An evaluation method of energy storage configuration

Abstract

The present application relates to the technical field of electric power, in particular to a kind of evaluation method of energy storage configuration.The present application includes the following steps: step S1, the power that each super capacitor and lithium battery in hybrid energy storage system needs to suppress is obtained;Step S2, a hybrid energy storage capacity configuration model is established, and the hybrid energy storage capacity configuration model is used to represent the correlation between the power that each super capacitor and lithium battery needs to suppress and the capacity of each;Step S3, the objective function and constraint condition of hybrid energy storage capacity configuration model are constructed;Step S4, the hybrid energy storage capacity configuration model is solved based on the objective function and constraint condition, and then the capacity configuration of super capacitor and lithium battery is obtained.The method in the present application can first obtain the capacity configuration in hybrid energy storage system based on the power that each super capacitor and lithium battery needs to suppress, by establishing hybrid energy storage capacity configuration model and constructing objective function and constraint condition and solving, preferably.

Description

Technical Field

[0001] This invention relates to the field of power technology, and more specifically, to an evaluation method for energy storage configuration. Background Technology

[0002] While renewable energy sources such as photovoltaic power generation are developing rapidly, especially in smart parks where they are widely promoted and applied, they are significantly affected by the environment, particularly temperature and sunlight, resulting in highly unstable power output and impacting power quality and reliability. Furthermore, with economic and technological development, the number of electric vehicles is growing rapidly, and their grid connection for charging has a significant impact on power quality.

[0003] For smart parks, microgrids need to be connected to the main grid via tie lines to maintain grid power balance and ensure reliable power supply to the smart park's microgrids. Due to the integration of photovoltaic power generation, the fluctuations in its output, combined with the fluctuations in electric vehicle charging loads, cause significant power fluctuations on the tie lines. To mitigate the power fluctuations on the tie lines, a certain amount of energy storage equipment needs to be configured.

[0004] Energy storage systems are mainly categorized into energy-type energy storage, power-type energy storage, and hybrid energy storage. Hybrid energy storage combines the advantages of both types of energy storage and can effectively mitigate power fluctuations in interconnect lines. However, due to the high cost of energy storage, both excessively large and insufficient storage capacity are undesirable. Therefore, finding the optimal energy storage capacity configuration in smart park distribution networks is extremely important. Summary of the Invention

[0005] This invention provides an evaluation method for energy storage configurations that can overcome some or all of the shortcomings of existing technologies.

[0006] An evaluation method for energy storage configuration according to the present invention includes the following steps:

[0007] Step S1: Obtain the power that the supercapacitor and lithium battery in the hybrid energy storage system need to be smoothed.

[0008] Step S2: Establish a hybrid energy storage capacity configuration model. The hybrid energy storage capacity configuration model is used to characterize the relationship between the power that supercapacitors and lithium batteries need to suppress and their respective capacities.

[0009] Step S3: Construct the objective function and constraints of the hybrid energy storage capacity configuration model;

[0010] Step S4: Solve the hybrid energy storage capacity configuration model based on the objective function and constraints to obtain the capacity configuration of supercapacitors and lithium batteries.

[0011] The method in this invention can first obtain the power that the supercapacitor and lithium battery need to suppress based on step S1, and then establish a hybrid energy storage capacity configuration model, construct the objective function and constraints, and solve the hybrid energy storage capacity configuration model to obtain the capacity configuration in the hybrid energy storage system in a better way.

[0012] Preferably, step S1 specifically includes the following steps.

[0013] Step S11: Construct a power mathematical model for the distribution network. The power mathematical model is used to characterize the relationship between the charging power of electric vehicles, the power generation power of renewable energy, the transmission power of the distribution network interconnection lines, and the power that the hybrid energy storage system needs to smooth out.

[0014] Step S12: Establish a tie-line power regulation model and obtain the transmission power of the distribution network tie-line after regulation;

[0015] Step S13: Obtain the operating power of the hybrid energy storage system, establish a high-pass filter algorithm model, and obtain the power that the supercapacitor and lithium battery in the hybrid energy storage system need to smooth.

[0016] Through the above, a better mathematical model of the power distribution network of a smart park containing electric vehicles and renewable energy can be established, which in turn can better express the original power of the power distribution network tie line. Then, based on the tie line power regulation model, it can be better ensured that the power of the tie line can change within a limited range. By recalculating the power that the hybrid energy storage needs to suppress and using a high-pass filtering algorithm model to allocate it, the power that the supercapacitor and lithium battery need to suppress respectively can be better determined.

[0017] Preferably, in step S11, the expression for the power mathematical model of the distribution network is:

[0018] P HESS =P EV -P PV -P grid ;

[0019] Among them, P HESS P EV P PV and P grid These are the power that the hybrid energy storage system needs to smooth out, the charging power of electric vehicles, the power generation of renewable energy, and the transmission power of grid interconnection lines.

[0020] Through the above methods, a better mathematical model of power distribution network can be established.

[0021] Preferably, in step S12, the expression for the tie-line power control model is:

[0022]

[0023] Among them, P grid 1 (t) is the regulated transmission power of the distribution network tie line in the t-th regulation cycle, a is a set parameter and 0 < a < 1, F1(t) is the power volatility of the distribution network tie line after regulation in the t-th regulation cycle, and P PV (t) is the power generation power of renewable energy in the t-th regulation cycle, and P EV (t) is the charging power of electric vehicles in the t-th regulation cycle;

[0024] Among them,

[0025]

[0026] Among them, m is the set mean benchmark, and P grid (t - i) represents the transmission power of the distribution network tie line in the (t - i)-th regulation cycle.

[0027] In the present invention, the established tie line power regulation model can be carried out based on the power volatility of the distribution network tie line, so that it can preferably ensure that the power volatility at the distribution network tie line is maintained within the specified range, and further preferably reduce the situation where the power of the distribution network tie line rises or falls sharply, and reduce the impact of the power fluctuation of the distribution network tie line on the transmission capacity of the tie line.

[0028] Preferably, in step S13, the expression of the high-pass filter algorithm model is

[0029]

[0030] Among them, P HESS (t) is the power that needs to be suppressed by the hybrid energy storage system in the t-th regulation cycle, P sc (t) is the power that needs to be suppressed by the supercapacitor in the hybrid energy storage system in the t-th regulation cycle, P b (t) is the power that needs to be suppressed by the lithium battery in the hybrid energy storage system in the t-th regulation cycle, s is the differential operator, and T f is the filter time constant.

[0031] Through the above, it can preferably calculate the power that needs to be suppressed by the hybrid energy storage system based on the power volatility as the energy storage suppression index, and allocate the power that needs to be suppressed by the supercapacitor and the lithium battery respectively through the high-pass filter algorithm.

[0032] Preferably, in step S2, the expression of the hybrid energy storage capacity configuration model is

[0033]

[0034] Among them, Q sc and Q b P represents the capacity of the supercapacitor and the lithium battery, respectively. sc (t) and P b (t) represents the power that the supercapacitor and lithium battery need to suppress during the t-th regulation cycle, respectively, where t0 is the discharge start time, and t p The duration of the electricity price parity period, t h For the duration of periods of high electricity prices, SOC sc，max and SOC sc，min These represent the maximum and minimum states of charge (SOC) of the supercapacitor, respectively. b，max and SOC b，min These are the power levels that each lithium battery needs to suppress.

[0035] Through the above, the capacity of supercapacitors and lithium batteries can be limited by their state of charge. Therefore, the characteristics of hybrid energy storage systems, such as charging at low electricity prices and discharging at flat and high electricity prices, can be fully considered. Specifically, supercapacitors or lithium batteries should meet the limitation of discharging from the maximum state of charge to the minimum state of charge when discharging at high and flat electricity prices.

[0036] As a preferred option, based on annual return C earn The objective function to be constructed is maximized, and the objective function constructed is:

[0037]

[0038] Among them, C income The direct profit of the hybrid energy storage system in a natural year is represented by C1, C2, and r, where r is the discount rate and l is the service life of the energy storage system.

[0039] in,

[0040]

[0041] C1=α sc P sc +α b P b +β sc Q sc +β b Q b ;

[0042] C2=ε sc Q sc +ε b Q b ;

[0043] Where K represents the number of days in a natural year, T represents the total number of control cycles in a day, Δt1 represents the charging and discharging time of the supercapacitor in the t-th control cycle, Δt2 represents the charging and discharging time of the lithium battery in the t-th control cycle, and R... t This represents the electricity price during the t-th regulation cycle;

[0044] Where, α sc α b β sc and β b These represent the unit price per unit power of supercapacitors, the unit price per unit power of lithium batteries, the unit price per unit capacity of supercapacitors, and the unit price per unit capacity of lithium batteries, respectively.

[0045] Where, ε sc ε represents the operating and maintenance cost coefficient for supercapacitors. b This represents the operating and maintenance cost coefficient for lithium batteries.

[0046] The above approach allows for a better realization of maximizing profit as the objective function.

[0047] As a preferred approach, constraint conditions are constructed based on energy conservation, output power, and state of charge. The constructed constraint conditions are as follows:

[0048]

[0049] Among them, P sc，min and P sc，max P represents the minimum and maximum charging and discharging power of the supercapacitor. b，min and P b，max P represents the minimum and maximum charging and discharging power of a lithium battery. PV，max This represents the maximum power generation capacity of renewable energy sources.

[0050] Through the above, it is possible to better achieve the constraints that electricity must always meet the supply and demand balance, the constraints that the output power of photovoltaic generators, supercapacitors and lithium batteries is limited by their own characteristics, and the constraints of avoiding overcharging and over-discharging.

[0051] As a preferred approach, the adaptive weighted particle swarm optimization algorithm is used to solve the hybrid energy storage capacity configuration model based on the objective function and constraints. Therefore, it can obtain a capacity configuration scheme with higher accuracy and stability.

[0052] Preferably, the value of 'a' is in the range of 0.15-0.25. Therefore, it can take into account the average value of the tie-line power fluctuation, the maximum value of the fluctuation, the configuration of energy storage, and the economics of energy storage. Attached Figure Description

[0053] Figure 1This is a flowchart of an evaluation method for an energy storage configuration in Example 1;

[0054] Figure 2 This is a diagram of the smart park power distribution network architecture in Example 1;

[0055] Figure 3 This is a graph showing the original transmission power curve of the distribution network tie line in Example 1;

[0056] Figure 4 The diagram shows the tie-line power before and after regulation in Example 1;

[0057] Figure 5 This is a diagram showing the power volatility of the tie-line before and after regulation in Example 1;

[0058] Figure 6 The power that the hybrid energy storage system in Example 1 needs to smooth out;

[0059] Figure 7 This is a diagram of the high-pass filtering algorithm in Example 1;

[0060] Figure 8 The power that the supercapacitor and lithium battery in Example 1 need to suppress;

[0061] Figure 9 The flowchart of the adaptive weighted particle swarm algorithm in Example 1 is shown below;

[0062] Figure 10 Here is a flowchart of the fitness algorithm in Example 1;

[0063] Figure 11 This is a graph showing the optimal return variation of the hybrid energy storage system in Example 1;

[0064] Figure 12 This is a graph showing the change in the state of charge of the energy storage in Example 1. Detailed Implementation

[0065] To further understand the content of this invention, a detailed description of the invention will be provided in conjunction with the accompanying drawings and embodiments. It should be understood that the embodiments are merely illustrative and not limiting of the invention.

[0066] Example 1

[0067] like Figure 1 As shown, this embodiment provides an evaluation method for energy storage configuration, which includes the following steps:

[0068] Step S1: Obtain the power that the supercapacitor and lithium battery in the hybrid energy storage system need to be smoothed.

[0069] Step S2: Establish a hybrid energy storage capacity configuration model. The hybrid energy storage capacity configuration model is used to characterize the relationship between the power that supercapacitors and lithium batteries need to suppress and their respective capacities.

[0070] Step S3: Construct the objective function and constraints of the hybrid energy storage capacity configuration model;

[0071] Step S4: Solve the hybrid energy storage capacity configuration model based on the objective function and constraints to obtain the capacity configuration of supercapacitors and lithium batteries.

[0072] The method in this embodiment can first obtain the power that the supercapacitor and lithium battery need to suppress based on step S1, and then establish a hybrid energy storage capacity configuration model, construct the objective function and constraints, and solve the hybrid energy storage capacity configuration model to obtain the capacity configuration in the hybrid energy storage system in a better way.

[0073] Step S1 in this embodiment specifically includes the following steps:

[0074] Step S11: Construct a power mathematical model for the distribution network. The power mathematical model is used to characterize the relationship between the charging power of electric vehicles, the power generation power of renewable energy, the transmission power of the distribution network interconnection lines, and the power that the hybrid energy storage system needs to smooth out.

[0075] Step S12: Establish a tie-line power regulation model and obtain the transmission power of the distribution network tie-line after regulation;

[0076] Step S13: Obtain the operating power of the hybrid energy storage system, establish a high-pass filter algorithm model, and obtain the power that the supercapacitor and lithium battery in the hybrid energy storage system need to smooth.

[0077] Through the above, a better mathematical model of the power distribution network of a smart park containing electric vehicles and renewable energy can be established, which in turn can better express the original power of the power distribution network tie line. Then, based on the tie line power regulation model, it can be better ensured that the power of the tie line can change within a limited range. By recalculating the power that the hybrid energy storage needs to suppress and using a high-pass filtering algorithm model to allocate it, the power that the supercapacitor and lithium battery need to suppress respectively can be better determined.

[0078] In step S11 of this embodiment, the expression for the power mathematical model of the distribution network is:

[0079] P HESS =P EV -P PV -P grid ;

[0080] Among them, P HESS PEV P PV and P grid These are the power that the hybrid energy storage system needs to smooth out, the charging power of electric vehicles, the power generation of renewable energy, and the transmission power of grid interconnection lines.

[0081] Through the above methods, a better mathematical model of power distribution network can be established.

[0082] Understandably, for smart parks, microgrids need to be connected to the main grid via distribution network interconnections to maintain grid power balance and ensure reliable power supply to the smart park's microgrids. Due to the integration of renewable energy sources (taking photovoltaic generators as an example in this embodiment), the fluctuations in their output, combined with the fluctuations in electric vehicle charging loads, cause significant power fluctuations on the distribution network interconnections. This may adversely affect the transmission capacity of the distribution network interconnections and even endanger grid security. Therefore, to mitigate the power fluctuations on the distribution network interconnections, a certain amount of energy storage equipment needs to be configured (taking a hybrid energy storage system constructed using supercapacitors and lithium batteries as an example in this embodiment).

[0083] Combination Figure 2 As shown, the smart park power distribution network architecture in this embodiment includes photovoltaic generators, electric vehicle (EV) charging systems, and hybrid energy storage systems connected to the power distribution network via the bus (i.e., tie line) shown in the figure. The photovoltaic generators release electrical energy to the power distribution network, the electric vehicle charging systems draw electrical energy from the grid (in this embodiment, the situation of electric vehicles discharging to the power distribution network is not considered), and the hybrid energy storage system can both release electrical energy to the power distribution network and draw electrical energy from the grid.

[0084] Hybrid energy storage systems are designed to mitigate power fluctuations from photovoltaic power generation and electric vehicle charging loads in the distribution network to ensure the balance of power supply and demand and stable operation of the grid. Therefore, they can better facilitate the establishment of mathematical models for power distribution networks.

[0085] Understandably, the power of the hybrid energy storage system is provided by supercapacitors and lithium batteries, that is, P HESS =P sc +P b .

[0086] In addition, the transmission power P of the uncontrolled distribution network tie line grid It can be represented as P grid =γ(P EV -P PV) where γ is a proportionality coefficient. When the power of the photovoltaic generator is greater than the charging power of the electric vehicle and γ is 0, the excess electric energy of the photovoltaic generator is stored in the hybrid energy storage system; when the electricity price is low, the proportionality coefficient γ can be greater than 1, so the power supply is mainly from the power grid; when the electricity price is at parity or high, the proportionality coefficient γ can be less than 1, so the power supply is mainly from the hybrid energy storage system. Thus, the economy can be taken into account.

[0087] In Figure 3 , the power transmission curve of the unregulated distribution network tie line in a specific distribution network within one day is given. From Figure 3 it can be seen that between 9:00 - 17:00 and 20:00 - 22:00, the power transmission of the distribution network tie line fluctuates greatly, which greatly affects the safe and stable operation of the power grid.

[0088] In this embodiment, in step S12, the expression of the tie line power regulation model is

[0089]

[0090] where P grid (t) and P grid 1 (t) are the transmission powers of the distribution network tie line before and after regulation in the t - th regulation period, a is a set parameter and 0 < a < 1, F1(t) is the power volatility of the distribution network tie line after regulation in the t - th regulation period, P PV (t) is the power generation of renewable energy in the t - th regulation period, and P EV (t) is the charging power of the electric vehicle in the t - th regulation period;

[0091] where

[0092]

[0093] where m is the set mean benchmark, and P grid (t - i) represents the transmission power of the distribution network tie line in the (t - i) - th regulation period.

[0094] In this embodiment, the established tie line power regulation model can be based on the power volatility of the distribution network tie line, so it can preferably ensure that the power volatility at the distribution network tie line is kept within the limited range, and further preferably reduce the situation where the power of the distribution network tie line rises or falls sharply, and reduce the impact of the power fluctuation of the distribution network tie line on the transmission capacity of the tie line.

[0095] In this embodiment, by taking the power volatility at the distribution network tie line as the energy storage smoothing index, it can preferably realize the evaluation of the smoothing effect of the energy storage and avoid excessive or insufficient smoothing of the energy storage.

[0096] In this embodiment, the goal of energy storage in mitigating power fluctuations is to ensure that the power fluctuation rate does not exceed the upper and lower limits of the set power fluctuation rate within a specified time window of the operating cycle through energy storage charging and discharging. In this embodiment, the average power transmitted by the distribution network tie line is used as the benchmark value to calculate the power fluctuation rate, thus resulting in a higher smoothness of the power transmitted by the distribution network tie line after regulation.

[0097] Given a time window length of L and a sampling number of m, the transmission power P of the uncontrolled distribution network tie line within a certain period t is... grid The volatility F0(t) of (t) is,

[0098]

[0099] In this embodiment, with L = 20 min, a = 0.2, and a sampling interval of 5 min (m = 4), the tie-line power and power fluctuation rate before and after regulation of a specific power distribution network are compared. Figure 4 and 5 As shown in the figure, it can be seen that after adjustment, the power of the tie line is greatly reduced, and the volatility of each point after adjustment can be kept within the set range as much as possible.

[0100] like Figure 6 As shown, in step S13 of this embodiment, the expression for the high-pass filtering algorithm model is:

[0101]

[0102] Among them, P HESS (t) represents the power that the hybrid energy storage system needs to smooth out during the t-th regulation cycle, P. sc (t) represents the power that the supercapacitor in the hybrid energy storage system needs to smooth out during the t-th regulation cycle, P. b (t) represents the power that the lithium battery in the hybrid energy storage system needs to suppress during the t-th regulation cycle, s is the differential operator, and T f This is the filtering time constant.

[0103] Based on the above, the power fluctuation rate can be used as an energy storage smoothing index to calculate the power that needs to be smoothed by the hybrid energy storage system, and the power that needs to be smoothed by the supercapacitor and lithium battery can be allocated by the high-pass filtering algorithm.

[0104] Understandably, P sc (t) represents the high-frequency fluctuation component after high-pass filtering, which can serve as an active command for controlling the energy storage of the supercapacitor; P b(t) represents the remaining power after processing by the high-pass filtering algorithm, which can serve as an active command for lithium battery energy storage.

[0105] In this embodiment, the filtering time constant T f The frequency band of power fluctuations that the supercapacitor energy storage system needs to smooth is determined, and it is usually on the order of seconds to minutes.

[0106] After analyzing a specific power distribution network as described above, the power curve that needs to be smoothed by the hybrid energy storage system is as follows: Figure 7 As shown, the hybrid energy storage system charges from 0:00 to 7:00, which is the period of low electricity price; from 7:00 to 12:00 and from 15:00 to 18:00, the hybrid energy storage consumes photovoltaic power; at other times, the hybrid energy storage discharges; the peak discharge of the hybrid energy storage is concentrated between 12:00 and 17:00.

[0107] In this embodiment, in a specific power distribution network described above, the power curves that the supercapacitor and lithium battery need to smooth are as follows: Figure 8 As shown.

[0108] In step S2 of this embodiment, the expression for the hybrid energy storage capacity configuration model is:

[0109]

[0110] Among them, Q sc and Q b P represents the capacity of the supercapacitor and the lithium battery, respectively. sc (t) and P b (t) represents the power that the supercapacitor and lithium battery need to suppress during the t-th regulation cycle, respectively, where t0 is the discharge start time, and t p The duration of the electricity price parity period, t h For the duration of periods of high electricity prices, SOC sc，max and SOC sc，min These represent the maximum and minimum states of charge (SOC) of the supercapacitor, respectively. b，max and SOC b，min These are the power levels that each lithium battery needs to suppress.

[0111] Through the above, the capacity of supercapacitors and lithium batteries can be limited by their state of charge. Therefore, the characteristics of hybrid energy storage systems, such as charging at low electricity prices and discharging at flat and high electricity prices, can be fully considered. Specifically, supercapacitors or lithium batteries should meet the limitation of discharging from the maximum state of charge to the minimum state of charge when discharging at high and flat electricity prices.

[0112] It is understood that the state of charge (SOC) is used to measure the remaining capacity of an energy storage unit (the supercapacitor or lithium battery in this embodiment). The SOC calculation formula is as follows:

[0113]

[0114] Where SOC(t) and SOC(t-1) represent the state of charge of the energy storage unit at time t and time t-1, respectively; P(t) represents the charging and discharging power at time t (greater than 0 indicates discharging, less than 0 indicates charging); E N Indicates the capacity of the energy storage unit; η c and η d Let represent the charging and discharging efficiency, δ represent the self-discharge rate, and Δt represent the step size between time t and time t-1.

[0115] In this embodiment, based on the annual return C earn The objective function to be constructed is maximized, and the objective function constructed is:

[0116]

[0117] Among them, C income The direct profit of the hybrid energy storage system in a natural year is represented by C1, C2, and r, where r is the discount rate and l is the service life of the energy storage system.

[0118] in,

[0119]

[0120] C1=α sc P sc +α b P b +β sc Q sc +β b Q b ;

[0121] C2=ε sc Q sc +ε b Q b ;

[0122] Where K represents the number of days in a natural year, T represents the total number of control cycles in a day, Δt1 represents the charging and discharging time of the supercapacitor in the t-th control cycle, Δt2 represents the charging and discharging time of the lithium battery in the t-th control cycle, and R... t This represents the electricity price during the t-th regulation cycle;

[0123] Where, α sc α b β sc and β bThese represent the unit price per unit power of supercapacitors, the unit price per unit power of lithium batteries, the unit price per unit capacity of supercapacitors, and the unit price per unit capacity of lithium batteries, respectively.

[0124] Where, ε sc ε represents the operating and maintenance cost coefficient for supercapacitors. b This represents the operating and maintenance cost coefficient for lithium batteries.

[0125] The above approach allows for a better realization of maximizing profit as the objective function.

[0126] In this specific embodiment, K=365 and T=288 are set.

[0127] In this embodiment, constraint conditions are constructed based on energy conservation, output power, and state of charge. The constructed constraint conditions are as follows:

[0128]

[0129] Among them, P sc，min and P sc，max P represents the minimum and maximum charging and discharging power of the supercapacitor. b，min and P b，max P represents the minimum and maximum charging and discharging power of a lithium battery. PV，max This represents the maximum power generation capacity of renewable energy sources.

[0130] Through the above, it is possible to better achieve the constraints that electricity must always meet the supply and demand balance, the constraints that the output power of photovoltaic generators, supercapacitors and lithium batteries is limited by their own characteristics, and the constraints of avoiding overcharging and over-discharging.

[0131] In this embodiment, the adaptive weighted particle swarm optimization algorithm is used to solve the hybrid energy storage capacity configuration model based on the objective function and constraints. Therefore, it is possible to obtain a capacity configuration scheme with higher accuracy and stability.

[0132] In this embodiment, the Adaptive Particle Swarm Optimization (APSO) algorithm is selected to optimize the hybrid energy storage capacity configuration model. The algorithm flow is as follows: Figure 9 As shown, its fitness algorithm flow is as follows: Figure 10 As shown.

[0133] In this embodiment, with a = 0.2, a population size of 80, a particle iteration count of 80, a maximum and minimum inertia weight coefficient of 0.9 and 0.4 respectively, a learning factor of 2, a simulation step size of 5 min, and relevant parameters set as shown in Table 1 below, the capacity optimization configuration results of the hybrid energy storage system are shown in Table 2. Figure 11 and Figure 12 As shown.

[0134] Table 1 Parameter Configuration

[0135]

[0136] Table 2. Capacity configuration and system benefits of the hybrid energy storage system before and after regulation.

[0137] Consider tie line power regulation 922 111 1.3733 Tie line power regulation not considered 909 111 1.143

[0138] As shown in Table 2, although the capacity configuration results before and after regulation are not significantly different, the power fluctuation rate of the tie line after regulation can be kept within the set range, resulting in less impact on the tie line transmission capacity and better economic efficiency. Meanwhile, from... Figure 12 It can be seen that throughout the entire operating cycle, the state of charge of the regulated supercapacitor and lithium battery varies within their respective state of charge ranges and satisfies the energy conservation constraint.

[0139] In this embodiment, the value of 'a' ranges from 0.15 to 0.25. Therefore, it can take into account the average value of tie-line power fluctuation, the maximum value of fluctuation, the configuration of energy storage, and the economics of energy storage.

[0140] Considering that even with timely adjustment of the tie-line power control model, significant power fluctuations still occur, potentially negatively impacting the tie-line's transmission capacity, this embodiment uses the average and maximum power fluctuations within a set time window of length L as evaluation indicators to assess the range of values ​​for 'a'.

[0141] Understandably, the smaller the average power fluctuation, the less likely the tie line power will fluctuate significantly; the smaller the maximum power fluctuation, the less impact it has on the tie line's transmission capacity.

[0142] In this embodiment, at a certain period t, the power fluctuation ΔP of the tie line is... grid 1 (t′) is,

[0143] ΔP grid 1 (t)=|P grid 1 (t)-P grid (t-1)|.

[0144] In this embodiment, the average value of the power fluctuation ΔP grid，avg Calculated using the following formula:

[0145]

[0146] In this embodiment, the average value and maximum value of the power fluctuation of the tie line are shown in Table 3 when 'a' takes different values.

[0147] Table 3a shows the average and maximum values ​​of tie-line power fluctuations for different values.

[0148]

[0149] As shown in Table 3, the average power fluctuation of the uncontrolled tie line is 16.5 kW, and the maximum power fluctuation is 173 kW. After regulation, the smaller the value of 'a', the smaller the average and maximum power fluctuations of the tie line, and the smaller the impact on the transmission capacity of the tie line. However, when 'a' exceeds 0.2, the maximum power fluctuation of the tie line changes more rapidly. That is, the larger the allowable range of tie line power fluctuations, the more points of large random power fluctuations occur in the tie line, which has a significant impact on the transmission capacity of the tie line.

[0150] Meanwhile, in order to further explore the impact of the value of 'a' on energy storage configuration and economics, different values ​​of 'a' were used, and the resulting energy storage configuration results and annual revenue of the energy storage system are shown in Table 4.

[0151] Table 4. Relationship between different values ​​of a and energy storage configuration results and annual returns of energy storage systems

[0152]

[0153] As can be seen from the table above, the value of 'a' has a significant impact on the annual returns of energy storage. Furthermore, when the value of 'a' ranges from 0.05 to 0.2, it has a substantial impact on energy storage configuration; when 'a' is greater than 0.2, it has a very small impact on energy storage configuration.

[0154] Therefore, in this embodiment, considering the average value of the tie line power fluctuation, the maximum value of the fluctuation, the configuration of energy storage, and the economics of energy storage, the range of 'a' that can be selected is 0.15-0.25.

[0155] The method in this invention can first use the power fluctuation rate of the tie line as the smoothing index of the hybrid energy storage system, establish a tie line power regulation model, keep the power of each point of the tie line within a limited range, and determine the power that the supercapacitor and lithium battery need to smooth. By establishing a hybrid energy storage capacity configuration model and constructing objective functions and constraints and solving them, the capacity configuration in the hybrid energy storage system can be obtained better.

[0156] The present invention and its embodiments have been described above illustratively. This description is not restrictive, and the figures shown are only one embodiment of the present invention; the actual structure is not limited thereto. Therefore, if those skilled in the art are inspired by this description and design similar structures and embodiments without departing from the spirit of the present invention, such designs should fall within the protection scope of the present invention.

Claims

1. An evaluation method for energy storage configuration, comprising the following steps: Step S1: Obtain the power that the supercapacitor and lithium battery in the hybrid energy storage system need to be smoothed. Step S2: Establish a hybrid energy storage capacity configuration model. The hybrid energy storage capacity configuration model is used to characterize the relationship between the power that supercapacitors and lithium batteries need to suppress and their respective capacities. Step S3: Construct the objective function and constraints of the hybrid energy storage capacity configuration model; Step S4: Solve the hybrid energy storage capacity configuration model based on the objective function and constraints to obtain the capacity configuration of supercapacitors and lithium batteries. Step S1 specifically includes the following steps: Step S11: Construct a power mathematical model for the distribution network. The power mathematical model is used to characterize the relationship between the charging power of electric vehicles, the power generation power of renewable energy, the transmission power of the distribution network interconnection lines, and the power that the hybrid energy storage system needs to smooth out. Step S12: Establish a tie-line power regulation model to obtain the regulated transmission power of the distribution network tie-lines. The expression for the tie-line power regulation model is as follows: ； wherein, and is the transmission power of the tie-line of the power distribution network before and after the regulation at the tth regulation period, a is a set parameter and 0 < a < 1, is the power fluctuation rate of the tie-line of the power distribution network after the regulation at the tth regulation period. in, ； Where m is the set mean benchmark. This represents the transmission power of the distribution network tie line during the ti-th control cycle; Step S13: Obtain the operating power of the hybrid energy storage system, establish a high-pass filter algorithm model, and obtain the power that the supercapacitor and lithium battery in the hybrid energy storage system need to smooth.

2. The evaluation method for energy storage configuration according to claim 1, characterized in that: In step S11, the expression for the power mathematical model of the distribution network is: ； in, , , and These are the power that the hybrid energy storage system needs to smooth out, the charging power of electric vehicles, the power generation of renewable energy, and the transmission power of grid interconnection lines.

3. The evaluation method for energy storage configuration according to claim 1, characterized in that: In step S13, the expression for the high-pass filtering algorithm model is: ； in, Let the power generation of renewable energy be the power output during the t-th regulation period. Let be the charging power of the electric vehicle during the t-th control cycle; Let t be the power that the hybrid energy storage system needs to smooth out during the t-th regulation cycle. Let be the power that the supercapacitor in the hybrid energy storage system needs to smooth out during the t-th control cycle. Let be the power that the lithium battery in the hybrid energy storage system needs to suppress during the t-th regulation cycle, and s be the differential operator. This is the filtering time constant.

4. The evaluation method for energy storage configuration according to claim 1, characterized in that: In step S2, the expression for the hybrid energy storage capacity configuration model is: ； in, and These refer to the capacities of supercapacitors and lithium batteries, respectively. and Let be the power that the supercapacitor and the lithium battery need to suppress during the t-th adjustment cycle, respectively. The discharge initiation time. The duration of the electricity price parity period. The duration of periods with high electricity prices. and These represent the maximum and minimum values ​​of the state of charge of the supercapacitor, respectively. and These are the power levels that each lithium battery needs to suppress.

5. The evaluation method for energy storage configuration according to claim 4, characterized in that: Based on annual returns The objective function to be constructed is maximized, and the objective function constructed is: ； in, For the direct profit of a hybrid energy storage system in a calendar year, This indicates the construction cost of a hybrid energy storage system. This represents the maintenance cost of a hybrid energy storage system, where r is the discount rate. l The lifespan of the energy storage; in, ； ； ； in, It represents the number of days in a calendar year. This indicates the total number of control cycles within a day. This represents the charging and discharging time of the supercapacitor during the t-th control cycle. This represents the charging and discharging time of the lithium battery during the t-th control cycle. This represents the electricity price during the t-th regulation cycle; in, , , and These represent the unit price per unit power of supercapacitors, the unit price per unit power of lithium batteries, the unit price per unit capacity of supercapacitors, and the unit price per unit capacity of lithium batteries, respectively. in, This represents the operating and maintenance cost coefficient for supercapacitors. This represents the operating and maintenance cost coefficient for lithium batteries.

6. The evaluation method for energy storage configuration according to claim 5, characterized in that: Based on energy conservation, output power, and state of charge, constraint conditions are constructed. The constructed constraint conditions are as follows: ； in, and These represent the minimum and maximum charging and discharging power of the supercapacitor. and These represent the minimum and maximum charging and discharging power of the lithium battery. This represents the maximum power generation capacity of renewable energy sources.

7. The evaluation method for energy storage configuration according to claim 6, characterized in that: The adaptive weighted particle swarm optimization algorithm is used to solve the hybrid energy storage capacity configuration model based on the objective function and constraints.

8. The evaluation method for energy storage configuration according to claim 1, characterized in that: The value of a ranges from 0.15 to 0.25.

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