Power grid energy storage optimization configuration method, device, electronic equipment and storage medium

Abstract

The present invention belongs to the field of power grid energy storage configuration, and discloses a power grid energy storage optimization configuration method, device, electronic equipment and storage medium; the method comprises: obtaining power grid peak and valley period data; constructing an effective arbitrage duration calculation formula based on the power grid peak and valley period data; obtaining data related to extreme risk events in the power grid; constructing a multidimensional risk impact factor based on the data related to risk events in the power grid; constructing an internal rate of return implicit equation by integrating the effective arbitrage duration calculation formula and the multidimensional risk impact factor; converting the internal rate of return implicit equation into a linear equation to obtain an internal rate of return probability function; converting the internal rate of return probability function into a constraint condition, solving a pre-constructed optimization configuration model that comprehensively considers economic efficiency and power grid stability, and obtaining the optimized configuration scale of power grid energy storage; the present invention comprehensively considers economic efficiency and power grid stability, and can obtain an energy storage configuration scale that meets the requirements of economic efficiency and power grid stability.

Description

Technical Field

[0001] The present invention belongs to the field of power grid energy storage configuration, and in particular relates to a power grid energy storage optimization configuration method, device, electronic equipment and storage medium. Background Art

[0002] With the increasing penetration of renewable energy and the gradual improvement of electricity market mechanisms, the economic value of energy storage systems in scenarios such as peak-valley arbitrage and frequency regulation ancillary services is becoming increasingly prominent. However, the current scale configuration (capacity, power, etc.) of energy storage systems still faces many problems, which restrict their coordinated optimization of economic and technical efficiency. Numerous uncertainties and risks affect the accuracy of calculating the internal rate of return (IRR) over the entire life cycle of energy storage projects. Traditional deterministic IRR models are unable to accurately quantify the coupled impact of uncertainty risks and user behavior heterogeneity on returns, making it difficult for users to accurately assess the optimal energy storage configuration scale.

[0003] If the energy storage configuration cannot meet the needs of the power grid, the peak-shaving and valley-filling capabilities will be weak, which will increase the risk of instability in the safe and stable operation of the power grid. At the same time, it will not be possible to absorb new energy in a timely manner, resulting in energy waste. Summary of the Invention

[0004] The purpose of the present invention is to provide a method, device, electronic device and storage medium for optimizing the configuration of power grid energy storage, which can accurately optimize the configuration of power grid energy storage and meet the requirements of power grid energy storage configuration economy and power grid stability.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] In a first aspect, the present invention provides a method for optimizing energy storage configuration in a power grid, comprising:

[0007] Obtaining grid peak and valley period data; building an effective arbitrage duration calculation formula based on the grid peak and valley period data;

[0008] Obtain data related to extreme risk events in the power grid; construct multidimensional risk impact factors based on the data related to extreme risk events in the power grid;

[0009] The implicit equation of internal rate of return is constructed by combining the effective arbitrage period calculation formula and multi-dimensional risk influencing factors;

[0010] Convert the implicit internal rate of return equation into a linear equation to obtain the internal rate of return probability function;

[0011] The internal rate of return probability function is converted into constraint conditions, and a pre-built optimization configuration model that comprehensively considers economic efficiency and grid stability is solved to obtain the optimal configuration scale of grid energy storage.

[0012] A further improvement of the present invention is that the objective function of the optimization configuration model includes: minimizing configuration cost and maximizing grid stability;

[0013] The constraints of the optimization configuration model include: a maximum rated capacity constraint acceptable to users, a maximum rated power constraint acceptable to users, an actual grid frequency constraint, a grid node voltage constraint, and an internal rate of return constraint.

[0014] A further improvement of the present invention is that the optimization configuration model is:

[0015]

[0016] in, is the configuration cost, f2 is the grid stability function; Represents the start of the constraint execution, is the rated capacity of the energy storage system, is the target rated power, The maximum rated capacity acceptable to the user. The maximum rated power acceptable to the user; is the internal rate of return probability function; % represents the value of internal rate of return, and Y% represents the value of internal rate of return. % probability; Indicates the actual frequency of the power grid at time t; represents the voltage of the i-th node of the power grid at time t; is the frequency stability weight, is the voltage stability weight, is the power smoothness weight.

[0017] A further improvement of the present invention is that in the step of constructing an effective arbitrage duration calculation formula based on the power grid peak and valley period data, the effective arbitrage duration calculation formula is:

[0018]

[0019] in, represents the daily effective arbitrage duration that changes with time, where Charging power for the energy storage system, is the discharge power of the energy storage system, is the rated capacity of the energy storage system, is the time period weight factor; is the daily peak period of the power grid, They are the daily off-peak hours of the power grid.

[0020] A further improvement of the present invention is that in the step of constructing a multidimensional risk impact factor based on data related to extreme risk events in the power grid, the constructed multidimensional risk impact factor is:

[0021]

[0022] in, is the risk influencing factor, is the impact rate of progressive risk impact, is the annual probability of occurrence of the kth type of extreme risk event, is the impact intensity of the kth type of extreme risk event on returns; K is the total number of extreme risk events.

[0023] A further improvement of the present invention is that: in the step of constructing an implicit internal rate of return equation based on the comprehensive effective arbitrage time calculation formula and the multi-dimensional risk impact factors, a cash flow calculation formula is constructed based on the comprehensive effective arbitrage time calculation formula and the multi-dimensional risk impact factors; the cash flow calculation formula is calculated to obtain the net income R(t) in the tth year;

[0024] The implicit internal rate of return equation based on the net income R(t) in year t is:

[0025]

[0026] T represents the construction and operation years of the energy storage system in the power grid, is the net income in year t, and IRR is the internal rate of return.

[0027] A further improvement of the present invention is that: in the step of converting the internal rate of return implicit equation into a linear equation to obtain the internal rate of return probability function, at a known reference point Perform Taylor first-order expansion nearby to transform the implicit equation of internal rate of return into a linear equation:

[0028]

[0029] The internal rate of return probability function is obtained as:

[0030]

[0031] in, is the standard deviation of the internal rate of return, is the variance of the internal rate of return, is the mean internal rate of return, is the Euler number, and x is the internal rate of return.

[0032] A further improvement of the present invention is that: in the step of solving the pre-built optimal configuration model that comprehensively considers economy and grid stability and obtaining the optimal configuration scale of grid energy storage, by traversing the candidate energy storage configurations, The solution is performed to generate the internal rate of return distribution and grid stability under different energy storage configurations, and the energy storage configuration that meets the preset economic and grid stability requirements is used as the final optimized grid energy storage configuration scale.

[0033] A further improvement of the present invention is that, in the optimization configuration model:

[0034]

[0035] in, The unit cost of the initial investment in energy storage;

[0036]

[0037] in, is the frequency stability function, is the voltage stability function, is the power fluctuation smoothness function.

[0038] In a second aspect, the present invention provides a power grid energy storage optimization configuration device, comprising:

[0039] The first construction module is used to obtain power grid peak and valley period data; and to construct an effective arbitrage duration calculation formula based on the power grid peak and valley period data;

[0040] The second construction module is used to obtain data related to extreme risk events in the power grid; and construct multidimensional risk impact factors based on the data related to extreme risk events in the power grid;

[0041] The third construction module is used to construct an implicit internal rate of return equation by integrating the effective arbitrage period calculation formula and multi-dimensional risk influencing factors;

[0042] The fourth building block is used to transform the internal rate of return implicit equation into a linear equation to obtain the internal rate of return probability function;

[0043] The optimization configuration module is used to convert the internal rate of return probability function into constraint conditions, solve the pre-built optimization configuration model that comprehensively considers economic efficiency and grid stability, and obtain the optimal configuration scale of grid energy storage.

[0044] A further improvement of the present invention is that the objective function of the optimization configuration model includes: minimizing configuration cost and maximizing grid stability;

[0045] The constraints of the optimization configuration model include: a maximum rated capacity constraint acceptable to users, a maximum rated power constraint acceptable to users, an actual grid frequency constraint, a grid node voltage constraint, and an internal rate of return constraint.

[0046] A further improvement of the present invention is that the optimization configuration model is:

[0047]

[0048] in, is the configuration cost, f2 is the grid stability function; Represents the start of the constraint execution, is the target rated capacity, is the target rated power, The maximum rated capacity acceptable to the user. The maximum rated power acceptable to the user; is the internal rate of return probability function; % represents the value of internal rate of return, and Y% represents the value of internal rate of return. % probability; Indicates the actual frequency of the power grid at time t; represents the voltage of the i-th node of the power grid at time t; is the frequency stability weight, is the voltage stability weight, is the power smoothness weight.

[0049] A further improvement of the present invention is that the optimization configuration module is specifically configured to: traverse the energy storage candidate configurations, The solution is performed to generate the internal rate of return distribution and grid stability under different energy storage configurations, and the energy storage configuration that meets the preset economic and grid stability requirements is used as the final optimized grid energy storage configuration scale.

[0050] In a third aspect, the present invention provides an electronic device comprising a processor and a memory, wherein the processor is configured to execute a computer program stored in the memory to implement the method for optimizing configuration of power grid energy storage.

[0051] In a fourth aspect, the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores at least one instruction, and when the at least one instruction is executed by a processor, the power grid energy storage optimization configuration method is implemented.

[0052] Compared with the prior art, the present invention has the following beneficial effects:

[0053] The present invention provides a method for optimizing the configuration of power grid energy storage, comprising: obtaining power grid peak and valley period data; constructing an effective arbitrage duration calculation formula based on the power grid peak and valley period data; obtaining data related to extreme risk events in the power grid; constructing a multidimensional risk impact factor based on the data related to risk events in the power grid; constructing an implicit internal rate of return equation by integrating the effective arbitrage duration calculation formula and the multidimensional risk impact factor; converting the implicit internal rate of return equation into a linear equation to obtain an internal rate of return probability function; converting the internal rate of return probability function into a constraint condition, solving a pre-constructed optimization configuration model that comprehensively considers economic efficiency and power grid stability, and obtaining an optimized configuration scale of power grid energy storage. The present invention comprehensively considers the effective arbitrage duration and uncertainty risk, improves the accuracy of IRR calculation, converts the pre-constructed optimization configuration model that comprehensively considers economic efficiency and power grid stability into a constraint condition solution based on the optimized IRR, and thus obtains an energy storage configuration scale that meets the requirements of economic efficiency and power grid stability.

[0054] Furthermore, the present invention constructs a dynamic evaluation framework based on uncertainty parameters, and measures the uncertainty distribution of IRR by introducing parameters such as risk factors, electricity price fluctuation covariance, and user preference weight function.

[0055] Furthermore, the present invention transforms the static peak-valley period assumption into a dynamically adjustable time function, solving the problem of overestimation or underestimation of arbitrage time caused by traditional models ignoring market dynamic changes.

[0056] Furthermore, the present invention introduces a sensitivity coefficient for dividing peak and valley periods, and incorporates the user's risk preference into the calculation of IRR.

[0057] Furthermore, traditional models ignore uncertainty risks or only use scenario analysis, while the present invention unifies the modeling of continuous and discrete risk shocks, and innovatively introduces probabilistic risk discounting into IRR calculations.

[0058] Furthermore, the present invention incorporates market volatility and uncertainty risks into the same multiplicative framework, avoiding the risk of traditional models underestimating the synergistic effects of multiple factors.

[0059] Furthermore, the present invention comprehensively considers the effective arbitrage period and uncertainty risk, improves the accuracy of IRR calculation, and then optimizes the configuration scale of energy storage to find the energy storage configuration scale with the highest return rate; at the same time, it takes into account the user's requirements for grid stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] The accompanying drawings, which constitute part of the present invention, are intended to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are intended to explain the present invention and do not constitute an undue limitation of the present invention. In the accompanying drawings:

[0061] Figure 1 A schematic diagram of a flow chart of a method for optimizing energy storage configuration in a power grid according to an embodiment of the present invention;

[0062] Figure 2 A schematic flow chart of a method for optimizing power grid energy storage configuration according to another embodiment of the present invention;

[0063] Figure 3 This is a structural diagram of a power grid energy storage optimization configuration device according to the present invention;

[0064] Figure 4 The figure is a schematic structural diagram of an electronic device of the present invention. DETAILED DESCRIPTION

[0065] The present invention will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that, in the absence of conflict, the embodiments and features of the embodiments of the present invention can be combined with each other.

[0066] The following detailed description is an exemplary description and is intended to provide further detailed description of the present invention. Unless otherwise indicated, all technical terms used in the present invention have the same meaning as those generally understood by those skilled in the art to which the present invention belongs. The terms used in the present invention are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present invention.

[0067] See also Figure 1 As shown, an embodiment of the present invention provides a method for optimizing energy storage configuration in a power grid, comprising the following steps:

[0068] S1: Establish a dynamic change model for electricity price peak and valley periods to quantify the effective arbitrage duration for users with future changes;

[0069] In order to solve the impact of future spot market electricity price fluctuations on IRR calculation, considering the impact of changes in market conditions such as electricity prices on users' peak-valley arbitrage duration, the concept of dynamic peak-valley duration is introduced to dynamically capture changes in peak-valley periods.

[0070] S11. First, define the peak and valley period values ​​of electricity prices:

[0071]

[0072] in, is the electricity price at time t, is the daily average electricity price, k is the sensitivity coefficient, which is used to reflect the user's control over the tightness of the definition of peak and valley periods (input by the user), is the daily standard deviation of electricity price.

[0073] S12. Construct the calculation formula for effective arbitrage duration:

[0074] After defining and expressing the peak and valley periods of the electricity spot market using formulas, based on the duration of the peak and valley periods 、 A formula for calculating the effective arbitrage duration was constructed, taking into account the impact of different dates on the arbitrage duration and adding a date weighting factor. The constraints considered in constructing the effective arbitrage duration calculation formula include: the energy storage system's charging energy during off-peak periods is limited by the off-peak charging duration and storage capacity, while the discharge duration is limited by the energy release rate and the duration of the peak period.

[0075]

[0076] in, represents the daily effective arbitrage duration that changes with time, where Charging power for the energy storage system, is the discharge power of the energy storage system, is the rated capacity of the energy storage system, is the time period weight factor, where W is the non-summer working day =1.0, non-summer holiday W =0.6, summer working day W =1.2, summer holiday W =1.0.

[0077] S2: Considering the existence of uncertain continuous or discrete risks, construct multidimensional risk influencing factors.

[0078] In order to consider the impact of continuous or discrete risks such as price controls and carbon tax increases on electricity prices that may arise during the development of the power market, a multidimensional risk impact factor is constructed. An exponential term is used to simulate the gradual nature of the impact of continuous risks. For example, subsidy reductions usually decrease in steps on an annual basis, and the exponential form can be used to fit a smooth attenuation. A Poisson-Gamma composite term is used to simulate the probability and impact intensity of discrete risk impacts, and its expected loss is quantified through weighted summation. The constructed multidimensional risk impact factor is:

[0079]

[0080] in, is the risk impact factor, ranging from 0 to 1, the smaller the value, the greater the risk. is the impact rate of the gradual risk impact (if the gradual risk is subsidy reduction, represents the annual subsidy reduction rate). is the annual probability of occurrence of the kth type of extreme risk event, is the impact intensity of the kth type of extreme risk event on returns; K is the total number of extreme risk events. 、 、 It can be retrieved from the Internet, or obtained from a preset database.

[0081] S3: Taking into account the effective arbitrage period and risk factors, construct a cash flow calculation formula and IRR implicit equation.

[0082] IRR is the discount rate that makes the project's Net Present Value (NPV) equal to zero. Due to the influence of the above factors, the project's cash flow is uncertain. Therefore, IRR itself becomes a random variable, and its distribution needs to reflect the uncertainty under the influence of the above uncertain factors. Taking into account the effective arbitrage period and risk factors, the expression for IRR is constructed:

[0083] (1) Construct a cash flow calculation formula, net income in year t for:

[0084]

[0085] in, is the net income in year t, For initial investment, represents the peak-to-valley price difference in year t, is the rated capacity of the energy storage system, is the annual capacity attenuation rate of the battery in the energy storage system, is the depth of charge and discharge of the battery, represents the annual average of the effective arbitrage duration over time, is the annual average value of the risk impact factor, is the dynamic cost in year t.

[0086] (2) The implicit equation for IRR based on cash flow is:

[0087] Based on the relationship between cash flow and IRR, an implicit equation for IRR is constructed. Since the equation is nonlinear, IRR cannot be directly solved as an explicit function of risk factors.

[0088]

[0089] T represents the construction and operation period of the energy storage system in the power grid, and t represents the tth year.

[0090] S4: The IRR implicit equation is converted to By expanding it, the nonlinear equation is transformed into an approximate linear equation, and the "probability calculation function of IRR of energy storage investment that comprehensively considers the effective arbitrage period and uncertainty risk" is derived.

[0091] In order to obtain the analytical solution of IRR, that is, to directly derive the probability density function of IRR through mathematical formulas, it is necessary to make the implicit equation of IRR (that is, the equation with NPV=0) explicit. Therefore, the implicit equation of IRR is expanded at the reference point and the nonlinear equation is converted into an approximate linear equation.

[0092] Assume that the IRR is at the reference point Fluctuates slightly around the known reference point Perform Taylor first-order expansion near the equation to linearize the implicit equation of IRR:

[0093]

[0094] It follows that the IRR probability function is:

[0095]

[0096] in, is the standard deviation of IRR, is the variance of IRR, is the mean of IRR, is the Euler number, and x is the internal rate of return (IRR).

[0097] S5: Convert the probability distribution of IRR into constraints, set the objective function with the goal of minimizing configuration costs and improving multi-dimensional grid stability, and achieve dynamic adjustment through the opportunity constraint model.

[0098] (1) Convert the probability distribution of IRR into constraints and construct the objective function that minimizes the configuration cost:

[0099] Objective function 1:

[0100]

[0101] Constraints:

[0102]

[0103] in, is the initial investment, i.e. configuration cost, is the rated capacity of the energy storage system, is the unit cost of the initial investment in energy storage, % represents the value of IRR, and Y% represents the value of IRR % probability.

[0104] (2) Constructing the objective function for improving the multi-dimensional grid stability and the constraints for the safe operation of the grid:

[0105] Objective function 2:

[0106]

[0107] in, is the frequency stability function, which indicates the frequency fluctuation caused by the change of energy storage scale. ∈[0,1], the larger the value, the better the energy storage suppresses frequency fluctuations; the frequency stability function The expression is:

[0108]

[0109] is the voltage stability function, which indicates the voltage fluctuation caused by the change of energy storage scale. ∈[0,1], the larger the value, the smaller the voltage fluctuation; voltage stability function The expression is:

[0110]

[0111] is the power fluctuation smoothness function, which represents the impact of energy storage scale changes on grid smoothing. ∈(-∞,1], the larger the value, the better the smoothing effect; power fluctuation smoothness function:

[0112]

[0113] Constraints:

[0114] 1) Weight constraint:

[0115]

[0116] 2) Grid security constraints:

[0117]

[0118]

[0119] in, represents the actual frequency of the power grid at time t (Hz), is the statistical time window (such as 24 hours, 1 month), represents the voltage of the ith node of the grid at time t, is the nominal voltage, represents the number of key nodes in the power grid, Represents the standard deviation of the original output of renewable energy (photovoltaic, wind power), Represents the standard deviation of grid-connected power after energy storage intervention. is the frequency stability weight, is the voltage stability weight, is the power smoothness weight.

[0120] (3) Taking into account the economic and grid stability goals, the final optimization configuration model is obtained:

[0121]

[0122] in, Represents the start of the constraint execution, is the rated capacity of the energy storage system (target rated capacity), is the target rated power, The maximum rated capacity acceptable to the user. It is the maximum rated power acceptable to the user.

[0123] By traversing the candidate energy storage configurations, The solution is obtained and the IRR distribution and grid stability under different configurations are generated. Based on the above constrained optimization model, the energy storage configuration scale that meets the economic and grid stability requirements is found under the joint action of the objective function and constraints.

[0124] See also Figure 2 As shown, an embodiment of the present invention provides a method for optimizing energy storage configuration in a power grid, comprising:

[0125] S100, obtaining power grid peak and valley period data; constructing an effective arbitrage duration calculation formula based on the power grid peak and valley period data;

[0126] S200, obtaining data related to extreme risk events in the power grid; constructing a multidimensional risk impact factor based on the data related to extreme risk events in the power grid;

[0127] S300, the internal rate of return implicit equation is constructed by integrating the effective arbitrage period calculation formula and multi-dimensional risk influencing factors;

[0128] S400, converting the internal rate of return implicit equation into a linear equation to obtain the internal rate of return probability function;

[0129] S500: Convert the internal rate of return probability function into constraint conditions, solve a pre-built optimization configuration model that comprehensively considers economic efficiency and grid stability, and obtain the optimal configuration scale of grid energy storage.

[0130] In a specific embodiment, the objective function of the optimization configuration model includes: minimizing configuration cost and maximizing grid stability;

[0131] The constraints of the optimization configuration model include: a maximum rated capacity constraint acceptable to users, a maximum rated power constraint acceptable to users, an actual grid frequency constraint, a grid node voltage constraint, and an internal rate of return constraint.

[0132] In a specific embodiment, the optimization configuration model is:

[0133]

[0134] in, is the configuration cost, f2 is the grid stability function; Represents the start of the constraint execution, is the rated capacity of the energy storage system, is the target rated power, The maximum rated capacity acceptable to the user. The maximum rated power acceptable to the user; is the internal rate of return probability function; % represents the value of internal rate of return, and Y% represents the value of internal rate of return. % probability; Indicates the actual frequency of the power grid at time t; represents the voltage of the i-th node of the power grid at time t; is the frequency stability weight, is the voltage stability weight, is the power smoothness weight.

[0135] In a specific embodiment, the peak and valley period data of the power grid are obtained:

[0136]

[0137] in, is the electricity price at time t, is the daily average electricity price, k is the sensitivity coefficient, which is used to reflect the user's control over the tightness of the definition of peak and valley periods (input by the user), is the daily standard deviation of electricity price.

[0138] In a specific embodiment, in the step of constructing an effective arbitrage duration calculation formula based on the power grid peak and valley period data, the effective arbitrage duration calculation formula is:

[0139]

[0140] in, represents the daily effective arbitrage duration that changes with time, where Charging power for the energy storage system, is the discharge power of the energy storage system, is the rated capacity of the energy storage system, is the time period weight factor, non-summer working days W =1.0, non-summer holiday W =0.6, summer working day W =1.2, summer holiday W =1.0; is the daily peak period of the power grid, They are the daily off-peak hours of the power grid.

[0141] In a specific embodiment, obtaining data related to extreme risk events in the power grid includes: obtaining the total number K of extreme risk event types, the impact intensity of each type of extreme risk event on revenue, and the annual probability of occurrence of each type of extreme risk event.

[0142] In a specific embodiment, in the step of constructing a multidimensional risk impact factor based on data related to extreme risk events in the power grid, the constructed multidimensional risk impact factor is:

[0143]

[0144] in, is the risk influencing factor, is the impact rate of progressive risk impact, is the annual probability of occurrence of the kth type of extreme risk event, is the impact intensity of the kth type of extreme risk event on returns; K is the total number of extreme risk events.

[0145] In a specific embodiment, in the step of constructing an implicit internal rate of return equation based on the comprehensive effective arbitrage period calculation formula and the multi-dimensional risk impact factors, a cash flow calculation formula is constructed based on the comprehensive effective arbitrage period calculation formula and the multi-dimensional risk impact factors; the cash flow calculation formula is calculated to obtain the net income R(t) in the tth year;

[0146] The implicit internal rate of return equation based on the net income R(t) in year t is:

[0147]

[0148] T represents the construction and operation years of the energy storage system in the power grid, is the net income in year t, and IRR is the internal rate of return.

[0149] In one embodiment, the net income in year t for:

[0150]

[0151] in, is the net income in year t, For initial investment, represents the peak-to-valley price difference in year t, is the rated capacity of the energy storage system, is the annual capacity attenuation rate of the battery in the energy storage system, is the depth of charge and discharge of the battery, represents the annual average of the effective arbitrage duration over time, Risk influencing factors The annual average value of is the dynamic cost in year t.

[0152] In a specific embodiment, in the step of converting the internal rate of return implicit equation into a linear equation to obtain the internal rate of return probability function, at a known reference point Perform Taylor first-order expansion nearby to transform the implicit equation of internal rate of return into a linear equation:

[0153]

[0154] The internal rate of return probability function is obtained as:

[0155]

[0156] in, is the standard deviation of the internal rate of return, is the variance of the internal rate of return, is the mean internal rate of return, is the Euler number, and x is the value of IRR.

[0157] In a specific embodiment, in the step of solving the pre-built optimization configuration model that comprehensively considers economy and grid stability to obtain the optimal configuration scale of grid energy storage, by traversing the candidate energy storage configurations, The solution is performed to generate the internal rate of return distribution and grid stability under different energy storage configurations, and the energy storage configuration that meets the preset economic and grid stability requirements is used as the final optimized grid energy storage configuration scale.

[0158] In a specific embodiment, the objective function for minimizing the configuration cost is constructed as follows:

[0159]

[0160] Constraints:

[0161]

[0162] in, is the initial investment, i.e. configuration cost, is the rated capacity of the energy storage system, is the unit cost of the initial investment in energy storage, % represents the value of IRR, and Y% represents the value of IRR % probability.

[0163] The objective function for improving the multi-dimensional grid stability is:

[0164]

[0165] in, is the frequency stability function, which indicates the frequency fluctuation caused by the change of energy storage scale. ∈[0,1], the larger the value, the better the energy storage suppresses frequency fluctuations; the frequency stability function The expression is:

[0166]

[0167] is the voltage stability function, which indicates the voltage fluctuation caused by the change of energy storage scale. ∈[0,1], the larger the value, the smaller the voltage fluctuation; voltage stability function The expression is:

[0168]

[0169] is the power fluctuation smoothness function, which represents the impact of energy storage scale changes on grid smoothing. ∈(-∞,1], the larger the value, the better the smoothing effect; power fluctuation smoothness function:

[0170]

[0171] Constraints:

[0172] 1) Weight constraint:

[0173]

[0174] 2) Grid security constraints:

[0175]

[0176]

[0177] in, represents the actual frequency of the power grid at time t (Hz), is the statistical time window (such as 24 hours, 1 month), represents the voltage of the ith node of the grid at time t, is the nominal voltage, represents the number of key nodes in the power grid, Represents the standard deviation of the original output of renewable energy (photovoltaic, wind power), Represents the standard deviation of grid-connected power after energy storage intervention. is the frequency stability weight, is the voltage stability weight, is the power smoothness weight.

[0178] See also Figure 3 As shown, the present invention provides a power grid energy storage optimization configuration device, comprising:

[0179] The first construction module is used to obtain power grid peak and valley period data; and to construct an effective arbitrage duration calculation formula based on the power grid peak and valley period data;

[0180] The second construction module is used to obtain data related to extreme risk events in the power grid; and construct multidimensional risk impact factors based on the data related to extreme risk events in the power grid;

[0181] The third construction module is used to construct an implicit internal rate of return equation by integrating the effective arbitrage period calculation formula and multi-dimensional risk influencing factors;

[0182] The fourth building block is used to transform the internal rate of return implicit equation into a linear equation to obtain the internal rate of return probability function;

[0183] The optimization configuration module is used to convert the internal rate of return probability function into constraint conditions, solve the pre-built optimization configuration model that comprehensively considers economic efficiency and grid stability, and obtain the optimal configuration scale of grid energy storage.

[0184] In a specific embodiment, the objective function of the optimization configuration model includes: minimizing configuration cost and maximizing grid stability;

[0185] The constraints of the optimization configuration model include: a maximum rated capacity constraint acceptable to users, a maximum rated power constraint acceptable to users, an actual grid frequency constraint, a grid node voltage constraint, and an internal rate of return constraint.

[0186] In a specific embodiment, the optimization configuration model is:

[0187]

[0188] in, is the configuration cost, f2 is the grid stability function; Represents the start of the constraint execution, is the rated capacity of the energy storage system, is the target rated power, The maximum rated capacity acceptable to the user. The maximum rated power acceptable to the user; is the internal rate of return probability function; % represents the value of internal rate of return, and Y% represents the value of internal rate of return. % probability; Indicates the actual frequency of the power grid at time t; represents the voltage of the i-th node of the power grid at time t; is the frequency stability weight, is the voltage stability weight, is the power smoothness weight.

[0189] In a specific embodiment, the peak and valley period data of the power grid are obtained:

[0190]

[0191] in, is the electricity price at time t, is the daily average electricity price, k is the sensitivity coefficient, which is used to reflect the user's control over the tightness of the definition of peak and valley periods (input by the user), is the daily standard deviation of electricity price.

[0192] In a specific embodiment, in the step of constructing an effective arbitrage duration calculation formula based on the power grid peak and valley period data, the effective arbitrage duration calculation formula is:

[0193]

[0194] in, represents the daily effective arbitrage duration that changes with time, where Charging power for the energy storage system, is the discharge power of the energy storage system, is the rated capacity of the energy storage system, is the time period weight factor, non-summer working days W =1.0, non-summer holiday W =0.6, summer working day W =1.2, summer holiday W =1.0; is the daily peak period of the power grid, They are the daily off-peak hours of the power grid.

[0195] In a specific embodiment, obtaining data related to extreme risk events in the power grid includes: obtaining the total number K of extreme risk event types, the impact intensity of each type of extreme risk event on revenue, and the annual probability of occurrence of each type of extreme risk event.

[0196] In a specific embodiment, in the step of constructing a multidimensional risk impact factor based on data related to extreme risk events in the power grid, the constructed multidimensional risk impact factor is:

[0197]

[0198] in, is the risk influencing factor, is the impact rate of progressive risk impact, is the annual probability of occurrence of the kth type of extreme risk event, is the impact intensity of the kth type of extreme risk event on returns; K is the total number of extreme risk events.

[0199] In a specific embodiment, in the step of constructing an implicit internal rate of return equation based on the comprehensive effective arbitrage period calculation formula and the multi-dimensional risk impact factors, a cash flow calculation formula is constructed based on the comprehensive effective arbitrage period calculation formula and the multi-dimensional risk impact factors; the cash flow calculation formula is calculated to obtain the net income R(t) in the tth year;

[0200] The implicit internal rate of return equation based on the net income R(t) in year t is:

[0201]

[0202] T represents the construction and operation years of the energy storage system in the power grid, is the net income in year t, and IRR is the internal rate of return.

[0203] In one embodiment, the net income in year t for:

[0204]

[0205] in, is the net income in year t, For initial investment, represents the peak-to-valley price difference in year t, is the rated capacity of the energy storage system, is the annual capacity attenuation rate of the battery in the energy storage system, is the depth of charge and discharge of the battery, represents the annual average of the effective arbitrage duration over time, Risk influencing factors The annual average value of is the dynamic cost in year t.

[0206] In a specific embodiment, in the step of converting the internal rate of return implicit equation into a linear equation to obtain the internal rate of return probability function, at a known reference point Perform Taylor first-order expansion nearby to transform the implicit equation of internal rate of return into a linear equation:

[0207]

[0208] The internal rate of return probability function is obtained as:

[0209]

[0210] in, is the standard deviation of the internal rate of return, is the variance of the internal rate of return, is the mean internal rate of return, is the Euler number, and x is the value of IRR.

[0211] In a specific embodiment, in the step of solving the pre-built optimization configuration model that comprehensively considers economy and grid stability to obtain the optimal configuration scale of grid energy storage, by traversing the candidate energy storage configurations, The solution is performed to generate the internal rate of return distribution and grid stability under different energy storage configurations, and the energy storage configuration that meets the preset economic and grid stability requirements is used as the final optimized grid energy storage configuration scale.

[0212] In a specific embodiment, the objective function for minimizing the configuration cost is constructed as follows:

[0213]

[0214] Constraints:

[0215]

[0216] in, is the initial investment, i.e. configuration cost, is the rated capacity of the energy storage system, is the unit cost of the initial investment in energy storage, % represents the value of IRR, and Y% represents the value of IRR % probability.

[0217] The objective function for improving the multi-dimensional grid stability is:

[0218]

[0219] in, is the frequency stability function, which indicates the frequency fluctuation caused by the change of energy storage scale. ∈[0,1], the larger the value, the better the energy storage suppresses frequency fluctuations; the frequency stability function The expression is:

[0220]

[0221] is the voltage stability function, which indicates the voltage fluctuation caused by the change of energy storage scale. ∈[0,1], the larger the value, the smaller the voltage fluctuation; voltage stability function The expression is:

[0222]

[0223] is the power fluctuation smoothness function, which represents the impact of energy storage scale changes on grid smoothing. ∈(-∞,1], the larger the value, the better the smoothing effect; power fluctuation smoothness function:

[0224]

[0225] Constraints:

[0226] 1) Weight constraint:

[0227]

[0228] 2) Grid security constraints:

[0229]

[0230]

[0231] in, represents the actual frequency of the power grid at time t (Hz), is the statistical time window (such as 24 hours, 1 month), represents the voltage of the ith node of the grid at time t, is the nominal voltage, represents the number of key nodes in the power grid, Represents the standard deviation of the original output of renewable energy (photovoltaic, wind power), Represents the standard deviation of grid-connected power after energy storage intervention. is the frequency stability weight, is the voltage stability weight, is the power smoothness weight.

[0232] See also Figure 4 As shown, an embodiment of the present invention provides an electronic device 100 for implementing a method for optimizing configuration of power grid energy storage; the electronic device 100 includes a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and executable on the at least one processor 102, and at least one communication bus 104.

[0233] The memory 101 can be used to store the computer program 103. The processor 102 implements the steps of the power grid energy storage optimization configuration method by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101. The memory 101 can mainly include a program storage area and a data storage area. The program storage area can store an operating system, at least one application required for a function (such as a sound playback function, an image playback function, etc.); the data storage area can store data (such as audio data) created based on the use of the electronic device 100. In addition, the memory 101 can include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (SmartMedia Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device.

[0234] The at least one processor 102 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor 102 may be a microprocessor or any conventional processor, etc. The processor 102 is the control center of the electronic device 100 and connects various parts of the entire electronic device 100 using various interfaces and lines.

[0235] The memory 101 in the electronic device 100 stores a plurality of instructions to implement a method for optimizing energy storage configuration of a power grid. The processor 102 can execute the plurality of instructions to implement:

[0236] Obtaining grid peak and valley period data; building an effective arbitrage duration calculation formula based on the grid peak and valley period data;

[0237] Obtain data related to extreme risk events in the power grid; construct multidimensional risk impact factors based on the data related to extreme risk events in the power grid;

[0238] The implicit equation of internal rate of return is constructed by combining the effective arbitrage period calculation formula and multi-dimensional risk influencing factors;

[0239] Convert the implicit internal rate of return equation into a linear equation to obtain the internal rate of return probability function;

[0240] The internal rate of return probability function is converted into constraints to solve the pre-built optimization configuration model that comprehensively considers economic efficiency and grid stability, and obtain the optimal configuration scale of grid energy storage;

[0241] The optimized configuration model is:

[0242]

[0243] in, is the configuration cost, f2 is the grid stability function; Represents the start of the constraint execution, is the rated capacity of the energy storage system, is the target rated power, The maximum rated capacity acceptable to the user. The maximum rated power acceptable to the user; is the internal rate of return probability function; % represents the value of internal rate of return, and Y% represents the value of internal rate of return. % probability; Indicates the actual frequency of the power grid at time t; represents the voltage of the i-th node of the power grid at time t; is the frequency stability weight, is the voltage stability weight, is the power smoothness weight.

[0244] If the module / unit integrated in the electronic device 100 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the process in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory and read-only memory (ROM, Read-Only Memory).

[0245] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0246] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0247] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0248] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0249] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. A method for optimizing the configuration of power grid energy storage, characterized in that: include: Obtain power grid peak and valley period data; Build a calculation formula for effective arbitrage duration based on power grid peak and valley period data; Obtain data related to extreme risk events in the power grid; Construct multidimensional risk impact factors based on data related to extreme risk events in the power grid; The implicit equation of internal rate of return is constructed by combining the effective arbitrage period calculation formula and multi-dimensional risk influencing factors; Convert the implicit internal rate of return equation into a linear equation to obtain the internal rate of return probability function; The internal rate of return probability function is converted into constraints to solve the pre-built optimization configuration model that comprehensively considers economic efficiency and grid stability, and obtain the optimal configuration scale of grid energy storage; The objective functions of the optimization configuration model include: minimizing configuration cost and maximizing grid stability; The constraints of the optimization configuration model include: a maximum rated capacity constraint acceptable to the user, a maximum rated power constraint acceptable to the user, an actual frequency constraint of the power grid, a voltage constraint of the power grid node, and an internal rate of return constraint; In the step of constructing an effective arbitrage duration calculation formula based on the power grid peak and valley period data, the effective arbitrage duration calculation formula is: in, represents the daily effective arbitrage duration that changes with time, where Charging power for the energy storage system, is the discharge power of the energy storage system, is the rated capacity of the energy storage system, is the time period weight factor; is the daily peak period of the power grid, are the daily valley period duration of the power grid; In the step of constructing a multidimensional risk impact factor based on the data related to extreme risk events in the power grid, the constructed multidimensional risk impact factor is: in, is the risk influencing factor, is the impact rate of progressive risk impact, is the annual probability of occurrence of the kth type of extreme risk event, is the impact intensity of the kth type of extreme risk event on returns; K is the total number of extreme risk events.

2. The method for optimizing power grid energy storage configuration according to claim 1, characterized in that: The optimization configuration model is: in, is the configuration cost, f2 is the grid stability function; Represents the start of the constraint execution, is the rated capacity of the energy storage system, is the target rated power, The maximum rated capacity acceptable to the user. The maximum rated power acceptable to the user; is the internal rate of return probability function; % represents the value of internal rate of return, and Y% represents the value of internal rate of return. % probability; Indicates the actual frequency of the power grid at time t; represents the voltage of the i-th node of the power grid at time t; is the frequency stability weight, is the voltage stability weight, is the power smoothness weight.

3. The method for optimizing the configuration of power grid energy storage according to claim 1, characterized in that: In the step of constructing an implicit internal rate of return equation based on the comprehensive effective arbitrage time calculation formula and multi-dimensional risk impact factors, a cash flow calculation formula is constructed based on the comprehensive effective arbitrage time calculation formula and multi-dimensional risk impact factors; Calculate the cash flow formula to obtain the net income R(t) in year t; The implicit internal rate of return equation based on the net income R(t) in year t is: T represents the construction and operation years of the energy storage system in the power grid, is the net income in year t, and IRR is the internal rate of return.

4. The method for optimizing the configuration of power grid energy storage according to claim 2, characterized in that: In the optimization configuration model: in, The unit cost of the initial investment in energy storage; in, is the frequency stability function, is the voltage stability function, is the power fluctuation smoothness function.

5. A power grid energy storage optimization configuration device, characterized in that: include: The first building block is used to obtain power grid peak and valley period data; Build a calculation formula for effective arbitrage duration based on power grid peak and valley period data; The second building block is used to obtain data related to extreme risk events in the power grid; Construct multidimensional risk impact factors based on data related to extreme risk events in the power grid; The third construction module is used to construct an implicit internal rate of return equation by integrating the effective arbitrage period calculation formula and multi-dimensional risk influencing factors; The fourth building block is used to transform the internal rate of return implicit equation into a linear equation to obtain the internal rate of return probability function; The optimization configuration module is used to convert the internal rate of return probability function into constraints, solve the pre-built optimization configuration model that comprehensively considers economic efficiency and grid stability, and obtain the optimal configuration scale of grid energy storage; The objective functions of the optimization configuration model include: minimizing configuration cost and maximizing grid stability; The constraints of the optimization configuration model include: a maximum rated capacity constraint acceptable to the user, a maximum rated power constraint acceptable to the user, an actual frequency constraint of the power grid, a voltage constraint of the power grid node, and an internal rate of return constraint; In the step of constructing an effective arbitrage duration calculation formula based on the power grid peak and valley period data, the effective arbitrage duration calculation formula is: in, represents the daily effective arbitrage duration that changes with time, where Charging power for the energy storage system, is the discharge power of the energy storage system, is the rated capacity of the energy storage system, is the time period weight factor; is the daily peak period of the power grid, are the daily valley period duration of the power grid; In the step of constructing a multidimensional risk impact factor based on the data related to extreme risk events in the power grid, the constructed multidimensional risk impact factor is: in, is the risk influencing factor, is the impact rate of progressive risk impact, is the annual probability of occurrence of the kth type of extreme risk event, is the impact intensity of the kth type of extreme risk event on returns; K is the total number of extreme risk events.

6. An electronic device, characterized in that: It comprises a processor and a memory, wherein the processor is used to execute a computer program stored in the memory to implement the power grid energy storage optimization configuration method as described in any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that The computer-readable storage medium stores at least one instruction, and when the at least one instruction is executed by a processor, the power grid energy storage optimization configuration method according to any one of claims 1 to 4 is implemented.

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