Real-time feedback optimization method for electro-hydrogen hybrid energy storage system

Abstract

The application discloses a real-time feedback optimization method of an electric-hydrogen composite energy storage system. The real-time feedback optimization method of the electric-hydrogen composite energy storage system comprises the following steps: constructing a real-time feedback optimization model of energy self-adaptive adjustment of the electric-hydrogen composite energy storage system; constructing operation constraints of the electric-hydrogen composite energy storage system, including electrochemical energy storage operation constraints and hydrogen energy storage operation constraints; and calculating the charging and discharging power of the electrochemical energy storage and the hydrogen energy storage; with the aid of online measurement of the energy state of the hydrogen energy storage and the electrochemical cell unit, a real-time feedback optimization strategy of the electric-hydrogen composite energy storage system based on the measurement feedback is designed. The application can comprehensively consider the wind power fluctuation suppression demand and the physical operation characteristics of the electrochemical energy storage system and the hydrogen energy storage system, obtain better control results of the electric-hydrogen composite energy storage system, and has the advantages of scientific and reasonable method, strong applicability, good effect and the like.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of wind farm electric-hydrogen composite energy storage system control, and particularly relates to a real-time feedback optimization method for reducing wind farm output power prediction deviation of an electric-hydrogen composite energy storage system. BACKGROUND

[0002] New energy stations need to have certain regulation capacity, and certain energy storage is configured as necessary to improve the adjustability of the new energy station. Among them, the electrochemical energy storage and hydrogen energy storage have certain complementarity in power density and energy density, and configuring a certain capacity of electric-hydrogen composite energy storage system at the new energy station side is an effective means to improve the adjustability of the new energy station. However, the electrochemical energy storage and hydrogen energy storage have large differences in physical characteristics, and the cost is high, so how to control the electric-hydrogen composite energy storage system to reduce the wind farm output power prediction deviation demand is crucial.

[0003] The prior art discloses a control method of an electric-hydrogen composite energy storage system, an optimal control model and a target function of the electric-hydrogen composite energy storage system are established, and most studies are to realize the rapid distribution of power through filtering or heuristic electric-hydrogen composite energy storage system regulation strategies.

[0004] However, the regulation target and scene applicable to the above method are limited, the internal power distribution of the electric-hydrogen composite energy storage system is not comprehensively considered, and the adjustment of the operation state of the electric-hydrogen composite energy storage system according to the difference in wind power smoothing demand in each period is not considered, which may lead to a relatively conservative control strategy. SUMMARY

[0005] In view of the problems in the prior art, the purpose of the present application is to provide an optimal control strategy for an electric-hydrogen composite energy storage system, which is scientific and reasonable, has strong applicability, and has excellent effect, and aims to comprehensively consider the wind farm output power prediction deviation reduction demand and the physical operation characteristics of the electrochemical energy storage and hydrogen energy storage to obtain the optimal power of the electrochemical energy storage and hydrogen energy storage.

[0006] In a first aspect, the present application provides a real-time feedback optimization method for an electric-hydrogen composite energy storage system, which comprises:

[0007] An optimization target function of the electric-hydrogen composite energy storage system and corresponding operation constraints are constructed;

[0008] A feedback optimization strategy is constructed based on the wind power of the wind farm output and the optimization target function;

[0009] The power of the electrochemical energy storage system and the power of the hydrogen energy storage system are updated in real time according to the feedback optimization strategy;

[0010] The wind power after energy storage adjustment is calculated according to the updated power of the electrochemical energy storage system and the power of the hydrogen energy storage system;

[0011] If the adjusted wind power exceeds the upper and lower limit range of the pre-set predicted wind power, the parameters of the feedback optimization strategy are adjusted, and the power of the electrochemical energy storage system and the power of the hydrogen energy storage system are re-updated.

[0012] As preferred, the optimization objective function of the electro-hydrogen hybrid energy storage system comprises:

[0013]

[0014]

[0015] wherein Min represents minimization, f is the loss cost of the electro-hydrogen hybrid energy storage system, C V is the operation cost of the electro-hydrogen hybrid energy storage system, is the power of the i-th battery unit at time t, is the power of the i-th hydrogen storage unit at time t, λ BESS and λ HESS are the operation cost coefficients of the electrochemical energy storage system and the hydrogen energy storage system respectively, Δt is a unit time interval, is the penalty function about state of charge, is the penalty function about state of hydrogen storage; γ is a penalty factor, δ1 and δ2 are parameters of the electrochemical energy storage system and the hydrogen energy storage system respectively, SOC t.i is the state of charge of the i-th battery unit at time t, SOC max and SOC min are the upper and lower limits of the state of charge of a single battery unit, SOH t.i is the state of hydrogen storage of the hydrogen tank in the i-th hydrogen storage unit at time t, SOH max and SOH min are the upper and lower limits of the state of hydrogen storage of the hydrogen tank.

[0016] As preferred, the operation constraints comprise operation constraints of the electrochemical energy storage system and operation constraints of the hydrogen energy storage system, and the operation constraints of the electrochemical energy storage system comprise:

[0017]

[0018] wherein, is the energy of the i-th battery unit at time t, η BESS.E and η BESS.F are the charging and discharging efficiencies of the battery unit, Q BESS is the capacity of the battery unit, is the power of the electrolyzer in the i-th hydrogen storage unit at time t, is the power of the fuel cell in the i-th hydrogen storage unit at time t;

[0019] The operating constraints of the hydrogen energy storage system include:

[0020] Electrolyzer Constraints:

[0021]

[0022] Where, is the power of the electrolyzer in the i-th hydrogen storage unit at time t, η elec is the hydrogen production efficiency of the electrolyzer of the hydrogen storage unit, is the mass of hydrogen produced by the electrolytic cell in the i-th hydrogen storage unit at time t, H is the calorific value of hydrogen, P elec.max and P elec.min are the upper and lower limits of the input power of a single electrolyzer of the hydrogen storage unit, The maximum amount of hydrogen flowing into the hydrogen storage tank per unit time for a single electrolyzer of the hydrogen storage unit;

[0023] Hydrogen storage tank constraints:

[0024]

[0025] SOH min ≤SOH t.i ≤SOH max

[0026] Where Q HS is the capacity of the hydrogen storage tank, is the amount of hydrogen consumed by the fuel cell in the i-th hydrogen storage unit at time t; is the amount of hydrogen consumed by the fuel cell in the i-th hydrogen storage unit at time t;

[0027] Fuel cell constraints:

[0028]

[0029] Where η FC The efficiency of converting hydrogen energy into electrical energy in the fuel cell of the hydrogen storage unit, is the power of the fuel cell in the i-th hydrogen storage unit at time t, P HESS.F.max and P HESS.F.min are the upper and lower limits of the output power of a single fuel cell, The maximum amount of hydrogen that flows from the hydrogen storage tank to a single fuel cell per unit time.

[0030] Preferably, the feedback optimization strategy includes:

[0031] The Lagrangian function of the electric-hydrogen composite energy storage system is constructed, and the Lagrangian function is:

[0032]

[0033] wherein L is a Lagrange function, f is a loss cost of the electro-hydrogen composite energy storage system, is the wind power adjusted by energy storage, is the predicted power of the wind farm at t, t W.up is the upper limit of the predicted power of the wind farm at t, t W.dn is the lower limit of the predicted power of the wind farm at t, up and x dn are the upper and lower limit coefficients of the predicted power of the wind farm, respectively, and μ t are the upper and lower multipliers of the Lagrange function at t, respectively, and s are the correction factors of the upper and lower limits of the wind power, respectively, and β is a step factor.

[0034] Preferably, the real-time updating of the electrochemical energy storage system power and the hydrogen energy storage system power according to the feedback optimization strategy comprises:

[0035] the partial derivative of the Lagrange function with respect to the power of the battery unit to obtain the updated electrochemical energy storage system power:

[0036]

[0037] wherein is a limited range function, limiting y in , and r is a penalty parameter;

[0038] the partial derivative of the Lagrange function with respect to the power of the hydrogen storage unit to obtain the updated hydrogen energy storage system power:

[0039]

[0040] wherein is a limited range function, limiting y in , and r is a penalty parameter.

[0041] Preferably, based on the updated electrochemical energy storage system power and the hydrogen energy storage system power, the wind power adjusted by energy storage is calculated according to the following formula:

[0042]

[0043] wherein is the maximum wind power output of the wind farm at t, is the curtailed wind power at t.

[0044] Preferably, the adjustment of the parameters of the feedback optimization strategy comprises:

[0045]

[0046] In a second aspect, the present application provides a real-time feedback optimization device for an electric-hydrogen hybrid energy storage system, comprising:

[0047] a first construction module for constructing an optimization objective function and operation constraints of the electric-hydrogen hybrid energy storage system;

[0048] a second construction module for constructing a feedback optimization strategy according to wind power output by a wind farm and the optimization objective function;

[0049] an updating module for updating electrochemical energy storage system power and hydrogen energy storage system power in real time according to the feedback optimization strategy;

[0050] a calculation module for calculating wind power after energy storage adjustment according to the updated electrochemical energy storage system power and hydrogen energy storage system power;

[0051] an adjustment module for adjusting parameters of the feedback optimization strategy, and re-updating the electrochemical energy storage system power and hydrogen energy storage system power, if the wind power after energy storage adjustment exceeds a pre-set upper and lower limit range of predicted wind power.

[0052] In a third aspect, the present application provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements the real-time feedback optimization method for an electric-hydrogen hybrid energy storage system.

[0053] Compared with the prior art, the present application has the following beneficial effects:

[0054] The present application can update the feedback optimization strategy when the predicted wind power exceeds the pre-set range, and fully considers the prediction deviation reduction requirement of wind power output by the wind farm. Meanwhile, the present application can update the electrochemical energy storage system power and hydrogen energy storage system power in real time under the objective function and constraint conditions through the feedback optimization strategy, and can make the updated electrochemical energy storage system power and hydrogen energy storage system power under different wind farm operation states have better rationality by taking the state of charge of the battery unit and the state of hydrogen storage of the hydrogen storage unit as the penalty function, fully considers the physical operation characteristics of the electrochemical energy storage and hydrogen energy storage to obtain better power of the electrochemical energy storage and hydrogen energy storage, and has the advantages of scientific rationality, strong applicability, and good effect. BRIEF DESCRIPTION OF DRAWINGS

[0055] Figure 1 A flowchart of the real-time feedback optimization method for the electric-hydrogen hybrid energy storage system in Example 1;

[0056] Figure 2 A comparison chart of wind power before and after smoothing in the application example;

[0057] Figure 3 This is a comparison chart of the power exceeding the upper and lower limits before and after smoothing in the application example;

[0058] Figure 4 The SOC and power variation diagram of ten battery cells in the application example;

[0059] Figure 5 The SOH and power variation diagram of the ten hydrogen storage units in the application example;

[0060] Figure 6 This is an enlarged image exceeding the upper limit and its gradient effect image in the application example;

[0061] Figure 7 This is a diagram showing the charging power effects of electrochemical energy storage and hydrogen energy storage in the application example;

[0062] Figure 8 This is an enlarged image below the lower limit in the application example and its gradient effect image;

[0063] Figure 9 This is a diagram showing the discharge power effects of electrochemical energy storage and hydrogen energy storage in the application example;

[0064] Figure 10 This is an enlarged diagram of the wind power within the range and a gradient effect diagram of the five intervals in the application example. DETAILED DESCRIPTION

[0065] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, features defined as "first", "second", etc. may explicitly or implicitly include one or more of the features. In the description of the present invention, unless otherwise specified, "multiple" means two or more. In the description of the present invention, it should be noted that, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed connections, detachable connections, or integral connections; mechanical connections or electrical connections; direct connections or indirect connections through an intermediate medium; and internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0066] The application will be further described below with reference to the accompanying drawings. The following examples are only used to more clearly illustrate the technical solutions of the application, and cannot be used to limit the protection scope of the application.

[0067] Example 1

[0068] In combination Figure 1 , the embodiment provides a real-time feedback optimization method for an electric-hydrogen hybrid energy storage system, which comprises the following steps:

[0069] Step 1, constructing an optimization objective function for realizing energy self-adaptive adjustment of the electric-hydrogen hybrid energy storage system. The objective function is as follows:

[0070]

[0071] In the formula, Min represents minimization, f is the loss cost of the electric-hydrogen hybrid energy storage system, C V is the operation cost of the electric-hydrogen hybrid energy storage system, is the power of the i th battery unit at time t, is the power of the i th hydrogen storage unit at time t, λ BESS and λ HESS are respectively the operation cost coefficients of the electrochemical energy storage system and the hydrogen energy storage system, Δt is a unit time interval, is a penalty function about state of charge, is a penalty function about state of hydrogen storage;

[0072] In order to facilitate solving the objective function formula (1) under the inequality constraint conditions of SOC and SOH, the inequality constraint conditions are integrated into the objective function in the form of a penalty function, and then an augmented objective function is constructed, which is as shown in formula (2).

[0073] Penalty function of state of charge:

[0074]

[0075] In the formula, γ is a penalty factor, δ1 is a parameter of electrochemical energy storage, SOC t.i is the state of charge of the i th battery unit at time t, SOC max and SOC min are respectively the upper and lower limits of the state of charge of a single battery unit.

[0076] Penalty function of state of hydrogen storage:

[0077]

[0078] In the formula, γ is a penalty factor, δ1 and δ2 are respectively parameters of electrochemical energy storage and hydrogen energy storage, SOC t.iSOCi(t) is the state of charge of the i-th battery unit at time t max and SOC min are the upper and lower limits of the state of charge of a single battery unit, SOH t.i SOH i(t) is the state of hydrogen storage of the i-th hydrogen storage unit at time t max and SOH min are the upper and lower limits of the state of hydrogen storage of a single hydrogen storage tank.

[0079] Step 2, build the operation constraints of hydrogen storage system and electrochemical energy storage system.

[0080] 1) The operation constraints of electrochemical energy storage system are:

[0081]

[0082] where, ηi(t) is the energy of the i-th battery unit at time t BESS.E and η BESS.F are the charge and discharge efficiencies of the battery, Q BESS is the capacity of the battery, Pi(t) is the power of the electrolyzer in the i-th battery unit at time t Pi(t) is the power of the fuel cell in the i-th battery unit at time t

[0083] 2) The operation constraints of hydrogen storage system are:

[0084] (a) Electrolyzer constraints:

[0085]

[0086] where, η elec is the hydrogen production efficiency of the electrolyzer of the hydrogen storage unit, H i(t) is the mass of hydrogen produced by the electrolyzer in the i-th hydrogen storage unit at time t, H is the heat value of hydrogen, P elec.max and P elec.min are the upper and lower limits of the input power of a single electrolyzer of the hydrogen storage unit, The maximum amount of hydrogen flowing into the hydrogen storage tank per unit time of a single electrolyzer of the hydrogen storage unit is Pmax, from the inequality constraint of formula (7) and the relationship expression of formula (6), the power constraint at this time can be derived as formula (8), the electrolyzer has the above two constraints of formula (7) and (8), and the smaller value of the two constraints is taken as the upper limit of the input power of the electrolyzer, so that the constraint of the input power of the electrolyzer is obtained, which is specifically shown in formula (9).

[0087] (b) Hydrogen storage tank constraints:

[0088]

[0089] SOH min≤ SOH t.i ≤ SOH max (11)

[0090] where Q HS is the capacity of the hydrogen storage tank, is the amount of hydrogen consumed by the fuel cell at time t in the i-th hydrogen storage unit.

[0091] (c) Fuel cell constraints:

[0092]

[0093] where η FC is the efficiency of the fuel cell in the hydrogen storage unit in converting hydrogen energy into electrical energy, P HESS.F.max and P HESS.F.min are the upper and lower limits of the output power of a single fuel cell, is the maximum amount of hydrogen flowing from the hydrogen storage tank into a single fuel cell per unit time. From the inequality constraint of formula (13) and the relationship expression in formula (12), the power constraint at this time can be derived as formula (14). The fuel cell has the above two constraints of formula (13) and (14), and the smaller value of the two constraints is taken as the upper limit of the output power of the fuel cell, thereby obtaining the constraint of the output power of the fuel cell, which is specifically shown in formula (15).

[0094] Step 3, design the real-time feedback optimization strategy of the electricity-hydrogen composite energy storage system based on measurement feedback. The Lagrange function of the electricity-hydrogen composite energy storage system based on measurement feedback is:

[0095]

[0096] where L is the Lagrange function, f is the loss cost of the electricity-hydrogen composite energy storage system, is the wind power after energy storage adjustment, is the predicted power of the wind farm at time t, P t W.up is the upper limit of the predicted power of the wind farm at time t, P t W.dn is the lower limit of the predicted power of the wind farm at time t, x up and x dn are the upper and lower limit coefficients of the predicted power of the wind farm, and μ t are the upper and lower multipliers of the Lagrange function at time t, and s are the correction factors for exceeding the upper and lower limits of the wind power, and β is the step factor;

[0097] The real-time feedback optimization strategy of the electricity-hydrogen composite energy storage system based on measurement feedback is designed, which includes taking the partial derivative of the Lagrange function, and specifically:

[0098] (a) the partial derivative of the Lagrangian function with respect to the power of the battery unit is given by:

[0099]

[0100] where, is a limiting function that limits y to , and r is a penalty parameter. (b) the partial derivative of the Lagrangian function with respect to the power of the hydrogen storage unit is given by:

[0101]

[0102] The initial distribution of power of the electro-hydrogen hybrid energy storage system can be obtained according to formula (19) and (21), and the adjusted energy storage power of wind power is calculated according to , the maximum wind power output of the wind farm at time t is , and the curtailed wind power at time t is

[0103] If the adjusted wind power at this time is not within the upper and lower limit range of the predicted wind power, the strategy in this paper needs to be used to adjust the upper and lower multipliers, that is:

[0104]

[0105] Step 4, analyze and adjust the real-time feedback optimization strategy based on measurement feedback.

[0106] According to the different intervals of SOC and SOH, in the interval of the penalty function of formula (3), from low to high, the first SOC interval, the second SOC interval, the third SOC interval, the fourth SOC interval, the fifth SOC interval, in the interval of the penalty function of formula (4), from low to high, the first SOH interval, the second SOH interval, the third SOH interval, the fourth SOH interval, the fifth SOH interval, and P t W.act The relationship between P t W.dn The relationship between P

[0107] (1) When SOC is in the first SOC interval or the second SOC interval

[0108] a) SOH is in the first SOH interval or the second SOH interval, when P t W.act P t W.dn , the energy storage needs to be charged, and if the power of the battery unit at this time is higher than its maximum power, then P t.i ​BESS.E = P BESS.E.max , if the power of the hydrogen storage unit at this time is higher than its maximum power, then as shown in equation (23):

[0109]

[0110] When P t W.act < P t W.dn , the energy storage needs to be discharged, since the electrolytic tank of the battery unit is frequently started and stopped, which has a large loss and has a great impact on the service life of the electrolytic tank, so the electrolytic tank of the battery unit is operated at the hot standby power, that is, P t.i BESS.E = P elec.min .

[0111] b) SOH is in the third SOH interval, the fourth SOH interval or the fifth SOH interval, when P t W.act > P t W.dn , the energy storage needs to be charged, at this time, the state of SOC is low, and the state of SOH is high, the electrochemical energy storage system is preferentially charged; when P t W.act < P t W.dn , the energy storage needs to be discharged, the state of SOC is lower than the state of SOH, the hydrogen energy storage system is preferentially discharged.

[0112] (2) When SOC is in the third SOC interval

[0113] a) SOH is in the first SOH interval or the second SOH interval, when P t W.act > P t W.dn , the energy storage needs to be charged, at this time, the state of SOC is high, and the state of SOH is low, the hydrogen energy storage system is preferentially charged; when P t W.act < P t W.dn , the energy storage needs to be discharged, the state of SOC is higher than the state of SOH, the electrochemical energy storage system is preferentially discharged.

[0114] b) SOH is in the third SOH interval, the fourth SOH interval or the fifth SOH interval, when P t W.act > P t W.dn , the energy storage needs to be charged, at this time, the state of SOC is low, and the state of SOH is high, the electrochemical energy storage system is preferentially charged; when P t W.act < Pt W.dn When the SOC needs to be discharged, the state of the SOC is lower than the state of the SOH, and the hydrogen energy storage system is preferentially discharged.

[0115] (3) When the SOC is in the fourth SOC interval or the fifth SOC interval

[0116] a) When the SOH is in the first SOH interval, the second SOH interval or the third SOH interval, and P t W.act >P t W.dn When the SOC needs to be charged, the state of the SOC is higher than the state of the SOH, and the hydrogen energy storage system is preferentially charged; when P t W.act <P t W.dn When the SOC needs to be discharged, the state of the SOC is higher than the state of the SOH, and the electrochemical energy storage system is preferentially discharged.

[0117] b) When the SOH is in the fourth SOH interval or the fifth SOH interval, and P t W.act >P t W.dn When the SOC needs to be charged, the state of the SOC is higher than the state of the SOH, and the hydrogen energy storage system is preferentially charged, if the SOH t.i ≤ SOHmax when the electrolytic cell is operated at the minimum power, then the charging can continue, if the SOH t.i > SOHmax when the electrolytic cell is operated at the minimum power, the fuel cell needs to be discharged for the safety of the hydrogen storage tank; when P t W.act <P t W.dn When the SOC needs to be discharged, the state of the SOC is higher than the state of the SOH, and the electrochemical energy storage system is preferentially discharged, if the electrochemical energy storage power is higher than the maximum power thereof at this time, then P t.i BESS.F = P BESS.F.max , if the hydrogen storage unit power is higher than the maximum power thereof at this time, then as shown in equation (24):

[0118]

[0119] The embodiment also provides a real-time feedback optimization strategy of an electricity-hydrogen composite energy storage system for reducing prediction deviation of output power of a wind farm.

[0120] Application example

[0121] Based on a certain installed 200MW wind farm, the capacity of the electrochemical energy storage system is 15MW, and the capacity of the hydrogen energy storage system is 20MW, the effectiveness of the method is analyzed. Among them, the configuration parameters of electrochemical energy storage and hydrogen energy storage are shown in Table 1 and Table 2. In order to verify the effectiveness of the strategy proposed in this paper, the sampling period is 7 days, and the sampling interval is 1 second.

[0122] Table 1 Electrochemical energy storage related parameters

[0123] parameter Numerical Rated power 5MW Rated capacity 15KWh State of charge upper / lower limit 0.9 / 0.1 Charge / discharge efficiency 0.85～09 electrochemical cell 10

[0124] Table 2 Hydrogen energy storage related parameters

[0125] parameter Numerical Number of electrolytic cell units 10 Calorific value 33.3KWh / kg Electrolyzer input power upper / lower limit 5 / 0.5MW Maximum hydrogen production per unit time of electrolyzer 10kg / h Electrolyzer efficiency 0.62～0.67 Number of hydrogen storage tank units 10 Maximum amount of hydrogen flowing in / out of the storage tank per unit time 20 / 20kg / h Upper / lower limit of hydrogen storage state 0.9 / 0.1 Hydrogen storage tank capacity 5.6m3 Number of fuel cell units 10 Fuel cell efficiency 0.62～0.67 Fuel cell power limit 5MW

[0126] Using the method of the application, the wind power before and after smoothing and the power exceeding the upper and lower limits before and after smoothing are compared, and the results are as follows Figure 2 、 Figure 3 From the figure, it is found that the wind power before smoothing exceeds the upper limit at 740 to 800 seconds, and is lower than the lower limit at 3200 to 3400 seconds, while the wind power after smoothing is within the upper and lower limit range in the above interval. In the figure of power exceeding the upper and lower limits before and after smoothing, the part of wind power exceeding the upper limit is represented by "xx", and the part of wind power lower than the lower limit is represented by "circle". After adjusting according to the strategy proposed in this paper, the power exceeding the upper and lower limits after smoothing is about 0.1MW, while the power exceeding the upper and lower limits before adjustment is about 3MW. It can be seen that the wind power curve after regulation tends to be smooth, and the fluctuation is significantly reduced. This shows that the regulation strategy used in this paper can effectively reduce the volatility of wind power, making it more controllable, so as to better meet the requirements of power system for wind power output.

[0127] The power of the battery unit and the SOC and the power of the hydrogen storage unit and the SOH are compared, and the results are shown in Figure 4 、 Figure 5 , Figure 4The distribution of the internal power of the battery units is shown, where all the power fluctuates in the range of -0.5 MW to 0.5 MW, and there is no case exceeding this range. This indicates that the internal power distribution strategy of the battery units proposed in this paper has good effectiveness. Due to the difference in the internal power of the battery units and the introduction of the penalty function, the SOC of each battery unit is also different, but the overall trend of change is the same, and the SOC of each battery unit changes relatively gently in the range of 0.3 to 0.7, and the curve changes tend to be stable; in the range of 0.2 to 0.3 and 0.7 to 0.8, the SOC curve changes faster; in the range of 0.1 to 0.2 and 0.8 to 0.9, the SOC curve changes significantly faster. By quickly adjusting the power distribution, the system can effectively pull the SOC back to a more ideal range, thereby avoiding the risk of overcharging or discharging. These results prove that the strategy in this paper can effectively manage the SOC of the battery units under various working conditions, ensuring the safety and effectiveness of the system.

[0128] Figure 5 The distribution of the internal power of the hydrogen storage units is shown, where all the power fluctuates in the range of -0.5 MW to 0.5 MW, and there is no case exceeding this range. Due to the difference in the internal power of the hydrogen storage units and the introduction of the penalty function, the SOH of each hydrogen storage unit is also different, but the trend of change of SOH in each interval is the same as that of SOC. Considering the high loss of hydrogen storage energy charging and discharging and the low energy density of electrochemical energy storage, the penalty function of hydrogen storage energy is set to be greater than that of electrochemical energy storage, so the SOC changes frequently and the SOH changes relatively slowly. Overall, by comparing the SOC and SOH curves of electrochemical energy storage and hydrogen storage energy, the dynamic response characteristics of different energy storage systems under the corresponding control strategy can be seen, and hydrogen storage energy and electrochemical energy storage respectively undertake long and short time charging and discharging tasks, reducing the loss of hydrogen storage energy and maintaining the battery SOC within the allowed range.

[0129] Further, in order to more detailedly study the distribution and change of power, we further amplify and analyze the specific interval. As shown in Figure 6 , the time period between 740 and 800 seconds is selected to amplify the display of wind power exceeding the upper limit. When the actual wind power exceeds its upper limit, according to the strategy in this paper, the gradient is increased by adjusting the upper multiplier, and the larger gradient leads to the power of the next moment tending to negative value (in this paper, negative value represents charging, and positive value represents discharging), therefore, the increase of the gradient will make the system tend to perform more charging operation at the next moment, and the charging power of electrochemical energy storage and hydrogen storage energy increases with the increase of the gradient, and the energy storage system can effectively reduce the power after energy storage adjustment when charging. As shown in Figure 8As shown, the time period between 3200 and 3400 seconds is selected to magnify the case that the wind power is below the lower limit. When the actual wind power is below the lower limit, the lower multiplier should be adjusted to reduce the gradient according to the strategy in this paper, and the reduction of the gradient will cause the power at the next moment to tend to be positive. Therefore, the reduction of the gradient will make the system tend to perform more discharge operation at the next moment, and the energy storage system can effectively increase the power after the energy storage adjustment when discharging. The effect diagram of the charging power of the electrochemical energy storage and hydrogen energy storage is as shown in Figure 7 As shown, the effect diagram of the discharging power of the electrochemical energy storage and hydrogen energy storage is as shown in Figure 9 As shown, the time period between 3200 and 3400 seconds is selected to magnify the case that the wind power is below the lower limit. When the actual wind power is below the lower limit, the lower multiplier should be adjusted to reduce the gradient according to the strategy in this paper, and the reduction of the gradient will cause the power at the next moment to tend to be positive. Therefore, the reduction of the gradient will make the system tend to perform more discharge operation at the next moment, and the energy storage system can effectively increase the power after the energy storage adjustment when discharging. The effect diagram of the charging power of the electrochemical energy storage and hydrogen energy storage is as shown in Figure 10 As shown, the time period between 3200 and 3400 seconds is selected to magnify the case that the wind power is below the lower limit. When the actual wind power is below the lower limit, the lower multiplier should be adjusted to reduce the gradient according to the strategy in this paper, and the reduction of the gradient will cause the power at the next moment to tend to be positive. Therefore, the reduction of the gradient will make the system tend to perform more discharge operation at the next moment, and the energy storage system can effectively increase the power after the energy storage adjustment when discharging. The effect diagram of the charging power of the electrochemical energy storage and hydrogen energy storage is as shown in

[0130] Those skilled in the art will appreciate that embodiments of the present application can be readily used as a method, a system, or a computer program product. Accordingly, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application can take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage devices, etc.) embodying computer readable program code thereon for use by or in connection with an instruction execution system. For the purposes of this description, a computer-usable or computer readable storage medium can be any apparatus that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.

[0131] The present application is described in relation to flowcharts and / or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, as well as combinations of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general purpose computer, special purpose computer, embedded processing system or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowcharts and / or block diagrams. Figure 1 The flowcharts and / or block diagrams can include one or more flows and / or blocks that can represent code, circuits, circuitry, hardware components, or combinations thereof. In this regard, it should be understood that each flow and / or block in the flowcharts and / or block diagrams can represent a module, segment, or portion of code, which comprises one or more executable instructions to implement the specified logical function(s). It should also be understood that each flow and / or block in the flowcharts and / or block diagrams and a combination of flows and / or blocks in the flowcharts and / or block diagrams can represent a module, segment, or portion of code, which comprises one or more executable instructions to implement the specified logical function(s). Figure 1 The present application is described in relation to flowcharts and / or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, as well as combinations of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general purpose computer, special purpose computer, embedded processing system or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowcharts and / or block diagrams.

[0132] These computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture including instructions which implement the Figure 1 function specified in the flow or flows and / or blocks Figure 1 of the block or blocks.

[0133] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions that are executed on the computer or other programmable apparatus provide steps for implementing the Figure 1 function specified in the flow or flows and / or blocks Figure 1 of the block or blocks.

[0134] The embodiments of the present application described above are merely illustrative and not limiting, and the above described specific embodiments are only illustrative, not limiting, and those of ordinary skill in the art can make many forms under the inspiration of the present application without departing from the purpose of the present application and the scope protected by the claims, which are all within the protection of the present application.

Claims

1. A real-time feedback optimization method for an electro-hydrogen hybrid energy storage system, characterized in that, The method comprises the following steps: constructing an optimization objective function of the electricity-hydrogen hybrid energy storage system and corresponding operation constraints; constructing a feedback optimization strategy based on the wind power output of the wind farm and the optimization objective function; updating the power of the electrochemical energy storage system and the power of the hydrogen energy storage system in real time according to the feedback optimization strategy; calculating the wind power adjusted by energy storage according to the updated power of the electrochemical energy storage system and the power of the hydrogen energy storage system; if the adjusted wind power exceeds the upper and lower limit range of the predicted wind power, adjusting the parameters of the feedback optimization strategy, and updating the power of the electrochemical energy storage system and the power of the hydrogen energy storage system again; the optimization objective function of the electricity-hydrogen hybrid energy storage system comprises: where Min denotes minimization, f is the loss cost of the electro-hydrogen hybrid energy storage system, C V is the operation cost of the electro-hydrogen hybrid energy storage system, is the power of the ith battery unit at time t, is the power of the ith hydrogen storage unit at time t, λ BESS and λ HESS are the operation cost coefficients of the electrochemical energy storage system and the hydrogen energy storage system, respectively, Δt is the unit time interval, is the penalty function on state of charge, is the penalty function on state of hydrogen; γ is the penalty factor, δ1 and δ2 are the parameters of the electrochemical energy storage system and the hydrogen energy storage system, respectively, SOC t.i is the state of charge of the ith battery unit at time t, SOC max and SOC min are the upper and lower limits of the state of charge of a single battery unit, SOH t.i is the state of hydrogen of the hydrogen tank in the ith hydrogen storage unit at time t, SOH max and SOH min are the upper and lower limits of the state of hydrogen of a hydrogen tank.

2. The real-time feedback optimization method for an electro-hydrogen hybrid energy storage system according to claim 1, wherein, the operation constraints comprise operation constraints of the electrochemical energy storage system and operation constraints of the hydrogen energy storage system, and the operation constraints of the electrochemical energy storage system comprise: wherein, Ei(t) is the energy of the ith battery unit at time t, η BESS.E and η BESS.F are the charge and discharge efficiencies of the battery unit, Q BESS is the capacity of the battery unit, Pi(t) is the power of the ith battery unit at time t, Pi(t) is the power of the ith battery unit at time t, the operation constraints of the hydrogen energy storage system comprise: electrolyzer constraints: wherein, Pti is the power of the electrolyzer t in the i th hydrogen storage unit, η elec η is the hydrogen production efficiency of the electrolyzer of the hydrogen storage unit, Hti is the mass of hydrogen produced by the electrolyzer t in the i th hydrogen storage unit, H is the heat value of hydrogen, P elec.max and P elec.min Pmax and Pmin are the upper and lower limits of the input power of a single electrolyzer of the hydrogen storage unit, respectively, Pmax is the maximum amount of hydrogen flowing into the hydrogen storage tank per unit time of a single electrolyzer of the hydrogen storage unit; hydrogen storage tank constraints: SOH min ≤SOH t.i ≤SOH max wherein Q HS is the capacity of the hydrogen storage tank, is the amount of hydrogen consumed by the fuel cell at time t in the i-th hydrogen storage unit; is the amount of hydrogen consumed by the fuel cell at time t in the i-th hydrogen storage unit; fuel cell constraints: where η FC is the efficiency of the fuel cell hydrogen energy conversion to electricity for the hydrogen storage unit, is the power of the fuel cell at time t in the i-th hydrogen storage unit, P HESS.F.max and P HESS.F.min are the upper and lower limits of the output power of a single fuel cell, respectively, is the maximum amount of hydrogen flowing from the hydrogen storage tank to a single fuel cell per unit time.

3. The real-time feedback optimization method for an electro-hydrogen hybrid energy storage system according to claim 2, wherein, the feedback optimization strategy comprises: constructing a Lagrange function of the electricity-hydrogen hybrid energy storage system, wherein the Lagrange function is: where L is the Lagrange function, f is the loss cost of the electro-hydrogen composite energy storage system, is the wind power after energy storage adjustment, is the predicted power of the wind farm at time t, P t W.up is the upper limit of the predicted power of the wind farm at time t, P t W.dn is the lower limit of the predicted power of the wind farm at time t, x up and x dn are the upper and lower limit coefficients of the predicted power of the wind farm, respectively, and μ t are the upper and lower multipliers of the Lagrange function at time t, respectively, and s are the correction factors for exceeding the upper and lower limits of the wind power, respectively, and β is the step factor.

4. The real-time feedback optimization method for an electro-hydrogen hybrid energy storage system according to claim 3, wherein, the updating of the power of the electrochemical energy storage system and the power of the hydrogen energy storage system in real time according to the feedback optimization strategy comprises: the partial derivative of the Lagrange function with respect to the power of the battery unit is obtained to obtain the updated power of the electrochemical energy storage system: wherein is a bounded range function that bounds y to and r is a penalty parameter. the partial derivative of the Lagrange function with respect to the power of the hydrogen storage unit is obtained to obtain the updated power of the hydrogen energy storage system: wherein is a bounded range function that bounds y to and r is a penalty parameter.

5. The real-time feedback optimization method for an electro-hydrogen hybrid energy storage system according to claim 4, wherein, based on the updated power of the electrochemical energy storage system and the power of the hydrogen energy storage system, the wind power adjusted by energy storage is calculated according to the following formula: wherein, is the maximum wind power output of the wind farm at time t, is the curtailed wind power at time t.

6. The real-time feedback optimization method of an electro-hydrogen hybrid energy storage system according to claim 5, wherein, the adjustment of the parameters of the feedback optimization strategy comprises:

7. A real-time feedback optimization device for an electro-hydrogen hybrid energy storage system, comprising: a real-time feedback optimization method for an electricity-hydrogen hybrid energy storage system according to any one of claims 1-6, comprising: a first construction module for constructing an optimization objective function of the electricity-hydrogen hybrid energy storage system and operation constraints; a second construction module for constructing a feedback optimization strategy according to the wind power output of the wind farm and the optimization objective function; an updating module for updating the power of the electrochemical energy storage system and the power of the hydrogen energy storage system in real time according to the feedback optimization strategy; a calculation module for calculating the wind power adjusted by energy storage according to the updated power of the electrochemical energy storage system and the power of the hydrogen energy storage system; an adjustment module for adjusting the parameters of the feedback optimization strategy if the adjusted wind power exceeds the upper and lower limit range of the predicted wind power, and updating the power of the electrochemical energy storage system and the power of the hydrogen energy storage system again.

8. A storage medium having a computer program stored thereon, wherein the computer program is executed by a processor to implement the real-time feedback optimization method for an electricity-hydrogen hybrid energy storage system according to any one of claims 1-6.

Images