Energy storage capacity planning method and device

Abstract

The invention provides an energy storage capacity planning method and device, and belongs to the field of energy storage capacity planning. The method comprises the following steps: obtaining capacity data and operation data of target energy storage equipment, and energy influence characteristics of renewable energy sources in different scenes; determining a net load curve of the target energy storage equipment according to the energy influence characteristics and the operation data of the renewable energy source in different scenes; determining a total energy storage capacity lower limit value of the target energy storage equipment according to the net load curve and the capacity data; taking the minimum cost as an objective function, and obtaining an energy storage capacity planning scheme of the target energy storage equipment under the constraint of the constraint condition; wherein the constraint condition is determined based on the lower limit value of the total energy storage capacity. According to the invention, the adequacy of the system capacity of the energy storage system can be improved, so that the requirement of a novel power system for flexible resources in a spot environment is met.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage capacity planning, and in particular to an energy storage capacity planning method and device. Background Art

[0002] Promoting the consumption of new energy is a key factor to be considered in the design of the power market. The randomness, intermittency and anti-peaking output characteristics of wind and light resources will seriously affect the balance of supply and demand in the power system. China's thermal power is mainly coal-fired, with poor regulation capabilities, and urgently needs the support of more adjustable capacity resources. The construction of new energy storage and other flexible power sources can effectively alleviate the peak-shaving pressure and meet the needs of all aspects of the system's generation, transmission, distribution and use. However, new energy storage still lacks a clear business model, which has become the main reason hindering social capital investment in energy storage installations.

[0003] At present, China's electricity market is in its early stages of development, and the market system is not yet perfect. This is reflected in the relatively low clearing prices of the spot electricity market and the ancillary service market, and some market players are unable to recover fixed costs. The construction cost of energy storage is high, and it also faces the above difficulties. The investment benefits of energy storage will also be affected by the actual operation of the system. Among the mainstream capacity pricing mechanisms, the scarce electricity price mechanism may lead to problems such as price supervision difficulties and high market risks; while the capacity market mechanism has high requirements for the system capacity demand forecasting ability and market control capabilities. Although North China and other regions have begun to organize peak-shaving capacity markets, the capacity market mechanism under the spot environment has not yet been designed. However, the current system capacity is not sufficient and cannot meet the demand for flexible resources in the new power system under the spot environment. Summary of the invention

[0004] The embodiments of the present invention provide a method and device for energy storage capacity planning to solve the problem that the system capacity is not sufficient at the current stage and cannot meet the demand for flexible resources in the new power system under the spot environment.

[0005] In a first aspect, an embodiment of the present invention provides a method for energy storage capacity planning, comprising:

[0006] Obtain capacity data and operation data of target energy storage equipment, as well as energy impact characteristics of renewable energy in different scenarios;

[0007] Determine the net load curve of the target energy storage device based on the energy impact characteristics and operation data of renewable energy in different scenarios; and determine the lower limit of the total energy storage capacity of the target energy storage device based on the net load curve and capacity data;

[0008] Taking the minimum cost as the objective function, under the constraints of the constraints, the energy storage capacity planning scheme of the target energy storage equipment is obtained; wherein the constraints are determined based on the lower limit of the total energy storage capacity.

[0009] In a possible implementation, the capacity data includes a maximum capacity and a rated capacity;

[0010] According to the net load curve and capacity data, determine the lower limit of the total energy storage capacity of the target energy storage equipment, including:

[0011] According to the net load curve, maximum capacity and rated capacity, determine the lower limit of the total energy storage capacity of the target energy storage equipment.

[0012] In a possible implementation, the net load curve, the maximum capacity, the rated capacity, and the lower limit of the total energy storage capacity of the target energy storage device satisfy the following relationship:

[0013]

[0014] Among them, C RL is the lower limit of the total energy storage capacity of the target energy storage device; t2 and t1 are time; P max is the maximum capacity; P net (t) is the net load curve; is the rated capacity; h is the discharge time at the rated capacity.

[0015] In a possible implementation, the net load curve of the target energy storage device is determined according to the energy impact characteristics and operation data of renewable energy in different scenarios, including:

[0016] Determine the characteristic probability of renewable energy based on the energy impact characteristics of renewable energy in different scenarios;

[0017] Cluster the characteristic probabilities of renewable energy and obtain the renewable energy output data under different probability scenarios;

[0018] Determine the net load curve of the target energy storage device based on the renewable energy output data and operation data under different probability scenarios.

[0019] In one possible implementation, the constraints include: system balance constraints, system dispatch constraints, and operation constraints of short-term and long-term energy storage;

[0020] Among them, in the operation constraints of short-term and long-term energy storage, the charging and discharging power constraints of the energy storage and the upper limit value of the available energy of the energy storage are based on determining the lower limit value of the total energy storage capacity.

[0021] In a possible implementation, the method further includes:

[0022] Obtain cost data and economic life of target energy storage equipment;

[0023] Determine the corresponding benefits of the target energy storage equipment at equivalent capacity based on cost data, economic life, lower limit of total energy storage capacity and rated capacity;

[0024] Based on the energy storage capacity planning scheme of the target energy storage device and the corresponding revenue of the target energy storage device under equivalent capacity, the clearing price of the spot market in which the energy storage device participates is obtained.

[0025] In one possible implementation, the corresponding benefit of the target energy storage device at equivalent capacity is determined based on cost data, economic life, lower limit of total energy storage capacity and rated capacity, including:

[0026] Determine the investment cost of the target energy storage equipment based on the cost data;

[0027] Determine the annual investment value of the target energy storage equipment based on investment cost and economic life;

[0028] Determine the capacity cost of the target energy storage equipment based on cost data and economic life;

[0029] According to the capacity cost, the lower limit of the total energy storage capacity and the rated capacity, the corresponding benefit of the target energy storage device under the equivalent capacity is determined; wherein the equivalent capacity is determined based on the power capacity and the proportionality coefficient of the target energy storage device; the proportionality coefficient is used to characterize the ratio of the lower limit of the total energy storage capacity to the rated capacity of the target energy storage device.

[0030] In a possible implementation, taking the minimum cost as the objective function and subject to the constraints, an energy storage capacity planning scheme for the target energy storage device is obtained, including:

[0031] Under the constraints, the objective function is solved to obtain the capacity data and charge and discharge data of the target energy storage device;

[0032] Energy storage capacity planning is carried out based on the capacity data and charge and discharge data of the target energy storage equipment.

[0033] In a possible implementation, based on the energy storage capacity planning scheme of the target energy storage device and the corresponding revenue of the target energy storage device under equivalent capacity, the clearing price of the spot market in which the energy storage device participates is obtained, including:

[0034] Based on the capacity data, charge and discharge data of the target energy storage device and the corresponding income of the target energy storage device under equivalent capacity, the clearing price of the spot market in which the energy storage device participates is obtained.

[0035] In a second aspect, an embodiment of the present invention provides an energy storage capacity planning device, including:

[0036] The acquisition module is used to obtain the capacity data and operation data of the target energy storage equipment, as well as the energy impact characteristics of renewable energy in different scenarios;

[0037] A determination module is used to determine the net load curve of the target energy storage device according to the energy impact characteristics and operation data of renewable energy in different scenarios; and to determine the lower limit of the total energy storage capacity of the target energy storage device according to the net load curve and capacity data;

[0038] The planning module is used to obtain the energy storage capacity planning scheme of the target energy storage device with the minimum cost as the objective function under the constraints of the constraints; wherein the constraints are determined based on the lower limit of the total energy storage capacity.

[0039] The embodiment of the present invention provides a method and device for energy storage capacity planning. Compared with the prior art, the embodiment of the present invention determines the lower limit of the total energy storage capacity based on the capacity data and operation data of the target energy storage device and the energy impact characteristics of renewable energy in different scenarios, and imposes new constraints on the relevant parameters in the constraint conditions according to the lower limit of the total energy storage capacity, which can improve the calculation accuracy of the output of the energy storage device and the overall capacity. Finally, with the minimum cost as the objective function, under the constraints of the constraints, the capacity of each target energy storage device and the planning scheme of the charging and discharging power can be obtained to improve the adequacy of the system capacity of the energy storage system, thereby meeting the demand for flexible resources in the new power system under the spot environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, 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 some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0041] Figure 1 is a flow chart of an implementation method of energy storage capacity planning provided by an embodiment of the present invention;

[0042] Figure 2 is a flow chart of an implementation method of energy storage capacity planning provided by an embodiment of the present invention;

[0043] Figure 3 It is a structural schematic diagram of an energy storage capacity planning device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0044] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present invention. However, it should be clear to those skilled in the art that the present invention may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present invention.

[0045] In order to make the purpose, technical solutions and advantages of the present invention more clear, specific embodiments will be described below in conjunction with the accompanying drawings.

[0046] Figure 1 The following is a flowchart of the energy storage capacity planning method provided in the embodiment of the present invention. Figure 1 As shown, the method includes:

[0047] Step 110: Acquire capacity data and operation data of the target energy storage device, as well as energy impact characteristics of renewable energy in different scenarios.

[0048] In this embodiment, since there is at least one type of energy storage device in any power scenario at this stage, in this embodiment, the target energy storage device can be of multiple types, for example, it can include traditional thermal power units and new energy storage devices. Accordingly, when planning, it is necessary to obtain the capacity data and operation data corresponding to each target energy storage device, as well as the energy impact characteristics of renewable energy in different scenarios.

[0049] Among them, the capacity data corresponding to the target energy storage device may include the maximum capacity and the rated capacity. The operating data may include the charging and discharging power, the maximum discharge time, the load data, the load loss power and the output data at each moment, etc., which are not limited here. For wind energy, the energy impact characteristics of renewable energy in different scenarios may include data such as average wind speed, wind direction, maximum wind speed and instantaneous wind speed. For photovoltaics, the energy impact characteristics of renewable energy in different scenarios may include data such as light intensity, light incident angle, maximum illumination, etc. In addition, it may also include characteristics that affect power conversion, such as temperature, humidity, etc., which are not limited here.

[0050] Step 120: Determine the net load curve of the target energy storage device according to the energy impact characteristics and operation data of renewable energy in different scenarios; and determine the lower limit of the total energy storage capacity of the target energy storage device according to the net load curve and capacity data.

[0051] In this embodiment, energy storage is used to smooth the fluctuation of renewable energy. Considering the renewable energy supply and load demand, an energy storage capacity planning model is established, that is, the lower limit of the total energy storage capacity of the target energy storage device is determined. Accordingly, the lower limit of the total energy storage capacity of the target energy storage device can be expressed as:

[0052]

[0053] Among them, C RL is the lower limit of the total energy storage capacity of the target energy storage device; t2 and t1 are time; P max is the maximum capacity; P net (t) is the net load curve; is the rated capacity; h is the discharge time at the rated capacity.

[0054] Among them, the net load curve can be determined by the following method:

[0055] According to the energy impact characteristics of renewable energy in different scenarios, the characteristic probability of renewable energy is determined.

[0056] The characteristic probabilities of renewable energy are clustered to obtain the renewable energy output data under different probability scenarios.

[0057] Determine the net load curve of the target energy storage device based on the renewable energy output data and operation data under different probability scenarios.

[0058] In this embodiment, for any renewable energy source, the characteristic probability corresponding to the renewable energy source is determined according to the energy impact characteristics of the renewable energy source in different scenarios.

[0059] The KL asymmetry metric is used to cluster the characteristic probabilities of renewable energy and obtain the renewable energy output data under different probability scenarios. In the embodiment of the present invention, the net load curve can be obtained by subtracting the renewable energy output from the load in the study area. Since Gaussian distribution is often used to represent the distribution of different renewable energy sources, such as the characteristics of wind and solar power to the scene. However, the existence of some outliers in the Gaussian distribution will have a greater impact on the results of clustering analysis. The embodiment of the present invention uses t distribution for representation. At the same time, in order to improve the credibility of clustering and enhance the clustering performance, the following formula is used as the clustering distribution probability function:

[0060]

[0061] Among them, q ij is the characteristic probability of renewable energy, z j is the energy impact characteristic, c i For the scene; ∥z j -c i′ ∥ 2is the sum of the distances from the renewable energy feature to all scenarios, α is the degree of freedom of the distribution; p ij is the probability function after clustering, is the sum of the probabilities of renewable energy characteristics to different scenarios, f i It is the sum of the probabilities of all features to a specific scene.

[0062] Correspondingly, the net load curve can be expressed as:

[0063]

[0064] Among them, N W is the number of renewable energy units; N b is the number of nodes included in the energy storage system; is the power load of node n; P l n,t is the load loss power, is the output of the renewable energy unit at time t.

[0065] Step 130: Taking the minimum cost as the objective function, under the constraints of the constraints, an energy storage capacity planning scheme for the target energy storage device is obtained; wherein the constraints are determined based on the lower limit value of the total energy storage capacity.

[0066] In this embodiment, the objective function may be:

[0067]

[0068] in, is the price quoted by thermal power unit i at time t; is the output of the thermal power unit at time t; is the abandoned power of renewable energy unit w; is the discharge power of the energy storage; is the discharge cost of energy storage; c is the penalty price for renewable energy abandonment; l is the penalty price for load loss; N G is the number of thermal power units.

[0069] The system balance constraints can be:

[0070]

[0071] in, is the output of the renewable energy unit at time t, is the charging power of the energy storage.

[0072] Since energy storage has different operating durations, the operating constraints for short-term and long-term energy storage can be:

[0073]

[0074] E k,T =E k,0

[0075]

[0076] Among them, π k,t Indicates the state of energy storage charging and discharging; E k,t It is the available energy of the stored battery; TIME k is the maximum discharge time of energy storage; E k,t and E k,0 is the available energy stored at times T and 0; and is the charging and discharging efficiency of energy storage, Ω is the combination of short-term and long-term energy storage, and Represent short-term and long-term energy storage devices respectively. In addition, constraints can also include system scheduling constraints.

[0077] Based on the above objective function and constraint conditions, the capacity data and charge and discharge data of the target energy storage device are obtained; based on the capacity data and charge and discharge data of the target energy storage device, energy storage capacity planning is performed.

[0078] Figure 2 The implementation flowchart of the implementation flowchart method provided by the embodiment of the present invention is as follows: Figure 2 As shown, the method also includes:

[0079] Step 210: Obtain cost data and economic life of the target energy storage equipment.

[0080] Step 220: Determine the corresponding benefit of the target energy storage device at equivalent capacity based on the cost data, economic life, lower limit of total energy storage capacity and rated capacity.

[0081] Step 230: Based on the energy storage capacity planning scheme of the target energy storage device and the corresponding revenue of the target energy storage device under equivalent capacity, obtain the clearing price of the spot market in which the energy storage device participates.

[0082] In this embodiment, step 210 and step 220 may be before step 130, step 230 may be after step 130, or steps 210 to 230 may be used after step 130. Specifically, based on the calculation formula of the lower limit of the total energy storage capacity of the target energy storage device, when t2-t1>h, there is and With C RLThe difference is the shortfall caused by the inability of the energy storage equipment to support the system. To compensate for this shortfall, it is necessary to consider the current market revenue of energy storage and calculate the cost recovery capacity of energy storage capacity, so as to neither over-compensate nor under-compensate, thereby optimizing the energy storage planning capacity and reasonably determining the scale of energy storage operation.

[0083] Based on this, determining the corresponding benefit of the target energy storage device at equivalent capacity according to the cost data, economic life, lower limit of total energy storage capacity and rated capacity in step 210 may include:

[0084] The investment cost of the target energy storage equipment is determined based on the cost data.

[0085] Determine the annual investment value of the target energy storage equipment based on investment cost and economic life.

[0086] Determine the capacity cost of the target energy storage equipment based on cost data and economic life.

[0087] According to the capacity cost, the lower limit of the total energy storage capacity and the rated capacity, the corresponding benefit of the target energy storage device under the equivalent capacity is determined; wherein the equivalent capacity is determined based on the power capacity and the proportionality coefficient of the target energy storage device; the proportionality coefficient is used to characterize the ratio of the lower limit of the total energy storage capacity to the rated capacity of the target energy storage device.

[0088] In this embodiment, energy storage can fully release the peak load regulation, frequency regulation and ramp climbing capabilities, effectively deal with the absorption problems caused by large-scale grid connection of new energy, and is an important part of the new power system. However, at this stage, there are also problems such as low utilization rate, single market model and weak profitability, and it is urgent to support the development of corresponding market and price mechanisms.

[0089] In this embodiment, the cost data of the target energy storage device may include the unit investment cost of power capacity and the unit investment cost of energy capacity. Accordingly, the investment cost of the target energy storage device may be divided into two parts, namely, power capacity cost and energy capacity cost. Then, the investment cost of the target energy storage device may be calculated as follows:

[0090]

[0091] in, is the unit investment cost of the power capacity of the target energy storage device k, is the unit investment cost of the energy capacity of the target energy storage device k; is the power capacity scale; h k It is the energy storage duration. It should be noted here that since the target energy storage equipment includes short-term energy storage and long-term energy storage, the energy storage durations of the two energy storage equipment are different, so their investment costs need to be calculated separately.

[0092] In order to ensure the uniformity of the time value of money, the investment cost of the target energy storage equipment needs to be converted into the annual investment value of the target energy storage equipment according to the discount rate. Correspondingly, the annual investment value of the target energy storage equipment can be expressed as:

[0093]

[0094] Among them, N k is the economic life of the target energy storage equipment; r is the discount rate.

[0095] In order to calculate the energy storage capacity benefit, it is necessary to first calculate the capacity cost of the target energy storage device. Accordingly, the capacity cost of the target energy storage device can be expressed as:

[0096]

[0097] in, is the annual operation and maintenance cost of energy storage; is the revenue of energy storage in the electric energy market, K is the number of energy storages; e is the charge and discharge price; and It is the charge and discharge capacity.

[0098] Since different energy storage devices can provide different storage durations and support different systems, their equivalent capacities need to be further analyzed. The reason is that energy storage devices that play the same role in supporting the system (which can be considered to have the same power and charge and discharge duration) and energy storage devices that play different roles (which can be considered to have different power and charge and discharge duration) should be compensated differently, and the compensation ratio should be equivalent to the ratio of their equivalent capacity (that is, the system support capacity rather than the rated capacity). To this end, the power capacity of each target energy storage device is calculated by the coefficient Converted into equivalent capacity, the corresponding benefit under the consideration of equivalent capacity can be calculated by the following formula:

[0099]

[0100] This income can be considered as compensation for the system support provided by energy storage in different roles, which can be used for cost recovery. Market investors or policymakers can consider the specific investment scale or formulate compensation standards based on this income.

[0101] In the electric energy market, there are many types of market participants, such as thermal power, renewable energy units (wind power, photovoltaic) and energy storage. Each market player bids freely in the electric energy market according to its own conditions. The independent system operator will optimize the bidding behavior of all market participants according to the market clearing conditions, and then derive the optimal energy storage capacity scale and market clearing price. Correspondingly, step 230 can also use the same objective function and constraints as step 130, and then obtain the clearing price of the spot market in which the energy storage device participates based on the capacity data and charge and discharge data of the target energy storage device obtained in step 130, and the corresponding income of the target energy storage device under equivalent capacity determined by the above formula.

[0102] In summary, the embodiments of the present invention have the following advantages over the prior art: the embodiments of the present invention perform cluster analysis on renewable energy characteristics through KL asymmetry measurement, determine the overall energy storage capacity cost recovery plan based on the new energy output scenario and load demand, and improve the accuracy of new energy output and overall capacity calculation; establish a capacity cost recovery model through energy storage equivalent capacity measurement, which neither over-compensates nor under-compensates, and optimizes the scale of energy storage commissioning. Therefore, the embodiments of the present invention can be widely used in the field of new energy storage planning and its participation in market operations.

[0103] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0104] The following is an embodiment of the device of the present invention. For details not described in detail therein, reference may be made to the corresponding method embodiment described above.

[0105] Figure 3 The following is a schematic diagram showing the structure of an energy storage capacity planning device provided by an embodiment of the present invention. For ease of explanation, only the parts related to the embodiment of the present invention are shown, which are described in detail as follows:

[0106] like Figure 3 As shown, an energy storage capacity planning device 3 includes:

[0107] The acquisition module 31 is used to obtain the capacity data and operation data of the target energy storage device, as well as the energy impact characteristics of renewable energy in different scenarios;

[0108] A determination module 32 is used to determine the net load curve of the target energy storage device according to the energy impact characteristics and operation data of the renewable energy in different scenarios; and determine the lower limit of the total energy storage capacity of the target energy storage device according to the net load curve and capacity data;

[0109] The planning module 33 is used to obtain the energy storage capacity planning scheme of the target energy storage device with the minimum cost as the objective function under the constraints of the constraints; wherein the constraints are determined based on the lower limit value of the total energy storage capacity.

[0110] In a possible implementation, the capacity data includes a maximum capacity and a rated capacity;

[0111] The determination module 32 is specifically used for:

[0112] According to the net load curve, maximum capacity and rated capacity, determine the lower limit of the total energy storage capacity of the target energy storage equipment.

[0113] In a possible implementation, the net load curve, the maximum capacity, the rated capacity, and the lower limit of the total energy storage capacity of the target energy storage device satisfy the following relationship:

[0114]

[0115] Among them, C RL is the lower limit of the total energy storage capacity of the target energy storage device; t2 and t1 are time; P max is the maximum capacity; P net (t) is the net load curve; is the rated capacity; h is the discharge time at the rated capacity.

[0116] In a possible implementation, the determination module 32 is specifically configured to:

[0117] Determine the characteristic probability of renewable energy based on the energy impact characteristics of renewable energy in different scenarios;

[0118] Cluster the characteristic probabilities of renewable energy and obtain the renewable energy output data under different probability scenarios;

[0119] Determine the net load curve of the target energy storage device based on the renewable energy output data and operation data under different probability scenarios.

[0120] In one possible implementation, the constraints include: system balance constraints, system dispatch constraints, and operation constraints of short-term and long-term energy storage;

[0121] Among them, in the operation constraints of short-term and long-term energy storage, the charging and discharging power constraints of the energy storage and the upper limit value of the available energy of the energy storage are based on determining the lower limit value of the total energy storage capacity.

[0122] In a possible implementation, the acquisition module 31 is also used to obtain cost data and economic life of the target energy storage device;

[0123] The determination module 23 is further used to determine the corresponding benefit of the target energy storage device under equivalent capacity according to the cost data, economic life, lower limit value of total energy storage capacity and rated capacity;

[0124] The planning module 33 is also used to obtain the clearing price of the spot market in which the energy storage device participates based on the energy storage capacity planning scheme of the target energy storage device and the corresponding income of the target energy storage device under equivalent capacity.

[0125] In a possible implementation, the determination module 23 is specifically configured to:

[0126] Determine the investment cost of the target energy storage equipment based on the cost data;

[0127] Determine the annual investment value of the target energy storage equipment based on investment cost and economic life;

[0128] Determine the capacity cost of the target energy storage equipment based on cost data and economic life;

[0129] According to the capacity cost, the lower limit of the total energy storage capacity and the rated capacity, the corresponding benefit of the target energy storage device under the equivalent capacity is determined; wherein the equivalent capacity is determined based on the power capacity and the proportionality coefficient of the target energy storage device; the proportionality coefficient is used to characterize the ratio of the lower limit of the total energy storage capacity to the rated capacity of the target energy storage device.

[0130] In a possible implementation, the planning module 33 is specifically configured to:

[0131] Under the constraints, the objective function is solved to obtain the capacity data and charge and discharge data of the target energy storage device;

[0132] Energy storage capacity planning is carried out based on the capacity data and charge and discharge data of the target energy storage equipment.

[0133] In a possible implementation, the planning module 33 is specifically configured to:

[0134] Based on the capacity data, charge and discharge data of the target energy storage device and the corresponding income of the target energy storage device under equivalent capacity, the clearing price of the spot market in which the energy storage device participates is obtained.

[0135] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0136] Those of ordinary skill in the art will appreciate that the templates, units, and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. 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 can 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 the present invention.

[0137] If the module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, it can implement the steps of the above-mentioned energy storage capacity planning method embodiments. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory, random access memory, electrical carrier signal, telecommunication signal and software distribution medium.

[0138] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A method for energy storage capacity planning, characterized in that: include: Obtain capacity data and operation data of target energy storage equipment, as well as energy impact characteristics of renewable energy in different scenarios; Determine the net load curve of the target energy storage device according to the energy impact characteristics of the renewable energy in different scenarios and the operating data; and determine the lower limit of the total energy storage capacity of the target energy storage device according to the net load curve and the capacity data; Taking cost minimization as the objective function, under the constraints of constraints, an energy storage capacity planning scheme for the target energy storage device is obtained; wherein the constraints are determined based on the lower limit value of the total energy storage capacity.

2. The energy storage capacity planning method according to claim 1, characterized in that: The capacity data includes maximum capacity and rated capacity; Determining the lower limit value of the total energy storage capacity of the target energy storage device according to the net load curve and the capacity data includes: A lower limit value of a total energy storage capacity of the target energy storage device is determined according to the net load curve, the maximum capacity and the rated capacity.

3. The energy storage capacity planning method according to claim 2, characterized in that: The net load curve, the maximum capacity, the rated capacity and the lower limit of the total energy storage capacity of the target energy storage device satisfy the following relationship: Among them, C RL is the lower limit of the total energy storage capacity of the target energy storage device; t2 and t1 are time; P max is the maximum capacity; P net (t) is the net load curve; is the rated capacity; h is the discharge time at the rated capacity.

4. The energy storage capacity planning method according to claim 1, characterized in that: The determining the net load curve of the target energy storage device according to the energy impact characteristics of the renewable energy in different scenarios and the operation data includes: Determining characteristic probabilities of renewable energy sources according to energy impact characteristics of the renewable energy sources in different scenarios; Clustering the characteristic probabilities of the renewable energy sources to obtain renewable energy output data under different probability scenarios; The net load curve of the target energy storage device is determined according to the renewable energy output data and the operation data under the different probability scenarios.

5. The energy storage capacity planning method according to claim 1, characterized in that: The constraints include: system balance constraints, system dispatch constraints, and short-term and long-term energy storage operation constraints; Among them, in the operation constraints of the short-term and long-term energy storage, the charging and discharging power constraints of the energy storage and the upper limit value of the available energy of the energy storage are based on determining the lower limit value of the total energy storage capacity.

6. The energy storage capacity planning method according to claim 1, characterized in that: The method further comprises: Obtaining cost data and economic life of the target energy storage device; Determine the corresponding benefit of the target energy storage device at equivalent capacity according to the cost data, the economic life, the lower limit of the total energy storage capacity and the rated capacity; Based on the energy storage capacity planning scheme of the target energy storage device and the corresponding revenue of the target energy storage device under equivalent capacity, a clearing price of the spot market in which the energy storage device participates is obtained.

7. The energy storage capacity planning method according to claim 6, characterized in that: The determining of the corresponding benefit of the target energy storage device at equivalent capacity according to the cost data, the economic life, the lower limit of the total energy storage capacity and the rated capacity includes: Determining the investment cost of the target energy storage device based on the cost data; Determine the annual investment value of the target energy storage equipment according to the investment cost and the economic life; Determining the capacity cost of the target energy storage device according to the cost data and the economic life; According to the capacity cost, the lower limit value of the total energy storage capacity and the rated capacity, the corresponding benefit of the target energy storage device under the equivalent capacity is determined; wherein the equivalent capacity is determined based on the power capacity and the proportionality coefficient of the target energy storage device; the proportionality coefficient is used to characterize the ratio of the lower limit value of the total energy storage capacity to the rated capacity of the target energy storage device.

8. The energy storage capacity planning method according to claim 6, characterized in that: The energy storage capacity planning scheme of the target energy storage device is obtained by taking the minimum cost as the objective function and under the constraints of the constraints, including: Under the constraints of the constraints, solving the objective function to obtain capacity data and charge and discharge data of the target energy storage device; Energy storage capacity planning is performed based on the capacity data and charge and discharge data of the target energy storage device.

9. The energy storage capacity planning method according to claim 8, characterized in that: The energy storage capacity planning scheme based on the target energy storage device and the corresponding revenue of the target energy storage device under equivalent capacity is used to obtain the clearing price of the spot market in which the energy storage device participates, including: Based on the capacity data, charge and discharge data of the target energy storage device and the corresponding income of the target energy storage device under equivalent capacity, the clearing price of the spot market in which the energy storage device participates is obtained.

10. An energy storage capacity planning device, characterized in that: include: The acquisition module is used to obtain the capacity data and operation data of the target energy storage equipment, as well as the energy impact characteristics of renewable energy in different scenarios; A determination module, configured to determine a net load curve of the target energy storage device according to the energy impact characteristics of the renewable energy in different scenarios and the operation data; and determine a lower limit value of the total energy storage capacity of the target energy storage device according to the net load curve and the capacity data; The planning module is used to obtain the energy storage capacity planning scheme of the target energy storage device under the constraints of the constraints with the minimum cost as the objective function; wherein the constraints are determined based on the lower limit value of the total energy storage capacity.

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