Distributed Control Method and System for DC Microgrids Based on Optimal Generation Cost

Abstract

This disclosure provides a distributed control method and system for DC microgrids based on optimal power generation cost, relating to the field of distributed control technology for DC microgrids. Distributed control is achieved through controllers installed in each converter. Specifically, the method involves: acquiring operating data from adjacent converters to estimate global information of the DC microgrid; estimating the optimal current value based on the global information, and calculating the adaptive droop gain in real time using the optimal current value; compensating for voltage deviation based on the calculated adaptive droop gain and the line resistance of each converter to obtain the compensated bus voltage; and performing droop control and secondary voltage regulation control on the DC microgrid based on the adaptive droop gain and the compensated bus voltage to ensure that the output current of each converter tracks the optimal current value in real time, ensuring that the bus voltage remains stable at its rated value. Even under resistive load variations, this invention can still guarantee the optimal output current of each distributed converter, ensuring minimum power generation cost and achieving voltage stability.

Description

Technical Field

[0001] This disclosure relates to the field of distributed control technology for DC microgrids, specifically to a distributed control method and system for DC microgrids based on optimal power generation cost. Background Technology

[0002] DC microgrids, with their high efficiency, ease of integration with renewable energy generation systems, and high flexibility, have become an important component of smart grids and distributed energy systems. In a DC microgrid, multiple distributed power sources and loads are interconnected via a DC bus to achieve efficient utilization of clean energy. However, the operation of DC microgrids faces numerous challenges, including voltage stability, power distribution coordination, and the complexity of maintaining system economy under dynamic load changes. With the increasing proportion of renewable energy generation, the uncertainty of distributed power source output characteristics further exacerbates these challenges. How to achieve efficient and low-cost energy distribution while ensuring voltage stability has become one of the core research issues.

[0003] Currently, most control methods for DC microgrids are based on traditional droop control, which achieves power sharing by adjusting the voltage-current characteristics of each distributed power source. However, traditional droop control struggles to balance voltage recovery and generation cost optimization under dynamic load changes: fixed control parameters cannot adapt to load fluctuations and the differences in characteristics of distributed power sources, potentially leading to uneven power distribution and decreased system efficiency. Furthermore, the generation cost of distributed power sources in a microgrid is typically non-linearly related to output power. Under frequent load changes, traditional control methods alone cannot dynamically achieve optimal power distribution and effectively reduce system operating costs. Therefore, existing methods cannot balance real-time economics and voltage stability, impacting the overall performance of the DC microgrid. Summary of the Invention

[0004] To address the aforementioned issues, this disclosure proposes a distributed control method and system for DC microgrids based on optimal power generation cost. Even under varying resistive load conditions, each distributed converter can still guarantee optimal output current, ensure minimum power generation cost, and achieve voltage stability.

[0005] According to some embodiments, the present disclosure adopts the following technical solutions:

[0006] The distributed control method for DC microgrids based on optimal power generation cost uses controllers located at each converter for distributed control. Specifically:

[0007] The system acquires the operating data of adjacent converters and estimates the global information of the DC microgrid to be controlled through a dual dynamic compensation consistency algorithm.

[0008] Based on the estimated global information, with the goal of minimizing the overall power generation cost, the optimal current value is estimated, and the adaptive droop gain is calculated in real time using the optimal current value.

[0009] Based on the calculated adaptive droop gain and the line resistance of each converter, and based on the designed virtual voltage drop parameters, the voltage deviation is compensated to obtain the compensated bus voltage.

[0010] Based on adaptive droop gain and compensated bus voltage, droop control and secondary voltage regulation are performed on the DC microgrid to ensure that the output current of each converter tracks the optimal current value in real time and that the bus voltage remains stable at the rated value.

[0011] According to some embodiments, the present disclosure adopts the following technical solutions:

[0012] The distributed control system for DC microgrids based on optimal power generation cost performs distributed control through controllers located at each converter, including a global information estimation module, an optimal current solution module, a bus voltage compensation module, and a microgrid control module.

[0013] The global information estimation module is configured to: acquire the operating data of adjacent converters and estimate the global information of the DC microgrid to be controlled through a dual dynamic compensation consistency algorithm;

[0014] The optimal current solution module is configured to: estimate the optimal current value based on the estimated global information, with the goal of minimizing the overall power generation cost, and calculate the adaptive droop gain in real time using the optimal current value;

[0015] The bus voltage compensation module is configured to: compensate for voltage deviation based on the calculated adaptive droop gain and the line resistance of each converter, and based on the designed virtual voltage drop parameters, to obtain the compensated bus voltage.

[0016] The microgrid control module is configured to perform droop control and secondary voltage regulation on the DC microgrid based on adaptive droop gain and compensated bus voltage, ensuring that the output current of each converter tracks the optimal current value in real time and that the bus voltage remains stable at the rated value.

[0017] According to some embodiments, the present disclosure adopts the following technical solutions:

[0018] A computer program product includes a computer program that, when executed by a processor, implements the aforementioned distributed control method for DC microgrids based on optimal power generation cost.

[0019] According to some embodiments, the present disclosure adopts the following technical solutions:

[0020] A non-transitory computer-readable storage medium is provided for storing computer instructions, which, when executed by a processor, implement the aforementioned distributed control method for DC microgrids based on optimal power generation cost.

[0021] According to some embodiments, the present disclosure adopts the following technical solutions:

[0022] An electronic device includes a processor, a memory, and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory to enable the electronic device to implement the distributed control method for DC microgrids based on optimal power generation cost.

[0023] Compared with the prior art, the beneficial effects of this disclosure are as follows:

[0024] This invention provides a distributed control method for DC microgrids based on optimal power generation cost. By solving the power generation cost function of each power generation unit, the optimal output current value is estimated, and an adaptive droop gain is designed on this basis to ensure that the droop gain can change in real time under load changes, thereby achieving optimal current output and minimum power generation cost.

[0025] By analyzing the voltage deviation caused by line resistance and droop gain, a secondary voltage regulator controller based on virtual voltage drop was designed to ensure that the voltage is stable at the rated value. To meet the requirements of distributed control, a static average consensus algorithm and a dynamic average consensus algorithm were designed to estimate the global information in the controller and achieve distributed control with optimal power generation cost. Attached Figure Description

[0026] The accompanying drawings, which form part of this disclosure, are used to provide a further understanding of this disclosure. The illustrative embodiments of this disclosure and their descriptions are used to explain this disclosure and do not constitute an undue limitation of this disclosure.

[0027] Figure 1 This is a flowchart of the method in Example 1.

[0028] Figure 2 This is a schematic diagram of the DC microgrid structure and control method of Example 1.

[0029] Figure 3 This is a schematic diagram of the microgrid simulation verification in Example 1.

[0030] Figure 4 This is a graph showing the results of a step load change in Example 1.

[0031] Figure 5 The graph shows the results of a sinusoidal load variation in Example 1. Detailed Implementation

[0032] The present disclosure will be further described below with reference to the accompanying drawings and embodiments.

[0033] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of this disclosure. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains.

[0034] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this disclosure. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms “comprising” and / or “including” are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0035] Example 1

[0036] One embodiment of this disclosure provides a distributed control method for a DC microgrid based on optimal power generation cost, which performs distributed control through controllers located at each converter, specifically:

[0037] Furthermore, the controllers installed at each converter are used to construct a DC microgrid topology containing multiple converters. A controller for droop control and secondary voltage regulation is installed at each converter, and each converter is connected to a distributed communication network to ensure that adjacent converters have information exchange capabilities.

[0038] Step S1: Obtain the operating data of adjacent converters and estimate the global information of the DC microgrid to be controlled through a dual dynamic compensation consistency algorithm.

[0039] Furthermore, the global information of the DC microgrid to be controlled is estimated using a dual dynamic compensation consistency algorithm, expressed by the following formula:

[0040]

[0041] Among them, y i For estimated global information, including static global information and dynamic global information I avg u i ; α and β are adjustment parameters, N i Let x be the set of adjacent transformers of the i-th transformer. i m is an intermediate variable. i The dynamic input compensation term, ζ and ψ are adjustment factors used to adjust the algorithm's convergence speed and system stability, g iThis represents the local input information corresponding to the i-th transformer.

[0042] Step S2: Based on the estimated global information, with the goal of minimizing the overall power generation cost, estimate the optimal current value, and calculate the adaptive droop gain in real time using the optimal current value.

[0043] Furthermore, the estimation of the optimal current value with the goal of minimizing the overall power generation cost specifically involves:

[0044] Based on the power generation capacity of the converter, the power generation efficiency of the distributed generation unit, the operating cost and the environmental cost, the power generation cost of each distributed generation unit is calculated, and then the cost function of the overall power generation cost is constructed.

[0045] Based on the cost function and constraints, the optimal current value is obtained by solving the problem with the goal of minimizing the overall power generation cost.

[0046] Furthermore, the adaptive droop gain is calculated in real time using the optimal current value, and the specific formula is as follows:

[0047]

[0048] in, For the adaptive droop gain of the i-th converter, Let σ be the optimal current value for the i-th converter, and σ be an adjustable parameter used to achieve a trade-off between current sharing and voltage recovery accuracy.

[0049] Step S3: Based on the calculated adaptive droop gain and the line resistance of each converter, and based on the designed virtual voltage drop parameters, compensate for the voltage deviation to obtain the compensated bus voltage.

[0050] Furthermore, the compensated bus voltage is expressed by the formula:

[0051]

[0052] Among them, V bus For the compensated bus voltage, V ref R is the voltage rating. i Let u be the line impedance of the i-th converter. i For virtual voltage drop compensation parameters, Let N be the adaptive droop gain of the i-th converter, and N be the number of converters.

[0053] Step S4: Based on the adaptive droop gain and the compensated bus voltage, perform droop control and secondary voltage regulation control on the DC microgrid to ensure that the output current of each converter tracks the optimal current value in real time and ensure that the bus voltage is stable at the rated value.

[0054] As one embodiment, the distributed control method for DC microgrids based on optimal power generation cost disclosed herein can ensure that each distributed converter can still output the optimal current, guarantee the minimum power generation cost and achieve voltage stability even under resistive load changes. The specific implementation process is described below.

[0055] First, we will introduce the structure of a DC microgrid and the necessary graph theory knowledge:

[0056] like Figure 2 As shown, the DC microgrid in this embodiment includes N DC-DC converters. Distributed generation units are connected to the same bus through the DC-DC converters, and the loads are powered by the DC bus.

[0057] In a DC microgrid, N DC-DC converters interact with each other through a distributed communication network. Consider a communication network containing N converters, defined as G = (V, E, A), where V is the set of nodes, and each node is a converter. Let be the set of all edges, and be the communication connections between nodes; Let [a] be an adjacency matrix. ij ] is the weighting coefficient, when (v i ,v j When )∈E, a ij =1, and all others are 0.

[0058] Define N i Let D be the set of neighboring nodes of the i-th node, and D be the degree matrix of the communication network. in =D out =diag{d i},in The Laplace matrix is ​​defined as L = DA, and when i = j, When i≠j, L ij =-a ij .

[0059] The control method of this embodiment, such as Figure 1 As shown in the lower half, the DC microgrid is distributedly controlled through droop control and secondary voltage regulation control to ensure that the output current of each converter tracks the optimal current value in real time and ensures that the bus voltage is stable at the rated value.

[0060] First, the cost function for each power generation unit is given below:

[0061]

[0062] Among them, C i (P i Let P be the power generation cost of the i-th power generation unit. i Let r be the power output of the i-th converter. i wi , and q i These represent the power generation efficiency, operating cost, and environmental cost of the i-th power generation unit, respectively.

[0063] Subsequently, the cost function and constraints for the overall power generation cost of the DC microgrid are obtained as follows:

[0064]

[0065] Among them, P i opt V represents the optimal power output of the i-th converter. ref R is the reference voltage, i.e., the voltage rating. L It is a variable resistive load.

[0066] The optimal current can be obtained by solving the optimization problem as follows:

[0067]

[0068] Based on circuit transmission losses, the conversion between bus voltage and converter voltage can be obtained as follows:

[0069]

[0070] Among them, V i and I i Let R be the output voltage and current of the i-th converter, respectively. i Let I be the line resistance of the i-th converter circuit. load This represents the total current on the load side.

[0071] Then, the optimal current estimate can be obtained as follows:

[0072]

[0073] in, as well as The above parameters are global information and need to be estimated using a dynamic averaging consensus algorithm. The formula is as follows:

[0074]

[0075] Among them, y i For estimated global information, α and β are adjustment parameters, and N i Let x be the set of adjacent transformers of the i-th transformer. i m is an intermediate variable. i The dynamic input compensation term, ζ and ψ are adjustment factors used to adjust the algorithm's convergence speed and system stability, g i This represents the local input information corresponding to the i-th transformer.

[0076] Based on the obtained optimal current estimation information, the adaptive droop gain is designed as follows:

[0077]

[0078] Here, σ is an adjustable parameter that allows for a trade-off between current sharing and voltage recovery accuracy.

[0079] Subsequently, to stabilize the bus voltage at its rated value, the bus voltage expression including secondary control compensation values ​​is given as follows:

[0080]

[0081] Among them, V bus For the compensated bus voltage, V ref R is the voltage rating. i Let u be the line impedance of the i-th converter. i For virtual voltage drop compensation parameters, Let be the adaptive droop gain of the i-th converter.

[0082] Subsequently, based on the droop gain and line resistance, the virtual voltage drop compensation parameters are designed as follows:

[0083]

[0084] Finally, simulation experiments were conducted on the two cases using the Matlab / Simulink simulation platform to verify the effectiveness of the proposed method. Consider a microgrid circuit consisting of four buck converters, the schematic diagram of which is shown below. Figure 3 As shown, the algorithm parameters are set as α = 50, β = 200, γ = 0.3, and σ = 3. The remaining circuit parameters are shown in Table 1.

[0085] Table 1 Circuit Parameters

[0086]

[0087] Case 1: Step load change

[0088] To study how the converter can still achieve optimal power generation cost in real time under load step changes, this case study sets up the following four stages:

[0089] 1) Stage 1 (0-2s): When t=0, a resistive load R=5Ω is connected to the circuit, and only the primary control of the circuit is working.

[0090] 2) Stage 2 (2-4s): The secondary controller starts working at t=2.

[0091] 3) Stage 3 (4-6s): At t=4, the resistive load becomes R=2.5Ω.

[0092] 4) Stage 4 (6-8s): At t=4, the resistive load recovers to R=5Ω.

[0093] Case results as follows Figure 4 As shown, where, Figure 4 (a) shows the current results. Figure 4 (b) shows the voltage results. Figure 4 (c) shows the adaptive droop gain variation. Figure 4 The middle (d) chart is a comparison of the overall power generation cost. The experimental results show that the proposed algorithm can guarantee the optimal output current in real time under the condition of a step change in load, thus ensuring the optimal power generation cost.

[0094] Case 2: Sinusoidal Load Variation

[0095] In this case study, to investigate whether the converter can still achieve the optimal power generation cost in real time under sinusoidal load changes, the simulated sinusoidal resistance change trend is R = 0.5*sin(0.8*π) + 2.

[0096] Case results as follows Figure 5 As shown, where, Figure 5 (a) shows the current results. Figure 5 (b) shows the voltage results. Figure 5 (c) shows the adaptive droop gain variation. Figure 5 Figure (d) shows a comparison of overall power generation costs. The experimental results demonstrate that the proposed algorithm can guarantee the optimal current output in real time under sinusoidal load variations, thus ensuring optimal power generation costs.

[0097] Example 2

[0098] One embodiment of this disclosure provides a distributed control system for a DC microgrid based on optimal power generation cost. Distributed control is achieved through controllers located at each converter, including a global information estimation module, an optimal current calculation module, a bus voltage compensation module, and a microgrid control module.

[0099] The global information estimation module is configured to: acquire the operating data of adjacent converters and estimate the global information of the DC microgrid to be controlled through a dual dynamic compensation consistency algorithm;

[0100] The optimal current solution module is configured to: estimate the optimal current value based on the estimated global information, with the goal of minimizing the overall power generation cost, and calculate the adaptive droop gain in real time using the optimal current value;

[0101] The bus voltage compensation module is configured to: compensate for voltage deviation based on the calculated adaptive droop gain and the line resistance of each converter, and based on the designed virtual voltage drop parameters, to obtain the compensated bus voltage.

[0102] The microgrid control module is configured to perform droop control and secondary voltage regulation on the DC microgrid based on adaptive droop gain and compensated bus voltage, ensuring that the output current of each converter tracks the optimal current value in real time and that the bus voltage remains stable at the rated value.

[0103] Example 3

[0104] One embodiment of this disclosure provides a computer program product, including a computer program that, when executed by a processor, implements the aforementioned distributed control method for DC microgrids based on optimal power generation cost.

[0105] Example 4

[0106] One embodiment of this disclosure provides a non-transitory computer-readable storage medium for storing computer instructions. When these computer instructions are executed by a processor, they implement the distributed control method for DC microgrids based on optimal power generation cost.

[0107] Example 5

[0108] One embodiment of this disclosure provides an electronic device, including a processor, a memory, and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory to enable the electronic device to implement the DC microgrid distributed control method based on optimal power generation cost.

[0109] This disclosure is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0110] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1The steps of the function specified in one or more boxes.

[0111] While the specific embodiments of this disclosure have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of this disclosure. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of this disclosure are still within the scope of protection of this disclosure.

Claims

1. A distributed control method for DC microgrids based on optimal power generation cost, characterized in that, Distributed control is achieved through controllers located at each converter, specifically: The system acquires the operating data of adjacent converters and estimates the global information of the DC microgrid to be controlled through a dual dynamic compensation consistency algorithm. Based on the estimated global information, with the goal of minimizing the overall power generation cost, the optimal current value is estimated, and the adaptive droop gain is calculated in real time using the optimal current value. Based on the calculated adaptive droop gain and the line resistance of each converter, and based on the designed virtual voltage drop parameters, the voltage deviation is compensated to obtain the compensated bus voltage. Based on adaptive droop gain and compensated bus voltage, droop control and secondary voltage regulation control are performed on DC microgrid to ensure that the output current of each converter tracks the optimal current value in real time and that the bus voltage is stable at the rated value. The global information of the DC microgrid to be controlled is estimated using a dual dynamic compensation consistency algorithm, expressed by the following formula: in, For estimated global information, including static global information , and dynamic global information , ; and To adjust the parameters, For the first The set of adjacent converters of a converter As an intermediate variable, For dynamically input compensation items, and This is an adjustment factor used to adjust the algorithm's convergence speed and system stability. For the first Local input information corresponding to each converter; The adaptive droop gain is calculated in real time using the optimal current value, and the specific formula is as follows: in, For the first Adaptive droop gain of the converter For the first The optimal current value for the converter This is an adjustable parameter used to achieve a trade-off between current sharing and voltage recovery accuracy. The compensated bus voltage is expressed by the formula: in, This is the compensated bus voltage. This is the rated voltage. For the first Line resistance of the converter For virtual voltage drop compensation parameters, For the first Adaptive droop gain of the converter The number of converters.

2. The distributed control method for DC microgrids based on optimal power generation cost as described in claim 1, characterized in that, The controllers installed at each converter are used to construct a DC microgrid topology containing multiple converters. Each converter is equipped with a controller for droop control and secondary voltage regulation, and each converter is connected to a distributed communication network to ensure that adjacent converters have the ability to exchange information.

3. The distributed control method for DC microgrids based on optimal power generation cost as described in claim 1, characterized in that, The goal of minimizing overall power generation cost is to estimate the optimal current value, specifically as follows: Based on the power generation capacity of the converter, the power generation efficiency of the distributed generation unit, the operating cost and the environmental cost, the power generation cost of each distributed generation unit is calculated, and then the cost function of the overall power generation cost is constructed. Based on the cost function and constraints, the optimal current value is obtained by solving the problem with the goal of minimizing the overall power generation cost.

4. A distributed control system for a DC microgrid based on optimal power generation cost, characterized in that, The distributed control method for DC microgrids based on optimal power generation cost, as described in any one of claims 1-3, is employed for distributed control through controllers located at each converter. This method includes a global information estimation module, an optimal current calculation module, a bus voltage compensation module, and a microgrid control module. The global information estimation module is configured to: acquire the operating data of adjacent converters and estimate the global information of the DC microgrid to be controlled through a dual dynamic compensation consistency algorithm; The optimal current solution module is configured to: estimate the optimal current value based on the estimated global information, with the goal of minimizing the overall power generation cost, and calculate the adaptive droop gain in real time using the optimal current value; The bus voltage compensation module is configured to: compensate for voltage deviation based on the calculated adaptive droop gain and the line resistance of each converter, and based on the designed virtual voltage drop parameters, to obtain the compensated bus voltage. The microgrid control module is configured to perform droop control and secondary voltage regulation on the DC microgrid based on adaptive droop gain and compensated bus voltage, ensuring that the output current of each converter tracks the optimal current value in real time and that the bus voltage remains stable at the rated value.

5. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the distributed control method for DC microgrids based on optimal power generation cost as described in any one of claims 1-3.

6. A non-transitory computer-readable storage medium, characterized in that, The non-transitory computer-readable storage medium is used to store computer instructions, which, when executed by a processor, implement the distributed control method for DC microgrids based on optimal power generation cost as described in any one of claims 1-3.

7. An electronic device, characterized in that, include: The device includes a processor, a memory, and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory to enable the electronic device to implement the distributed control method for DC microgrids based on optimal power generation cost as described in any one of claims 1-3.

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