A nonlinear multi-objective capacity configuration optimization method for hydrogen-supported isolated island microgrids

Abstract

The present invention belongs to the technical field of microgrid capacity configuration, and discloses a nonlinear multi-objective capacity configuration optimization method for a hydrogen-powered isolated island microgrid, comprising: constructing a capacity configuration model with the objective functions of minimizing the lifecycle cost and the curtailment rate of the isolated island microgrid; and constructing operating constraints for the isolated microgrid including a maximum curtailment rate constraint; and solving the capacity configuration model under the operating constraints to obtain an optimal capacity configuration result. The present invention can quickly and accurately solve the dual-objective optimization problem of minimizing the curtailment rate and minimizing the nonlinear lifecycle cost of a hydrogen-powered microgrid. The lifecycle cost objective function of the optimization model greatly restores the real-world scenario (including the operating costs related to power distribution and the capital costs related to the initial configuration), ensuring the authenticity and validity of the optimal capacity configuration solution.

Description

Technical Field

[0001] The present invention belongs to the technical field of microgrid capacity configuration, and more specifically, relates to a nonlinear multi-objective capacity configuration optimization method for a hydrogen energy-supported isolated island microgrid. Background Art

[0002] The optimal capacity configuration of a microgrid aims to determine the optimal capacity of each device in the microgrid to achieve economic, reliable, and environmentally friendly goals. The capacity optimization configuration problem for a microgrid is a nonlinear multi-objective capacity optimization configuration problem. Hydrogen-powered microgrids (hydrogen-electric hybrid grids) combine hydrogen energy, batteries, and other grid technologies. They use electrolysis to generate hydrogen for energy storage. Fuel cells or other hydrogen energy technologies, such as solid oxide fuel cell (SOFC) systems, convert hydrogen into electricity to achieve energy storage and balance. At the same time, batteries quickly respond to energy demands, making hybrid grids more flexible and reliable. An island microgrid is a small grid system that operates independently and is powered by local energy (such as solar and wind energy).

[0003] In the capacity optimization and configuration of hydrogen-powered, isolated microgrids, heuristic and exact algorithms are commonly used to address nonlinear, multi-objective capacity optimization and configuration tasks. While heuristic algorithms, such as particle swarm search and genetic algorithms, can effectively optimize complex, nonlinear, multi-objective tasks, they require complex grid energy management strategies in practice. These strategies are often highly subjective. Improperly designed strategies can lead to suboptimal energy allocation, impacting the final optimization results. Exact algorithms, such as linear programming and integer linear programming, while not requiring complex grid energy management strategies, typically only address convex optimization problems and impose strict requirements on the optimization objective function and constraints. When addressing microgrid capacity optimization and configuration tasks, they typically ignore or replace the nonlinear components of the nonlinear objective function, transforming it into a linear objective function for solution. For example, for a cost minimization objective function, the cost of each device (capital cost) in the microgrid is simply ignored, or the operation and maintenance cost is set as a fixed percentage of the capital cost, making the objective function linear. This results in less realistic and accurate capacity allocation solutions. Summary of the Invention

[0004] In response to the above-mentioned defects or improvement needs of the prior art, the present invention provides a nonlinear multi-objective capacity configuration optimization method for a hydrogen-supported island microgrid, which aims to improve the authenticity and accuracy of the capacity configuration results.

[0005] To achieve the above objectives, according to a first aspect of the present invention, a nonlinear multi-objective capacity configuration optimization method for a hydrogen-powered isolated island microgrid is provided. The isolated island microgrid includes a photovoltaic power generation unit (PV) and a SOFC system. The PV is used to provide new energy generation for the isolated island microgrid, and the SOFC system is used to convert hydrogen energy into electrical energy. The method comprises:

[0006] Construct a capacity configuration model with the minimum life cycle cost and the minimum abandonment rate of the island microgrid as the objective function; and construct a capacity configuration model including the maximum abandonment rate R max The island microgrid operation constraints are included in the constraints; under the operation constraints, solving the capacity configuration model includes:

[0007] S1. Initialize the number of PVs to n PV,ini , the number of SOFC units is 0; and the maximum abandoned light rate R max Divided into steps from 0 to R according to the preset step size max An array of multiple fixed light abandonment rates;

[0008] S2, at the current PV number n PV and the number of SOFC units n SOFC In this case, each fixed abandoned light rate in the array is used as a constraint of the objective function, and linear programming is used to solve the objective function under the corresponding constraints;

[0009] S3. If there is a fixed abandoned light rate in the array that makes the objective function solvable, then retain the local optimal solution and the corresponding decision variable value, and set n SOFC Add one and jump to S2; if each fixed abandonment rate in the array makes the objective function unsolvable, then n PV Add one and set n SOFC Set to 0 and jump to S2; if each fixed abandonment rate in the array makes the objective function solvable, jump to S4;

[0010] S4. Compare all retained local optimal solutions and select the better ones to form an optimal solution set. The decision variable values ​​corresponding to the optimal solution set are the optimal capacity configuration results.

[0011] Furthermore, in S1, the number of PVs n is initialized. PV,ini The calculation method is:

[0012]

[0013] Wherein, Life represents the life cycle of the island microgrid in years; * represents a multiplication operation; represents the user load power at time t; It represents the power generation of a single photovoltaic power generation unit PV at time t.

[0014] Furthermore, the island microgrid further comprises: a lithium battery energy storage unit, a hydrogen production unit and a hydrogen energy storage unit;

[0015] The life cycle cost function LCC of the island microgrid is:

[0016] LCC=Cap all +OM all

[0017] Among them, Cap all is the total capital cost, OM all is the total operation and maintenance cost; Cap all and OM all The calculation method is:

[0018]

[0019]

[0020] Where, Life represents the life cycle of the island microgrid, in years; * represents multiplication operation; n PV 、C LB 、 n SOFC and is the decision variable, C LB and Respectively represent the rated capacity of the lithium battery energy storage unit and the hydrogen energy storage unit, Indicates the rated power of the hydrogen production unit; C PV,cap 、C LB,cap 、C ele,cap 、C SOFC,cap and C HSS,cap are the capital cost coefficients of PV, lithium battery energy storage unit, hydrogen production unit, SOFC system and hydrogen energy storage unit respectively; and are decision variables, representing the power generation of a single PV unit, the abandoned solar power, the charging power and discharging power of the lithium battery energy storage unit, the power generation of a single SOFC unit, and the hydrogen production power of the hydrogen production unit at time t; C PV,OM 、C LB,OM 、C SOFC,OM 、C ele,OM and C HSS,OM is the operation and maintenance cost coefficient of PV, lithium battery energy storage unit, SOFC system, hydrogen production unit and hydrogen energy storage unit.

[0021] Furthermore, the operation constraints also include: power balance constraints, upper and lower power constraints of each unit, and upper and lower capacity constraints of each energy storage unit; the units include PV, SOFC system, lithium battery energy storage unit, hydrogen production unit and hydrogen energy storage unit; the energy storage units include lithium battery energy storage unit and hydrogen energy storage unit;

[0022] The power balance constraint is:

[0023]

[0024] in, Represents the user load power at time t.

[0025] Furthermore, in S4, all retained local optimal solutions are compared, and the better ones are selected to form the optimal solution set, including:

[0026] For any two retained local optimal solutions A(LCC i , R i )、B(LCC j , R j ), where LCC i and LCC j Represent the full life cycle costs in the local optimal solutions A and B, R i and R j They represent the abandonment rates in the local optimal solutions A and B respectively, and the comparison method is:

[0027] If LCC i >LCC j And R i >R j , then keep the local optimal solution A and discard the local optimal solution B;

[0028] If LCC i <LCC j And R i <R j , then keep the local optimal solution B and discard the local optimal solution A;

[0029] If LCC i >LCC j And R i <R j , then retain the local optimal solutions A and B;

[0030] If LCC i <LCC j And R i >R j , then retain the local optimal solutions A and B;

[0031] If LCC i=LCC j And R i =R j , then retain the local optimal solutions A and B;

[0032] All retained local optimal solutions are traversed, and the final set of local optimal solutions obtained is the optimal solution set.

[0033] According to a second aspect of the present invention, a nonlinear multi-objective capacity configuration optimization device for a hydrogen-supported isolated island microgrid is provided, which is used to execute the nonlinear multi-objective capacity configuration optimization method according to any one of the first aspects. The isolated island microgrid includes a photovoltaic power generation unit (PV) and a SOFC system, wherein the PV is used to provide new energy generation for the isolated island microgrid, and the SOFC system is used to convert hydrogen energy into electrical energy. The nonlinear multi-objective capacity configuration optimization device includes:

[0034] A capacity configuration model construction module is used to construct a capacity configuration model with the minimum life cycle cost and the minimum curtailment rate of the island microgrid as the objective function;

[0035] Run the constraint building module to build the maximum abandonment rate R max The operation constraints of the island microgrid including the constraints;

[0036] A solution module, configured to solve the capacity configuration model under the operation constraints, comprising:

[0037] S1. Initialize the number of PVs to n PV,ini , the number of SOFC units is 0; and the maximum abandoned light rate R max Divided into steps from 0 to R according to the preset step size max An array of multiple fixed light abandonment rates;

[0038] S2, at the current PV number n PV and the number of SOFC units n SOFC In this case, each fixed abandoned light rate in the array is used as a constraint of the objective function, and linear programming is used to solve the objective function under the corresponding constraints;

[0039] S3. If there is a fixed abandoned light rate in the array that makes the objective function solvable, then retain the local optimal solution and the corresponding decision variable value, and set n SOFC Add one and jump to S2; if each fixed abandonment rate in the array makes the objective function unsolvable, then n PV Add one and set n SOFC Set to 0 and jump to S2; if each fixed abandonment rate in the array makes the objective function solvable, jump to S4;

[0040] S4. Compare all retained local optimal solutions and select the better ones to form an optimal solution set. The decision variable values ​​corresponding to the optimal solution set are the optimal capacity configuration results.

[0041] According to a third aspect of the present invention, there is provided an electronic device comprising a computer-readable storage medium and a processor;

[0042] The computer-readable storage medium is used to store executable instructions;

[0043] The processor is configured to read the executable instructions stored in the computer-readable storage medium to execute the nonlinear multi-objective capacity configuration optimization method according to any one of the first aspects.

[0044] According to a fourth aspect of the present invention, a computer-readable storage medium is provided, on which a computer program is stored. When the program is executed by a processor, the nonlinear multi-objective capacity configuration optimization method as described in any one of the first aspects is implemented.

[0045] According to a fifth aspect of the present invention, a computer program product is provided. When the computer program product is run on a computer, the computer is enabled to execute the nonlinear multi-objective capacity configuration optimization method according to any one of the first aspects.

[0046] In general, the above technical solutions conceived by the present invention can achieve the following beneficial effects:

[0047] (1) The nonlinear multi-objective capacity configuration optimization method of the hydrogen energy-supported isolated island microgrid of the present invention solves the nonlinear dual-objective function of minimizing the life cycle cost of the isolated island microgrid and minimizing the abandonment rate by first initializing the number of photovoltaic power generation units PV n PV,ini and SOFC units, under the current fixed number of PV units and SOFC units, and by setting the maximum abandoned light rate R max The system is divided into a series of fixed abandonment rates, and each fixed abandonment rate is used as a solution constraint in turn. The integer decision variable part in the nonlinear objective function is separated by linear search. Under the premise of fixed integer decision variables, the nonlinear objective function is converted into a linear objective function. In this way, the corresponding objective function can be solved by linear programming. At this time, the optimal solution obtained by the solution is the local optimal solution. The local optimal solutions are merged to obtain the final optimal solution set of the model. The decision variable values ​​corresponding to the optimal solution set are the optimal capacity configuration results. Compared with the heuristic algorithm, the method of the present invention does not require the configuration of the power grid energy management strategy, and the solution is simple. Compared with the existing accurate algorithm, the present invention realizes the solution of the nonlinear multi-objective capacity configuration optimization problem of hydrogen energy supported island microgrid, and improves the authenticity and accuracy of the capacity configuration results.

[0048] (2) As a preferred method, the present invention designs a method for initializing the number of photovoltaic power generation units PV n PV,ini The calculation method is based on the initial number of photovoltaic power generation units n obtained by this calculation formula. PV,ini , which can improve the convergence speed of the algorithm.

[0049] (3) Furthermore, the objective function of the present invention directly adopts a nonlinear life cycle cost function, which greatly restores the real scenario (including the operating costs related to power allocation and the capital costs related to the initial configuration), ensuring the authenticity and effectiveness of the optimal capacity configuration solution.

[0050] In summary, the present invention solves the problem of solving the optimization model for the optimal capacity configuration of hydrogen-supported island power grid with the minimum life cycle cost and the minimum abandonment rate as the objective function, and can provide a feasible construction based on the user's cost and the demand for new energy consumption. The life cycle cost objective function of the optimization model greatly restores the real scene (including the operating costs related to power distribution and the capital costs related to the initial configuration), ensuring the authenticity and effectiveness of the solution of the optimal capacity configuration. The solution method of the present invention has the advantages of solving complex problems, fast optimization speed and high solution accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 This is a flow chart of a nonlinear multi-objective capacity configuration optimization method for a hydrogen-powered isolated island microgrid in an embodiment of the present invention.

[0052] Figure 2 Schematic diagram of the structural model of the hydrogen energy-supported island microgrid in an embodiment of the present invention.

[0053] Figure 3 Schematic diagram of the relationship between the number of photovoltaic power generation units and the number of SOFCs during the solution process in an embodiment of the present invention. DETAILED DESCRIPTION

[0054] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only intended to illustrate the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.

[0055] Example 1

[0056] like Figure 1 and Figure 2As shown, the various devices in the hydrogen-powered island microgrid in the embodiment of the present invention mainly include: a photovoltaic power generation unit (PV), a hydrogen production unit, a hydrogen energy storage unit, a SOFC system, and a lithium battery energy storage unit. The photovoltaic power generation unit provides new energy generation for the island microgrid. The photovoltaic power generation unit, hydrogen production unit, SOFC system, and lithium battery energy storage unit are all connected to the DC main line. The air inlet of the hydrogen energy storage unit is connected to the air outlet of the hydrogen production unit to store the hydrogen produced by the hydrogen production unit. The air outlet of the hydrogen energy storage unit is connected to the air inlet of the SOFC system to provide hydrogen to the SOFC system for power generation. The power on the DC main line is converted to DC power for the user's load via an AC / DC converter. Figure 2 In, n PV Indicates the number of photovoltaic power generation units PV, It represents the power generation of a single photovoltaic power generation unit PV at time t; Indicates user load power; and They represent the rated power of the hydrogen production unit and the hydrogen production power at time t respectively; n SOFC Indicates the number of SOFC units, represents the power generation of a single SOFC at time t; C LB 、 They represent the rated capacity of the lithium battery energy storage unit, the discharge power at time t, and the charging power at time t respectively; They represent the rated capacity of the hydrogen energy storage unit, the hydrogen output at time t, and the hydrogen input rate at time t, respectively.

[0057] In an embodiment of the present invention, a nonlinear multi-objective capacity configuration optimization method for a hydrogen-powered isolated island microgrid includes:

[0058] Construct the objective function of the island microgrid capacity configuration model, which is to minimize the life cycle cost and the curtailment rate of the island microgrid. The decision variables are the capacity configuration and input and output power distribution of each device in the island microgrid.

[0059] Construct the operation constraints of the isolated microgrid; the operation constraints include: the maximum abandonment rate constraint, that is, the abandonment rate of the isolated microgrid throughout its life cycle does not exceed the preset maximum abandonment rate R max , in order to maximize the use of photovoltaic energy, improve energy efficiency, and reduce energy production waste; In the embodiment of the present invention, the preset maximum value R max Choose based on experience;

[0060] Under the operating constraints of the island microgrid, solving the above model includes:

[0061] S1. Initialize the number of photovoltaic power generation units PV to nPV,ini , the number of SOFC units is 0; and the maximum abandoned light rate R max Divided into steps from 0 to R according to the preset step size max Different fixed abandonment rates of light, these different fixed abandonment rates form an array;

[0062] S2, the number of photovoltaic power generation units n PV and the number of SOFC units n SOFC In this case, each fixed abandonment rate in the array is taken as a constraint of the above objective function, and linear programming is used to solve the objective function under the corresponding constraints;

[0063] S3. If there is a fixed abandoned light rate in the array that makes the objective function solvable, then the local optimal solution (local optimal objective function value) and the corresponding decision variable value are retained, and the number of SOFC units n SOFC Add one and jump to S2; if each fixed abandonment rate in the array makes the objective function unsolvable, then the number of current photovoltaic power generation units PV n PV Add one, and the number of SOFC units n SOFC Set to 0 and jump to S2; if each fixed abandonment rate in the array makes the objective function solvable, jump to S4;

[0064] S4. Compare all local optimal solutions to obtain an optimal solution set; the decision variable values ​​corresponding to the optimal solution set are the optimal decision variables, that is, the optimal capacity configuration result.

[0065] Specifically, in the embodiment of the present invention, the objective function S of the island microgrid capacity configuration model is constructed as follows:

[0066] S = min(LCC, R curtail )

[0067] Where LCC represents the life cycle cost function, which is calculated as follows:

[0068] LCC=Cap all +OM all

[0069] Cap all is the total capital cost, OM all is the total operation and maintenance cost.

[0070]

[0071] Where, Life represents the life cycle of the island microgrid (unit: year), * represents multiplication operation; C PV,cap 、C LB,cap 、C ele,cap 、C SOFC,cap and C HSS,capThey are the capital cost coefficients of the photovoltaic power generation unit, lithium battery energy storage unit, hydrogen production unit, SOFC system and hydrogen energy storage unit respectively; in the embodiment of the present invention, the capital cost system of the corresponding unit is set according to experience.

[0072]

[0073] Among them, C PV,OM 、C LB,OM 、C SOFC,OM 、C ele,OM and C HSS,OM is the operation and maintenance cost coefficient of the photovoltaic power generation unit, the lithium battery energy storage unit, the SOFC system, the hydrogen production unit, and the hydrogen energy storage unit. In the embodiment of the present invention, it is set based on experience; Indicates the discarded optical power at time t.

[0074] Life cycle abandonment rate R curtail The calculation is as follows:

[0075]

[0076] In the objective function constructed in the embodiment of the present invention, the decision variables include two categories. One category is the capacity configuration of each device in the isolated island microgrid, including the number n of photovoltaic power generation units PV PV 、Number of SOFC units n SOFC , Rated capacity C of lithium battery energy storage unit LB , Rated capacity of hydrogen energy storage unit and rated power of hydrogen production unit The other type is the power allocation at different decision moments, that is, the input and output power allocation of each unit, including: and

[0077] In the embodiment of the present invention, the operation constraints of the island microgrid further include: power balance constraints, upper and lower power constraints of each unit, and upper and lower capacity constraints of each energy storage unit.

[0078] The power balance constraint is:

[0079]

[0080] Furthermore, in S1, the number n of photovoltaic power generation units PV is initialized. PV,ini It can be set to 0 or set based on the formula designed below to improve the convergence speed of the method.

[0081]

[0082] Preferably, in S4, all local optimal solutions are compared to obtain an optimal solution set, including:

[0083] For any two retained local optimal solutions A(LCC i , R i )、B(LCC j , R j ), where LCC i and LCC j Represent the full life cycle costs in the local optimal solutions A and B, R i and R j Represent the abandonment rate in the local optimal solution A and B, that is, to obtain LCC i or LCC j When , the current fixed abandonment rate in the array is compared as follows:

[0084] If LCC i >LCC j And R i >R j , then keep the local optimal solution A and discard the local optimal solution B;

[0085] If LCC i <LCC j And R i <R j , then keep the local optimal solution B and discard the local optimal solution A;

[0086] If LCC i >LCC j And R i <R j , then retain the local optimal solutions A and B;

[0087] If LCC i <LCC j And R i >R j , then retain the local optimal solutions A and B;

[0088] If LCC i =LCC j And R i =R j , then the local optimal solutions A and B are retained.

[0089] The final set of local optimal solutions is the optimal solution set, and the optimal capacity configuration result is the Pareto solution frontier corresponding to the optimal solution set.

[0090] The nonlinear multi-objective capacity configuration optimization method of hydrogen energy-supported isolated island microgrid of the present invention solves the nonlinear dual-objective function of minimizing the life cycle cost of the isolated island microgrid and minimizing the abandonment rate by first initializing the number n of photovoltaic power generation units PV. PV,iniand SOFC units, under the current fixed number of PV units and SOFC units, and by setting the maximum abandoned light rate R max The system is divided into a series of fixed abandonment rates, and each fixed abandonment rate is used as a solution constraint in turn. The integer decision variable part in the nonlinear objective function is separated by linear search. Under the premise of fixed integer decision variables, the nonlinear objective function is converted into a linear objective function. In this way, the corresponding objective function can be solved by linear programming. At this time, the optimal solution obtained by the solution is the local optimal solution. The local optimal solutions are merged to obtain the final optimal solution set of the model. The decision variable values ​​corresponding to the optimal solution set are the optimal capacity configuration results. Compared with the heuristic algorithm, the method of the present invention does not require the configuration of the power grid energy management strategy, and the solution is simple. Compared with the existing accurate algorithm, the present invention realizes the solution of the nonlinear multi-objective capacity configuration optimization problem of hydrogen energy supported island microgrid, and improves the authenticity and accuracy of the capacity configuration results.

[0091] Furthermore, the method of the present invention can demonstrate the advantages and disadvantages of the role played by hydrogen energy related units (hydrogen production unit, hydrogen energy storage unit and SOFC system) in the power grid compared with lithium battery energy storage units, such as Figure 3 As shown, a visualization of the solution process generally generated after the solution is obtained by the method of the present invention is shown. It can be seen that:

[0092] (1) SOFC systems have a weaker ability to absorb renewable energy than battery energy storage: Increasing the number of SOFC units makes it easier for the number of photovoltaic units that can be solved by the model to reach the upper limit. This is because excessive photovoltaic power generation exceeds the response range of the SOFC system. Therefore, the maximum wind and solar curtailment rate limits the application of SOFC systems in the power grid. SOFC systems have a weaker ability to absorb renewable energy than battery energy storage.

[0093] (2) The SOFC system makes it easier for the microgrid to operate successfully when renewable energy generation is low: When the number of SOFC units is zero, the microgrid is completely regulated by the battery, and the lower limit of the number of photovoltaic units is greater than when the number of SOFC units is not zero. This means that hydrogen energy-related units can help the microgrid system achieve power supply and power generation balance in the case of low photovoltaic power generation, revealing the role of hydrogen energy equipment in helping microgrids operate safely and stably.

[0094] The objective function in the present invention directly adopts a nonlinear full life cycle cost function, which greatly restores the real scenario (including the operating costs related to power allocation and the capital costs related to initial configuration), and ensures the authenticity and effectiveness of the optimal capacity configuration solution.

[0095] In summary, the present invention is an accuracy solution method that can quickly and accurately solve the dual-objective optimization problem of minimizing the curtailment rate of hydrogen energy microgrids and minimizing the nonlinear full life cycle cost, and can intuitively demonstrate the regulatory role of hydrogen energy-related equipment in the microgrid.

[0096] Example 2

[0097] The embodiment of the present invention provides a nonlinear multi-objective capacity configuration optimization device for a hydrogen-powered isolated island microgrid, which is used to execute the nonlinear multi-objective capacity configuration optimization method in Example 1, including:

[0098] The capacity configuration model construction module is used to build a capacity configuration model with the objective function of minimizing the life cycle cost and the curtailment rate of the island microgrid;

[0099] Run the constraint building module to build the maximum abandonment rate R max The operation constraints of the island microgrid including the constraints;

[0100] The solver module is used to solve the capacity configuration model under operational constraints, including:

[0101] S1. Initialize the number of PVs to n PV,ini , the number of SOFC units is 0; and the maximum abandoned light rate R max Divided into steps from 0 to R according to the preset step size max An array of multiple fixed light abandonment rates;

[0102] S2, at the current PV number n PV and the number of SOFC units n SOFC In this case, each fixed abandonment rate in the array is taken as a constraint of the objective function, and linear programming is used to solve the objective function under the corresponding constraints.

[0103] S3. If there is a fixed abandonment rate in the array that makes the objective function solvable, then retain the local optimal solution and the corresponding decision variable value, and set n SOFC Add one and jump to S2; if each fixed abandonment rate in the array makes the objective function unsolvable, then n PV Add one and set n SOFC Set to 0 and jump to S2; if each fixed abandonment rate in the array makes the objective function solvable, jump to S4;

[0104] S4. Compare all retained local optimal solutions and select the better ones to form the optimal solution set. The decision variable values ​​corresponding to the optimal solution set are the optimal capacity configuration results.

[0105] The specific execution process of the relevant modules can be found in the corresponding description in the above embodiment 1, which will not be repeated here.

[0106] Example 3

[0107] An embodiment of the present invention provides an electronic device, including a computer-readable storage medium and a processor;

[0108] Computer-readable storage media for storing executable instructions;

[0109] The processor is configured to read the executable instructions stored in the computer-readable storage medium and execute the nonlinear multi-objective capacity configuration optimization method in Example 1. For related technical solutions, please refer to the corresponding description in the above Example 1 and will not be repeated here.

[0110] Example 4

[0111] An embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon. When executed by a processor, the program implements the nonlinear multi-objective capacity allocation optimization method described in Example 1. For related technical solutions, see the corresponding description in Example 1 above and will not be repeated here.

[0112] Example 5

[0113] An embodiment of the present invention provides a computer program product that, when executed on a computer, causes the computer to execute the nonlinear multi-objective capacity allocation optimization method of Embodiment 1. For related technical solutions, see the corresponding description in Embodiment 1 above and will not be repeated here.

[0114] It will be easily understood by those skilled in the art that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A nonlinear multi-objective capacity configuration optimization method for hydrogen-powered isolated island microgrids, characterized in that: The isolated island microgrid includes a photovoltaic power generation unit PV and a SOFC system, wherein the PV is used to provide new energy for the isolated island microgrid, and the SOFC system is used to convert hydrogen energy into electrical energy, including: Construct a capacity configuration model with the minimum life cycle cost and the minimum abandonment rate of the island microgrid as the objective function; and construct a capacity configuration model including the maximum abandonment rate R max The island microgrid operation constraints are included in the constraints; under the operation constraints, solving the capacity configuration model includes: S1. Initialize the number of PVs to n PV,ini , the number of SOFC units is 0; and the maximum abandoned light rate R max Divided into steps from 0 to R according to the preset step size max An array of multiple fixed light abandonment rates; S2, at the current PV number n PV and the number of SOFC units n SOFC In this case, each fixed abandoned light rate in the array is used as a constraint of the objective function, and linear programming is used to solve the objective function under the corresponding constraints; S3. If there is a fixed abandoned light rate in the array that makes the objective function solvable, then retain the local optimal solution and the corresponding decision variable value, and set n SOFC Add one and jump to S2; if each fixed abandonment rate in the array makes the objective function unsolvable, then n PV Add one and set n SOFC Set to 0 and jump to S2; if each fixed abandonment rate in the array makes the objective function solvable, jump to S4; S4. Compare all retained local optimal solutions and select the better ones to form an optimal solution set. The decision variable values ​​corresponding to the optimal solution set are the optimal capacity configuration results.

2. The nonlinear multi-objective capacity allocation optimization method according to claim 1, characterized in that: In S1, initialize the number of PVs n PV,ini The calculation method is: Wherein, Life represents the life cycle of the island microgrid in years; * represents a multiplication operation; represents the user load power at time t; It represents the power generation of a single photovoltaic power generation unit PV at time t.

3. The nonlinear multi-objective capacity allocation optimization method according to claim 1 or 2, characterized in that: The island microgrid further comprises: a lithium battery energy storage unit, a hydrogen production unit and a hydrogen energy storage unit; The life cycle cost function LCC of the island microgrid is: LCC=Cap all +OM all Among them, Cap all is the total capital cost, OM all is the total operation and maintenance cost; Cap all and OM all The calculation method is: Where, Life represents the life cycle of the island microgrid, in years; * represents multiplication operation; n PV 、C LB 、 n SOFC and is the decision variable, C LB and Respectively represent the rated capacity of the lithium battery energy storage unit and the hydrogen energy storage unit, Indicates the rated power of the hydrogen production unit; C PV,cap 、C LB,cap 、C ele,cap 、C SOFC,cap and C HSS,cap are the capital cost coefficients of PV, lithium battery energy storage unit, hydrogen production unit, SOFC system and hydrogen energy storage unit respectively; and are decision variables, representing the power generation of a single PV unit, the abandoned solar power, the charging power and discharging power of the lithium battery energy storage unit, the power generation of a single SOFC unit, and the hydrogen production power of the hydrogen production unit at time t; C PV,OM 、C LB,OM 、C SOFC,OM 、C ele,OM and C HSS,OM is the operation and maintenance cost coefficient of PV, lithium battery energy storage unit, SOFC system, hydrogen production unit and hydrogen energy storage unit.

4. The nonlinear multi-objective capacity allocation optimization method according to claim 3, characterized in that: The operation constraints also include: power balance constraints, upper and lower power constraints of each unit, and upper and lower capacity constraints of each energy storage unit; the units include PV, SOFC system, lithium battery energy storage unit, hydrogen production unit and hydrogen energy storage unit; the energy storage units include lithium battery energy storage unit and hydrogen energy storage unit; The power balance constraint is: in, Represents the user load power at time t.

5. The nonlinear multi-objective capacity allocation optimization method according to claim 1, characterized in that: In S4, all retained local optimal solutions are compared, and the better ones are selected to form the optimal solution set, including: For any two retained local optimal solutions A(LCC i , R i )、B(LCC j , R j ), where LCC i and LCC j Represent the full life cycle costs in the local optimal solutions A and B, R i and R j They represent the abandonment rates in the local optimal solutions A and B respectively, and the comparison method is: If LCC i >LCC j And R i >R j , then keep the local optimal solution A and discard the local optimal solution B; If LCC i <LCC j And R i <R j , then keep the local optimal solution B and discard the local optimal solution A; If LCC i >LCC j And R i <R j , then retain the local optimal solutions A and B; If LCC i <LCC j And R i >R j , then retain the local optimal solutions A and B; If LCC i =LCC j And R i =R j , then retain the local optimal solutions A and B; All retained local optimal solutions are traversed, and the final set of local optimal solutions obtained is the optimal solution set.

6. A nonlinear multi-objective capacity configuration optimization device for hydrogen-powered isolated island microgrid, characterized in that: Used to execute the nonlinear multi-objective capacity configuration optimization method according to any one of claims 1 to 5, the isolated island microgrid includes a photovoltaic power generation unit PV and a SOFC system, the PV is used to provide new energy power generation for the isolated island microgrid, and the SOFC system is used to convert hydrogen energy into electrical energy, and the nonlinear multi-objective capacity configuration optimization device includes: A capacity configuration model construction module is used to construct a capacity configuration model with the minimum life cycle cost and the minimum curtailment rate of the island microgrid as the objective function; Run the constraint building module to build the maximum abandonment rate R max The operation constraints of the island microgrid including the constraints; A solution module, configured to solve the capacity configuration model under the operation constraints, comprising: S1. Initialize the number of PVs to n PV,ini , the number of SOFC units is 0; and the maximum abandoned light rate R max Divided into steps from 0 to R according to the preset step size max An array of multiple fixed light abandonment rates; S2, at the current PV number n PV and the number of SOFC units n SOFC In this case, each fixed abandoned light rate in the array is used as a constraint of the objective function, and linear programming is used to solve the objective function under the corresponding constraints; S3. If there is a fixed abandoned light rate in the array that makes the objective function solvable, then retain the local optimal solution and the corresponding decision variable value, and set n SOFC Add one and jump to S2; if each fixed abandonment rate in the array makes the objective function unsolvable, then n PV Add one and set n SOFC Set to 0 and jump to S2; if each fixed abandonment rate in the array makes the objective function solvable, jump to S4; S4. Compare all retained local optimal solutions and select the better ones to form an optimal solution set. The decision variable values ​​corresponding to the optimal solution set are the optimal capacity configuration results.

7. An electronic device, characterized in that: comprising a computer-readable storage medium and a processor; The computer-readable storage medium is used to store executable instructions; The processor is configured to read the executable instructions stored in the computer-readable storage medium to execute the nonlinear multi-objective capacity configuration optimization method according to any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the nonlinear multi-objective capacity configuration optimization method according to any one of claims 1 to 5 is implemented.

9. A computer program product, characterized in that When the computer program product is run on a computer, the computer is enabled to execute the nonlinear multi-objective capacity configuration optimization method according to any one of claims 1 to 5.

Images