Direct-current micro-grid distributed control method and system based on optimal power generation cost

Abstract

The invention provides a direct current micro-grid distributed control method and system based on optimal power generation cost, and relates to the technical field of direct current micro-grid distributed control, distributed control is carried out through controllers arranged on all converters, and the method specifically comprises the steps that operation data of adjacent converters are acquired, and global information of a direct current micro-grid is estimated; estimating an optimal current value based on the global information, and calculating an adaptive droop gain in real time through the optimal current value; compensating voltage deviation according to the calculated adaptive droop gain and the line resistance of each converter to obtain a compensated bus voltage; based on the self-adaptive droop gain and the compensated bus voltage, droop control and secondary voltage stabilization control are carried out on the DC micro-grid, it is ensured that the output current of each converter tracks the optimal current value in real time, and it is ensured that the bus voltage is stabilized at a rated value; even under the condition that the resistive load changes, each distributed converter can still output the optimal current, the minimum power generation cost is guaranteed, and voltage stability is achieved.

Description

Technical Field

[0001] The present disclosure relates to the technical field of distributed control of a direct current microgrid, and in particular to a distributed control method and system of a direct current microgrid based on optimal power generation cost. Background Art

[0002] DC microgrids have become an important component of smart grids and distributed energy systems due to their high efficiency, easy integration with renewable energy generation systems, and strong flexibility. In a DC microgrid, multiple distributed power sources and loads are interconnected through a DC bus to achieve efficient use of clean energy; however, the operation of DC microgrids faces many challenges, including voltage stability, power distribution coordination, and the complexity of maintaining system economy when the load changes dynamically. With the increase in the proportion of renewable energy generation, the uncertainty of the output characteristics of distributed power sources has further exacerbated these challenges. How to achieve efficient and low-cost energy distribution while ensuring voltage stability has become one of the core research issues.

[0003] At present, 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 is difficult to balance voltage recovery and power generation cost optimization when the load changes dynamically: fixed control parameters cannot adapt to load fluctuations and differences in the characteristics of distributed power sources, which may lead to unbalanced power distribution and reduced system efficiency. At the same time, the power generation cost of distributed power sources in microgrids is usually nonlinearly related to output power. When the load changes frequently, it is difficult to dynamically achieve optimal power distribution and effectively reduce system operating costs by relying solely on traditional control methods. Therefore, the existing methods cannot balance real-time economy and voltage stability, affecting the overall performance of DC microgrids. Summary of the invention

[0004] In order to solve the above problems, the present disclosure proposes a DC microgrid distributed control method and system based on optimal power generation cost. Even when the resistive load changes, each distributed converter can still ensure the output of optimal current, guarantee the 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 of the DC microgrid based on the optimal power generation cost is implemented by a controller set at each converter, specifically:

[0007] Obtain the operating data of adjacent converters and estimate the global information of the DC microgrid to be controlled through a dual dynamic compensation consensus algorithm;

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

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

[0010] Based on the adaptive droop gain and the compensated bus voltage, the DC microgrid is subjected to 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 that the bus voltage is stabilized at the rated value.

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

[0012] The DC microgrid distributed control system based on the optimal power generation cost performs distributed control through the controller set 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: obtain the operation data of the adjacent converters and estimate the global information of the DC microgrid to be controlled through a dual dynamic compensation consensus algorithm;

[0014] The optimal current solving module is configured to: estimate the optimal current value based on the estimated global information and take the overall power generation cost as the minimum, and calculate the adaptive droop gain in real time through the optimal current value;

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

[0016] The microgrid control module is configured to: perform 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 and to ensure that the bus voltage is stable at the rated value.

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

[0018] A computer program product comprises a computer program, wherein when the computer program is executed by a processor, the distributed control method of a DC microgrid based on optimal power generation cost is implemented.

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

[0020] A non-transitory computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by a processor, the DC microgrid distributed control method based on optimal power generation cost is implemented.

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

[0022] An electronic device comprises: 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 so that the electronic device executes the DC microgrid distributed control method based on the optimal power generation cost.

[0023] Compared with the prior art, the present invention has the following beneficial effects:

[0024] The present invention provides a DC microgrid distributed control method based on optimal power generation cost. The optimal output current value is estimated by solving the power generation cost function of each power generation unit, and an adaptive droop gain is designed on this basis to ensure that the droop gain can change in real time under load changes to achieve 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 is designed to ensure that the voltage is stable at the rated value. To meet the needs of distributed control, a static average consensus algorithm and a dynamic average consensus algorithm are designed to estimate the global information in the controller to achieve distributed control with optimal power generation cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The accompanying drawings constituting a part of the present disclosure are used to provide a further understanding of the present disclosure. The illustrative embodiments of the present disclosure and their descriptions are used to explain the present disclosure and do not constitute an improper limitation on the present disclosure.

[0027] Figure 1 This is a flow chart of the method of 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 a microgrid for simulation verification of Example 1.

[0030] Figure 4 This is the result diagram of Example 1 under load step change.

[0031] Figure 5 This is the result diagram of Example 1 under sinusoidal load variation. DETAILED DESCRIPTION

[0032] The present disclosure is further described below in conjunction with the accompanying drawings and embodiments.

[0033] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present disclosure. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present disclosure belongs.

[0034] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present disclosure. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0035] Example 1

[0036] In one embodiment of the present disclosure, a DC microgrid distributed control method based on optimal power generation cost is provided, wherein distributed control is performed by a controller provided at each converter, specifically:

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

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

[0039] Furthermore, the global information of the DC microgrid to be controlled is estimated by the dual dynamic compensation consensus algorithm, which is expressed as follows:

[0040]

[0041] Among them, y i is the estimated global information, including static global information and dynamic global information I avg 、u i ; α and β are adjustment parameters, N i is the set of neighboring transformers of the ith transformer, x i is the intermediate variable, m i is the dynamic input compensation term, ζ and ψ are adjustment factors used to adjust the algorithm convergence speed and system stability, g iis 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, the optimal current value is estimated, and the adaptive droop gain is calculated in real time through the optimal current value.

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

[0044] According to the power generation power of the converter, the power generation efficiency, operation cost and environmental cost of the distributed power generation unit, the power generation cost of each distributed power 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 through the optimal current value, and the specific formula is:

[0047]

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

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

[0050] Furthermore, the compensated bus voltage is expressed as follows:

[0051]

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

[0053] Step S4: Based on the adaptive droop gain and the compensated bus voltage, the DC microgrid is subjected to 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 that the bus voltage is stabilized at the rated value.

[0054] As an embodiment, the DC microgrid distributed control method based on optimal power generation cost disclosed in the present invention can ensure that each distributed converter can output the optimal current, guarantee the minimum power generation cost and achieve voltage stability even when the resistive load changes. The specific implementation process is described below.

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

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

[0057] The N DC-DC converters in the DC microgrid exchange information through a distributed communication network. Consider a communication network containing N converters, defined as G = (V, E, A), where V is a node set, a node is a converter, is the set of all edges, which are the communication connections between nodes; is the adjacency matrix, [a ij ] is the weight coefficient, when (v i ,v j )∈E ij =1, and all others are 0.

[0058] Definition N i is the set of neighboring nodes of the ith node, D is the degree matrix of the communication network, D 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 is as follows: Figure 1 As shown in the lower part of , through droop control and secondary voltage regulation control, the DC microgrid is distributedly controlled 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.

[0060] First, the cost function of each power generation unit is given as follows:

[0061]

[0062] Among them, C i (P i ) is the power generation cost of the i-th power generation unit, P i is the power generation of the ith converter, r i 、wi , and q i are the power generation efficiency, operating cost and environmental cost of the ith power generation unit respectively.

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

[0064]

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

[0066] By optimizing the solution, the optimal current can be obtained as follows:

[0067]

[0068] According to the circuit transmission loss, the bus voltage and converter voltage conversion can be obtained as follows:

[0069]

[0070] Among them, V i and I i are the output voltage and current of the i-th converter, R i is the line resistance of the i-th converter circuit, I load is the total current on the load side.

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

[0072]

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

[0074]

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

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

[0077]

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

[0079] Subsequently, in order to achieve bus voltage stability at the rated value, the bus voltage expression including the secondary control compensation value is given as follows:

[0080]

[0081] Among them, V bus is the compensated bus voltage, V ref is the voltage rating, R i is the line resistance of the ith converter, u i is the virtual voltage drop compensation parameter, is the adaptive droop gain of the i-th converter.

[0082] Then, according to the droop gain and line resistance, the virtual voltage drop compensation parameters are designed as follows:

[0083]

[0084] Finally, two cases are simulated by Matlab / Simulink simulation platform to verify the effectiveness of the proposed method. Consider a microgrid circuit consisting of four buck converters, as shown in the schematic diagram. Figure 3 As shown, the algorithm parameters are set to 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 Change Load

[0088] In order to study the control algorithm in this case, the converter can still achieve the optimal power generation cost in real time under load step changes. The following four stages are set:

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

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

[0091] 3) Phase 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 such as Figure 4 As shown, Figure 4 (a) is the current result diagram. Figure 4 (b) is the voltage result diagram. Figure 4 (c) is the adaptive droop gain change diagram. Figure 4 (d) is a comparison chart of the overall power generation cost. The experimental results show that the proposed algorithm can guarantee the real-time output of the optimal current and the optimal power generation cost in the case of load step changes.

[0094] Case 2: Sinusoidally varying load

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

[0096] Case results such as Figure 5 As shown, Figure 5 (a) is the current result diagram. Figure 5 (b) is the voltage result diagram. Figure 5 (c) is the adaptive droop gain change diagram. Figure 5 (d) is a comparison chart of the overall power generation cost. The experimental results show that the proposed algorithm can guarantee the real-time output of the optimal current and the optimal power generation cost when the load changes sinusoidally.

[0097] Example 2

[0098] In one embodiment of the present disclosure, a DC microgrid distributed control system based on optimal power generation cost is provided, and distributed control is performed by a controller arranged at each converter, including a global information estimation module, an optimal current solution module, a bus voltage compensation module and a microgrid control module:

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

[0100] The optimal current solving module is configured to: estimate the optimal current value based on the estimated global information and take the overall power generation cost as the minimum, and calculate the adaptive droop gain in real time through the optimal current value;

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

[0102] The microgrid control module is configured to: perform 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 and to ensure that the bus voltage is stable at the rated value.

[0103] Example 3

[0104] In one embodiment of the present disclosure, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the DC microgrid distributed control method based on optimal power generation cost is implemented.

[0105] Example 4

[0106] In one embodiment of the present disclosure, a non-transitory computer-readable storage medium is provided, wherein the non-transitory computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by a processor, the DC microgrid distributed control method based on optimal power generation cost is implemented.

[0107] Example 5

[0108] In one embodiment of the present disclosure, an electronic device is provided, 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, so that the electronic device executes the DC microgrid distributed control method based on the optimal power generation cost.

[0109] The present disclosure is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present disclosure. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0110] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1The steps for the functions specified in one or more boxes.

[0111] Although the above describes the specific implementation methods of the present disclosure in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present disclosure. Technical personnel in the relevant field should understand that on the basis of the technical solution of the present disclosure, various modifications or variations that can be made by those skilled in the art without creative work are still within the scope of protection of the present disclosure.

Claims

1. A DC microgrid distributed control method based on optimal power generation cost, characterized in that: Distributed control is performed by the controllers set at each converter, specifically: Obtain the operating data of adjacent converters and estimate the global information of the DC microgrid to be controlled through a dual dynamic compensation consensus algorithm; Based on the estimated global information, the optimal current value is estimated with the goal of minimizing the overall power generation cost, and the adaptive droop gain is calculated in real time through the optimal current value; According to the calculated adaptive droop gain and the line resistance of each converter, based on the designed virtual voltage drop parameter, the voltage deviation is compensated to obtain the compensated bus voltage; Based on the adaptive droop gain and the compensated bus voltage, the DC microgrid is subjected to 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 that the bus voltage is stabilized at the rated value.

2. The DC microgrid distributed control method based on optimal power generation cost according to claim 1, characterized in that: The controller set at each converter is to construct a DC microgrid topology structure containing multiple converters, set a droop control and secondary voltage regulation control controller at each converter, and connect each converter to a distributed communication network to ensure that adjacent converters have information exchange capabilities.

3. The DC microgrid distributed control method based on optimal power generation cost according to claim 1, characterized in that: The global information of the DC microgrid to be controlled is estimated by the dual dynamic compensation consensus algorithm, which is expressed as follows: Among them, y i is the estimated global information, including static global information and dynamic global information I avg 、u i ; α and β are adjustment parameters, N i is the set of neighboring transformers of the ith transformer, x i is the intermediate variable, m i is the dynamic input compensation term, ζ and ψ are adjustment factors used to adjust the algorithm convergence speed and system stability, g i is the local input information corresponding to the i-th transformer.

4. The DC microgrid distributed control method based on optimal power generation cost according to claim 1, characterized in that: The optimal current value is estimated with the goal of minimizing the overall power generation cost, specifically: According to the power generation power of the converter, the power generation efficiency, operation cost and environmental cost of the distributed power generation unit, the power generation cost of each distributed power 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.

5. The DC microgrid distributed control method based on optimal power generation cost according to claim 1, characterized in that: The adaptive droop gain is calculated in real time through the optimal current value, and the specific formula is: in, is the adaptive droop gain of the ith converter, is the optimal current value of the i-th converter, and σ is an adjustable parameter used to achieve a trade-off between current sharing and voltage recovery accuracy.

6. The DC microgrid distributed control method based on optimal power generation cost according to claim 1, characterized in that: The compensated bus voltage is expressed by the formula: Among them, V bus is the compensated bus voltage, V ref is the voltage rating, R i is the line resistance of the ith converter, u i is the virtual voltage drop compensation parameter, is the adaptive droop gain of the i-th converter, and N is the number of converters.

7. A DC microgrid distributed control system based on optimal power generation cost, characterized in that: Distributed control is performed through controllers set at each converter, including a global information estimation module, an optimal current solution module, a bus voltage compensation module, and a microgrid control module: The global information estimation module is configured to: obtain the operation data of the adjacent converters and estimate the global information of the DC microgrid to be controlled through a dual dynamic compensation consensus algorithm; The optimal current solving module is configured to: estimate the optimal current value based on the estimated global information and take the overall power generation cost as the minimum, and calculate the adaptive droop gain in real time through the optimal current value; The bus voltage compensation module is configured to: compensate the voltage deviation based on the calculated adaptive droop gain and the line resistance of each converter and the designed virtual voltage drop parameter to obtain a compensated bus voltage; The microgrid control module is configured to: perform 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 and to ensure that the bus voltage is stable at the rated value.

8. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the DC microgrid distributed control method based on optimal power generation cost according to any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium, characterized in that: The non-transitory computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by the processor, the DC microgrid distributed control method based on optimal power generation cost as described in any one of claims 1 to 6 is implemented.

10. An electronic device, characterized in that: include: 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 so that the electronic device executes the DC microgrid distributed control method based on the optimal power generation cost as described in any one of claims 1-6.

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