A Distributed Resource Power Allocation Method, Device, Equipment and Storage Medium

Abstract

The present invention discloses a distributed resource power allocation method, device, equipment and storage medium, which are used to solve the technical problem of poor power allocation in the existing primary frequency regulation of the power grid. The present invention includes: calculating the total droop coefficient of the primary frequency regulation system of the power grid; calculating the output power increment of each distributed resource; generating a distributed resource output constraint by using the output power increment and the total droop coefficient; taking the minimum unit-time frequency regulation cost of the distributed resource as the optimization objective, and taking the output power increment constraint and the distributed resource output constraint as constraint conditions to construct a primary frequency regulation power allocation model; solving the primary frequency regulation power allocation model to obtain the power allocation result of the distributed resource.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy power distribution, and particularly to a distributed resource power distribution method, device, equipment and storage medium. Background Art

[0002] Under the background of the continuous reduction of thermal power installed capacity and the continuous increase of the proportion of new energy, how to ensure the safe and stable operation of the power grid and maintain the real-time balance of power supply and demand is an urgent problem to be solved at present. The large-scale grid connection of renewable energy power generation will lead to a reduction in the equivalent inertia of the power system, and the difficulty of regulating the power grid only by traditional regulating resources is increasing continuously. The regulation space of traditional regulating resources is getting smaller and smaller, and exploratory frequency modulation means are needed. As the terminal power grid facing users in the receiving end system, the distribution network contains a large number of controllable distributed resources, such as micro gas turbines, distributed photovoltaics, distributed energy storage, etc. Therefore, exploring their frequency modulation capabilities has high research value.

[0003] At present, there are two ways for distributed resources to participate in the primary frequency modulation of the power grid: the first is the droop control method, where each distributed resource determines a droop coefficient and determines its own frequency modulation output according to the product of the power grid frequency deviation and the droop coefficient; the second is to establish a frequency modulation output optimization model, with the goal of minimizing the frequency modulation cost of distributed resources, and determine the frequency modulation output of each distributed resource. The solution of the optimization model can be divided into two categories: centralized solution and distributed solution. The centralized solution mainly uses quadratic programming algorithms, fuzzy logic algorithms, evolutionary algorithms, etc., and the distributed solution mainly uses distributed algorithms such as the alternating direction multiplier method and the consensus algorithm.

[0004] For the two ways of distributed resources participating in the primary frequency modulation of the power grid, the droop control is simple and practical, but the problems of this method are how to reasonably determine the droop coefficient of distributed resources and how to ensure that distributed resources can fully respond to the power adjustment amount obtained by the droop control. Establishing a frequency modulation output distribution model for distributed resources can ensure that distributed resources fully respond to the primary frequency modulation requirements of the power grid. However, distributed resources have the characteristics of large quantity and scattered distribution. If traditional centralized control is adopted, it will significantly increase the computational load of the control center and cause the curse of dimensionality problem. Currently, the distributed algorithms applied to frequency modulation generally require multiple iterations to converge, resulting in a large communication pressure. Summary of the Invention

[0005] The present invention provides a distributed resource power distribution method, device, equipment and storage medium, which are used to solve the technical problem of poor power distribution in the existing primary frequency modulation of the power grid.

[0006] The present invention provides a distributed resource power distribution method, which is applied to a primary frequency modulation system of a power grid; the method includes:

[0007] Calculate the total droop coefficient of the power grid primary frequency modulation system;

[0008] Calculate the output power increment of each distributed resource;

[0009] Generate the distributed resource output constraint by using the output power increment and the total droop coefficient;

[0010] Construct a primary frequency modulation power distribution model with the minimum frequency modulation cost per unit time of the distributed resource as the optimization objective and the output power increment constraint and the distributed resource output constraint as the constraint conditions;

[0011] Solve the primary frequency modulation power distribution model to obtain the power distribution result of the distributed resource.

[0012] Optionally, the step of calculating the total droop coefficient of the power grid primary frequency modulation system includes:

[0013] Obtain the frequency response model of the power grid primary frequency modulation system;

[0014] Calculate the frequency deviation of the frequency response model;

[0015] Calculate the total droop coefficient of the power grid primary frequency modulation system by using the frequency deviation.

[0016] Optionally, the distributed resource includes a micro gas turbine, a distributed photovoltaic, and a distributed energy storage device; the output power increment constraint includes a micro gas turbine constraint, a distributed photovoltaic constraint, and a distributed energy storage constraint; the primary frequency modulation power distribution model is:

[0017]

[0018] where N p is the prediction step; C MT (k + n|k) is the frequency modulation cost of the micro gas turbine at time k + n; C PV (k + n|k) is the frequency modulation cost of the distributed photovoltaic at time k + n; C B (k + n|k) is the frequency modulation cost of the distributed energy storage at time k + n;

[0019] The constraint conditions are:

[0020]

[0021]

[0022]

[0023]

[0024]

[0025]

[0026] where, ΔP MT,i (k + n|k) is the output power increment of the i-th micro gas turbine at time k + n, is the upper limit of the output power increment of the i-th micro gas turbine at time k + n, is the lower limit of the output power increment of the i-th micro gas turbine at time k + n; ΔP PV,i (k + n|k) is the output power increment of the i-th distributed photovoltaic at time k + n, is the upper limit of the output power increment of the i-th distributed photovoltaic at time k + n, is the lower limit of the output power increment of the i-th distributed photovoltaic at time k + n; ΔP B,i (k + n|k) is the output power increment of the i-th distributed energy storage at time k + n, is the upper limit of the output power increment of the i-th distributed energy storage at time k + n, is the lower limit of the output power increment of the i-th distributed energy storage at time k + n; S B,i (k + n|k) is the output power increment of the SOC of the i-th distributed energy storage, T s is the simulation step size, and are the upper and lower limits of the SOC of the i-th distributed energy storage respectively; N MT is the number of micro gas turbines, N PV is the number of distributed photovoltaics, N B is the number of distributed energy storage devices, K sum is the total droop coefficient of the primary frequency regulation system of the power grid.

[0027] Optionally, the step of solving the primary frequency regulation power distribution model to obtain the power distribution result of the distributed resources includes:

[0028] Obtain the original variables and the original dual variables of each distributed resource;

[0029] Calculate the compensation gap of each distributed resource using the original variables and the original dual variables, and determine the maximum compensation gap;

[0030] Judge whether the maximum compensation gap satisfies being less than a preset convergence standard value;

[0031] If so, output the original dual variables as the power distribution result;

[0032] If not, convert the primary frequency regulation power distribution model into an unconstrained quadratic programming model;

[0033] Solve the unconstrained quadratic programming model using each distributed resource to obtain the correction amount of the coupling variable;

[0034] Calculate the correction amount of the internal variable and the correction amount of the dual variable using the correction amount of the coupling variable;

[0035] Update the primal-dual variable using the correction amount of the internal variable and the correction amount of the dual variable to obtain the updated dual variable;

[0036] Replace the original primal-dual variable with the updated dual variable, and return to the step of calculating the compensation gap of each distributed resource using the original variable and the original primal-dual variable, and determining the maximum value of the compensation gap.

[0037] The present invention also provides a distributed resource power distribution device, which is applied to the primary frequency modulation system of the power grid; the device includes:

[0038] A total droop coefficient calculation module, configured to calculate the total droop coefficient of the primary frequency modulation system of the power grid;

[0039] An output power increment calculation module, configured to calculate the output power increment of each distributed resource;

[0040] A distributed resource output constraint generation module, configured to generate distributed resource output constraints using the output power increment and the total droop coefficient;

[0041] A model construction module, configured to construct a primary frequency modulation power distribution model with the minimum unit-time frequency modulation cost of the distributed resource as the optimization objective and the output power increment constraint and the distributed resource output constraint as the constraint conditions;

[0042] A solution module, configured to solve the primary frequency modulation power distribution model to obtain the power distribution result of the distributed resource.

[0043] Optionally, the total droop coefficient calculation module includes:

[0044] A frequency response model acquisition sub-module, configured to acquire the frequency response model of the primary frequency modulation system of the power grid;

[0045] A frequency deviation calculation sub-module, configured to calculate the frequency deviation of the frequency response model;

[0046] A total droop coefficient calculation sub-module, configured to calculate the total droop coefficient of the primary frequency modulation system of the power grid using the frequency deviation.

[0047] Optionally, the distributed resources include micro gas turbines, distributed photovoltaics, and distributed energy storage devices; the output power increment constraints include micro gas turbine constraints, distributed photovoltaic constraints, and distributed energy storage constraints; the primary frequency regulation power distribution model is:

[0048]

[0049] where N p is the prediction step; C MT (k + n|k) is the frequency regulation cost of the micro gas turbine at time k + n; C PV (k + n|k) is the frequency regulation cost of the distributed photovoltaic at time k + n; C B (k + n|k) is the frequency regulation cost of the distributed energy storage at time k + n;

[0050] The constraint conditions are:

[0051]

[0052]

[0053]

[0054]

[0055]

[0056]

[0057] where ΔP MT,i (k + n|k) is the output power increment of the i-th micro gas turbine at time k + n, is the upper limit of the output power increment of the i-th micro gas turbine at time k + n, is the lower limit of the output power increment of the i-th micro gas turbine at time k + n; ΔP PV,i (k + n|k) is the output power increment of the i-th distributed photovoltaic at time k + n, is the upper limit of the output power increment of the i-th distributed photovoltaic at time k + n, is the lower limit of the output power increment of the i-th distributed photovoltaic at time k + n; ΔP B,i (k + n|k) is the output power increment of the i-th distributed energy storage at time k + n, is the upper limit of the output power increment of the i-th distributed energy storage at time k + n, is the lower limit of the output power increment of the i-th distributed energy storage at time k + n; S B,i (k + n|k) is the output power increment of the SOC of the i-th distributed energy storage, T sis the simulation step size, and are the upper and lower limits of the SOC of the i-th distributed energy storage respectively; N MT is the number of micro gas turbines, N PV is the number of distributed photovoltaics, N B is the number of distributed energy storage devices, K sum is the total droop coefficient of the power grid primary frequency regulation system.

[0058] Optionally, the solving module includes:

[0059] An original variable and original dual variable acquisition sub-module, configured to acquire the original variables and original dual variables of each distributed resource;

[0060] A compensation gap maximum value determination sub-module, configured to calculate the compensation gaps of each distributed resource by using the original variables and the original dual variables, and determine the maximum compensation gap value;

[0061] A judgment sub-module, configured to judge whether the maximum compensation gap value satisfies being less than a preset convergence standard value;

[0062] An output sub-module, configured to, if so, output the original dual variables as the power distribution result;

[0063] An unconstrained quadratic programming model conversion sub-module, configured to, if not, convert the primary frequency regulation power distribution model into an unconstrained quadratic programming model;

[0064] A coupling variable correction amount calculation sub-module, configured to solve the unconstrained quadratic programming model by using each distributed resource to obtain a coupling variable correction amount;

[0065] An internal variable correction amount and dual variable correction amount calculation sub-module, configured to calculate an internal variable correction amount and a dual variable correction amount by using the coupling variable correction amount;

[0066] An update sub-module, configured to update the original dual variables by using the internal variable correction amount and the dual variable correction amount to obtain updated dual variables;

[0067] A return sub-module, configured to replace the original dual variables with the updated dual variables, and return to the step of calculating the compensation gaps of each distributed resource by using the original variables and the original dual variables, and determining the maximum compensation gap value.

[0068] The present invention also provides an electronic device, and the device includes a processor and a memory:

[0069] The memory is used for storing program codes and transmitting the program codes to the processor;

[0070] The processor is configured to execute the distributed resource power allocation method according to any one of the above instructions in the program code.

[0071] The present invention also provides a computer-readable storage medium for storing program code for executing the distributed resource power allocation method according to any one of the above.

[0072] As can be seen from the above technical solutions, the present invention has the following advantages: The present invention discloses a distributed resource power allocation method, which specifically includes: calculating the total droop coefficient of the power grid primary frequency regulation system; calculating the output power increment of each distributed resource; generating the output constraint of the distributed resource by using the output power increment and the total droop coefficient; taking the minimum unit-time frequency regulation cost of the distributed resource as the optimization target, and taking the output power increment constraint and the output constraint of the distributed resource as the constraint conditions to construct a primary frequency regulation power allocation model; solving the primary frequency regulation power allocation model to obtain the power allocation result of the distributed resource. The present invention realizes the effective allocation of distributed energy by calculating the total droop coefficient of the power grid primary frequency regulation system, and at the same time realizes the optimal allocation of distributed resources by constructing a primary frequency regulation power allocation model with the minimum unit-time frequency regulation cost as the optimization target. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention, and those of ordinary skill in the art can also obtain other drawings without creative efforts based on these drawings.

[0074] Figure 1 It is a flowchart of the steps of a distributed resource power allocation method provided by an embodiment of the present invention

[0075] Figure 2 It is an architecture diagram for providing primary frequency regulation services for multiple types of distributed resources provided by an embodiment of the present invention;

[0076] Figure 3 It is a schematic structural diagram of the frequency response model of the power grid primary frequency regulation system provided by an embodiment of the present invention;

[0077] Figure 4 It is a schematic structural diagram of a transmission and distribution joint system including distributed resources provided by an embodiment of the present invention;

[0078] Figure 5 It is a schematic diagram of the change of the primary frequency regulation system frequency provided by an embodiment of the present invention;

[0079] Figure 6Schematic diagram of the output curves of various distributed resources under the scenario of sudden load increase;

[0080] Figure 7 Structural block diagram of a distributed resource power distribution device provided by an embodiment of the present invention. Detailed implementation manners

[0081] Embodiments of the present invention provide a distributed resource power distribution method, device, equipment and storage medium, which are used to solve the technical problem of poor power distribution in the existing primary frequency regulation of the power grid.

[0082] In order to make the objectives, features, and advantages of the present invention more obvious and understandable, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the embodiments described below are only a part of the embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0083] Please refer to Figure 1 , Figure 1 Steps flowchart of a distributed resource power distribution method provided by an embodiment of the present invention.

[0084] A distributed resource power distribution method provided by the present invention may specifically include the following steps: Step 101, calculate the total droop coefficient of the primary frequency regulation system of the power grid;

[0085] The total droop coefficient is the primary frequency regulation demand of the primary frequency regulation system of the power grid.

[0086] Primary frequency regulation refers to an automatic control process in which once the frequency of the power grid deviates from the rated value, the control system of the units in the power grid automatically controls the increase and decrease of the active power of the units, restricts the change of the power grid frequency, and keeps the power grid frequency stable.

[0087] When power disturbances occur in the power grid, such as generator set failures, tie line disconnections, etc., resulting in large fluctuations in power, the dispatching center can calculate the primary frequency regulation demand of the power grid according to the power disturbance situation and send it to each distributed resource. Each distributed resource can determine the optimal frequency modulation power processing scheme through information interaction to ensure the stability of the power grid frequency. Figure 2 System architecture for providing primary frequency regulation services for multiple types of distributed resources provided by an embodiment of the present invention.

[0088] In the embodiments of the present invention, the step of calculating the total droop coefficient of the primary frequency regulation system of the power grid may include the following sub-steps:

[0089] S11, obtain the frequency response model of the primary frequency regulation system of the power grid;

[0090] S12. Calculate the frequency deviation of the frequency response model;

[0091] S13. Calculate the total droop coefficient of the power grid primary frequency regulation system using the frequency deviation.

[0092] To achieve the rapidity of primary frequency regulation, the distributed resources participating in frequency regulation usually adopt droop control and adjust their output power according to the droop coefficient. However, the setting of the droop coefficient needs to be combined with the system characteristics. Therefore, it is necessary to optimize the total droop coefficient of the distributed resources according to the frequency response model of the power grid primary frequency regulation system first.

[0093] As Figure 3 shown, Figure 3 is the frequency response model of the power grid primary frequency regulation system provided by the embodiment of the present invention. The frequency response model describes the frequency change process after the power grid is disturbed. On the basis of the traditional low-order frequency response model, the embodiment of the present invention introduces the frequency regulation link of distributed resources. Among them, K sum is the total droop coefficient of the distributed resources; K m is the output power gain coefficient of the steam turbine; F H is the ratio of the output power of the high-pressure cylinder to the total output power; T R is the equivalent inertia time constant of the steam turbine; R is the droop coefficient of the governor; D is the load damping coefficient; H is the equivalent inertia time constant of the system; ΔP d is the load disturbance power increment; ΔP G is the output power increment of the synchronous machine; ΔP MT is the output power increment of the micro gas turbine; ΔP PV is the output power increment of the distributed photovoltaic; ΔP B is the output power increment of the distributed resources; Δf is the system frequency deviation.

[0094] According to Figure 3 the shown frequency response model, the frequency domain expression of the frequency deviation can be deduced, and the time domain expression of the frequency deviation of the power grid primary frequency regulation system can be obtained by performing the inverse Laplace transform on it, that is:

[0095]

[0096] Among them,

[0097]

[0098]

[0099]

[0100]

[0101]

[0102] Let the partial derivative of Equation (1) with respect to time be 0, and the moment t corresponding to the extreme point of the frequency deviation of the primary frequency modulation system can be obtained. nadir Furthermore, the expression for the extreme value of the system frequency deviation is obtained as:

[0103]

[0104] Next, the extreme point of the frequency deviation can be set to 0.5 Hz, and thus the total droop coefficient of the distributed resources participating in the primary frequency modulation is obtained as:

[0105]

[0106] After calculating the total droop coefficient of the primary frequency modulation, the corresponding frequency modulation demand can be sent to each distributed resource, and the distributed resources determine the frequency modulation power distribution scheme of each distributed resource through the distributed interior point method to maintain the stability of the power grid frequency.

[0107] Step 102, calculate the output power increment of each distributed resource;

[0108] In the embodiment of the present invention, the distributed resources may include micro gas turbines, distributed photovoltaics, and distributed energy storage devices.

[0109] Calculating the output power increment of the distributed resources can analyze the influence of the power grid power change on each distributed resource.

[0110] Step 103, generate the output constraint of the distributed resources by using the output power increment and the total droop coefficient;

[0111] After calculating the output power increment of each distributed resource, the output constraint of the distributed resources can be generated by using the output power increment and the total droop coefficient.

[0112] In a specific implementation, the sum of the output power increments of all distributed resources should be equal to the primary frequency modulation demand of the primary frequency modulation system, that is:

[0113]

[0114] Wherein, N MT is the number of micro gas turbines, N PV is the number of distributed photovoltaics, N B is the number of distributed energy storage devices, and K sum is the total droop coefficient of the power grid primary frequency modulation system.

[0115] Step 104: Construct a primary frequency regulation power distribution model with the minimum unit - time frequency regulation cost of distributed resources as the optimization objective and the output power increment constraint and the distributed resource output constraint as the constraint conditions.

[0116] In the embodiment of the present invention, the primary frequency regulation power distribution model is:

[0117]

[0118] where N p is the prediction step; C MT (k + n|k) is the frequency regulation cost of the micro - gas turbine at time k + n; C PV (k + n|k) is the frequency regulation cost of the distributed photovoltaic at time k + n; C B (k + n|k) is the frequency regulation cost of the distributed energy storage at time k + n.

[0119] The frequency regulation cost C MT (k + n|k) of the micro - gas turbine at time k + n is:

[0120]

[0121] where a MT,i and b MT,i are the cost coefficients of the i - th micro - gas turbine; ΔP MT,i (k + n|k) is the output power increment of the i - th micro - gas turbine at time k + n; P MT,i (k + n|k) is the planned output power of the i - th micro - gas turbine at time k + n, and N MT is the number of micro - gas turbines in the distribution network.

[0122] The frequency regulation cost of the distributed photovoltaic can be expressed by a quadratic function of the output power increment of the distributed photovoltaic, that is:

[0123]

[0124] where a PV,i is the frequency regulation cost coefficient of the i - th distributed photovoltaic; ΔP PV,i (k + n|k) is the output power increment of the i - th distributed photovoltaic at time k + n; N PV is the number of distributed photovoltaics in the distribution network.

[0125] The frequency regulation cost of the distributed energy storage mainly includes the equipment loss cost caused by the SOC offset and the output power change, and can be described by the energy storage SOC offset and the output power increment, that is:

[0126]

[0127] where aB,i is the output power deviation cost coefficient of the i-th distributed energy storage; a SOC,i is the SOC offset cost coefficient of the i-th distributed energy storage; ΔP B,i (k + n|k) is the output power increment of the i-th distributed energy storage at time k + n; S B,i (k + n|k) is the SOC of the i-th distributed energy storage at time k + n, S ref,i is the reference value of the SOC of the i-th distributed energy storage; E B,i is the rated capacity of the i-th distributed energy storage; N B is the number of distributed energy storages in the distribution network.

[0128] The constraint conditions include: micro gas turbine constraints, distributed photovoltaic constraints, distributed energy storage constraints, and distributed resource output constraints.

[0129] 1) Micro gas turbine constraints

[0130] The output power increment of the micro gas turbine should satisfy:

[0131]

[0132] Among them, ΔP MT,i (k + n|k) is the output power increment of the i-th micro gas turbine at time k + n, and are the upper and lower limits of the output power increment of the i-th micro gas turbine at time k + n.

[0133] 2) Distributed photovoltaic constraints

[0134] The active power increment of the distributed photovoltaic should satisfy:

[0135]

[0136] Among them, ΔP PV,i (k + n|k) is the output power increment of the i-th distributed photovoltaic at time k + n, is the upper limit of the output power increment of the i-th distributed photovoltaic at time k + n, is the lower limit of the output power increment of the i-th distributed photovoltaic at time k + n.

[0137] 3) Distributed energy storage constraints

[0138] The active power increment output by the energy storage device should satisfy:

[0139]

[0140] Among them, ΔP B,i(k + n|k) is the output power increment of the i-th distributed energy storage at time k + n. is the upper limit of the output power increment of the i-th distributed energy storage at time k + n. is the lower limit of the output power increment of the i-th distributed energy storage at time k + n.

[0141] The SOC of the distributed energy storage should satisfy:

[0142]

[0143]

[0144] Among them, S B,i (k + n|k) is the output power increment of the SOC of the i-th distributed energy storage, T s is the simulation step size, and are the upper and lower limits of the SOC of the i-th distributed energy storage respectively;

[0145] 4) Output power constraint of distributed resources

[0146] The sum of the output power increments of all distributed resources should be equal to the primary frequency regulation demand of the system, as shown in formula (4), that is:

[0147]

[0148] Step 105, solve the primary frequency regulation power distribution model to obtain the power distribution results of distributed resources.

[0149] In the embodiment of the present invention, the primary frequency regulation power distribution model can be written in the following compact form:

[0150]

[0151] s.t.g(x a ) = 0, a ∈ A (19b)

[0152]

[0153] Among them, the decision variable x of distributed resource a a = [x a(I) ; x a(B) , x a(I) is the internal variable of distributed resource a, and x a(B) is the coupling variable between distributed resource a and other distributed resources. Equation (19b) includes equality constraints (9) and (18), and equation (19c) includes inequality constraints (14) - (17).

[0154] For the problems shown in formulas (19a)-(19c), the distributed interior point method can be used to transform them into optimization problems of multiple distributed resources for distributed solution, thereby protecting the privacy of each distributed resource. The solution process will be introduced below.

[0155] In one example, step 105 may include the following sub-steps:

[0156] S51, obtain the original variables and original dual variables of each distributed resource;

[0157] S52, calculate the compensation gap of each distributed resource using the original variables and original dual variables, and determine the maximum compensation gap;

[0158] S53, determine whether the maximum compensation gap satisfies being less than the preset convergence standard value;

[0159] S54, if so, output the original dual variable as the power distribution result;

[0160] S55, if not, convert the primary frequency regulation power distribution model into an unconstrained quadratic programming model;

[0161] S56, use each distributed resource to solve the unconstrained quadratic programming model to obtain the coupling variable correction amount;

[0162] S57, calculate the internal variable correction amount and the dual variable correction amount using the coupling variable correction amount;

[0163] S58, update the original dual variable using the internal variable correction amount and the dual variable correction amount to obtain the updated dual variable;

[0164] S59, replace the original dual variable with the updated dual variable, and return to the step of calculating the compensation gap of each distributed resource using the original variables and original dual variables and determining the maximum compensation gap.

[0165] In a specific implementation, the derivation and conversion method of the unconstrained quadratic programming model is as follows:

[0166] Taking distributed resource a as an example, rewrite its primary frequency regulation power distribution rolling optimization model as follows:

[0167] minf a (x a ) (20a)

[0168] s.t.g(x a )=0 (20b)

[0169]

[0170] 1) Construct the Lagrangian function:

[0171] For equations (20b) and (20c), introduce the slack variable m a , the Lagrange multiplier (or dual variable) y a and z a , and the following Lagrangian function can be constructed:

[0172]

[0173] where m a ≥0, z a ≤0, r is the number of inequality constraints, and μ a is the barrier factor of distributed resource a.

[0174] 2) Derive the Karush-Kuhn-Tucker (KKT) conditions

[0175]

[0176]

[0177]

[0178]

[0179] where Z a = diag(z a ), M a = diag(m a ), and e is the unit column vector.

[0180] Define the compensation gap of distributed resource a:

[0181] Gap a =(z a ) T m a (23)

[0182] From equations (22d) and (23), we can obtain:

[0183]

[0184] 3) Derive the correction equation

[0185] For the KKT conditions (22a)-(22d), the Newton method can be used to solve them. Retaining Δx a and Δy a , eliminating other variables, the following simplified correction equation can be obtained:

[0186]

[0187] where:

[0188]

[0189]

[0190] 4) Derive the unconstrained quadratic programming model

[0191] For the correction equation shown in Equation (25), it can be converted into the following unconstrained quadratic programming problem:

[0192]

[0193] By combining the unconstrained quadratic programming problems of each distributed resource, the total unconstrained quadratic programming model can be obtained:

[0194]

[0195] For the unconstrained quadratic programming problem shown in Equation (27), it can be written as a quadratic function expression represented by the correction amount of the coupling variable. Each distributed resource communicates with each other to obtain the total quadratic function, and the correction amount of the coupling variable is obtained by independently solving this function, and then the original dual variable is updated. The specific process is as follows:

[0196] 1) Form the unconstrained quadratic programming problem containing the correction amount of the coupling variable

[0197] For the unconstrained quadratic programming problem shown in Equation (27), arrange the correction amount of the regional variable in the order of the correction amount of the internal variable and the correction amount of the coupling variable, Δx a =[Δx a(I) ; Δx a(B) , and make corresponding adjustments to the correction amount of the dual variable and the coefficient matrix, and we can get:

[0198]

[0199] Among them, the correction amount of the coupling variable is a parameter. Therefore, Equation (29) can be written as the following correction equation system:

[0200]

[0201] According to Equation (25), express the correction amount of the internal variable and the correction amount of the dual variable in terms of the correction amount of the coupling variable:

[0202] Δx a(I) =R x Δx a(B) +r x (31a)

[0203] Δy a =R y Δx a(B) +r y(31b)

[0204] Substituting Equation (31) back into Equation (29), we get:

[0205]

[0206] where represents the quadratic function expression after parameter-optimized distributed resource a, and c a are the quadratic term coefficient matrix, the linear term coefficient vector, and the constant term, respectively.

[0207] 2) Solve for the correction amount of the coupling variable

[0208] After each distributed resource calculates the quadratic function expression (32) for the correction amount of the coupling variable, it shares its own with other distributed resources through the communication network, and the total unconstrained quadratic programming problem (33) can be obtained. Solving this problem can get the correction amount of the coupling variable.

[0209]

[0210] 3) Update the primal variable and the dual variable

[0211] Substituting the correction amount of the coupling variable into Equation (31) can obtain the correction amount of the internal variable and the correction amount of the dual variable, and then substituting them into the following formula to update the primal-dual variable:

[0212]

[0213] where α p and α d are the update steps of the primal-dual variable.

[0214] In the specific implementation, the solution steps of the distributed interior point method are summarized as follows:

[0215] Step 0: Given the iteration count k = 0 and the convergence criterion ε = 10 -4 , for distributed resource a, given the initial values of its primal variables x a (k), m a (k) and dual variables y a (k), z a (k);

[0216] Step 1: Calculate the compensation gap Gap a (k) of each distributed resource, and through information sharing, obtain the maximum value Gap max (k) of the compensation gap; Determine whether the convergence condition is satisfied: Gap max (k) < ε. If satisfied, output the primal-dual variable and exit the iteration; otherwise, go to the next step;

[0217] Step 2: Each distributed resource independently generates formula (32), and through information sharing, obtains the overall optimization model formula (33);

[0218] Step 3: Each distributed resource independently solves formula (33) to obtain Δx a(B) (k), and then calculates Δx a(I) (k), Δy a (k), Δm a (k), and Δz a (k);

[0219] Step 4: Update the original dual variables x a (k + 1), y a (k + 1), m a (k + 1), and z a (k + 1) according to formula (34);

[0220] Step 5: Let k = k + 1, and return to Step 1.

[0221] The present invention realizes the effective allocation of distributed energy by calculating the total droop coefficient of the power grid primary frequency modulation system. At the same time, by constructing a primary frequency modulation power distribution model with the minimum frequency modulation cost per unit time as the optimization goal, the optimal allocation of distributed resources is realized.

[0222] For easy understanding, the embodiments of the present invention are described below through specific examples:

[0223] The embodiments of the present invention adopt a transmission and distribution integrated system with distributed resources as shown in Figure 4 to verify the effectiveness of the embodiments of the present invention.

[0224] The parameter settings of the frequency response model of the simulation system are shown in Table 1. The parameters of the distributed energy storage device are shown in Table 2. The distributed photovoltaic adopts a load shedding control strategy, and its parameters are shown in Table 3. The parameters of the distributed gas turbine are shown in Table 4. The simulation step of the system is 0.05 s, the control period is 0.2 s, the prediction time domain is 2 s, and the convergence accuracy ε of the distributed interior point method is 10-4.

[0225] Table 1 Parameter settings of the frequency response model of the system

[0226]

[0227]

[0228] Table 2 Distributed energy storage parameters

[0229]

[0230] Table 3 Distributed photovoltaic parameters

[0231]

[0232] Table 4 Micro Gas Turbine Parameters

[0233]

[0234] After the system load suddenly increases by 40 MW, according to Equation (3), the total droop coefficient KB of the distributed resources at this time can be obtained as 21, and the change in the system frequency is as Figure 5 shown. From Figure 5 it can be seen that after a 40 MW load step increase occurs in the system, if the distributed resources do not participate in frequency regulation, the lowest point of the system frequency is 49.35 Hz, and the maximum frequency deviation reaches 0.65 Hz, which does not meet the frequency requirements for the safe and stable operation of the power grid. After the distributed resources participate in frequency regulation, the maximum frequency deviation of the system drops to 0.5 Hz, and the steady-state frequency rises from 49.67 Hz to 49.72 Hz, and the process of system frequency change is improved.

[0235] Figure 6 is the output curve of each distributed resource under the scenario of sudden load increase. From Figure 6 it can be known that after the system disturbance occurs, various distributed resources will correspondingly increase their own outputs to meet the primary frequency regulation requirements of the power grid. In the early stage of frequency regulation, due to the relatively low cost, distributed energy storage will undertake most of the frequency regulation tasks. Among them, distributed energy storage 1 has the smallest power deviation cost and the largest output in the early stage of frequency regulation, and distributed energy storage 3 has a relatively high power deviation cost and the smallest output in the early stage of frequency regulation. As the primary frequency regulation progresses, due to maintaining a high output, the SOC of each distributed energy storage drops rapidly, and the deviation from the SOC reference value increases, resulting in a relatively high SOC offset cost. Each distributed energy storage will reduce its own output, and the micro gas turbine and distributed photovoltaic will increase their outputs to meet the frequency regulation requirements.

[0236] To illustrate the effectiveness and superiority of the proposed solution algorithm, the alternating direction method of multipliers is selected for comparison with the distributed interior point method adopted in the present invention, and the result of solving the original model (20) using the quadratic programming solver Quadprog in MATLAB is used as the comparison benchmark. The ADMM parameter settings are as follows: ρ = 0.05, λ0 = 1, ε = 10 -4 .

[0237] The number of iterations, solution time, and total frequency regulation cost of the three solution algorithms under the scenario of sudden load increase are shown in Table 5. It can be seen from Table 5 that the distributed interior point method is consistent with Quadprog in terms of solution accuracy, and has obvious advantages compared with ADMM in terms of solution time and number of iterations. The above comparison results show that the distributed interior point method can ensure the speed and accuracy of the solution while protecting the privacy information of each distributed resource.

[0238]

[0239] Table 5 Comparison of Results of Three Algorithms

[0240] Please refer to Figure 7 , Figure 7 which is the structural block diagram of a distributed resource power allocation device provided by an embodiment of the present invention.

[0241] An embodiment of the present invention provides a distributed resource power allocation device, which is applied to the primary frequency regulation system of the power grid; the device includes:

[0242] A total droop coefficient calculation module 701, configured to calculate the total droop coefficient of the primary frequency regulation system of the power grid;

[0243] An output power increment calculation module 702, configured to calculate the output power increment of each distributed resource;

[0244] A distributed resource output constraint generation module 703, configured to generate distributed resource output constraints by using the output power increment and the total droop coefficient;

[0245] A model construction module 704, configured to construct a primary frequency regulation power allocation model with the minimum unit-time frequency regulation cost of the distributed resource as the optimization objective and the output power increment constraint and the distributed resource output constraint as the constraint conditions;

[0246] A solution module 705, configured to solve the primary frequency regulation power allocation model to obtain the power allocation result of the distributed resource.

[0247] In the embodiment of the present invention, the total droop coefficient calculation module 701 includes:

[0248] A frequency response model acquisition sub-module, configured to acquire the frequency response model of the primary frequency regulation system of the power grid;

[0249] A frequency deviation calculation sub-module, configured to calculate the frequency deviation of the frequency response model;

[0250] A total droop coefficient calculation sub-module, configured to calculate the total droop coefficient of the primary frequency regulation system of the power grid by using the frequency deviation.

[0251] In the embodiment of the present invention, the distributed resources include micro gas turbines, distributed photovoltaics, and distributed energy storage devices; the output power increment constraints include micro gas turbine constraints, distributed photovoltaic constraints, and distributed energy storage constraints; the primary frequency regulation power allocation model is:

[0252]

[0253] Among them, N p is the prediction step; CMT (k + n|k) is the frequency regulation cost of the micro gas turbine at time k + n; C PV (k + n|k) is the frequency regulation cost of the distributed photovoltaic at time k + n; C B (k + n|k) is the frequency regulation cost of the distributed energy storage at time k + n;

[0254] The constraint conditions are:

[0255]

[0256]

[0257]

[0258]

[0259]

[0260]

[0261] Among them, ΔP MT,i (k + n|k) is the output power increment of the i-th micro gas turbine at time k + n, is the upper limit of the output power increment of the i-th micro gas turbine at time k + n, is the lower limit of the output power increment of the i-th micro gas turbine at time k + n; ΔP PV,i (k + n|k) is the output power increment of the i-th distributed photovoltaic at time k + n, is the upper limit of the output power increment of the i-th distributed photovoltaic at time k + n, is the lower limit of the output power increment of the i-th distributed photovoltaic at time k + n; ΔP B,i (k + n|k) is the output power increment of the i-th distributed energy storage at time k + n, is the upper limit of the output power increment of the i-th distributed energy storage at time k + n, is the lower limit of the output power increment of the i-th distributed energy storage at time k + n; S B,i (k + n|k) is the output power increment of the SOC of the i-th distributed energy storage, T s is the simulation step size, and are the upper and lower limits of the SOC of the i-th distributed energy storage respectively; N MT is the number of micro gas turbines, N PV is the number of distributed photovoltaics, N B is the number of distributed energy storage devices, K sum is the total droop coefficient of the power grid primary frequency regulation system.

[0262] In an embodiment of the present invention, the solving module 705 includes:

[0263] An original variable and original dual variable acquisition sub-module, configured to acquire the original variables and original dual variables of each distributed resource;

[0264] A compensation gap maximum value determination sub-module, configured to calculate the compensation gaps of each distributed resource by using the original variables and original dual variables, and determine the maximum compensation gap value;

[0265] A judgment sub-module, configured to judge whether the maximum compensation gap value satisfies being less than a preset convergence standard value;

[0266] An output sub-module, configured to, if so, output the original dual variables as the power distribution result;

[0267] An unconstrained quadratic programming model conversion sub-module, configured to, if not, convert the primary frequency modulation power distribution model into an unconstrained quadratic programming model;

[0268] A coupling variable correction amount calculation sub-module, configured to solve the unconstrained quadratic programming model by using each distributed resource to obtain a coupling variable correction amount;

[0269] An internal variable correction amount and dual variable correction amount calculation sub-module, configured to calculate an internal variable correction amount and a dual variable correction amount by using the coupling variable correction amount;

[0270] An update sub-module, configured to update the original dual variables by using the internal variable correction amount and the dual variable correction amount to obtain updated dual variables;

[0271] A return sub-module, configured to replace the original dual variables with the updated dual variables, and return to the step of calculating the compensation gaps of each distributed resource by using the original variables and the original dual variables, and determining the maximum compensation gap value.

[0272] An embodiment of the present invention further provides an electronic device, which includes a processor and a memory:

[0273] The memory is used to store program codes and transmit the program codes to the processor;

[0274] The processor is configured to execute the distributed resource power distribution method according to the instructions in the program codes.

[0275] An embodiment of the present invention further provides a computer-readable storage medium, which is used to store program codes, and the program codes are used to execute the distributed resource power distribution method according to the embodiments of the present invention.

[0276] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0277] Each embodiment in this specification is described in a progressive manner. Each embodiment focuses on the differences from other embodiments. For the same or similar parts among the embodiments, reference can be made to each other.

[0278] Those skilled in the art should understand that the embodiments of the present invention can be provided as methods, devices, or computer program products. Therefore, the embodiments of the present invention can take the form of all-hardware embodiments, all-software embodiments, or embodiments combining software and hardware aspects. Moreover, the embodiments of the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program codes.

[0279] The embodiments of the present invention are described with reference to the flowcharts and / or block diagrams of methods, terminal devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be realized by computer program instructions. These computer program instructions can be provided to the processors of general-purpose computers, special-purpose computers, embedded processors, or other programmable data processing terminal devices to generate a machine, such that the instructions executed by the processors of the computer or other programmable data processing terminal devices generate a device for realizing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0280] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing terminal device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured product including an instruction device, and the instruction device realizes the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0281] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal device, such that a series of operation steps are executed on the computer or other programmable terminal device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable terminal device provide for realizing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1Steps of the functions specified in one or more boxes.

[0282] Although the preferred embodiments of the embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications to these embodiments once they know the basic creative concept. Therefore, the appended claims are intended to be construed to include the preferred embodiments and all changes and modifications falling within the scope of the embodiments of the present invention.

[0283] Finally, it should also be noted that in this text, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or terminal device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or terminal device. Without further limitation, an element defined by the statement "comprising one..." does not exclude the presence of additional identical elements in the process, method, article or terminal device comprising the said element.

[0284] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A distributed resource power allocation method, characterized in that: Applied to the primary frequency modulation system of a power grid; the method comprises: Calculating a total droop coefficient of the primary frequency regulation system of the power grid; Calculating the output power increment of each of the distributed resources; generating a distributed resource output constraint using the output power increment and the total droop coefficient; Taking the minimum unit time frequency regulation cost of distributed resources as the optimization goal, taking the output power increment constraint and the output constraint of the distributed resources as the constraint conditions, a primary frequency regulation power allocation model is constructed; Solving the primary frequency modulation power allocation model to obtain a power allocation result of the distributed resources; The distributed resources include micro gas turbines, distributed photovoltaics and distributed energy storage devices; the output power increment constraints include micro gas turbine constraints, distributed photovoltaic constraints and distributed energy storage constraints; the primary frequency modulation power allocation model is: Among them, N p is the prediction step length; C MT (k+n|k) is the frequency regulation cost of the micro gas turbine at time k+n; C PV (k+n|k) is the frequency regulation cost of distributed photovoltaic at time k+n; C B (k+n|k) is the frequency regulation cost of distributed energy storage at time k+n; The constraints are: Among them, ΔP MT,i (k+n|k) is the output power increment of the i-th micro gas turbine at time k+n, is the upper limit of the output power increment of the i-th micro gas turbine at time k+n, is the lower limit of the output power increment of the i-th micro gas turbine at time k+n; ΔP PV,i (k+n|k) is the output power increment of the i-th distributed photovoltaic at time k+n, is the upper limit of the output power increment of the i-th distributed photovoltaic at time k+n, is the lower limit of the output power increment of the i-th distributed photovoltaic at time k+n; ΔP B,i (k+n|k) is the output power increment of the i-th distributed energy storage at time k+n, is the upper limit of the output power increment of the i-th distributed energy storage at time k+n, is the lower limit of the output power increment of the i-th distributed energy storage at time k+n; S B,i (k+n|k) is the output power increment of the i-th distributed energy storage SOC, T s is the simulation step length, and are the upper and lower limits of the SOC of the i-th distributed energy storage; N MT is the number of micro gas turbines, N PV is the number of distributed photovoltaics, N B is the number of distributed energy storage devices, K sum It is the total droop coefficient of the primary frequency regulation system of the power grid.

2. The method according to claim 1, characterized in that The step of calculating the total droop coefficient of the primary frequency regulation system of the power grid comprises: Obtaining a frequency response model of the primary frequency regulation system of the power grid; calculating a frequency deviation of the frequency response model; The frequency deviation is used to calculate the total droop coefficient of the primary frequency regulation system of the power grid.

3. The method according to claim 1, characterized in that The step of solving the primary frequency modulation power allocation model to obtain the power allocation result of the distributed resources includes: Obtain the primal variables and primal dual variables of each distributed resource; Calculating the compensation gap of each distributed resource using the original variable and the original dual variable, and determining the maximum value of the compensation gap; Determine whether the maximum value of the compensation gap satisfies a preset convergence standard value; If so, output the original dual variable as the power allocation result; If not, converting the primary frequency modulation power allocation model into an unconstrained quadratic programming model; Using each distributed resource to solve the unconstrained quadratic programming model to obtain a correction amount of the coupling variable; The coupled variable correction amount is used to calculate the internal variable correction amount and the dual variable correction amount; Using the internal variable correction amount and the dual variable correction amount to update the original dual variable to obtain an updated dual variable; The updated dual variable is used to replace the original dual variable, and the step of returning to the step of using the original variable and the original dual variable to calculate the compensation gap of each distributed resource and determining the maximum value of the compensation gap is performed.

4. A distributed resource power allocation device, characterized in that: Applicable to the primary frequency modulation system of the power grid; the device comprises: A total droop coefficient calculation module, used to calculate the total droop coefficient of the primary frequency regulation system of the power grid; An output power increment calculation module, used to calculate the output power increment of each of the distributed resources; A distributed resource output constraint generation module, configured to generate a distributed resource output constraint by using the output power increment and the total droop coefficient; A model building module, used to build a primary frequency modulation power allocation model with the minimum unit time frequency modulation cost of distributed resources as the optimization goal, and with the output power increment constraint and the distributed resource output constraint as the constraint conditions; A solution module, used for solving the primary frequency modulation power allocation model to obtain the power allocation result of the distributed resources; The distributed resources include micro gas turbines, distributed photovoltaics and distributed energy storage devices; the output power increment constraints include micro gas turbine constraints, distributed photovoltaic constraints and distributed energy storage constraints; the primary frequency modulation power allocation model is: Among them, N p is the prediction step length; C MT (k+n|k) is the frequency regulation cost of the micro gas turbine at time k+n; C PV (k+n|k) is the frequency regulation cost of distributed photovoltaic at time k+n; C B (k+n|k) is the frequency regulation cost of distributed energy storage at time k+n; The constraints are: Among them, ΔP MT,i (k+n|k) is the output power increment of the i-th micro gas turbine at time k+n, is the upper limit of the output power increment of the i-th micro gas turbine at time k+n, is the lower limit of the output power increment of the i-th micro gas turbine at time k+n; ΔP PV,i (k+n|k) is the output power increment of the i-th distributed photovoltaic at time k+n, is the upper limit of the output power increment of the i-th distributed photovoltaic at time k+n, is the lower limit of the output power increment of the i-th distributed photovoltaic at time k+n; ΔP B,i (k+n|k) is the output power increment of the i-th distributed energy storage at time k+n, is the upper limit of the output power increment of the i-th distributed energy storage at time k+n, is the lower limit of the output power increment of the i-th distributed energy storage at time k+n; S B,i (k+n|k) is the output power increment of the i-th distributed energy storage SOC, T s is the simulation step length, and are the upper and lower limits of the SOC of the i-th distributed energy storage; N MT is the number of micro gas turbines, N PV is the number of distributed photovoltaics, N B is the number of distributed energy storage devices, K sum It is the total droop coefficient of the primary frequency regulation system of the power grid.

5. The device according to claim 4, characterized in that The total droop coefficient calculation module includes: A frequency response model acquisition submodule, used to acquire a frequency response model of the primary frequency regulation system of the power grid; A frequency deviation calculation submodule, used to calculate the frequency deviation of the frequency response model; The total droop coefficient calculation submodule is used to calculate the total droop coefficient of the primary frequency regulation system of the power grid using the frequency deviation.

6. The device according to claim 4, characterized in that The solution module comprises: The primitive variable and primitive dual variable acquisition submodule is used to obtain the primitive variables and primitive dual variables of each distributed resource; A compensation gap maximum value determination submodule, used to calculate the compensation gap of each distributed resource by using the original variable and the original dual variable, and determine the compensation gap maximum value; A judgment submodule, used to judge whether the maximum value of the compensation gap satisfies a preset convergence standard value; An output submodule, for outputting the original dual variable as a power allocation result if yes; An unconstrained quadratic programming model conversion submodule, for converting the primary frequency modulation power allocation model into an unconstrained quadratic programming model if no; A coupling variable correction amount calculation submodule is used to solve the unconstrained quadratic programming model using each distributed resource to obtain a coupling variable correction amount; An internal variable correction amount and a dual variable correction amount calculation submodule, used for calculating an internal variable correction amount and a dual variable correction amount by using the coupling variable correction amount; An updating submodule, configured to update the original dual variable using the internal variable correction amount and the dual variable correction amount to obtain an updated dual variable; The return submodule is used to replace the original dual variable with the updated dual variable, and return to the step of calculating the compensation gap of each distributed resource by using the original variable and the original dual variable, and determining the maximum value of the compensation gap.

7. An electronic device, characterized in that: The device comprises a processor and a memory: The memory is used to store program code and transmit the program code to the processor; The processor is used to execute the distributed resource power allocation method according to any one of claims 1-3 according to the instructions in the program code.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium is used to store program codes, and the program codes are used to execute the distributed resource power allocation method according to any one of claims 1 to 3.

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