Multi-region cooperative control method for hydrogen energy supply chain under uncertain demand

Abstract

The invention discloses a multi-region cooperative control method for a hydrogen energy supply chain under an uncertain demand, and belongs to the technical field of intelligent control of an energy system, and the method comprises the steps: building a hydrogen energy supply chain operation dynamic mechanism model; converting the operation dynamic mechanism model into a state space control model; based on Lyapunov-Krasovskii time-delay stability analysis and a large system control theory, carrying out multi-region cooperative performance analysis and cooperative control design on a production-inventory control system of the state space control model; and verifying the effectiveness through the three-region hydrogen energy supply model of the power system. According to the invention, a cooperative control strategy is provided to make production-inventory cooperation of each region, and a dynamic mechanism model operating in a short time scale is established and converted into a state space control model; based on a Lyapunov-Krasovskii time-delay dependency analysis method and a large system control theory, an operation decision coefficient of a cooperative control strategy is designed; resource sharing and cooperative scheduling among different areas are realized, and supply and demand imbalance caused by information islands in local areas is avoided.

Description

Technical Field

[0001] This invention relates to the field of intelligent control technology for energy systems, and in particular to a method for multi-regional collaborative control of the hydrogen energy supply chain under uncertain demand. Background Technology

[0002] With the continued advancement of the "dual carbon" goals, hydrogen energy, as a clean and efficient secondary energy source, is gradually becoming an important component of the future energy system. The hydrogen energy supply chain encompasses multiple stages, including hydrogen production, storage, transportation, and utilization. Its operational efficiency and coordination capabilities directly impact the economic viability and feasibility of hydrogen energy. In actual operation, hydrogen demand in different regions is affected by various factors (such as weather, industrial structure, and traffic flow), resulting in significant uncertainty. This, in turn, places higher demands on the overall scheduling and control of the supply chain.

[0003] Currently, the control methods for the hydrogen energy supply chain generally suffer from the following problems: 1) Centralized dispatching is slow to respond and difficult to adapt to rapidly changing regional demands; 2) There is a lack of effective modeling and response mechanisms for demand uncertainty, which can easily lead to resource waste or insufficient supply; 3) There is a lack of coordination mechanisms among multiple regions, with each region acting independently and failing to achieve resource complementarity and system optimization.

[0004] 4) Existing control methods do not fully utilize intelligent optimization and prediction algorithms, resulting in a low level of system intelligence.

[0005] Existing research on hydrogen energy supply chain planning within the context of power systems has yielded some results. However, in uncertain environments, the short-term daily operation of the hydrogen energy supply chain may deviate from the expected supply chain plan. Without proactive and effective control, this could affect the long-term performance goals of the hydrogen energy supply chain and even its safe operation. Therefore, further research is needed on the synergy between the hydrogen energy supply chain and the power system at the short-term operational level. Summary of the Invention

[0006] The purpose of this invention is to provide a method for multi-regional collaborative control of the hydrogen energy supply chain under uncertain demand. Addressing the problem of actual operation deviating from expected planning in the hydrogen energy supply chain under uncertain demand, this invention proposes a collaborative control strategy to achieve coordinated production and inventory across regions. A dynamic mechanism model for operation on a short timescale is established and transformed into a state-space control model. Based on the Lyapunov-Krasovskii time-delay dependency analysis method and large-scale system control theory, the operational decision coefficients of the collaborative control strategy are designed. This enables resource sharing and collaborative scheduling between different regions, avoiding supply and demand imbalances caused by information silos in local areas. While ensuring stable system operation, this invention effectively improves energy utilization efficiency and reduces overall operating costs.

[0007] To achieve the above objectives, this invention provides a method for multi-regional collaborative control of the hydrogen energy supply chain under uncertain demand, comprising the following steps:

[0008] S1. Considering internal and external factors such as hydrogen storage loss and hydrogen transportation delay, establish a dynamic mechanism model for the operation of the hydrogen energy supply chain.

[0009] S2. To meet the multi-regional collaborative control objectives under uncertain demands, the dynamic mechanism model of operation is transformed into a state-space control model.

[0010] S3. Based on Lyapunov-Krasovskii time-delay stability analysis and large-system control theory, multi-regional collaborative performance analysis and collaborative control design are carried out on the production-inventory control system of state-space control model.

[0011] S4. Verify the effectiveness through a three-region hydrogen energy supply model for the power system.

[0012] Preferably, in S1, the dynamic mechanism model of the hydrogen energy supply chain operation is described as follows:

[0013]

[0014] Where i takes values ​​of 1, 2, 3, ..., N, representing the hydrogen energy supply chain region; h i (t) represents the amount of hydrogen stored at time t; Δp i (t) represents the local hydrogen production during time Δt; s ij This represents the proportion of hydrogen transported from region i to region j within time interval Δt, relative to the total hydrogen production of region j; d ij (t) represents the transportation delay caused by transporting hydrogen from region j to region i; Δl i (t) represents the amount of hydrogen demanded by local customers within time Δt; r i Indicates the hydrogen production loss coefficient;

[0015] Taking the limit of equation (1):

[0016]

[0017] Where, p i (t) represents the hydrogen production rate at time t, l i (t) represents the customer's demand rate for hydrogen at time t.

[0018] The preferred dynamic model for regulating hydrogen production rate in region i is as follows:

[0019]

[0020] Among them, E(h) i (t) represents the desired hydrogen production regulation rate, which is related to the hydrogen storage inventory h.i (t) related; T i Indicates adaptive time; u i (t) represents the actively applied adjustment amount; adjustment experience This indicates a safe level of hydrogen storage inventory.

[0021] Preferably, in S2, the state-space control model is:

[0022]

[0023] in, w i (t) represents the uncertainty of demand fluctuations, and

[0024]

[0025] in, The planned value for stable hydrogen demand.

[0026] Preferably, the actively applied adjustment amount u i (t) Based on the feedback of operational information, the design is as follows:

[0027] u i (t)=K i x i (t) (6);

[0028] Among them, K i It is the local operational decision coefficient to be designed.

[0029] Preferably, the closed-loop equations of the state-space control model system are:

[0030]

[0031] Preferably, in the state-space control model, the multi-regional collaborative objective under uncertain requirements is equivalent to how to design the operational decision coefficient K. i This makes the closed-loop system internally asymptotically stable and for a preset coefficient γ i >0 satisfies the following performance indicators:

[0032]

[0033] Among them, z i (t)=G i x i (t)+M i w i (t), G i and M i It is a known constant real matrix.

[0034] Preferably, in S3,

[0035] The collaborative performance analysis process is as follows:

[0036] For a given scalar and d ij satisfy If γ > 0, and a 2×2 matrix P exists... i >0, Q i >0, R i >0, S i >0, U i >0, N ij P i1 P i2 and a 1×2 matrix K i , making

[0037]

[0038] Where, Δ ij It has elements A 7×7 block symmetric matrix, and

[0039]

[0040] Therefore, for w i (t) = 0, the closed-loop equation of the state-space control model system is asymptotically stable, and for w i The system performance index (t)∈L2[0,+∞) is ensured;

[0041] The collaborative control design process is as follows:

[0042] Nonlinear terms exist in the collaborative performance analysis. and Operational decision coefficient K i The decision coefficients cannot be directly obtained by solving matrix inequalities; therefore, a cooperative control strategy for the state-space control model system is designed, and the corresponding decision coefficients K are solved. i The details are as follows:

[0043] For a given scalar and satisfy γ>0, ε i >0 if a 2×2 matrix exists and 1×2 matrix Y i , making

[0044]

[0045] in, It has elements A 7×7 block symmetric matrix, and

[0046]

[0047] Therefore, for w i (t) = 0, the closed-loop equation of the state-space control model system is asymptotically stable, and for w i The system performance index (t)∈L2[0,+∞) is ensured.

[0048] Preferably, step S4 specifically includes the following steps:

[0049] S41. Construct a three-region hydrogen energy supply model for the power system and set the operating parameters for each hydrogen supply region;

[0050] S42. Based on the operating parameter settings, design a controller to ensure multi-region production-inventory coordination of the hydrogen energy supply model. Through the coordinated control design, calculate the operating decision coefficients for each region. Apply the calculated operating decision coefficients to each region of the hydrogen energy supply model and calculate the monthly hydrogen storage inventory deviation and hydrogen production rate deviation.

[0051] S43. Study the impact of internal and external factors of hydrogen energy loss and hydrogen transport delay on the performance of coordinated control, and formulate specific scenarios to study the impact of internal hydrogen loss.

[0052] S44. The collaborative control design does not consider the actual time delay prediction conditions, and formulates specific scenarios to study the impact of external hydrogen transportation.

[0053] Therefore, this invention adopts the aforementioned method for multi-regional collaborative control of the hydrogen energy supply chain under uncertain demand. Addressing the problem of actual operation deviating from expected planning under uncertain demand, it proposes a collaborative control strategy to enable coordinated production and inventory across regions. A dynamic mechanism model for operation on a short timescale is established and transformed into a state-space control model, considering internal hydrogen loss and external hydrogen transportation delays. Based on the Lyapunov-Krasovskii time-delay dependency analysis method and large-scale system control theory, the operational decision coefficients of the collaborative control strategy are designed. This achieves resource sharing and collaborative scheduling between different regions, avoiding supply-demand imbalances caused by information silos in local areas. While ensuring stable system operation, it effectively improves energy utilization efficiency and reduces overall operating costs.

[0054] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0055] Figure 1 This is a flowchart of an embodiment of a method for multi-regional collaborative control of the hydrogen energy supply chain under uncertain demand according to the present invention;

[0056] Figure 2 This is a conceptual diagram of electrolytic hydrogen-hydrogen supply for a multi-regional collaborative control method embodiment of the present invention, which describes a method for multi-regional collaborative control of the hydrogen energy supply chain under uncertain demand.

[0057] Figure 3 This is a schematic diagram of the IEEE PJM 5-node power system three-region hydrogen energy supply model used in an embodiment of the method for multi-regional collaborative control of hydrogen energy supply chain under uncertain demand of the present invention.

[0058] Figure 4 This is an embodiment of the method for multi-regional collaborative control of the hydrogen energy supply chain under uncertain demand of the present invention, showing the hydrogen demand fluctuation curves of each region's hydrogen energy supply model during intraday operation.

[0059] Figure 5 This is an embodiment of the method for multi-regional collaborative control of the hydrogen energy supply chain under uncertain demand, and the evolution diagram of the state of hydrogen storage inventory deviation and hydrogen production rate deviation in each region after proposing a collaborative control strategy.

[0060] Figure 6 This is an example of the method for multi-regional collaborative control of the hydrogen energy supply chain under uncertain demand according to the present invention. When no collaborative control strategy is adopted, the state evolution diagram of hydrogen storage inventory deviation and hydrogen production rate deviation in each region is shown.

[0061] Figure 7 This is an embodiment of the present invention, which describes a method for multi-regional collaborative control of the hydrogen energy supply chain under uncertain demand. It shows the evolution of the hydrogen production-storage state norm of each region under the scenario of changing hydrogen loss levels.

[0062] Figure 8 This is an embodiment of the present invention, which describes a method for multi-regional collaborative control of the hydrogen energy supply chain under uncertain demand. It shows the evolution of the hydrogen production-storage state norm in each region under the scenario of varying hydrogen transportation delay. Detailed Implementation

[0063] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0064] Unless otherwise defined, the technical or scientific terms used in this invention shall have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0065] Example 1

[0066] This invention provides a method for multi-regional collaborative control of the hydrogen energy supply chain under uncertain demand, in order to reduce the impact of long-term planning performance and ensure the safe operation of the hydrogen energy supply chain in multiple regions. The flowchart of the method is as follows: Figure 1 As shown, it includes the following steps:

[0067] S1. Considering internal and external factors such as hydrogen storage loss and hydrogen transportation delay, establish a dynamic mechanism model for the operation of the hydrogen energy supply chain; among which, the conceptual diagram of multi-regional collaborative control of electrolytic hydrogen-hydrogen supply is as follows: Figure 2 As shown. The dynamic mechanism model of the hydrogen energy supply chain operation includes hydrogen production, hydrogen storage, hydrogen transportation delays, and hydrogen demand, specifically described as follows:

[0068]

[0069] Where i takes values ​​of 1, 2, 3, ..., N, representing the hydrogen energy supply chain region; h i (t) represents the amount of hydrogen stored at time t; Δp i (t) represents the local hydrogen production during time Δt; s ij This represents the proportion of hydrogen transported from region i to region j within time interval Δt, relative to the total hydrogen production of region j; d ij (t) represents the transportation delay caused by transporting hydrogen from region j to region i; Δl i (t) represents the amount of hydrogen demanded by local customers within time Δt; r i Indicates the hydrogen production loss coefficient;

[0070] Taking the limit of equation (1):

[0071]

[0072] Where, p i(t) represents the hydrogen production rate at time t, l i (t) represents the customer's demand rate for hydrogen at time t.

[0073] The dynamic model for hydrogen production rate regulation in region i is as follows:

[0074]

[0075] Among them, E(h) i (t) represents the desired hydrogen production regulation rate, which is related to the hydrogen storage inventory h. i (t) related; T i Indicates adaptive time; u i (t) represents the actively applied adjustment amount; adjustment experience This indicates a safe level of hydrogen storage inventory.

[0076] S2. To address the multi-regional collaborative control objective under uncertain conditions, the operational dynamic mechanism model is transformed into a state-space control model; the state-space control model is as follows:

[0077]

[0078] in, w i (t) represents the uncertainty of demand fluctuations, and

[0079]

[0080] in, The planned value for stable hydrogen demand.

[0081] Actively applied adjustment amount u i (t) can be designed based on the feedback of operational information as follows:

[0082] u i (t)=K i x i (t) (6);

[0083] Among them, K i It is the local operational decision coefficient to be designed.

[0084] Therefore, the closed-loop equation of the state-space control model system can be obtained as follows:

[0085]

[0086] Under uncertain demands, the equivalent of multi-regional collaborative goals is how to design the operational decision coefficient K. i This makes the closed-loop system internally asymptotically stable and for a preset coefficient γ i >0 satisfies the following performance indicators:

[0087]

[0088] Among them, z i (t)=G i x i (t)+M i w i (t), G i and M i It is a known constant real matrix.

[0089] S3. Based on Lyapunov-Krasovskii time-delay stability analysis and large-scale system control theory, multi-regional collaborative performance analysis and collaborative control design are carried out on the production-inventory control system of state-space control model.

[0090] The collaborative performance analysis process is as follows:

[0091] For a given scalar and d ij satisfy If γ > 0, and a 2×2 matrix P exists... i >0, Q i >0, R i >0, S i >0, U i >0, N ij P i1 P i2 and a 1×2 matrix K i , making

[0092]

[0093] Where, Δ ij It has elements A 7×7 block symmetric matrix, and

[0094]

[0095] Therefore, for w i (t) = 0, the closed-loop equation of the state-space control model system is asymptotically stable, and for w i The system performance index (t)∈L2[0,+∞) is ensured.

[0096] The proof process of the collaborative performance analysis is as follows:

[0097] Select Lyapunov-Krasovskii candidate functionals: where...

[0098]

[0099] Taking the derivative of V(x(t)), we can derive...

[0100]

[0101] in,

[0102]

[0103] right Using Jensen's inequality on the integral term, we can obtain...

[0104]

[0105] Among them, Ξ ij (t)=x j (t)-x j (t- d ij ).

[0106] right The integral term uses Jensen's inequality and the interactive convex combination method, when the condition is... When satisfied, it can be scaled up to obtain

[0107]

[0108] in,

[0109] Now, let w i (t) = 0, and the following 0 terms:

[0110]

[0111] Add to formula In addition, it should be noted that Satisfying the Lipschitz condition, i.e.

[0112]

[0113] Where H = [1 0] T .

[0114] Then, from equations (a)-(f), we can deduce

[0115]

[0116] in,

[0117]

[0118] And formula and Δ is defined ij Item and item.

[0119] when It can be known That is, the internal asymptotic stability of the closed-loop equations of the state-space control model system can be guaranteed. In fact, this can be known from the Schur complement property. Equivalent to

[0120] And when conditions When satisfied, the formula The equivalence relation is valid. Based on the above equivalence relation, the asymptotic stability of the closed-loop equations of the state-space control model system is proven.

[0121] The following command w i (t)≠0, similar to steps (a)-(g), we can deduce

[0122]

[0123] Where, σ i (t)=col{θ i (t),w i (t)},

[0124] Similar to the steps above, using the Schur complement property, it can be known that when the condition... At the time of its establishment, the project Right now

[0125]

[0126] Notice that V(x(0)) = 0, therefore we have

[0127]

[0128] And note Let t→+∞, we can obtain

[0129]

[0130] Therefore, the performance indicators of the state-space control model system are satisfied, thus completing the proof.

[0131] The collaborative control design process is as follows:

[0132] Nonlinear terms exist in the collaborative performance analysis. and Operational decision coefficient K i This cannot be obtained directly by solving matrix inequalities. Therefore, a cooperative control strategy for the state-space control model system is designed, and the corresponding decision coefficients K are solved. i The details are as follows:

[0133] For a given scalar and satisfy γ>0, ε i >0 if a 2×2 matrix exists and 1×2 matrix Y i , making

[0134]

[0135] in, It has elements A 7×7 block symmetric matrix, and

[0136]

[0137] Therefore, for w i (t) = 0, the closed-loop equation of the state-space control model system is asymptotically stable, and for w i The system performance index (t)∈L2[0,+∞) is ensured.

[0138] The proof process for the collaborative control design is as follows:

[0139] In the process of proving the synergistic performance analysis, a fine-tuning parameter ε is introduced. i , making P i2 =ε i P i1 And ordered Make

[0140]

[0141] Pair Pre-ride After Derivative Reverse formula Pre-ride After The formula is obtained Q.E.D.

[0142] S4. Verify the effectiveness through a three-region hydrogen energy supply model for the power system.

[0143] The schematic diagram of the IEEE PJM 5-node power system three-region hydrogen supply model used in this embodiment is shown below. Figure 3 As shown.

[0144] S41, according to the original plan for the hydrogen energy supply chain, Region I will transport a portion of its hydrogen energy to Regions II and III in accordance with the resource-sharing agreement. Similarly, Region II will share hydrogen energy for transportation to Region III, and Region III will share hydrogen energy for transportation to Region I. Under this scenario, based on the established mechanism model, the operating parameters for each hydrogen supply region can be set as follows:

[0145]

[0146] Therefore, the system matrix A can be calculated separately. i F i and the correlation matrix S ij The calculation results are not listed here.

[0147] Assume the upper and lower bounds of the transportation delay for the hydrogen energy transportation network are:

[0148]

[0149] S42, based on the operating parameter settings from the previous section, we will now begin designing the controller to ensure multi-regional production-inventory coordination in the hydrogen supply model. Let ε i =0.7, γ=3, G i = [0.1 0.7], M i =0.8, the hydrogen demand fluctuation curves of each region's hydrogen energy supply model during intraday operation are shown in the figure. Figure 4 As shown.

[0150] Based on the collaborative control design, the operational decision coefficients for each region can be calculated as follows:

[0151]

[0152] The solved operational decision coefficients are applied to each region of the hydrogen supply model to calculate the monthly hydrogen storage inventory deviation and hydrogen production rate deviation, as shown in the following evolution diagram. Figure 5 As shown.

[0153] To further demonstrate the effectiveness of the designed coordinated control, when the hydrogen supply model system relies only on general regulation experience E(h) i (t)), when no active decision-making feedback information is applied, i.e., u i When (t) = 0, the evolution of the deviations in hydrogen storage capacity and hydrogen production rate in each region is as follows: Figure 6 As shown.

[0154] S43, In order to study the impact of internal and external factors such as hydrogen loss and hydrogen transport delay on the performance of coordinated control, this embodiment first formulates six scenarios to study the impact of internal hydrogen loss.

[0155] Scenario 1 (Hydrogen Loss 1): The hydrogen supply model has a relatively high level of hydrogen loss, and no active decision feedback information is applied. Therefore, for the closed-loop equation of the state-space control model system, let ε1 = 0.63, ε2 = 0.61, ε3 = 0.63, and other parameters be set as in S42.

[0156] Scenario 2 (Hydrogen Loss 2): Active decision feedback information is applied under the conditions of Scenario 1, with parameter settings similar to Scenario 1 and S42. The corresponding operational decision coefficients are then calculated through collaborative control design; these are not listed here.

[0157] Scenario 3 (Hydrogen Loss 3): The hydrogen loss within the hydrogen supply model is at a moderate level, and no active decision-making feedback information is applied. Therefore, for the closed-loop equation, let ε1 = 0.33, ε2 = 0.31, ε3 = 0.33, and other parameters be set as in S42.

[0158] Scenario 4 (Hydrogen Loss 4): Active decision feedback information is applied under the conditions of Scenario 3, with parameter settings similar to Scenario 3 and S42. The corresponding operational decision coefficients are then calculated through collaborative control design; these are not listed here.

[0159] Scenario 5 (Hydrogen Loss 5): The hydrogen loss within the hydrogen energy supply model is at a relatively low level, and no active decision-making feedback information is applied. Therefore, for the closed-loop equation, let ε1 = 0.13, ε2 = 0.11, ε3 = 0.13, and other parameters set as in S42.

[0160] Scenario 6 (Hydrogen Loss 6): Active decision feedback information is applied under the conditions of Scenario 5, with parameter settings as in Scenario 5 and Section S42. The corresponding operational decision coefficients are then obtained through collaborative control design, which are not listed here.

[0161] For ease of analysis under the above six scenarios, the evolution diagram of the hydrogen production and storage state norm in each region of the hydrogen supply model is shown below. Figure 7 As shown.

[0162] S44, the cooperative control design does not consider the actual time delay prediction conditions. In this embodiment, six more scenarios will be developed to study the impact of external hydrogen transportation.

[0163] Scenario 7 (Hydrogen Transportation 1): The external hydrogen transportation delay in the hydrogen energy supply model is relatively short, and no active decision-making feedback information is applied. Therefore, for the closed-loop equation, let... d 12 =2.01, d 13 = d 23 = d 31 = d 12 Other parameters are set as shown in S42.

[0164] Scenario 8 (Hydrogen Transport 2): Active decision feedback information is applied under the conditions of Scenario 7, with parameter settings similar to those in Scenario 7 and S42. The corresponding operational decision coefficients are then calculated through collaborative control design; these are not listed here.

[0165] Scenario 9 (Hydrogen Transportation 3): The external hydrogen transportation delay in the hydrogen energy supply model is of a moderate length, and no active decision-making feedback information is applied. Therefore, for the closed-loop equation, let... d 12 =6.01, d 13 = d 23 = d 31 = d 12 Other parameters are set as shown in S42.

[0166] Scenario 10 (Hydrogen Transport 4): Apply proactive decision feedback information as in Scenario 9, with parameter settings similar to Scenario 9 and S42. The corresponding operational decision coefficients are then calculated through collaborative control design, and are not listed here.

[0167] Scenario 11 (Hydrogen Transportation 5): The external hydrogen transportation delay in the hydrogen energy supply model is relatively long, and no active decision-making feedback information is applied. Therefore, for the closed-loop equation, let... d 12 =9.01, d 13 = d 23 = d 31 = d 12 Other parameters are set as shown in S42.

[0168] Scenario 12 (Hydrogen Transport 6): Apply proactive decision feedback information under the conditions of Scenario 11, with parameter settings as in Scenario 11 and S42. Then, calculate the corresponding operational decision coefficients through collaborative control design, which will not be listed here.

[0169] from Figure 5 It can be observed that under the designed control strategy, the deviations in hydrogen storage and production rates in each region tend to approach 0, meaning that the actual operation does not deviate from the expected plan, the production-storage coordination in each region can be guaranteed, and the hydrogen energy supply model operates safely.

[0170] Depend on Figure 5 and Figure 6 The comparison shows that without active decision-making feedback, the deviations in hydrogen storage and production in each region of the hydrogen supply model tend to be non-zero, indicating discrepancies. This makes it impossible to ensure production-inventory coordination in each region, and its effectiveness is not as good as the designed coordinated control strategy.

[0171] Depend on Figure 7It can be observed that in scenarios 1, 3, and 5, facing different levels of internal hydrogen loss, without proactive decision-making, the actual operation of the hydrogen energy supply model deviates further and further from the expected plan as the loss increases. However, when the proposed collaborative control strategy is applied, even when facing different levels of hydrogen loss (scenarios 2, 4, and 6), under uncertain hydrogen demand, the system evolution can reach the expected state, achieving coordinated production and inventory across different regions.

[0172] Depend on Figure 8 It can be observed that in scenarios 7, 9, and 11, facing varying degrees of external hydrogen transportation delays, without proactive decision-making, the actual operation of the hydrogen supply model deviates further from the expected plan as the transportation delay increases, especially in Region I. This is because, compared to Regions II and III, Region I, according to the hydrogen supply chain contract, has a more complex transportation topology with other regions, more frequent information exchanges, and is more affected by delays. However, when the proposed collaborative control strategy is applied, even facing varying degrees of transportation delays (Scenarios 8, 10, and 12), under uncertain hydrogen demand, the system evolution can reach the expected state, achieving production-inventory coordination in each region.

[0173] The study of the above 12 scenarios found that, under uncertain demand, the proposed collaborative control strategy has a good ability to cope with uncertainty, can reduce the actual operational risks of the hydrogen energy supply chain, and provide auxiliary decision-making solutions for hydrogen energy supply chain managers.

[0174] Therefore, this invention employs the aforementioned method for multi-regional collaborative control of the hydrogen energy supply chain under uncertain demand. Addressing the issue of actual operation deviating from expected planning under uncertain demand, a collaborative control strategy is proposed to ensure coordinated production and inventory across regions. A dynamic mechanism model for operation on a short timescale is established and transformed into a state-space control model, considering internal hydrogen loss and external hydrogen transportation delays. Based on the Lyapunov-Krasovskii time-delay dependency analysis method and large-scale system control theory, the operational decision coefficients of the collaborative control strategy are designed. The effectiveness of the proposed control strategy is demonstrated using a three-region hydrogen energy supply model of the IEEE PJM5 node power grid system.

[0175] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for multi-regional collaborative control of the hydrogen energy supply chain under uncertain demand, characterized in that: Includes the following steps: S1. Considering internal and external factors such as hydrogen storage loss and hydrogen transportation delay, establish a dynamic mechanism model for the operation of the hydrogen energy supply chain. S2. To meet the multi-regional collaborative control objectives under uncertain demands, the dynamic mechanism model of operation is transformed into a state-space control model. S3. Based on Lyapunov-Krasovskii time-delay stability analysis and large-system control theory, multi-regional collaborative performance analysis and collaborative control design are carried out on the production-inventory control system of state-space control model. S4. Verify the effectiveness through a three-region hydrogen energy supply model for the power system.

2. The method for multi-regional collaborative control of the hydrogen energy supply chain under uncertain demand as described in claim 1, characterized in that: In S1, the dynamic mechanism model of the hydrogen energy supply chain operation is described as follows: Where i takes values ​​of 1, 2, 3, ..., N, representing the hydrogen energy supply chain region; h i (t) represents the amount of hydrogen stored at time t; Δp i (t) represents the local hydrogen production during time Δt; s ij This represents the proportion of hydrogen transported from region i to region j within time interval Δt, relative to the total hydrogen production of region j; d ij (t) represents the transportation delay caused by transporting hydrogen from region j to region i; This represents the amount of hydrogen demanded by local customers within a time interval Δt; r i Indicates the hydrogen production loss coefficient; Taking the limit of equation (1): Where, p i (t) represents the hydrogen production rate at time t. This represents the customer demand rate for hydrogen at time t.

3. The method for multi-regional collaborative control of the hydrogen energy supply chain under uncertain demand as described in claim 2, characterized in that: The dynamic model for hydrogen production rate regulation in region i is as follows: Among them, E(h) i (t) represents the desired hydrogen production regulation rate, which is related to the hydrogen storage inventory h. i (t) related; T i Indicates adaptive time; u i (t) represents the actively applied adjustment amount; adjustment experience This indicates a safe level of hydrogen storage inventory.

4. The method for multi-regional collaborative control of the hydrogen energy supply chain under uncertain demand as described in claim 3, characterized in that: In S2, the state-space control model is: in, w i (t) represents the uncertainty of demand fluctuations, and in, The planned value for stable hydrogen demand.

5. The method for multi-regional collaborative control of the hydrogen energy supply chain under uncertain demand as described in claim 4, characterized in that: Actively applied adjustment amount u i (t) Based on the feedback of operational information, the design is as follows: u i (t)=K i x i (t) (6); Among them, K i It is the local operational decision coefficient to be designed.

6. The method for multi-regional collaborative control of the hydrogen energy supply chain under uncertain demand as described in claim 5, characterized in that: The closed-loop equation of the state-space control model system is:

7. The method for multi-regional collaborative control of the hydrogen energy supply chain under uncertain demand as described in claim 6, characterized in that: In state-space control models, the equivalent of designing operational decision coefficients K under uncertain demand for multi-region collaborative objectives is how to design these coefficients. i This makes the closed-loop system internally asymptotically stable and for a preset coefficient γ i >0 satisfies the following performance indicators: Among them, z i (t)=G i x i (t)+M i w i (t), G i and M i It is a known constant real matrix.

8. The method for multi-regional collaborative control of the hydrogen energy supply chain under uncertain demand as described in claim 7, characterized in that: In S3, The collaborative performance analysis process is as follows: For a given scalar and d ij satisfy (i,j=1,2,...,N), γ>0, if there exists a 2×2 matrix P i >0, Q i >0, R i >0, S i >0, U i >0, N ij P i1 P i2 and a 1×2 matrix K i , making Where, Δ ij It has elements A 7×7 block symmetric matrix, and Therefore, for w i (t) = 0, the closed-loop equation of the state-space control model system is asymptotically stable, and for w i The system performance index (t)∈L2[0,+∞) is ensured; The collaborative control design process is as follows: Nonlinear terms exist in the collaborative performance analysis. and Operational decision coefficient K i The decision coefficients cannot be directly obtained by solving matrix inequalities; therefore, a cooperative control strategy for the state-space control model system is designed, and the corresponding decision coefficients K are solved. i The details are as follows: For a given scalar and satisfy (i,j=1,2,...,N), γ>0, ε i >0 if a 2×2 matrix exists and 1×2 matrix Y i , making in, It has elements A 7×7 block symmetric matrix, and Therefore, for w i (t) = 0, the closed-loop equation of the state-space control model system is asymptotically stable, and for w i The system performance index (t)∈L2[0,+∞) is ensured.

9. A method for multi-regional collaborative control of a hydrogen energy supply chain under uncertain demand, as described in claim 8, characterized in that: S4 specifically includes the following steps: S41. Construct a three-region hydrogen energy supply model for the power system and set the operating parameters for each hydrogen supply region; S42. Based on the operating parameter settings, design a controller to ensure multi-region production-inventory coordination of the hydrogen energy supply model. Calculate the operational decision coefficients for each region based on the coordinated control design. The solved operational decision coefficients are applied to each region of the hydrogen energy supply model to calculate the monthly hydrogen storage inventory deviation and hydrogen production rate deviation. S43. Study the impact of internal and external factors of hydrogen energy loss and hydrogen transport delay on the performance of coordinated control, and formulate specific scenarios to study the impact of internal hydrogen loss. S44. The collaborative control design does not consider the actual time delay prediction conditions, and formulates specific scenarios to study the impact of external hydrogen transportation.

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