An energy management optimization method, system, device and storage medium for reducing voltage fluctuation and improving hydrogen production efficiency

Abstract

This invention provides an energy management optimization method, system, device, and storage medium for reducing voltage fluctuations and improving hydrogen production efficiency. The method is based on a wind-solar hydrogen production system, which includes a grid unit, a photovoltaic power generation unit, a wind power generation unit, and an electrolyzer unit. The grid unit is connected to node 1, the photovoltaic power generation unit is connected to node 3, the wind power generation unit is connected to node 4, and the electrolyzer unit is connected to node 5. Nodes 1, 3, 4, and 5 are each connected to node 2. The energy management optimization method provided by this invention does not backcharge reactive power to the grid, avoiding the release of excess reactive power from photovoltaic and wind power. The wind-solar hydrogen production system can improve hydrogen production efficiency by optimizing the voltage at the electrolyzer unit's connection point. The system can improve hydrogen production efficiency while optimizing line losses to avoid energy waste.

Description

Technical Field

[0001] This invention relates to the field of power, and more specifically to an energy management optimization method, system, device, and storage medium for reducing voltage fluctuations and improving hydrogen production efficiency. Background Technology

[0002] Hydrogen energy is an environmentally friendly energy source and one of the country's important strategic energy sources. Hydrogen production processes can be categorized into green hydrogen, blue hydrogen, and gray hydrogen based on their environmental pollution levels, with green hydrogen production currently being a key area of ​​national focus. Wind and solar power hydrogen production systems utilize renewable energy sources such as wind and solar power, and are one of the effective methods for producing green hydrogen. Existing systems include large-scale projects such as the Narisong Photovoltaic Hydrogen Production Industry Demonstration Project, the Xinjiang Kuqa Green Hydrogen Demonstration Project, and the Henan 100-kilowatt SOEC water electrolysis hydrogen production project, all of which primarily operate with grid connection.

[0003] Research on the hydrogen production efficiency of electrolyzers in wind-solar-hydrogen grid-connected systems can improve the system's economic benefits, reduce the cost of green hydrogen production, and facilitate the conversion of blue and gray hydrogen into green hydrogen. The hydrogen production efficiency of electrolyzers is affected by voltage fluctuations at the connection point. These fluctuations alter the reactive power demand of transformers and converters, change the DC input power of the electrolyzer, and cause fluctuations in the electrolysis current, thereby reducing hydrogen production efficiency. Summary of the Invention

[0004] The first objective of this invention is to provide an energy management optimization method for reducing voltage fluctuations and improving hydrogen production efficiency, addressing the aforementioned problems.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] An energy management optimization method for reducing voltage fluctuations and improving hydrogen production efficiency is disclosed. The energy management optimization method is based on a wind-solar hydrogen production system, which includes a grid unit, a photovoltaic power generation unit, a wind power generation unit, and an electrolyzer unit. The grid unit is connected to node 1, the photovoltaic power generation unit is connected to node 3, the wind power generation unit is connected to node 4, and the electrolyzer unit is connected to node 5. Nodes 1, 3, 4, and 5 are all connected to node 2.

[0007] And includes the following steps:

[0008] S1: Determine the required model based on the composition of the wind and solar hydrogen production system;

[0009] S2: Determine system constraints based on the actual operation of the wind and solar hydrogen production system;

[0010] S3: Determine the system objective function based on the requirements of the wind and solar hydrogen production system;

[0011] S4: Outputs the active power of the photovoltaic, wind power, and electrolyzer of the wind-solar hydrogen production system;

[0012] S5: Input system parameters, including apparent power of photovoltaic converter, power factor of wind turbine, rated voltage of system, line impedance, etc.

[0013] S6: Based on the constraints in step S2, initialize the algorithm population, including photovoltaic reactive power output, wind turbine reactive power output, and electrolytic cell connection point voltage;

[0014] S7: Calculate the reactive power demand of the electrolyzer based on the reactive power model of the electrolyzer in step S1, determine the active power and reactive power of nodes 1-5, calculate the active power and reactive power of the power grid based on the loss model of line 12 (line 12 is the line between node 1 and node 2, and the same applies to other lines or lines in the following text) in step S1, and sum the power of node 2 to calculate the active power and reactive power of the power grid.

[0015] S8: Determine whether the power grid absorbs reactive power. If it does, proceed to step S9; if it does not, proceed to step S10.

[0016] S9: Adjust the reactive power output of the power grid to 0, adjust the reactive power output of photovoltaic and wind turbines, and maintain the power balance of the system.

[0017] S10: Output the voltage of each node and the power of the line, and calculate the system indicators based on the objective function in step S3;

[0018] S11: Regenerate the population using the Strength Pareto Evolutionary algorithm 2 (SPEA2), and determine whether the algorithm iteration has ended. If it has ended, proceed to step S12; otherwise, proceed to step S7.

[0019] S12: Compare the system objectives and select the optimal population based on the weight of each objective.

[0020] While adopting the above technical solutions, the present invention may also adopt or combine the following technical solutions:

[0021] As a preferred technical solution of the present invention: In step S1, the photovoltaic power generation and wind power generation units are calculated using power source substitution, and also include the electrolyzer reactive power model, hydrogen production efficiency model and power flow calculation model.

[0022] The alkaline electrolyzer operates at a constant active power at the system connection point, while the reactive power varies due to voltage fluctuations at the connection point. The specific reactive power-voltage relationship model is as follows:

[0023]

[0024] In the formula, The reactive power of the electrolytic cell is expressed in Mvar; t represents the t-th minute of system operation. X is the voltage at the connection point of the electrolytic cell, in kV; el This is the equivalent reactance of the electrolytic cell, in Ω; This represents the active power of the electrolytic cell, measured in MW.

[0025] The hydrogen production efficiency of an electrolyzer is affected by voltage fluctuations; the greater the voltage fluctuation, the lower the efficiency. The rate of decrease in hydrogen production efficiency is initially slow, then accelerates. After a short-term drop, the efficiency gradually recovers, with the recovery rate initially rapid and then slowing down. Using surface fitting at characteristic points, the following model of voltage fluctuation and hydrogen production efficiency is obtained:

[0026]

[0027] In the formula, For voltage fluctuations The percentage of hydrogen production efficiency recovered at time ts, expressed in %. For voltage fluctuations The actual hydrogen production efficiency recovered at time ts, in %; The actual maximum hydrogen production efficiency of the electrolyzer is expressed as a percentage (%). As an intermediate variable; This represents the percentage of voltage fluctuation in the electrolytic cell, expressed as a percentage. U is the voltage at the connection point of the electrolytic cell at minute t-1, in kV; el This is the rated voltage of the electrolytic cell, in kV.

[0028] Since voltage fluctuations have an instantaneous impact on electrolyzer efficiency, and to compare hydrogen production efficiency and yield per unit time scale, an integral method is used to calculate the average hydrogen production efficiency. The specific model is as follows:

[0029]

[0030] In the formula, The average hydrogen production efficiency at time t is expressed in %;

[0031] The node voltage is determined by the active and reactive power outputs of wind power, solar power, and the power grid. Changes in the reactive power of the electrolyzer also affect the voltage at each node of the system. The voltage models for nodes 1 and 2 are as follows:

[0032]

[0033] In the formula, Let be the voltage at node 2 at time t, in kV. Let be the voltage at node 1 at time t, in kV. This represents the longitudinal component of the voltage drop at node 1, in kV. The voltage drop across node 1 is represented by the transverse component, in kV. R represents the active power of node 1, in MW; line12 The resistance of line 12 is in Ω; X represents the reactive power at node 1, in Mvar. line12 The reactance of line 12 is in Ω;

[0034] The voltage model for node 3 is as follows:

[0035]

[0036] In the formula, Let be the voltage at node 3 at time t, in kV. This represents the longitudinal component of the voltage drop at node 2, in kV. The voltage drop across node 2 is represented by the transverse component, in kV. The active power transmitted from node 2 to node 3, in MW; R line23 The resistance of line 23 is in Ω; The reactive power transferred from node 2 to node 3, in Mvar; X line23 The reactance of line 23 is in Ω;

[0037] The voltage model for node 4 is as follows:

[0038]

[0039] In the formula, Let be the voltage at node 4 at time t, in kV. The active power transferred from node 2 to node 4, in MW; R line24 The resistance of the circuit is 24Ω; The reactive power transmitted from node 2 to node 4 is expressed in Mvar; X line24 The line reactance is 24Ω;

[0040] The voltage model for node 5 is as follows:

[0041]

[0042] In the formula, Let be the voltage at node 5 at time t, in kV. The active power transmitted from node 2 to node 5, in MW; R line25 The circuit resistance is 25Ω. The reactive power transmitted from node 2 to node 5 is expressed in Mvar; X line25 The line reactance is 25Ω;

[0043] The active and reactive power of a line are determined by line impedance, node power, and node voltage, which affect the system power allocation decision. The loss model for line 12 is as follows:

[0044]

[0045] In the formula, The active power of line 12 is expressed in MW. The reactive power of line 12 is expressed in Mvar; the loss model for line 23 is as follows:

[0046]

[0047] In the formula, The active power of line 23 is expressed in MW. The reactive power of line 23 is expressed in Mvar; the loss model for line 24 is as follows:

[0048]

[0049] In the formula, The active power of line 24 is in MW. The reactive power of line 24 is expressed in Mvar; the loss model for line 25 is as follows:

[0050]

[0051] In the formula, The active power of the line is 25 MW. The reactive power of the line is 25 Mvar.

[0052] As a preferred embodiment of the present invention: in step S2, the constraints include:

[0053] The reactive power output of a photovoltaic unit is limited by the converter capacity and the active power output, namely:

[0054]

[0055] In the formula, S represents the upper limit of photovoltaic reactive power output at time t, in Mvar; pv The apparent power of the photovoltaic converter is expressed in MVA. The photovoltaic active power output at time t is expressed in MW.

[0056] The reactive power output of a wind turbine unit is limited by the power factor and its active power output, namely:

[0057]

[0058] In the formula, The upper limit of reactive power output of the wind turbine at time t, in Mvar; The active power output of the wind turbine at time t is expressed in MW. This is the lower limit of the power factor of the wind turbine;

[0059] In a hydrogen production system, both active and reactive power must be balanced, that is:

[0060]

[0061] In the formula, The reactive power of the line at time t is expressed in MW or Mvar. The active and reactive power absorbed by photovoltaic power at time t are expressed in MW and Mvar, respectively. The active and reactive power consumed by the wind turbine at time t is expressed in MW and Mvar.

[0062] As a preferred embodiment of the present invention: In step S3, the system objectives include:

[0063] Voltage fluctuations at the electrolyzer inlet point reduce the efficiency of hydrogen production through electrolysis. The system energy management aims to minimize these voltage fluctuations, as shown in the following model:

[0064]

[0065] In the formula, U di The voltage fluctuation value of the electrolytic cell is given; T is the system calculation period.

[0066] Reducing line losses can increase the system's hydrogen production capacity, thereby improving the system's economic benefits. This is also an important indicator, and the system aims to minimize line losses.

[0067]

[0068] In the formula, Total active power loss of the line, in MW; The active power loss of line 23 is expressed in MW. The active power loss of line 24 is expressed in MW. The active power loss of line 25 is expressed in MW.

[0069] The voltage deviation calculation model is as follows:

[0070]

[0071] In the formula, U represents the voltage shift of the electrolytic cell at time t, expressed as a percentage. N This is the system's operating reference voltage, in kV.

[0072] Photovoltaic and wind power units have reactive power output capabilities. Excessive power output fluctuations and alternating power output are detrimental to stable system operation. Therefore, the power output curves need to be optimized while meeting other objectives. The specific model is as follows:

[0073]

[0074] In the formula, Q di This represents the total output fluctuation, measured in Mvar. The photovoltaic reactive power output at times t and t-1 is expressed in Mvar. The reactive power output of the wind turbine at times t and t-1 is expressed in Mvar.

[0075] The second objective of this invention is to provide an electronic device.

[0076] Therefore, the above-mentioned objective of the present invention is achieved through the following technical solution:

[0077] An electronic device includes a memory and a processor, the memory storing an executable program, and the processor being configured to run the executable program to perform the steps of the energy management optimization method for reducing voltage fluctuations and improving hydrogen production efficiency as described above.

[0078] The third objective of this invention is to provide a non-volatile storage medium.

[0079] Therefore, the above-mentioned objective of the present invention is achieved through the following technical solution:

[0080] A non-volatile storage medium storing an executable program, which, when executed by a processor, implements the energy management optimization method steps described above for reducing voltage fluctuations and improving hydrogen production efficiency.

[0081] This invention provides an energy management optimization method, system, device, and storage medium to reduce voltage fluctuations and improve hydrogen production efficiency, which has the following beneficial effects: The energy management optimization method provided by this invention will not charge reactive power back to the grid, avoiding the release of excess reactive power from photovoltaic and wind power; the wind and solar hydrogen production system can improve hydrogen production efficiency by optimizing the voltage at the electrolyzer unit access point (node ​​5); the system can improve hydrogen production efficiency while optimizing line loss to avoid energy waste. Attached Figure Description

[0082] Figure 1 The flowchart illustrates the energy management optimization method for wind and solar hydrogen production systems provided by this invention.

[0083] Figure 2 This is a schematic diagram of a wind-solar hydrogen production system.

[0084] Figure 3This is a schematic diagram of the active power of photovoltaic, wind power, and electrolytic cells.

[0085] Figure 4 A schematic diagram illustrating the optimization of photovoltaic reactive power output before and after optimization.

[0086] Figure 5 A schematic diagram to optimize the reactive power output of wind power before and after.

[0087] Figure 6 This is a schematic diagram of the reactive power output of the power grid.

[0088] Figure 7 A schematic diagram for optimizing the active power output of the power grid before and after.

[0089] Figure 8 A schematic diagram illustrating the optimization of reactive power requirements for the electrolytic cells before and after the process.

[0090] Figure 9 A schematic diagram for optimizing the voltage at nodes 2-5 before and after.

[0091] Figure 10 A schematic diagram to optimize the active and reactive power losses of the preceding and following lines 12.

[0092] Figure 11 A schematic diagram to optimize the active and reactive power losses of the preceding and following lines 23.

[0093] Figure 12 A schematic diagram to optimize the active and reactive power losses of the preceding and following lines 24.

[0094] Figure 13 A schematic diagram to optimize the active and reactive power losses of the preceding and following lines 25.

[0095] Figure 14 A schematic diagram to optimize the total active and reactive power losses of the preceding and following lines.

[0096] Figure 15 A schematic diagram illustrating the optimization of hydrogen production efficiency in the electrolyzers before and after the process. Detailed Implementation

[0097] The present invention will be described in further detail with reference to the accompanying drawings and specific embodiments.

[0098] like Figure 1 As shown, an energy management optimization method for reducing voltage fluctuations and improving hydrogen production efficiency is proposed. The energy management optimization method is based on a wind-solar hydrogen production system, which includes a grid unit, a photovoltaic power generation unit, a wind power generation unit, and an electrolyzer unit. The grid unit is connected to node 1, the photovoltaic power generation unit is connected to node 3, the wind power generation unit is connected to node 4, and the electrolyzer unit is connected to node 5. Nodes 1, 3, 4, and 5 are respectively connected to node 2.

[0099] And includes the following steps:

[0100] S1: Determine the required model based on the composition of the wind and solar hydrogen production system;

[0101] The photovoltaic and wind power generation units use power source substitution calculations, and also include reactive power models for electrolyzers, hydrogen production efficiency models, and power flow calculation models.

[0102] The alkaline electrolyzer operates at a constant active power at the system connection point, while the reactive power varies due to voltage fluctuations at the connection point. The specific reactive power-voltage relationship model is as follows:

[0103]

[0104] In the formula, The reactive power of the electrolytic cell is expressed in Mvar; t represents the t-th minute of system operation. X is the voltage at the connection point of the electrolytic cell, in kV; el This is the equivalent reactance of the electrolytic cell, in Ω; This represents the active power of the electrolytic cell, measured in MW.

[0105] The hydrogen production efficiency of an electrolyzer is affected by voltage fluctuations; the greater the voltage fluctuation, the lower the efficiency. The rate of decrease in hydrogen production efficiency is initially slow, then accelerates. After a short-term drop, the efficiency gradually recovers, with the recovery rate initially rapid and then slowing down. Using surface fitting at characteristic points, the following model of voltage fluctuation and hydrogen production efficiency is obtained:

[0106]

[0107] In the formula, For voltage fluctuations The percentage of hydrogen production efficiency recovered at time ts, expressed in %. For voltage fluctuations The actual hydrogen production efficiency recovered at time ts, in %; The actual maximum hydrogen production efficiency of the electrolyzer is expressed as a percentage (%). As an intermediate variable; This represents the percentage of voltage fluctuation in the electrolytic cell, expressed as a percentage. U is the voltage at the connection point of the electrolytic cell at minute t-1, in kV; el This is the rated voltage of the electrolytic cell, in kV.

[0108] Since voltage fluctuations have an instantaneous impact on electrolyzer efficiency, and to compare hydrogen production efficiency and yield per unit time scale, an integral method is used to calculate the average hydrogen production efficiency. The specific model is as follows:

[0109]

[0110] In the formula, The average hydrogen production efficiency at time t is expressed in %;

[0111] The node voltage is determined by the active and reactive power outputs of wind power, solar power, and the power grid. Changes in the reactive power of the electrolyzer also affect the voltage at each node of the system. The voltage models for nodes 1 and 2 are as follows:

[0112]

[0113] In the formula, Let be the voltage at node 2 at time t, in kV. Let be the voltage at node 1 at time t, in kV. This represents the longitudinal component of the voltage drop at node 1, in kV. The voltage drop across node 1 is represented by the transverse component, in kV. R represents the active power of node 1, in MW; line12 The resistance of line 12 is in Ω; X represents the reactive power at node 1, in Mvar. line12 The reactance of line 12 is in Ω;

[0114] The voltage model for node 3 is as follows:

[0115]

[0116] In the formula, Let be the voltage at node 3 at time t, in kV. This represents the longitudinal component of the voltage drop at node 2, in kV. The voltage drop across node 2 is represented by the transverse component, in kV. The active power transmitted from node 2 to node 3, in MW; R line23 The resistance of line 23 is in Ω; The reactive power transferred from node 2 to node 3, in Mvar; X line23 The reactance of line 23 is in Ω;

[0117] The voltage model for node 4 is as follows:

[0118]

[0119] In the formula, Let be the voltage at node 4 at time t, in kV. The active power transferred from node 2 to node 4, in MW; R line24 The resistance of the circuit is 24Ω; The reactive power transmitted from node 2 to node 4 is expressed in Mvar; X line24 The line reactance is 24Ω;

[0120] The voltage model for node 5 is as follows:

[0121]

[0122] In the formula, Let be the voltage at node 5 at time t, in kV. The active power transmitted from node 2 to node 5, in MW; R line25 The circuit resistance is 25Ω. The reactive power transmitted from node 2 to node 5 is expressed in Mvar; X line25 The line reactance is 25Ω;

[0123] The active and reactive power of a line are determined by line impedance, node power, and node voltage, which affect the system power allocation decision. The loss model for line 12 is as follows:

[0124]

[0125] In the formula, The active power of line 12 is expressed in MW. The reactive power of line 12 is expressed in Mvar.

[0126] The loss model for line 23 is as follows:

[0127]

[0128] In the formula, The active power of line 23 is expressed in MW. The reactive power of line 23 is expressed in Mvar.

[0129] The loss model for line 24 is as follows:

[0130]

[0131] In the formula, The active power of line 24 is in MW. The reactive power of the line is 24 Mvar;

[0132] The loss model for line 25 is as follows:

[0133]

[0134] In the formula, The active power of the line is 25 MW. The line has 25 reactive power units, measured in Mvar.

[0135] S2: Determine system constraints based on the actual operation of the wind and solar hydrogen production system;

[0136] The constraints include:

[0137] The reactive power output of a photovoltaic unit is limited by the converter capacity and the active power output, namely:

[0138]

[0139] In the formula, S represents the upper limit of photovoltaic reactive power output at time t, in Mvar; pv The apparent power of the photovoltaic converter is expressed in MVA. The photovoltaic active power output at time t is expressed in MW.

[0140] The reactive power output of a wind turbine unit is limited by the power factor and its active power output, namely:

[0141]

[0142] In the formula, The upper limit of reactive power output of the wind turbine at time t, in Mvar; The active power output of the wind turbine at time t is expressed in MW. This is the lower limit of the power factor of the wind turbine;

[0143] In a hydrogen production system, both active and reactive power must be balanced, that is:

[0144]

[0145] In the formula, The reactive power of the line at time t is expressed in MW or Mvar. The active and reactive power absorbed by photovoltaic power at time t are expressed in MW and Mvar, respectively. The active and reactive power absorbed by the wind turbine at time t is expressed in MW and Mvar.

[0146] S3: Determine the system objective function based on the requirements of the wind and solar hydrogen production system;

[0147] System objectives include:

[0148] Voltage fluctuations at the electrolyzer inlet point reduce the efficiency of hydrogen production through electrolysis. The system energy management aims to minimize these voltage fluctuations, as shown in the following model:

[0149]

[0150] In the formula, U di The voltage fluctuation value of the electrolytic cell is given; T is the system calculation period.

[0151] Reducing line losses can increase the system's hydrogen production capacity, thereby improving the system's economic benefits. This is also an important indicator, and the system aims to minimize line losses.

[0152]

[0153] In the formula, Total active power loss of the line, in MW; The active power loss of line 23 is expressed in MW. The active power loss of line 24 is expressed in MW. The active power loss of line 25 is expressed in MW.

[0154] The voltage deviation calculation model is as follows:

[0155]

[0156] In the formula, U represents the voltage shift of the electrolytic cell at time t, expressed as a percentage. N This is the system's operating reference voltage, in kV.

[0157] Photovoltaic and wind power units have reactive power output capabilities. Excessive power output fluctuations and alternating power output are detrimental to stable system operation. Therefore, the power output curves need to be optimized while meeting other objectives. The specific model is as follows:

[0158]

[0159] In the formula, Q di This represents the total output fluctuation, measured in Mvar. The photovoltaic reactive power output at times t and t-1 is expressed in Mvar. The reactive power output of the wind turbine at times t and t-1 is expressed in Mvar.

[0160] S4: Outputs the active power of the photovoltaic, wind power, and electrolyzer of the wind-solar hydrogen production system;

[0161] S5: Input system parameters, including apparent power of photovoltaic converter, power factor of wind turbine, rated voltage of system, line impedance, etc.

[0162] S6: Based on the constraints in step S2, initialize the algorithm population, including photovoltaic reactive power output, wind turbine reactive power output, and electrolytic cell connection point voltage;

[0163] S7: Calculate the reactive power demand of the electrolyzer based on the reactive power model of the electrolyzer in step S1, determine the active power and reactive power of nodes 1-5, calculate the active power and reactive power of the power grid based on the loss model of line 12 in step S1, and sum the power of node 2. Node 2 is the central node.

[0164] S8: Determine whether the power grid absorbs reactive power. If it does, proceed to step S9; if it does not, proceed to step S10.

[0165] S9: Adjust the reactive power output of the power grid to 0, adjust the reactive power output of photovoltaic and wind turbines, and maintain the power balance of the system.

[0166] S10: Output the voltage of each node and the power of the line, and calculate the system indicators based on the objective function in step S3;

[0167] S11: Regenerate the population using the Strength Pareto Evolutionary algorithm 2 (SPEA2), and determine whether the algorithm iteration has ended. If it has ended, proceed to step S12; otherwise, proceed to step S7.

[0168] S12: Compare the system objectives and select the optimal population based on the weight of each objective.

[0169] like Figure 2 As shown, the energy of the wind-solar hydrogen production system consists of a photovoltaic power generation unit, a wind power generation unit, and an electrolyzer unit, and it adopts a grid-connected operation mode. The active power output of the photovoltaic and wind power generation units is selected according to the actual situation, and the reactive power output participates in the system voltage adjustment. The electrolyzer unit includes a transformer, a converter, and a DC electrolysis unit, and it operates with a fixed active power. The reactive power is affected by the voltage at the connection point. The system parameters are shown in Table 1.

[0170] Table 1 Parameters of Wind-Solar Hydrogen Production System

[0171]

[0172] The effectiveness of improving hydrogen production efficiency by reducing voltage fluctuations at the electrolyzer connection point was verified using energy management optimization methods for wind-solar hydrogen production systems.

[0173] like Figure 3 As shown, the hydrogen production system selects the actual active power output of photovoltaic cells, wind turbines, and electrolyzers; such as Figure 4-8 As shown, after introducing an energy management optimization method to improve efficiency, the reactive power of photovoltaics, wind turbines, power grid, and electrolyzer, as well as the active power of the power grid, are solved using the SPEA2 algorithm; for example... Figure 9 As shown, the voltage fluctuation is significantly reduced after optimization; as Figure 10-14 As shown, the line losses of lines 12, 23, 24, and 25, as well as the total line loss, did not increase significantly before and after optimization; as Figure 15 As shown, the hydrogen production efficiency is significantly improved before and after optimization. Considering the voltage fluctuation strategy, hydrogen production efficiency can be improved and hydrogen production output increased while ensuring the electricity used for hydrogen production in the electrolyzer, full utilization of generated electricity, and a slight increase in line losses. Hydrogen production increased by 2677 Nm³. 3 The improvement rate was 7.51%, indicating that the energy management strategy for wind and solar hydrogen production systems, which takes into account the impact of electrolyzer voltage fluctuations, is effective.

[0174] An electronic device includes a memory and a processor, the memory storing an executable program and the processor being configured to run the executable program to perform the steps of the energy management optimization method for reducing voltage fluctuations and improving hydrogen production efficiency as described above.

[0175] A non-volatile storage medium storing an executable program, which, when executed by a processor, implements the energy management optimization method steps described above for reducing voltage fluctuations and improving hydrogen production efficiency.

[0176] The above specific embodiments are used to explain and illustrate the present invention, and are only preferred embodiments of the present invention, not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made to the present invention within the spirit and scope of the claims shall fall within the protection scope of the present invention.

Claims

1. An energy management optimization method for reducing voltage fluctuations and improving hydrogen production efficiency, characterized in that: The energy management optimization method is based on a wind-solar hydrogen production system, which includes a grid unit, a photovoltaic power generation unit, a wind power generation unit, and an electrolyzer unit. The grid unit is connected to node 1, the photovoltaic power generation unit is connected to node 3, the wind power generation unit is connected to node 4, and the electrolyzer unit is connected to node 5. Nodes 1, 3, 4, and 5 are all connected to node 2. And includes the following steps: S1: Determine the required model based on the composition of the wind and solar hydrogen production system; S2: Determine system constraints based on the actual operation of the wind and solar hydrogen production system; S3: Determine the system objective function based on the requirements of the wind and solar hydrogen production system; S4: Outputs the active power of the photovoltaic, wind power, and electrolyzer of the wind-solar hydrogen production system; S5: Input system parameters, including apparent power of photovoltaic converter, power factor of wind turbine, rated voltage of system, line impedance, etc. S6: Based on the constraints in step S2, initialize the algorithm population, including photovoltaic reactive power output, wind turbine reactive power output, and electrolytic cell connection point voltage; S7: Calculate the reactive power demand of the electrolyzer based on the reactive power model of the electrolyzer in step S1, determine the active power and reactive power of nodes 1-5, calculate the active power and reactive power of the power grid based on the loss model of line 12 in step S1 and sum it with the power of node 2. S8: Determine whether the power grid absorbs reactive power. If it does, proceed to step S9; if it does not, proceed to step S10. S9: Adjust the reactive power output of the power grid to 0, adjust the reactive power output of photovoltaic and wind turbines, and maintain the power balance of the system. S10: Output the voltage of each node and the power of the line, and calculate the system indicators based on the objective function in step S3; S11: Regenerate the population using the Intensive Pareto Evolutionary Algorithm 2, and determine whether the algorithm iteration has ended. If it has ended, proceed to step S12; otherwise, proceed to step S7. S12: Compare the system objectives and select the optimal population based on the weight of each objective.

2. The energy management optimization method for reducing voltage fluctuations and improving hydrogen production efficiency according to claim 1, characterized in that: In step S1, the photovoltaic power generation and wind power generation units are calculated using power source substitution. In addition, the electrolyzer reactive power model, hydrogen production efficiency model, and power flow calculation model are also included. The alkaline electrolyzer operates at a constant active power at the system connection point, while the reactive power varies due to voltage fluctuations at the connection point. The specific reactive power-voltage relationship model is as follows: In the formula, The reactive power of the electrolytic cell is expressed in Mvar; t represents the t-th minute of system operation. X is the voltage at the connection point of the electrolytic cell, in kV; el This is the equivalent reactance of the electrolytic cell, in Ω; This represents the active power of the electrolytic cell, measured in MW. The hydrogen production efficiency of an electrolyzer is affected by voltage fluctuations; the greater the voltage fluctuation, the lower the efficiency. The rate of decrease in hydrogen production efficiency is initially slow, then accelerates. After a short-term drop, the efficiency gradually recovers, with the recovery rate initially rapid and then slowing down. Using surface fitting at characteristic points, the following model of voltage fluctuation and hydrogen production efficiency is obtained: In the formula, For voltage fluctuations The percentage of hydrogen production efficiency recovered at time ts, expressed in %. For voltage fluctuations The actual hydrogen production efficiency recovered at time ts, in %; The actual maximum hydrogen production efficiency of the electrolyzer is expressed as a percentage (%). As an intermediate variable; This represents the percentage of voltage fluctuation in the electrolytic cell, expressed as a percentage. U is the voltage at the connection point of the electrolytic cell at minute t-1, in kV; el This is the rated voltage of the electrolytic cell, in kV. Since voltage fluctuations have an instantaneous impact on electrolyzer efficiency, and to compare hydrogen production efficiency and yield per unit time scale, an integral method is used to calculate the average hydrogen production efficiency. The specific model is as follows: In the formula, The average hydrogen production efficiency at time t is expressed in %; The node voltage is determined by the active and reactive power outputs of wind power, solar power, and the power grid. Changes in the reactive power of the electrolyzer also affect the voltage at each node of the system. The voltage models for nodes 1 and 2 are as follows: In the formula, Let be the voltage at node 2 at time t, in kV. Let be the voltage at node 1 at time t, in kV. This represents the longitudinal component of the voltage drop at node 1, in kV. The voltage drop across node 1 is represented by the transverse component, in kV. R represents the active power of node 1, in MW; line12 The resistance of line 12 is in Ω; X represents the reactive power at node 1, in Mvar. line12 The reactance of line 12 is in Ω; The voltage model for node 3 is as follows: In the formula, Let be the voltage at node 3 at time t, in kV. This represents the longitudinal component of the voltage drop at node 2, in kV. The voltage drop across node 2 is represented by the transverse component, in kV. The active power transmitted from node 2 to node 3, in MW; R line23 The resistance of line 23 is in Ω; The reactive power transferred from node 2 to node 3, in Mvar; X line23 The reactance of line 23 is in Ω; The voltage model for node 4 is as follows: In the formula, Let be the voltage at node 4 at time t, in kV. The active power transferred from node 2 to node 4, in MW; R line24 The resistance of the circuit is 24Ω; The reactive power transmitted from node 2 to node 4 is expressed in Mvar; X line24 The line reactance is 24Ω; The voltage model for node 5 is as follows: In the formula, The voltage at node 5 at time t is in kV. The active power transmitted from node 2 to node 5, in MW; R line25 The resistance of the line is 25Ω; The reactive power transmitted from node 2 to node 5 is expressed in Mvar; X line25 The line reactance is 25Ω; The active and reactive power of a line are determined by line impedance, node power, and node voltage, which affect the system power allocation decision. The loss model for line 12 is as follows: In the formula, The active power of line 12 is expressed in MW. The reactive power of line 12 is expressed in Mvar. The loss model for line 23 is as follows: In the formula, The active power of line 23 is expressed in MW. The reactive power of line 23 is expressed in Mvar. The loss model for line 24 is as follows: In the formula, The active power of line 24 is in MW. The reactive power of the line is 24 Mvar; The loss model for line 25 is as follows: In the formula, The active power of the line is 25 MW. The reactive power of the line is 25 Mvar.

3. The energy management optimization method for reducing voltage fluctuations and improving hydrogen production efficiency according to claim 1, characterized in that: In step S2, the constraints include: The reactive power output of a photovoltaic unit is limited by the converter capacity and the active power output, namely: In the formula, S represents the upper limit of photovoltaic reactive power output at time t, in Mvar; pv The apparent power of the photovoltaic converter is expressed in MVA. The photovoltaic active power output at time t is expressed in MW. The reactive power output of a wind turbine unit is limited by the power factor and its active power output, namely: In the formula, The upper limit of reactive power output of the wind turbine at time t, in Mvar; The active power output of the wind turbine at time t is expressed in MW. This is the lower limit of the power factor of the wind turbine; In a hydrogen production system, both active and reactive power must be balanced, that is: In the formula, The reactive power of the line at time t is expressed in MW or Mvar. The active and reactive power absorbed by photovoltaic power at time t are expressed in MW and Mvar, respectively. The active and reactive power consumed by the wind turbine at time t is expressed in MW and Mvar.

4. The energy management optimization method for reducing voltage fluctuations and improving hydrogen production efficiency according to claim 1, characterized in that: In step S3, the system objectives include: Voltage fluctuations at the electrolyzer inlet point reduce the efficiency of hydrogen production through electrolysis. The system energy management aims to minimize these voltage fluctuations, as shown in the following model: In the formula, U di The voltage fluctuation value of the electrolytic cell is given; T is the system calculation period. Reducing line losses can increase the system's hydrogen production capacity, thereby improving the system's economic benefits. This is also an important indicator, and the system aims to minimize line losses. In the formula, Total active power loss of the line, in MW; The active power loss of line 23 is expressed in MW. The active power loss of line 24 is expressed in MW. The active power loss of line 25 is expressed in MW. The voltage deviation calculation model is as follows: In the formula, U represents the voltage shift of the electrolytic cell at time t, expressed as a percentage. N This is the system's operating reference voltage, in kV. Photovoltaic and wind power units have reactive power output capabilities. Excessive power output fluctuations and alternating power output are detrimental to stable system operation. Therefore, the power output curves need to be optimized while meeting other objectives. The specific model is as follows: In the formula, Q di This represents the total output fluctuation, expressed in Mvar. The photovoltaic reactive power output at times t and t-1 is expressed in Mvar. The reactive power output of the wind turbine at times t and t-1 is expressed in Mvar.

5. An electronic device, comprising a memory and a processor, characterized in that: The memory stores an executable program, and the processor is configured to run the executable program to perform the steps of the energy management optimization method for reducing voltage fluctuations and improving hydrogen production efficiency as claimed in any one of claims 1-4.

6. A non-volatile storage medium, characterized in that: The non-volatile storage medium stores an executable program, which, when executed by a processor, implements the energy management optimization method steps for reducing voltage fluctuations and improving hydrogen production efficiency as described in any one of claims 1-4.

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