Energy storage full life cycle optimization configuration method based on cluster division

Abstract

The invention discloses an energy storage full-life-cycle optimal configuration method based on cluster division, and belongs to the technical field of energy storage planning, and the method comprises the steps: inputting obtained equipment parameters and algorithm parameters into an energy storage double-layer planning model, and obtaining a final energy storage configuration scheme, wherein the energy storage double-layer planning model comprises an upper-layer planning model and a lower-layer planning model; in the upper-layer planning model, the following steps are executed: an initial parent population is generated according to equipment parameters and algorithm parameters, and each population comprises a plurality of energy storage configuration schemes; calculating the fitness value of each parent population, performing crossover and mutation operation on the parent populations to generate a new offspring population, and calculating the fitness value of the offspring population; the fitness value of the child population is selected to update the fitness value of the parent population, and after iteration is performed for set times, a final energy storage configuration scheme is obtained; the method can give full play to the regional autonomous capability of the power distribution network, enhances the new energy consumption capability of the power distribution network, and improves the operation economy of the power distribution network.

Description

Technical Field

[0001] The invention relates to a full life cycle optimization configuration method for energy storage based on cluster division, and belongs to the technical field of energy storage planning. Background Art

[0002] With the global energy transformation and the increasing complexity of the power system, the distribution network is facing unprecedented challenges. In particular, the large-scale access of renewable energy, although it helps to reduce carbon emissions, is large in number and dispersed in location, which is prone to local line flow reversal and even reverse flow overload. At the same time, its intermittent and uncertain nature also brings new challenges to the stable operation of the power grid. Energy storage technology has become the key to alleviating this problem. By controlling the power grid by cluster control, it is possible to achieve local energy consumption between regions, reduce the impact of power flow between regions on the power grid, and thus reduce the occurrence of reverse flow overload. Existing research on the control strategy of energy storage participating in distributed new energy cluster division is relatively insufficient, and it is difficult to take into account the economic efficiency of energy storage operation and the advantages of cluster division control. Therefore, in order to give full play to the self-control ability of the distribution network area, a full life cycle optimization configuration method of energy storage based on cluster division is proposed. Summary of the invention

[0003] The purpose of the present invention is to overcome the deficiencies in the prior art and provide a method for optimizing the configuration of energy storage throughout its life cycle based on cluster division, which can give full play to the regional autonomy of the distribution network, enhance the new energy absorption capacity of the distribution network, and improve the economy of the distribution network operation.

[0004] To achieve the above object, the present invention is implemented by adopting the following technical solutions: In a first aspect, the present invention provides a method for optimizing the configuration of energy storage throughout its life cycle based on cluster division, comprising: Input the acquired equipment parameters and algorithm parameters into a pre-built energy storage two-layer planning model to obtain a final energy storage configuration plan, wherein the energy storage two-layer planning model includes an upper-layer planning model and a lower-layer planning model; In the upper-level planning model, the following steps are performed: Generate an initial parent population based on device parameters and algorithm parameters, each population containing multiple energy storage configuration schemes; Calculate the fitness value of each parent population, perform crossover and mutation operations on the parent population, generate a new child population, and calculate the fitness value of the child population; Select the fitness value of the offspring population to update the fitness value of the parent population, and after iterating a set number of times, obtain the final energy storage configuration plan; In the lower-level planning model, the following steps are performed: According to the equipment parameters and algorithm parameters, Gurobi is called to solve the second-order cone programming problem in parallel to obtain the annual operating cost of the equipment; The annual operating cost of the equipment is returned to the upper-level planning model to improve the fitness values ​​of the parent and child populations.

[0005] Furthermore, the expression of the upper-level planning model is: ; ; in, The total investment cost of the energy storage system, including the battery cost, power conversion system cost and auxiliary equipment cost; The cost of replacing the energy storage system, including the cost of replacing the battery and power conversion system if the life cycle does not meet the full project life cycle; The cost of handling the energy storage system when it is shut down at the end of its life; Recover revenue for energy storage systems; To delay the benefits of grid expansion; is the operating cost of the energy storage system, which is the objective function of the lower-level planning model; in, and are the rated capacity and rated power of the energy storage system respectively; is the unit capacity cost of the energy storage system; is the unit power cost of the energy storage system; The unit capacity cost of auxiliary facilities of the energy storage system; is the discount rate; N For the project cycle; The total number of battery replacements; i For the replacement order; n For battery life; is the unit power handling cost; is the recovery rate; Delaying grid capacity expansion for energy storage; The investment cost of expanding the unit capacity of traditional power grid lines; The number of years to delay grid upgrades.

[0006] Furthermore, the constraints of the upper-level planning model are as follows: ; in, Indicates j The capacity of each energy storage configuration; Indicates the maximum capacity of energy storage allowed to be configured; Indicates j The power of each energy storage configuration; Indicates the maximum power allowed for energy storage configuration.

[0007] Furthermore, the expression of the lower-level planning model is: ; ; ; in represents the lower-level planning model, a is the overall modularity index; b It is the active power balance index; m is the sum of all edge weights of the distribution network; and Percentile and node i and nodes j The sum of the edge weights of connected edges; c is the total number of clusters; For partitioning; For Node i With Node j The electrical distance between for t Moment k Clusters t Net power value at the moment; T is the scene time scale; and are the weight coefficients of the overall modularity and active power balance, respectively, and .

[0008] Furthermore, the objective function of the lower-level planning model is as follows: ; ; in, Cost of curtailing wind and solar power; and Wind power and photovoltaic power t The predicted output at the moment; and Wind power and photovoltaic power t Actual output at the moment; is the system congestion cost; for t Active power change at every moment; For line exist t Active power transmitted at any moment; To focus on the threshold; For line transmission capacity; and are the number of wind power and photovoltaic power respectively; and They are the unit electricity price of wind power abandonment and the unit electricity price of photovoltaic power abandonment respectively; is the unit time; It is the system peak electricity price.

[0009] Furthermore, the constraints of the lower-level planning model are as follows: Power flow constraints: ; in, For line exist t The transmission power at the moment; For The set of all line end nodes from the starting point; For is the set of all nodes of the starting point; for t Timeline ki The square of the transmitted current; For line ki The resistance of for t Time Node The injected power; , , They are t Wind power output, photovoltaic output and load at each moment; and They are t It is the charging power and discharging power of energy storage at all times; for t Time Node j The square of the voltage; M is a sufficiently large positive number; is a 0-1 variable; For line ki exist t The transmission power at the moment; for t Time Node The square of the voltage; For line The resistance of for t Timeline The square of the transmitted current; Branch power limitation: ; Wind power output constraints: ; Photovoltaic output constraints: ; Energy storage output constraints: ; ; ; ; ; in, for The actual output of energy storage at all times; and for Energy storage charging and discharging power at all times; Maximum output for energy storage; for Energy storage capacity at any time; and They are Energy storage charging and discharging efficiency at all times; The depth of energy storage charge and discharge; The maximum energy storage capacity.

[0010] In a second aspect, the present invention provides a device for optimizing the configuration of energy storage throughout its life cycle based on cluster division, comprising: A configuration module, used to input the acquired device parameters and algorithm parameters into a pre-built energy storage two-layer planning model to obtain a final energy storage configuration solution, wherein the energy storage two-layer planning model includes an upper-layer planning model and a lower-layer planning model; The upper-level planning model execution module is used to perform the following steps: Generate an initial parent population based on device parameters and algorithm parameters, each population containing multiple energy storage configuration schemes; Calculate the fitness value of each parent population, perform crossover and mutation operations on the parent population, generate a new child population, and calculate the fitness value of the child population; Select the fitness value of the offspring population to update the fitness value of the parent population, and after iterating a set number of times, obtain the final energy storage configuration plan; The lower-level planning model execution module is used to perform the following steps: According to the equipment parameters and algorithm parameters, Gurobi is called to solve the second-order cone programming problem in parallel to obtain the annual operating cost of the equipment; The annual operating cost of the equipment is returned to the upper-level planning model to improve the fitness values ​​of the parent and child populations.

[0011] In a third aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the aforementioned methods.

[0012] In a fourth aspect, the present invention provides a computer device, comprising: Memory, for storing computer programs / instructions; A processor is used to execute the computer program / instructions to implement the steps of any of the aforementioned methods.

[0013] In a fifth aspect, the present invention provides a computer program product, comprising a computer program / instruction, which implements the steps of any one of the aforementioned methods when executed by a processor.

[0014] Compared with the prior art, the present invention has the following beneficial effects: The present invention provides a method for optimizing the configuration of energy storage throughout its life cycle based on cluster division. By constructing a two-layer planning model for energy storage, the method optimizes the configuration of energy storage throughout its life cycle, which can give full play to the regional autonomy of the distribution network, enhance the new energy consumption capacity of the distribution network, and improve the economy of the distribution network operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a flow chart of a method for optimizing configuration of energy storage throughout its life cycle based on cluster division provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0016] The present invention will be further described below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and cannot be used to limit the protection scope of the present invention.

[0017] Embodiment 1, as Figure 1 As shown, this embodiment introduces a method for optimizing the configuration of energy storage throughout its life cycle based on cluster division, including: Input the acquired equipment parameters and algorithm parameters into a pre-built energy storage two-layer planning model to obtain a final energy storage configuration plan, wherein the energy storage two-layer planning model includes an upper-layer planning model and a lower-layer planning model; In the upper-level planning model, the following steps are performed: Generate an initial parent population based on device parameters and algorithm parameters, each population containing multiple energy storage configuration schemes; Calculate the fitness value of each parent population, perform crossover and mutation operations on the parent population, generate a new child population, and calculate the fitness value of the child population; Select the fitness value of the offspring population to update the fitness value of the parent population, and after iterating a set number of times, obtain the final energy storage configuration plan; In the lower-level planning model, the following steps are performed: According to the equipment parameters and algorithm parameters, Gurobi is called to solve the second-order cone programming problem in parallel to obtain the annual operating cost of the equipment; The annual operating cost of the equipment is returned to the upper-level planning model to improve the fitness values ​​of the parent and child populations.

[0018] The energy storage life cycle optimization configuration method based on cluster division provided in this embodiment specifically involves the following steps in its application process: (1) Construct an investment planning model for the entire life cycle of upper-layer energy storage; In the step (1) of constructing the upper-level energy storage full life cycle investment planning model, the upper-level planning model is responsible for solving the location and capacity of the fixed energy storage connected to the distribution network, so as to achieve the goal of minimizing the annual comprehensive cost of the distribution network operation and energy storage investment including distributed new energy. The specific expression is: ; ; in, The total investment cost of the energy storage system, including the battery cost, power conversion system cost and auxiliary equipment cost; The cost of replacing the energy storage system, including the cost of replacing the battery and power conversion system if the life cycle does not meet the full project life cycle; The cost of handling the energy storage system when it is shut down at the end of its life; Recover revenue for energy storage systems; To delay the benefits of grid expansion; is the operating cost of the energy storage system, which is the objective function of the lower-level planning model; in, and are the rated capacity and rated power of the energy storage system respectively; is the unit capacity cost of the energy storage system; is the unit power cost of the energy storage system; The unit capacity cost of auxiliary facilities of the energy storage system; is the discount rate; N For the project cycle; The total number of battery replacements; i For the replacement order; n For battery life; is the unit power handling cost; is the recovery rate; Delaying grid capacity expansion for energy storage; The investment cost of expanding the unit capacity of traditional power grid lines; The number of years to delay grid upgrades.

[0019] The constraints are as follows: ; in, Indicates j The capacity of each energy storage configuration; Indicates the maximum capacity of energy storage allowed to be configured; Indicates j The power of each energy storage configuration; Indicates the maximum power allowed for energy storage configuration.

[0020] (2) Constructing a lower-level energy storage optimization operation model based on cluster division; The step (2) constructs a lower layer energy storage optimization operation model based on cluster division. The lower layer model optimizes the operation strategy of the energy storage system in the distribution network. On the basis of cluster division, the energy storage operation is optimized to achieve the best economic efficiency with the goal of absorbing new energy.

[0021] First, the cluster division model is established by integrating the overall modularity and active power balance of the distribution network: ; ; ; in represents the lower-level planning model, a is the overall modularity index; b It is the active power balance index; m is the sum of all edge weights of the distribution network; and Percentile and node i and nodes j The sum of the edge weights of connected edges; c is the total number of clusters; For partitioning; For Node i With Node j The electrical distance between for t Moment k Clusters t Net power value at the moment; T is the scene time scale; and are the weight coefficients of the overall modularity and active power balance, respectively, and .

[0022] On the basis of this cluster division, the optimal economic efficiency of energy storage operation is optimized with the goal of alleviating system congestion and absorbing new energy. The objective function is as follows: ; ; in, Cost of curtailing wind and solar power; and Wind power and photovoltaic power t The predicted output at the moment; and Wind power and photovoltaic power t Actual output at the moment; is the system congestion cost; for t Active power change at every moment; For line exist t Active power transmitted at any moment; To focus on the threshold; For line transmission capacity; and are the number of wind power and photovoltaic power respectively; and They are the unit electricity price of wind power abandonment and the unit electricity price of photovoltaic power abandonment respectively; is the unit time; It is the system peak electricity price.

[0023] The constraints are as follows: (1) Power flow constraints: ; in, For line exist t The transmission power at the moment; For The set of all line end nodes from the starting point; For is the set of all nodes of the starting point; for t Timeline ki The square of the transmitted current; For line ki The resistance of for t Time Node The injected power; , , They are t Wind power output, photovoltaic output and load at each moment; and They are t It is the charging power and discharging power of energy storage at all times; for t Time Node jThe square of the voltage; M is a sufficiently large positive number; is a 0-1 variable; For line ki exist t The transmission power at the moment; for t Time Node The square of the voltage; For line The resistance of for t Timeline The square of the transmitted current; (2) Branch power limitation: ; (3) Wind power output constraints: ; (4) Photovoltaic output constraints: ; (5) Energy storage output constraints: ; ; ; ; ; in, for The actual output of energy storage at all times; and for Energy storage charging and discharging power at all times; Maximum output for energy storage; for Energy storage capacity at any time; and They are Energy storage charging and discharging efficiency at all times; The depth of energy storage charge and discharge; The maximum energy storage capacity.

[0024] (3) Propose a solution method for the double-layer energy storage model; The step (3) proposes a method for solving the energy storage double-layer model. The above-mentioned energy storage double-layer planning model is solved by upper and lower layer patching bags. In the upper model, an improved genetic algorithm is used to generate configuration scheme individuals through crossover and mutation operations, and then the configuration schemes with high fitness values ​​are selected through the selection operator, and the planning model is solved iteratively in sequence; for the lower model, the nonlinear constraints in the tidal equation have been relaxed by the second-order cone, and then the ProcessPoolExecutor module in Python is used to call Gurobi to solve the mixed integer second-order cone programming problems in each scenario of the lower layer in parallel to accelerate the calculation. After the solution, the operating cost of each scenario is returned to the upper layer to calculate the fitness value of the individual in the improved AGA. The overall solution process is as follows: Figure 1 shown.

[0025] Embodiment 2: This embodiment provides a device for optimizing the configuration of energy storage throughout its life cycle based on cluster division, including: A configuration module, used to input the acquired device parameters and algorithm parameters into a pre-built energy storage two-layer planning model to obtain a final energy storage configuration solution, wherein the energy storage two-layer planning model includes an upper-layer planning model and a lower-layer planning model; The upper-level planning model execution module is used to perform the following steps: Generate an initial parent population based on device parameters and algorithm parameters, each population containing multiple energy storage configuration schemes; Calculate the fitness value of each parent population, perform crossover and mutation operations on the parent population, generate a new child population, and calculate the fitness value of the child population; Select the fitness value of the offspring population to update the fitness value of the parent population, and after iterating a set number of times, obtain the final energy storage configuration plan; The lower-level planning model execution module is used to perform the following steps: According to the equipment parameters and algorithm parameters, Gurobi is called to solve the second-order cone programming problem in parallel to obtain the annual operating cost of the equipment; The annual operating cost of the equipment is returned to the upper-level planning model to improve the fitness values ​​of the parent and child populations.

[0026] The specific functional implementation of each of the above modules can be found in the relevant contents of the method in Example 1 and will not be elaborated here.

[0027] Embodiment 3, this embodiment provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the steps of any one of the methods described in Embodiment 1 are implemented.

[0028] Embodiment 4: This embodiment provides a computer device, including: Memory, for storing computer programs / instructions; A processor, configured to execute the computer program / instructions to implement the steps of any one of the methods described in Example 1.

[0029] Embodiment 5: This embodiment provides a computer program product, including a computer program / instruction, which implements the steps of any method described in Embodiment 1 when executed by a processor.

[0030] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

[0031] It should be understood by those skilled in the art that the embodiments of the present disclosure may be provided as methods, systems or computer program products. Therefore, the present disclosure may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware aspects. Moreover, the present disclosure may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0032] The present disclosure is described with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present disclosure. It should be understood that each process and / or block in the flowchart and / or block diagram and the combination of processes and / or blocks in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0033] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0034] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0035] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present disclosure rather than to limit its protection scope. Although the present disclosure has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that after reading the present disclosure, those skilled in the art can still make various changes, modifications or equivalent substitutions to the specific implementation methods of the invention, but these changes, modifications or equivalent substitutions are all within the protection scope of the disclosed claims to be approved.

Claims

1. A method for optimizing the configuration of energy storage throughout its life cycle based on cluster division, characterized in that: include: Input the acquired equipment parameters and algorithm parameters into a pre-built energy storage two-layer planning model to obtain a final energy storage configuration plan, wherein the energy storage two-layer planning model includes an upper-layer planning model and a lower-layer planning model; In the upper-level planning model, the following steps are performed: Generate an initial parent population based on device parameters and algorithm parameters, each population containing multiple energy storage configuration schemes; Calculate the fitness value of each parent population, perform crossover and mutation operations on the parent population, generate a new child population, and calculate the fitness value of the child population; Select the fitness value of the offspring population to update the fitness value of the parent population, and after iterating a set number of times, obtain the final energy storage configuration plan; In the lower-level planning model, the following steps are performed: According to the equipment parameters and algorithm parameters, Gurobi is called to solve the second-order cone programming problem in parallel to obtain the annual operating cost of the equipment; The annual operating cost of the equipment is returned to the upper-level planning model to improve the fitness values ​​of the parent and child populations.

2. The energy storage full life cycle optimization configuration method based on cluster division according to claim 1 is characterized in that: The expression of the upper-level planning model is: ； ； in, The total investment cost of the energy storage system, including the battery cost, power conversion system cost and auxiliary equipment cost; The cost of replacing the energy storage system, including the cost of replacing the battery and power conversion system if the life cycle does not meet the full project life cycle; The cost of handling the energy storage system when it is shut down at the end of its life; Recover revenue for energy storage systems; To delay the benefits of grid expansion; is the operating cost of the energy storage system, which is the objective function of the lower-level planning model; in, and are the rated capacity and rated power of the energy storage system respectively; is the unit capacity cost of the energy storage system; is the unit power cost of the energy storage system; The unit capacity cost of auxiliary facilities of the energy storage system; is the discount rate; N For the project cycle; The total number of battery replacements; i For the replacement order; n For battery life; is the unit power handling cost; is the recovery rate; Delaying grid capacity expansion for energy storage; The investment cost of expanding the unit capacity of traditional power grid lines; The number of years to delay grid upgrades.

3. The energy storage full life cycle optimization configuration method based on cluster division according to claim 1 is characterized in that: The constraints of the upper-level planning model are as follows: ； in, Indicates j The capacity of each energy storage configuration; Indicates the maximum capacity of energy storage allowed to be configured; Indicates j The power of each energy storage configuration; Indicates the maximum power allowed for energy storage configuration.

4. The energy storage full life cycle optimization configuration method based on cluster division according to claim 1 is characterized in that: The expression of the lower-level planning model is: ； ； ； in represents the lower-level planning model, a is the overall modularity index; b It is the active power balance index; m is the sum of all edge weights of the distribution network; and Percentile and node i and nodes j The sum of the edge weights of connected edges; c is the total number of clusters; For partitioning; For Node i With Node j The electrical distance between for t Moment k Clusters t Net power value at the moment; T is the scene time scale; and are the weight coefficients of the overall modularity and active power balance, respectively, and .

5. The energy storage full life cycle optimization configuration method based on cluster division according to claim 1 is characterized in that: The objective function of the lower-level planning model is as follows: ； ； in, Cost of curtailing wind and solar power; and Wind power and photovoltaic power t The predicted output at the moment; and Wind power and photovoltaic power t Actual output at the moment; is the system congestion cost; for t Active power change at every moment; For Line exist t Active power transmitted at any moment; To focus on the threshold; For Line transmission capacity; and are the number of wind power and photovoltaic power respectively; and They are the unit electricity price of wind power abandonment and the unit electricity price of photovoltaic power abandonment respectively; is the unit time; It is the system peak electricity price.

6. The energy storage full life cycle optimization configuration method based on cluster division according to claim 5 is characterized in that: The constraints of the lower-level planning model are as follows: Power flow constraints: ； in, For Line exist t The transmission power at the moment; For The set of all line end nodes from the starting point; For is the set of all nodes of the starting point; for t Timeline ki The square of the transmitted current; For Line ki Resistance; for t Time Node The injected power; , , They are t Wind power output, photovoltaic output and load at each moment; and They are t It is the charging power and discharging power of energy storage at all times; for t Time Node j The square of the voltage; M is a sufficiently large positive number; is a 0-1 variable; For Line ki exist t The transmission power at the moment; for t Time Node The square of the voltage; For Line Resistance; for t Timeline The square of the transmitted current; Branch power limitation: ； Wind power output constraints: ； Photovoltaic output constraints: ； Energy storage output constraints: ； ； ； ； ； in, for The actual output of energy storage at all times; and for Energy storage charging and discharging power at all times; Maximum output for energy storage; for Energy storage capacity at any time; and They are Energy storage charging and discharging efficiency at all times; The depth of energy storage charge and discharge; The maximum energy storage capacity.

7. A device for optimizing the configuration of energy storage throughout its life cycle based on cluster division, characterized in that: include: A configuration module, used to input the acquired device parameters and algorithm parameters into a pre-built energy storage two-layer planning model to obtain a final energy storage configuration solution, wherein the energy storage two-layer planning model includes an upper-layer planning model and a lower-layer planning model; The upper-level planning model execution module is used to perform the following steps: Generate an initial parent population based on device parameters and algorithm parameters, each population containing multiple energy storage configuration schemes; Calculate the fitness value of each parent population, perform crossover and mutation operations on the parent population, generate a new child population, and calculate the fitness value of the child population; Select the fitness value of the offspring population to update the fitness value of the parent population, and after iterating a set number of times, obtain the final energy storage configuration plan; The lower-level planning model execution module is used to perform the following steps: According to the equipment parameters and algorithm parameters, Gurobi is called to solve the second-order cone programming problem in parallel to obtain the annual operating cost of the equipment; The annual operating cost of the equipment is returned to the upper-level planning model to improve the fitness values ​​of the parent and child populations.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method described in any one of claims 1 to 6 are implemented.

9. A computer device, characterized in that: include: Memory, for storing computer programs / instructions; A processor, configured to execute the computer program / instructions to implement the steps of the method according to any one of claims 1 to 6.

10. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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