Optimal configuration method and device for hybrid energy storage capacity of power system and medium

Abstract

The invention discloses a power system hybrid energy storage capacity optimal configuration method and device and a medium, and relates to the technical field of power system optimal configuration, and the method comprises the steps: obtaining a net load time sequence of a power system, carrying out the preprocessing of the net load time sequence, and obtaining a preprocessed net load time sequence; processing the preprocessed net load time sequence by adopting a complete set empirical mode decomposition method to obtain a spectral characteristic curve; determining a target separation frequency based on the spectral characteristic curve by using a trial and error method, and determining frequency bands loaded by the pumped storage power station and the electrochemical energy storage power station in the power system based on the target separation frequency; and establishing a hybrid energy storage capacity optimal configuration model, and solving the hybrid energy storage capacity optimal configuration model to obtain an optimal configuration scheme of the pumped storage power station and the electrochemical energy storage power station. According to the invention, the adjusting capability of the energy storage system combining pumped storage and electrochemical energy storage is improved.

Description

Technical Field

[0001] The present application relates to the technical field of power system optimization configuration, and in particular to a method, device and medium for optimizing configuration of hybrid energy storage capacity of a power system. Background Art

[0002] New energy is developing more and more rapidly, but the randomness and intermittency of renewable energy output poses a great risk to the stability of the power system, so energy storage technology is urgently needed to ensure the stable operation of the power grid. In order to solve this problem, when renewable energy such as wind power and solar power are connected to the grid, energy storage systems should be equipped to smooth out fluctuations and improve the stability of the power system. Selecting an appropriate scale of pumped storage can not only save resources, but also improve the guarantee for the stable operation of the power grid.

[0003] However, a single type of energy storage is unlikely to meet the new grid's demand for high power, large capacity, and high-frequency charging and discharging of energy storage systems. Pumped storage and compressed air energy storage have the potential for medium- and long-term energy storage and can be adjusted during the day; while electrochemical energy storage is generally used for intraday peak regulation or frequency regulation. The comprehensive use of pumped storage and electrochemical energy storage can not only achieve complementary advantages, but also effectively reduce the total investment cost of the energy storage system, achieving the effect of "1+1>2".

[0004] In terms of optimal configuration of energy storage capacity, the key lies in the energy distribution of hybrid energy storage. The existing adaptive sliding average filtering method for energy distribution of hybrid energy storage can avoid the influence of subjective factors, but taking the average power of all sampling points in a sliding window as the sliding target can easily cause the target power curve to deviate from the original power curve, making the regulation ability of hybrid energy storage poor. Summary of the invention

[0005] The purpose of this application is to provide a method, device and medium for optimizing the configuration of hybrid energy storage capacity in an electric power system, thereby improving the regulation capability of an energy storage system combining pumped storage and electrochemical energy storage.

[0006] To achieve the above objectives, this application provides the following solutions:

[0007] In a first aspect, the present application provides a method for optimizing configuration of hybrid energy storage capacity in a power system, comprising:

[0008] Obtaining a net load time series of the power system, and preprocessing the net load time series to obtain a preprocessed net load time series; the preprocessing is to maximize the minimum value of each net load in the net load time series while meeting the requirements of the comprehensive utilization rate of new energy and the proportion of electricity;

[0009] The complete set empirical mode decomposition method is used to process the preprocessed net load time series to obtain the spectrum characteristic curve;

[0010] Determine the target separation frequency based on the frequency spectrum characteristic curve by trial and error method, and determine the frequency band borne by the pumped storage power station and the electrochemical energy storage power station in the power system based on the target separation frequency;

[0011] A hybrid energy storage capacity optimization configuration model is established, and the hybrid energy storage capacity optimization configuration model is solved to obtain the optimal configuration scheme of the pumped-storage power station and the electrochemical energy storage power station; the hybrid energy storage capacity optimization configuration model includes: an objective function and constraints, the objective function is to minimize the full life cycle cost of the energy storage system in the power system as the optimization goal, and the constraints include: power balance constraints, pumped-storage power station constraints, electrochemical energy storage power station constraints, wind and solar power consumption indicators and node voltage constraints.

[0012] Optionally, the preprocessed net load time series is processed using a complete set empirical mode decomposition method to obtain a spectrum characteristic curve, including:

[0013] By using the complete set empirical mode decomposition method, each net load in the preprocessed net load time series is reconstructed by frequency division to obtain multiple intrinsic mode function components of the corresponding net load;

[0014] The Hilbert transform is performed on each intrinsic mode function component of each net load to obtain the instantaneous frequency of the corresponding intrinsic mode function component, thereby obtaining the frequency spectrum characteristic curve.

[0015] Optionally, the objective function includes:

[0016] min E all =E C +E OM +E R +E cut +E n ;

[0017] E C =P phsN α phs +P bsN α bs ;

[0018] E OM =P phsN β phs +P bsN β bs ;

[0019] E R =κ bs P bsN γ bs ;

[0020]

[0021] Among them, E all is the life cycle cost of the energy storage system; E C is the initial investment cost of the energy storage system; E OM is the operation and maintenance cost of the energy storage system; E R is the replacement cost of the energy storage system; E cut E is the penalty cost for wind and solar curtailment of the energy storage system; n is the operating cost of thermal power units; P phsN is the installed capacity of the pumped storage power station; α phs P is the initial investment cost unit price of the pumped storage power station; bsN is the installed capacity of the electrochemical energy storage power station; α bs is the initial investment cost unit price of the electrochemical energy storage power station; β phs is the unit price of operation and maintenance cost of pumped storage power station; bs is the unit price of operation and maintenance cost of electrochemical energy storage power station; κ bs is the number of replacements of the electrochemical energy storage power station; γ bs is the replacement cost unit price of the electrochemical energy storage power station; ρ w is the wind abandonment penalty coefficient; T is the total number of moments; is the wind power abandoned at time t; Δt is the time interval; ρ pv is the penalty coefficient for abandoning light; is the abandoned optical power at time t; n P is the unit output cost of thermal power units; n,t is the output of the thermal power unit at time t.

[0022] Optionally, the power balance constraint includes:

[0023] P pv,t +P wind,t +P phs_ele,t +P bs_dis,t +P n,t =P load,t +P phs_ch,t +P bs_ch,t +P loss,t ;

[0024] Among them, P pv,t is the power generation of the photovoltaic power station at time t; P wind,t is the wind farm power generation at time t; P phs_ele,t is the power generation capacity of the pumped storage power station at time t; P bs_dis,t is the discharge power of the electrochemical energy storage power station at time t; P n,t is the power generated by conventional power supply at time t; P load,t is the load power at time t; P phs_ch,t is the pumping power of the pumped storage power station at time t; Pbs_ch,t is the charging power of the electrochemical energy storage power station at time t; P loss,t is the system loss at time t.

[0025] Optionally, the pumped storage power station constraints include:

[0026]

[0027] Among them, U phs,ele (t) is the power generation state of the pumped storage power station at time t, U phs,ele (t) = 1 means power generation, U phs,ele (t) = 0 means no power generation; U phs,ch (t) is the pumping state of the pumped storage power station at time t, U phs,ch (t) = 1 means pumping, U phs,ch (t) = 0 means no pumping; Q phsN is the total water storage volume of the upper reservoir; Q phs (t) is the water storage capacity of the upper reservoir at time t; Q phsN Q phs (0) is the water storage capacity of the upper reservoir at the initial moment; Q phs (24) is the water storage capacity of the upper reservoir at the last moment.

[0028] Optionally, the electrochemical energy storage power station constraints include:

[0029]

[0030] Among them, U bs_ch (t) is the charging state of the electrochemical energy storage power station at time t, U bs_ch (t) = 1 means charging, U bs_ch (t) = 0 means no charging; U bs_dis (t) is the discharge state of the electrochemical energy storage power station at time t, U bs_dis (t) = 1 means discharge, U bs_dis (t) = 0 means no discharge; SOC min is the lower limit of the state of charge of the battery in the electrochemical energy storage power station; SOC(t) is the state of charge of the battery in the electrochemical energy storage power station at time t; SOC max is the upper limit of the state of charge of the battery of the electrochemical energy storage power station; SOC(0) is the state of charge of the battery of the electrochemical energy storage power station at the initial moment; SOC(24) is the state of charge of the battery of the electrochemical energy storage power station at the last moment.

[0031] Optionally, the wind and solar power consumption index includes:

[0032]

[0033] Among them, λw,pv is the wind and solar power abandonment rate; w is the wind abandonment rate; pv is the light abandonment rate.

[0034] Optionally, the node voltage constraint includes:

[0035] V min ≤V i ≤V max ;

[0036] Among them, V min is the lower limit of node voltage; V i is the node voltage; V max is the upper limit of node voltage.

[0037] In a second aspect, the present application provides a computer device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement any of the above-mentioned methods for optimizing configuration of hybrid energy storage capacity of a power system.

[0038] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the above-mentioned methods for optimizing the configuration of hybrid energy storage capacity in a power system.

[0039] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0040] The present application discloses a method, device and medium for optimizing the configuration of hybrid energy storage capacity in an electric power system. With the electric power system as the application scenario, the advantages of large pumped storage capacity and low cost are utilized to construct a hybrid energy storage system of pumped storage and electrochemical energy storage, to adjust the peak and fill the valley, and to control the voltage of each node within a certain range. First, the complete set empirical mode decomposition method is used to reconstruct the net load time series by frequency division, and then the frequency division points suitable for pumped storage and electrochemical energy storage are selected, and the high-frequency components are smoothed by electrochemical energy storage, and the low-frequency components are smoothed by pumped storage. The rated power and rated capacity of pumped storage and electrochemical energy storage are established as the optimization target, the annual comprehensive cost of the system is used as the objective function, and the energy storage life and system stability constraints are comprehensively considered to finally determine the corresponding hybrid energy storage system optimization configuration plan. The transfer and transmission problems between high and low frequencies of white noise are effectively solved, and the decomposed net load time series spectrum characteristic curve is more accurate, which improves the regulation ability of the energy storage system combining pumped storage and electrochemical energy storage. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0042] Figure 1 A schematic diagram of a flow chart of a method for optimizing configuration of hybrid energy storage capacity in a power system provided in an embodiment of the present application;

[0043] Figure 2 A schematic diagram of the solution process for the optimal configuration of hybrid energy storage capacity based on the complete set empirical mode decomposition method;

[0044] Figure 3 It is a schematic diagram of the net load power curve for the whole year before and after pretreatment;

[0045] Figure 4 It is a schematic diagram of the power curve of some net loads before and after pretreatment;

[0046] Figure 5 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0047] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0048] The purpose of this application is to provide a method, device and medium for optimizing the configuration of hybrid energy storage capacity in an electric power system, aiming to improve the regulation capability of an energy storage system combining pumped storage and electrochemical energy storage.

[0049] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0050] Renewable energy is developing more and more rapidly. The proportion of renewable energy such as wind power and solar energy in the power system is gradually increasing. The randomness and intermittency of renewable energy output pose a great risk to the stability of the power system. Therefore, energy storage technology is urgently needed to ensure the stable operation of the power grid. In order to solve this problem and improve the stability and reliability of the power system, when renewable energy such as wind power and solar energy are connected to the grid, energy storage systems should be equipped to smooth out fluctuations and improve the stability of the power system. Large-scale energy storage technology can effectively balance the volatility of new energy. At present, the most economical and mature energy storage technology is pumped storage. Selecting an appropriate scale of pumped storage can not only save resources, but also improve the guarantee for the stable operation of the power grid.

[0051] However, a single type of energy storage can hardly meet the needs of new power grids for high power, large capacity, and high frequency charging and discharging of energy storage systems. A hybrid energy storage system that combines energy-type and power-type energy storage can greatly improve the regulation capabilities of the energy storage system in both power and energy dimensions, and can also meet the needs of new power systems for high power, large capacity, and high frequency response of energy storage technology. Under existing engineering and technical conditions, pumped storage and compressed air energy storage have the potential for medium- and long-term energy storage and can be adjusted during the day; while electrochemical energy storage is generally used for intraday peak regulation or frequency regulation. The comprehensive use of pumped storage and electrochemical energy storage can not only achieve complementary advantages, but also effectively reduce the total investment cost of the energy storage system, achieving the effect of "1+1>2".

[0052] In terms of optimal configuration of energy storage capacity, the key lies in the energy allocation of hybrid energy storage. Existing methods include a decomposition method based on discrete Fourier transform, which analyzes signals through simple time-frequency transformation, but this method decomposes the frequency characteristics under long time scales and cannot determine the specific time period when a certain output signal occurs; the wavelet packet decomposition method performs multi-scale decomposition and reconstruction of wind power signals, and the power decomposition is more refined, but the energy storage configuration result of this method is closely related to the number of wavelet packet decomposition layers determined artificially; the adaptive sliding average filtering method can avoid the influence of subjective factors, but the average power of all sampling points in a sliding window is used as the sliding target, which easily causes the target power curve to deviate from the original power curve. This application proposes a hybrid energy storage capacity optimization configuration method for power systems based on complete ensemble empirical mode decomposition (CEEMDAN). This method can effectively solve the modal aliasing of signals, produce smaller deviations when reconstructing signals, and in dealing with white noise problems, its effect is greatly improved compared with the ensemble empirical mode decomposition method.

[0053] In an exemplary embodiment, Figure 1 and Figure 2 As shown, the method for optimizing configuration of hybrid energy storage capacity of a power system in this embodiment includes:

[0054] Step 1: Obtain the net load time series of the power system and preprocess the net load time series to obtain the preprocessed net load time series.

[0055] The preprocessing is to maximize the minimum value of each net load in the net load time series while meeting the requirements of comprehensive utilization rate of new energy and power ratio.

[0056] Specifically, the net load L of the power system at time t is t It is defined as the difference between the total load demand of the system at time t and the total output of traditional power sources and renewable energy sources:

[0057] L t =D t -W t -S t -F t ,t=1,...,T.

[0058] Among them, D t is the total load demand at time t; W t is the actual wind power output at time t; S t is the actual photovoltaic output at time t; F t is the output of traditional power supply at time t; T is the total number of times.

[0059] According to the power balance, the net load of the power system at any time should be balanced by the hybrid energy storage system.

[0060] L t =H t +B t ,t=1,...,T.

[0061] Among them, H t B is the power provided by the pumped storage power station at time t; t They are the powers provided by the electrochemical energy storage power station at time t.

[0062] As the proportion of renewable energy access continues to increase, the DC component of net load power decreases and the fluctuation amplitude increases. In extreme cases where the wind and solar output is very large, the net load power curve may show a large negative value. Since the output of thermal power units cannot be negative, the net load negative power can only be charged and absorbed by the energy storage system. Continuous and large-value negative power will significantly increase the capacity burden of the energy storage system and reduce the economic efficiency of energy storage investment. In order to reduce the impact of extreme situations, a small amount of wind and solar power can be abandoned within a certain range to reduce the peak value of net load negative power. For the net load at time T throughout the year, the following pre-processing method is adopted: while meeting the requirements of comprehensive utilization rate of new energy and power ratio, the minimum value of net load is maximized, which can be expressed as:

[0063] max L .

[0064] in, L It is an auxiliary variable, indicating the minimum value of net load.

[0065] The net load power curves before and after pretreatment are as follows: Figure 3 and Figure 4 shown.

[0066] Step 2: Use the complete set empirical mode decomposition method to process the preprocessed net load time series to obtain the spectrum characteristic curve.

[0067] As an optional implementation, step 2 includes:

[0068] Step 21: Using the complete set empirical mode decomposition method, each net load in the preprocessed net load time series is reconstructed by frequency division to obtain multiple intrinsic mode function components of the corresponding net load.

[0069] Step 22: Perform Hilbert transform on each intrinsic mode function component of each net load to obtain the instantaneous frequency of the corresponding intrinsic mode function component, thereby obtaining a frequency spectrum characteristic curve.

[0070] Specifically, the complete ensemble empirical mode decomposition method is an improved method based on the ensemble empirical mode decomposition method. The ensemble empirical mode decomposition adds white noise interference to the original signal to change the extreme point characteristics of the signal to suppress aliasing. However, white noise has the problem of high- and low-frequency transfer and transmission. The complete ensemble empirical mode decomposition method uses the calculation of each first-order intrinsic mode function (IMF) component, re-adds white noise to the residual value and calculates the mean of the IMF component, and repeats it until the function is monotonous and cannot be decomposed. This application uses this method to perform spectral decomposition on the net load time series, and the specific calculation is as follows:

[0071] P X (t)+W c (t) = F i 1 (t)+r i (t).

[0072] Among them, P X (t) is the pre-processed net load at time t; W c (t) is the disturbance after adding c times of white noise at time t; F i 1 (t) is the i-th order IMF component in the initial calculation at time t; r i (t) is the i-th order residual component at time t.

[0073] The IMF component means are calculated as follows:

[0074]

[0075] Where N is the total order; F 1 (t) is the first-order IMF component.

[0076] The updating formula of the residual value component is:

[0077] r i (t) = P X (t)-F 1 (t).

[0078] After K calculations, the function cannot be decomposed, and the final result is:

[0079]

[0080] Among them, F k (t) is the kth order IMF component at time t; r k (t) is the kth order residual component at time t.

[0081] The K-th order IMF component F K (t) is convolved with the impulse signal h(t) to obtain the Hilbert transform HF K (t).

[0082]

[0083] F K (t) is the real part, HF K (t) is the imaginary part constructor IMF K (t):

[0084] IMF K (t) = F K (t)+jHF K (t).

[0085] IMF K The phase of (t) is expressed as The instantaneous frequency f of the Kth order IMF component K (t) is calculated as follows:

[0086]

[0087] According to the instantaneous frequency f 1 (t),f 2 (t)…f K (t) Get the frequency spectrum characteristic curve.

[0088] Step 3: Use the trial and error method to determine the target separation frequency based on the spectrum characteristic curve, and determine the frequency band borne by the pumped storage power station and the electrochemical energy storage power station in the power system based on the target separation frequency.

[0089] Specifically, after obtaining the spectrum characteristic curve of the net load time series, it is necessary to determine the separation frequency f D .f D If the value is different, the capacity allocated to the electrochemical energy storage power station and the pumped storage power station will be different. D The smaller the value, the greater the capacity shared by the electrochemical energy storage power station, and the better the effect of smoothing the power fluctuation of new energy sources in the power grid and maintaining the stability of the voltage and frequency of the power grid. Therefore, after the time-frequency transformation of the net load time series, its spectrum characteristics are analyzed, and the trial-and-error method is used to determine the separation frequency f that meets the system requirements. D .f D Take values ​​from high frequency in sequence, for any f D value, calculate [f D ,f K ] frequency band load power is borne by electrochemical energy storage grid stability and energy storage cost, and comprehensively determine the optimal f D value.

[0090] f D After the value is determined, [f 1 ,f D The load power in the frequency band is borne by the electrochemical energy storage power station, [f D ,f K The load power in the frequency band is borne by the pumped storage power station. Therefore, the reference power of the electrochemical energy storage power station and the pumped storage power station are:

[0091]

[0092] Among them, P B (t) is the reference power of the electrochemical energy storage power station; P H (t) is the reference power of the pumped storage power station.

[0093] Step 4: Establish a hybrid energy storage capacity optimization configuration model and solve the hybrid energy storage capacity optimization configuration model to obtain the optimal configuration plan of the pumped storage power station and the electrochemical energy storage power station.

[0094] Among them, the hybrid energy storage capacity optimization configuration model includes: objective function and constraints. The objective function is to minimize the full life cycle cost of the energy storage system in the power system. The constraints include: power balance constraints, pumped storage power station constraints, electrochemical energy storage power station constraints, wind and solar power consumption indicators and node voltage constraints.

[0095] Specifically, by calling the GUROBI solver in Python to solve the hybrid energy storage capacity optimization configuration model, the optimal configuration scheme of the pumped storage power station and the electrochemical energy storage power station can be obtained.

[0096] As an optional implementation, the objective function includes:

[0097] minE all =E C +E OM +E R +E cut +E n .

[0098] E C =P phsN α phs +P bsN α bs .

[0099] E OM =P phsN β phs +P bsN β bs .

[0100] E R =κ bs P bsN γ bs .

[0101]

[0102] Among them, E all is the life cycle cost of the energy storage system; E C is the initial investment cost of the energy storage system; E OM is the operation and maintenance cost of the energy storage system; E R is the replacement cost of the energy storage system; E cut E is the penalty cost for wind and solar curtailment of the energy storage system; n is the operating cost of thermal power units; P phsN is the installed capacity of the pumped storage power station; α phs P is the initial investment cost unit price of the pumped storage power station; bsN is the installed capacity of the electrochemical energy storage power station; α bs is the initial investment cost unit price of the electrochemical energy storage power station; β phs is the unit price of operation and maintenance cost of pumped storage power station; bs is the unit price of operation and maintenance cost of electrochemical energy storage power station; κ bs is the number of replacements of the electrochemical energy storage power station; γ bs is the replacement cost unit price of the electrochemical energy storage power station; ρ wis the wind abandonment penalty coefficient; T is the total number of moments; is the wind power abandoned at time t; Δt is the time interval; ρ pv is the penalty coefficient for abandoning light; is the abandoned optical power at time t; n P is the unit output cost of thermal power units; n,t is the output of the thermal power unit at time t.

[0103] Specifically, the life cycle cost (LCC) mainly refers to the life cycle cost of the equipment, mainly referring to all expenses incurred during its research and development, production, operation, maintenance and decommissioning. The core of the life cycle cost of this application is to take the construction cost and operation cost of a project as a whole, comprehensively consider various cost items, and achieve the purpose of minimizing the life cycle cost. Among them, the initial investment cost generally refers to the material cost, labor cost, etc. consumed in power station construction, equipment purchase, equipment installation, etc.

[0104] As an optional implementation manner, the power balance constraint includes:

[0105] P pv,t +P wind,t +P phs_ele,t +P bs_dis,t +P n,t =P load,t +P phs_ch,t +P bs_ch,t +P loss,t .

[0106] Among them, P pv,t is the power generation of the photovoltaic power station at time t; P wind,t is the wind farm power generation at time t; P phs_ele,t is the power generation capacity of the pumped storage power station at time t; P bs_dis,t is the discharge power of the electrochemical energy storage power station at time t; P n,t is the power generated by conventional power supply at time t; P load,t is the load power at time t; P phs_ch,t is the pumping power of the pumped storage power station at time t; P bs_ch,t is the charging power of the electrochemical energy storage power station at time t; P loss,t is the system loss at time t.

[0107] As an optional implementation, the pumped storage power station constraints include:

[0108]

[0109] Among them, U phs,ele (t) is the power generation state of the pumped storage power station at time t, Uphs,ele (t) = 1 means power generation, U phs,ele (t) = 0 means no power generation; U phs,ch (t) is the pumping state of the pumped storage power station at time t, U phs,ch (t) = 1 means pumping, U phs,ch (t) = 0 means no pumping; Q phsN is the total water storage volume of the upper reservoir; Q phs (t) is the water storage capacity of the upper reservoir at time t; Q phsN Q phs (0) is the water storage capacity of the upper reservoir at the initial moment; Q phs (24) is the water storage capacity of the upper reservoir at the last moment.

[0110] As an optional implementation, the electrochemical energy storage power station constraints include:

[0111]

[0112] Among them, U bs_ch (t) is the charging state of the electrochemical energy storage power station at time t, U bs_ch (t) = 1 means charging, U bs_ch (t) = 0 means no charging; U bs_dis (t) is the discharge state of the electrochemical energy storage power station at time t, U bs_dis (t) = 1 means discharge, U bs_dis (t) = 0 means no discharge; SOC min is the lower limit of the state of charge of the battery in the electrochemical energy storage power station; SOC(t) is the state of charge of the battery in the electrochemical energy storage power station at time t; SOC max is the upper limit of the state of charge of the battery of the electrochemical energy storage power station; SOC(0) is the state of charge of the battery of the electrochemical energy storage power station at the initial moment; SOC(24) is the state of charge of the battery of the electrochemical energy storage power station at the last moment.

[0113] As an optional implementation method, the wind and solar power consumption indicators include:

[0114]

[0115] Among them, λ w,pv is the wind and solar power abandonment rate; w is the wind curtailment rate; pv is the light abandonment rate.

[0116] As an optional implementation, the node voltage constraint includes:

[0117] V min ≤V i ≤V max .

[0118] Among them, V min is the lower limit of node voltage; V i is the node voltage; V max is the upper limit of node voltage.

[0119] Beneficial effects of this application:

[0120] 1. Compared with the ensemble empirical mode decomposition method, the complete ensemble empirical mode decomposition method adopted in this application effectively solves the transfer and transmission problems between high and low frequencies of white noise, and the decomposed net load time series spectrum characteristic curve is more accurate.

[0121] 2. Compared with a single energy storage system, combining energy-type pumped storage with power-type electrochemical energy storage can greatly improve the regulation capacity of the energy storage system in both power and energy dimensions.

[0122] 3. Compared with the traditional single energy storage configuration model, this application adopts a full life cycle model of different types of energy storage configurations as the basis for constructing its investment cost, which includes the comprehensive costs of different types of energy storage during the purchase, operation and maintenance, replacement and other periods. It can better reflect the differences between different types of energy storage and better reflect the comprehensive costs of different types of energy storage.

[0123] In an exemplary embodiment, a computer device is provided, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement a method for optimizing configuration of hybrid energy storage capacity in a power system.

[0124] In an exemplary embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, a method for optimizing configuration of hybrid energy storage capacity of a power system is implemented.

[0125] In an exemplary embodiment, a computer program product is provided, including a computer program, which, when executed by a processor, implements a method for optimizing configuration of hybrid energy storage capacity in a power system.

[0126] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 5As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for optimizing the configuration of hybrid energy storage capacity of a power system is implemented.

[0127] Those skilled in the art will understand that Figure 5 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0128] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0129] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0130] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0131] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0132] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for optimizing the configuration of hybrid energy storage capacity in a power system, characterized in that: The method for optimizing configuration of hybrid energy storage capacity of a power system comprises: Obtaining a net load time series of the power system, and preprocessing the net load time series to obtain a preprocessed net load time series; the preprocessing is to maximize the minimum value of each net load in the net load time series while meeting the requirements of the comprehensive utilization rate of new energy and the proportion of electricity; The complete set empirical mode decomposition method is used to process the preprocessed net load time series to obtain the spectrum characteristic curve; Determine the target separation frequency based on the frequency spectrum characteristic curve by trial and error method, and determine the frequency band borne by the pumped storage power station and the electrochemical energy storage power station in the power system based on the target separation frequency; A hybrid energy storage capacity optimization configuration model is established, and the hybrid energy storage capacity optimization configuration model is solved to obtain the optimal configuration scheme of the pumped-storage power station and the electrochemical energy storage power station; the hybrid energy storage capacity optimization configuration model includes: an objective function and constraints, the objective function is to minimize the full life cycle cost of the energy storage system in the power system as the optimization goal, and the constraints include: power balance constraints, pumped-storage power station constraints, electrochemical energy storage power station constraints, wind and solar power consumption indicators and node voltage constraints.

2. The method for optimizing the configuration of hybrid energy storage capacity in a power system according to claim 1, characterized in that: The complete set empirical mode decomposition method is used to process the preprocessed net load time series to obtain the spectrum characteristic curve, including: By using the complete set empirical mode decomposition method, each net load in the preprocessed net load time series is reconstructed by frequency division to obtain multiple intrinsic mode function components of the corresponding net load; The Hilbert transform is performed on each intrinsic mode function component of each net load to obtain the instantaneous frequency of the corresponding intrinsic mode function component, thereby obtaining the frequency spectrum characteristic curve.

3. The method for optimizing the configuration of hybrid energy storage capacity in a power system according to claim 1, characterized in that: The objective function includes: me all =E C +E OM +E R +E cut +E n ; E C =P phsN a phs +P bsN a bs ; E OM =P phsN b phs +P bsN b bs ; E R =k bs P bsN c bs ; Among them, E all is the life cycle cost of the energy storage system; E C is the initial investment cost of the energy storage system; E OM is the operation and maintenance cost of the energy storage system; E R is the replacement cost of the energy storage system; E cut E is the penalty cost for wind and solar curtailment of the energy storage system; n is the operating cost of thermal power units; P phsN is the installed capacity of the pumped storage power station; α phs P is the initial investment cost unit price of the pumped storage power station; bsN is the installed capacity of the electrochemical energy storage power station; α bs is the initial investment cost unit price of the electrochemical energy storage power station; β phs is the unit price of operation and maintenance cost of pumped storage power station; bs is the unit price of operation and maintenance cost of electrochemical energy storage power station; κ bs is the number of replacements of the electrochemical energy storage power station; γ bs is the replacement cost unit price of the electrochemical energy storage power station; ρ w is the wind abandonment penalty coefficient; T is the total number of moments; is the wind power abandoned at time t; Δt is the time interval; ρ pv is the penalty coefficient for abandoning light; is the abandoned optical power at time t; n is the unit output cost of thermal power units; P n,t is the output of the thermal power unit at time t.

4. The method for optimizing configuration of hybrid energy storage capacity in a power system according to claim 3, characterized in that: The power balance constraint includes: P pv,t +P wind,t +P phs_ele,t +P bs_dis,t +P n,t =P load,t +P phs_ch,t +P bs_ch,t +P loss,t ; Among them, P pv,t is the power generation of the photovoltaic power station at time t; P wind,t is the wind farm power generation at time t; P phs_ele,t is the power generation capacity of the pumped storage power station at time t; P bs_dis,t is the discharge power of the electrochemical energy storage power station at time t; P n,t P is the power generated by conventional power supply at time t; load,t is the load power at time t; P phs_ch,t is the pumping power of the pumped storage power station at time t; P bs_ch,t is the charging power of the electrochemical energy storage power station at time t; P loss,t is the system loss at time t.

5. The method for optimizing configuration of hybrid energy storage capacity in a power system according to claim 4, characterized in that: The pumped storage power station constraints include: Among them, U phs,ele (t) is the power generation state of the pumped storage power station at time t, U phs,ele (t) = 1 means power generation, U phs,ele (t) = 0 means no power generation; U phs,ch (t) is the pumping state of the pumped storage power station at time t, U phs,ch (t) = 1 means pumping, U phs,ch (t) = 0 means no pumping; Q phsN is the total water storage volume of the upper reservoir; Q phs (t) is the water storage capacity of the upper reservoir at time t; Q phsN Q phs (0) is the water storage capacity of the upper reservoir at the initial moment; Q phs (24) is the water storage capacity of the upper reservoir at the last moment.

6. The method for optimizing the configuration of hybrid energy storage capacity in a power system according to claim 5, characterized in that: The electrochemical energy storage power station constraints include: Among them, U bs_ch (t) is the charging state of the electrochemical energy storage power station at time t, U bs_ch (t) = 1 means charging, U bs_ch (t) = 0 means no charging; U bs_dis (t) is the discharge state of the electrochemical energy storage power station at time t, U bs_dis (t) = 1 means discharge, U bs_dis (t) = 0 means no discharge; SOC min is the lower limit of the state of charge of the battery in the electrochemical energy storage power station; SOC(t) is the state of charge of the battery in the electrochemical energy storage power station at time t; SOC max is the upper limit of the state of charge of the battery of the electrochemical energy storage power station; SOC(0) is the state of charge of the battery of the electrochemical energy storage power station at the initial moment; SOC(24) is the state of charge of the battery of the electrochemical energy storage power station at the last moment.

7. The method for optimizing the configuration of hybrid energy storage capacity in a power system according to claim 6, characterized in that: The wind and solar power consumption indicators include: Among them, λ w,pv is the wind and solar power abandonment rate; w is the wind curtailment rate; pv is the light abandonment rate.

8. The method for optimizing configuration of hybrid energy storage capacity in a power system according to claim 7, characterized in that: The node voltage constraint includes: In min ≤V i ≤V max ; Among them, V min is the lower limit of node voltage; V i is the node voltage; V max is the upper limit of node voltage.

9. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for optimizing configuration of hybrid energy storage capacity in a power system as described in any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for optimizing the configuration of hybrid energy storage capacity in a power system as described in any one of claims 1 to 8 is implemented.

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