New energy power system energy storage optimization configuration method, device, equipment and storage medium

Abstract

The invention discloses an energy storage optimization configuration method, device and equipment for a new energy power system and a storage medium, and the method comprises the steps: constructing an upper-layer energy storage optimization configuration model with the total cost of the new energy power system as an upper-layer objective function and a lower-layer energy storage optimization operation model with the system operation cost of the new energy power system as a lower-layer objective function; the system operation cost is characterized by adopting the spectral risk measurement function, the acceptance degree difference of investors on different risk losses is considered, and the optimal configuration effect is improved.

Description

Technical Field

[0001] The present invention relates to power grid energy storage optimization technology, and in particular to a new energy power system energy storage optimization configuration method, device, equipment and storage medium. Background Art

[0002] As the penetration rate of new energy in the power system continues to rise, it can be foreseen that the supply and demand relationship of power grid flexibility will become more tense in the future. At the same time, the demand for energy storage equipment will become stronger and stronger. Therefore, it is necessary to analyze the optimal configuration of energy storage in the new energy power system.

[0003] There are random factors such as wind power and photovoltaics in the new energy power system, and the uncertainty losses caused will constitute the risk of insufficient flexibility, which needs to be quantified by corresponding methods. However, the existing commonly used risk measurement methods, such as value at risk (VaR) and conditional value at risk (CVaR), often fail to consider tail risks or give the same weight to tail risks, resulting in poor optimization configuration results. Summary of the invention

[0004] The present invention provides a new energy power system energy storage optimization configuration method, device, equipment and storage medium to improve the optimization configuration effect.

[0005] In a first aspect, the present invention provides a method for optimizing energy storage configuration of a new energy power system, comprising:

[0006] Constructing an upper-level energy storage optimization configuration model with the total cost of the new energy power system as the upper-level objective function and a lower-level energy storage optimization operation model with the system operation cost of the new energy power system as the lower-level objective function, wherein the total cost of the new energy power system includes the new energy storage investment cost and the system operation cost, and the system operation cost is characterized by a spectral risk measurement function;

[0007] The particle swarm algorithm is used to iteratively calculate and solve the upper energy storage optimization configuration model;

[0008] Determine whether the upper-layer energy storage optimization configuration model meets the iteration termination conditions;

[0009] When the iteration termination condition of the upper energy storage optimization configuration model is not met, a mathematical programming optimization algorithm is used to solve the solution of the lower energy storage optimization operation model as the particle fitness of the upper energy storage optimization configuration model;

[0010] Calculate the total cost of the new energy power system based on the particle fitness, and return to execute the step of determining whether the upper energy storage optimization configuration model meets the iteration termination condition;

[0011] When the iteration termination condition of the upper-level energy storage optimization configuration model is met, the energy storage optimization configuration result is output.

[0012] Optionally, the upper layer energy storage optimization configuration model is:

[0013]

[0014] Among them, F(x) is the upper layer objective function, F I is the new energy storage investment cost of the system, F O is the system operation cost, x i With x o They represent the energy storage investment decision variables and optimal operation decision variables of the system respectively. I(x) and E(x) represent the inequality constraints and equality constraints of the upper-level energy storage optimization configuration model respectively.

[0015] Optionally, the lower-layer energy storage optimization operation model is:

[0016]

[0017] Among them, f(x) is the lower-level objective function, i(x) and e(x) represent the inequality constraints and equality constraints of the lower-level energy storage optimization operation model, respectively, and ξ is a random variable representing the influence of system uncertainty.

[0018] Optionally, the investment cost of new energy storage is:

[0019]

[0020] Among them, c p represents the unit power cost of energy storage; c e represents the unit capacity cost of energy storage; r is the discount rate, T e is the energy storage service life, Y d is the total number of days in the operating year, is the upper limit of energy storage capacity, It is the maximum charging and discharging power value of energy storage.

[0021] Optionally, the system running cost is:

[0022]

[0023] f O =F g +F R L+F m +F p

[0024] in, represents the spectral risk measurement function under a given confidence level, α and β are the given confidence levels, ω1 and ω2 are the risk calculation weights in the low loss and high loss intervals, CVaR is the conditional risk value under a given confidence level, is the system operation cost value of the random sample k, F g is the operating cost of conventional thermal power units, F RL F is the interruption load compensation fee, m is the energy storage operation and maintenance cost, F p Penalty losses for system power abandonment and load shedding.

[0025] Optionally, a particle swarm algorithm is used to iteratively calculate and solve the upper energy storage optimization configuration model, including:

[0026] Cluster the input planning scenario set data of the new energy power station to obtain the typical output scenario of the new energy power station;

[0027] Sampling typical output scenarios to generate sample scenarios;

[0028] Set the population's initial position, speed, iteration parameters and constraints, and start traversing all sample scenes;

[0029] Calculate the total cost for all sample scenarios.

[0030] Optionally, judging whether the upper-layer energy storage optimization configuration model satisfies the iteration termination condition includes:

[0031] Determine whether the total cost of all sample scenarios is less than the preset value.

[0032] In a second aspect, the present invention further provides a new energy power system energy storage optimization configuration device, comprising:

[0033] A model building module, used to build an upper energy storage optimization configuration model with the total cost of the new energy power system as the upper objective function and a lower energy storage optimization operation model with the system operation cost of the new energy power system as the lower objective function, wherein the total cost of the new energy power system includes the investment cost of the newly built energy storage and the system operation cost, and the system operation cost is characterized by a spectral risk measurement function;

[0034] A solution module is used to iteratively calculate and solve the upper energy storage optimization configuration model using a particle swarm algorithm;

[0035] A judgment module is used to judge whether the upper energy storage optimization configuration model meets the iteration termination condition;

[0036] A fitness calculation module is used to use a mathematical programming optimization algorithm to solve the solution of the lower energy storage optimization operation model as the particle fitness of the upper energy storage optimization configuration model when the iteration termination condition of the upper energy storage optimization configuration model is not met;

[0037] Returning to the execution module, which is used to calculate the total cost of the new energy power system based on the particle fitness, and returning to the step of determining whether the upper-layer energy storage optimization configuration model meets the iteration termination condition;

[0038] The configuration result output module is used to output the energy storage optimization configuration result when the iteration termination condition of the upper-level energy storage optimization configuration model is met.

[0039] In a third aspect, the present invention further provides an electronic device, comprising:

[0040] one or more processors;

[0041] A storage device for storing one or more programs;

[0042] When the one or more programs are executed by the one or more processors, the one or more processors implement the new energy power system energy storage optimization configuration method provided in the first aspect of the present invention.

[0043] In a third aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for optimizing energy storage configuration of a new energy power system as provided in the first aspect of the invention.

[0044] The present invention solves XX problem and achieves XX effect through XX.

[0045] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. 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 creative work.

[0047] Figure 1 A flowchart of a new energy power system energy storage optimization configuration method provided by the present invention;

[0048] Figure 2 Schematic diagram of the double-layer model structure for energy storage optimization configuration;

[0049] Figure 3 Schematic diagram of the overall solution process of the energy storage optimization configuration model;

[0050] Figure 4A schematic diagram of the structure of a new energy power system energy storage optimization configuration device provided by the present invention;

[0051] Figure 5 The present invention provides a schematic structural diagram of an electronic device.

[0052] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0053] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. 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 creative work should fall within the scope of protection of the present invention.

[0054] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0055] Figure 1 This is a flow chart of a method for optimizing energy storage configuration of a new energy power system provided by the present invention. This embodiment can be used to optimize the configuration of energy storage equipment in a new energy power system. The method can be executed by a device for optimizing energy storage configuration of a new energy power system provided by the present invention. The device can be implemented by software and / or hardware and is usually configured in an electronic device, such as Figure 1 As shown, the energy storage optimization configuration method of the new energy power system includes the following steps:

[0056] S101. Construct an upper-level energy storage optimization configuration model with the total cost of the new energy power system as the upper-level objective function and a lower-level energy storage optimization operation model with the system operation cost of the new energy power system as the lower-level objective function.

[0057] In an embodiment of the present invention, an upper-level energy storage optimization configuration model with the total cost of the new energy power system as the upper-level objective function and a lower-level energy storage optimization operation model with the system operation cost of the new energy power system as the lower-level objective function are constructed. The total cost of the new energy power system includes the investment cost of the new energy storage and the system operation cost, and the system operation cost is characterized by the spectral risk measurement function.

[0058] Although CVaR (conditional value at risk) can consider the expected value of tail risk after specifying the probability quantile, it has the consistency of risk measurement. However, since it calculates tail risk by averaging, it does not take into account the differences in investors' acceptance of risk losses of different sizes, but gives the same weight to tail risks for consideration. In actual situations, investors tend to care more about risk losses with larger values, so the calculation weight of risk should also increase with the increase of loss value.

[0059] Spectral risk measurement is a risk measurement method that meets consistency and takes into account the differences in risk acceptance. Constructing spectral risk measurement for risk measurement can be used as an effective problem-solving method. Considering the advantages of spectral risk measurement in reflecting risk losses, the present invention uses it as a method to measure the risk of insufficient flexibility of wind power systems. Under the premise of comprehensively considering the practicality and theoretical analysis value, based on CVaR, an admissible spectral density function suitable for the energy storage optimization configuration model of power systems containing new energy is constructed.

[0060] The present invention will establish an energy storage optimization configuration model that takes into account the risk of insufficient system flexibility based on a wind power power system. In a wind power power system, energy storage, as an important flexibility resource that provides regulation capabilities, plays a vital role in achieving a balance between the flexibility supply and demand of the system and optimizing economic benefits. In the energy storage optimization configuration model, it is necessary to form an optimal energy storage equipment investment plan by selecting the type, installed capacity and power of energy storage equipment, and to achieve overall economic optimization with the goal of minimizing the total cost of the system. According to the characteristics of the wind power power system, its total cost can be mainly divided into two parts: the investment cost of new energy storage and the system operation cost. The corresponding decision variables can also be separated into two parts, including energy storage investment decision variables and optimized operation decision variables.

[0061] The selection of the optimal energy storage configuration scheme can be regarded as a process of weighing the pros and cons between the system operating cost and the new energy storage investment cost. The reduction of energy storage configuration capacity and power will reduce the system investment cost, but the flexibility margin that can be provided will also decrease. The increased risk of insufficient flexibility will lead to greater wind abandonment and load shedding losses, which will increase the system operating cost; and vice versa. At the same time, considering that the uncertainty of the wind power power system mainly comes from the prediction errors of wind power and load, it means that random variables will mainly affect the operating cost of the system through changes in flexibility demand, and it is necessary to use risk measurement methods to quantify it. Therefore, the present invention establishes an energy storage optimization configuration model that takes into account the risk of insufficient system flexibility with a two-layer model structure. The lower layer conducts system optimization operation research and considers the risk of insufficient flexibility; the upper layer studies the energy storage optimization configuration scheme to seek the optimal total cost of the system.

[0062] Exemplarily, the upper layer energy storage optimization configuration model is:

[0063]

[0064] Among them, F(x) is the upper layer objective function, F I is the new energy storage investment cost of the system, F O is the system operation cost, x i With x o They represent the energy storage investment decision variables and optimal operation decision variables of the system respectively. I(x) and E(x) represent the inequality constraints and equality constraints of the upper-level energy storage optimization configuration model respectively.

[0065] The optimization operation model of the lower energy storage is:

[0066]

[0067] Among them, f(x) is the lower-level objective function, i(x) and e(x) represent the inequality constraints and equality constraints of the lower-level energy storage optimization operation model, respectively, and ξ is a random variable representing the influence of system uncertainty.

[0068] From the above, we can see that the energy storage investment decision variables of the upper model will affect the operating constraints of the lower model, and the optimization operation results of the lower model will directly affect the objective function of the upper model. Therefore, during the operation process, the operation results of the upper and lower models will be transmitted and affect each other. The structure and connotation of the two-layer model of energy storage optimization configuration considering the risk of insufficient system flexibility are as follows: Figure 2 As shown, the model divides the optimization configuration problem into two stages for processing, and can apply mathematical optimization solution algorithms and intelligent optimization algorithms to the solutions of the upper and lower models respectively. While the model logic is clear, it also supports the organic combination of the two types of solution algorithms, optimizing the speed and feasibility of model solution.

[0069] The optimization goal of the upper model is to optimize the total system cost, including the new energy storage investment cost and the system operation cost. The expression is as follows:

[0070] min F=F I +F O

[0071] The system operation cost is calculated by optimizing the lower-level model, and the new energy storage investment cost mainly examines the annual value of the investment in energy storage facilities. The new energy storage investment cost is:

[0072]

[0073] Among them, c p represents the unit power cost of energy storage; c e represents the unit capacity cost of energy storage; r is the discount rate, T e is the energy storage service life, Y d is the total number of days in the operating year, generally 365 days. is the upper limit of energy storage capacity, It is the maximum charging and discharging power value of energy storage.

[0074] The role of the lower model is to solve the optimal solution for the system's optimized operating cost and pass it to the upper model. In the energy storage optimization operation model, the uncertainty of the new energy system needs to be considered. Because there is a prediction error between the net load forecast curve and the actual curve, it will greatly affect the flexibility of the system. The balance of supply and demand will lead to a substantial increase in the penalty costs for wind abandonment and load shedding. In order to configure enough energy storage equipment to cope with the uncertain system flexibility requirements, in the system operation cost function F O Considering the risk of insufficient flexibility, the expression is as follows:

[0075]

[0076] represents the spectral risk measure function under a given confidence level.

[0077] Due to the wind power and load forecast errors of the system, the system operating cost will be affected by uncertainty factors. In order to reflect the risk of insufficient system flexibility under the influence of wind power forecast errors, the spectral risk measure is used. The system loss is measured, and the system operating cost is used as the economic loss of the system, and it is analyzed under given confidence levels α and β. Considering that random sampling methods are often used in energy storage optimization configuration models to represent system uncertainty factors, the formula Expressed in discrete form:

[0078]

[0079] fO =F g +F RL +F m +F p

[0080] in, represents the spectral risk measurement function under a given confidence level, α and β are the given confidence levels, ω1 and ω2 are the risk calculation weights in the low loss and high loss intervals, CVaR is the conditional risk value under a given confidence level, is the system operation cost value of the random sample k, F g is the operating cost of conventional thermal power units, F RL F is the interruption load compensation fee, m is the energy storage operation and maintenance cost, F p Penalty losses for system power abandonment and load shedding.

[0081] Among them, the operating cost of conventional thermal power units is g , Interruption load compensation fee F RL , Energy storage operation and maintenance cost F m and system power curtailment load shedding penalty loss F p The calculation formula is as follows:

[0082]

[0083] Where N g is the total number of thermal power units; is the fuel cost coefficient of unit i; is the output power of thermal power unit i at time t; and are the startup and shutdown cost coefficients of unit i respectively; T = 24 is the total number of time periods in one operation cycle of the system, and the time scale τ is 1; is the incentive demand-side response cost compensation coefficient of user n; c m The annual operation and maintenance cost coefficient of energy storage needs to be divided by the total number of days in the operation year to average the cost to a single operation cycle; c L With c w are the penalty cost coefficients for system load shedding and wind abandonment respectively; is the load shedding power at time t; is the amount of wind power curtailment at time t.

[0084] The constraints include:

[0085] (1) Energy storage type constraints

[0086] According to the results of energy storage technology selection, the energy storage technology type with better evaluation results is selected for optimal configuration.

[0087] (2) Energy storage capacity scale constraints

[0088]

[0089] In the formula, Represents the upper limit of the system's energy storage capacity.

[0090] (3) Energy storage power scale constraints

[0091]

[0092] In the formula, Represents the upper limit of the system energy storage power scale.

[0093] (4) System constraints

[0094] The system constraint means that the source-load power balance state needs to be achieved at every moment, and its expression is as follows:

[0095]

[0096] Where P t ch , P t dis are the charging and discharging power of energy storage at time t; P t w , P t w,l represents the wind power output and abandoned power at time t; L t Indicates the load size of the system at time t; They represent the load power increase and decrease of the transferable load at time t respectively; It represents the total power reduction of the system's load interruption users at time t.

[0097] (5) Energy storage operation constraints

[0098] ① Energy storage energy constraints

[0099]

[0100] In the formula, is the energy storage capacity at time t; η ch , η dis Represents the charging and discharging efficiency of energy storage.

[0101] ②Energy storage power constraints

[0102]

[0103] ③ Energy storage charging and discharging state constraints

[0104] The energy storage charging state conflicts with the discharging state, and the two cannot proceed at the same time. Therefore, the value of one of the parameters of the charging and discharging power is 0, and the product of the two is also 0.

[0105] P t dis ·P t ch =0

[0106] (6) Demand-side response load constraints

[0107] ①Interruptible load constraints

[0108]

[0109] In the formula, and is the minimum and maximum load interruption power of the interruptible load n at time t, Indicates the interruption time of the interruptible load within one operating cycle; The upper limit of the interruption time.

[0110] ②Transferable load constraints

[0111] The user of transferable load can change the load output value at each moment, but the total load power in one operating cycle remains unchanged.

[0112]

[0113] In the formula, is the upper limit of the increase and decrease of the transferable load m at time t.

[0114] (7) Constraints on conventional thermal power units

[0115] ① Output constraints of thermal power units

[0116]

[0117] In the formula, Indicates the maximum rated output and minimum technical output of the unit; Indicates the start and stop status of the unit.

[0118] ② Thermal power unit climbing constraints

[0119]

[0120] In the formula, Indicates the upward and downward climbing rates of thermal power units.

[0121] ③ Constraints on the start and stop of thermal power units

[0122]

[0123] In the formula, Indicates the shutdown time and minimum shutdown time of the thermal power unit.

[0124] S102. Using a particle swarm algorithm to iteratively calculate and solve the upper energy storage optimization configuration model.

[0125] For example, the overall solution process of the model is as follows Figure 3 As shown, the input planning scenario set data of the new energy power station is clustered to obtain the typical output scenario of the new energy power station, sample the typical output scenario, generate sample scenarios, set the initial position, speed, iteration parameters and constraints of the population, and start traversing all sample scenarios to calculate the total cost of all sample scenarios.

[0126] S103: Determine whether the upper-layer energy storage optimization configuration model satisfies the iteration termination condition.

[0127] like Figure 3 As shown, it is determined whether the upper energy storage optimization configuration model meets the iteration termination condition. For example, it is determined whether the total cost of all sample scenarios is less than a preset value. If not, S104 is executed; if so, S106 is executed.

[0128] S104. Using a mathematical programming optimization algorithm to solve the solution of the lower energy storage optimization operation model is used as the particle fitness of the upper energy storage optimization configuration model.

[0129] For example, Figure 3 As shown, when the iteration termination condition of the upper energy storage optimization configuration model is not met, the mathematical programming optimization algorithm is used to solve the solution of the lower energy storage optimization operation model as the particle fitness of the upper energy storage optimization configuration model.

[0130] S105. Calculate the total cost of the new energy power system based on the particle fitness.

[0131] like Figure 3 As shown, the total cost of the new energy power system is calculated based on the particle fitness. Exemplarily, the system operation cost of multiple wind power scenarios is calculated cyclically based on the particle fitness until the system operation cost of all sample scenarios is calculated completely (i.e., j=n), and then m samples are taken from all sample scenarios (m is the number of typical output scenarios), and the total cost of the new energy power system is recalculated, and the step of judging whether the upper-level energy storage optimization configuration model meets the iteration termination condition is returned, and the cycle is repeated until the iteration termination condition is met.

[0132] S106. Output the energy storage optimization configuration result.

[0133] For example, Figure 3As shown, when the iteration termination condition of the upper-level energy storage optimization configuration model is met, the energy storage optimization configuration result is output.

[0134] The method for optimizing energy storage configuration of a new energy power system provided by the present invention constructs an upper energy storage optimization configuration model with the total cost of the new energy power system as the upper objective function and a lower energy storage optimization operation model with the system operation cost of the new energy power system as the lower objective function, wherein the total cost of the new energy power system includes the investment cost of newly built energy storage and the system operation cost, the system operation cost is characterized by a spectral risk measurement function, a particle swarm algorithm is used to iteratively calculate and solve the upper energy storage optimization configuration model, and it is judged whether the upper energy storage optimization configuration model meets the iteration termination condition. When the iteration termination condition of the upper energy storage optimization configuration model is not met, a mathematical programming optimization algorithm is used to solve the solution of the lower energy storage optimization operation model as the particle fitness of the upper energy storage optimization configuration model, the total cost of the new energy power system is calculated based on the particle fitness, and the step of judging whether the upper energy storage optimization configuration model meets the iteration termination condition is returned to execute. When the iteration termination condition of the upper energy storage optimization configuration model is met, the energy storage optimization configuration result is output, and the system operation cost is characterized by a spectral risk measurement function, and the difference in investors' acceptance of risk losses of different sizes is considered, thereby improving the optimization configuration effect.

[0135] Figure 4 A schematic diagram of a new energy power system energy storage optimization configuration device provided by the present invention is shown in FIG. Figure 4 As shown, the energy storage optimization configuration device of the new energy power system includes:

[0136] A model building module 201 is used to build an upper energy storage optimization configuration model with the total cost of the new energy power system as the upper objective function and a lower energy storage optimization operation model with the system operation cost of the new energy power system as the lower objective function, wherein the total cost of the new energy power system includes the new energy storage investment cost and the system operation cost, and the system operation cost is characterized by a spectral risk measurement function;

[0137] A solution module 202 is used to perform iterative calculation and solution on the upper energy storage optimization configuration model using a particle swarm algorithm;

[0138] A judgment module 203 is used to judge whether the upper energy storage optimization configuration model meets the iteration termination condition;

[0139] The fitness calculation module 204 is used to use a mathematical programming optimization algorithm to solve the solution of the lower energy storage optimization operation model as the particle fitness of the upper energy storage optimization configuration model when the iteration termination condition of the upper energy storage optimization configuration model is not met;

[0140] Return to the execution module 205, which is used to calculate the total cost of the new energy power system based on the particle fitness, and return to the step of determining whether the upper energy storage optimization configuration model meets the iteration termination condition;

[0141] The configuration result output module 206 is used to output the energy storage optimization configuration result when the iteration termination condition of the upper-layer energy storage optimization configuration model is met.

[0142] In some embodiments of the present invention, the upper layer energy storage optimization configuration model is:

[0143]

[0144] Among them, F(x) is the upper layer objective function, F I is the new energy storage investment cost of the system, F O is the system operation cost, x i With x o They represent the energy storage investment decision variables and optimal operation decision variables of the system respectively. I(x) and E(x) represent the inequality constraints and equality constraints of the upper-level energy storage optimization configuration model respectively.

[0145] In some embodiments of the present invention, the lower layer energy storage optimization operation model is:

[0146]

[0147] Among them, f(x) is the lower-level objective function, i(x) and e(x) represent the inequality constraints and equality constraints of the lower-level energy storage optimization operation model, respectively, and ξ is a random variable representing the influence of system uncertainty.

[0148] In some embodiments of the present invention, the new energy storage investment cost is:

[0149]

[0150] Among them, c p represents the unit power cost of energy storage; c e represents the unit capacity cost of energy storage; r is the discount rate, T e is the energy storage service life, Y d is the total number of days in the operating year, is the upper limit of energy storage capacity, It is the maximum charging and discharging power value of energy storage.

[0151] In some embodiments of the present invention, the system operation cost is:

[0152]

[0153] f O =F g +FRL +F m +F p

[0154] in, represents the spectral risk measurement function under a given confidence level, α and β are the given confidence levels, ω1 and ω2 are the risk calculation weights in the low loss and high loss intervals, CVaR is the conditional risk value under a given confidence level, is the system operation cost value of the random sample k, F g is the operating cost of conventional thermal power units, F RL F is the interruption load compensation fee, m is the energy storage operation and maintenance cost, F p Penalty losses for system power abandonment and load shedding.

[0155] In some embodiments of the present invention, the solution module 202 includes:

[0156] The clustering submodule is used to cluster the input planning scenario set data of the new energy power station to obtain the typical output scenario of the new energy power station;

[0157] The sampling submodule is used to sample typical output scenarios and generate sample scenarios;

[0158] The initialization submodule is used to set the population's initial position, speed, iteration parameters and constraints, and start traversing all sample scenes;

[0159] The total cost calculation submodule is used to calculate the total cost of all sample scenarios.

[0160] In some embodiments of the present invention, the determination module 203 includes:

[0161] The judgment submodule is used to judge whether the total cost of all sample scenarios is less than a preset value.

[0162] The above-mentioned new energy power system energy storage optimization configuration device can execute the new energy power system energy storage optimization configuration method provided by the aforementioned embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the new energy power system energy storage optimization configuration method.

[0163] Figure 5A schematic diagram of the structure of an electronic device provided by the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0164] like Figure 5 As shown, the electronic device includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0165] A number of components in the electronic device are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0166] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The processor 11 executes the various methods and processes described above, such as a new energy power system energy storage optimization configuration method.

[0167] In some embodiments, the new energy power system energy storage optimization configuration method can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on an electronic device via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the new energy power system energy storage optimization configuration method described above can be executed. Alternatively, in other embodiments, the processor 11 can be configured to execute the new energy power system energy storage optimization configuration method in any other appropriate manner (for example, by means of firmware).

[0168] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0169] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0170] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0171] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0172] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0173] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0174] An embodiment of the present invention further provides a computer program product, including a computer program, which, when executed by a processor, implements the method for optimizing energy storage configuration of a new energy power system as provided in any embodiment of the present application.

[0175] In the process of implementation, the computer program product can be written in one or more programming languages ​​or a combination thereof to perform the computer program code of the present invention, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, using an Internet service provider to connect through the Internet).

[0176] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0177] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for optimizing energy storage configuration of a new energy power system, characterized in that: include: Constructing an upper-level energy storage optimization configuration model with the total cost of the new energy power system as the upper-level objective function and a lower-level energy storage optimization operation model with the system operation cost of the new energy power system as the lower-level objective function, wherein the total cost of the new energy power system includes the new energy storage investment cost and the system operation cost, and the system operation cost is characterized by a spectral risk measurement function; The particle swarm algorithm is used to iteratively calculate and solve the upper energy storage optimization configuration model; Determine whether the upper-layer energy storage optimization configuration model meets the iteration termination conditions; When the iteration termination condition of the upper energy storage optimization configuration model is not met, a mathematical programming optimization algorithm is used to solve the solution of the lower energy storage optimization operation model as the particle fitness of the upper energy storage optimization configuration model; Calculate the total cost of the new energy power system based on the particle fitness, and return to execute the step of determining whether the upper energy storage optimization configuration model meets the iteration termination condition; When the iteration termination condition of the upper-level energy storage optimization configuration model is met, the energy storage optimization configuration result is output.

2. The method for optimizing energy storage configuration of a new energy power system according to claim 1, characterized in that: The upper-layer energy storage optimization configuration model is: Among them, F(x) is the upper layer objective function, F I is the new energy storage investment cost of the system, F O is the system operation cost, x i With x o They represent the energy storage investment decision variables and optimal operation decision variables of the system respectively. I(x) and E(x) represent the inequality constraints and equality constraints of the upper-level energy storage optimization configuration model respectively.

3. The method for optimizing energy storage configuration of a new energy power system according to claim 2, characterized in that: The optimization operation model of the lower energy storage is: Among them, f(x) is the lower-level objective function, i(x) and e(x) represent the inequality constraints and equality constraints of the lower-level energy storage optimization operation model, respectively, and ξ is a random variable representing the influence of system uncertainty.

4. The method for optimizing energy storage configuration of a new energy power system according to claim 3, characterized in that: The investment cost of new energy storage is: Among them, c p represents the unit power cost of energy storage; c e represents the unit capacity cost of energy storage; r is the discount rate, T e is the energy storage service life, Y d is the total number of days in the operating year, is the upper limit of energy storage capacity, It is the maximum charging and discharging power value of energy storage.

5. The method for optimizing energy storage configuration of a new energy power system according to claim 4, characterized in that: The system operating costs are: f O =F g +f RL +F m +F p in, represents the spectral risk measurement function under a given confidence level, α and β are the given confidence levels, ω1 and ω2 are the risk calculation weights in the low loss and high loss intervals, CVaR is the conditional risk value under a given confidence level, is the system operation cost value of the random sample k, F g is the operating cost of conventional thermal power units, FRL is the interruption load compensation fee, m is the energy storage operation and maintenance cost, F p Penalty losses for system power abandonment and load shedding.

6. The method for optimizing energy storage configuration of a new energy power system according to claim 1, characterized in that: The particle swarm algorithm is used to iteratively calculate and solve the upper energy storage optimization configuration model, including: Cluster the input planning scenario set data of the new energy power station to obtain the typical output scenario of the new energy power station; Sampling typical output scenarios to generate sample scenarios; Set the population's initial position, speed, iteration parameters and constraints, and start traversing all sample scenes; Calculate the total cost for all sample scenarios.

7. The method for optimizing energy storage configuration of a new energy power system according to claim 6, characterized in that: Determine whether the upper-layer energy storage optimization configuration model meets the iteration termination conditions, including: Determine whether the total cost of all sample scenarios is less than the preset value.

8. A new energy power system energy storage optimization configuration device, characterized in that: include: A model building module, used to build an upper energy storage optimization configuration model with the total cost of the new energy power system as the upper objective function and a lower energy storage optimization operation model with the system operation cost of the new energy power system as the lower objective function, wherein the total cost of the new energy power system includes the investment cost of the newly built energy storage and the system operation cost, and the system operation cost is characterized by a spectral risk measurement function; A solution module is used to iteratively calculate and solve the upper energy storage optimization configuration model using a particle swarm algorithm; A judgment module is used to judge whether the upper energy storage optimization configuration model meets the iteration termination condition; A fitness calculation module is used to use a mathematical programming optimization algorithm to solve the solution of the lower energy storage optimization operation model as the particle fitness of the upper energy storage optimization configuration model when the iteration termination condition of the upper energy storage optimization configuration model is not met; Returning to the execution module, which is used to calculate the total cost of the new energy power system based on the particle fitness, and returning to the step of determining whether the upper-layer energy storage optimization configuration model meets the iteration termination condition; The configuration result output module is used to output the energy storage optimization configuration result when the iteration termination condition of the upper-level energy storage optimization configuration model is met.

9. An electronic device, characterized in that: include: one or more processors; A storage device for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the new energy power system energy storage optimization configuration method as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method for optimizing energy storage configuration of a new energy power system as described in any one of claims 1 to 7 is implemented.

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