used to represent the regulatory effect of the unit modification status on the output lower limit. This reveals the coupling relationship between unit modification scale and energy storage configuration, supporting coordinated thermal and energy storage planning.

[0036] Specifically, a thermal power-energy storage multi-stage collaborative optimization model is constructed according to the following formula:

[0037] ;

[0038] in, is the total operating cost of the thermal power unit, is the total flexibility transformation cost of thermal power units, is the number of thermal power units, is the unit price of coal used for power generation by thermal power units at stage k, is the total output of thermal power unit g in stage k, is the flexibility transformation cost of a single thermal power unit, is the number of thermal power units that have undergone flexibility transformation in stage k;

[0039] The state of charge dynamic model is constructed according to the following formula:

[0040] ;

[0041] in, is the self-discharge rate of energy storage, 、 are the storage capacity of energy storage at time t and time t-1 respectively, and are the charging and discharging power of the energy storage at time t, and are the charging and discharging efficiency of energy storage respectively; is the state of charge of the energy storage at time t, is the rated capacity of the energy storage battery; and They are the upper and lower limits of the state of charge of the energy storage battery respectively.

[0042] Step S103: constructing a first objective function with the goal of minimizing the total system operation cost, wherein the total system operation cost includes the energy storage construction cost, the coal burning cost of the thermal power units, the coal burning cost of the thermal power units, and the peak-valley arbitrage income in each stage;

[0043] It's important to note that this step, during multi-stage energy storage planning, primarily considers overall construction and operating costs, including the cost of retrofitting thermal power units, the cost of energy storage equipment, and the system operating costs of coal-fired units. With the goal of reducing overall costs, coordinated optimization is conducted to arrive at the optimal planning decision.

[0044] Specifically, the first objective function is constructed according to the following formula:

[0045] ;

[0046] in, is the total cost of system operation, The construction cost of energy storage; is the peak-valley arbitrage profit at the kth stage; is the number of years in the kth stage; is the discount rate; T is the actual operating life of the energy storage system; is the power cost of unit energy storage at the kth stage; is the unit capacity cost of energy storage at the kth stage; The energy storage power planned and configured in the kth stage; The energy storage capacity planned for the kth stage; is the electricity sales revenue of the k-th stage discharge; The charging cost when charging the kth stage; is the electricity price at time t, is the wind power output in the kth stage, is the output of thermal power unit g at stage k, is the charging power of the energy storage in the kth stage, is the discharge amount of energy storage at time t in the kth stage.

[0047] Step S104: setting multi-stage coordinated constraints for the first objective function, wherein the multi-stage coordinated constraints include thermal power unit output operation constraints, thermal power unit ramping constraints, system power balance constraints, wind farm operation constraints, energy storage charge and discharge power constraints, energy storage operation state constraints, and new energy penetration rate constraints;

[0048] It should be noted that this step ensures the feasibility of system operation by setting multi-dimensional constraints, including thermal power unit output operation constraints, thermal power unit ramping constraints, system power balance constraints, wind farm operation constraints, energy storage charging and discharging power constraints, energy storage operation status constraints, and new energy penetration constraints. These constraints together constitute the boundary conditions for multi-stage collaborative optimization.

[0049] Specifically, the output operation constraints of thermal power units are constructed according to the following formula:

[0050] ;

[0051] in, is the actual output of thermal power unit g at time t; The lower output limit of the thermal power unit before flexibility modification; 、 They are the lower and upper limits of the output of thermal power units after flexibility transformation; is a 0-1 variable, which takes the value 1 when unit g undergoes flexibility modification, and 0 otherwise;

[0052] The thermal power unit ramp constraint is constructed according to the following formula:

[0053] ;

[0054] in, is the ramp rate of thermal power unit g before transformation; is the ramp rate of thermal power unit g after transformation, is the actual output of thermal power unit g at time t-1;

[0055] The system power balance constraint is constructed according to the following formula:

[0056] ;

[0057] in, is the wind power curtailment at time t; is the actual power that the wind farm can generate at time t; For the Maximum permissible wind curtailment rate of renewable energy in the system per year; is the wind power grid-connected power at time t, is the charging power of the energy storage system at time t, is the load power at time t;

[0058] The wind farm operation constraints are constructed according to the following formula:

[0059] ;

[0060] The energy storage charging and discharging power constraints are constructed according to the following formula:

[0061] ;

[0062] in, are the discharge power of energy storage at the kth stage respectively; is a 0-1 variable; M is an infinite number. By using the 0-1 variable and the infinite number M, only charging or discharging can be performed in the same period, which makes the model linear and easy to solve;

[0063] The energy storage operation state constraint is constructed according to the following formula:

[0064] ;

[0065] in, 、 They are the SOC values ​​of the energy storage at the 24th and 1st moments of the operation day, respectively;

[0066] The new energy penetration rate constraint is constructed according to the following formula:

[0067]

[0068] By converting the penetration rate target into a constraint on the proportion of renewable energy power generation, we ensure that the planning scheme and the target are dynamically coordinated.

[0069] Step S105: constructing a multi-stage energy storage planning model according to the first objective function and the multi-stage coordination constraint conditions, and solving the multi-stage energy storage planning model.

[0070] In this step, based on the above objective function and constraints, a multi-stage energy storage planning model is constructed, and the energy storage configuration power and capacity are used as decision variables to solve the optimization solution with the lowest multi-stage total cost.

[0071] Specifically, a multi-stage energy storage planning model is constructed according to the following formula:

[0072] ;

[0073] in, As the overall goal, the optimization model requires its minimum value, is the total cost function, where the most important variables are the power and capacity that the energy storage needs to configure at each stage. is the constraint function, is a 0-1 variable in the kth stage, which takes the value 1 when unit g undergoes flexibility modification, and takes the value 0 otherwise. is the wind power abandoned by the wind farm at time t in stage k, is the energy storage state of the energy storage system at time t in the kth stage, is the SOC state of the energy storage system at time t in the kth stage.

[0074] This multi-stage energy storage planning model uses the planned power and capacity of energy storage at each stage as the primary decision variables, with the objective function being to minimize total cost within the energy storage planning cycle. Constraints include thermal power unit output constraints, thermal power unit ramping constraints, system power balance constraints, wind farm operation constraints, energy storage charge and discharge power constraints, and energy storage operational status constraints. Based on this model, the multi-stage energy storage planning results are solved, ultimately determining the required energy storage power and capacity for each stage, the flexibility modification plan for thermal power units, and the output of wind, thermal, and energy storage units on a typical day for each stage.

[0075] In summary, the embodiments of the present invention have the following advantages:

[0076] (1) This invention dynamically simulates the penetration rate growth under the carbon neutrality target based on the logistic growth model, overcomes the deviation of traditional linear prediction, and improves the long-term adaptability of new energy planning and thermal power-storage coordinated configuration.

[0077] (2) The present invention constructs a multi-stage energy storage capacity optimization model, and achieves a reduction in the cost of the entire life cycle and an improvement in resource utilization by coupling the flexibility transformation of thermal power units.

[0078] like Figure 2 As shown, an embodiment of the present invention further provides an energy storage planning system that considers the evolution path of the proportion of new energy, and the system includes:

[0079] An evolution path generation module 10 is used to generate an evolution path of new energy penetration driven by the carbon neutrality goal using a logistic growth model;

[0080] An optimization model construction module 20 is used to construct a multi-stage collaborative optimization model for thermal power generation and energy storage that includes the cost of thermal power unit transformation and the cost of coal combustion for thermal power units. A dynamic state-of-charge model for self-discharge rate and efficiency decay is constructed using lithium-ion batteries as the energy storage medium.

[0081] An objective function construction module 30 is configured to construct a first objective function with the goal of minimizing the total system operation cost, wherein the total system operation cost includes the energy storage construction cost, the coal burning cost of the thermal power units, the coal burning cost of the thermal power units, and the peak-valley arbitrage income in each stage;

[0082] a constraint condition construction module 40 for setting multi-stage coordinated constraint conditions for the first objective function, wherein the multi-stage coordinated constraint conditions include thermal power unit output operation constraint, thermal power unit ramping constraint, system power balance constraint, wind farm operation constraint, energy storage charge and discharge power constraint, energy storage operation state constraint, and new energy penetration constraint;

[0083] The planning model building module 50 is used to build a multi-stage energy storage planning model according to the first objective function and the multi-stage coordination constraint conditions, and solve the multi-stage energy storage planning model.

[0084] On the other hand, the present invention further proposes a storage medium having one or more programs stored thereon, which, when executed by a processor, implements the above-mentioned energy storage planning method considering the evolution path of the proportion of new energy.

[0085] On the other hand, the present invention also proposes an electronic device, including a memory and a processor, wherein the memory is used to store computer programs, and the processor is used to execute the computer programs stored in the memory to implement the above-mentioned energy storage planning method considering the evolution path of the proportion of new energy.

[0086] Those skilled in the art will appreciate that the logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device), or in conjunction with such instruction execution system, apparatus, or device. For purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transmit a program for use by an instruction execution system, apparatus, or device, or in conjunction with such instruction execution system, apparatus, or device.

[0087] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting, or processing it in another suitable manner as necessary, and then storing it in a computer memory.

[0088] It should be understood that various components of the present invention may be implemented using hardware, software, firmware, or a combination thereof. In the aforementioned embodiments, multiple steps or methods may be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one or a combination of the following technologies known in the art may be used: a discrete logic circuit having logic gate circuits for implementing logic functions on data signals, an application-specific integrated circuit having suitable combinational logic gate circuits, a programmable gate array (PGA), a field-programmable gate array (FPGA), etc.

[0089] While the embodiments of the present invention have been described in detail above, it will be apparent to those skilled in the art that various modifications and variations of these embodiments are possible. However, it should be understood that such modifications and variations are within the scope and spirit of the present invention as set forth in the claims. Furthermore, the invention described herein is susceptible to other embodiments and may be practiced or implemented in a variety of ways.

Claims

1. A method for energy storage planning that considers the evolution path of the proportion of new energy, characterized in that: The method comprises: Using the logistic growth model, we generate the evolution path of new energy penetration driven by the carbon neutrality goal; The new energy penetration rate evolution path is generated according to the following formula: ； in, is the new energy penetration rate in year t; is the upper limit of the policy target, is the base year penetration rate, r is the comprehensive growth rate, is the base year, is the target value of new energy penetration rate in the corresponding year of the kth stage, and each stage corresponds to ten years; A multi-stage collaborative optimization model for thermal power and energy storage was constructed, including the cost of thermal power unit transformation and the cost of coal combustion. A dynamic state-of-charge model for self-discharge rate and efficiency decay was constructed using lithium-ion batteries as the energy storage medium. The first objective function is constructed with the goal of minimizing the total system operating cost. The total system operating cost includes the energy storage construction cost, the coal burning cost of the thermal power units, the total flexibility modification cost of the thermal power units, and the peak-valley arbitrage income in each stage. The first objective function is constructed according to the following formula: ； in, is the total cost of system operation, The construction cost of energy storage is is the coal burning cost of thermal power units, is the total flexibility modification cost of thermal power units; is the peak-valley arbitrage profit at the kth stage; is the number of years in the kth stage; is the discount rate; T is the actual operating life of the energy storage system; is the power cost of unit energy storage at the kth stage; is the unit capacity cost of energy storage at the kth stage; The energy storage power planned and configured in the kth stage; The energy storage capacity planned for the kth stage; is the electricity sales revenue of the k-th stage discharge; The charging cost when charging the kth stage; is the electricity price at time t, is the wind power output in the kth stage, is the output of thermal power unit g at stage k, is the charging power of the energy storage in the kth stage, is the discharge amount of energy storage at time t in the kth stage; Setting multi-stage coordinated constraints on the first objective function, the multi-stage coordinated constraints including thermal power unit output operation constraints, thermal power unit ramping constraints, system power balance constraints, wind farm operation constraints, energy storage charging and discharging power constraints, energy storage operation state constraints, and new energy penetration rate constraints; A multi-stage energy storage planning model is constructed according to the first objective function and the multi-stage coordination constraint conditions, and the multi-stage energy storage planning model is solved.

2. The energy storage planning method considering the evolution path of the proportion of new energy according to claim 1 is characterized in that: The steps of constructing a multi-stage collaborative optimization model for thermal power generation and energy storage that includes the cost of thermal power unit transformation and the cost of coal combustion for thermal power units, and constructing a state of charge dynamic model for self-discharge rate and efficiency decay using lithium-ion batteries as energy storage carriers include: A multi-stage collaborative optimization model of thermal power and energy storage is constructed according to the following formula: ； in, is the coal burning cost of thermal power units, is the total flexibility transformation cost of thermal power units, is the number of thermal power units, is the unit price of coal used for power generation by thermal power units at stage k, is the total output of thermal power unit g in stage k, is the flexibility transformation cost of a single thermal power unit, is the number of thermal power units that have undergone flexibility transformation in stage k; The state of charge dynamic model is constructed according to the following formula: ； in, is the self-discharge rate of energy storage, 、 are the storage capacity of energy storage at time t and time t-1 respectively, and are the charging and discharging power of the energy storage at time t, and are the charging and discharging efficiency of energy storage respectively; is the state of charge of the energy storage at time t, is the rated capacity of the energy storage battery; and They are the upper and lower limits of the state of charge of the energy storage battery respectively.

3. The energy storage planning method considering the evolution path of the proportion of new energy according to claim 2 is characterized in that: The step of setting multi-stage coordinated constraint conditions for the first objective function, wherein the multi-stage coordinated constraint conditions include thermal power unit output operation constraint, thermal power unit ramping constraint, system power balance constraint, wind farm operation constraint, energy storage charging and discharging power constraint, energy storage operation state constraint, and new energy penetration rate constraint, comprises: The output operation constraints of thermal power units are constructed according to the following formula: ； in, is the actual output of thermal power unit g at time t; The lower output limit of the thermal power unit before flexibility modification; 、 They are the lower and upper limits of the output of thermal power units after flexibility transformation; is a 0-1 variable, which takes the value 1 when unit g undergoes flexibility modification, and 0 otherwise; The thermal power unit ramp constraint is constructed according to the following formula: ； in, is the ramp rate of thermal power unit g before transformation; is the ramp rate of thermal power unit g after transformation, is the actual output of thermal power unit g at time t-1; The system power balance constraint is constructed according to the following formula: ； in, is the wind power curtailment at time t; is the actual power that the wind farm can generate at time t; For the The maximum permissible wind curtailment rate of renewable energy in the system in a year, is the wind power grid-connected power at time t, is the charging power of the energy storage system at time t, is the load power at time t; The wind farm operation constraints are constructed according to the following formula: ； The energy storage charging and discharging power constraints are constructed according to the following formula: ； in, are the discharge power of energy storage at the kth stage respectively; is a 0-1 variable; M is an infinite number; The energy storage operation state constraint is constructed according to the following formula: ； in, 、 They are the SOC values ​​of the energy storage at the 24th and 1st moments of the operation day, respectively; The new energy penetration rate constraint is constructed according to the following formula: 。 4. The energy storage planning method considering the evolution path of the proportion of new energy according to claim 3 is characterized in that: The steps of constructing a multi-stage energy storage planning model according to the first objective function and the multi-stage coordination constraint conditions, and solving the multi-stage energy storage planning model include: A multi-stage energy storage planning model is constructed according to the following formula: ； in, For the overall optimization goal, is the total cost function, is the constraint function, is a 0-1 variable in the kth stage, which takes the value 1 when unit g undergoes flexibility modification, and takes the value 0 otherwise. is the wind power abandoned by the wind farm at time t in stage k, is the energy storage state of the energy storage system at time t in the kth stage, is the SOC state of the energy storage system at time t in the kth stage.

5. An energy storage planning system that considers the evolution path of the proportion of new energy, characterized by: The system comprises: An evolution path generation module, which uses a logistic growth model to generate an evolution path for new energy penetration driven by the carbon neutrality goal; The new energy penetration rate evolution path is generated according to the following formula: ； in, is the new energy penetration rate in year t; is the upper limit of the policy target, is the base year penetration rate, r is the comprehensive growth rate, is the base year, is the target value of new energy penetration rate in the corresponding year of the kth stage, and each stage corresponds to ten years; An optimization model construction module is used to construct a multi-stage collaborative optimization model for thermal power and energy storage, including the cost of thermal power unit modification and the cost of coal combustion. It also uses lithium-ion batteries as the energy storage medium to construct a dynamic state-of-charge model for self-discharge rate and efficiency decay. An objective function construction module is configured to construct a first objective function with the goal of minimizing the total system operation cost, wherein the total system operation cost includes the energy storage construction cost, the coal burning cost of the thermal power units, the total flexibility modification cost of the thermal power units, and the peak-valley arbitrage income in each stage; The first objective function is constructed according to the following formula: ； in, is the total cost of system operation, The construction cost of energy storage is is the coal burning cost of thermal power units, is the total flexibility modification cost of thermal power units; is the peak-valley arbitrage profit at the kth stage; is the number of years in the kth stage; is the discount rate; T is the actual operating life of the energy storage system; is the power cost of unit energy storage at the kth stage; is the unit capacity cost of energy storage at the kth stage; The energy storage power planned and configured in the kth stage; The energy storage capacity planned for the kth stage; is the electricity sales revenue of the k-th stage discharge; The charging cost when charging the kth stage; is the electricity price at time t, is the wind power output in the kth stage, is the output of thermal power unit g at stage k, is the charging power of the energy storage in the kth stage, is the discharge amount of energy storage at time t in the kth stage; a constraint construction module, configured to set multi-stage coordinated constraint conditions for the first objective function, wherein the multi-stage coordinated constraint conditions include thermal power unit output operation constraint, thermal power unit ramping constraint, system power balance constraint, wind farm operation constraint, energy storage charge and discharge power constraint, energy storage operation state constraint, and new energy penetration constraint; The planning model construction module is used to construct a multi-stage energy storage planning model according to the first objective function and the multi-stage coordination constraint conditions, and solve the multi-stage energy storage planning model.

6. A storage medium, characterized in that The storage medium stores one or more programs, which, when executed by a processor, implement the energy storage planning method considering the evolution path of the proportion of new energy as described in any one of claims 1-4.

7. An electronic device, characterized in that: The electronic device comprises a memory and a processor, wherein: The memory is used to store computer programs; When the processor is used to execute the computer program stored in the memory, it implements the energy storage planning method that considers the evolution path of the proportion of new energy as described in any one of claims 1-4.

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