Short-time energy storage marginal effect characteristic analysis method based on pinch point principle and time sequence operation simulation

Abstract

The invention provides a short-time energy storage marginal effect characteristic analysis method based on a pinch point principle and time sequence operation simulation, and the method comprises the steps: generating a planning scene based on historical time sequence data, and obtaining a theoretical available value and load time sequence data of new energy; acquiring amplitude-frequency characteristics of the net load by using a frequency domain decomposition method, and constructing a source-trap combination curve; preliminary configuration of thermal power and energy storage installed capacity is carried out through a pinch point principle; and constructing an operation characteristic model of short-time energy storage, and obtaining an energy storage installed capacity and new energy utilization rate scatter set through a time sequence operation simulation method. And drawing a marginal effect curve of the new energy utilization rate based on a simulation result, and establishing an economic evaluation index of short-time energy storage. The method provided by the invention can effectively improve the consumption capability of new energy, optimize the capacity configuration of the short-time energy storage system, provide economic analysis indexes, and provide a scientific basis for planning and operation of a power system.

Description

Technical Field

[0001] The present invention belongs to the field of energy planning, and in particular relates to a method for analyzing the marginal effect characteristics of short-term energy storage based on the pinch point principle and timing operation simulation. Background Art

[0002] As the proportion of renewable energy in the power system gradually increases, the supply and demand balance of the power system is affected by its intermittency and volatility. To ensure the stable operation of the system, short-term energy storage, as an important means of regulation, can effectively absorb excess renewable energy and improve the reliability of the system. However, how to reasonably configure energy storage capacity and maximize the utilization of renewable energy while meeting the needs of the power system remains a technical challenge. The existing energy storage capacity configuration method mainly relies on static planning, and lacks dynamic optimization that considers the volatility characteristics of renewable energy and the operating characteristics of energy storage. The marginal effect characteristic analysis method based on the pinch point principle and time series operation simulation avoids the problems of difficult solution and cumbersome algorithms in traditional optimization models, and provides a more scientific and economical energy storage configuration solution. Summary of the invention

[0003] In order to solve the above technical problems, the present invention provides a method for analyzing the marginal effect characteristics of short-term energy storage based on the pinch point principle and timing operation simulation.

[0004] In order to achieve the above object, the present invention adopts the following technical solution:

[0005] A method for analyzing the marginal effect characteristics of short-term energy storage based on the pinch point principle and sequential operation simulation, including:

[0006] Generate planning scenarios based on historical time series data to obtain theoretical available values ​​of new energy and load time series data;

[0007] The amplitude-frequency characteristic curve of the net load of the power grid is obtained by the frequency domain decomposition method. The net load is the difference between the total load of the system and the total output of new energy. The time domain data of the net load is converted into frequency domain data by discrete Fourier transform, and the typical periodic component and its amplitude and frequency are obtained by amplitude-frequency characteristic analysis. Discrete Fourier transform is represented by DFT.

[0008] Based on the amplitude-frequency characteristic curve, a source-sink combination curve is constructed. The source represents the frequency characteristics of the thermal power unit, and the sink represents the frequency characteristics of the short-term energy storage system. The frequency-amplitude combination of the source and the sink is determined by frequency domain decomposition of the net load to form a source-sink combination curve.

[0009] The pinch point principle is used to configure the installed capacity of thermal power and short-term energy storage, and the initial planning values ​​of thermal power installed capacity and short-term energy storage installed capacity are determined by matching the source-sink combination curve;

[0010] Construct an operating characteristic model of a short-term energy storage system, including the charging and discharging model of the energy storage system, the battery life model, and the charging and discharging efficiency model, to simulate the charging and discharging process of the energy storage system under the conditions of power demand and new energy fluctuations;

[0011] Based on the planning scenario data, energy storage charging and discharging model and thermal power installed capacity, optimization is performed through time series operation simulation method to minimize the annual abandoned power of renewable energy and ensure the maximum consumption of renewable energy; the installed capacity is updated iteratively to obtain the scattered point set between energy storage installed capacity and renewable energy utilization rate;

[0012] Based on the optimization results of energy storage capacity and abandoned renewable energy, draw the marginal effect curve between energy storage installed capacity and renewable energy utilization rate, and determine the saturation point of short-term energy storage to improve renewable energy utilization rate;

[0013] Establish economic evaluation indicators for short-term energy storage, comprehensively consider the initial investment cost, operation and maintenance cost, battery replacement cost, and charging and discharging losses of the energy storage system, evaluate the economic feasibility under different energy storage installed capacities, and optimize the configuration of the energy storage system.

[0014] Furthermore, the discrete Fourier transform converts the net load time domain data into frequency domain data so as to analyze the typical periodic components of the net load and obtain the amplitude and frequency information thereof.

[0015] Furthermore, the source-sink combination curve is obtained by combining the frequency characteristics of the thermal power unit with the frequency response of the energy storage system, and matching them based on the maximum charge and discharge capacity of the energy storage and its frequency response characteristics.

[0016] Furthermore, the charging and discharging model of the short-term energy storage system includes the state equation of the battery and the charging and discharging control strategy, and further considers the charging and discharging efficiency of the energy storage system and the life model of the battery.

[0017] Furthermore, the timing operation simulation method includes simulating the output timing of new energy, load timing and the output of thermal power and energy storage units in the planning year, taking into account the peak-shaving capacity, power supply structure and supply guarantee needs of the power system.

[0018] Furthermore, the construction of the marginal effect curve of energy storage installed capacity and new energy utilization rate is achieved by fitting a set of scattered points between the energy storage installed capacity and the new energy utilization rate to obtain a relationship curve between the energy storage installed capacity and the new energy utilization rate, and further determining the optimal configuration of the energy storage capacity by identifying the saturation point of the new energy utilization rate.

[0019] Furthermore, the short-term energy storage economic evaluation indicators include initial investment cost, operation and maintenance cost, battery replacement cost, charging and discharging loss and battery life.

[0020] Furthermore, the short-term energy storage economic evaluation index evaluates the economic efficiency of the energy storage system by calculating the unit electricity cost.

[0021] Furthermore, the optimization of short-term energy storage installed capacity in the short-term energy storage marginal effect characteristic analysis method is to make the optimal configuration by considering the maximum power, capacity, charge and discharge efficiency, system operation time and battery life of the battery, comprehensively evaluating the impact of energy storage capacity on the new energy consumption rate and system economy.

[0022] Furthermore, in the short-term energy storage marginal effect characteristic analysis method, the new energy utilization marginal effect curve is used to determine the maximum utilization rate and optimal energy storage configuration of new energy under different short-term energy storage installed capacities, thereby optimizing the new energy consumption efficiency.

[0023] Beneficial effects:

[0024] The method proposed in the present invention can effectively improve the absorption capacity of new energy, optimize the capacity configuration of short-term energy storage systems, and provide economic analysis indicators, thus providing a scientific basis for the planning and operation of power systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 It is a flow chart of a method for analyzing the marginal effect characteristics of short-term energy storage based on the pinch point principle and timing operation simulation of the present invention;

[0026] Figure 2 It is the rated frequency-amplitude diagram of the net load curve after Fourier transformation and partitioning; among them, (a) is the frequency-amplitude diagram after Fourier transformation, and (b) is the frequency-amplitude diagram after partitioning;

[0027] Figure 3 fA curve diagram of energy storage and net load. DETAILED DESCRIPTION

[0028] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in each embodiment of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0029] The present invention aims to provide a method for analyzing the marginal effect characteristics of short-term energy storage based on the pinch point principle and sequential operation simulation, including planning scenario generation, net load amplitude-frequency characteristic acquisition, source-sink combination curve construction, thermal power energy storage installed capacity pre-configuration, short-term energy storage operation characteristic model construction, energy storage installed capacity and new energy utilization rate scatter point set acquisition, marginal effect curve construction and economic evaluation index establishment, such as Figure 1As shown, the specific steps include:

[0030] Step 1: Generate planning scenarios based on historical time series data:

[0031] Taking hours as the time scale, we collected 8760 hours of historical data on the theoretical available output of new energy in the planning area. Combined with the ratio between the installed capacity of new energy in the planning year and the installed capacity in historical years, we calculated and predicted according to formula (1) to obtain the theoretical available value of the annual power generation of new energy in the planning year.

[0032] (1)

[0033] Among them, P new,obj With P new,past They represent the theoretically available output sequence of new energy power predicted in the planning year and the theoretically available output sequence of new energy power in the historical year respectively; C new,obj and C new,past Respectively represent the installed capacity of new energy in the planning year and historical year.

[0034] In terms of load, the method for generating the theoretical available value of the annual power generation of new energy is the same as that for generating the theoretical available value of the annual power generation of new energy. Based on the 8760-hour time series data of the load in the historical years and the total annual power consumption in the planning year and the historical years, the load sequence at the hourly scale of the planning level is generated, as shown in formula (2):

[0035] (2)

[0036] Among them, P load,obj With P load,past They represent the load time series of the planning level year and the historical year respectively. load,obj With E load,past Respectively represent the total annual electricity consumption in planning level years and historical years.

[0037] Step 2: Obtain the net load amplitude-frequency characteristic curve based on the frequency domain decomposition method:

[0038] Net load of power grid is defined as the difference between the total system load and the total output of renewable energy:

[0039] (3)

[0040] Where L(t) represents the net load at time t; P load,obj (t) represents the total load at time t in the planning year; P new,obj (t) represents the output of new energy at time t in the planning year, where wind power and photovoltaic power are considered; T is the length of the statistical period.

[0041] Through Discrete Fourier Transform (DFT) The net load is converted from discrete data in the time domain to discrete data in the frequency domain. The conversion formula is:

[0042] (4)

[0043] Where x(n) represents the time domain data, X(k) represents the frequency domain data sequence; N is the number of sampling points, that is, the number of time domain data; f s is the sampling frequency, n represents the index value, j is the imaginary unit, and f(k) represents the frequency corresponding to the kth frequency component in the frequency domain.

[0044] The net load power L(t) can be regarded as a sampling point with N sampling points and a sampling frequency of f. s The time domain discrete signal with a sampling period of t s , the net load power can be regarded as a cycle The signal has a base frequency of , through Discrete Fourier Transform (DFT), the net load power is converted into the form of the sum of the DC component, the fundamental frequency period component and the multiplied frequency period component. According to the sampling theorem, the frequency domain sequence is about the Nyquist frequency Conjugate symmetry means that only the first half needs to be considered when analyzing the amplitude-frequency characteristics. Therefore, after transformation, the frequency domain net load sequence Z is obtained:

[0045] (5)

[0046] in, is the number of frequency domain data, Indicates downward value.

[0047] Amplitude A, Phase The calculation method is as follows:

[0048] (6)

[0049] (7)

[0050] Where Re[Z(m)], Im[Z(m)] are the real and imaginary parts of the net load sequence Z(m), m is the index of the frequency component, N n is the total number of frequency components.

[0051] According to the amplitude-frequency characteristic curve after DFT transformation, some typical periodic components contained in the net load curve can be determined. The amplitudes of these typical periodic components are significantly higher than the amplitudes of nearby frequencies and are mainly concentrated in the low-frequency area, such as Figure 2 Therefore, based on the typical periodic component, the spectrum is divided into several sub-spectra, such as Figure 2 The calculation method of the sub-spectrum after partitioning is as follows:

[0052] (8)

[0053] (9)

[0054] Among them, N s is the number of typical periodic components; N i is the period length of the i-th typical periodic component; Y is the complex sequence after spectrum partitioning, and F is the frequency sequence after spectrum partitioning.

[0055] Step 3: Construct source-sink combination curve based on amplitude-frequency characteristic curve:

[0056] The DC component of the net load is absorbed by the thermal power unit, and the remaining component is borne by the short-term energy storage. The thermal power unit can operate at constant power, so the present invention intends to set the thermal power frequency to 0. Different types of energy storage have different energy storage characteristics. The maximum charge and discharge time and power are two key parameters. The present invention takes the short-term energy storage of lithium iron phosphate battery as an example for research. Based on this, the lowest frequency of load power fluctuation that it can withstand is calculated, and the calculation method is as follows:

[0057] (10)

[0058] in, is the maximum operation cycle of energy storage, It is the maximum charging and discharging time of energy storage, both in seconds. The minimum frequency of power fluctuation that can be absorbed by energy storage, in Hz. This minimum frequency is determined by the maximum charge and discharge time of energy storage. In actual operation, energy storage can absorb fluctuations higher than Net load power, but cannot withstand fluctuations below Net load power.

[0059] The pinch point analysis method has two important indicators when applied in the industrial field, which are used to measure quality and flow. The present invention uses the frequency after the net load frequency domain decomposition as the quality indicator and the amplitude as the flow indicator, the maximum acceptable fluctuation frequency of the energy storage as its quality indicator and the maximum charge and discharge power of the energy storage as its flow indicator, and constructs a source-sink combination curve with energy storage as the source and net load as the sink.

[0060] The source combination curve is drawn according to the amplitude-frequency characteristic curve of the energy storage, and the sink combination curve is drawn based on the spectrum after the net load frequency domain decomposition and partitioning. The drawing method is as follows:

[0061] (1) The horizontal axis is amplitude and the vertical axis is frequency;

[0062] (2) Each horizontal line segment represents a source or sink. The length of the line segment represents the amplitude of the source / sink. The value on the vertical axis corresponding to the horizontal position of the line segment represents the frequency.

[0063] (3) The lateral movement of the horizontal line segment does not affect its meaning. The source and sink line segments are connected separately to form a source / sink combination curve.

[0064] According to this method, the fA curve representing energy storage and net load is drawn, such as Figure 3 According to the operating characteristics of energy storage, energy storage can accept a net load component whose frequency is greater than its charging and discharging frequency. Therefore, when the fA curve of the net load is above the energy storage, it means that the component can be absorbed by the energy storage located below this section.

[0065] Step 4: Configure thermal power and energy storage installed capacity according to the pinch point method:

[0066] Since the source and sink combination curves use the length of each horizontal line to represent the amplitude, the position of each line segment on the horizontal axis is meaningless, and the horizontal movement of the curve does not change its meaning. Therefore, the sink combination curve is translated to the right until it is completely located above the source combination curve. At this time, the intersection of the two curves is the pinch point. The source and sink are matched above and below the pinch point, but the matching cannot be performed across the pinch point. The distance moved to the right represents the deficit capacity, and the distance at the end of the source and sink curves represents the remaining capacity. The deficit capacity corresponds to the installed capacity of the thermal power unit, while the remaining capacity is the unused capacity of the energy storage.

[0067] By matching the source and sink above and below the pinch point, the frequency domain split points corresponding to the thermal power unit and each type of energy storage are determined, and the net load components that each needs to bear are calculated based on the frequency domain split points. The frequency domain component expression is as follows:

[0068] (11)

[0069] Among them, d 1 d 2 ,…,d n-1 is n-1 frequency domain breakpoints, dividing the spectrum into n sub-spectra. 1 , D 2 ,…,D n There are n sub-spectrums.

[0070] Finally, the corresponding time domain sequence is obtained through Inverse Discrete Fourier Transform (IDFT). The IDFT formula is as shown in formula (12). The capacity requirement of each unit is determined according to the timing curve.

[0071] (12)

[0072] Step 5: Construction of short-term energy storage operation characteristic model:

[0073] Based on the above steps, when the load and new energy 8760h time series data are known, the planning values ​​of thermal power installed capacity and short-term energy storage installed capacity can be obtained through the pinch point principle. However, the pinch point principle is only based on the perspective of power and electricity balance, and considers the configuration of thermal power and energy storage installed capacity under the ideal premise of full consumption of new energy. The thermal power installed capacity obtained at this time is the minimum planning value under the power and electricity balance constraint. In the actual operation scenario, it is necessary to comprehensively consider factors such as power structure, peak load regulation capacity, supply guarantee demand, and new energy output characteristics to constrain the power system. Therefore, the above short-term energy storage installed capacity configuration results obtained by the pinch point principle are used as the initial value, and the time series operation simulation method is used to further optimize the short-term energy storage installed capacity from the technical and economic levels.

[0074] (1) Charging and discharging model of energy storage system

[0075] The energy storage and release process of lithium iron phosphate batteries can be expressed by the battery state equation (SOC: battery state of charge):

[0076] (13)

[0077] Where SOC(t) is the state of charge at time t (between 0 and 1); P charge is the charging power, P discharge is the discharge power, C rated is the rated capacity of the battery in kWh and Δt is the time step in hours.

[0078] The charging and discharging process of the energy storage system is based on the battery status and the charging and discharging control strategy. The battery charging and discharging process can be described by the following mathematical formula:

[0079] Battery charging power P charge and the battery charge E charge Satisfies the following formula:

[0080] (14)

[0081] The discharge power of the battery is P discharge and the battery discharge capacity E discharge Satisfies the following formula:

[0082] (15)

[0083] (2) Charge and discharge efficiency model

[0084] The battery loses some energy during the charging and discharging process, so the efficiency of charging and discharging needs to be considered. Assume that the charging efficiency of the battery is η charge , the discharge efficiency is η discharge , the actual charge and discharge capacity can be expressed by the following formula:

[0085] (16)

[0086] (17)

[0087] (3) Battery energy storage capacity and state estimation

[0088] The energy storage capacity of the battery is determined by the maximum energy storage capacity E of the battery. stored,max The relationship between the energy storage capacity and the state of the battery is as follows:

[0089] (18)

[0090] Among them, E stored (t) is the energy stored in the battery at time t.

[0091] The battery capacity changes over time and is affected by charging and discharging. Assume that the battery storage capacity at a certain time t is E stored (t), then the capacity of the battery at the next moment is E stored (t+Δt) is determined by the charge and discharge process:

[0092] (19)

[0093] (4) Battery discharge depth (DOD)

[0094] The depth of discharge (DOD) of a battery indicates the ratio of the battery's discharge from a fully charged state to its current state. Usually, the DOD calculation formula is:

[0095] (20)

[0096] Among them, SOC max The maximum state of charge of the battery.

[0097] (5) Battery life model

[0098] The life of the battery is closely related to the number of charge and discharge cycles. For lithium iron phosphate batteries, the cycle life is usually expressed in terms of the number of charge and discharge cycles. cycleAs the number of charge and discharge cycles of the battery increases, the performance of the battery will gradually decline. The estimation of battery life is usually based on the cycle depth (DOD) and usage conditions.

[0099] Battery cycle life L cycle The relationship with DOD can be expressed by the empirical formula:

[0100] (twenty one)

[0101] Among them, L cycle,ref is the cycle life under reference conditions. DOD max is the maximum allowable discharge depth of the battery (usually 100%). α is an empirical coefficient related to the battery type and usage conditions.

[0102] Step 6: Obtaining a scattered set of energy storage installed capacity and new energy utilization rate based on the time series simulation method:

[0103] The output of new energy, charging and discharging of energy storage systems, and the output of conventional units are regarded as sequences that change over time. By comprehensively considering factors such as power structure, peak-shaving capacity, supply demand, and new energy output characteristics, and based on the theoretical available time series data of new energy, load time series data, planned installed capacity of thermal power, and initial value of short-term energy storage installed capacity for the planning year predicted in the above steps, the time series operation simulation method is used to simulate the operation of 8760 hours throughout the year.

[0104] (1) Objective function

[0105] Taking the minimum annual abandoned power of new energy as the optimization goal, the expression is as follows:

[0106] (twenty two)

[0107] Among them, Q new P represents the total amount of abandoned electricity of new energy in the planning year. PV,theo (t) and P Wind,theo (t) represents the theoretical available value of photovoltaic and wind power at time t in the planning year, and the unit of t is hour. PV,grid (t) and P Wind,grid (t) represent the actual grid-connected values ​​of photovoltaic and wind power at time t in the planning year respectively.

[0108] (2) Constraints

[0109] The optimization constraints mainly include: power balance constraints (such as formula (23)), upper and lower limit constraints of power unit output (such as formula (24)), and short-term energy storage operation constraints as shown in formula (25) to (26). The normal operation of the generator set must meet the maximum and minimum power generation constraints, the unit start and stop time constraints, and the conventional unit climbing constraints, which will not be repeated here.

[0110] (twenty three)

[0111] Among them, P G It is the total output of thermal power units.

[0112] (twenty four)

[0113] Among them, P G,max With P G,min Respectively represent the upper and lower limits of the total output of thermal power units, where P G,max is the planning value of thermal power installed capacity obtained by the pinch point principle, P G,min By P G,max Calculated based on the peak load regulation capability.

[0114] (25)

[0115] (26)

[0116] Among them, X charge (t) and X discharge (t) respectively represent the working state of the short-time energy storage at time t, 1 represents charging or discharging at time t, and 0 represents standby state at time t; P stored,max and E stored,max Indicates the maximum power and capacity of short-term energy storage installed.

[0117] Step 7: Construction of marginal effect curve of new energy utilization rate:

[0118] Based on the above model, the installed capacity of short-term energy storage is input in the input area, and the optimization is carried out with the goal of minimizing the total amount of renewable energy abandoned in the planning year, and the utilization rate of renewable energy in the planning year is obtained in the output area.

[0119] (27)

[0120] Among them, R new,obj Indicates the utilization rate of new energy in the planning year.

[0121] Based on the initial value of the short-term energy storage installed capacity planning, the installed capacity is continuously increased with a specific step size. At the same time, the corresponding new energy utilization rate is calculated based on the time series operation simulation method. A scatter plot between the energy storage installed capacity and the new energy utilization rate is drawn, and the scatter points are fitted with a curve. Finally, the marginal effect curve between the short-term energy storage installed capacity and the new energy utilization rate is obtained.

[0122] As the scale of energy storage continues to increase, the increase in the utilization rate of new energy continues to decrease. When the increase in the utilization rate of new energy is lower than a certain threshold ε, it can be considered that short-term energy storage has reached the saturation point in improving the utilization rate of new energy.

[0123] (28)

[0124] Among them, ΔR new,obj is the increase in the utilization rate of new energy in the planning year, and ε is the threshold corresponding to the saturation point of the utilization rate of new energy.

[0125] Step 8: Establishment of short-term energy storage economic evaluation indicators:

[0126] The marginal effect curve of new energy consumption can determine the energy storage scale corresponding to the saturation point of new energy consumption rate. However, in order to maximize the marginal benefit of the energy storage system and avoid unnecessary redundant construction, it is necessary to further establish short-term energy storage economic evaluation indicators.

[0127] When considering the comprehensive operating costs of lithium iron phosphate battery energy storage systems, the following aspects are mainly considered: initial investment cost (equipment purchase and installation costs), operation and maintenance costs (regular maintenance and monitoring), battery replacement costs (battery degradation and replacement), charge and discharge losses (efficiency losses), battery life (number of charge and discharge cycles), and system annual energy output (actual operating energy of the system). These factors together determine the comprehensive economic efficiency of the energy storage system. The following is a detailed economic model and corresponding supporting formula after considering these factors.

[0128] (1) Initial investment cost C initial

[0129] Initial investment costs usually include the purchase, installation and infrastructure construction costs of batteries. For lithium iron phosphate battery energy storage systems, they can be calculated separately by capacity and power.

[0130] (29)

[0131] Among them, C capacity is the investment cost per unit capacity; C power is the investment cost per unit power; E stored,max is the maximum capacity of the energy storage system; P stored,max is the maximum power of the energy storage system; C installationIt is the installation cost, including civil engineering, equipment commissioning, etc.

[0132] (2) Operation and maintenance cost C O&M

[0133] The operation and maintenance costs of energy storage systems usually include daily maintenance of the system, personnel costs, insurance and monitoring costs, etc. The annual operation and maintenance costs can be estimated based on the ratio of capacity to power.

[0134] (30)

[0135] Where: C O&M,capacity is the annual operation and maintenance cost per unit capacity; C O&M,power is the annual operation and maintenance cost per unit power; C O&M,operation It is the fixed annual operation and maintenance cost related to the operation.

[0136] (3) Battery replacement cost C replacement

[0137] The life of lithium iron phosphate batteries is limited, usually measured in charge and discharge cycles. cycle The battery replacement cost can be estimated based on the battery life and the actual use of the system. Assume that the battery life is L cycle , the battery needs to be replaced according to the actual number of cycles used by the system. Battery replacement cost C replacement It can be calculated as follows:

[0138] (31)

[0139] Where: L cycle is the design life of the battery; N cycles is the actual number of charge and discharge cycles of the system; This means rounding up to ensure that the system can still meet the capacity requirements after each replacement.

[0140] (4) Charge and discharge loss E loss

[0141] The battery will produce energy loss during the charging and discharging process. Consider the charging efficiency η charge and discharge efficiency η discharge , the charge and discharge losses are:

[0142] (32)

[0143] Among them, E charge is the charging capacity, η charge For charging efficiency.

[0144] (33)

[0145] Among them, E discharge is the discharge capacity, η discharge is the discharge efficiency.

[0146] (5) Battery life and depth of charge and discharge (DOD)

[0147] The cycle life of the battery is affected by the depth of charge and discharge (DOD). Deeper discharge will accelerate the aging of the battery. The relationship between battery life and DOD can usually be expressed by an empirical formula:

[0148] (34)

[0149] Where: L cycle,ref DOD is the battery life under reference conditions (unit: times); max is the maximum discharge depth of the battery (usually 100%); α is an empirical coefficient, which is usually determined by the battery type and usage conditions.

[0150] (6) Levelized cost of electricity (LCOE)

[0151] Levelized cost of electricity (LCOE) is a key indicator for measuring the economic feasibility of energy storage systems, indicating the comprehensive cost per unit of electricity. The calculation formula for LCOE is:

[0152] (35)

[0153] Among them, C total It is the total cost of the energy storage system, including initial investment cost, operation and maintenance cost, replacement cost, etc. is the system during the evaluation period T eval The total amount of power provided.

[0154] (7) Comprehensive economic model

[0155] Taking all cost factors into consideration, the total cost of the energy storage system (including initial investment, operation and maintenance, and battery replacement) can be expressed as:

[0156] (36)

[0157] Therefore, the LCOE formula is:

[0158] (37)

[0159] After obtaining the marginal effect curve between the energy storage installed capacity and the new energy utilization rate, combined with its investment cost and operating cost, and comprehensively considering the new energy consumption rate and the economy of the energy storage system, the most efficient energy storage capacity configuration plan is selected.

Claims

1. A method for analyzing the marginal effect characteristics of short-term energy storage based on the pinch point principle and sequential operation simulation, characterized in that: include: Generate planning scenarios based on historical time series data to obtain theoretical available values ​​of new energy and load time series data; The amplitude-frequency characteristic curve of the net load of the power grid is obtained by the frequency domain decomposition method. The net load is the difference between the total load of the system and the total output of new energy. The time domain data of the net load is converted into frequency domain data by discrete Fourier transform, and the typical periodic component and its amplitude and frequency are obtained by amplitude-frequency characteristic analysis. Discrete Fourier transform is represented by DFT. Based on the amplitude-frequency characteristic curve, a source-sink combination curve is constructed. The source represents the frequency characteristics of the thermal power unit, and the sink represents the frequency characteristics of the short-term energy storage system. The frequency-amplitude combination of the source and the sink is determined by frequency domain decomposition of the net load to form a source-sink combination curve. The pinch point principle is used to configure the installed capacity of thermal power and short-term energy storage, and the initial planning values ​​of thermal power installed capacity and short-term energy storage installed capacity are determined by matching the source-sink combination curve; Construct an operating characteristic model of a short-term energy storage system, including the charging and discharging model of the energy storage system, the battery life model, and the charging and discharging efficiency model, to simulate the charging and discharging process of the energy storage system under the conditions of power demand and new energy fluctuations; Based on the planning scenario data, energy storage charging and discharging model and thermal power installed capacity, optimization is performed through time series operation simulation method to minimize the annual abandoned power of renewable energy and ensure the maximum consumption of renewable energy; the installed capacity is updated iteratively to obtain the scattered point set between energy storage installed capacity and renewable energy utilization rate; Based on the optimization results of energy storage capacity and renewable energy curtailment, draw the marginal effect curve between energy storage installed capacity and renewable energy utilization rate, and determine the saturation point of short-term energy storage to improve renewable energy utilization rate; Establish economic evaluation indicators for short-term energy storage, comprehensively consider the initial investment cost, operation and maintenance cost, battery replacement cost, and charging and discharging losses of the energy storage system, evaluate the economic feasibility under different energy storage installed capacities, and optimize the configuration of the energy storage system.

2. According to claim 1, a method for analyzing the marginal effect characteristics of short-term energy storage based on the pinch point principle and timing operation simulation is characterized in that: The discrete Fourier transform converts the net load time domain data into frequency domain data so as to analyze the typical periodic component of the net load and obtain its amplitude and frequency information.

3. According to claim 1, a method for analyzing the marginal effect characteristics of short-term energy storage based on the pinch point principle and timing operation simulation is characterized in that: The source-sink combination curve is obtained by combining the frequency characteristics of the thermal power unit with the frequency response of the energy storage system, and matching them based on the maximum charge and discharge capacity of the energy storage and its frequency response characteristics.

4. According to claim 1, a method for analyzing the marginal effect characteristics of short-term energy storage based on the pinch point principle and timing operation simulation is characterized in that: The charging and discharging model of the short-term energy storage system includes the battery state equation and the charging and discharging control strategy, and further considers the charging and discharging efficiency of the energy storage system and the battery life model.

5. According to claim 1, a method for analyzing the marginal effect characteristics of short-term energy storage based on the pinch point principle and timing operation simulation is characterized in that: The timing operation simulation method includes simulating the output timing of new energy, load timing and the output of thermal power and energy storage units in the planning year, taking into account the peak-shaving capacity, power supply structure and supply guarantee needs of the power system.

6. According to claim 1, a method for analyzing the marginal effect characteristics of short-term energy storage based on the pinch point principle and timing operation simulation is characterized in that: The marginal effect curve of energy storage installed capacity and new energy utilization rate is constructed by fitting a set of scattered points between energy storage installed capacity and new energy utilization rate to obtain a relationship curve between energy storage installed capacity and new energy utilization rate, and is further determined by identifying the saturation point of new energy utilization rate to optimize the configuration of energy storage capacity.

7. According to claim 1, a method for analyzing the marginal effect characteristics of short-term energy storage based on the pinch point principle and timing operation simulation is characterized in that: The short-term energy storage economic evaluation indicators include initial investment cost, operation and maintenance cost, battery replacement cost, charging and discharging loss and battery life.

8. A method for analyzing the marginal effect characteristics of short-term energy storage based on the pinch point principle and sequential operation simulation according to claim 7, characterized in that: The short-term energy storage economic evaluation index evaluates the economic efficiency of the energy storage system by calculating the unit electricity cost.

9. The method for analyzing the marginal effect characteristics of short-term energy storage based on the pinch point principle and sequential operation simulation according to claim 1 is characterized in that: The optimization of short-term energy storage installed capacity in the short-term energy storage marginal effect characteristic analysis method is to make the optimal configuration by considering the maximum power, capacity, charging and discharging efficiency, system operation time and battery life of the battery, comprehensively evaluating the impact of energy storage capacity on the new energy consumption rate and system economy.

10. The method for analyzing the marginal effect characteristics of short-term energy storage based on the pinch point principle and timing operation simulation according to claim 1 is characterized in that: In the short-term energy storage marginal effect characteristic analysis method, the new energy utilization marginal effect curve is used to determine the maximum utilization rate and optimal energy storage configuration of new energy under different short-term energy storage installed capacities, thereby optimizing the new energy consumption efficiency.

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