Economic Coordination Strategy for Scheduling Power Systems Based on Dynamic Diffusion Algorithm

Abstract

This invention discloses an economic coordination strategy for dispatchable power systems based on a dynamic diffusion algorithm, mainly comprising an incremental cost calculation module, a communication module, a cooperative control module, and a voltage-current dual closed-loop control module. In the incremental cost calculation module, the incremental cost of different batteries is calculated for fuel cells and storage batteries. In the communication module, the dynamic diffusion algorithm is used to exchange only one communication variable with adjacent communication nodes to obtain the required average value information. In the cooperative control module, only an integrator is needed to maintain the DC bus voltage near the bus voltage reference value while ensuring that the incremental costs of each dispatchable power source are consistent, thereby achieving bus voltage recovery and minimizing power generation costs. This invention designs a novel cooperative controller to achieve voltage regulation and economic coordination goals based on the same time scale, thereby realizing stable bus voltage recovery and optimal output power allocation of the dispatchable power system.

Description

Technical Field

[0001] This invention relates to the field of distributed energy storage systems in DC microgrids, and in particular to an economic coordination strategy for dispatchable power systems based on a dynamic diffusion algorithm. Background Technology

[0002] To address the economic dispatching problem of dispatchable power systems, the time scales of the tertiary economic coordination control layer and the secondary voltage recovery control layer differ, preventing them from coordinating control and optimization on the same time scale. This time scale mismatch not only limits the improvement of the operating efficiency of the dispatchable power system but also makes it difficult for each dispatchable power source to maintain its optimal operating state in real time, resulting in voltage deviation and unreasonable power allocation issues. To adjust and optimize the tertiary economic coordination control layer and the secondary voltage recovery control layer of the dispatchable power system and solve the problem of inconsistent time scales between different control layers, this invention designs an economic coordination strategy for dispatchable power systems based on a dynamic diffusion algorithm. By designing a novel cooperative controller, the goals of voltage regulation and economic coordination are achieved on the same time scale, thereby realizing stable recovery of the bus voltage and optimal allocation of output power in the dispatchable power system, ensuring system stability and economy. Furthermore, the communication network of this invention is constructed based on a multi-agent diffusion algorithm, requiring only the transmission of an average virtual regulation factor between adjacent dispatchable power sources, effectively reducing the system's communication burden. Summary of the Invention

[0003] To achieve the above objectives, the technical solution provided by this invention is as follows:

[0004] Step S1: The dispatchable power sources in the dispatchable power system include fuel cells and batteries. At the beginning of each sampling period, the DC-side inductor current of each dispatchable power source is... DC side output voltage Samples were taken separately;

[0005] Step S2: In the incremental cost calculation module, incremental cost represents the increased cost per unit of output, including the incremental cost of the dispatchable power supply. Divided into fuel cells with slightly increased costs Slightly increased cost of storage batteries For fuel cells, the DC-side output power of the fuel cell will be... Multiply by 2 times the fuel cell cost factor The result is then added to the fuel cell incremental coefficient. Achieving a slight increase in fuel cell cost Its expression is:

[0006] (1)

[0007] For batteries, subtract the battery's state of charge from 1. Multiply the resulting value by 6 times the maximum charging and discharging power of the battery. In addition, double the DC output power of the battery. The result is multiplied by the battery cost factor. In addition to the battery increment coefficient This results in a slight increase in the cost of the battery. Its expression is:

[0008] (2)

[0009] Step S3: In the communication module, the virtual adjustment factors of each schedulable power source are collected. The average virtual regulation factor of a schedulable power system is obtained using a dynamic diffusion algorithm. ;

[0010] Step S4: In the collaborative control module, the virtual incremental cost factor is... Subtract the slight increase in cost of schedulable power sources The obtained value is multiplied by the adjustment coefficient. Then, take the sine function of the result to obtain the virtual transition factor. Virtual transition factor Multiply by the DC-side output voltage Obtain virtual regulation factor Its expression is:

[0011] (3)

[0012] Average of virtual adjustment factor Divided by virtual transition factor Obtain the corrected voltage, then use the bus reference voltage value. Subtract the correction voltage and then obtain the coordinated control voltage compensation amount through an integral step. Coordinated control of voltage compensation amount The expression is:

[0013] (4)

[0014] Step S5: In the voltage and current dual closed-loop control module, the bus voltage reference value is... and coordinated control voltage compensation amount Add them together, then subtract the DC-side output voltage. Then, through the voltage outer loop PI controller Obtain the DC-side reference current and compare it with the DC-side inductor current. After subtraction, the current passes through the inner loop PI controller. The driving voltage is obtained, and then compared with the triangular carrier wave to obtain the PWM modulation signal.

[0015] Furthermore, in step S2, the fuel cell cost coefficient The value range is 0.1 1. Incremental coefficient of fuel cells The value range is 1. 10. Battery cost coefficient The value range is 0.1 1. Battery Increment Coefficient The value range is 1. 10; In step S4, the adjustment coefficient The value range is 0.1 1.

[0016] Compared with existing technologies, the principles and advantages of this solution are as follows:

[0017] This invention discloses an economic coordination strategy for dispatchable power systems based on a dynamic diffusion algorithm, mainly comprising an incremental cost calculation module, a communication module, a cooperative control module, and a voltage-current dual closed-loop control module. In the incremental cost calculation module, the incremental cost of different batteries is calculated for fuel cells and storage batteries. In the communication module, the dynamic diffusion algorithm is used to exchange only one communication variable with adjacent communication nodes to obtain the required average value information. In the cooperative control module, only an integrator is needed to maintain the DC bus voltage near the bus voltage reference value while ensuring that the incremental costs of each dispatchable power source are consistent, thereby achieving bus voltage recovery and minimizing power generation costs. This invention designs a novel cooperative controller to achieve voltage regulation and economic coordination goals based on the same time scale, thereby realizing stable bus voltage recovery and optimal output power allocation of the dispatchable power system. Attached Figure Description

[0018] Figure 1 This is the main circuit diagram of the schedulable power supply system in an embodiment of the present invention;

[0019] Figure 2 This is a control block diagram of the economic coordination strategy for a schedulable power system based on the dynamic diffusion algorithm in an embodiment of the present invention.

[0020] Figure 3 This is a waveform diagram of the bus voltage in an embodiment of the present invention;

[0021] Figure 4 This is a waveform diagram showing the slight increase in cost in an embodiment of the present invention. Detailed Implementation

[0022] The present invention will be further described below with reference to specific embodiments:

[0023] Figure 1 The diagram shows the main circuit of a dispatchable power system, consisting of two fuel cells and two batteries connected in parallel via a DC-DC converter. i =1, 2, 3, 4, This is a reference value for the bus voltage of a dispatchable power system. For the DC-side inductor current of each schedulable power supply, For the DC-side output voltage of each schedulable power supply, For the DC-side output current of each schedulable power supply, For the load resistance of each bus, The line impedances for each of the four dispatchable power sources are 0.1. 0.15 0.2 0.25 , The line impedances between the different dispatchable power sources are 0.3, and the line impedances between the four dispatchable power sources are respectively 0.3. 0.33 0.36 0.4 .

[0024] Figure 2 The diagram shows the control block diagram of an economic coordination strategy for a schedulable power system based on a dynamic diffusion algorithm, including the following steps:

[0025] Step S1: The dispatchable power sources in the dispatchable power system include fuel cells and batteries. At the beginning of each sampling period, the DC-side inductor current of each dispatchable power source is... DC side output voltage Samples were taken separately;

[0026] Step S2: In the incremental cost calculation module, incremental cost represents the increased cost per unit of output, including the incremental cost of the dispatchable power supply. Divided into fuel cells with slightly increased costs Slightly increased cost of storage batteries For fuel cells, the DC-side output power of the fuel cell will be... Multiply by 2 times the fuel cell cost factor The result is then added to the fuel cell incremental coefficient. Achieving a slight increase in fuel cell cost Its expression is:

[0027] (1)

[0028] For batteries, subtract the battery's state of charge from 1. Multiply the resulting value by 6 times the maximum charging and discharging power of the battery. In addition, double the DC output power of the battery. The result is multiplied by the battery cost factor. In addition to the battery increment coefficient This results in a slight increase in the cost of the battery. Its expression is:

[0029] (2)

[0030] Step S3: In the communication module, the virtual adjustment factors of each schedulable power source are collected. The average virtual regulation factor of a schedulable power system is obtained using a dynamic diffusion algorithm. ;

[0031] Step S4: In the collaborative control module, the virtual incremental cost factor is... Subtract the slight increase in cost of schedulable power sources The obtained value is multiplied by the adjustment coefficient. Then, take the sine function of the result to obtain the virtual transition factor. Virtual transition factor Multiply by the DC-side output voltage Obtain virtual regulation factor Its expression is:

[0032] (3)

[0033] Average of virtual adjustment factor Divided by virtual transition factor Obtain the corrected voltage, then use the bus reference voltage value. Subtract the correction voltage and then obtain the coordinated control voltage compensation amount through an integral step. Coordinated control of voltage compensation amount The expression is:

[0034] (4)

[0035] Step S5: In the voltage and current dual closed-loop control module, the bus voltage reference value is... and coordinated control voltage compensation amount Add them together, then subtract the DC-side output voltage. Then, through the voltage outer loop PI controller Obtain the DC-side reference current and compare it with the DC-side inductor current. After subtraction, the current passes through the inner loop PI controller. The driving voltage is obtained, and then compared with the triangular carrier wave to obtain the PWM modulation signal.

[0036] Furthermore, in step S2, the fuel cell cost coefficient The value range is 0.1 1. Incremental coefficient of fuel cells The value range is 1. 10. Battery cost coefficient The value range is 0.1 1. Battery Increment Coefficient The value range is 1. 10; In step S4, the adjustment coefficient The value range is 0.1 1.

[0037] Figure 3 The diagram shows the waveform of the bus voltage of the dispatchable power system. When the strategy implemented in this invention is adopted, the bus voltage of the dispatchable power system can recover to the reference value of around 400V within 0.1s, with a voltage drop percentage of less than 0.5%. This demonstrates that the method can effectively restore the bus voltage of a dispatchable power system and ensure the stable operation of the system.

[0038] Figure 4 The diagram shows the waveforms of incremental costs for each dispatchable power source. The strategy implemented in this invention can promote the rapid convergence of incremental costs for each dispatchable power source. The incremental costs between fuel cells and between batteries are almost consistent from the beginning. At 0.6s, the incremental costs of each dispatchable power source are consistent and remain at around 525 cents per watt. This indicates that the method can effectively and quickly achieve equal incremental cost operation of the dispatchable power system, minimizing the system's power generation cost and ensuring the system's economic efficiency.

[0039] As can be seen from the above analysis, this invention designs an economic coordination strategy for a dispatchable power system based on a dynamic diffusion algorithm. By designing a novel cooperative controller, the goals of voltage regulation and economic coordination are achieved on the same time scale, so as to realize the stable recovery of the bus voltage and the optimal allocation of output power of the dispatchable power system, thus ensuring the stability and economy of the system.

[0040] The above-described embodiments are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Therefore, any changes made in accordance with the shape and principle of the present invention should be covered within the protection scope of the present invention.

Claims

1. An economic coordination strategy for schedulable power systems based on a dynamic diffusion algorithm, characterized in that, Includes the following steps: Step S1: The dispatchable power sources in the dispatchable power system include fuel cells and batteries. At the beginning of each sampling period, the DC-side inductor current of each dispatchable power source is... DC side output voltage Samples were taken separately; Step S2: In the incremental cost calculation module, incremental cost represents the increased cost per unit of output, including the incremental cost of the dispatchable power supply. Divided into fuel cells with slightly increased costs Slightly increased cost of storage batteries For fuel cells, the DC-side output power of the fuel cell will be... Multiply by 2 times the fuel cell cost factor The result is then added to the fuel cell incremental coefficient. Achieving a slight increase in fuel cell cost Its expression is: (1) For batteries, subtract the battery's state of charge from 1. Multiply the resulting value by 6 times the maximum charging and discharging power of the battery. In addition, double the DC output power of the battery. The result is multiplied by the battery cost factor. In addition to the battery increment coefficient This results in a slight increase in the cost of the battery. Its expression is: (2) Step S3: In the communication module, the virtual adjustment factors of each schedulable power source are collected. The average virtual regulation factor of a schedulable power system is obtained using a dynamic diffusion algorithm. ; Step S4: In the collaborative control module, the virtual incremental cost factor is... Subtract the slight increase in cost of schedulable power sources The obtained value is multiplied by the adjustment coefficient. Then, take the sine function of the result to obtain the virtual transition factor. Virtual transition factor Multiply by the DC-side output voltage Obtain virtual regulation factor Its expression is: (3) Average of virtual adjustment factor Divided by virtual transition factor Obtain the corrected voltage, then use the bus reference voltage value. Subtract the correction voltage and then obtain the coordinated control voltage compensation amount through an integral step. Coordinated control of voltage compensation amount The expression is: (4) Step S5: In the voltage and current dual closed-loop control module, the bus voltage reference value is... and coordinated control voltage compensation amount Add them together, then subtract the DC-side output voltage. Then, through the voltage outer loop PI controller Obtain the DC-side reference current and compare it with the DC-side inductor current. After subtraction, the current passes through the inner loop PI controller. The driving voltage is obtained, and then compared with the triangular carrier wave to obtain the PWM modulation signal.

2. The economic coordination strategy for schedulable power systems based on dynamic diffusion algorithm according to claim 1, characterized in that, In step S2, the fuel cell cost coefficient The value range is 0.1 1. Incremental coefficient of fuel cells The value range is 1.

10. Battery cost coefficient The value range is 0.1 1. Battery Increment Coefficient The value range is 1.

10.

3. The economic coordination strategy for schedulable power systems based on dynamic diffusion algorithm according to claim 1, characterized in that, In step S4, the adjustment coefficient The value range is 0.1 1.

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