A power distribution method of a light storage hydrogen direct current micro-grid considering SOC and SOH cooperation

Abstract

To address the issue of extreme operating conditions such as deep charging and discharging that energy storage batteries are prone to encounter when rapidly responding to grid or load disturbances, a photovoltaic-hydrogen storage DC microgrid system with maximum output power was designed. This system comprises photovoltaic cells, proton exchange membrane fuel cells, an electrolyzer, energy storage batteries, and a hydrogen storage tank. A bidirectional adaptive weighting factor power allocation strategy was proposed, which integrates the state of charge (SOC) of the energy storage batteries and the SOC of the hydrogen storage tank. High and low thresholds for the SOC of the energy storage units were set, and the power allocation weighting factor was calculated based on their SOC values ​​to dynamically allocate the power load between the energy storage batteries and the electrolyzer. During operation, considering both SOC and SOC, the 750V rated bus voltage fluctuation was less than 1.6%, and the SOC of the energy storage batteries remained within a reasonable operating range of 30% to 70%, without exhibiting extreme operating conditions under rapid grid and load disturbances. This achieved adaptive power allocation for the photovoltaic-hydrogen storage DC microgrid system, improving operational reliability.

Description

Technical Field

[0001] This invention relates to the field of photovoltaic hydrogen storage DC microgrids, and in particular to a power allocation method for photovoltaic hydrogen storage DC microgrids that takes into account the synergy of SOC and SOH. Background Technology

[0002] Driven by the global energy structure transformation, photovoltaic (PV) power generation, with its abundant resources and green, low-carbon advantages, has become an important component of DC microgrids. However, the inherent volatility and strong randomness of intermittent power sources pose severe challenges to the stable operation of power systems. Power systems not only need to ensure power balance but also require sufficient flexibility to address power imbalances caused by renewable energy generation. Photovoltaic-hydrogen-storage DC microgrids enhance the absorption capacity of PV power generation, achieving efficient energy storage and utilization, and have become an emerging research direction. Hydrogen-electric energy storage technology converts surplus electricity into hydrogen energy for storage through water electrolysis. When PV output is insufficient, fuel cell power generation feeds back into the grid, achieving cross-period energy regulation. Compared to traditional battery energy storage, hydrogen energy storage has advantages such as high energy density, no long-term storage degradation, and cross-seasonal regulation, making it suitable for large-scale renewable energy absorption.

[0003] However, as the core components of photovoltaic-hydrogen storage DC microgrids, energy storage batteries and hydrogen energy devices are rarely considered in current technologies regarding their state of charge, state of hydrogen charge, and power distribution strategies. This leads to the energy storage system being prone to extreme operating conditions such as deep charging and discharging when responding quickly to grid and load disturbances, which reduces its operating life and stability. Summary of the Invention

[0004] Therefore, it is necessary to propose a power allocation method for photovoltaic-hydrogen storage DC microgrids that considers the synergy of SOC (State of Charge) and SOH (State of Health). First, a photovoltaic-hydrogen storage DC microgrid system with a maximum output power of 200kW was designed, comprising photovoltaic cells, fuel cells, electrolyzers, energy storage batteries, and hydrogen storage tanks. Then, a bidirectional adaptive weighting factor power allocation strategy that coordinates the state of charge of the energy storage batteries and the state of charge of the hydrogen storage tanks was proposed. High and low thresholds for the state of the energy storage units were set, and the power allocation weighting factor was calculated based on their state variables to dynamically allocate the power load of the energy storage batteries and electrolyzers. During rapid disturbances to the grid and load, the bus voltage remained stable at its rated voltage, and the energy storage batteries did not exhibit extreme operating conditions. This achieved adaptive power allocation for the photovoltaic-hydrogen storage DC microgrid system, improving operational reliability.

[0005] This invention discloses a power allocation method for a photovoltaic hydrogen storage DC microgrid that considers the synergy of SOC and SOH. It includes the following steps:

[0006] Step 1: Construct a multi-physics coupling model of a photovoltaic hydrogen storage DC microgrid system;

[0007] Step 2: Design a bidirectional adaptive weighting factor (BAW) power allocation strategy to establish a power allocation mechanism that coordinates SOC and SOH. Set high and low thresholds for SOC and SOH, and divide the system into four operating modes: reasonable SOC / SOH, too low SOC, too high SOC, and power deficit. Dynamically allocate the remaining power through the weighting factor w.

[0008] Step 3: Based on the model built in Step 1 and the BAW strategy designed in Step 2, conduct multi-condition experiments to verify the results. Then, simulate normal operating conditions such as sudden load changes and sudden changes in light intensity to verify that the bus voltage is stable at 750V and the SOC is maintained in a reasonable range of 30%~70%. Through deep discharge and deep charge tests, compare the power and SOC curves of BAW and traditional strategies. Finally, conduct 200kW full load and gradual light change tests to confirm that the bus voltage is stable and the SOC fluctuates reasonably.

[0009] The beneficial effects of this invention are as follows:

[0010] This invention discloses a power allocation method for a photovoltaic-hydrogen storage DC microgrid that considers the synergy of State of Charge (SOC) and State of Hypoxia (SOH). Regarding the SOC balancing of the energy storage battery, compared to traditional power allocation strategies, the BAW-based power allocation strategy keeps the SOC of the energy storage battery within a reasonable operating range of 30% to 70%, preventing the battery from reaching its extreme operating conditions. Faced with fluctuations in light intensity and load power disturbances, the output power of each energy module within the system is minimally affected, the bus voltage remains stable at its rated voltage, and the electro-hydrogen coupled microgrid system can operate stably under the corresponding test conditions. Attached Figure Description

[0011] Figure 1 Flowchart of the two-way adaptive weight factor control strategy;

[0012] Figure 2 Microgrid power allocation strategy diagram;

[0013] Figure 3 Power curves for microgrid systems under multiple operating conditions;

[0014] Figure 4 The bus voltage and SOC curves under multiple operating conditions;

[0015] Figure 5 The power curve of a microgrid system based on BAW control strategy in the deep discharge range;

[0016] Figure 6 The power curve of a microgrid system based on traditional control strategies;

[0017] Figure 7 The SOC curves are based on BAW and traditional control strategies;

[0018] Figure 8 The power curve of a microgrid system based on BAW control strategy in the deep discharge range;

[0019] Figure 9 The power curve of a microgrid system based on a traditional control strategy in the deep discharge range;

[0020] Figure 10 The SOC curves for the deep discharge range are based on BAW and traditional control strategies.

[0021] Figure 11 The full-load power curve of the DC microgrid system;

[0022] Figure 12 The full-load bus voltage and SOC curve are both included.

[0023] Figure 13 Structure of a photovoltaic hydrogen storage DC microgrid system Detailed Implementation

[0024] To facilitate understanding of the present invention, a more complete description will be given below with reference to the accompanying drawings. Preferred embodiments of the invention are shown in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to provide a thorough and complete understanding of the disclosure of the invention.

[0025] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0026] The power allocation method for photovoltaic hydrogen storage DC microgrids that considers the synergy of SOC and SOH, as described in this invention, specifically includes the following steps:

[0027] Step 1: Construct a multi-physics coupling model for a photovoltaic hydrogen storage DC microgrid system.

[0028] The system includes a photovoltaic array, energy storage batteries, a proton exchange membrane fuel cell, an electrolyzer, and hydrogen storage equipment. The UI characteristic equation of the photovoltaic cell is:

[0029]

[0030] In the formula, I SC This is the short-circuit current; U OC This is the open-circuit voltage; and These represent the peak current and peak voltage, respectively; I and U are the output current and output voltage of the photovoltaic cell; C1 = (1-I m / I sc )exp(-U m / C2U oc ),C2=(U m / U oc -1)[ln(1-I m / I sc )]-1.

[0031] The mathematical model for energy storage batteries is as follows:

[0032]

[0033] In the formula, This refers to the open-circuit voltage across the dynamic response circuit. The internal resistance of the dynamic circuit; and For two series Voltage across the circuit; S soc,t ,S soc,t-1 For the states of charge at times t and t-1, respectively; C t U bat,t and P bat,t These represent the calibration capacitance, battery voltage, and battery power of the energy storage battery at time t, respectively.

[0034] Voltage U of a single electrolysis unit in an electrolytic cell cell for:

[0035]

[0036] In the formula, This is the voltage of a reversible battery; r2 and r2 are ohmic resistance parameters; T el I represents the temperature of the electrolytic cell; A represents the electrode area of ​​the electrolytic cell; I represents the electrode area of ​​the electrolytic cell. el The current in the electrolytic cell; k el k T1 k T2 and k T3 This represents the electrode overvoltage coefficient.

[0037]

[0038] U ae N is the voltage of the electrolytic cell. aeThe number of strings connected in series.

[0039] The output voltage U of a single proton exchange membrane fuel cell fc_cell for:

[0040]

[0041] In the formula, For Nernst potential, Activation voltage; U ohmic U is the ohmic voltage; con Concentration voltage; N fc The number of individual battery cells; U fc This is the PEMFC voltage.

[0042] According to the van der Waals equation, the pressure inside the hydrogen storage tank... for:

[0043]

[0044] In the formula, el The rate at which hydrogen is injected into the electrolyzer; fc The rate at which the fuel cell absorbs hydrogen; n sto R represents the total amount of hydrogen already in the tank. c V is Avogadro's constant; K is the Kelvin temperature; V sto denoted as , where is the volume of the hydrogen storage tank; 'n' is the amount of hydrogen gas in the storage tank; 'a' is the volume of the hydrogen storage tank. m b m a is the proportionality constant. m Pick =0.02476 Pa•m 6 •mol -2 b m =2.661×10 -5 m 3 •mol -1 .

[0045] The equivalent hydrogen charge state of the hydrogen storage tank is:

[0046]

[0047] In the formula, This is the maximum allowable pressure for the hydrogen storage tank.

[0048] Step 2: Design a bidirectional adaptive weighting factor (BAW) power allocation strategy to establish a power allocation mechanism that coordinates SOC and SOH. Set high and low thresholds for SOC and SOH, and divide the system into four operating modes: reasonable SOC / SOH, too low SOC, too high SOC, and power deficit. Dynamically allocate the remaining power through the weighting factor w.

[0049] Set high and low thresholds for SOC and SOH. min SOC max SOH min and SOH max Real-time data collection of SOC of energy storage battery and SOH of hydrogen storage tank, photovoltaic power P pv and load power P load The adaptive weighting factor ω is calculated to determine the electrolytic cell power P in the simulation. el With energy storage battery power P bat .

[0050] When the energy storage battery system is in the deep discharge range, the calculation process for ω is as follows:

[0051]

[0052] In the formula, ω base Based on the weights, D c and D dc These are the charging demand factor and discharging demand factor for energy storage batteries, respectively, D. soh This is the hydrogen demand coefficient for the hydrogen storage tank.

[0053]

[0054] When the energy storage battery system is in the deep charging range, the calculation process for ω is as follows:

[0055]

[0056] Electrolytic cell power P el With energy storage battery power P bat The calculation is as follows:

[0057]

[0058] Furthermore, based on the operating scenarios, the real-time power allocation strategy of the photovoltaic-hydrogen storage DC microgrid system is divided into four modes. Mode 1: When both the SOC of the energy storage battery and the SOH of the hydrogen storage tank are within a reasonable operating range, the electrolyzer fully utilizes its remaining power to electrolyze water to produce hydrogen, maximizing hydrogen energy output. Mode 2: When the SOC of the energy storage battery is below the lower threshold, the system allocates power according to the SOH of the hydrogen storage tank. The system prioritizes increasing the SOC to avoid the risk of over-discharge. If the SOH of the hydrogen storage tank is within a reasonable operating range or above the upper threshold, the electrolyzer still prioritizes consuming the remaining power to produce hydrogen, using a small amount of the remaining power for charging the energy storage battery; if the SOH of the hydrogen storage tank is also below the lower threshold, the system switches to a dynamic power allocation mode based on multi-objective optimization of SOC and SOH, coordinating the power allocation between the electrolyzer and the energy storage battery. Mode 3: When the SOC of the energy storage battery is above the upper threshold, the energy storage battery discharges to reduce the SOC, avoiding the risk of overcharging. If the SOH of the hydrogen storage tank is within a reasonable range or below the lower threshold, the electrolyzer, after fully absorbing the remaining power, will additionally utilize the discharge power of the energy storage battery to further increase hydrogen production capacity. If the SOH of the hydrogen storage tank is above the upper threshold, the system will also switch to a dynamic power allocation mode based on multi-objective optimization of SOC and SOH, rationally allocating the discharge power of the energy storage battery. Mode 4: When photovoltaic output is insufficient, the energy storage battery discharges to suppress grid disturbances, and the PEMFC supplements the system's power deficit.

[0059] Table 1 Microgrid System Operation Modes

[0060] model <![CDATA[P e ]]> SOC work area SOH working area 1 > 0 <![CDATA[S Clow ≤S C ≤ S Chigh ]]> <![CDATA[S Hlow ≤ S H ≤ S Hhigh ]]> 2 > 0 <![CDATA[S C < S Clow ]]> <![CDATA[S Hlow ≤S H ≤S Hhigh / S H > S Hhigh ]]> 3 > 0 <![CDATA[S C > S Chigh ]]> <![CDATA[S Hlow ≤S H ≤S Hhigh / S H < S Hlow ]]> 4 < 0 - -

[0061] Step 3: Based on the model built in Step 1 and the BAW strategy designed in Step 2, conduct multi-condition experimental verification. First, simulate normal operating conditions such as sudden load changes and sudden changes in light intensity. Then, compare the power and SOC curves of BAW and traditional strategies through deep discharge and deep charge tests. Finally, conduct a gradual light change test under a 200kW full load condition to confirm the stability of the system bus voltage and reasonable SOC fluctuations. Table 2 shows the relevant parameters of each distributed energy source in the microgrid.

[0062] Table 2 Distributed Energy Parameters

[0063] parameter unit numerical values DC bus voltage V 750 Energy storage battery capacity A·h 500 Electrolytic cell rated power kW 150 Fuel cell rated power kW 100 Maximum power of photovoltaic array kW 200

[0064] Figure 3 , Figure 4 The figures show the power curve and bus voltage / SOC curve of the DC microgrid system. Under normal operating conditions such as sudden load changes and sudden changes in irradiance, the bus voltage remains basically around the set reference value of 750 V; the SOC of the energy storage battery varies within a reasonable operating range with the photovoltaic power generation and load disturbances. The above results verify the effectiveness of the invention and indicate that the system has good operational stability.

[0065] Figure 5 and Figure 6 The figure shows the power curves of a DC microgrid system based on BAW control strategy and a traditional control strategy under deep discharge conditions. Figure 7 The image shows a comparison of the SOC curves of an energy storage battery under the initial conditions of deep discharge, using a BAW-based control strategy and a traditional control strategy. Under deep charging conditions, the DC microgrid system using the traditional control strategy does not allocate power to the energy storage battery, and the battery SOC shows no significant change trend, remaining consistently in the deep charging range. In contrast, the BAW-based control strategy effectively coordinates the power flow between the power source, load, and energy storage system, effectively preventing the energy storage battery from operating in the extreme state of deep charging for extended periods.

[0066] Figure 8 , Figure 9 The figure shows the power curves based on BAW and traditional control strategies under the initial conditions of deep discharge of the energy storage battery at SOC. Figure 10 The figures show the SOC curves under deep charging conditions based on BAW and conventional control strategies. Under deep discharge operating conditions, the DC microgrid system based on the conventional control strategy does not allocate power to the energy storage battery, and the battery SOC shows no significant trend, remaining consistently in the deep charging range. In contrast, the BAW-based control strategy effectively coordinates the power flow between the power source, load, and energy storage system, effectively preventing the energy storage battery from operating in the extreme state of deep discharge for extended periods.

[0067] Figure 11 , Figure 12 The figures show the full-load power curve and the full-load bus voltage / SOC curve of the DC microgrid system. Under full-load conditions, the BAW-based control strategy effectively coordinates the power flow between the power source, load, and energy storage system. The DC bus voltage remains relatively stable around the set reference value of 750V, and the SOC of the energy storage battery varies within a reasonable operating range depending on fluctuations in photovoltaic power generation and load disturbances. These results verify the effectiveness of the invention, demonstrating that the system possesses good operational stability and dynamic response sensitivity.

[0068] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0069] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this invention patent should be determined by the appended claims.

Claims

1. A power allocation method for a photovoltaic hydrogen storage DC microgrid considering the synergy of SOC and SOH, characterized in that, Includes the following steps: Step 1: Construct a multi-physics coupling model of a photovoltaic-hydrogen storage DC microgrid system; the model includes the following: photovoltaic array, energy storage battery, proton exchange membrane fuel cell, energy storage battery, electrolyzer, and hydrogen storage device; Step 2: Design a bidirectional adaptive weighting factor power allocation strategy to establish a power allocation mechanism that coordinates SOC and SOH; set high and low thresholds for SOC and SOH, and divide the system into four operating modes: reasonable SOC / SOH, low SOC, high SOC, and power deficit. Dynamically allocate remaining power using a bidirectional adaptive weighting factor ω; based on the bidirectional adaptive weighting factor control strategy for the energy storage battery SOC and the hydrogen storage tank SOH, dynamically calculate the power load of the energy storage battery and electrolyzer; set high and low thresholds for SOC and SOH. min SOC max SOH min and SOH max Real-time data collection of SOC of energy storage battery and SOH of hydrogen storage tank, photovoltaic power P pv and load power P load The adaptive weighting factor ω is calculated to determine the electrolytic cell power P in the simulation. el With energy storage battery power P bat .

2. The method according to claim 1, characterized in that, In Step 1, establishing the multiphysics coupling model of the photovoltaic-hydrogen storage DC microgrid system includes the following: The UI characteristic equation of a photovoltaic cell is: In the formula, I SC U is the short-circuit current. OC This is the open-circuit voltage; and These represent the peak current and peak voltage, respectively; I and U are the output current and output voltage of the photovoltaic cell; C1 = (1-I m / I sc )exp(-U m / C2U oc ),C2=(U m / U oc -1)[ln(1-I m / I sc )]-1; The mathematical model for energy storage batteries is as follows: In the formula, This refers to the open-circuit voltage across the dynamic response circuit. The internal resistance of the dynamic circuit; and For two series Voltage across the circuit; S soc,t ,S soc,t-1 For the states of charge at times t and t-1; C t U bat,t and P bat,t These are the calibration capacitance, battery voltage, and battery power of the energy storage battery at time t, respectively. Voltage U of a single electrolysis unit in an electrolytic cell cell for: In the formula, This is the voltage of a reversible battery; r2 and r2 are ohmic resistance parameters; T el I represents the temperature of the electrolytic cell; A represents the electrode area of ​​the electrolytic cell; I represents the electrode area of ​​the electrolytic cell. el The current in the electrolytic cell; k el k T1 k T2 and k T3 Electrode overvoltage coefficient; U ae N is the voltage of the electrolytic cell. ae The number of strings connected in series; The output voltage U of a single proton exchange membrane fuel cell fc_cell for: In the formula, For Nernst potential, Activation voltage; U ohmic U is the ohmic voltage; con Concentration voltage; N fc This refers to the number of individual battery cells; U fc PEMFC voltage; According to the van der Waals equation, the pressure inside the hydrogen storage tank... for: In the formula, el Hydrogen injection rate into the electrolyzer; #imgpt23# fc #imgpt24# represents the hydrogen absorption rate of the fuel cell; n sto #imgpt25# represents the total amount of hydrogen already in the tank; R c #imgpt26# represents Avogadro's constant; K is the Kelvin temperature; V sto denoted as , where is the volume of the hydrogen storage tank; 'n' is the amount of hydrogen gas in the storage tank; 'a' is the volume of the hydrogen storage tank. m b m a is the proportionality constant. m Take #imgpt27# = 0.02476 Pa•m 6 •mol -2 b m =2.661×10 -5 m 3 •mol -1 ; The equivalent hydrogen charge state of the hydrogen storage tank is: In the formula, #imgpt29# represents the maximum allowable pressure of the hydrogen storage tank.

3. The method according to claim 1, characterized in that, In Step 2, when the energy storage battery system is in the deep discharge range, the calculation process for ω is as follows: In the formula, ω base Based on the weights, D c and D dc These are the charging demand factor and discharging demand factor for energy storage batteries, respectively, D. soh The hydrogen demand coefficient for the hydrogen storage tank; 4. The method according to claim 3, characterized in that, When the energy storage battery system is in the deep charging range, the calculation process for ω is as follows: Electrolytic cell power P el With energy storage battery power P bat The calculation is as follows:

5. The method according to claim 3, characterized in that, The real-time power allocation strategy for the photovoltaic-hydrogen storage DC microgrid system is as follows: Mode 1: When both the SOC of the energy storage battery and the SOH of the hydrogen storage tank are within a reasonable operating range, the electrolyzer fully utilizes its remaining power to electrolyze water to produce hydrogen, maximizing hydrogen energy output; Mode 2: When the SOC of the energy storage battery is lower than the lower threshold, the system allocates power according to the SOH of the hydrogen storage tank; the system prioritizes increasing the SOC to avoid the risk of over-discharge; if the SOH of the hydrogen storage tank is in a reasonable operating range or higher than the upper threshold, the electrolyzer still prioritizes using the remaining power to produce hydrogen, and uses a small amount of the remaining power for charging the energy storage battery; if the SOH of the hydrogen storage tank is also lower than the lower threshold, the system switches to a dynamic power allocation mode based on multi-objective optimization of SOC and SOH, and coordinates the power allocation between the electrolyzer and the energy storage battery. Mode 3: When the SOC of the energy storage battery is higher than the upper limit threshold, the energy storage battery discharges to reduce the SOC and avoid the risk of overcharging; if the SOH of the hydrogen storage tank is in a reasonable range or lower than the lower limit threshold, the electrolyzer will utilize the remaining power and additionally utilize the discharge power of the energy storage battery to further improve the hydrogen production power; if the SOH of the hydrogen storage tank is higher than the upper limit threshold, the system will also switch to a dynamic power allocation mode based on multi-objective optimization of SOC and SOH to reasonably allocate the discharge power of the energy storage battery. Mode 4: When photovoltaic output is insufficient, the energy storage battery discharges to suppress grid disturbances, and PEMFC makes up for the system's power deficit.

6. The method according to claim 1, characterized in that, It also includes Step 3, which involves conducting multi-condition experimental verification based on the model built in Step 1 and the BAW strategy designed in Step 2. First, it simulates normal operating conditions such as sudden load changes and sudden changes in light intensity to verify that the bus voltage is stable at 750V and the SOC is maintained in a reasonable range of 30% to 70%. Then, it compares the power and SOC curves of BAW and traditional strategies through deep discharge and deep charge tests. Finally, it conducts a 200kW full load and gradual light change test to confirm that the bus voltage is stable and the SOC fluctuates reasonably.

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