Virtual power plant distributed power generation instruction distribution method and system for power distribution network

Abstract

The invention discloses a virtual power plant distributed power generation instruction distribution method and system for a power distribution network, and the method comprises the steps: firstly carrying out the linearization of a power distribution network AC power flow model, carrying out the modeling of the line active power and node voltage in the power distribution network, and representing the node voltage and the line active power as the mapping of the node power variation; setting constraint conditions including a power balance constraint, a node voltage constraint, a line capacity constraint, an adjustable unit power adjustment upper and lower limit constraint and a climbing rate constraint of each adjustable unit, and constructing a power generation instruction scheduling optimization model taking the highest frequency modulation performance and the lowest carbon emission as double optimization targets; and finally, solving the power generation instruction scheduling optimization model in each control period by adopting a Nesterov momentum acceleration-based distributed algorithm to realize rapid and accurate solving of the instruction, and setting a communication condition to reduce the communication pressure during distributed information exchange and improve the convergence speed of the model. The method is high in convergence speed and high in precision.

Description

Technical Field

[0001] The present invention belongs to the field of internal power instruction allocation of virtual power plants, and particularly relates to a distributed generation instruction allocation method and system for virtual power plants facing distribution networks. Background Art

[0002] With the global transition to zero carbon emissions, the number of distributed energy resources (hereinafter referred to as DER) is increasing. However, a large part of distributed energy resources are not directly managed by the power system. Virtual power plant (hereinafter referred to as VPP) technology provides an effective solution to achieve coordinated control and optimized management of internal DER. By aggregating and precisely controlling various distributed energy resources, virtual power plants can provide auxiliary frequency regulation services. From the perspective of an externally controlled power source, the VPP tracks the power regulation command (hereinafter referred to as PRC) sent by the grid dispatching center and adjusts the power of the VPP in real time to meet the requirements of power system frequency regulation. From the perspective of internal regulation and control allocation, it is necessary to efficiently allocate internal distributed energy resources with different characteristics through generation command dispatch (hereinafter referred to as GCD). Most traditional generation command decompositions are centralized, with limited ability to process massive data, prone to single-point failures during communication, and difficult to meet the requirements of unit privacy. Therefore, the GCD of virtual power plants is gradually developing towards a decentralized, flexible and open solution, and distributed algorithms have become an effective method to solve GCD.

[0003] There are three major pain points in the distributed GCD of virtual power plants: First, when designing the objective function, most assume that all DERs of the VPP are connected to an equivalent node, ignoring power flow changes in the network, voltage node over-limit, etc. Since traditional loads change slowly and are highly predictable, traditional GCD based on slower time-scale economic dispatch will not cause significant losses. However, in modern VPPs with high renewable energy penetration, short-term power fluctuations will increase significantly and be difficult to predict, and the internal topology of the VPP is complex. Therefore, traditional distributed GCD methods cannot quickly respond to frequency modulation commands while reducing carbon emissions within the virtual power plant, achieving a cleaner and lower-carbon virtual power plant operation effect, and the distributed GCD model becomes complex and time-sensitive. Summary of the Invention

[0004] Aiming at the deficiencies of the existing technology, the present invention provides a method and system for distributing generation instructions of a virtual power plant for a distribution network, introducing a linearized alternating current power flow model to model the active power and node voltage in the virtual power plant, and taking the power adjustment upper and lower limits of each frequency modulation resource, line capacity constraints, and node voltage constraints as important constraint conditions, and designing an optimal GCD model with the highest frequency modulation performance and reduced carbon emissions as the dual optimization objectives. In the algorithm solving part, a distributed algorithm based on Nesterov accelerated gradient is used to achieve fast and accurate solution of the instructions, and communication conditions are set to reduce the communication pressure during distributed information exchange and improve the convergence speed of distributed GCD iteration.

[0005] The specific technical solution of the present invention is as follows:

[0006] A method for distributing generation instructions of a virtual power plant for a distribution network, the method comprising the following steps:

[0007] Step 1: Linearize the alternating current power flow model of the distribution network, model the line active power and node voltage in the distribution network, and represent the node voltage and line active power as mappings of the node power change amount;

[0008] Step 2: Set constraint conditions including power balance constraints, node voltage constraints, line capacity constraints, adjustable unit power adjustment upper and lower limit constraints, and ramp rate constraints of each adjustable unit, and construct a generation instruction scheduling optimization model with the highest frequency modulation performance and the lowest carbon emissions as the dual optimization objectives;

[0009] Step 3: Use a distributed algorithm based on Nesterov momentum acceleration to solve the generation instruction scheduling optimization model in each control cycle, achieve fast and accurate solution of the instructions, and set communication conditions to reduce the communication pressure during distributed information exchange and improve the convergence speed of the generation instruction scheduling optimization model.

[0010] Further, the specific steps of the step 3 include the following sub-steps:

[0011] S3.1: Decouple the Lagrange multipliers in the generation instruction scheduling optimization model into n sub-problems, so that each adjustable unit can locally solve the Lagrange multipliers corresponding to itself;

[0012] S3.2: Initialize the Lagrange multipliers, iteration step sizes, and iteration times, and introduce the performance deficiency indicators of each adjustable unit and the power adjustment value at the previous moment;

[0013] S3.3: Update the Lagrange multiplier using a distributed algorithm based on Nesterov momentum acceleration, specifically including: calculating the actual power command of the $i$-th adjustable unit at time $t$ corresponding to iteration round $k + 1$ according to the update result of the Lagrange multiplier intermediate variable ; combining the power adjustment upper and lower limit constraints and ramp rate constraints of each adjustable unit, is corrected to obtain the actual power command of the $i$-th adjustable unit at the best time $t$ corresponding to iteration round $k + 1$;

[0014] S3.4: Perform repeated iteration according to the update result of the Lagrange multiplier until the iteration termination condition is reached, and obtain the best actual power command of the $i$-th adjustable unit at time $t$.

[0015] Furthermore, the S3.3 is implemented through the following sub-steps:

[0016] S3.3.1: Use the Nesterov momentum term to replace the Lagrange multiplier, perform gradient update on the Nesterov momentum term, and calculate the weighted sum $v$ of the Nesterov momentum gradient using the exponential weighted moving average method i ;

[0017] S3.3.2: Introduce a correction step to eliminate the difference between the local incremental cost $\psi$ obtained by Nesterov accelerated gradient descent i and the weighted sum $v$ of the Nesterov momentum gradient i so that $\psi$ i can be stabilized at the optimal point during the iteration process;

[0018] S3.3.3: Combine the correction information of neighbor nodes to achieve global consistency.

[0019] Furthermore, in the third step, set communication conditions to reduce the communication pressure during distributed information exchange, specifically:

[0020] During the communication process of the adjustable unit, if the change amount of the Lagrange multiplier in two adjacent iterations is less than the set termination condition, then stop the information interaction.

[0021] Furthermore, in the second step,

[0022] the power balance constraint is that when responding to the total command issued by the dispatching center, the total FM mileage and FM demand are balanced;

[0023] the node voltage constraint is that the node voltage of important nodes is within the upper and lower limits of the voltage safety range; the important nodes are the nodes where virtual power plant adjustable units are connected;

[0024] The line capacity constraint ensures that the power of all lines is within the upper and lower limits of the current line power during the dispatching process of the generation command;

[0025] The upper and lower limits of the adjustable unit power regulation constraint mean that the regulation power of each adjustable unit is within the upper and lower limits of the power adjustment;

[0026] The ramp rate constraint means that the power regulation rate of each adjustable unit is within the upper and lower limits of the power regulation rate of the adjustable unit.

[0027] Furthermore, the generation command dispatching optimization model is the linear weighted sum of the frequency modulation contribution functions and the carbon emission functions of all adjustable units;

[0028] The frequency modulation contribution function of the adjustable unit is specifically the square of the difference between the power adjustment value of the adjustable unit at the current moment and the power adjustment value at the previous moment, and then multiplied by the performance deficiency index σ of the corresponding adjustable unit i ;

[0029] The carbon emission function of the adjustable unit is specifically a quadratic function of the actual power command of the adjustable unit at the current moment, and the coefficient of the quadratic function is the carbon emission coefficient.

[0030] Furthermore, in the first step,

[0031] Linearize the AC power flow model of the distribution network, specifically: decouple the voltage amplitude and phase angle of the AC power flow model of the distribution network;

[0032] Express the node voltage as a mapping of the node power change, specifically: introduce the active-voltage sensitivity matrix and express the node voltage as the relationship of the active power change of each node;

[0033] Express the line active power as a mapping of the node power change, specifically: based on the linearized AC power flow model of the distribution network, introduce the active-voltage sensitivity matrix and express the node voltage as the relationship of the active power change of each node; introduce the power transfer distribution matrix and express the node phase angle difference as the relationship of the total output power of each node.

[0034] Furthermore, it includes one or more processors for implementing the distributed generation command allocation method for the virtual power plant facing the distribution network.

[0035] An electronic device includes:

[0036] One or more processors;

[0037] A storage device for storing one or more programs, which, when executed by the electronic device, enable the electronic device to implement a method for distributing distributed generation instructions of a virtual power plant for a distribution network.

[0038] A computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements a method for distributing distributed generation instructions of a virtual power plant for a distribution network.

[0039] The beneficial effects of the present invention are as follows:

[0040] (1) The present invention introduces a linearized AC power flow model to model the distribution network where the virtual power plant is located, and takes node voltage constraints and line capacity constraints as one of the constraints of the multi-objective optimization problem to prevent overvoltage and over-power of adjustable units during power adjustment.

[0041] (2) The present invention uses a distributed algorithm based on Nesterov momentum acceleration to achieve fast and accurate solution of instructions, speeds up the convergence rate, and ensures the convergence accuracy. The method of the present invention is not prone to single-point failures, has high scalability and flexibility, and protects the privacy of adjustable units in the virtual power plant;

[0042] (3) In the iterative process of the present invention, communication conditions are set to reduce the communication pressure during distributed information exchange and improve the convergence rate of distributed GCD. Description of the Drawings

[0043] 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 drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can also obtain the drawings of other embodiments without creative efforts.

[0044] Figure 1 It is a flowchart of a method for quickly distributing distributed generation instructions of a virtual power plant for a low-carbon distribution network according to an embodiment of the present invention.

[0045] Figure 2 It is a power residual comparison chart of an improved distributed algorithm based on Nesterov momentum acceleration, an exact diffusion algorithm, a standard diffusion algorithm, and a consensus algorithm.

[0046] Figure 3 It is a result chart of the power adjustment values of each adjustable unit, and the abscissa is time (s).

[0047] Figure 4 It is the power curve of the virtual power plant tracking P RRC The abscissa is time (s).

[0048] Figure 5 It is the carbon emission curve during the participation of the virtual power plant in secondary frequency regulation. The abscissa is time (s). Specific implementation manners

[0049] The present invention will be described in detail below according to the accompanying drawings and preferred embodiments. The purpose and effect of the present invention will become clearer. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0050] The method for quickly allocating distributed generation instructions of a virtual power plant for a low-carbon distribution network provided by the present invention first introduces a linearized AC power flow model. Using the voltage sensitivity matrix and the power transfer distribution matrix, the active power flow of the line is expressed as a mapping relationship of the node power change. The node voltage constraint and the line capacity constraint are used as important constraints for the multi-objective optimization problem to prevent the adjustable unit from exceeding the limit during power adjustment. When constructing the optimization problem, a GCD model with improving frequency regulation performance and reducing carbon emissions as the dual optimization objectives is designed to achieve the improvement of frequency regulation performance and the reduction of carbon emissions. For this optimization model, the Lagrange multipliers in the optimization model are decoupled into n sub-problems, so that the problem can be locally solved in each adjustable unit; a distributed algorithm based on Nesterov momentum acceleration is adopted, and the Nesterov momentum gradient is introduced to replace the traditional gradient term to accelerate the convergence speed and ensure the convergence accuracy. Communication conditions are set during the iteration process to reduce the communication pressure during distributed information exchange and improve the convergence speed of the distributed GCD.

[0051] This embodiment is introduced by taking a virtual power plant composed of n distributed resources as an example. As Figure 1 shown, the method for quickly allocating distributed generation instructions of a virtual power plant for a low-carbon distribution network specifically includes the following steps:

[0052] S1: Linearize the AC power flow model of the distribution network, model the active power of the lines and the node voltages in the distribution network, and express the node voltages and the active power of the lines as mappings of the node power change amounts.

[0053] The element Y in the admittance matrix Y of the power system pq can be expressed as:

[0054]

[0055] In the formula, G pq is the real part of the element Y in the admittance matrix of the power system pq , and B pq is the imaginary part of the element Y in the admittance matrix of the power system pq . y pp is the shunt admittance of node p, y pq is the admittance of line l between nodes p and q, ypq = g pq + j*b pq where g pq represents the conductance of line l between nodes p and q, and b pq represents the susceptance of line l between nodes p and q.

[0056] Without considering reactive power, the AC power flow equations of the distribution network are expressed as follows:

[0057]

[0058] where P p represents the AC power flow of node p, num represents the total number of nodes, v p is the voltage magnitude of node p, v q is the voltage magnitude of node q, and θ pq is the voltage phase angle difference between nodes p and q.

[0059] Since the power flow of the distribution network has two characteristics:

[0060] 1) The voltage magnitude of the nodes is close to 1 p.u.;

[0061] 2) The phase angle difference between the two ends is very small.

[0062] Therefore, by decoupling the voltage magnitude and phase angle of the AC power flow equations, we can obtain:

[0063]

[0064] Therefore, the AC power flow equations in branch power form can be linearly expressed as:

[0065]

[0066] where P pq represents the AC power flow from node p to node q.

[0067] According to the voltage sensitivity formula:

[0068]

[0069] where v p,0 is the voltage of node p at steady state, S pf is the element in the p-th row and f-th column of the active-power voltage sensitivity matrix S, and ΔP f is the change in the active power output at node f. Therefore, g pq (v p - v q ) can be expressed as:

[0070]

[0071] According to the power transfer distribution matrix, -b pq θ pq can be expressed as:

[0072]

[0073]

[0074] In the formula, P f is the total output power of node f, P f,0 is the initial power of node f, ρ lf is an element in the power transfer distribution matrix ρ, ρ = [ρ lf ] ∈ R m×num , and m is the total number of lines. The power transfer distribution matrix ρ can also be expressed as:

[0075]

[0076] In the formula, B line is the line-node admittance matrix, B x is the node admittance matrix calculated based on Kirchhoff's law, and the specific calculation formula is as follows:

[0077]

[0078]

[0079] where X i,j represents the line reactance from node i to j.

[0080] The active power flow formula after linearizing the distribution network is as follows:

[0081]

[0082] To make the representation of the optimization function constraints more concise later, in the active power flow formula after linearizing the distribution network, the relationship between the active power flow P l of line l and the change in active power output ΔP f at node f can be written in the form of a mapping,

[0083]

[0084] where is a function of ΔP f . Since the present invention only considers the nodes with virtual power plant resources in the distribution network and the loads of other nodes are known constants, the above mapping formula can be simplified to,

[0085]

[0086] Among them, n is the number of adjustable resources of the virtual power plant in the distribution network, which represents the power adjustment value of the i-th adjustable resource.

[0087] S2: Set the constraint conditions including the power balance constraint, node voltage constraint, line capacity constraint, upper and lower limits of adjustable unit power regulation constraint, and ramp rate constraint of each adjustable unit, and construct a GCD optimization model with the highest frequency modulation performance and the lowest carbon emission as the dual optimization objectives.

[0088] The GCD optimization model is expressed as the linear weighted sum of the frequency modulation contribution function and the carbon emission function of the adjustable unit, as follows:

[0089]

[0090] In the formula, represents the frequency modulation contribution function of the i-th adjustable resource, represents the carbon emission function of the i-th adjustable resource, and ϵ is the weight coefficient of linear weighting;

[0091]

[0092]

[0093]

[0094] In the formula, ΔT is the implementation period of each control interval, t represents the current time point, t = ΔT, 2*ΔT,..., N*ΔT, and N is the total number of control periods; a Co2,i , b Co2,i , c Co2,i are the three carbon emission coefficients of each adjustable unit, represents the actual power command of the i-th adjustable unit at time t, represents the initial power of the i-th adjustable unit at time t; is the power adjustment value of the i-th adjustable unit at time t; σ i represents the performance deficiency index of the i-th adjustable unit.

[0095] The constraints of the GCD optimization model specifically include:

[0096] Power command balance constraint. When responding to the total command P PRC (t) issued by the dispatching center, the balance of the total mileage of frequency modulation and the demand for frequency modulation is satisfied:

[0097]

[0098] Node voltage constraint. To ensure the safety of the node voltage in the virtual power plant during the power regulation process, the important node voltage constraint is set as follows:

[0099]

[0100] Wherein, v j is the node voltage of the important node j. In the present invention, the node with the virtual power plant adjustable unit connected is regarded as the important node, and are respectively the upper and lower limits of the voltage safety range of the important node j.

[0101] The line capacity constraint is used to prevent exceeding the line capacity during the GCD process and ensure the safe and stable operation of the power grid:

[0102]

[0103]

[0104]

[0105] Wherein, and represent the upper and lower limits of the power of line l, L max and L min represent the set of upper and lower limits of the line capacity;

[0106] The power regulation lower limit constraint is used to ensure that the adjustment power of each adjustable unit cannot exceed the power adjustment limit,

[0107]

[0108] Wherein, and respectively represent the upper and lower limits of the power adjustment of the i-th adjustable unit.

[0109] The ramp rate constraint specifically means that the adjustment power of each adjustable unit should not exceed the limit of the adjustment rate:

[0110]

[0111] Wherein, is the power adjustment output of the i-th adjustable unit in the previous control period t - ΔT, V i is the ramp rate of the i-th adjustable unit in the virtual power plant.

[0112] S3: Use the distributed algorithm based on Nesterov momentum acceleration to solve the GCD optimization model in each control period, realize the fast and accurate solution of the instruction, and set the communication conditions to reduce the communication pressure during the distributed information exchange and improve the convergence speed of the distributed GCD iteration.

[0113] Solving the GCD optimization model in each control period by using an improved distributed algorithm based on Nesterov momentum acceleration includes the following sub-steps:

[0114] S3.1: Decouple the Lagrange multipliers in the GCD optimization model into n sub-problems, so that each adjustable unit can locally solve the Lagrange multiplier corresponding to itself.

[0115] Among them, the objective function of the GCD model including power instruction constraints, node voltage constraints, line capacity constraints, upper and lower limits of power adjustment of each adjustable unit, and ramp rate constraints is rewritten in the form of a Lagrangian dual function, which is expressed as follows:

[0116]

[0117] Among them, λ is the Lagrange multiplier of the equality constraint, and are the Lagrange multipliers of the voltage constraint of the important node j, and are the Lagrange multipliers of the line capacity constraint of the line l.

[0118] Decouple the coupled Lagrangian function into n sub-problems, and each adjustable unit can locally solve the Lagrange multiplier corresponding to itself, which is expressed as follows:

[0119]

[0120]

[0121]

[0122]

[0123] In the formula, λ i is the Lagrange multiplier of the equality constraint in the decoupled adjustable unit i, and respectively represent the Lagrange multipliers of the voltage constraint of the important node j in the decoupled adjustable unit i, and respectively represent the Lagrange multipliers of the line capacity constraint in the decoupled adjustable unit i, OF i represents the objective function of the i-th adjustable unit.

[0124] S3.2: Initialize the Lagrange multiplier, iteration step size and number of iterations, and introduce the performance deficiency index of each adjustable unit and the power adjustment value at the previous moment;

[0125] S3.3: Update the Lagrange multipliers using a distributed algorithm based on Nesterov momentum acceleration, specifically including: calculating the actual power command of the $i$-th adjustable unit at time $t$ corresponding to iteration round $k + 1$ according to the Lagrange multiplier update result intermediate variable ; combined with the power adjustment upper and lower limit constraints and ramp rate constraints of each adjustable unit, correct to obtain the actual power command of the $i$-th adjustable unit at the optimal time $t$ corresponding to iteration round $k + 1$.

[0126] Since $\lambda$ i , , , and are updated independently of each other and do not affect each other, taking the update of $\lambda$ i as an example, the iterative process of the distributed algorithm is as follows:

[0127] Step 1, use the Nesterov momentum term to replace the Lagrange multiplier, and perform gradient update on the Nesterov momentum term, and calculate the weighted sum $v$ i of the Nesterov momentum gradient using the exponentially weighted moving average method, specifically as follows:

[0128]

[0129] where $k$ is the iteration number of the distributed algorithm, $v$ i is the Nesterov momentum term, $\beta$ is the Nesterov momentum delay coefficient, is the Nesterov momentum coefficient, is the local incremental cost obtained by Nesterov accelerated gradient descent, is the iteration step size. .

[0130] Step 2, in order to make the local incremental cost stable at the optimal point during the iteration process, introduce a correction step to eliminate the difference between and $v$ i :

[0131]

[0132] where is the local incremental cost considering bias correction.

[0133] Step 3, information diffusion step, combine the correction information of neighbor nodes to achieve global consistency:

[0134]

[0135] In the formula, Φ W = [ω i,j ] ∈ R n×n represents the set of line weights. If , then ω i,j > 0, indicating that the ith adjustable unit and the jth adjustable unit are neighbors of each other; if , then ω i,j = 0, and Φ E represents the set of communication lines; the specific weight definition is as follows:

[0136]

[0137] where, n i , n j respectively represent the number of neighbor nodes of the ith adjustable unit and the jth adjustable unit; and are updated in a similar way to the update of λ i and can be executed in parallel.

[0138] Calculate the power adjustment command of the ith adjustable unit at the best t moment corresponding to the (k + 1)th iteration round according to the update result of the Lagrange multiplier of the intermediate variable ; Based on the intermediate variable and combined with the upper and lower limits of the power adjustment of each distributed resource for correction to obtain the actual power command of the ith adjustable unit at the best t moment of the (k + 1)th iteration round, as follows:

[0139]

[0140]

[0141] S3.4: Perform repeated iterations according to the update result of the Lagrange multiplier until the iteration termination condition is reached, and obtain the best actual power command of the ith adjustable unit at t moment.

[0142] Set communication conditions during the iteration process to reduce the communication pressure during distributed information exchange and improve the convergence speed of the distributed GCD process, as follows:

[0143] During the communication process of the adjustable units, if the change amount of the Lagrange multiplier in two adjacent iterations is less than the set termination condition, then the information exchange will be stopped.

[0144]

[0145] where, ε is the termination condition set by the system. When the above conditions are met, the neighbor nodes will use the last to update local information.

[0146] The effects of the present invention will be further described below in conjunction with a specific embodiment.

[0147] In this embodiment, a virtual power plant composed of 5 distributed frequency regulation units of the distributed adjustable units 1-5 (1: photovoltaic, 2: micro gas turbine, 3: micro gas turbine, 4: energy storage, 5: small hydropower) is used to verify the effectiveness of this method, and the weight coefficient is set to 0.9 in this paper. When P PRC = 10MW, the power residuals of the improved distributed algorithm based on Nesterov momentum acceleration, the exact diffusion algorithm, the standard diffusion algorithm, and the consensus algorithm are compared as Figure 2 shown. The calculation formula of the power residual is as follows:

[0148]

[0149] where is the set of frequency regulation mileage commands of each adjustable unit in the k-th iteration, is the set of optimal values obtained by the solver. The power residual curve is used to compare the convergence speeds of the four consensus algorithms. Compared with the remaining three methods, the method of the present invention has a faster convergence speed and higher convergence accuracy.

[0150] Modeling is carried out on the MATLAB / Simulink platform, and a virtual power plant system composed of 5 distributed frequency regulation units of the distributed adjustable units 1-5 (1: photovoltaic, 2: micro gas turbine, 3: micro gas turbine, 4: energy storage, 5: small hydropower) is used to verify the response performance of the virtual power plant when P PRC = 10MW at 200s. The total input duration in the system is 600s. Figure 3 is the instruction allocation situation of the 5 adjustable units during the GCD real-time control process. It can be seen that there are differences in the frequency regulation performance and carbon emissions between different adjustable units, which will affect the output size of the power. The frequency regulation response speed of the gas turbine is slow and the carbon emissions are large, so the frequency regulation power is low; the carbon emissions during the operation of distributed resources dominated by renewable energy such as photovoltaic are low, and its carbon emission coefficient quantifies the carbon emissions during the entire operation cycle (including transportation, installation, and operation and maintenance), which is generally low, so it undertakes more power adjustment. In addition, there are effects of node voltage constraints and line capacity constraints, and the power adjustment values of the adjustable units are all within the constraints. In addition, four methods (Method 1: the method using a centralized solver, regarded as a reference value for other algorithms; Method 2: the method of fixed ratio allocation; Method 3: the consensus algorithm; the present invention) are compared. Figure 4 is the virtual power plant response P calculated by the four methods PRCFrom the response curve of the instruction, it can be seen that the fixed ratio allocation method has a poor response ability to track instructions because there is no dynamic optimization process, that is, the frequency modulation performance is poor. The accuracy of the consensus algorithm is low, the convergence speed is slow, and it is also impossible to calculate the optimal solution of the frequency modulation performance. In contrast, the frequency modulation performance of the present invention is the best, which proves the improvement of the present invention in terms of frequency modulation performance. Figure 5 By comparing the carbon emissions of four methods, it is found that the method proposed in the present invention also has the smallest cumulative carbon emissions.

[0151] Those of ordinary skill in the art can understand that the above are only preferred examples of the invention and are not used to limit the invention. Although the invention has been described in detail with reference to the foregoing examples, for those skilled in the art, they can still modify the technical solutions described in the foregoing examples, or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, etc. made within the spirit and principle of the invention shall be included within the protection scope of the invention.

Claims

1. A method for distributing distributed generation instructions of a virtual power plant for a distribution network, characterized in that, The method includes the following steps: Step 1: Linearize the AC power flow model of the distribution network, model the active power of the lines and the node voltages in the distribution network, and represent the node voltages and the active power of the lines as mappings of the changes in node power. Step 2: Set constraint conditions including power balance constraints, node voltage constraints, line capacity constraints, upper and lower limits of adjustable unit power regulation constraints, and ramp rate constraints for each adjustable unit, and construct a generation command scheduling optimization model with the dual optimization objectives of the highest frequency regulation performance and the lowest carbon emissions. Step 3: Adopt a distributed algorithm based on Nesterov momentum acceleration to solve the generation command scheduling optimization model in each control cycle, realize fast and accurate solution of the commands, and set communication conditions to reduce the communication pressure during distributed information exchange and improve the convergence speed of the generation command scheduling optimization model.

2. The distributed generation instruction allocation method for a virtual power plant oriented to a distribution network according to claim 1, wherein The specific steps of Step 3 include the following sub-steps: S3.1: Decouple the Lagrange multipliers in the generation command scheduling optimization model into n sub-problems, so that each adjustable unit can locally solve the Lagrange multipliers corresponding to itself. S3.2: Initialize the Lagrange multipliers, iteration step sizes, and iteration times, and introduce the performance deficiency indicators of each adjustable unit and the power adjustment value at the previous moment. S3.3: Update the Lagrange multiplier by using a distributed algorithm based on Nesterov momentum acceleration, specifically including: calculating the actual power command of the i-th adjustable unit at time t corresponding to the (k + 1)-th iteration round according to the update result of the Lagrange multiplier intermediate variable ; combining the upper and lower limits constraints and ramp rate constraints of the power regulation of each adjustable unit, correct it to obtain the actual power command of the i-th adjustable unit at the best time t corresponding to the (k + 1)-th iteration round; S3.4: Perform repeated iterations according to the updated results of the Lagrange multipliers until the iteration termination condition is reached, and obtain the optimal actual power command of the i-th adjustable unit at time t.

3. The method for distributing distributed generation instructions of a virtual power plant for a distribution network according to claim 2, characterized in that, The S3.3 is realized through the following sub-steps: S3.3.1: Replace the Lagrange multiplier with the Nesterov momentum term, perform gradient update on the Nesterov momentum term, and calculate the weighted sum v of the Nesterov momentum gradients using the exponentially weighted moving average method i ; S3.3.2: Introduce a correction step to eliminate the difference between the local incremental cost ψ obtained by Nesterov accelerated gradient descent i and the weighted sum v of Nesterov momentum gradients i so that ψ i can be stabilized at the optimal point during the iteration process; S3.3.3: Combine the correction information of neighbor nodes to achieve global consistency.

4. The method for distributing distributed generation instructions of a virtual power plant for a distribution network according to claim 1, wherein In Step 3, setting communication conditions to reduce the communication pressure during distributed information exchange specifically means: During the communication process of the adjustable units, if the change amount of the Lagrange multipliers in two adjacent iterations is less than the set termination condition, then the information interaction will be stopped.

5. The method for distributing distributed generation instructions of a virtual power plant for a distribution network according to claim 1, characterized in that, In Step 2, The power balance constraint is to satisfy the balance of the total frequency regulation mileage and the frequency regulation demand when responding to the total command issued by the dispatching center. The node voltage constraint is that the node voltages of important nodes are within the upper and lower limits of the voltage safety range; the important nodes are the nodes where virtual power plant adjustable units are connected. The line capacity constraint is to ensure that the power of all lines is within the upper and lower limits of the current line power during the generation command scheduling process. The upper and lower limits of adjustable unit power regulation constraints are that the adjustment power of each adjustable unit is within the upper and lower limits of power adjustment. The ramp rate constraint is that the power regulation rate of each adjustable unit is within the upper and lower limits of the power regulation rate of the adjustable unit.

6. The method for distributing distributed generation instructions of a virtual power plant for a distribution network according to claim 1, wherein The generation command scheduling optimization model is the linear weighted sum of the frequency regulation contribution degree functions and carbon emission functions of all adjustable units. The frequency modulation contribution function of the adjustable unit is specifically the square of the difference between the power adjustment value of the adjustable unit at the current moment and the power adjustment value at the previous moment, and then multiplied by the performance deficiency index σ of the corresponding adjustable unit i ; The carbon emission function of the adjustable unit is specifically a quadratic function of the actual power command of the adjustable unit at the current moment, and the coefficient of the quadratic function is the carbon emission coefficient.

7. The method for distributing distributed generation instructions of a virtual power plant for a distribution network according to claim 1, wherein In Step 1, To linearize the AC power flow model of the distribution network specifically means: decouple the voltage amplitude and phase angle of the AC power flow model of the distribution network. Express the node voltage as a mapping of the change in node power, specifically: introduce the active power-voltage sensitivity matrix and express the node voltage as a relationship with the change in active power of each node. Express the line active power as a mapping of the change in node power, specifically: based on the linearized AC power flow model of the distribution network, introduce the active power-voltage sensitivity matrix and express the node voltage as a relationship with the change in active power of each node; introduce the power transfer distribution matrix and express the node phase angle difference as a relationship with the total output power of each node.

8. A distributed generation instruction allocation system for a virtual power plant oriented to a distribution network, characterized in that, Include one or more processors for implementing the distribution method of virtual power plant distributed generation instructions for the distribution network according to any one of claims 1 to 7.

9. An electronic device, characterized in that, Include: One or more processors; A storage device for storing one or more programs, which when executed by the electronic device, cause the electronic device to implement the distribution method of virtual power plant distributed generation instructions for the distribution network according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, A program is stored thereon, which when executed by a processor, implements the distribution method of virtual power plant distributed generation instructions for the distribution network according to any one of claims 1 to 7.

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