Economic operation control method for off-grid hydrogen production system based on distributed quasi-newton method

Abstract

This invention discloses an economical operation control method for off-grid hydrogen production systems based on a distributed quasi-Newton method. The method includes: establishing a renewable energy hydrogen production system, comprising a photovoltaic system, an energy storage system, a fuel cell, and a hydrogen production unit; establishing operating cost functions for each distributed power source, wherein the distributed power sources are divided into dispatchable clean energy and non-dispatchable clean energy; differentiating the operating cost functions of each distributed power source to obtain the incremental rate function of the power generation cost of each distributed power source; establishing an optimization target model and defining constraints; and iteratively updating the optimization target model using a distributed quasi-Newton method to calculate the target power of each distributed power source. This invention improves the dynamic response speed and control accuracy of power allocation, significantly enhances the economy in dynamic processes, and is more suitable for scenarios with frequent fluctuations in load and power supply status.

Description

Technical Field

[0001] This invention relates to the field of economic operation control technology for distributed DC microgrid hydrogen production systems, specifically to an economic operation control method for off-grid hydrogen production systems based on the distributed quasi-Newton method. Background Technology

[0002] With the rapid development of renewable energy and the continuous transformation of the energy structure, DC microgrids play an important role in the access and integration of distributed energy resources due to their advantages such as simple structure, high efficiency, and flexible control. Meanwhile, hydrogen energy, as a clean and efficient secondary energy carrier, places high demands on power quality and power supply economy in its production process (especially water electrolysis), making hydrogen production systems based on DC microgrids a research hotspot.

[0003] In DC microgrids, multiple distributed power sources often operate in parallel via power electronic converters. While traditional droop control strategies can allocate power proportionally to capacity, they fail to adequately consider the generation costs, operating efficiency, and environmental impact of each power source, resulting in low overall system economics. Therefore, an economical droop control strategy has been proposed. This strategy adjusts the droop coefficient by incorporating factors such as generation costs and pollution control costs, guiding lower-cost power sources to output more power and higher-cost power sources to output less, thereby optimizing system operating costs. Traditional DC microgrid power allocation largely relies on droop control. This method simulates the frequency regulation characteristics of synchronous generators and achieves autonomous power allocation based on the local voltage-power droop relationship. However, droop control is essentially a static allocation strategy without communication. Its allocation result is only related to a preset droop coefficient and cannot reflect the real-time generation cost differences of each distributed power source, thus making it difficult to achieve economical system operation. Although subsequent studies proposed "economic droop control," which optimized the allocation results to some extent by incorporating factors such as power generation costs into the droop coefficient design, it is still within the droop control framework and has problems such as the need for secondary adjustment of steady-state voltage deviation, limited dynamic response, and sensitivity to line impedance. Moreover, it usually still relies on prior knowledge of cost curves or centralized coordination, which limits its adaptability in dynamically changing environments. Summary of the Invention

[0004] Most existing economic operation control strategies still rely heavily on droop control frameworks. Although economic droop control introduces generation cost factors into the traditional droop control, it is essentially still a static allocation mechanism based on the local voltage-power linear relationship, with inherent steady-state voltage deviations and allocation accuracy easily affected by line impedance. Furthermore, most existing strategies rely on centralized coordination or preset global cost parameters, resulting in insufficient system reliability and "plug-and-play" capabilities. Fully distributed economic dispatch algorithms often converge slowly due to their use of first-order gradient methods, making it difficult to quickly track the optimal operating point in dynamic scenarios such as load fluctuations and random switching of distributed power sources. These limitations are particularly pronounced in DC microgrid hydrogen production systems, which have high requirements for power supply economy, dynamic response, and operational flexibility.

[0005] The purpose of this invention is to provide an economical operation control method for off-grid hydrogen production systems based on the distributed quasi-Newton method, so as to solve the problems existing in the above-mentioned prior art.

[0006] To achieve the above functions, this invention designs an economic operation control method for off-grid hydrogen production systems based on the distributed quasi-Newton method, executing the following steps S1-S5 to complete the economic operation control of the renewable energy hydrogen production system:

[0007] Step S1: Establish a renewable energy hydrogen production system, including a photovoltaic system, an energy storage system, a fuel cell, and a hydrogen production unit; wherein, the photovoltaic system, fuel cell, energy storage system, electrolyzer, and constant power load in the hydrogen production unit are connected to the DC bus through DC / DC converters respectively;

[0008] Step S2: Establish the operating cost function for each distributed power source. Distributed power sources are divided into dispatchable clean energy and non-dispatchable clean energy. The operating cost function for dispatchable clean energy includes the operating cost function for fuel cells and the operating cost function for energy storage systems. The operating cost function for non-dispatchable clean energy includes the operating cost function for photovoltaic systems.

[0009] Step S3: Differentiate the operating cost function of each distributed power source to obtain the incremental rate function of the power generation cost of each distributed power source.

[0010] Step S4: Establish an optimization objective model based on the incremental rate function of the generation cost of each distributed power source, and establish constraint conditions according to the power balance constraint, the output constraint of each distributed power source, and the constraint of minimizing the system generation cost.

[0011] Step S5: For the optimization target model, the distributed quasi-Newton method is used for iterative updates to calculate the target power of each distributed power source. Based on the target power, the output power of each distributed power source is controlled to complete the economic operation control of the renewable energy hydrogen production system.

[0012] As a preferred embodiment of the present invention: the operating cost function of the fuel cell in step S2 As shown in the following formula:

[0013] ;

[0014] In the formula, The output power of the converter, The maintenance cost per kilowatt-hour of fuel cell electricity. For the cost per kilowatt-hour of fuel cell electricity, F FC =C n / L,C n Where L is the fuel price and L is the lower calorific value of the fuel. This is the fixed loss reduction factor for fuel cells. The variable loss coefficient of the fuel cell. This represents the higher-order loss coefficient of the fuel cell;

[0015] Operating cost function of energy storage system As shown in the following formula:

[0016] ;

[0017] In the formula, The maintenance cost per kilowatt-hour of battery power. This is the fixed loss reduction factor for the energy storage system. The variable loss coefficient of the energy storage system. This represents the higher-order loss coefficient of the energy storage system.

[0018] Operating cost function of photovoltaic system As shown in the following formula:

[0019] ;

[0020] In the formula, The maintenance cost per kilowatt-hour of battery power. This is the fixed loss reduction factor for photovoltaic systems. The variable loss factor of the photovoltaic system. This refers to the higher-order loss coefficient of the photovoltaic system.

[0021] The operating cost function for both dispatchable and undispatchable clean energy can be uniformly expressed as the following formula:

[0022] ;

[0023] Among them, P i Let be the power output of the i-th distributed power source. Let a represent the operating cost function of the i-th distributed power source. ib i c i These are the quadratic, linear, and constant terms in the cost function of the i-th distributed power source, respectively.

[0024] As a preferred embodiment of the present invention, the incremental rate function of the power generation cost of each distributed power source obtained in step S3 is as follows:

[0025] ;

[0026] In the formula, Let P be the incremental rate function representing the generation cost of the i-th distributed power source. i Let a be the power output of the i-th distributed power source. i b i These are the quadratic and linear terms in the power generation cost function of the i-th distributed power source, respectively.

[0027] As a preferred technical solution of the present invention: the optimization target model is established in step S4 as follows:

[0028] ;

[0029] ;

[0030] In the formula, n is the total number of distributed power sources, and P i Let P be the power output of the i-th distributed power source. D and P loss These are line loss and load power, respectively. and They are respectively The maximum and minimum values; The incremental rate function represents the rate of increase in the generation cost of the i-th distributed power source; The incremental rate function represents the rate of increase in the generation cost of the j-th distributed power source;

[0031] make and order Based on equation (5), equation (6) can be rewritten as:

[0032] ;

[0033] in, Represents the system's cost function. Represent the cost function of the i-th distributed power source;

[0034] by As the decision variable for the k-th iteration of the system, the decision variable for the i-th distributed source in the system at the k-th iteration is as follows:

[0035] ;

[0036] In the formula, Let be the weight between the i-th and j-th distributed power sources in the communication network. Let be the step size of the gradient, and k be the number of iterations. It is the decision variable of the i-th distributed source in the k-th iteration. It is the decision variable of the i-th distributed source in the (k+1)-th iteration. It is the decision variable of the j-th distributed power source in the k-th iteration; express The gradient; Let the decision variable of the i-th distributed power source in the k-th iteration be: Cost function at time;

[0037] Rewritten in matrix form, we get the following formula:

[0038] ;

[0039] In the formula, , It is an n×n identity matrix. ; These are the decision variables of the system in the (k+1)th iteration. These are the decision variables of the system in the k-th iteration;

[0040] The objective function is constructed as follows:

[0041] ;

[0042] In the formula, Describe the objective function. Let be the step size of the gradient. Let represent the cost function of the i-th distributed power source.

[0043] As a preferred technical solution of the present invention: in step S5, the gradient of the kth iteration is obtained according to equation (11). As shown in the following formula:

[0044] ;

[0045] In the formula, Describe the objective function The gradient; This represents the objective function at the k-th iteration.

[0046] The Hessian matrix is ​​calculated as follows:

[0047] ;

[0048] In the formula, Represents the Hessian matrix, It is a diagonal matrix; Describe the objective function The second-order gradient;

[0049] The iterative equation for the second-order gradient is:

[0050] ;

[0051] In the formula, For Newton's direction, , It is the inverse of the Hessian matrix. For about Step size;

[0052] For diagonal matrix Its diagonal elements are:

[0053] ;

[0054] In the formula, Represents a diagonal matrix The diagonal elements, The cost function of the i-th distributed power source The second-order gradient; Let represent the quadratic term in the cost function of the i-th distributed power source;

[0055] Hessian matrix Decomposed into two matrices: H k =D k -B;

[0056] Among them, matrix As shown in the following formula:

[0057] ;

[0058] In the formula, α∈(0,1), is the adjustment diagonal matrix. The coefficient;

[0059] matrix As shown in the following formula:

[0060] ;

[0061] Hessian matrix Rewrite it and find the inverse. :

[0062] ;

[0063] Using the Taylor series expansion theorem to Further rewriting yields Newton's direction as:

[0064] ;

[0065] In the formula, This represents the number of inner iterations. In the Newton direction;

[0066] Approximate Newtonian direction Considering only the first r+1 terms of the infinite series, we finally obtain The iterative equation is:

[0067] ;

[0068] In the formula, This represents the Newton direction in the r-th inner iteration. This represents the Newton direction in the (r+1)th inner iteration;

[0069] The Newton direction for the i-th distributed source is represented as:

[0070] ;

[0071] In the formula, This represents the Newton direction of the i-th distributed power source in the k-th and (r+1)-th inner iterations. This represents the Newton direction of the j-th distributed power source in the k-th and r-th inner iterations; Representation matrix The elements in Representation matrix The diagonal elements;

[0072] According to equation (12), we get for:

[0073] ;

[0074] In the formula, This represents the gradient of the i-th distributed power source in the k-th iteration. Let be the weight between the i-th and j-th distributed power sources in the communication network. Let P be the neighborhood of the i-th distributed power source, and n be the number of distributed power sources; D and P loss These represent line loss and load power, respectively, P i Let a be the power output of the i-th distributed power source; i This is the quadratic term in the cost function of the i-th distributed power source; [It is the i-th distributed power source]

[0075] The decision variables at the k-th iteration;

[0076] Equation (21) can be rewritten as:

[0077] ;

[0078] In the formula, This is the offset coefficient. , These are the bus voltage sample values ​​at iteration k and iteration k-1, respectively;

[0079] When performing the k-th iteration of the Newton direction, the specific recursive process is as follows: First, let r=0, and set the inner iteration number r=3; then calculate the initial value of the Newton direction method. Determine if t is less than or equal to 3. If yes, end the iteration; otherwise, determine the Newton direction of the i-th distributed power source in step t. Exchange Newton directions with neighbors and perform Newton direction recursion calculation according to equation (21) until r≤3 is satisfied and exit the iteration;

[0080] Based on the Newton direction obtained recursively, update equation (14) for local Newton iteration; the iteration process is as follows: change the local Newton direction... Send to neighbors and collect what neighbors send. ,calculate and Then distributed iteration Finally, update according to equation (14). ,judge and If they are equal, exit the iteration; otherwise, re-enter the local... Send to neighbors and collect what neighbors send. Repeat the iteration until... and equal;

[0081] The target power of each distributed power source needs to be set based on the system decision variable x obtained from the iteration:

[0082] ;

[0083] In the formula, This represents the target power of the i-th distributed power source; , These are the quadratic and linear terms in the power generation cost function of the i-th distributed power source, respectively.

[0084] Based on the calculated target power This controls the power output of each distributed power source.

[0085] The present invention also designs an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the aforementioned economic operation control method for off-grid hydrogen production system based on distributed quasi-Newton method.

[0086] The present invention also designs a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the economic operation control method for off-grid hydrogen production systems based on the distributed quasi-Newton method.

[0087] Beneficial effects: Compared with the prior art, the advantages of the present invention include:

[0088] (1) Compared with the traditional economic droop control loop, the present invention directly solves the optimal power command from the optimization problem, avoids the steady-state voltage deviation problem caused by droop characteristics, simplifies the control structure, and fundamentally improves the dynamic response speed and control accuracy of power distribution.

[0089] (2) The quasi-Newton method is used to introduce second-order gradient information for distributed optimization. Compared with the traditional first-order consensus algorithm, it can converge to the optimal operating point with the lowest system power generation cost at a faster iteration speed, which significantly improves the economy in the dynamic process and is more suitable for scenarios with frequent fluctuations in load and power supply status.

[0090] (3) Based on a fully distributed architecture, it relies only on sparse neighbor communication, without the need for a central controller or global parameter presets, making the system robust and scalable. The built-in dynamic node commissioning and decommissioning protocol supports the "plug and play" of distributed power sources, facilitating flexible system reconfiguration and expansion. Attached Figure Description

[0091] Figure 1 This is a flowchart of an economic operation control method for an off-grid hydrogen production system based on a distributed quasi-Newton method, according to an embodiment of the present invention.

[0092] Figure 2 This is a simulation diagram of the marginal cost of each unit under load shedding conditions according to an embodiment of the present invention;

[0093] Figure 3 This is a simulation diagram of the output power of each unit under load-cutting conditions according to an embodiment of the present invention;

[0094] Figure 4 This is a simulation diagram of the node voltage of each unit under load shedding conditions according to an embodiment of the present invention;

[0095] Figure 5 This is a simulation diagram of the marginal cost of each unit that generates a power supply failure according to an embodiment of the present invention;

[0096] Figure 6 This is a simulation diagram of the output power of each unit that causes a power supply failure according to an embodiment of the present invention;

[0097] Figure 7 This is a simulation diagram of the node voltages of each unit that are faulty, provided by an embodiment of the present invention. Detailed Implementation

[0098] The present invention will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and should not be used to limit the scope of protection of the present invention.

[0099] The economic operation control method for off-grid hydrogen production systems based on the distributed quasi-Newton method provided in this embodiment of the invention refers to... Figure 1 Perform the following steps S1-S5 to complete the economic operation control of the renewable energy hydrogen production system:

[0100] Step S1: Establish a renewable energy hydrogen production system, including a photovoltaic system, an energy storage system, a fuel cell, and a hydrogen production unit; wherein, the photovoltaic system, fuel cell, energy storage system, electrolyzer, and constant power load in the hydrogen production unit are connected to the DC bus through DC / DC converters respectively;

[0101] Step S2: Establish the operating cost function for each distributed power source. Distributed power sources are divided into dispatchable clean energy and non-dispatchable clean energy. The operating cost function for dispatchable clean energy includes the operating cost function for fuel cells and the operating cost function for energy storage systems. The operating cost function for non-dispatchable clean energy includes the operating cost function for photovoltaic systems.

[0102] The operating cost of a fuel cell can be approximated as the cost of fuel and maintenance. Taking losses into account, the operating cost function of a fuel cell is... As shown in the following formula:

[0103] ;

[0104] In the formula, The output power of the converter, The maintenance cost per kilowatt-hour of fuel cell electricity. For the cost per kilowatt-hour of fuel cell electricity, F FC =C n / L,C n fuel price ($ / m 3 L represents the lower calorific value of the fuel (kWh / m³). 3 ), This is the fixed loss reduction factor for fuel cells, which is independent of output power; This is the variable loss coefficient of the fuel cell, which is proportional to the output power. This is the higher-order loss coefficient of the fuel cell, which is proportional to the square of the output power.

[0105] The operating cost of energy storage mainly considers maintenance costs, and its cost function is similar to equation (1), but without fuel costs; the operating cost function of the energy storage system As shown in the following formula:

[0106] ;

[0107] In the formula, The maintenance cost per kilowatt-hour of battery power. This is the fixed loss conversion factor for the energy storage system, and it is independent of the output power. This is the variable loss coefficient of the energy storage system, which is proportional to the output power. This is the higher-order loss coefficient of the energy storage system, which is proportional to the square of the output power.

[0108] For non-dispatchable clean energy sources like photovoltaics, they are typically integrated with energy storage systems for dispatch. The cost is the sum of the maintenance costs of the energy storage system and the renewable energy source. (Photovoltaic system operating cost function) As shown in the following formula:

[0109] ;

[0110] In the formula, The maintenance cost per kilowatt-hour of battery power. This is the fixed loss reduction factor for the photovoltaic system, which is independent of the output power. is the variable loss coefficient of the photovoltaic system, which is proportional to the output power; This is the higher-order loss coefficient of the photovoltaic system, which is proportional to the square of the output power.

[0111] The operating cost function for both dispatchable and undispatchable clean energy can be uniformly expressed as the following formula:

[0112] ;

[0113] Among them, P i Let be the power output of the i-th distributed power source. Let a represent the operating cost function of the i-th distributed power source. i b i c i These are the quadratic, linear, and constant terms in the power generation cost function of the i-th distributed power source, respectively, and their values ​​depend on the type of distributed power source.

[0114] Step S3: Differentiate the operating cost function of each distributed power source to obtain the incremental rate function of the power generation cost of each distributed power source.

[0115] The incremental rate function of the generation cost of each distributed power source obtained in step S3 is as follows:

[0116] ;

[0117] In the formula, Let P be the incremental rate function representing the generation cost of the i-th distributed power source. i Let a be the power output of the i-th distributed power source. i b i These are the quadratic and linear terms in the power generation cost function of the i-th distributed power source, respectively.

[0118] Step S4: Establish an optimization objective model based on the incremental rate function of the generation cost of each distributed power source, and establish constraint conditions according to the power balance constraint, the output constraint of each distributed power source, and the constraint of minimizing the system generation cost.

[0119] During system operation, three conditions must be met: power balance, output constraints of each distributed power source, and minimum system generation cost. According to the equal consumption incremental rate criterion, when the consumption incremental rates of each distributed generator (DG) are equal, load allocation is optimal and system generation cost is minimized. Therefore, the optimization objective model established in step S4 is as follows:

[0120] ;

[0121] ;

[0122] In the formula, n is the total number of distributed power sources, and P i Let P be the power output of the i-th distributed power source. D and P loss These are line loss and load power, respectively. and They are respectively The maximum and minimum values; The incremental rate function represents the rate of increase in the generation cost of the i-th distributed power source; The incremental rate function represents the rate of increase in the generation cost of the j-th distributed power source;

[0123] make and order Based on equation (5), equation (6) can be rewritten as:

[0124] ;

[0125] in, Represents the system's cost function. Represent the cost function of the i-th distributed power source;

[0126] by As the decision variable for the k-th iteration of the system, the decision variable for the i-th distributed source in the system at the k-th iteration is as follows:

[0127] ;

[0128] In the formula, Let be the weight between the i-th and j-th distributed power sources in the communication network. Let be the step size of the gradient, and k be the number of iterations. It is the decision variable of the i-th distributed source in the k-th iteration. It is the decision variable of the i-th distributed source in the (k+1)-th iteration. It is the decision variable of the j-th distributed power source in the k-th iteration; express The gradient; Let the decision variable of the i-th distributed power source in the k-th iteration be: Cost function at time;

[0129] Rewritten in matrix form, we get the following formula:

[0130] ;

[0131] In the formula, , It is an n×n identity matrix. ; These are the decision variables of the system in the (k+1)th iteration. These are the decision variables of the system in the k-th iteration;

[0132] The objective function is constructed as follows:

[0133] ;

[0134] In the formula, Describe the objective function. Let be the step size of the gradient. Let represent the cost function of the i-th distributed power source.

[0135] When x1=x2=…=x n hour, ,therefore, This is the embodiment of the incremental rate of equal consumption in equation (11). When the step size σ is small enough, the optimization result of the new objective function equation (11) is close to the optimization result of equation (8).

[0136] Step S5: For the optimization target model, the distributed quasi-Newton method is used for iterative updates to calculate the target power of each distributed power source. Based on the target power, the output power of each distributed power source is controlled to complete the economic operation control of the renewable energy hydrogen production system.

[0137] In step S5, the gradient of the k-th iteration is obtained according to equation (11). As shown in the following formula:

[0138] ;

[0139] In the formula, Describe the objective function The gradient; This represents the objective function at the k-th iteration.

[0140] The Hessian matrix is ​​calculated as follows:

[0141] ;

[0142] In the formula, Represents the Hessian matrix, It is a diagonal matrix; Describe the objective function The second-order gradient;

[0143] The iterative equation for the second-order gradient is:

[0144] ;

[0145] In the formula, For Newton's direction, , It is the inverse of the Hessian matrix. For about Step size;

[0146] For diagonal matrix Its diagonal elements are:

[0147] ;

[0148] In the formula, Represents a diagonal matrix The diagonal elements, The cost function of the i-th distributed power source The second-order gradient; Let represent the quadratic term in the cost function of the i-th distributed power source;

[0149] Hessian matrix Decomposed into two matrices: H k =D k -B;

[0150] Among them, matrix As shown in the following formula:

[0151] ;

[0152] In the formula, α∈(0,1), is the adjustment diagonal matrix. The coefficient;

[0153] matrix As shown in the following formula:

[0154] ;

[0155] Hessian matrix Rewrite it and find the inverse. :

[0156] ;

[0157] Using the Taylor series expansion theorem to Further rewriting yields Newton's direction as:

[0158] ;

[0159] In the formula, This represents the number of inner iterations. In the Newton direction;

[0160] Approximate Newtonian direction Considering only the first r+1 terms of the infinite series, we finally obtain The iterative equation is:

[0161] ;

[0162] In the formula, This represents the Newton direction in the r-th inner iteration. This represents the Newton direction in the (r+1)th inner iteration;

[0163] The Newton direction for the i-th distributed source is represented as:

[0164] ;

[0165] In the formula, This represents the Newton direction of the i-th distributed power source in the k-th and (r+1)-th inner iterations. This represents the Newton direction of the j-th distributed power source in the k-th and r-th inner iterations; Representation matrix The elements in Representation matrix The diagonal elements;

[0166] According to equation (12), we get for:

[0167] ;

[0168] In the formula, This represents the gradient of the i-th distributed power source in the k-th iteration. Let be the weight between the i-th and j-th distributed power sources in the communication network. Let P be the neighborhood of the i-th distributed power source, and n be the number of distributed power sources; D and P loss These represent line loss and load power, respectively, P i Let a be the power output of the i-th distributed power source; i This is the quadratic term in the cost function of the i-th distributed power source; It is the decision variable of the i-th distributed power source in the k-th iteration;

[0169] Due to g i,k Power offset term in Global information is required but cannot be obtained locally. However, power and voltage are strongly correlated, so equation (21) can be rewritten as:

[0170] ;

[0171] In the formula, This is the offset coefficient. , These are the bus voltage sample values ​​at iteration k and iteration k-1, respectively;

[0172] When performing the k-th iteration of the Newton direction, the specific recursive process is as follows: First, let r=0, and set the inner iteration number r=3; then calculate the initial value of the Newton direction method. Determine if t is less than or equal to 3. If yes, end the iteration; otherwise, determine the Newton direction of the i-th distributed power source in step t. Exchange Newton directions with neighbors and perform Newton direction recursion calculation according to equation (21) until r≤3 is satisfied and exit the iteration;

[0173] Based on the Newton direction obtained recursively, update equation (14) for local Newton iteration; the iteration process is as follows: change the local Newton direction... Send to neighbors and collect what neighbors send. ,calculate and Then distributed iteration Finally, update according to equation (14). ,judge and If they are equal, exit the iteration; otherwise, re-enter the local... Send to neighbors and collect what neighbors send. Repeat the iteration until... and equal;

[0174] The target power of each distributed power source needs to be set based on the system decision variable x obtained from the iteration:

[0175] ;

[0176] In the formula, This represents the target power of the i-th distributed power source; , These are the quadratic and linear terms in the power generation cost function of the i-th distributed power source, respectively.

[0177] Based on the calculated target power This controls the power output of each distributed power source.

[0178] In one application example, the total load power was 18kW in the first second, and increased to 24kW after 1 second. The results are as follows. Figures 2-5 As shown. Figure 2 This represents the marginal cost of each unit. Figure 2 It can be seen that the marginal costs of each unit gradually converge under the action of the distributed quasi-Newton algorithm, achieving the optimal total system cost. Figure 3 The output power of each unit. From Figure 3 As can be seen, the lower the cost, the greater the output power. Figure 4 For the output voltage of each unit, from Figure 4 It can be seen that the voltage of each unit can be maintained at the set voltage of 600V, and when the load is cut off, the voltage fluctuation is small and each unit can quickly recover to around 600V. Figure 5 , Figure 6 , Figure 7 These represent the marginal cost, output power, and voltage of each unit when a power supply failure occurs. Figures 5-7 As can be seen, even after the fourth power supply fails within 1 second, the remaining 3 power units can still achieve the lowest total cost.

[0179] This invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the aforementioned economic operation control method for an off-grid hydrogen production system based on a distributed quasi-Newton method.

[0180] This invention also provides a computer-readable storage medium storing a computer program thereon, characterized in that the computer program, when executed by a processor, implements the aforementioned economic operation control method for an off-grid hydrogen production system based on a distributed quasi-Newton method.

[0181] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the above embodiments. Within the scope of knowledge possessed by those skilled in the art, various changes can be made without departing from the spirit of the present invention.

Claims

1. An economical operation control method for off-grid hydrogen production systems based on the distributed quasi-Newton method, characterized in that, Perform the following steps S1-S5 to complete the economic operation control of the renewable energy hydrogen production system: Step S1: Establish a renewable energy hydrogen production system, including a photovoltaic system, an energy storage system, a fuel cell, and a hydrogen production unit; wherein, the photovoltaic system, fuel cell, energy storage system, electrolyzer, and constant power load in the hydrogen production unit are connected to the DC bus through DC / DC converters respectively; Step S2: Establish the operating cost function for each distributed power source. Distributed power sources are divided into dispatchable clean energy and non-dispatchable clean energy. The operating cost function for dispatchable clean energy includes the operating cost function for fuel cells and the operating cost function for energy storage systems. The operating cost function for non-dispatchable clean energy includes the operating cost function for photovoltaic systems. Step S3: Differentiate the operating cost function of each distributed power source to obtain the incremental rate function of the power generation cost of each distributed power source. Step S4: Establish an optimization objective model based on the incremental rate function of the generation cost of each distributed power source, and establish constraint conditions according to the power balance constraint, the output constraint of each distributed power source, and the constraint of minimizing the system generation cost. Step S5: For the optimization target model, the distributed quasi-Newton method is used for iterative updates to calculate the target power of each distributed power source. Based on the target power, the output power of each distributed power source is controlled to complete the economic operation control of the renewable energy hydrogen production system.

2. The economic operation control method for off-grid hydrogen production systems based on the distributed quasi-Newton method according to claim 1, characterized in that, The operating cost function of the fuel cell in step S2 As shown in the following formula: ； In the formula, The output power of the converter, The maintenance cost per kilowatt-hour of fuel cell electricity. For the cost per kilowatt-hour of fuel cell electricity, F FC =C n / L,C n Where L is the fuel price and L is the lower calorific value of the fuel. This is the fixed loss reduction factor for fuel cells. The variable loss coefficient of the fuel cell. This represents the higher-order loss coefficient of the fuel cell; Operating cost function of energy storage system As shown in the following formula: ； In the formula, The maintenance cost per kilowatt-hour of battery power. This is the fixed loss reduction factor for the energy storage system. The variable loss coefficient of the energy storage system. This represents the higher-order loss coefficient of the energy storage system. Operating cost function of photovoltaic system As shown in the following formula: ； In the formula, The maintenance cost per kilowatt-hour of battery power. This is the fixed loss reduction factor for photovoltaic systems. The variable loss factor of the photovoltaic system. This refers to the higher-order loss coefficient of the photovoltaic system. The operating cost function for both dispatchable and undispatchable clean energy can be uniformly expressed as the following formula: ； Among them, P i Let be the power output of the i-th distributed power source. Let a represent the operating cost function of the i-th distributed power source. i b i c i These are the quadratic, linear, and constant terms in the cost function of the i-th distributed power source, respectively.

3. The economic operation control method for off-grid hydrogen production systems based on the distributed quasi-Newton method according to claim 2, characterized in that, The incremental rate function of the generation cost of each distributed power source obtained in step S3 is as follows: ； In the formula, Let P be the incremental rate function representing the generation cost of the i-th distributed power source. i Let a be the power output of the i-th distributed power source. i b i These are the quadratic and linear terms in the power generation cost function of the i-th distributed power source, respectively.

4. The economic operation control method for off-grid hydrogen production systems based on the distributed quasi-Newton method according to claim 3, characterized in that, In step S4, the optimization objective model is established as follows: ； ； In the formula, n is the total number of distributed power sources, and P i Let P be the power output of the i-th distributed power source. D and P loss These are line loss and load power, respectively. and They are respectively The maximum and minimum values; The incremental rate function represents the rate of increase in the generation cost of the i-th distributed power source; The incremental rate function represents the rate of increase in the generation cost of the j-th distributed power source; make and order Based on equation (5), equation (6) can be rewritten as: ； in, Represents the system's cost function. Represent the cost function of the i-th distributed power source; by As the decision variable for the k-th iteration of the system, the decision variable for the i-th distributed source in the system at the k-th iteration is as follows: ； In the formula, Let be the weight between the i-th and j-th distributed power sources in the communication network. Let be the step size of the gradient, and k be the number of iterations. It is the decision variable of the i-th distributed source in the k-th iteration. It is the decision variable of the i-th distributed source in the (k+1)-th iteration. It is the decision variable of the j-th distributed power source in the k-th iteration; express The gradient; Let the decision variable of the i-th distributed power source in the k-th iteration be: Cost function at time; Rewritten in matrix form, we get the following formula: ； In the formula, , It is an n×n identity matrix. ; These are the decision variables of the system in the (k+1)th iteration. These are the decision variables of the system in the k-th iteration; The objective function is constructed as follows: ； In the formula, Describe the objective function. Let be the step size of the gradient. Let represent the cost function of the i-th distributed power source.

5. The economic operation control method for off-grid hydrogen production systems based on the distributed quasi-Newton method according to claim 4, characterized in that, In step S5, the gradient of the k-th iteration is obtained according to equation (11). As shown in the following formula: ； In the formula, Describe the objective function The gradient; This represents the objective function at the k-th iteration. The Hessian matrix is ​​calculated as follows: ； In the formula, Represents the Hessian matrix, It is a diagonal matrix; Describe the objective function The second-order gradient; The iterative equation for the second-order gradient is: ； In the formula, For Newton's direction, , It is the inverse of the Hessian matrix. For about Step size; For diagonal matrix Its diagonal elements are: ； In the formula, Represents a diagonal matrix The diagonal elements, The cost function of the i-th distributed power source The second-order gradient; Let represent the quadratic term in the cost function of the i-th distributed power source; Hessian matrix Decomposed into two matrices: H k =D k -B; Among them, matrix As shown in the following formula: ； In the formula, α∈(0,1), is the adjustment diagonal matrix. The coefficient; matrix As shown in the following formula: ； Hessian matrix Rewrite it and find the inverse. : ； Using the Taylor series expansion theorem to Further rewriting yields Newton's direction as: ； In the formula, This represents the number of inner iterations. In the Newton direction; Approximate Newtonian direction Considering only the first r+1 terms of the infinite series, we finally obtain The iterative equation is: ； In the formula, This represents the Newton direction in the r-th inner iteration. This represents the Newton direction in the (r+1)th inner iteration; The Newton direction for the i-th distributed source is represented as: ； In the formula, This represents the Newton direction of the i-th distributed power source in the k-th and (r+1)-th inner iterations. This represents the Newton direction of the j-th distributed power source in the k-th and r-th inner iterations; Representation matrix The elements in Representation matrix The diagonal elements; According to equation (12), we get for: ； In the formula, This represents the gradient of the i-th distributed power source in the k-th iteration. Let be the weight between the i-th and j-th distributed power sources in the communication network. Let P be the neighborhood of the i-th distributed power source, and n be the number of distributed power sources; D and P loss These represent line loss and load power, respectively, P i Let a be the power output of the i-th distributed power source; i This is the quadratic term in the cost function of the i-th distributed power source; [It is the i-th distributed power source] The decision variables at the k-th iteration; Equation (21) can be rewritten as: ； In the formula, This is the offset coefficient. , These are the bus voltage sample values ​​at iteration k and iteration k-1, respectively; When performing the k-th iteration of the Newton direction, the specific recursive process is as follows: First, let r=0, and set the inner iteration number r=3; then calculate the initial value of the Newton direction method. Determine if t is less than or equal to 3. If yes, end the iteration; otherwise, determine the Newton direction of the i-th distributed power source in step t. Exchange Newton directions with neighbors and perform Newton direction recursion calculation according to equation (21) until r≤3 is satisfied and exit the iteration; Based on the Newton direction obtained recursively, update equation (14) for local Newton iteration; the iteration process is as follows: change the local Newton direction... Send to neighbors and collect what neighbors send. ,calculate and Then distributed iteration Finally, update according to equation (14). ,judge and If they are equal, exit the iteration; otherwise, re-enter the local... Send to neighbors and collect what neighbors send. Repeat the iteration until... and equal; The target power of each distributed power source needs to be set based on the system decision variable x obtained from the iteration: ； In the formula, This represents the target power of the i-th distributed power source; , These are the quadratic and linear terms in the power generation cost function of the i-th distributed power source, respectively. Based on the calculated target power This controls the power output of each distributed power source.

6. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the economic operation control method for off-grid hydrogen production system based on distributed quasi-Newton method as described in any one of claims 1-5.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the economic operation control method for off-grid hydrogen production systems based on the distributed quasi-Newton method as described in any one of claims 1-5.

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