Energy storage configuration method based on price sensitivity

Abstract

The present application relates to the technical field of electric power, in particular to a kind of energy storage configuration method based on price sensitivity.It includes: step S1, current time t and the transmission power of m power distribution network tie line before current time t are collected uniformly spaced;Step S2, establish power distribution network tie line transmission power control model, and based on the power fluctuation rate F (t) of tie line, the transmission power P grid1 (t) of power distribution network tie line after control in current time t is obtained;Step S3, based on the transmission power P grid1 (t) of power distribution network tie line after control in current time t, the power fluctuation rate F (t) of tie line in current time t is calculated, and the price is adjusted based on the power fluctuation rate F (t) of tie line.The present application can preferably realize the optimization management of energy storage system by price adjustment.

Description

Technical Field

[0001] This invention relates to the field of power technology, and more specifically, to a method for configuring energy storage based on price sensitivity. 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.

[0005] In addition, to regulate power fluctuations on the interconnection line, from the perspective of demand-side management, users can be guided to change their electricity consumption behavior by adjusting or compensating for power supply prices, so as to ultimately achieve the purpose of regulating load and "peak shaving and valley filling". Summary of the Invention

[0006] This invention provides a price-sensitive energy storage configuration method that can overcome some or all of the shortcomings of existing technologies.

[0007] According to a price-sensitive energy storage configuration method of the present invention, the method includes the following steps:

[0008] Step S1: Collect the transmission power of the current time t and m distribution network tie lines before the current time t at uniform intervals;

[0009] Step S2: Establish a power transmission regulation model for distribution network tie lines, and base it on the power fluctuation rate of the tie lines. Obtain the transmission power of the distribution network tie line after adjustment at the current time t. ;

[0010] Step S3: Based on the transmission power of the distribution network tie lines after regulation Calculate the power fluctuation rate of the tie line at the current time t. And based on the power volatility of the tie line Adjust electricity prices.

[0011] Through the above methods, electricity price-based regulation can be effectively achieved. By adjusting or compensating for electricity prices, users can be guided to change their electricity consumption behavior, ultimately achieving the goal of load regulation and peak shaving.

[0012] Preferably, in step S2, the expression for the tie-line power control model is:

[0013]

[0014] in, Let be the actual power transmitted through the distribution network tie line at the current time t, and 'a' be a set parameter and 0. <a<1, This represents the power transmitted through the distribution network tie line at the i-th sampling time.

[0015] The established tie-line power regulation model can be based on the power fluctuation rate of the distribution network tie-line, thus better ensuring that the power fluctuation rate at the distribution network tie-line is kept within a limited range, thereby better reducing the situation of sharp rise or fall in the power of the distribution network tie-line and reducing the impact of the power fluctuation of the distribution network tie-line on the transmission capacity of the tie-line.

[0016] As a preferred option

[0017] ,

[0018] ;

[0019] Where m is the total number of samples, This represents the transmission power of the distribution network tie line during the ti-th control cycle.

[0020] 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.

[0021] Preferably, in step S3, the electricity price adjustment formula is as follows:

[0022] ;

[0023] in, The electricity price is adjusted at time t. Let t be the electricity price before the adjustment. For price volatility sensitivity, To set the adjustment base, Therefore, it can better control electricity prices.

[0024] As a preferred option

[0025] ;

[0026] in, The power fluctuation rate of the tie line before adjustment at the current time t, This represents the power fluctuation rate of the tie line at the previous sampling time t. Therefore, it can better control electricity prices.

[0027] 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

[0028] Figure 1 This is a flowchart of an evaluation method for an energy storage configuration in Example 1;

[0029] Figure 2 This is a diagram of the smart park power distribution network architecture in Example 1;

[0030] Figure 3 This is a graph showing the original transmission power curve of the distribution network tie line in Example 1;

[0031] Figure 4 The diagram shows the tie-line power before and after regulation in Example 1;

[0032] Figure 5 This is a graph showing the power fluctuation rate of the tie line before and after regulation in Example 1;

[0033] Figure 6 The power that the hybrid energy storage system in Example 1 needs to smooth out;

[0034] Figure 7 This is a diagram of the high-pass filtering algorithm in Example 1;

[0035] Figure 8 The power required to be smoothed for the supercapacitor and lithium battery in Example 1;

[0036] Figure 9 The flowchart of the adaptive weighted particle swarm algorithm in Example 1 is shown below;

[0037] Figure 10 Here is a flowchart of the fitness algorithm in Example 1;

[0038] Figure 11 This is a graph showing the optimal return variation of the hybrid energy storage system in Example 1;

[0039] Figure 12 This is a graph showing the change in the state of charge of the energy storage in Example 1;

[0040] Figure 13 This is a flowchart of the price-sensitive energy storage configuration method in Example 2. Detailed Implementation

[0041] 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.

[0042] Example 1

[0043] like Figure 1 As shown, this embodiment provides an evaluation method for energy storage configuration, which includes the following steps:

[0044] Step S1: Obtain the power that the supercapacitor and lithium battery in the hybrid energy storage system need to be smoothed.

[0045] 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.

[0046] Step S3: Construct the objective function and constraints of the hybrid energy storage capacity configuration model;

[0047] 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.

[0048] 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.

[0049] Step S1 in this embodiment specifically includes the following steps:

[0050] 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.

[0051] Step S12: Establish a tie-line power regulation model and obtain the transmission power of the distribution network tie-line after regulation;

[0052] 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.

[0053] 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.

[0054] In step S11 of this embodiment, the expression for the power mathematical model of the distribution network is:

[0055] ;

[0056] 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.

[0057] Through the above methods, a better mathematical model of power distribution network can be established.

[0058] 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).

[0059] 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) in the figure. The photovoltaic generators release electrical energy to the power distribution network, the electric vehicle charging system draws 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.

[0060] 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.

[0061] Understandably, the power of the hybrid energy storage system is provided by supercapacitors and lithium batteries, that is, .

[0062] In addition, the transmission power of uncontrolled distribution network interconnects It can be represented as, , This is the proportionality constant. When the power output of the photovoltaic generator exceeds the charging power of the electric vehicle, the excess electrical energy is stored in the hybrid energy storage system. When electricity prices are low, the proportional gain is [missing value]. The ratio can be greater than 1, therefore it can be mainly supplied by the power grid; when the electricity price is at par or high, the proportionality coefficient... Since the value can be less than 1, it can be primarily powered by a hybrid energy storage system, thus achieving both economic efficiency and cost-effectiveness.

[0063] exist Figure 3 The paper presents a daily power transmission curve for an uncontrolled distribution network tie line in a specific distribution network. Figure 3 It can be seen that the transmission power of the distribution network tie lines fluctuates greatly between 9:00-17:00 and 20:00-22:00, which greatly affects the safe and stable operation of the power grid.

[0064] In this embodiment, in step S12, the expression for the tie-line power control model is:

[0065] ;

[0066] in, and Let be the transmission power of the distribution network tie line before and after regulation in the t-th regulation cycle, where 'a' is a set parameter and 0. <a<1, Let be the power fluctuation rate after the distribution network tie line is regulated in the t-th regulation cycle. 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;

[0067] in,

[0068] ;

[0069] Where m is the set mean benchmark. This represents the transmission power of the distribution network tie line during the ti-th control cycle.

[0070] In this embodiment, the established tie-line power regulation model can be based on the power fluctuation rate of the distribution network tie-line. Therefore, it can better ensure that the power fluctuation rate at the distribution network tie-line is kept within a limited range, thereby better reducing the situation where the power of the distribution network tie-line rises or falls sharply, and reducing the impact of the power fluctuation of the distribution network tie-line on the transmission capacity of the tie-line.

[0071] In this embodiment, by using the power fluctuation rate at the distribution network interconnection line as the energy storage smoothing index, the smoothing effect of energy storage can be better evaluated, avoiding excessive or insufficient smoothing of energy storage.

[0072] 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.

[0073] Given a time window length of L and a sampling number of m, the transmission power of the uncontrolled distribution network tie line within a certain period t is... volatility for,

[0074] .

[0075] 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.

[0076] like Figure 6 As shown, in step S13 of this embodiment, the expression for the high-pass filtering algorithm model is:

[0077] ;

[0078] in, 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.

[0079] 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.

[0080] Understandable The high-frequency fluctuation component, processed by a high-pass filtering algorithm, can serve as an active command for controlling the energy storage of supercapacitors. The remaining power after processing by a high-pass filtering algorithm can serve as an active command for lithium battery energy storage.

[0081] In this embodiment, the filtering time constant 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.

[0082] 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.

[0083] 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.

[0084] In step S2 of this embodiment, the expression for the hybrid energy storage capacity configuration model is:

[0085] ;

[0086] 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.

[0087] 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.

[0088] 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:

[0089] ;

[0090] in, and Let P(t) and E(t) 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(t) represents 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(t) represents the state of charge of the energy storage unit at time t and time t-1, respectively; E ... N Indicates the capacity of the energy storage unit; and These represent the charging and discharging efficiencies, respectively. Self-discharge rate, Let be the step size between time t and time t-1.

[0091] In this embodiment, based on annual income The objective function to be constructed is maximized, and the objective function constructed is:

[0092] ;

[0093] Among them, 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;

[0094] in,

[0095] ;

[0096] ;

[0097] ;

[0098] 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;

[0099] 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.

[0100] in, This represents the operating and maintenance cost coefficient for supercapacitors. This represents the operating and maintenance cost coefficient for lithium batteries.

[0101] The above approach allows for a better realization of maximizing profit as the objective function.

[0102] In this specific embodiment, K=365 and T=288 are set.

[0103] In this embodiment, constraint conditions are constructed based on energy conservation, output power, and state of charge. The constructed constraint conditions are as follows:

[0104] ;

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] In this embodiment, with a=0.2, population size of 80, particle iterations of 80, maximum and minimum values ​​of inertia weight coefficients of 0.9 and 0.4 respectively, learning factor of 2, 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.

[0110] Table 1 Parameter Configuration

[0111] parameter Parameter value parameter Parameter value Supercapacitor self-discharge rate 0.05 Lithium battery self-discharge rate 0.05 Supercapacitor charge and discharge efficiency 0.95 Lithium battery charge and discharge efficiency 0.85 Supercapacitor Variation Range 0.15-0.95 Lithium battery variation range 0.10-0.90 <![CDATA[Unit power cost of supercapacitor (yuan·kW -1 )]]> 450 <![CDATA[Unit power cost of lithium battery / (yuan·kW -1 )]]> 1500 <![CDATA[Unit cost of supercapacitor per unit capacity (yuan·(kWh)) -1 )]]> 2000 <![CDATA[Unit cost of lithium battery capacity / (yuan·(kWh) -1 )]]> 860 <![CDATA[Operation and maintenance cost of supercapacitor (yuan·(kWh) -1 )]]> 14 <![CDATA[Operating and maintenance cost of lithium battery / (yuan·(kWh) -1 )]]> 0.005 Supercapacitor lifespan (years) 20 Lithium battery lifespan / year 15 discount rate 0.06

[0112] Table 2. Capacity configuration and system benefits of hybrid energy storage systems before and after regulation.

[0113] Lithium-ion battery (kWh) Supercapacitor (kWh) System revenue (ten thousand yuan) Consider tie line power regulation 922 111 1.3733 Tie line power regulation not considered 909 111 1.143

[0114] 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.

[0115] 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.

[0116] 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'.

[0117] 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.

[0118] In this embodiment, at a certain period t, the power fluctuation of the interconnect line for,

[0119] .

[0120] In this embodiment, the average value of power fluctuation Calculated using the following formula:

[0121] .

[0122] 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.

[0123] Table 3 shows the average and maximum values ​​of tie-line power fluctuations for different values ​​of a.

[0124] a 0.05 0.1 0.15 0.2 0.25 0.3 0.4 0.5 (kW) 73 69 78 84 106 129 173 173 (kW) 6.35 8.93 9.97 10.83 11.31 11.85 12.71 13.15

[0125] 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 fluctuation of the tie line, and the smaller the impact on the transmission capacity of the tie line; while when... a The greater the value of the tie line power fluctuation exceeds 0.2, the faster the maximum value of the tie line power fluctuation changes. In other words, the larger the range of allowable tie line power fluctuation, the more points of large random power fluctuations occur in the tie line, which has a great impact on the tie line transmission capacity.

[0126] Meanwhile, in order to further explore a The impact of the value of on energy storage configuration and economics, a The results of energy storage configuration and annual revenue of the energy storage system are shown in Table 4, which show the results of different values.

[0127] Table 4. Relationship between different 'a' values ​​and energy storage configuration results and annual revenue of energy storage systems

[0128] 0.05 0.1 0.15 0.2 0.25 0.3 0.4 0.5 Supercapacitor capacity (kWh) 128 115 114 111 110 110 110 110 Lithium battery capacity (kWh) 1066 957 933 922 922 920 919 918 Annual income (ten thousand yuan) -0.369 1.566 1.671 1.3733 1.175 1.054 0.8763 0.8096

[0129] As can be seen from the table above, a The value of has a significant impact on the annual returns of energy storage. Meanwhile, when a When the value ranges from 0.05 to 0.2, it has a significant impact on energy storage configuration. a When the value is greater than 0.2, the impact on energy storage configuration is very small.

[0130] Therefore, in this embodiment, considering the average and maximum power fluctuations of the tie line, the configuration of energy storage, and the economics of energy storage, the following can be selected: a The range is 0.15-0.25.

[0131] 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.

[0132] Example 2

[0133] Combination Figure 13 As shown, this embodiment, based on embodiment 1, also provides a price-sensitive energy storage configuration method, which includes the following steps:

[0134] Step S1: Collect the transmission power of the current time t and m distribution network tie lines before the current time t at uniform intervals;

[0135] Step S2: Establish a power transmission regulation model for distribution network tie lines, and base it on the power fluctuation rate of the tie lines. Obtain the transmission power of the distribution network tie line after adjustment at the current time t. ;

[0136] Step S3: Based on the transmission power of the distribution network tie lines after regulation Calculate the power fluctuation rate of the tie line at the current time t. And based on the power volatility of the tie line Adjust electricity prices.

[0137] Through the above methods, electricity price-based regulation can be effectively achieved. By adjusting or compensating for electricity prices, users can be guided to change their electricity consumption behavior, ultimately achieving the goal of load regulation and peak shaving.

[0138] In step S2, the expression for the tie-line power control model is:

[0139] ;

[0140] in, Let be the actual power transmitted through the distribution network tie line at the current time t, and 'a' be a set parameter and 0. <a<1, This represents the power transmitted through the distribution network tie line at the i-th sampling time.

[0141] Similar to Example 1, the tie-line power regulation model established in this example can be based on the power fluctuation rate of the distribution network tie-line. Therefore, it can better ensure that the power fluctuation rate at the distribution network tie-line is kept within a limited range, thereby better reducing the situation where the power of the distribution network tie-line rises or falls sharply, and reducing the impact of the power fluctuation of the distribution network tie-line on the transmission capacity of the tie-line.

[0142] in,

[0143] ,

[0144] ;

[0145] Where m is the total number of samples, This represents the transmission power of the distribution network tie line during the ti-th control cycle.

[0146] Similar to Example 1, the above method can better calculate the power that needs to be smoothed by the hybrid energy storage system based on the power fluctuation rate as the energy storage smoothing index, and allocate the power that needs to be smoothed by the supercapacitor and the lithium battery respectively through the high-pass filtering algorithm.

[0147] In step S3, the electricity price adjustment formula is:

[0148] ;

[0149] in, The electricity price is adjusted at time t. Let t be the electricity price before the adjustment. For price volatility sensitivity, Set the adjustment base. Therefore, it can better control electricity prices.

[0150] in,

[0151] ;

[0152] in, The power fluctuation rate of the tie line before adjustment at the current time t, This represents the power fluctuation rate of the tie line at the previous sampling time t. Therefore, it can better control electricity prices.

[0153] Wherein, the value of 'a' ranges from 0.15 to 0.25. Similar to Example 1, this approach 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.

[0154] 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. A price-sensitive energy storage configuration method, comprising the following steps: Step S1: Collect the transmission power of the current time t and m distribution network tie lines before the current time t at uniform intervals; Step S2: Establish a power transmission regulation model for distribution network tie lines, and base it on the power fluctuation rate of the tie lines. Obtain the transmission power of the distribution network tie line after adjustment at the current time t. ; Step S3: Based on the transmission power of the distribution network tie lines after regulation Calculate the power fluctuation rate of the tie line at the current time t. And based on the power volatility of the tie line Adjust electricity prices; In step S2, the expression for the tie-line power control model is: ； in, Let be the actual power transmitted through the distribution network tie line at the current time t, and 'a' be a set parameter and 0. <a<1, This represents the power transmitted through the distribution network tie line at the i-th sampling time. ， ； Where m is the total number of samples, This represents the transmission power of the distribution network tie line during the ti-th control cycle; In step S3, the electricity price adjustment formula is: ； in, The electricity price is adjusted at time t. Let t be the electricity price before the adjustment. For price volatility sensitivity, To set the adjustment base, ; ； in, The power fluctuation rate of the tie line before adjustment at the current time t, is the power fluctuation rate of the tie line at the previous sampling time t; the value of a ranges from 0.15 to 0.25.

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