Fuzzy random bilevel robust optimization method and device for energy storage frequency regulation transaction

Abstract

The application discloses a fuzzy random double-layer robust optimization method and device for energy storage frequency modulation transaction, determines an uncertainty variable of output power of a new energy unit according to output power information of the new energy unit; constructs a fuzzy random double-layer robust optimization model of participation of an energy storage battery in primary frequency modulation based on the uncertainty variable; and uses a column and constraint generation algorithm to operate the fuzzy random double-layer robust optimization model according to a preset constraint condition until a preset objective function reaches a minimum value, so as to obtain an output result of the fuzzy random double-layer robust optimization model. Thus, on the basis of considering randomness and volatility of new energy generation and charging and discharging of the energy storage battery, market electricity price of the energy storage battery in cooperative frequency modulation and primary frequency modulation power consumption are determined from an economic perspective, thereby providing necessary technical support for safe and stable operation of a power system.

Description

Technical Field

[0001] This application relates to the field of power system technology, and in particular to a fuzzy stochastic two-layer robust optimization method and apparatus for energy storage frequency regulation trading. Background Technology

[0002] A distributed generation system (DPS) is a system characterized by complex and interactive relationships involving both random and fuzzy uncertainties in events or parameters. Currently, the power generation and output of DPS systems are typically calculated using deterministic methods or probabilistic uncertainty-based methods. Deterministic methods usually assume that the inflow and flow rates of small hydropower stations, regional solar radiation intensity, and wind speed are all fixed, but the results are unique and deterministic, failing to accurately reflect the actual power generation, output, and capacity of the DPS system. Probabilistic uncertainty-based methods, on the other hand, typically assume only a single factor—such as the inflow and flow rates of small hydropower stations, regional solar radiation intensity, and wind speed—as uncertainties, providing probabilistic values ​​with a certain level of confidence.

[0003] However, in reality, the power generation, output, and installed capacity of distributed generation systems in power grids are affected by various uncertainties, which typically exhibit random or fuzzy uncertainties, usually existing as random and fuzzy uncertain events or parameters. Current calculation methods do not consider the uncertainty and randomness of these influencing factors, resulting in poor accuracy in power grid frequency regulation control. Summary of the Invention

[0004] This application provides a fuzzy stochastic two-layer robust optimization method and apparatus for energy storage frequency regulation trading, in order to solve the technical problem of poor accuracy of grid frequency regulation control caused by the uncertainty and randomness of influencing factors not being considered in the current calculation of power generation information.

[0005] To address the aforementioned technical problems, firstly, this application provides a fuzzy stochastic two-layer robust optimization method for energy storage frequency regulation trading, comprising:

[0006] Based on the output power information of the new energy generating units, determine the uncertain variables of the output power of the new energy generating units;

[0007] Based on the aforementioned uncertainties, a fuzzy stochastic two-layer robust optimization model is constructed for energy storage batteries participating in primary frequency regulation. The first-layer optimization variable of the fuzzy stochastic two-layer robust optimization model is the bid price for the output power of the energy storage battery, and the second-layer optimization variable is the bid amount for the output power of the energy storage battery.

[0008] Using a column and constraint generation algorithm, the fuzzy stochastic two-layer robust optimization model is calculated according to preset constraints until the preset objective function reaches its minimum value, and the output result of the fuzzy stochastic two-layer robust optimization model is obtained. The output result includes the market clearing price of the energy storage battery participating in primary frequency regulation and the service power of the energy storage battery output power.

[0009] In some implementations, determining the uncertainty variable of the output power of the new energy unit based on its output power information includes:

[0010] Obtain power generation influencing factor data of the new energy unit, including historical wind speed data and / or historical sunshine data;

[0011] Statistical analysis of the data on factors affecting power generation is performed to obtain the output power information of the new energy unit;

[0012] Based on a preset inequality, an uncertainty variable for the output power of the new energy unit is constructed.

[0013] In some implementations, the expression for the fuzzy stochastic two-layer robust optimization model is:

[0014]

[0015]

[0016] Among them, c T Let y represent the column vector of uncertainties in the output power of the new energy unit, where x represents the first-level optimization variables, u1 represents the charging power of energy storage participating in primary frequency regulation, u2 represents the discharging power of energy storage participating in primary frequency regulation, and y represents the variable y. u Let y(u1, u2) represent the output power and service capacity of the battery energy storage station operator, and d represent the second-level optimization variables. T H1~H represents the second-level optimization variable coefficient vector. 11 Let h1 and h6 represent the coefficient matrices corresponding to the equality constraints and inequality constraints, respectively. Let h1 to h6 represent the constant column vectors corresponding to various equality and inequality constraints, respectively. Let U1 represent the set of charging power for energy storage participating in primary frequency regulation, and let U2 represent the set of discharging power for energy storage participating in primary frequency regulation.

[0017] In some implementations, the expression for the preset objective function is:

[0018]

[0019] Where X represents the set of market-clearing prices for energy storage participating in primary frequency regulation, representing the first-level optimization variables, and Δf e(t) represents the frequency deviation at time t during a single frequency modulation, u represents the uncertainty variable, U represents the set of uncertainty variables, and C M1 This represents the transaction cost of ancillary services for participating in a frequency modulation.

[0020] In some implementations, the column and constraint generation algorithm is used to perform calculations on the fuzzy stochastic two-layer robust optimization model according to preset constraints until the preset objective function reaches its minimum value, thereby obtaining the output result of the fuzzy stochastic two-layer robust optimization model, including:

[0021] Using the column and constraint generation algorithm, the fuzzy random two-layer robust optimization model is decomposed to obtain the main optimization model and the sub-optimization model;

[0022] Based on the preset constraints, the main optimization model and the sub-optimization model are iterated alternately until the preset objective function reaches its minimum value, thereby obtaining the market clearing price of the main optimization model and the service volume of the sub-optimization model.

[0023] In some implementations, the expression for the master optimization model is:

[0024]

[0025] The expression for the sub-optimization model is:

[0026]

[0027] Where η represents an auxiliary variable, and k represents an uncertain scenario. Indicates when or The value of the post-valued dual problem in the k-th uncertain scenario is determined. This represents the first uncertainty value corresponding to the worst-case scenario generated by the iterative optimization model. This represents the second uncertainty value corresponding to the worst-case scenario generated by the iterative optimization model.

[0028] In some implementations, the preset constraints include frequency deviation constraints during the inertial response phase, frequency deviation constraints during the droop control phase, energy storage battery output power bid constraints, energy storage battery service power bid constraints, energy storage battery charging constraints, energy storage battery discharging constraints, energy storage battery stored capacity constraints, energy storage battery charging power constraints, energy storage battery charging time constraints, energy storage battery discharging power constraints, energy storage battery discharging time constraints, energy storage battery state of charge (SOC) constraints, constraints between energy storage battery SOC and load demand, and / or constraints between energy storage battery SOC and power system power deficit.

[0029] Secondly, this application also provides a fuzzy stochastic two-layer robust optimization device for energy storage frequency regulation trading, comprising:

[0030] The determination module is used to determine the uncertain variables of the output power of the new energy unit based on the output power information of the new energy unit;

[0031] A construction module is used to construct a fuzzy stochastic two-layer robust optimization model for energy storage batteries participating in primary frequency regulation based on the aforementioned uncertainty variables. The first-layer optimization variable of the fuzzy stochastic two-layer robust optimization model is the bid price of the energy storage battery output power, and the second-layer optimization variable of the fuzzy stochastic two-layer robust optimization model is the bid amount of the energy storage battery output power.

[0032] The computation module is used to perform calculations on the fuzzy stochastic two-layer robust optimization model according to preset constraints using a column and constraint generation algorithm until the preset objective function reaches its minimum value, and to obtain the output result of the fuzzy stochastic two-layer robust optimization model. The output result includes the market clearing price of the energy storage battery participating in primary frequency regulation and the service power of the energy storage battery output power.

[0033] Thirdly, this application also provides a computer device, including a processor and a memory, wherein the memory is used to store a computer program, and the computer program, when executed by the processor, implements the fuzzy stochastic two-layer robust optimization method for energy storage frequency regulation transactions as described in the first aspect.

[0034] Fourthly, this application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the fuzzy stochastic two-layer robust optimization method for energy storage frequency regulation transactions as described in the first aspect.

[0035] Compared with the prior art, this application has at least the following beneficial effects:

[0036] By analyzing the output power information of renewable energy generating units, the uncertainty variables of the renewable energy generating units' output power are determined to account for the randomness and volatility of renewable energy power generation and energy storage battery charging and discharging. Based on these uncertainties, a fuzzy stochastic two-layer robust optimization model for energy storage batteries participating in primary frequency regulation is constructed. The first-layer optimization variable of the fuzzy stochastic two-layer robust optimization model is the bid price of the energy storage battery's output power, and the second-layer optimization variable is the bid amount of the energy storage battery's output power, to consider the power generation of energy storage batteries participating in primary frequency regulation from an economic perspective. Finally, using a column and constraint generation algorithm, the fuzzy stochastic two-layer robust optimization model is calculated according to preset constraints until the preset objective function reaches its minimum value, yielding the output results of the fuzzy stochastic two-layer robust optimization model. The output results include the market clearing price of the energy storage battery participating in primary frequency regulation and the service electricity of the energy storage battery's output power. This approach, considering the randomness and volatility of renewable energy power generation and energy storage battery charging and discharging, and combining economic considerations, determines the market electricity price for energy storage battery-coordinated frequency regulation and the primary frequency regulation electricity consumption, providing necessary technical support for the safe and stable operation of the power system. Attached Figure Description

[0037] Figure 1 This is a flowchart illustrating a fuzzy stochastic two-layer robust optimization method for energy storage frequency regulation trading, as shown in an embodiment of this application.

[0038] Figure 2 This is a schematic diagram of the structure of a fuzzy stochastic two-layer robust optimization device for energy storage frequency regulation transactions, as shown in an embodiment of this application.

[0039] Figure 3 This is a schematic diagram of the structure of a computer device shown in an embodiment of this application. Detailed Implementation

[0040] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0041] Please refer to Figure 1 , Figure 1 This is a flowchart illustrating a fuzzy stochastic two-layer robust optimization method for energy storage frequency regulation trading, provided as an embodiment of this application. The fuzzy stochastic two-layer robust optimization method for energy storage frequency regulation trading in this embodiment can be applied to computer devices, including but not limited to smartphones, laptops, tablets, desktop computers, physical servers, and cloud servers. Figure 1 As shown, the fuzzy stochastic two-layer robust optimization method for energy storage frequency regulation trading in this embodiment includes steps S101 to S103, which are detailed below:

[0042] Step S101: Determine the uncertainty variables of the output power of the new energy unit based on the output power information of the new energy unit.

[0043] In this step, the power generation of new energy units is affected by factors such as wind speed and sunlight, so the uncertainty variables of the output power of new energy units can be determined based on the factors affecting power generation.

[0044] In some embodiments, step S101 includes:

[0045] Obtain power generation influencing factor data of the new energy unit, including historical wind speed data and / or historical sunshine data;

[0046] Statistical analysis of the data on factors affecting power generation is performed to obtain the output power information of the new energy unit;

[0047] Based on a preset inequality, an uncertainty variable for the output power of the new energy unit is constructed.

[0048] In this embodiment, based on the power models of wind turbines and photovoltaic cells, as well as statistical analysis of historical wind speed and historical sunshine data, a preset inequality is used to describe the uncertainty variables of the output power of wind turbines and photovoltaic cells:

[0049]

[0050] Among them, P i，t and These are the actual output power and the predicted output power of the i-th renewable energy source during time period t, respectively; and These are the upper and lower limits of the output power fluctuation of the i-th renewable energy source during time period t, respectively. These are the uncertainties in the output power of wind turbines and photovoltaic cells introduced under the worst-case scenario.

[0051] Step S102: Based on the uncertain variables, a fuzzy stochastic two-layer robust optimization model for the energy storage battery to participate in primary frequency regulation is constructed. The first-layer optimization variable of the fuzzy stochastic two-layer robust optimization model is the bid price of the energy storage battery output power, and the second-layer optimization variable of the fuzzy stochastic two-layer robust optimization model is the bid amount of the energy storage battery output power.

[0052] In this step, considering the randomness and volatility of energy storage battery charging and discharging, a fuzzy stochastic two-layer robust optimization model is constructed for energy storage to independently participate in primary frequency regulation. The first layer of optimization determines the market clearing price for primary frequency regulation, and the second layer of optimization determines the output power and service capacity of the battery energy storage station operator. The general form of the two-layer robust optimization model is as follows:

[0053]

[0054] Where x and y u These are the first-level optimization variables and the second-level optimization variables, respectively; A, B, C, D, E, and F are the coefficient matrices corresponding to the equality constraints and inequality constraints, respectively; h1-h4 are the constant column vectors corresponding to various equality constraints and inequality constraints, respectively; u and U are the uncertainty variables and the set of uncertainty variables, respectively.

[0055] Optionally, if the first-level optimization objective of the model is to minimize the frequency deviation value in primary frequency regulation, and the second-level optimization objective is to minimize the ancillary service transaction costs associated with primary frequency regulation, then the preset objective function is:

[0056]

[0057] Where X represents the set of market-clearing prices for energy storage participating in primary frequency regulation, representing the first-level optimization variables, and Δf e (t) represents the frequency deviation at time t during a single frequency modulation, u represents the uncertainty variable, U represents the set of uncertainty variables, and C M1 The ancillary service transaction cost for participating in a primary frequency regulation is determined by the battery storage station operator's battery output power and its quoted price, as well as the service volume and its quoted price, and can be expressed as:

[0058]

[0059] Where p Spi p SEi These are the energy storage battery output power quotes and service power quotes for each operator.

[0060] Step S103: Using a column and constraint generation algorithm, the fuzzy stochastic two-layer robust optimization model is calculated according to preset constraints until the preset objective function reaches its minimum value, and the output result of the fuzzy stochastic two-layer robust optimization model is obtained. The output result includes the market clearing price of the energy storage battery participating in primary frequency regulation and the service power of the energy storage battery output power.

[0061] In this step, considering the preset constraints, the expression for the fuzzy stochastic two-layer robust optimization model is:

[0062]

[0063]

[0064] Among them, c T Let y represent the column vector of uncertainties in the output power of the new energy unit, where x represents the first-level optimization variables, u1 represents the charging power of energy storage participating in primary frequency regulation, u2 represents the discharging power of energy storage participating in primary frequency regulation, and y represents the variable y. u Let y(u1, u2) represent the output power and service capacity of the battery energy storage station operator, and d represent the second-level optimization variables. T H1~H represents the second-level optimization variable coefficient vector. 11 Let h1 and h6 represent the coefficient matrices corresponding to the equality constraints and inequality constraints, respectively. Let h1 to h6 represent the constant column vectors corresponding to various equality and inequality constraints, respectively. Let U1 represent the set of charging power for energy storage participating in primary frequency regulation, and let U2 represent the set of discharging power for energy storage participating in primary frequency regulation.

[0065] In some embodiments, step S103 includes:

[0066] Using the column and constraint generation algorithm, the fuzzy random two-layer robust optimization model is decomposed to obtain the main optimization model and the sub-optimization model;

[0067] Based on the preset constraints, the main optimization model and the sub-optimization model are iterated alternately until the preset objective function reaches its minimum value, thereby obtaining the market clearing price of the main optimization model and the service volume of the sub-optimization model.

[0068] In this step, the fuzzy stochastic two-layer robust optimization model is a two-layer to three-layer robust optimization model with a min-max-min structure. The first layer contains a min optimization problem, corresponding to the bidding optimization problem for the output power of the corresponding energy storage battery. The second layer contains a max-min optimization problem, which includes a max and a min optimization problem, corresponding to the bidding quantity optimization problem for the output power of the energy storage battery. This model cannot be directly solved using commercial solvers, but the min optimization problem in the third layer can be transformed into a max optimization problem through dual transformation. Therefore, the min-max problem in the second layer can be merged into a single max optimization problem, thus transforming the min-max-min optimization model into a min-max optimization model. This type of min-max optimization problem can be solved using the column and constraint generation (C&CG) algorithm.

[0069] Optionally, the C&CG algorithm decomposes the original optimization problem into a master optimization problem (MP, i.e., the master optimization model) and a sub-optimization problem (SP, i.e., the sub-optimization model), and solves them iteratively by alternating between the master optimization problem MP and the sub-optimization problem SP. In each iteration, the master optimization problem MP passes the optimization result of the bid for the output power of the first-layer energy storage battery to the sub-optimization problem SP. The sub-optimization problem SP receives the optimization result of the first layer and can independently solve the bid quantity optimization problem of the second-layer energy storage battery output power, and returns the worst-case scenario and the corresponding constraints to the master optimization problem MP.

[0070] Optionally, the main optimization problem MP optimizes the price of the energy storage battery output power under the worst-case scenario reported by the sub-optimization problem SP in the current iteration, and its expression is:

[0071]

[0072] Optionally, the expression for the sub-optimization model is:

[0073]

[0074] Where η represents an auxiliary variable, and k represents an uncertain scenario. Indicates when or The value of the post-valued dual problem in the k-th uncertain scenario is determined. This represents the first uncertainty value corresponding to the worst-case scenario generated by the iterative optimization model. This represents the second uncertainty value corresponding to the worst-case scenario generated by the iterative optimization model.

[0075] Preferably, the SP problem has a max-min structure. Strong duality theory and the Big-M method are used to transform the inner layer into a single-layer max structure problem, thus converting the inner layer into a mixed-integer linear programming problem:

[0076]

[0077] Where β, γ, ν, and π are the dual variables corresponding to the constraints when the min optimization problem in the subproblem is transformed into a max problem. The subproblem contains a bilinear term h(u1, u2), and the dual problem obtains its optimal solution when the corresponding uncertainty is taken as the boundary value of its uncertainty set. Using the Big-M method for linearization, the subproblem can be transformed into the following model:

[0078]

[0079] in, and Δu1 and Δu2 are the charging and discharging power vectors of the energy storage battery in the predicted scenario, respectively; Z1 and Z2 are the auxiliary variables introduced; k1 and k2 are the coefficients of the uncertain variables; d1 and d2 are the column vectors of binary variables introduced by the Big-M method, used to select the values ​​of the uncertain variables; M is an auxiliary variable with a large positive real number.

[0080] Optionally, after the above transformation, the two-layer robust optimization model is converted into a main problem MP and subproblems SP with mixed-integer linear form, which can be solved using the column constraint generation algorithm (C&CG). The solution steps are as follows:

[0081] 1) Initialization: The upper bound f of the initial optimization problem u =∞, lower bound f l =-∞, convergence error ε is taken as a small positive number, iteration number k=0, scenario set

[0082] 2) Solve the main problem MP: Solve the main problem MP in the initial scenario to obtain the optimal solution to the main problem MP. And update the lower bound of the optimization problem.

[0083] 3) Solve SP: Find the optimal solution to the main problem MP. The problem is passed to subproblem SP, and the mixed-integer linear problem of subproblem SP is solved using the commercial solver Gurobi 9.0 to obtain the worst-case scenario of subproblem SP. The upper bound of the update optimization problem is

[0084] 4) Convergence criterion: If f u -f l If ≤ ε, then return the optimal solution. Otherwise, add variables. And the corresponding constraints (50) are applied to the main problem MP, and k = k + 1, K = K ∪ {k + 1} are updated, and the process returns to step 2).

[0085] In some embodiments, the preset constraints include frequency deviation constraints during the inertial response phase, frequency deviation constraints during the droop control phase, energy storage battery output power bid constraints, energy storage battery service power bid constraints, energy storage battery charging constraints, energy storage battery discharging constraints, energy storage battery stored capacity constraints, energy storage battery charging power constraints, energy storage battery charging time constraints, energy storage battery discharging power constraints, energy storage battery discharging time constraints, energy storage battery state of charge (SOC) constraints, constraints between energy storage battery SOC and load demand, and constraints between energy storage battery SOC and power system power deficit.

[0086] a) Frequency deviation constraint during the inertial response stage:

[0087] During the inertial response phase of the synchronous generator unit, the frequency deviation Δf eM (t) The constraint condition is that the value is not greater than the expected maximum value and not less than the expected minimum value.

[0088]

[0089] in, and f e M (t) represents the maximum and minimum expected frequency deviation during the inertial response phase in time period t, respectively.

[0090] b) Frequency deviation constraint during the droop control phase:

[0091] During the droop control phase, the frequency deviation Δf eC (t) The constraint condition is that the value is not greater than the expected maximum value and not less than the expected minimum value.

[0092]

[0093] in, and f e C (t) represents the maximum and minimum expected values ​​of frequency deviation during the drooping control phase in time period t, respectively.

[0094] c) Constraints on the output power of energy storage batteries in the bid:

[0095] The bid amount for the output power of energy storage battery i during time period t should meet the constraint that it is not greater than its maximum allowable value and not less than its minimum allowable value.

[0096]

[0097] in, and P S i(t) represents the maximum and minimum allowable output power of the energy storage battery k during time period t, respectively.

[0098] d) Constraints on the amount of electricity to be bid for energy storage battery services:

[0099] The bid amount for the energy storage battery i to serve the electricity in time period t should meet the constraint that it is not greater than the maximum allowable value of the energy storage capacity and not less than the minimum allowable value of the energy storage capacity.

[0100]

[0101] in, and E Si (t) represents the maximum and minimum allowable energy storage capacity of energy storage battery i during time period t, respectively.

[0102] e) Energy storage battery charging constraints:

[0103] Whether energy storage battery k enters charging operation during time period t depends on the time-of-use electricity price of the power system. When the time-of-use electricity price is lower than the price level at which the energy storage battery can be charged, the energy storage battery will charge, increasing its state of charge and improving its storage capacity. Once the energy storage battery enters the discharging operation state, it is not allowed to switch to the charging operation state unless the stored capacity has already fallen below its minimum allowable value. Energy storage battery i needs to satisfy the following constraints when charging during time period t:

[0104]

[0105] Where, μ i1 Let μ be the state-of-charge variable of energy storage battery i during time period t, and μ be the state-of-charge variable during charging. i1 =1, μ when not charging i1 =0; μ i2 Let μ be the state variable of energy storage battery i during time period t, and μ be the state variable during discharge. i2 =1, μ when not discharging i2 =0; p SYS For time-of-use pricing in the power system; p SCHi The acceptable electricity price for battery storage station operators to charge the storage batteries; E Si To store the energy in the energy storage battery i; E SCHi The amount of energy storage required for charging the battery i is acceptable to the battery storage station operator.

[0106] f) Discharge constraints of energy storage batteries:

[0107] Whether energy storage battery k enters discharge mode during time period t depends on the battery's stored capacity, the time-of-use electricity price, and the operating status and demand of the power system. When the time-of-use electricity price is higher than the price level at which the energy storage battery can discharge, the battery discharges, injecting power into the grid. During discharge, the battery continuously consumes its stored capacity, causing its state of charge (SOC) level to decrease. If the battery's stored capacity falls below the acceptable discharge capacity for energy storage battery i for the battery storage station operator, discharge should be stopped. Once the energy storage battery enters charging mode, it is not allowed to switch to discharging mode. Energy storage battery i must meet the following constraints during time period t to discharge:

[0108]

[0109] Where, p SDISCi The acceptable electricity price for battery storage station operators to discharge their batteries; E SDISCi The amount of energy that the battery storage device station operator needs to discharge is acceptable to the battery storage battery i.

[0110] g) Constraints on the energy storage capacity of the energy storage battery:

[0111] The amount of energy stored in battery i during time period t depends on its initial value E. S0i (t), state of charge / discharge, charging power, and charging time, are expressed mathematically as follows:

[0112]

[0113] Among them, E Si (t) represents the amount of energy stored in energy storage battery i during time period t, P CHi (t) and k CHi P represents the charging power and charging efficiency of energy storage battery i during time period t. DISCi (t) and k DISCi These represent the discharge power and discharge efficiency of energy storage battery i during time period t, respectively.

[0114] The energy storage capacity of battery i during time period t should meet the constraint that it is not greater than its maximum allowable value and not less than its minimum allowable value.

[0115]

[0116] h) Constraints on the charging power of energy storage batteries:

[0117] The charging power of energy storage battery i during time period t should meet the constraint that it is not greater than its maximum allowable value and not less than its minimum allowable value.

[0118]

[0119] in, and P SCHi (t) represents the maximum and minimum allowable charging power of energy storage battery i during time period t, respectively.

[0120] i) Constraints on energy storage battery charging time:

[0121] The energy storage battery i should satisfy the following constraints during the charging time t:

[0122]

[0123] j) Energy storage battery discharge power constraints:

[0124] The discharge power of energy storage battery i during time period t should meet the constraint that it is not greater than its maximum allowable value and not less than its minimum allowable value.

[0125]

[0126] in, and P SDISCi (t) represents the maximum and minimum allowable discharge power of energy storage battery i during time period t, respectively.

[0127] k) Energy storage battery discharge time constraint:

[0128] The energy storage battery i should satisfy the following constraints during the discharge time t:

[0129]

[0130] I) State of Charge (SOC) constraints for energy storage batteries:

[0131] The state of charge (SOC) of energy storage battery i during time period t should meet the constraint that it is not greater than its maximum allowable value and not less than its minimum allowable value.

[0132]

[0133] Where, δ Si (t) represents the state of charge of energy storage battery i during time period t. and δ Si (t) represents the maximum and minimum allowable state of charge of energy storage battery i during time period t, respectively.

[0134] o) Constraints between the state of charge of energy storage batteries and load demand:

[0135] During frequency regulation, to cope with peak loads with a variation period of less than 10ms, energy storage batteries can inject active power into the grid, helping to reduce frequency deviation. Therefore, within an operating cycle, the required state of charge (SOC) level of the energy storage battery can be specified based on the frequency and duration of peak loads. This is the constraint relationship between the SOC of the energy storage battery and load demand, and its mathematical expression is as follows:

[0136]

[0137] Among them, t DS and N DS These represent the period and frequency of peak load changes in the power system, respectively, k. SOCni (t)(n=1,2,3,4) is the regulation coefficient of the state of charge of energy storage battery i in time period t, 0≤k SOCni (t)≤1.

[0138] p) Constraints between the state of charge of energy storage batteries and the power deficit of the power system:

[0139] In a single frequency regulation cycle, to address power deficits with periods less than 10ms, energy storage batteries can inject active power into the grid, helping to reduce frequency deviation. Therefore, within an operating cycle, the required state of charge (SOC) level of the energy storage battery can be determined based on the frequency and duration of the power deficit. This is the constraint relationship between the SOC and the power deficit, expressed mathematically as follows:

[0140]

[0141] Among them, t GPV and N GPV These refer to the period and frequency of power deficit in the power system.

[0142] To implement the fuzzy stochastic two-layer robust optimization method for energy storage frequency regulation trading corresponding to the above method embodiments, in order to achieve the corresponding functions and technical effects. See Figure 2 , Figure 2 This diagram illustrates a structural block diagram of a fuzzy stochastic two-layer robust optimization device for energy storage frequency regulation transactions, according to an embodiment of this application. For ease of explanation, only the parts relevant to this embodiment are shown. The fuzzy stochastic two-layer robust optimization device for energy storage frequency regulation transactions provided in this embodiment includes:

[0143] The determination module 201 is used to determine the uncertainty variables of the output power of the new energy unit based on the output power information of the new energy unit;

[0144] Module 202 is used to construct a fuzzy stochastic two-layer robust optimization model for energy storage batteries participating in primary frequency regulation based on the aforementioned uncertainty variables. The first-layer optimization variable of the fuzzy stochastic two-layer robust optimization model is the bid price of the energy storage battery's output power, and the second-layer optimization variable is the bid amount for the energy storage battery's output power.

[0145] The calculation module 203 is used to perform calculations on the fuzzy random two-layer robust optimization model according to preset constraints using a column and constraint generation algorithm until the preset objective function reaches its minimum value, and obtain the output result of the fuzzy random two-layer robust optimization model. The output result includes the market clearing price of the energy storage battery participating in primary frequency regulation and the service power of the energy storage battery output power.

[0146] In some embodiments, the determining module 201 is specifically used for:

[0147] Obtain power generation influencing factor data of the new energy unit, including historical wind speed data and / or historical sunshine data;

[0148] Statistical analysis of the data on factors affecting power generation is performed to obtain the output power information of the new energy unit;

[0149] Based on a preset inequality, an uncertainty variable for the output power of the new energy unit is constructed.

[0150] In some embodiments, the expression for the fuzzy stochastic two-layer robust optimization model is:

[0151]

[0152]

[0153] Among them, c T Let y represent the column vector of uncertainties in the output power of the new energy unit, where x represents the first-level optimization variables, u1 represents the charging power of energy storage participating in primary frequency regulation, u2 represents the discharging power of energy storage participating in primary frequency regulation, and y represents the variable y. u Let y(u1, u2) represent the output power and service capacity of the battery energy storage station operator, and d represent the second-level optimization variables. T H1~H represents the second-level optimization variable coefficient vector. 11 Let h1 and h6 represent the coefficient matrices corresponding to the equality constraints and inequality constraints, respectively. Let h1 to h6 represent the constant column vectors corresponding to various equality and inequality constraints, respectively. Let U1 represent the set of charging power for energy storage participating in primary frequency regulation, and let U2 represent the set of discharging power for energy storage participating in primary frequency regulation.

[0154] In some embodiments, the expression of the preset objective function is:

[0155]

[0156] Where X represents the set of market-clearing prices for energy storage participating in primary frequency regulation, representing the first-level optimization variables, and Δf e (t) represents the frequency deviation at time t during a single frequency modulation, u represents the uncertainty variable, U represents the set of uncertainty variables, and C M1 This represents the transaction cost of ancillary services for participating in a frequency modulation.

[0157] In some embodiments, the arithmetic module 203 is specifically used for:

[0158] Using the column and constraint generation algorithm, the fuzzy random two-layer robust optimization model is decomposed to obtain the main optimization model and the sub-optimization model;

[0159] Based on the preset constraints, the main optimization model and the sub-optimization model are iterated alternately until the preset objective function reaches its minimum value, thereby obtaining the market clearing price of the main optimization model and the service volume of the sub-optimization model.

[0160] In some embodiments, the expression of the master optimization model is:

[0161]

[0162]

[0163] The expression for the sub-optimization model is:

[0164]

[0165] Where η represents an auxiliary variable, and k represents an uncertain scenario. Indicates when or The value of the post-valued dual problem in the k-th uncertain scenario is determined. This represents the first uncertainty value corresponding to the worst-case scenario generated by the iterative optimization model. This represents the second uncertainty value corresponding to the worst-case scenario generated by the iterative optimization model.

[0166] In some embodiments, the preset constraints include frequency deviation constraints during the inertial response phase, frequency deviation constraints during the droop control phase, energy storage battery output power bid constraints, energy storage battery service power bid constraints, energy storage battery charging constraints, energy storage battery discharging constraints, energy storage battery stored capacity constraints, energy storage battery charging power constraints, energy storage battery charging time constraints, energy storage battery discharging power constraints, energy storage battery discharging time constraints, energy storage battery state of charge (SOC) constraints, constraints between energy storage battery SOC and load demand, and constraints between energy storage battery SOC and power system power deficit.

[0167] The aforementioned fuzzy stochastic two-layer robust optimization device for energy storage frequency regulation transactions can implement the fuzzy stochastic two-layer robust optimization method for energy storage frequency regulation transactions described in the above method embodiments. The options in the above method embodiments are also applicable to this embodiment, and will not be detailed here. The remaining content of this application embodiment can be referred to the content of the above method embodiments, and will not be repeated in this embodiment.

[0168] Figure 3 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Figure 3 As shown, the computer device 3 of this embodiment includes: at least one processor 30 ( Figure 3 (Only one is shown in the diagram), memory 31, and computer program 32 stored in said memory 31 and executable on said at least one processor 30, wherein said processor 30 executes said computer program 32 to implement the steps in any of the above method embodiments.

[0169] The computer device 3 may be a smartphone, tablet, desktop computer, or cloud server, among other computing devices. This computer device may include, but is not limited to, a processor 30 and a memory 31. Those skilled in the art will understand that... Figure 3 The computer device 3 is merely an example and does not constitute a limitation on the computer device 3. It may include more or fewer components than shown in the figure, or combine certain components, or different components, such as input / output devices, network access devices, etc.

[0170] The processor 30 may be a Central Processing Unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0171] In some embodiments, the memory 31 may be an internal storage unit of the computer device 3, such as a hard disk or memory of the computer device 3. In other embodiments, the memory 31 may be an external storage device of the computer device 3, such as a plug-in hard disk, smart memory card (SMC), secure digital card (SD), flash card, etc., equipped on the computer device 3. Furthermore, the memory 31 may include both internal and external storage units of the computer device 3. The memory 31 is used to store the operating system, applications, bootloader, data, and other programs, such as the program code of the computer program. The memory 31 can also be used to temporarily store data that has been output or will be output.

[0172] In addition, embodiments of this application also provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps in any of the above method embodiments.

[0173] This application provides a computer program product that, when run on a computer device, enables the computer device to execute the steps described in the various method embodiments above.

[0174] In the several embodiments provided in this application, it will be understood that each block in the flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those shown in the figures. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved.

[0175] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0176] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of this application. It should be understood that the above descriptions are merely specific embodiments of this application and are not intended to limit the scope of protection of this application. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application for those skilled in the art.

Claims

1. A fuzzy stochastic two-layer robust optimization method for energy storage frequency regulation trading, characterized in that, include: Based on the output power information of the new energy generating units, determine the uncertain variables of the output power of the new energy generating units; Based on the aforementioned uncertainties, a fuzzy stochastic two-layer robust optimization model is constructed for energy storage batteries participating in primary frequency regulation. The first-layer optimization variable of the fuzzy stochastic two-layer robust optimization model is the bid price of the energy storage battery's output power, and the second-layer optimization variable of the fuzzy stochastic two-layer robust optimization model is the bid amount of the energy storage battery's output power. Using a column and constraint generation algorithm, the fuzzy stochastic two-layer robust optimization model is calculated based on preset constraints until the preset objective function reaches its minimum value, yielding the output results of the fuzzy stochastic two-layer robust optimization model. This includes: decomposing the fuzzy stochastic two-layer robust optimization model using the column and constraint generation algorithm to obtain a main optimization model and a sub-optimization model; iterating alternately between the main optimization model and the sub-optimization model based on the preset constraints until the preset objective function reaches its minimum value, obtaining the market clearing price of the main optimization model and the service power of the sub-optimization model. The output results include the market clearing price of the energy storage battery participating in primary frequency regulation and the service power of the energy storage battery's output power. The expression for the main optimization model is: ； st ； The expression for the sub-optimization model is: ； st ； in, Represents auxiliary variables. Indicates an uncertain scenario. Indicates when or The determined aftervalue dual problem is in the th Values ​​under uncertain scenarios This represents the first uncertainty value corresponding to the worst-case scenario generated by the iterative optimization model. This represents the second uncertainty value corresponding to the worst-case scenario generated by the iterative optimization model. This represents a column vector of uncertainties in the output power of new energy generating units. This represents the first-level optimization variables. This indicates the charging power of the energy storage system participating in primary frequency regulation. This indicates the discharge power of the energy storage participating in primary frequency regulation. This represents the second-level optimization variable. This indicates the output power and service electricity of the battery energy storage station operator. This represents the second-level optimization variable coefficient vector. These represent the coefficient matrices corresponding to the equality constraints and inequality constraints, respectively. These represent constant column vectors corresponding to various equal and unequal constraints, respectively. This represents the set of charging power that energy storage participates in primary frequency regulation. This represents the set of discharge power that the energy storage participates in primary frequency regulation.

2. The fuzzy stochastic two-layer robust optimization method for energy storage frequency regulation trading as described in claim 1, characterized in that, The determination of the uncertainty variables in the output power of the new energy generating unit based on its output power information includes: Obtain power generation influencing factor data of the new energy unit, including historical wind speed data and / or historical sunshine data; Statistical analysis of the data on factors affecting power generation is performed to obtain the output power information of the new energy unit; Based on a preset inequality, an uncertainty variable for the output power of the new energy unit is constructed.

3. The fuzzy stochastic two-layer robust optimization method for energy storage frequency regulation trading as described in claim 1, characterized in that, The expression for the fuzzy stochastic two-layer robust optimization model is: ； st ； in, This represents a column vector of uncertainties in the output power of new energy generating units. This represents the first-level optimization variables. This indicates the charging power of the energy storage system participating in primary frequency regulation. This indicates the discharge power of the energy storage participating in primary frequency regulation. This represents the second-level optimization variable. This indicates the output power and service electricity of the battery energy storage station operator. This represents the second-level optimization variable coefficient vector. These represent the coefficient matrices corresponding to the equality constraints and inequality constraints, respectively. These represent constant column vectors corresponding to various equal and unequal constraints, respectively. This represents the set of charging power that energy storage participates in primary frequency regulation. This represents the set of discharge power that the energy storage participates in primary frequency regulation.

4. The fuzzy stochastic two-layer robust optimization method for energy storage frequency regulation trading as described in claim 1, characterized in that, The expression for the preset objective function is: ; in, This represents the set of market-clearing prices for the first-level optimized variable energy storage participating in primary frequency regulation. Indicates the time in a single frequency modulation. Frequency deviation value, Represents uncertain variables. Represents a set of uncertain variables. This represents the transaction cost of ancillary services for participating in a frequency modulation.

5. The fuzzy stochastic two-layer robust optimization method for energy storage frequency regulation trading as described in claim 1, characterized in that, The preset constraints include frequency deviation constraints during the inertial response phase, frequency deviation constraints during the droop control phase, energy storage battery output power bid constraints, energy storage battery service power bid constraints, energy storage battery charging constraints, energy storage battery discharging constraints, energy storage battery stored capacity constraints, energy storage battery charging power constraints, energy storage battery charging time constraints, energy storage battery discharging power constraints, energy storage battery discharging time constraints, energy storage battery state of charge (SOC) constraints, constraints between energy storage battery SOC and load demand, and / or constraints between energy storage battery SOC and power system power deficit.

6. A fuzzy stochastic two-layer robust optimization device for energy storage frequency regulation trading, characterized in that, include: The determination module is used to determine the uncertain variables of the output power of the new energy unit based on the output power information of the new energy unit; A construction module is used to construct a fuzzy stochastic two-layer robust optimization model for energy storage batteries participating in primary frequency regulation based on the aforementioned uncertainty variables. The first-layer optimization variable of the fuzzy stochastic two-layer robust optimization model is the bid price of the energy storage battery output power, and the second-layer optimization variable of the fuzzy stochastic two-layer robust optimization model is the bid amount of the energy storage battery output power. The computation module is used to perform calculations on the fuzzy stochastic two-layer robust optimization model using a column and constraint generation algorithm, based on preset constraints, until the preset objective function reaches its minimum value, and to obtain the output result of the fuzzy stochastic two-layer robust optimization model. This includes: decomposing the fuzzy stochastic two-layer robust optimization model using the column and constraint generation algorithm to obtain a main optimization model and a sub-optimization model; iterating alternately between the main optimization model and the sub-optimization model based on the preset constraints until the preset objective function reaches its minimum value, obtaining the market clearing price of the main optimization model and the service power of the sub-optimization model. The output result includes the market clearing price of the energy storage battery participating in primary frequency regulation and the service power of the energy storage battery's output power. The expression for the main optimization model is: ； st ； The expression for the sub-optimization model is: ； st ； in, Represents auxiliary variables. Indicates an uncertain scenario. Indicates when or The determined aftervalue dual problem is in the th Values ​​under uncertain scenarios This represents the first uncertainty value corresponding to the worst-case scenario generated by the iterative optimization model. This represents the second uncertainty value corresponding to the worst-case scenario generated by the iterative optimization model. This represents a column vector of uncertainties in the output power of new energy generating units. This represents the first-level optimization variables. This indicates the charging power of the energy storage system participating in primary frequency regulation. This indicates the discharge power of the energy storage participating in primary frequency regulation. This represents the second-level optimization variable. This indicates the output power and service electricity of the battery energy storage station operator. This represents the second-level optimization variable coefficient vector. These represent the coefficient matrices corresponding to the equality constraints and inequality constraints, respectively. These represent constant column vectors corresponding to various equal and unequal constraints, respectively. This represents the set of charging power that energy storage participates in primary frequency regulation. This represents the set of discharge power that the energy storage participates in primary frequency regulation.

7. A computer device, characterized in that, It includes a processor and a memory, the memory being used to store a computer program, which, when executed by the processor, implements the fuzzy stochastic two-layer robust optimization method for energy storage frequency regulation transactions as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements the fuzzy stochastic two-layer robust optimization method for energy storage frequency regulation transactions as described in any one of claims 1 to 5.

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