Microgrid planning and processing method and device

Abstract

The present invention discloses a method and device for planning and processing a microgrid. The method includes: obtaining cost data and equipment operation data of each planned device in the target microgrid; inputting the cost data and equipment operation data into the microgrid planning model, so as to process the cost data and equipment operation data using the microgrid planning model, so as to obtain the output result of the microgrid planning model; determining the planning strategy of the target microgrid according to the output result, wherein the planning strategy includes: the number of each planned device, the planned location of each planned device; planning and processing the target microgrid according to the planning strategy within a predetermined area. The present invention solves the technical problem that the microgrid planning method in the related technology considers a single factor, resulting in a low ability of the microgrid to resist external interference and potential safety hazards.

Description

Technical Field

[0001] The present invention relates to the field of power grid safety technology, and in particular to a microgrid planning and processing method and device. Background Art

[0002] Microgrid is a small power generation-distribution-consumption system that integrates multiple distributed energy sources, energy storage systems and loads. It is very important as an important means to promote the distributed grid connection and consumption of renewable energy. At the same time, it is of great significance to achieve the low-carbon transformation of the park's energy through the low-carbon planning and design of distributed power sources, energy storage and other equipment in the park's microgrid system. In addition, affected by climate change, the frequency of natural disasters has increased, and various extreme disaster events have caused frequent distribution network failures, which may cause the microgrid to be in an isolated operation state; moreover, the distributed renewable energy in the microgrid is very susceptible to natural disasters and shut down. In order to ensure the support of important loads in the microgrid under emergency conditions, it is necessary to configure sufficient emergency reserves for the microgrid to cope with the impact of low-probability, high-impact events. Energy supply reliability, as an important indicator to measure the ability of microgrids to cope with emergencies, should also be considered in the planning and design of microgrids.

[0003] Existing microgrid planning methods mainly focus on investment economics, how to take into account the comprehensive performance of planning schemes in terms of investment cost, operating cost and energy utilization, and optimize the decision-making of microgrids in long-term investment and short-term operation scheduling. In addition to considering economic costs, some studies also take park carbon emissions as an objective function to achieve low-carbon planning of park microgrids.

[0004] However, there is no research that considers the three goals of economic benefits, carbon emission reduction and energy supply reliability at the same time to plan and design the park microgrid, which leads to the planning decision-making scheme focusing too much on economy and ignoring the optimization of low-carbon performance and the ability to resist external interference in emergency situations. In addition, for the solution of multi-objective problems, the conventional approach is to transform the multi-objective optimization problem into a single-objective optimization problem through a linear weighted method. This method is simple and convenient and is widely used. However, this method lacks a theoretical basis, and the weights of each goal cannot be accurately set, resulting in a certain loss and omission of information on the original goal, and it is impossible to guarantee the optimal solution. Another common idea is to use the Pareto model, which does not need to set weight parameters and operates directly based on the original objective function without losing information on the objective function. However, the optimal solution of the Pareto model is a set containing multiple optimal solutions, and it is difficult to find all Pareto optimal solutions.

[0005] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0006] The embodiments of the present invention provide a microgrid planning and processing method and device, so as to at least solve the technical problem that the microgrid planning method in the related art considers a single factor, resulting in the microgrid's low ability to resist external interference and the existence of safety hazards.

[0007] According to one aspect of an embodiment of the present invention, a method for planning and processing a microgrid is provided, comprising: obtaining cost data and device operation data of each planned device in a target microgrid, wherein the target microgrid is a microgrid that needs to be planned in a predetermined area; inputting the cost data and the device operation data into a microgrid planning model, so as to process the cost data and the device operation data using the microgrid planning model, so as to obtain an output result of the microgrid planning model, wherein the microgrid planning model is a model pre-trained by machine learning using multiple sets of training data, each of the multiple sets of training data comprising: sample cost data and sample device operation data, and sample output results corresponding to the sample cost data and the sample device operation data; determining a planning strategy for the target microgrid based on the output result, wherein the planning strategy comprises: the number of each of the planned devices and the planned location of each of the planned devices; and planning and processing the target microgrid in the predetermined area according to the planning strategy.

[0008] Optionally, the microgrid planning model includes a microgrid investment cost function, wherein the microgrid investment cost function is: , represents the total investment cost, represents the investment cost of the photovoltaic equipment in the planned equipment, represents the investment cost of the gas turbine in the planned plant, represents the investment cost of the electric energy storage device in the planning device; the microgrid planning model includes a microgrid operation cost objective function, wherein the microgrid operation cost objective function is: , represents the total operating cost, represents the annual operating cost of the photovoltaic equipment, represents the annual operating cost of the gas turbine, represents the annual operating cost of the energy storage device, represents the cost of purchasing electricity, Indicates the cost of abandoned light.

[0009] Optionally, the microgrid planning model includes a carbon emission objective function, wherein the carbon emission objective function is: , represents the total carbon emission cost, Indicates the equivalent carbon emissions generated by the purchased electricity, represents the carbon emissions generated by the power generation of the electric energy storage device in the planning device; the microgrid planning model includes an energy supply reliability objective function, wherein the energy supply reliability objective function is: , Indicates the number of days in each season, Represents the carbon emission cost at time t in scenario S.

[0010] Optionally, before inputting the cost data and the device operation data into the microgrid planning model to process the cost data and the device operation data using the microgrid planning model to obtain the output result of the microgrid planning model, the microgrid planning processing method further includes: generating constraints of the microgrid planning model; wherein generating the constraints of the microgrid planning model includes: generating the quantity constraints of the planned device according to the configuration quantity of photovoltaic devices, the configuration quantity of gas turbines, and the configuration quantity of energy storage devices in the planned device, wherein the quantity constraint formula corresponding to the quantity constraint of the planned device is: , represents the configuration quantity of the photovoltaic device, represents the configuration quantity of the gas turbine, represents the configuration quantity of the energy storage device, The maximum total number of configurations of the planned equipment; generating a power balance constraint of the microgrid planning model according to the output of the photovoltaic equipment, the output of the energy storage equipment, and the charging and discharging power of the gas turbine, wherein the balanced power constraint formula corresponding to the power balance constraint is: , represents the output of the photovoltaic device at time t in scene S, represents the output of the energy storage device at time t in scenario S, represents the discharge power of the gas turbine at time t in scenario S, represents the charging power of the gas turbine at time t in scenario S, represents the predicted load at time t; generating the distributed power output constraint of the microgrid planning model according to the output of the photovoltaic device and the output of the energy storage device, wherein the distributed power output constraint formula corresponding to the distributed power output constraint is: , represents the upper limit of the active output of the photovoltaic device at time t in scenario S, represents the amount of abandoned light at time t in scene S, represents the upper limit of active power output of the gas turbine.

[0011] Optionally, generating the constraint condition of the microgrid planning model includes: generating energy storage constraints of the energy storage device, wherein the energy storage constraints are: , Indicates whether the gas turbine is charged at time t in scenario S, Indicates whether the gas turbine is discharging at time t in scenario S, represents the upper limit of the charging rated power of the gas turbine, represents the upper limit of the discharge rated power of the gas turbine, represents the power consumption of the gas turbine at time t in scenario S, represents the charging efficiency of the gas turbine, represents the discharge efficiency of the gas turbine, represents the lower limit of the gas turbine in the state of charge, represents the upper limit of the gas turbine under the state of charge; generates the spare capacity constraint of the microgrid planning model, wherein the spare capacity constraint is: , represents the spare capacity of the gas turbine in the charging state at time t in scenario S, represents the reserve capacity of the gas turbine in the discharging state at time t in scenario S, represents the charging power of the gas turbine at time t in scenario S, represents the discharge power of the gas turbine at time t in scenario S; generates the planning-operation coupling constraint of the microgrid planning model, wherein the planning-operation coupling constraint is: , represents the predicted output of a single photovoltaic device at time t in scenario S, Indicates the upper limit of the power of a single energy storage device. represents the rated power of a single gas turbine, Indicates the rated capacity of a single gas turbine.

[0012] Optionally, after generating the constraint conditions of the microgrid planning model, the microgrid planning processing method further includes: linearizing the nonlinear constraint conditions in the constraint conditions; and linearizing the objective function in the microgrid planning model.

[0013] Optionally, before linearizing the objective function in the microgrid planning model, the microgrid planning processing method further includes: obtaining a first objective function in the objective function according to the investment cost, operation cost and carbon emission cost of the target microgrid, wherein the first objective function is: , represents the unit carbon emission penalty cost, f1 represents the first objective function; a second objective function in the objective function is generated according to the reliability index of the target microgrid, wherein the second objective function is: , f2 represents the second objective function, and Q represents the reliability index.

[0014] Optionally, the microgrid planning and processing method further includes: converting the microgrid planning model into a mixed integer linear programming model.

[0015] According to another aspect of an embodiment of the present invention, a microgrid planning and processing device is also provided, including: a first acquisition module, used to obtain cost data and device operation data of each planned device in a target microgrid, wherein the target microgrid is a microgrid that needs to be planned in a predetermined area; a first processing module, used to input the cost data and the device operation data into a microgrid planning model, so as to use the microgrid planning model to process the cost data and the device operation data to obtain an output result of the microgrid planning model, wherein the microgrid planning model is a model pre-trained by machine learning using multiple groups of training data, each of the multiple groups of training data includes: sample cost data and sample device operation data, and sample output results corresponding to the sample cost data and the sample device operation data; a first determination module, used to determine the planning strategy of the target microgrid according to the output result, wherein the planning strategy includes: the number of each of the planned devices and the planned location of each of the planned devices; a second processing module, used to plan and process the target microgrid in the predetermined area according to the planning strategy.

[0016] Optionally, the microgrid planning model includes a microgrid investment cost function, wherein the microgrid investment cost function is: , represents the total investment cost, represents the investment cost of the photovoltaic equipment in the planned equipment, represents the investment cost of the gas turbine in the planned plant, represents the investment cost of the electric energy storage device in the planning device; the microgrid planning model includes a microgrid operation cost objective function, wherein the microgrid operation cost objective function is: , represents the total operating cost, represents the annual operating cost of the photovoltaic equipment, represents the annual operating cost of the gas turbine, represents the annual operating cost of the energy storage device, represents the cost of purchasing electricity, Indicates the cost of abandoned light.

[0017] Optionally, the microgrid planning model includes a carbon emission objective function, wherein the carbon emission objective function is: , represents the total carbon emission cost, Indicates the equivalent carbon emissions generated by the purchased electricity, represents the carbon emissions generated by the power generation of the electric energy storage device in the planning device; the microgrid planning model includes an energy supply reliability objective function, wherein the energy supply reliability objective function is: , Indicates the number of days in each season, Represents the carbon emission cost at time t in scenario S.

[0018] Optionally, the planning and processing device of the microgrid further includes: a third processing module, which is used to generate the constraint conditions of the microgrid planning model before inputting the cost data and the equipment operation data into the microgrid planning model to process the cost data and the equipment operation data using the microgrid planning model to obtain the output result of the microgrid planning model; wherein the third processing module includes: a fourth processing unit, which is used to generate the quantity constraint of the planning device according to the configuration quantity of photovoltaic devices, the configuration quantity of gas turbines and the configuration quantity of energy storage devices in the planning device, wherein the quantity constraint formula corresponding to the quantity constraint of the planning device is: , represents the configuration quantity of the photovoltaic device, represents the configuration quantity of the gas turbine, represents the configuration quantity of the energy storage device, The maximum total number of configurations of the planned equipment; a fifth processing unit, used to generate a power balance constraint of the microgrid planning model according to the output of the photovoltaic device, the output of the energy storage device, and the charging and discharging power of the gas turbine, wherein the balanced power constraint formula corresponding to the power balance constraint is: , represents the output of the photovoltaic device at time t in scene S, represents the output of the energy storage device at time t in scenario S, represents the discharge power of the gas turbine at time t in scenario S, represents the charging power of the gas turbine at time t in scenario S, Represents the predicted load at time t; a sixth processing unit, used to generate a distributed power output constraint of the microgrid planning model according to the output of the photovoltaic device and the output of the energy storage device, wherein the distributed power output constraint formula corresponding to the distributed power output constraint is: , represents the upper limit of the active output of the photovoltaic device at time t in scenario S, represents the amount of abandoned light at time t in scene S, represents the upper limit of active power output of the gas turbine.

[0019] Optionally, the third processing module includes: a first processing unit, configured to generate an energy storage constraint of the energy storage device, wherein the energy storage constraint is: , Indicates whether the gas turbine is charged at time t in scenario S, Indicates whether the gas turbine is discharging at time t in scenario S, represents the upper limit of the charging rated power of the gas turbine, represents the upper limit of the discharge rated power of the gas turbine, represents the power consumption of the gas turbine at time t in scenario S, represents the charging efficiency of the gas turbine, represents the discharge efficiency of the gas turbine, represents the lower limit of the gas turbine in the state of charge, represents the upper limit of the gas turbine under the state of charge; a second processing unit is used to generate a spare capacity constraint of the microgrid planning model, wherein the spare capacity constraint is: , represents the spare capacity of the gas turbine in the charging state at time t in scenario S, represents the reserve capacity of the gas turbine in the discharging state at time t in scenario S, represents the charging power of the gas turbine at time t in scenario S, represents the discharge power of the gas turbine at time t in scenario S; a third processing unit, used to generate a planning-operation coupling constraint of the microgrid planning model, wherein the planning-operation coupling constraint is: , represents the predicted output of a single photovoltaic device at time t in scenario S, Indicates the upper limit of the power of a single energy storage device. represents the rated power of a single gas turbine, Indicates the rated capacity of a single gas turbine.

[0020] Optionally, the microgrid planning and processing device also includes: a fourth processing module, used to linearize the nonlinear constraints in the constraints after generating the constraints of the microgrid planning model; and a fifth processing module, used to linearize the objective function in the microgrid planning model.

[0021] Optionally, the microgrid planning and processing device further includes: a sixth processing module, which is used to obtain a first objective function in the objective function according to the investment cost, operation cost and carbon emission cost of the target microgrid before linearizing the objective function in the microgrid planning model, wherein the first objective function is: , represents the unit carbon emission penalty cost, f1 represents the first objective function; a seventh processing module is used to generate a second objective function in the objective function according to the reliability index of the target microgrid, wherein the second objective function is: , f2 represents the second objective function, and Q represents the reliability index.

[0022] Optionally, the microgrid planning processing device further includes: an eighth processing module, used to convert the microgrid planning model into a mixed integer linear programming model.

[0023] According to another aspect of an embodiment of the present invention, a microgrid planning and processing system is further provided. The microgrid planning and processing system uses any one of the microgrid planning and processing methods described above.

[0024] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is further provided, wherein the computer-readable storage medium includes a stored program, wherein the program executes any one of the above-mentioned microgrid planning and processing methods.

[0025] According to another aspect of an embodiment of the present invention, a processor is further provided, wherein the processor is used to run a program, wherein the program executes any one of the above-mentioned microgrid planning and processing methods when running.

[0026] According to another aspect of an embodiment of the present invention, a computer program product is further provided, comprising computer instructions, wherein when the computer instructions are executed by a processor, any one of the above-mentioned microgrid planning and processing methods is executed.

[0027] In an embodiment of the present invention, cost data and device operation data of each planned device in a target microgrid are obtained, wherein the target microgrid is a microgrid that needs to be planned in a predetermined area; the cost data and device operation data are input into a microgrid planning model, so as to use the microgrid planning model to process the cost data and the device operation data to obtain an output result of the microgrid planning model, wherein the microgrid planning model is a model pre-trained by machine learning using multiple sets of training data, each of the multiple sets of training data includes: sample cost data and sample device operation data, and sample output results corresponding to the sample cost data and the sample device operation data; a planning strategy for the target microgrid is determined based on the output result, wherein the planning strategy includes: the number of each planned device and the planned location of each planned device; and the target microgrid is planned and processed according to the planning strategy within the predetermined area. Through the technical solution provided by the present invention, a multi-objective optimization model for microgrid planning is established based on the planned equipment parameters and microgrid load data, and the nonlinear constraints in the model are converted into linear constraints. At the same time, an improved multi-objective problem solving method is adopted, and the converted optimization problem is solved to obtain the configuration scheme of each device in the microgrid, thereby achieving the purpose of improving calculation efficiency, promoting energy conservation and emission reduction of the microgrid while ensuring the planning economy, and improving the technical effect of improving the energy supply reliability of the microgrid, improving the reliability and safety of the microgrid, and thus solving the technical problem that the microgrid planning method in the related technology considers a single factor, resulting in a low ability of the microgrid to resist external interference and the existence of safety hazards. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0029] Figure 1 It is a hardware structure block diagram of a mobile terminal of a microgrid planning and processing method according to an embodiment of the present invention;

[0030] Figure 2 is a flow chart of a microgrid planning and processing method according to an embodiment of the present invention;

[0031] Figure 3 is a flow chart of an optional microgrid planning and processing method according to an embodiment of the present invention;

[0032] Figure 4 is a module schematic diagram of a microgrid planning and processing system according to an embodiment of the present invention;

[0033] Figure 5 is a schematic diagram of a microgrid planning and processing device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0034] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0035] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0036] As introduced in the background technology, the microgrid planning method in the related technology considers only a single factor, resulting in the microgrid's low ability to resist external interference and the technical problem of potential safety hazards. In view of the above defects, a microgrid planning and processing method and device, a microgrid planning and processing system, a computer-readable storage medium, a processor, and a computer program product are provided in the embodiments of the present invention.

[0037] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0038] The method embodiments provided in the embodiments of the present invention can be executed in a mobile terminal, a computer terminal or a similar computing device. Taking running on a mobile terminal as an example, Figure 1 FIG. 1 is a hardware structure block diagram of a mobile terminal of a microgrid planning and processing method according to an embodiment of the present invention. Figure 1 As shown, the mobile terminal may include one or more ( Figure 1 Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data, wherein the mobile terminal may also include a transmission device 106 and an input / output device 108 for communication functions. It can be understood by those skilled in the art that Figure 1The structure shown is only for illustration and does not limit the structure of the mobile terminal. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations shown.

[0039] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the planning and processing method of the microgrid in the embodiment of the present invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, the above method is implemented. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories can be connected to the mobile terminal via a network. Examples of the above-mentioned network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof. The transmission device 106 is used to receive or send data via a network. The above-mentioned specific network examples may include a wireless network provided by a communication provider of the mobile terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0040] According to an embodiment of the present invention, a method embodiment of a microgrid planning and processing method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0041] Figure 2 is a flow chart of a microgrid planning and processing method according to an embodiment of the present invention. Figure 2 As shown, the method comprises the following steps:

[0042] Step S202, obtaining cost data and equipment operation data of each planned equipment in the target microgrid, wherein the target microgrid is a microgrid that needs to be planned in a predetermined area.

[0043] Optionally, the above-mentioned planned equipment may include: photovoltaic equipment (photovoltaic generator, PV), micro-turbine generator (micro-turbine generator, MT) and energy storage system (energy storage system, ESS), the above-mentioned cost data may include: investment cost, operating cost and operating characteristic parameters of a single planned equipment, and the above-mentioned equipment operation data may include: load curve of a typical day of the microgrid and photovoltaic output prediction curve per unit installed capacity.

[0044] It should be noted that other relevant data information can also be obtained according to actual modeling and analysis needs, which will not be elaborated here.

[0045] In this embodiment, the cost data and equipment operation data of each planned equipment in the microgrid that needs to be planned in the predetermined area (ie, the target microgrid) can be obtained to facilitate the use of the data in the subsequent calculation process.

[0046] Step S204, input the cost data and the equipment operation data into the microgrid planning model, so as to use the microgrid planning model to process the cost data and the equipment operation data to obtain the output result of the microgrid planning model, wherein the microgrid planning model is a model pre-trained by machine learning using multiple sets of training data, and each of the multiple sets of training data includes: sample cost data and sample equipment operation data, and sample output results corresponding to the sample cost data and the sample equipment operation data.

[0047] In this embodiment, the acquired cost data and equipment operation data can be input into a model (i.e., a microgrid planning model) that has been pre-trained through machine learning using multiple sets of training data. The model is used to process the data to obtain the output results of the model, which can improve the accuracy and efficiency of the planning and adjust the planning scheme in a timely manner.

[0048] Optionally, each of the above-mentioned multiple groups of training data may include: sample cost data, sample device operation data, and sample output results corresponding to the sample cost data and the sample device operation data.

[0049] It should be noted that the above microgrid planning model is a multi-objective optimization model for microgrid planning.

[0050] Optionally, the multi-objective optimization model may include: a multi-objective function and constraints, wherein the multi-objective function may include: an economic cost objective function, a carbon emission objective function and a reliability objective function.

[0051] Step S206, determining a planning strategy for the target microgrid according to the output result, wherein the planning strategy includes: the number of each planned device and the planned location of each planned device.

[0052] Optionally, the above planning strategy may include: the number of photovoltaic devices, micro gas turbines, and energy storage devices configured in the microgrid, and the planned locations of the photovoltaic devices, micro gas turbines, and energy storage devices configured in the microgrid.

[0053] In this embodiment, the planning strategy of the target microgrid can be determined according to the output results of the microgrid planning model, and the optimal configuration scheme of the microgrid can be determined to achieve efficient operation of the microgrid and optimize energy utilization.

[0054] Step S208: planning the target microgrid in the predetermined area according to the planning strategy.

[0055] Optionally, the above-mentioned predetermined area may include: a city, a community, an industrial park and other specific areas.

[0056] It should be noted that the above-mentioned predetermined area is the geographical area covered by the microgrid, that is, the feasible domain of the target microgrid, which usually has relatively independent electricity demand and energy resources.

[0057] In this embodiment, the target microgrid can be planned and processed in the feasible domain according to the planning strategy, so that various resources of the microgrid can be reasonably configured to help optimize the operation efficiency and economy of the microgrid.

[0058] As can be seen from the above, in an embodiment of the present invention, the cost data and equipment operation data of each planned device in the target microgrid are obtained, wherein the target microgrid is a microgrid that needs to be planned in a predetermined area; the cost data and the equipment operation data are input into the microgrid planning model, so as to use the microgrid planning model to process the cost data and the equipment operation data to obtain the output result of the microgrid planning model, wherein the microgrid planning model is a model pre-trained by machine learning using multiple sets of training data, and each of the multiple sets of training data includes: sample cost data and sample equipment operation data, and sample output results corresponding to the sample cost data and the sample equipment operation data; the planning strategy of the target microgrid is determined according to the output result, wherein the planning strategy includes: the number of each planned device, and the planned location of each planned device; the target microgrid is planned and processed according to the planning strategy within the predetermined area. Through the technical solution provided by the present invention, a multi-objective optimization model for microgrid planning is established based on the planned equipment parameters and microgrid load data, and the nonlinear constraints in the model are converted into linear constraints. At the same time, an improved multi-objective problem solving method is adopted, and the converted optimization problem is solved to obtain the configuration scheme of each device in the microgrid, thereby achieving the purpose of improving calculation efficiency, promoting energy conservation and emission reduction of the microgrid while ensuring the planning economy, and improving the technical effect of improving the energy supply reliability of the microgrid, thereby improving the reliability and safety of the microgrid.

[0059] The technical solution provided by the embodiment of the present invention solves the technical problem that the microgrid planning method in the related art considers only a single factor, resulting in the microgrid's low ability to resist external interference and potential safety hazards.

[0060] According to the above embodiment of the present invention, the microgrid planning model includes a microgrid investment cost function, wherein the microgrid investment cost function is: , represents the total investment cost, represents the investment cost of photovoltaic equipment in the planned equipment, represents the investment cost of the gas turbine in the planned plant, Represents the investment cost of the electric energy storage equipment in the planned equipment; the microgrid planning model includes the microgrid operation cost objective function, where the microgrid operation cost objective function is: , represents the total operating cost, represents the annual operating cost of the photovoltaic equipment, represents the annual operating cost of the gas turbine, represents the annual operating cost of the energy storage equipment, represents the cost of purchasing electricity, Indicates the cost of abandoned light.

[0061] It should be noted that the above-mentioned microgrid investment cost function, that is, the economic cost objective function is an annual comprehensive cost including investment cost and operation cost.

[0062] Specifically, the investment cost objective function is:

[0063] in, They are the annualized coefficients of PV, ESS and MT respectively. The unit investment costs of PV, ESS and MT are respectively, are the configuration quantities of PV, ESS, and MT respectively, and are integer variables to be optimized; the operation cost objective function of microgrid planning is: ,in, It is a collection of photovoltaic output and load demand scenarios in different seasons. is the number of days in each season, are the unit electricity operation costs of PV and MT respectively, are the outputs of PV and MT at time t in scene s, is the unit charge and discharge cost of ESS, are the charging power and discharging power of ESS at time t in scenario s, is the electricity purchase price at time t, is the amount of electricity purchased at time t in scenario s, Penalty cost for abandoned light units, is the amount of abandoned light at time t in scene s.

[0064] In this embodiment, an economic cost objective function for campus microgrid planning can be established, and the optimization variable is the configuration quantity of the equipment to be planned, which can evaluate the costs involved in designing and operating the microgrid system.

[0065] Optionally, the economic cost objective function of the above microgrid planning may include: investment cost and operation cost.

[0066] According to the above embodiment of the present invention, the microgrid planning model includes a carbon emission objective function, wherein the carbon emission objective function is: , represents the total carbon emission cost, Indicates the equivalent carbon emissions generated by the purchased electricity, Represents the carbon emissions generated by the power generation of the electric energy storage equipment in the planned equipment; the microgrid planning model includes the energy supply reliability objective function, where the energy supply reliability objective function is: , Indicates the number of days in each season, Represents the carbon emission cost at time t in scenario S.

[0067] In this embodiment, a carbon emission objective function and an energy supply reliability objective function for campus microgrid planning can be established, which can help planners evaluate the environmental performance and energy supply reliability of the campus microgrid system.

[0068] Specifically, the carbon emission objective function of microgrid planning is: ,in, are the corresponding carbon emission coefficients respectively.

[0069] The energy supply reliability objective function of microgrid planning is: ,

[0070] ,in, is the upward reserve capacity of the microgrid at time t in scenario s, which is used to provide emergency load support for the microgrid in emergency scenarios. It represents the ratio of the total reserve capacity to the microgrid load demand at time t in scenario s.

[0071] According to the above embodiment of the present invention, before inputting the cost data and the equipment operation data into the microgrid planning model to process the cost data and the equipment operation data using the microgrid planning model to obtain the output result of the microgrid planning model, the microgrid planning processing method further includes: generating the constraint conditions of the microgrid planning model; wherein the constraint conditions of the microgrid planning model are generated, including: generating the quantity constraint of the planning equipment according to the configuration quantity of photovoltaic equipment, the configuration quantity of gas turbines, and the configuration quantity of energy storage equipment in the planning equipment, wherein the quantity constraint formula corresponding to the quantity constraint of the planning equipment is: , represents the number of photovoltaic devices configured, represents the number of gas turbines configured, Indicates the configuration quantity of energy storage devices, The maximum total number of planned equipment configurations; the power balance constraint of the microgrid planning model is generated according to the output of the photovoltaic equipment, the output of the energy storage equipment, and the charging and discharging power of the gas turbine. The balanced power constraint formula corresponding to the power balance constraint is: , represents the output of the photovoltaic equipment at time t in scenario S, represents the output of the energy storage device at time t in scenario S, represents the discharge power of the gas turbine at time t in scenario S, represents the charging power of the gas turbine at time t in scenario S, represents the predicted load at time t; the distributed power output constraint of the microgrid planning model is generated according to the output of the photovoltaic equipment and the output of the energy storage equipment, wherein the distributed power output constraint corresponding to the distributed power output constraint is: , represents the upper limit of the active output of the photovoltaic equipment at time t in scenario S, represents the amount of abandoned light at time t in scene S, Indicates the upper limit of the active power output of the gas turbine.

[0072] Optionally, the constraints of the above microgrid planning model may include: equipment quantity constraints, power balance constraints, distributed power output constraints, energy storage equipment constraints, backup capacity constraints and planning-operation coupling constraints.

[0073] It should be noted that the maximum number of configurations for the above-mentioned planned devices is Affected by the geographical area of ​​the microgrid, the predicted load at time t is is a known value, the upper limit of the active output of the above photovoltaic equipment at time t in scenario S The upper limit of the active output of the above gas turbine is related to the number of PVs configured. It is related to the number of configured MTs.

[0074] In this embodiment, device quantity constraints, power balance constraints, and distributed power output constraints can be established based on the operating characteristics of the campus microgrid equipment, which can limit the number of various devices in the campus microgrid system, ensure the balance of power supply and demand in the campus microgrid system, and limit the output power range of distributed power sources.

[0075] According to the above embodiment of the present invention, generating constraint conditions of a microgrid planning model includes: generating energy storage constraints of energy storage devices, wherein the energy storage constraints are: , Indicates whether the gas turbine is charged at time t in scenario S, Indicates whether the gas turbine is discharging at time t in scenario S, represents the upper limit of the charging rated power of the gas turbine, represents the upper limit of the discharge rated power of the gas turbine, represents the power consumption of the gas turbine at time t in scenario S, represents the charging efficiency of the gas turbine, represents the discharge efficiency of the gas turbine, represents the lower limit of the gas turbine in the state of charge, represents the upper limit of the gas turbine under the state of charge; generates the spare capacity constraint of the microgrid planning model, wherein the spare capacity constraint is: , represents the spare capacity of the gas turbine in the charging state at time t in scenario S, represents the reserve capacity of the gas turbine in the discharging state at time t in scenario S, represents the charging power of the gas turbine at time t in scenario S, represents the discharge power of the gas turbine at time t in scenario S; generates the planning-operation coupling constraint of the microgrid planning model, wherein the planning-operation coupling constraint is: , represents the predicted output of a single photovoltaic device at time t in scenario S, Indicates the upper limit of the power of a single energy storage device. represents the rated power of a single gas turbine, Indicates the rated capacity of a single gas turbine.

[0076] In this embodiment, energy storage equipment constraints, backup capacity constraints, and planning-operation coupling constraints may be established based on the operating characteristics of the campus microgrid equipment.

[0077] It should be noted that the above as well as is a 0-1 variable, the upper limit of the rated power of the gas turbine above And the upper limit of the discharge rated power of the gas turbine Depends on the number of configured ESSs.

[0078] According to the above embodiment of the present invention, after generating the constraint conditions of the microgrid planning model, the microgrid planning processing method further includes: linearizing the nonlinear constraint conditions in the constraint conditions; and linearizing the objective function in the microgrid planning model.

[0079] In this embodiment, the nonlinear constraints and nonlinear objective functions in the established microgrid planning model can be linearized, and the original nonlinear optimization problem can be converted into a mixed integer linear programming problem to facilitate the solution.

[0080] Specifically, for the energy storage device model, there are the following constraints containing bilinear terms: , including bilinear terms The constraints are transformed into: , including bilinear terms The constraints are transformed into: , the piecewise function in the reliability objective function is transformed into: ,in, is an introduced binary auxiliary variable, and M is a large real number.

[0081] According to the above embodiment of the present invention, before the objective function in the microgrid planning model is linearized, the microgrid planning processing method further includes: obtaining a first objective function in the objective function according to the investment cost, operation cost and carbon emission cost of the target microgrid, wherein the first objective function is: , represents the unit carbon emission penalty cost, f1 represents the first objective function; the second objective function in the objective function is generated according to the reliability index of the target microgrid, where the second objective function is: , f2 represents the second objective function, and Q represents the reliability index.

[0082] In this embodiment, the investment cost, operating cost and carbon emission cost of the microgrid can be added together to obtain the total economic cost as the first objective function in the objective function, and the reliability index of the microgrid can be used as the second objective function in the objective function, which can help the microgrid system achieve a balance between economic benefits and reliability.

[0083] It should be noted that the above objective function can be expressed as: .

[0084] According to the above embodiment of the present invention, the microgrid planning processing method further includes: converting the microgrid planning model into a mixed integer linear programming model.

[0085] In this embodiment, the microgrid planning model can be converted into a mixed integer linear programming model, which can better optimize the operation and planning of the microgrid system and achieve the best economic and environmental benefits.

[0086] The specific conversion process is as follows: after establishing the microgrid planning model, an improved method of directly solving the compromise solution of the multi-objective problem is adopted. The solution closest to the utopian point and farthest from the worst point is directly searched in the feasible domain of the problem as the compromise solution, and the GUROBI solver is called to solve the equivalent model to obtain the optimal configuration number of each device. The original problem can be converted into a mixed integer linear programming model. At the same time, the GUROBI solver is called to solve and obtain the optimization result. The specific steps are as follows: First, respectively and Solve the single-objective optimization model for the objective function and obtain the corresponding optimal solution, which is recorded as and , and obtain the corresponding payment matrix ; Establish a rectangular coordinate system with the two objective functions as the horizontal and vertical coordinates, then the coordinates of the Utopia point can be expressed as , obtain the coordinates of the worst point according to the maximum value of each column in the payment matrix ; In order to find a compromise solution that is closest to the utopia point and farthest from the worst point, the normalized distance from a solution to the utopia point within the feasible domain is minimized The square of its normalized distance to the worst point The square difference of is the objective function, and the following optimization problem is constructed: .

[0087] In addition, since the objective function of the optimization model is a non-convex function, it is difficult to solve directly and needs further processing: Decompose into horizontal axis components and the ordinate component ,Will Decompose into horizontal axis components and the ordinate component , the normalized distance between the utopia point and the worst point is recorded as , easy to know , based on the cosine theorem, we can finally get , substitute , we can obtain the following optimization model with convex function as the target: .

[0088] Figure 3 is a flow chart of an optional microgrid planning and processing method according to an embodiment of the present invention, that is, Figure 3 As shown in the figure, the nonlinear constraint, i.e., the objective function, is linearized, and then the improved multi-objective algorithm is used to directly search for the solution in the feasible domain that is closest to the utopian point and farthest from the worst point as a compromise solution. The multi-objective optimization model is solved to obtain the configuration plan of each device in the microgrid.

[0089] Taking the application of the low-carbon economic scheduling method of a data center park considering carbon trading as an example, Table 1 is a table of planned equipment investment cost parameters, and the investment cost parameter information of the equipment to be planned is shown in Table 1:

[0090] Table 1 Planning equipment investment cost parameters

[0091]

[0092] Table 2 is the operating cost parameter table. The operating cost related parameter information of the equipment to be planned is shown in Table 2:

[0093] Table 2 Operating cost parameters

[0094]

[0095] Table 3 is the electricity purchase price table. The electricity purchase price information is shown in Table 3:

[0096] Table 3 Power purchase price

[0097]

[0098] In order to fully verify the advancedness of the embodiments of the present invention, the following three solutions are adopted for comparative analysis:

[0099] Solution 1: Using economic cost and carbon emission cost as objective functions to solve the microgrid planning problem;

[0100] Solution 2: Taking energy supply reliability as the objective function, solving the microgrid planning problem;

[0101] Solution 3: Use the multi-objective optimization method provided in the embodiment of the present application to solve the multi-objective planning problem of the microgrid.

[0102] Table 4 is a comparison table of the optimization configuration results of the three solutions. The comparison of the optimization configuration results of Solution 1, Solution 2 and Solution 3 is shown in Table 4:

[0103] Table 4 Comparison of optimization configuration results of three solutions

[0104]

[0105] Table 5 is a comparison table of the objective functions of the three schemes. The objective function comparison is shown in Table 5:

[0106] Table 5 Comparison of objective functions of three schemes

[0107]

[0108] From the comparison of the results of the three schemes, it can be seen that: 1) Scheme 1, when only considering economic costs and carbon emission costs, the number of photovoltaic devices configured in the microgrid is relatively large. The main reason is that photovoltaics are clean energy and will not produce carbon emissions during operation. Compared with purchasing electricity from the power grid, carbon emissions will be greatly reduced, and the total cost can be reduced accordingly; however, this scheme only focuses on economic efficiency, which makes the energy supply reliability of the microgrid low. 2) Scheme 2, with the goal of maximizing the energy supply reliability of the microgrid, configures more energy storage devices, which can provide more upward backup capacity in emergency situations to support the normal operation of the microgrid load; however, because this scheme ignores economic costs and carbon emission costs, although the energy supply reliability is guaranteed, the annual comprehensive cost and carbon emissions of the configuration scheme are high. 3) Scheme 3 takes into account economic costs, carbon emissions, and energy supply reliability goals at the same time, and uses an improved multi-objective algorithm to obtain the optimal compromise solution. The total cost and reliability of this compromise solution are very close to the objective function value corresponding to the single-objective optimal solution, that is, it is close to the utopian point, which shows that this planning scheme not only helps to maintain the economic cost and carbon emissions at a low level, but also ensures the energy supply reliability of the microgrid, and achieves the coordinated optimization of economy and reliability.

[0109] Figure 4is a module schematic diagram of a microgrid planning and processing system according to an embodiment of the present invention, such as Figure 4 As shown in the figure, the system can include four modules: parameter information acquisition module, multi-objective optimization model construction module, model linearization module and optimization solution module. The specific functions are as follows: 1) Parameter information acquisition module, used to obtain the relevant parameters of the equipment to be planned and the load demand information of the microgrid. 2) Multi-objective optimization model construction module, used to establish a multi-objective optimization model for microgrid planning. 3) Model linearization module, used to linearize nonlinear objective functions and nonlinear constraints to make the model easy to solve. 4) Optimization solution module, based on the established multi-objective optimization model, uses an improved multi-objective optimization algorithm to directly search for a compromise solution in the feasible domain that is closest to the utopian point and farthest from the worst point, and obtains a microgrid configuration plan that takes into account economic costs, carbon emission reduction and energy supply reliability.

[0110] Through the technical solution provided by the above-mentioned embodiment of the present invention, a multi-objective optimization model for microgrid planning is first established based on the planned equipment parameters and the microgrid load data; the nonlinear constraints in the model are converted into linear constraints, and then an improved multi-objective problem solving method is adopted to solve the problem of low computational efficiency in traditional methods by searching for a solution that is closest to the utopian point and farthest from the worst point in the feasible domain as a compromise solution; the converted optimization problem is solved to obtain a configuration scheme for photovoltaic equipment, micro gas turbines, and energy storage equipment in the microgrid, thereby promoting energy conservation and emission reduction of the microgrid and improving the energy supply reliability of the microgrid while ensuring the planning economy.

[0111] That is, the technical solution provided by the embodiment of the present invention comprehensively considers multiple factors such as economy, reliability and low carbon, proposes a multi-objective optimization model from the three perspectives of economic cost, carbon emissions and energy supply reliability, and adopts an improved multi-objective algorithm to directly solve the compromise solution of the multi-objective problem, and obtain the best equipment configuration plan in the microgrid. It avoids the low computational efficiency of the traditional method of first finding the Pareto optimal solution set and then determining the compromise solution. It can solve the problem that the existing microgrid planning technology only considers a single goal and is not comprehensive enough. At the same time, it solves the problem of low computational efficiency of the existing multi-objective algorithm, which is helpful to achieve economic, low-carbon and reliable operation of the microgrid.

[0112] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0113] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, disk, CD), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods of each embodiment of the present application.

[0114] According to an embodiment of the present invention, a microgrid planning and processing device for implementing the above-mentioned microgrid planning and processing method is also provided. Figure 5 is a schematic diagram of a microgrid planning and processing device according to an embodiment of the present invention, such as Figure 5 As shown, the device includes: a first acquisition module 501, a first processing module 503, a first determination module 505 and a second processing module 507. The planning and processing device of the microgrid is described below.

[0115] The first acquisition module 501 is used to acquire cost data and equipment operation data of each planned equipment in the target microgrid, wherein the target microgrid is a microgrid that needs to be planned in a predetermined area.

[0116] The first processing module 503 is used to input the cost data and the equipment operation data into the microgrid planning model, so as to use the microgrid planning model to process the cost data and the equipment operation data to obtain the output result of the microgrid planning model, wherein the microgrid planning model is a model pre-trained by machine learning using multiple sets of training data, and each set of the multiple sets of training data includes: sample cost data and sample equipment operation data, and sample output results corresponding to the sample cost data and the sample equipment operation data.

[0117] The first determination module 505 is used to determine the planning strategy of the target microgrid according to the output result, wherein the planning strategy includes: the number of each planned device and the planned location of each planned device.

[0118] The second processing module 507 is used to perform planning processing on the target microgrid in a predetermined area according to the planning strategy.

[0119] It should be noted here that the above-mentioned first acquisition module 501, first processing module 503, first determination module 505 and second processing module 507 correspond to steps S202 to S208 in the above-mentioned embodiments, and the four modules are the same as the instances and application scenarios implemented by the corresponding steps, but are not limited to the contents disclosed in the above-mentioned embodiments.

[0120] As can be seen from the above, in the scheme recorded in the above-mentioned embodiment of the present invention, first, the first acquisition module can be used to obtain the cost data and equipment operation data of each planned equipment in the target microgrid, wherein the target microgrid is a microgrid that needs to be planned in a predetermined area; then, the first processing module can be used to input the cost data and equipment operation data into the microgrid planning model, so as to use the microgrid planning model to process the cost data and equipment operation data to obtain the output result of the microgrid planning model, wherein the microgrid planning model is a model obtained by machine learning training using multiple sets of training data in advance, and each set of the multiple sets of training data includes: sample cost data and sample equipment operation data, and sample output results corresponding to the sample cost data and the sample equipment operation data; secondly, the first confirmation module can be used The determination module determines the planning strategy of the target microgrid according to the output result, wherein the planning strategy includes: the number of each planned device and the planned location of each planned device; finally, the second processing module can be used to plan and process the target microgrid in a predetermined area according to the planning strategy, thereby achieving a multi-objective optimization model for microgrid planning based on the planning device parameters and microgrid load data, and converting the nonlinear constraints in the model into linear constraints. At the same time, an improved multi-objective problem solving method is adopted, and the converted optimization problem is solved to obtain the configuration scheme of each device in the microgrid, thereby achieving the purpose of improving calculation efficiency, promoting energy conservation and emission reduction of the microgrid and improving the energy supply reliability of the microgrid while ensuring the planning economy, thereby improving the reliability and safety of the microgrid.

[0121] The technical solution provided by the embodiment of the present invention solves the technical problem that the microgrid planning method in the related art considers only a single factor, resulting in the microgrid's low ability to resist external interference and potential safety hazards.

[0122] In an optional embodiment, the microgrid planning model includes a microgrid investment cost function, wherein the microgrid investment cost function is: , represents the total investment cost, represents the investment cost of photovoltaic equipment in the planned equipment, represents the investment cost of the gas turbine in the planned plant, Represents the investment cost of the electric energy storage equipment in the planned equipment; the microgrid planning model includes the microgrid operation cost objective function, where the microgrid operation cost objective function is: , represents the total operating cost, represents the annual operating cost of the photovoltaic equipment, represents the annual operating cost of the gas turbine, represents the annual operating cost of the energy storage equipment, represents the cost of purchasing electricity, Indicates the cost of abandoned light.

[0123] In an optional embodiment, the microgrid planning model includes a carbon emission objective function, wherein the carbon emission objective function is: , represents the total carbon emission cost, Indicates the equivalent carbon emissions generated by the purchased electricity, Represents the carbon emissions generated by the power generation of the electric energy storage equipment in the planned equipment; the microgrid planning model includes the energy supply reliability objective function, where the energy supply reliability objective function is: , Indicates the number of days in each season, Represents the carbon emission cost at time t in scenario S.

[0124] In an optional embodiment, the planning and processing device of the microgrid further includes: a third processing module, which is used to generate constraints of the microgrid planning model before inputting the cost data and the equipment operation data into the microgrid planning model to process the cost data and the equipment operation data using the microgrid planning model to obtain the output result of the microgrid planning model; wherein the third processing module includes: a fourth processing unit, which is used to generate a quantity constraint of the planning device according to the configuration quantity of photovoltaic devices, the configuration quantity of gas turbines, and the configuration quantity of energy storage devices in the planning device, wherein the quantity constraint formula corresponding to the quantity constraint of the planning device is: , represents the number of photovoltaic devices configured, represents the number of gas turbines configured, Indicates the configuration quantity of energy storage devices, The maximum total number of configurations of the planned equipment; a fifth processing unit, used to generate a power balance constraint of the microgrid planning model according to the output of the photovoltaic equipment, the output of the energy storage equipment, and the charging and discharging power of the gas turbine, wherein the balanced power constraint formula corresponding to the power balance constraint is: , represents the output of the photovoltaic equipment at time t in scenario S, represents the output of the energy storage device at time t in scenario S, represents the discharge power of the gas turbine at time t in scenario S, represents the charging power of the gas turbine at time t in scenario S, represents the predicted load at time t; a sixth processing unit, used to generate a distributed power output constraint of the microgrid planning model according to the output of the photovoltaic device and the output of the energy storage device, wherein the distributed power output constraint corresponding to the distributed power output constraint is: , represents the upper limit of the active output of the photovoltaic equipment at time t in scenario S, represents the amount of abandoned light at time t in scene S, Indicates the upper limit of the active power output of the gas turbine.

[0125] In an optional embodiment, the third processing module includes: a first processing unit, configured to generate an energy storage constraint of an energy storage device, wherein the energy storage constraint is: , Indicates whether the gas turbine is charged at time t in scenario S, Indicates whether the gas turbine is discharging at time t in scenario S, represents the upper limit of the charging rated power of the gas turbine, represents the upper limit of the discharge rated power of the gas turbine, represents the power consumption of the gas turbine at time t in scenario S, represents the charging efficiency of the gas turbine, represents the discharge efficiency of the gas turbine, represents the lower limit of the gas turbine in the state of charge, represents the upper limit of the gas turbine under the state of charge; a second processing unit is used to generate a spare capacity constraint of the microgrid planning model, wherein the spare capacity constraint is: , represents the spare capacity of the gas turbine in the charging state at time t in scenario S, represents the reserve capacity of the gas turbine in the discharging state at time t in scenario S, represents the charging power of the gas turbine at time t in scenario S, represents the discharge power of the gas turbine at time t in scenario S; a third processing unit, used to generate a planning-operation coupling constraint of a microgrid planning model, wherein the planning-operation coupling constraint is: , represents the predicted output of a single photovoltaic device at time t in scenario S, Indicates the upper limit of the power of a single energy storage device. represents the rated power of a single gas turbine, Indicates the rated capacity of a single gas turbine.

[0126] In an optional embodiment, the microgrid planning and processing device further includes: a fourth processing module for linearizing the nonlinear constraints in the constraints; and a fifth processing module for linearizing the objective function in the microgrid planning model.

[0127] In an optional embodiment, the microgrid planning and processing device further includes: a sixth processing module, configured to obtain a first objective function in the objective function according to the investment cost, operation cost and carbon emission cost of the target microgrid, wherein the first objective function is: , represents the unit carbon emission penalty cost, f1 represents the first objective function; the seventh processing module is used to generate the second objective function in the objective function according to the reliability index of the target microgrid, wherein the second objective function is: , f2 represents the second objective function, and Q represents the reliability index.

[0128] In an optional embodiment, the microgrid planning processing device further includes: an eighth processing module, used to convert the microgrid planning model into a mixed integer linear programming model.

[0129] According to another aspect of an embodiment of the present invention, a microgrid planning and processing system is further provided. The microgrid planning and processing system uses any one of the microgrid planning and processing methods described above.

[0130] According to another aspect of an embodiment of the present invention, a processor is further provided, and the processor is used to run a program, wherein when the program is run, any one of the above-mentioned microgrid planning and processing methods is executed.

[0131] According to another aspect of an embodiment of the present invention, a computer program product is provided, including computer instructions, and when the computer instructions are executed by a processor, any one of the above-mentioned microgrid planning and processing methods is executed.

[0132] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium including a stored program, wherein the program executes any one of the above-mentioned microgrid planning and processing methods.

[0133] Optionally, in this embodiment, the computer-readable storage medium may be located in any one of the computer terminals in a computer terminal group in a computer network, or in any one of the communication devices in a communication device group.

[0134] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0135] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0136] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0137] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0138] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0139] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk, etc. Various media that can store program codes.

[0140] 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 principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A microgrid planning and processing method, characterized in that: include: Obtaining cost data and equipment operation data of each planned equipment in a target microgrid, wherein the target microgrid is a microgrid that needs to be planned in a predetermined area; Inputting the cost data and the device operation data into a microgrid planning model, so as to process the cost data and the device operation data using the microgrid planning model, so as to obtain an output result of the microgrid planning model, wherein the microgrid planning model is a model obtained by machine learning training using multiple sets of training data in advance, and each of the multiple sets of training data includes: sample cost data and sample device operation data, and sample output results corresponding to the sample cost data and the sample device operation data; Determining a planning strategy for the target microgrid according to the output result, wherein the planning strategy includes: the number of each of the planned devices and the planned location of each of the planned devices; Performing planning processing on the target microgrid in the predetermined area according to the planning strategy; Wherein, before inputting the cost data and the equipment operation data into the microgrid planning model to process the cost data and the equipment operation data using the microgrid planning model to obtain the output result of the microgrid planning model, it also includes: generating constraint conditions of the microgrid planning model; The constraints for generating the microgrid planning model include: The quantity constraint of the planned device is generated according to the configuration quantity of photovoltaic devices, the configuration quantity of gas turbines and the configuration quantity of energy storage devices in the planned device, wherein the quantity constraint formula corresponding to the quantity constraint of the planned device is: , represents the configuration quantity of the photovoltaic device, represents the configuration quantity of the gas turbine, represents the configuration quantity of the energy storage device, The maximum total number of configurations of the planned devices; The power balance constraint of the microgrid planning model is generated according to the output of the photovoltaic device, the output of the energy storage device, and the charging and discharging power of the gas turbine, wherein the balanced power constraint formula corresponding to the power balance constraint is: , represents the output of the photovoltaic device at time t in scene S, represents the output of the energy storage device at time t in scenario S, represents the discharge power of the gas turbine at time t in scenario S, represents the charging power of the gas turbine at time t in scenario S, represents the predicted load at time t; The distributed power output constraint of the microgrid planning model is generated according to the output of the photovoltaic device and the output of the energy storage device, wherein the distributed power output constraint formula corresponding to the distributed power output constraint is: , represents the upper limit of the active output of the photovoltaic device at time t in scenario S, represents the amount of abandoned light at time t in scene S, represents the upper limit of the active output of the gas turbine; The constraints for generating the microgrid planning model include: Generate an energy storage constraint of the energy storage device, wherein the energy storage constraint is: , Indicates whether the gas turbine is charged at time t in scenario S, Indicates whether the gas turbine is discharging at time t in scenario S, represents the upper limit of the charging rated power of the gas turbine, represents the upper limit of the discharge rated power of the gas turbine, represents the power consumption of the gas turbine at time t in scenario S, represents the charging efficiency of the gas turbine, represents the discharge efficiency of the gas turbine, represents the lower limit of the gas turbine in the state of charge, represents the upper limit of the gas turbine under the state of charge; Generate a backup capacity constraint of the microgrid planning model, wherein the backup capacity constraint is: , represents the spare capacity of the gas turbine in the charging state at time t in scenario S, represents the reserve capacity of the gas turbine in the discharging state at time t in scenario S, represents the charging power of the gas turbine at time t in scenario S, represents the discharge power of the gas turbine at time t in scenario S; Generate a planning-operation coupling constraint of the microgrid planning model, wherein the planning-operation coupling constraint is: , represents the predicted output of a single photovoltaic device at time t in scenario S, Indicates the upper limit of the power of a single energy storage device. represents the rated power of a single gas turbine, Indicates the rated capacity of a single gas turbine.

2. The microgrid planning and processing method according to claim 1, characterized in that: The microgrid planning model includes a microgrid investment cost function, wherein the microgrid investment cost function is: , represents the total investment cost, represents the investment cost of the photovoltaic equipment in the planned equipment, represents the investment cost of the gas turbine in the planned plant, represents the investment cost of the electric energy storage device in the planning device; the microgrid planning model includes a microgrid operation cost objective function, wherein the microgrid operation cost objective function is: , represents the total operating cost, represents the annual operating cost of the photovoltaic equipment, represents the annual operating cost of the gas turbine, represents the annual operating cost of the energy storage device, represents the cost of purchasing electricity, Indicates the cost of abandoned light.

3. The microgrid planning and processing method according to claim 1, characterized in that: The microgrid planning model includes a carbon emission objective function, wherein the carbon emission objective function is: , represents the total carbon emission cost, Indicates the equivalent carbon emissions generated by the purchased electricity, represents the carbon emissions generated by the power generation of the electric energy storage device in the planning device; the microgrid planning model includes an energy supply reliability objective function, wherein the energy supply reliability objective function is: , Indicates the number of days in each season, Represents the carbon emission cost at time t in scenario S.

4. The microgrid planning and processing method according to claim 1, characterized in that: After generating the constraint conditions of the microgrid planning model, it also includes: Linearizing the nonlinear constraints in the constraints; The objective function in the microgrid planning model is linearized.

5. The microgrid planning and processing method according to claim 4, characterized in that: Before linearizing the objective function in the microgrid planning model, the method further includes: The first objective function in the objective function is obtained according to the investment cost, operation cost and carbon emission cost of the target microgrid, wherein the first objective function is: , represents the unit carbon emission penalty cost, f1 represents the first objective function; A second objective function in the objective function is generated according to the reliability index of the target microgrid, wherein the second objective function is: , f2 represents the second objective function, and Q represents the reliability index.

6. The microgrid planning and processing method according to claim 5, characterized in that: Also includes: The microgrid planning model is converted into a mixed integer linear programming model.

7. A microgrid planning and processing device, characterized in that: include: A first acquisition module is used to acquire cost data and equipment operation data of each planned equipment in a target microgrid, wherein the target microgrid is a microgrid that needs to be planned in a predetermined area; A first processing module is used to input the cost data and the equipment operation data into a microgrid planning model, so as to process the cost data and the equipment operation data using the microgrid planning model to obtain an output result of the microgrid planning model, wherein the microgrid planning model is a model obtained by machine learning training using multiple sets of training data in advance, and each of the multiple sets of training data includes: sample cost data and sample equipment operation data, and sample output results corresponding to the sample cost data and the sample equipment operation data; A first determination module is used to determine a planning strategy of the target microgrid according to the output result, wherein the planning strategy includes: the number of each of the planned devices and the planned location of each of the planned devices; A second processing module, configured to perform planning processing on the target microgrid in the predetermined area according to the planning strategy; Wherein, the device further includes: a third processing module, which is used to generate the constraint conditions of the microgrid planning model before inputting the cost data and the equipment operation data into the microgrid planning model to process the cost data and the equipment operation data using the microgrid planning model to obtain the output result of the microgrid planning model; wherein, the third processing module includes: a fourth processing unit, which is used to generate the quantity constraint of the planning device according to the configuration quantity of photovoltaic devices, the configuration quantity of gas turbines and the configuration quantity of energy storage devices in the planning device, wherein the quantity constraint formula corresponding to the quantity constraint of the planning device is: , represents the configuration quantity of the photovoltaic device, represents the configuration quantity of the gas turbine, represents the configuration quantity of the energy storage device, The maximum total number of configurations of the planned equipment; a fifth processing unit, used to generate a power balance constraint of the microgrid planning model according to the output of the photovoltaic device, the output of the energy storage device, and the charging and discharging power of the gas turbine, wherein the balanced power constraint formula corresponding to the power balance constraint is: , represents the output of the photovoltaic device at time t in scene S, represents the output of the energy storage device at time t in scenario S, represents the discharge power of the gas turbine at time t in scenario S, represents the charging power of the gas turbine at time t in scenario S, Represents the predicted load at time t; a sixth processing unit, used to generate a distributed power output constraint of the microgrid planning model according to the output of the photovoltaic device and the output of the energy storage device, wherein the distributed power output constraint formula corresponding to the distributed power output constraint is: , represents the upper limit of the active output of the photovoltaic device at time t in scenario S, represents the amount of abandoned light at time t in scene S, Indicates the upper limit of the active output of the gas turbine; The third processing module includes: a first processing unit, configured to generate an energy storage constraint of the energy storage device, wherein the energy storage constraint is: , Indicates whether the gas turbine is charged at time t in scenario S, Indicates whether the gas turbine is discharging at time t in scenario S, represents the upper limit of the charging rated power of the gas turbine, represents the upper limit of the discharge rated power of the gas turbine, represents the power consumption of the gas turbine at time t in scenario S, represents the charging efficiency of the gas turbine, represents the discharge efficiency of the gas turbine, represents the lower limit of the gas turbine in the state of charge, represents the upper limit of the gas turbine under the state of charge; a second processing unit is used to generate a spare capacity constraint of the microgrid planning model, wherein the spare capacity constraint is: , represents the spare capacity of the gas turbine in the charging state at time t in scenario S, represents the reserve capacity of the gas turbine in the discharging state at time t in scenario S, represents the charging power of the gas turbine at time t in scenario S, represents the discharge power of the gas turbine at time t in scenario S; a third processing unit, used to generate a planning-operation coupling constraint of the microgrid planning model, wherein the planning-operation coupling constraint is: , represents the predicted output of a single photovoltaic device at time t in scenario S, Indicates the upper limit of the power of a single energy storage device. represents the rated power of a single gas turbine, Indicates the rated capacity of a single gas turbine.

8. A computer program product comprising computer instructions, characterized in that: When the computer instructions are executed by a processor, a microgrid planning and processing method as described in any one of claims 1 to 6 is performed.

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