Multi-integrated energy station configuration method considering energy demand of semiconductor industry chain

Abstract

The invention discloses a multi-integrated energy station configuration method considering energy demands of a semiconductor industry chain. The method comprises the following steps: obtaining a typical daily load of each energy station in a semiconductor industry chain and an operation parameter of each unit in the energy station; randomly configuring the number and capacity of energy supply units and energy storage units in the energy station; constructing a comprehensive energy station model according to the operation parameters, the typical daily load and the number and capacity of the energy supply units and the energy storage units; constructing a multi-energy station resource generation model and a capacity configuration model; and based on the comprehensive energy station model and the multi-energy station resource generation model, solving the capacity configuration model to obtain the capacity configuration of each unit in the energy station. According to the method, the comprehensive energy station model, the multi-energy station resource generation model and the capacity configuration model are constructed, and the capacity configuration model is solved, so that the optimal allocation of energy can be realized, and the utilization efficiency of the energy is improved.

Description

Technical Field

[0001] Embodiments of the present invention relate to the technical field of energy station planning, and in particular, to a method for configuring multiple integrated energy stations that considers the energy demand of the semiconductor industry chain. Background Art

[0002] With the transformation of the global energy structure and the in-depth implementation of the sustainable development strategy, the integrated energy system, as an innovative energy supply model, has received increasing attention. By integrating multiple energy forms such as electricity, heat, and cold energy, the integrated energy system can not only achieve efficient utilization and optimized distribution of energy, but also significantly improve energy utilization efficiency, reduce energy consumption costs, and reduce environmental pollution. This is of great strategic significance for promoting the optimization and upgrading of the energy structure and achieving sustainable development of energy.

[0003] In the context of building a new power system, the industrial sector has an increasingly urgent need for an integrated energy system. The production processes of various industry chains have diverse energy demands and increasingly higher requirements for energy quality and reliability, which puts higher demands on the rational planning and efficient operation of integrated energy systems.

[0004] Take the semiconductor industry as an example. The semiconductor industry includes multiple process flows such as silicon raw material preparation, wafer processing, and chip manufacturing and testing. Due to the significant differences in energy demand in different links of the industrial chain, the demand for electricity, heat, and cold energy varies. Traditional energy supply methods can no longer meet such diverse and differentiated energy needs, resulting in low energy utilization efficiency, high energy costs, and difficulty in achieving optimal energy allocation. Summary of the invention

[0005] The present invention provides a multi-integrated energy station configuration method that takes into account the energy demand of the semiconductor industry chain, so as to solve the problem that the prior art cannot achieve optimal energy allocation, resulting in low energy utilization efficiency and high energy costs.

[0006] According to one aspect of the present invention, a method for configuring multiple integrated energy stations taking into account the energy demand of the semiconductor industry chain is provided, the method comprising: obtaining the typical daily load of each energy station in the semiconductor industry chain, and the operating parameters of each unit in the energy station; randomly configuring the number and capacity of the energy supply units and energy storage units in the energy station; constructing an integrated energy station model according to the operating parameters, the typical daily load, and the number and capacity of the energy supply units and energy storage units; constructing a multi-energy station resource generation model and a capacity configuration model; based on the integrated energy station model and the multi-energy station resource generation model, solving the capacity configuration model to obtain the capacity configuration of each unit in the energy station; wherein the integrated energy station model includes conventional unit operation constraints of each energy station, thermal load supply and demand balance constraints, electric load supply and demand balance constraints, and electric power interaction constraints between energy stations; the multi-energy station resource generation model takes the maximum sum of the interaction cost between the energy station and the power grid, the interaction cost between energy stations, and the amount of energy resources saved as the objective function; the capacity configuration model takes the minimum annual total resource consumption of the energy station as the objective function.

[0007] According to another aspect of the present invention, a multi-integrated energy station configuration device considering the energy demand of the semiconductor industry chain is provided, the device comprising: an acquisition module for acquiring the typical daily load of each energy station in the semiconductor industry chain and the operating parameters of each unit in the energy station; a configuration module for randomly configuring the number and capacity of the energy supply units and energy storage units in the energy station; a first construction module for constructing a comprehensive energy station model according to the operating parameters, the typical daily load and the number and capacity of the energy supply units and energy storage units; a second construction module for constructing a multi-energy station resource generation model and a capacity configuration model; A solution module is used to solve the capacity configuration model based on the comprehensive energy station model and the multi-energy station resource generation model to obtain the capacity configuration of each unit in the energy station; wherein the comprehensive energy station model includes the conventional unit operation constraints of each energy station, the thermal load supply and demand balance constraints, the electric load supply and demand balance constraints and the electric power interaction constraints between energy stations; the multi-energy station resource generation model takes the maximum sum of the interaction cost between the energy station and the power grid, the interaction cost between energy stations and the amount of energy resources saved as the objective function; the capacity configuration model takes the minimum annual total resource consumption of the energy station as the objective function.

[0008] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein,

[0009] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the multi-integrated energy station configuration method considering the energy demand of the semiconductor industry chain as described in any embodiment of the present invention.

[0010] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the multi-integrated energy station configuration method considering the energy demand of the semiconductor industry chain as described in any embodiment of the present invention when executed.

[0011] A method for configuring multiple integrated energy stations taking into account the energy demand of the semiconductor industry chain according to an embodiment of the present invention includes: obtaining the typical daily load of each energy station in the semiconductor industry chain and the operating parameters of each unit in the energy station; randomly configuring the number and capacity of the energy supply units and energy storage units in the energy station; constructing an integrated energy station model according to the operating parameters, the typical daily load, and the number and capacity of the energy supply units and energy storage units; constructing a multi-energy station resource generation model and a capacity configuration model; based on the integrated energy station model and the multi-energy station resource generation model, solving the capacity configuration model to obtain the capacity configuration of each unit in the energy station. This method can achieve the optimal allocation of energy and improve the efficiency of energy utilization by constructing an integrated energy station model, a multi-energy station resource generation model, and a capacity configuration model, and solving the capacity configuration model, thereby solving the problem that the existing technology cannot achieve the optimal allocation of energy, resulting in low energy utilization efficiency and high energy cost.

[0012] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0014] Figure 1 A flowchart of a method for configuring multiple integrated energy stations taking into account the energy demand of the semiconductor industry chain provided in the first embodiment of the present invention;

[0015] Figure 2 A schematic diagram of a comprehensive energy system for energy demand of a semiconductor industry chain provided by an embodiment of the present invention;

[0016] Figure 3 A flowchart of a method for configuring multiple integrated energy stations taking into account the energy demand of the semiconductor industry chain provided by an embodiment of the present invention;

[0017] Figure 4 A schematic diagram of the operation of a silicon raw material preparation energy station provided by an embodiment of the present invention;

[0018] Figure 5 A schematic diagram of the operation of a wafer processing energy station provided by an embodiment of the present invention;

[0019] Figure 6 A schematic diagram of the operation of a chip manufacturing test energy station provided by an embodiment of the present invention;

[0020] Figure 7 A schematic diagram of the structure of a multi-integrated energy station configuration device that takes into account the energy demand of the semiconductor industry chain, provided in the second embodiment of the present invention;

[0021] Figure 8 Schematic diagram of the structure of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0022] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiment of the present invention. Obviously, the described embodiment is only an embodiment of a part of the present invention, rather than all the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work should belong to the scope of protection of the present invention. It should be understood that the various steps recorded in the method implementation of the present invention can be performed in different orders and / or in parallel. In addition, the method implementation may include additional steps and / or omit the steps shown. The scope of the present invention is not limited in this respect.

[0023] The term "including" and its variations used herein are open inclusions, i.e., "including but not limited to". The term "based on" means "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". The relevant definitions of other terms will be given in the following description.

[0024] 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, any variations of the terms "including" and "having" 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 clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0025] It should be noted that the modifications of "one" and "plurality" mentioned in the present invention are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0026] The names of the messages or information exchanged between multiple devices in the embodiments of the present invention are only used for illustrative purposes, and are not used to limit the scope of these messages or information.

[0027] Embodiment 1

[0028] Figure 1 A flow chart of a method for configuring multiple integrated energy stations taking into account the energy demand of the semiconductor industry chain is provided in the first embodiment of the present invention. The method can be applied to the distribution of energy between different energy stations in an integrated energy system. The method can be executed by a device for configuring multiple integrated energy stations taking into account the energy demand of the semiconductor industry chain, wherein the device can be implemented by software and / or hardware and is generally integrated on an electronic device. In this embodiment, the electronic device includes but is not limited to: computers and other devices.

[0029] like Figure 1 As shown, a method for configuring multiple integrated energy stations taking into account the energy demand of the semiconductor industry chain provided by the first embodiment of the present invention includes the following steps:

[0030] S110. Obtain the typical daily load of each energy station in the semiconductor industry chain and the operating parameters of each unit in the energy station.

[0031] Among them, the energy station can be an energy station in the semiconductor industry chain, for example, the energy station can be a silicon raw material preparation energy station based on electricity, heat and gas energy, a wafer processing energy station based on electricity and heat energy, and a chip manufacturing test energy station based on electricity energy. The chip manufacturing test energy station consumes less power, and can provide electricity by configuring photovoltaic power generation equipment. When the photovoltaic power is sufficient and meets the needs of the station, it supplies power to the other two energy stations through the power connection line. The silicon raw material preparation energy station consumes more heat and generates more waste heat. It can be configured by configuring a low-temperature waste heat generator set to recycle a large amount of excess steam and waste heat into electricity, and supply power to the other two energy stations through the power connection line when the needs of the station are met. The wafer processing energy station consumes more power, there is no power generation device in the station, and it needs to obtain electricity from the power grid. At the same time, it obtains electricity from other energy stations through the power connection line to realize the power supply in the station. The typical daily load can be a common pattern or expected level of power load changes in a certain power system or power user within a specific time period (usually a whole day). The typical day in this embodiment can be classified by season, for example, divided into three types: spring, autumn, summer and winter. The unit may be any device in the energy station, and the operating parameters may be relevant parameters of the unit.

[0032] In this embodiment, the typical daily load corresponding to each energy station can be obtained, and the operating parameters of each unit in each energy station can be obtained. Figure 2 A schematic diagram of a comprehensive energy system for energy demand of a semiconductor industry chain provided by an embodiment of the present invention; Figure 2As shown, the method in this embodiment can be applied to different links of the semiconductor industry chain, wherein different energy stations can be connected through power lines. The silicon raw material preparation energy station involves operations such as high-temperature smelting, reduction, and polysilicon purification, and may include equipment such as electric arc furnaces and reduction furnaces. The electric arc furnace consumes a lot of electricity and produces heat, while the reduction furnace only consumes heat. Therefore, a gas boiler can be used in conjunction with a heat storage system to supply heat to the reduction furnace; excess steam and waste heat can be recycled and converted into electrical energy through an organic Rankine cycle (ORC) low-temperature waste heat generator set, and used in conjunction with electrical energy storage for equipment in the station. The wafer processing energy station involves operations such as oxidation, lithography, ion implantation, and chemical vapor deposition, and may include equipment such as an oxidation furnace, a lithography machine, an ion implanter, and chemical vapor deposition (CVD). The oxidation furnace consumes a lot of electricity and produces heat, the CVD equipment only consumes heat, and the lithography machine and ion implanter only consume electricity. Electric boilers can be used in conjunction with heat storage systems to supply heat to CVD. By obtaining electricity from the power grid, the power lines between stations are interconnected, and power supply is achieved in conjunction with electric energy storage. The chip manufacturing test energy station involves operations such as packaging, probe testing, welding and cleaning, and includes equipment such as molding machines, probe stations, wave soldering machines and ultrasonic cleaning machines. These devices only consume electricity, and the power consumption is relatively small. Photovoltaic power generation equipment can be configured to provide part of the electricity. When the photovoltaic power is insufficient or the cost of obtaining electricity from the power grid is high, the electric energy storage can be released for use by the equipment in the station.

[0033] S120. Randomly configure the number and capacity of energy supply units and energy storage units in the energy station.

[0034] The energy supply unit may be a unit that can provide energy, and the energy storage unit may be a unit that can generate energy. The capacity of the unit may be a generator set or a rated output power of the unit.

[0035] In this embodiment, the number and capacity of energy supply units and energy storage units in the energy station can be randomly configured.

[0036] S130. Construct a comprehensive energy station model according to the operating parameters, the typical daily load, and the number and capacity of the energy supply units and energy storage units.

[0037] Among them, the comprehensive energy station model can include the conventional unit operation constraints of each energy station, the thermal load supply and demand balance constraints, the electric load supply and demand balance constraints and the electric power interaction constraints between energy stations.

[0038] In this embodiment, a comprehensive energy station model can be constructed based on the operating parameters of each unit, the typical daily load, and the number and capacity of the energy supply units and energy storage units.

[0039] S140. Construct a multi-energy station resource generation model and a capacity configuration model.

[0040] The multi-energy station resource generation model can take the maximum sum of the interaction cost between the energy station and the power grid, the interaction cost between energy stations and the amount of energy resources saved as the objective function. The capacity configuration model can take the minimum annual total resource consumption of the energy station as the objective function.

[0041] In this embodiment, a multi-energy station resource generation model and a capacity configuration model may be constructed.

[0042] S150. Based on the integrated energy station model and the multi-energy station resource generation model, solve the capacity configuration model to obtain the capacity configuration of each unit in the energy station.

[0043] In this embodiment, the capacity configuration model can be solved based on the comprehensive energy station model and the multi-energy station resource generation model to obtain the capacity configuration of each unit in the energy station.

[0044] A method for configuring multiple integrated energy stations that takes into account the energy demand of the semiconductor industry chain is provided in a first embodiment of the present invention, including: obtaining the typical daily load of each energy station in the semiconductor industry chain, and the operating parameters of each unit in the energy station; randomly configuring the number and capacity of the energy supply units and energy storage units in the energy station; constructing an integrated energy station model according to the operating parameters, the typical daily load, and the number and capacity of the energy supply units and energy storage units; constructing a multi-energy station resource generation model and a capacity configuration model; based on the integrated energy station model and the multi-energy station resource generation model, solving the capacity configuration model to obtain the capacity configuration of each unit in the energy station. This method can achieve optimal energy allocation and improve energy utilization efficiency by constructing an integrated energy station model, a multi-energy station resource generation model, and a capacity configuration model, and solving the capacity configuration model, thereby solving the problem that the prior art cannot achieve optimal energy allocation, resulting in low energy utilization efficiency and high energy costs.

[0045] Based on the above embodiment, a variant embodiment of the above embodiment is proposed. It should be noted that in order to make the description concise, only the differences from the above embodiment are described in the variant embodiment.

[0046] In one embodiment, the capacity configuration model is solved based on the integrated energy station model and the multi-energy station resource generation model to obtain the capacity configuration of each unit in the energy station, including: determining the output status of each unit in the energy station on different typical days according to the electric load supply and demand balance constraints in the multi-energy station resource generation model and the integrated energy station model; solving the capacity configuration model based on the output status of each unit and the integrated energy station model to obtain the annual total resource consumption on different typical days; when the number of iterations is less than the maximum number of iterations, reconfiguring the number and capacity of the energy supply units and energy storage units in the energy station according to the capacity configuration model, and executing the step of solving the capacity configuration model; otherwise, determining the capacity configuration of each unit in the energy station according to the capacity configuration model corresponding to the lowest annual total resource consumption among the annual total resource consumption on different typical days.

[0047] The output state may be the actual power generation capacity and output level of a unit or the entire energy station at a specific time point. The annual total resource consumption may be the total amount of resources consumed by the energy station throughout the year.

[0048] In this embodiment, the number of iterations can be set. After determining the output status of each unit in the energy station on different typical days, the capacity configuration model is solved based on the output status of each unit and the comprehensive energy station model to obtain the annual total resource consumption on different typical days; when the number of iterations is less than the maximum number of iterations, the number and capacity of the energy supply units and energy storage units in the energy station are reconfigured according to the capacity configuration model, and the step of solving the capacity configuration model is executed; otherwise, the capacity configuration of each unit in the energy station is determined according to the capacity configuration model corresponding to the lowest annual total resource consumption among the annual total resource consumption on different typical days. Exemplary, Figure 3 A flowchart of a method for configuring multiple integrated energy stations taking into account the energy demand of the semiconductor industry chain provided by an embodiment of the present invention is shown in FIG. Figure 3 As shown, the number of units in each energy station and the capacity of energy storage equipment can be randomly configured. The number of iterations D=1 is set, and the iteration starts until the maximum number of iterations is reached.

[0049] In one embodiment, the objective function of the multi-energy station resource generation model is for:

[0050]

[0051]

[0052]

[0053]

[0054]

[0055] in, The cost of interaction between energy station m and the grid is generated, The cost of interaction between energy stations m is generated, R ORC (t) is the energy cost of waste heat recovery; R PV (t) the cost of energy saved by photovoltaic power generation equipment; is the electric energy output from energy station m to the grid on the i-th typical day, is the electric energy obtained by energy station m from the grid on the i-th typical day; is the electric power output of energy station m participating in the inter-station interaction on the i-th typical day, is the electric power obtained by energy station m participating in inter-station interaction on the i-th typical day; is the cost of energy station m obtaining electricity from the grid, The cost of energy station m exporting electricity to the grid; is the cost of obtaining electricity between energy stations m, is the cost of exporting electricity between energy stations m; is the waste heat power generation of the nth low-temperature waste heat power generation unit on the ith typical day, is the photovoltaic power generation of the nth photovoltaic power generation equipment on the i-th typical day; C e is the unit cost of obtaining electric energy from the grid, and N is the total number of unit types in the energy station.

[0056] In one embodiment, the objective function C of the capacity configuration model cost for:

[0057] C cost =min(C inv +C oper )

[0058]

[0059] Among them, C cost is the annual total resource consumption of the energy station, C inv is the investment cost of the energy station, C oper The operating cost of the energy station; is the unit input cost coefficient of the nth unit of the mth energy station, is the annual equal investment conversion coefficient of the nth unit of the mth energy station, is the fixed operation and maintenance coefficient of the nth unit of the mth energy station; S m,n is the configuration capacity of the nth unit of the mth energy station, L m,nis the life of the nth unit of the mth energy station, d is the resource loss rate, I is the total number of types on a typical day, λ i is the number of the i-th typical day, is the operating cost of the mth energy station during the tth period on the ith typical day;

[0060]

[0061] in, is the operating cost of the i-th typical daytime gas boiler at the m-th energy station, is the operating cost of the i-th typical daytime lithium bromide absorption chiller at the m-th energy station, is the operating cost of the low-temperature waste heat generator set in the i-th typical daytime at the m-th energy station, is the operating cost of the electric boiler in the mth energy station under the ith typical day, is the operating cost of the photovoltaic power generation equipment of the mth energy station under the i-th typical day; f GB is the operating cost coefficient of the gas boiler, f AR is the operating cost coefficient of the lithium bromide absorption chiller, f ORC is the operating cost coefficient of the low-temperature waste heat generator set, f EB is the operating cost coefficient of the electric boiler, f PV is the operating cost coefficient of photovoltaic power generation equipment; is the output of the ith typical daytime gas boiler at the mth energy station, is the output of the i-th typical daytime lithium bromide absorption chiller at the m-th energy station, is the output of the low-temperature waste heat generator set in the i-th typical daytime at the m-th energy station, is the output of the electric boiler in the mth energy station under the ith typical day, is the output of the photovoltaic power generation equipment of the mth energy station under the i-th typical day.

[0062] In one embodiment, the conventional unit operation constraints include at least a gas boiler model, a lithium bromide absorption chiller model, a low-temperature waste heat power generation unit model based on an organic Rankine cycle ORC, an electric boiler model, a photovoltaic power generation equipment model, an electric energy storage equipment model, and a thermal energy storage equipment model;

[0063] Wherein, the gas boiler model is:

[0064]

[0065] in, is the output power of the nth gas boiler in the mth energy station during period t, ε gas is the calorific value of the natural gas input to the gas boiler, ηGB is the heating efficiency of the gas boiler, is the gas consumption of the nth gas boiler in the mth energy station during period t, The upper limit of natural gas input for gas boilers, The lower limit of natural gas input for gas boilers, is the upper limit of the gas boiler’s climbing capacity. It is the lower limit of the climbing of the gas boiler;

[0066] The lithium bromide absorption refrigerator model is:

[0067]

[0068] in, is the thermal power absorbed by the nth lithium bromide absorption refrigerator in the mth energy station, is the cooling power generated by the nth lithium bromide absorption chiller in the mth energy station, η LBR is the refrigeration efficiency of the lithium bromide absorption refrigerator, is the upper limit of the heat power absorbed by the lithium bromide absorption refrigerator, It is the lower limit of heat power absorbed by lithium bromide absorption refrigerator;

[0069] The ORC low temperature waste heat power generation unit model is:

[0070]

[0071] in, is the waste heat power generation power of the nth ORC low-temperature waste heat power generation unit in the mth energy station during period t, is the waste heat power consumed by the nth ORC low-temperature waste heat power generation unit in the mth energy station during period t; η ORC The power generation efficiency of the ORC low-temperature waste heat power generation unit; is the maximum power of the ORC low-temperature waste heat generator set; is the maximum value of the climbing power of the ORC low-temperature waste heat generator set, It is the minimum value of the climbing power of the ORC low-temperature waste heat generator set;

[0072] The electric boiler model is:

[0073]

[0074] in, is the upper limit of the input power of the electric boiler, is the input power of the nth electric boiler in the mth energy station, is the output thermal power of the nth electric boiler in the mth energy station, η EB is the conversion efficiency of the electric boiler;

[0075] The photovoltaic power generation equipment model is:

[0076]

[0077] in, is the output power of the nth photovoltaic power generation equipment in the mth energy station during period t, η PV is the power generation efficiency of photovoltaic power generation equipment, S PV is the area of ​​photovoltaic power generation equipment, is the light intensity of the nth photovoltaic power generation equipment in the mth energy station during period t;

[0078] The electric energy storage device model is:

[0079]

[0080] in, is the charging power of the nth energy storage device at the mth energy station during period t, is the discharge power of the nth energy storage device in the mth energy station during period t, is the maximum charging power of the electric energy storage device, is the maximum discharge power of the energy storage device, η ES,in is the charging efficiency of the energy storage device, η ES,out is the discharge efficiency of the electrical energy storage device, is the charging state variable during period t, is the discharge state variable during period t, is the state of charge of the nth energy storage device in the mth energy station during time period t, S ES is the energy storage capacity of the electrical energy storage device, is the upper limit of the energy storage capacity of the electric energy storage device, It is the lower limit of energy storage capacity of electric energy storage equipment;

[0081] The thermal energy storage device model is:

[0082]

[0083] in, is the thermal storage power of the nth thermal energy storage device in the mth energy station during period t, is the heat release power of the nth thermal energy storage device in the mth energy station during period t, is the maximum heat storage power of the thermal energy storage device, is the maximum heat release power of the thermal energy storage device, η HS is the heat dissipation rate of the thermal energy storage device, η HS,in is the heat storage efficiency of the thermal energy storage device, η HS,outis the heat release efficiency of the thermal energy storage device, is the heat storage state variable of the thermal energy storage device during period t, is the heat release state variable of the thermal energy storage device during period t, is the capacity of the nth thermal energy storage device at the mth energy station during period t, is the upper limit of the capacity of the thermal energy storage device, It is the lower limit of the capacity of thermal energy storage equipment.

[0084] In this embodiment, the conventional unit operation constraints may include a gas boiler model, a lithium bromide absorption chiller model, a low-temperature waste heat power generation unit model based on an organic Rankine cycle ORC, an electric boiler model, a photovoltaic power generation equipment model, an electric energy storage equipment model, and a thermal energy storage equipment model.

[0085] In one embodiment, the heat load supply and demand balance constraint is:

[0086]

[0087] in, is the electric load of the mth energy station in period t, is the output power of the nth power supply unit of the mth energy station in period t, is the input power of the nth power consumption unit in the mth energy station during period t, is the charging power of the nth energy storage device at the mth energy station during period t, is the discharge power of the nth energy storage device in the mth energy station during period t, is the electric power obtained by the mth energy station from the power grid during period t, is the electric power output from the mth energy station to the grid during period t, is the electric power obtained by the interaction of the mth energy station unit in time period t, is the electric power of the interactive output of the mth energy station unit in period t, N E is the number of power supply units running in energy station m, N ES is the number of power storage devices running in energy station m, N cone is the number of power-consuming units running in energy station m;

[0088] The power load supply and demand balance constraint is:

[0089]

[0090] in, is the heat load of the mth energy station in period t, is the output power of the nth heating unit of the mth energy station in period t, is the input power of the nth heat-consuming unit in the mth energy station during period t, is the charging power of the nth heat storage device in the mth energy station during period t, is the energy release power of the nth heat storage device in the mth energy station during period t, N H is the number of heating units running in energy station m, N HS is the number of heat storage devices running in energy station m, N conh is the number of heat-consuming units operating in energy station m.

[0091] In one embodiment, the electric power interaction constraint between energy stations is:

[0092]

[0093] in, is the state variable of the interaction input between energy stations m in period t, is the state variable of the interaction output between energy stations m during period t, is the upper limit of the interactive power between energy stations m, is the actual interaction power between energy stations m, η ex is the power transmission efficiency between energy station m.

[0094] The embodiments of the present invention provide several specific implementation methods based on the technical solutions of the above embodiments.

[0095] As a specific implementation method of this embodiment, Figure 4 A schematic diagram of the operation of a silicon raw material preparation energy station provided by an embodiment of the present invention, Figure 5 A schematic diagram of the operation of a wafer processing energy station provided by an embodiment of the present invention, Figure 6 A schematic diagram of the operation of a chip manufacturing test energy station provided by an embodiment of the present invention. Figure 4-6 As shown in the figure, compared with the independent operation of each energy station, regional interconnection can significantly improve the utilization rate of energy by realizing power connection between energy stations. On the basis of self-sufficiency, the energy utilization rate can be increased by transmitting the power of energy stations with excess power to the power grid or other energy stations; through waste heat recovery and photovoltaic power generation equipment, the cost of using the external power grid can be effectively reduced.

[0096] Specifically speaking, the operation of energy stations in different seasons, taking autumn and winter as an example:

[0097] In autumn, the chip manufacturing and testing energy station has a large photovoltaic power generation capacity, which can fully meet the needs within the station, and the excess electricity is supplied to other energy stations through the power interconnection line; the silicon raw material preparation energy station has a small heating demand, generates less waste heat, has a small temperature difference between the waste heat and the environment, has low thermal engine efficiency, and generates little ORC power. It needs to obtain electricity from the interconnection line to meet the needs within the station, and the excess electricity is supplied to the wafer processing energy station through the power interconnection line.

[0098] In winter, the heat energy demand of the silicon raw material preparation energy station increases, gas boilers are used frequently, the heat supply to the reduction furnace through the heat storage system increases, the waste heat increases, and the ORC generates a large amount of electricity, which can fully meet the needs of the station. The excess electricity is supplied to other energy stations through the power interconnection line. The chip manufacturing and testing energy station has a small amount of photovoltaic power generation and needs to obtain electricity from the interconnection line to meet the needs of the station. The excess electricity is supplied to the wafer processing energy station through the power interconnection line.

[0099] Existing industrial integrated energy systems often focus on the energy needs of independent industrial links, lacking the interconnection between multiple energy stations in industrial clusters. Therefore, the technical solution of the embodiment of the present invention, by utilizing the role of electricity as a link in the integrated energy system, constructs a friendly interactive energy supply model that can meet the energy needs of regional integrated energy stations and various links in the semiconductor industry chain, realizes the collaborative cooperation between different integrated energy stations, optimizes energy distribution, and improves overall energy efficiency.

[0100] Embodiment 2

[0101] Figure 7 A schematic diagram of the structure of a multi-integrated energy station configuration device that takes into account the energy demand of the semiconductor industry chain provided in Example 2 of the present invention. The device can be used to distribute energy between different energy stations in an integrated energy system, wherein the device can be implemented by software and / or hardware and is generally integrated on an electronic device.

[0102] like Figure 7 As shown, the device comprises:

[0103] An acquisition module 210 is used to acquire a typical daily load of each energy station in the semiconductor industry chain and operating parameters of each unit in the energy station;

[0104] Configuration module 220, used for randomly configuring the number and capacity of energy supply units and energy storage units in the energy station;

[0105] A first construction module 230 is used to construct a comprehensive energy station model according to the operating parameters, the typical daily load, and the number and capacity of the energy supply units and the energy storage units;

[0106] The second construction module 240 is used to construct a multi-energy station resource generation model and a capacity configuration model;

[0107] A solving module 250, configured to solve the capacity configuration model based on the integrated energy station model and the multi-energy station resource generation model to obtain the capacity configuration of each unit in the energy station;

[0108] Among them, the comprehensive energy station model includes the conventional unit operation constraints of each energy station, the thermal load supply and demand balance constraints, the electric load supply and demand balance constraints and the electric power interaction constraints between energy stations; the multi-energy station resource generation model takes the maximum sum of the interaction cost between the energy station and the power grid, the interaction cost between energy stations and the amount of energy resources saved as the objective function; the capacity configuration model takes the minimum annual total resource consumption of the energy station as the objective function.

[0109] This embodiment provides a multi-comprehensive energy station configuration device that takes into account the energy demand of the semiconductor industry chain, including: an acquisition module, used to obtain the typical daily load of each energy station in the semiconductor industry chain, and the operating parameters of each unit in the energy station; a configuration module, used to randomly configure the number and capacity of the energy supply units and energy storage units in the energy station; a first construction module, used to construct a comprehensive energy station model according to the operating parameters, the typical daily load, and the number and capacity of the energy supply units and energy storage units; a second construction module, used to construct a multi-energy station resource generation model and a capacity configuration model; a solution module, used to solve the capacity configuration model based on the comprehensive energy station model and the multi-energy station resource generation model, and obtain the capacity configuration of each unit in the energy station. By constructing a comprehensive energy station model, a multi-energy station resource generation model and a capacity configuration model, and solving the capacity configuration model, it is possible to achieve optimal energy allocation and improve energy utilization efficiency, thereby solving the problem of low energy utilization efficiency and high energy cost caused by the inability of the prior art to achieve optimal energy allocation.

[0110] Furthermore, the solution module 250 includes:

[0111] Determine the output status of each unit in the energy station on different typical days according to the power load supply and demand balance constraints in the multi-energy station resource generation model and the integrated energy station model;

[0112] Solving the capacity configuration model based on the output status of each unit and the integrated energy station model to obtain the annual total resource consumption under different typical days;

[0113] When the number of iterations is less than the maximum number of iterations, reconfigure the number and capacity of the energy supply units and energy storage units in the energy station according to the capacity configuration model, and execute the step of solving the capacity configuration model;

[0114] Otherwise, the capacity configuration of each unit in the energy station is determined according to the capacity configuration model corresponding to the lowest annual total resource consumption among the annual total resource consumption on different typical days.

[0115] Furthermore, the objective function of the multi-energy station resource generation model is for:

[0116]

[0117]

[0118]

[0119]

[0120]

[0121] in, The cost of interaction between energy station m and the grid is generated, The cost of interaction between energy stations m is generated, R ORC (t) is the energy cost of waste heat recovery; R PV (t) the cost of energy saved by photovoltaic power generation equipment; is the electric energy output from energy station m to the grid on the i-th typical day, is the electric energy obtained by energy station m from the grid on the i-th typical day; is the electric power output of energy station m participating in the inter-station interaction on the i-th typical day, is the electric power obtained by energy station m participating in inter-station interaction on the i-th typical day; is the cost of energy station m obtaining electricity from the grid, The cost of energy station m exporting electricity to the grid; is the cost of obtaining electricity between energy stations m, is the cost of exporting electricity between energy stations m; is the waste heat power generation of the nth low-temperature waste heat power generation unit on the ith typical day, is the photovoltaic power generation of the nth photovoltaic power generation equipment on the i-th typical day; C e is the unit cost of obtaining electric energy from the grid, and N is the total number of unit types in the energy station.

[0122] Furthermore, the objective function C of the capacity configuration model cost for:

[0123] C cost =min(C inv +C oper )

[0124]

[0125] Among them, C cost is the annual total resource consumption of the energy station, C inv is the investment cost of the energy station, C oper The operating cost of the energy station; is the unit input cost coefficient of the nth unit of the mth energy station, is the annual equal investment conversion coefficient of the nth unit of the mth energy station, is the fixed operation and maintenance coefficient of the nth unit of the mth energy station; S m,n is the configuration capacity of the nth unit of the mth energy station, L m,n is the life of the nth unit of the mth energy station, d is the resource loss rate, I is the total number of types on a typical day, λ i is the number of the i-th typical day, is the operating cost of the mth energy station during the tth period on the ith typical day;

[0126]

[0127] in, is the operating cost of the i-th typical daytime gas boiler at the m-th energy station, is the operating cost of the i-th typical daytime lithium bromide absorption chiller at the m-th energy station, is the operating cost of the low-temperature waste heat generator set in the i-th typical daytime at the m-th energy station, is the operating cost of the electric boiler in the mth energy station under the ith typical day, is the operating cost of the photovoltaic power generation equipment of the mth energy station under the i-th typical day; f GB is the operating cost coefficient of the gas boiler, f AR is the operating cost coefficient of the lithium bromide absorption chiller, f ORC is the operating cost coefficient of the low-temperature waste heat generator set, f EB is the operating cost coefficient of the electric boiler, f PV is the operating cost coefficient of photovoltaic power generation equipment; is the output of the ith typical daytime gas boiler at the mth energy station, is the output of the i-th typical daytime lithium bromide absorption chiller at the m-th energy station, is the output of the low-temperature waste heat generator set in the i-th typical daytime at the m-th energy station, is the output of the electric boiler in the mth energy station under the ith typical day, is the output of the photovoltaic power generation equipment of the mth energy station under the i-th typical day.

[0128] Furthermore, the conventional unit operation constraints include at least a gas boiler model, a lithium bromide absorption chiller model, a low-temperature waste heat power generation unit model based on an organic Rankine cycle ORC, an electric boiler model, a photovoltaic power generation equipment model, an electric energy storage equipment model, and a thermal energy storage equipment model;

[0129] Wherein, the gas boiler model is:

[0130]

[0131] in, is the output power of the nth gas boiler in the mth energy station during period t, ε gas is the calorific value of the natural gas input to the gas boiler, η GB is the heating efficiency of the gas boiler, is the gas consumption of the nth gas boiler in the mth energy station during period t, The upper limit of natural gas input for gas boilers, The lower limit of natural gas input for gas boilers, is the upper limit of the gas boiler’s climbing capacity. It is the lower limit of the climbing of the gas boiler;

[0132] The lithium bromide absorption refrigerator model is:

[0133]

[0134] in, is the thermal power absorbed by the nth lithium bromide absorption refrigerator in the mth energy station, is the cooling power generated by the nth lithium bromide absorption chiller in the mth energy station, η LBR is the refrigeration efficiency of the lithium bromide absorption refrigerator, is the upper limit of the heat power absorbed by the lithium bromide absorption refrigerator, It is the lower limit of heat power absorbed by lithium bromide absorption refrigerator;

[0135] The ORC low temperature waste heat power generation unit model is:

[0136]

[0137] in, is the waste heat power generation power of the nth ORC low-temperature waste heat power generation unit in the mth energy station during period t, is the waste heat power consumed by the nth ORC low-temperature waste heat power generation unit in the mth energy station during period t; η ORC The power generation efficiency of the ORC low-temperature waste heat power generation unit; is the maximum power of the ORC low-temperature waste heat generator set; is the maximum value of the climbing power of the ORC low-temperature waste heat generator set, It is the minimum value of the climbing power of the ORC low-temperature waste heat generator set;

[0138] The electric boiler model is:

[0139]

[0140] in, is the upper limit of the input power of the electric boiler, is the input power of the nth electric boiler in the mth energy station, is the output thermal power of the nth electric boiler in the mth energy station, η EB is the conversion efficiency of the electric boiler;

[0141] The photovoltaic power generation equipment model is:

[0142]

[0143] in, is the output power of the nth photovoltaic power generation equipment in the mth energy station during period t, η PV is the power generation efficiency of photovoltaic power generation equipment, S PV is the area of ​​photovoltaic power generation equipment, is the light intensity of the nth photovoltaic power generation equipment in the mth energy station during period t;

[0144] The electric energy storage device model is:

[0145]

[0146] in, is the charging power of the nth energy storage device at the mth energy station during period t, is the discharge power of the nth energy storage device in the mth energy station during period t, is the maximum charging power of the electric energy storage device, is the maximum discharge power of the energy storage device, η ES,in is the charging efficiency of the energy storage device, η ES,out is the discharge efficiency of the electrical energy storage device, is the charging state variable during period t, is the discharge state variable during period t, is the state of charge of the nth energy storage device in the mth energy station during time period t, S ES is the energy storage capacity of the electrical energy storage device, is the upper limit of the energy storage capacity of the electric energy storage device, It is the lower limit of energy storage capacity of electric energy storage equipment;

[0147] The thermal energy storage device model is:

[0148]

[0149] in, is the thermal storage power of the nth thermal energy storage device in the mth energy station during period t, is the heat release power of the nth thermal energy storage device in the mth energy station during period t, is the maximum heat storage power of the thermal energy storage device, is the maximum heat release power of the thermal energy storage device, η HS is the heat dissipation rate of the thermal energy storage device, η HS,in is the heat storage efficiency of the thermal energy storage device, η HS,out is the heat release efficiency of the thermal energy storage device, is the heat storage state variable of the thermal energy storage device during period t, is the heat release state variable of the thermal energy storage device during period t, is the capacity of the nth thermal energy storage device at the mth energy station during period t, is the upper limit of the capacity of the thermal energy storage device, It is the lower limit of the capacity of thermal energy storage equipment.

[0150] Furthermore, the heat load supply and demand balance constraint is:

[0151]

[0152] in, is the electric load of the mth energy station in period t, is the output power of the nth power supply unit of the mth energy station in period t, is the input power of the nth power consumption unit in the mth energy station during period t, is the charging power of the nth energy storage device at the mth energy station during period t, is the discharge power of the nth energy storage device in the mth energy station during period t, is the electric power obtained by the mth energy station from the power grid during period t, is the electric power output from the mth energy station to the grid during period t, is the electric power obtained by the interaction of the mth energy station unit in time period t, is the electric power of the interactive output of the mth energy station unit in period t, N E is the number of power supply units running in energy station m, N ES is the number of power storage devices running in energy station m, N cone is the number of power-consuming units running in energy station m;

[0153] The power load supply and demand balance constraint is:

[0154]

[0155] in, is the heat load of the mth energy station in period t, is the output power of the nth heating unit of the mth energy station in period t, is the input power of the nth heat-consuming unit in the mth energy station during period t, is the charging power of the nth heat storage device in the mth energy station during period t, is the energy release power of the nth heat storage device in the mth energy station during period t, N H is the number of heating units running in energy station m, N HS is the number of heat storage devices running in energy station m, N conh is the number of heat-consuming units operating in energy station m.

[0156] Furthermore, the electric power interaction constraint between the energy stations is:

[0157]

[0158] in, is the state variable of the interaction input between energy stations m in period t, is the state variable of the interaction output between energy stations m during period t, is the upper limit of the interactive power between energy stations m, is the actual interaction power between energy stations m, η ex is the power transmission efficiency between energy station m.

[0159] The above-mentioned multi-integrated energy station configuration device considering the energy demand of the semiconductor industry chain can execute the multi-integrated energy station configuration method considering the energy demand of the semiconductor industry chain provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0160] Embodiment 3

[0161] Figure 8A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0162] like Figure 8 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0163] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0164] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The processor 11 executes the various methods and processes described above, such as a multi-integrated energy station configuration method considering the energy needs of the semiconductor industry chain.

[0165] In some embodiments, the multi-integrated energy station configuration method considering the energy demand of the semiconductor industry chain can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the multi-integrated energy station configuration method considering the energy demand of the semiconductor industry chain described above can be executed. Alternatively, in other embodiments, the processor 11 can be configured to execute the multi-integrated energy station configuration method considering the energy demand of the semiconductor industry chain by any other appropriate means (for example, by means of firmware).

[0166] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0167] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0168] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0169] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0170] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0171] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0172] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0173] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for configuring multiple integrated energy stations considering the energy demand of the semiconductor industry chain, characterized in that: The method comprises: Obtain the typical daily load of each energy station in the semiconductor industry chain and the operating parameters of each unit in the energy station; Randomly configure the number and capacity of energy supply units and energy storage units in the energy station; Constructing a comprehensive energy station model according to the operating parameters, the typical daily load, and the number and capacity of the energy supply units and energy storage units; Construct resource generation model and capacity configuration model for multiple energy stations; Based on the integrated energy station model and the multi-energy station resource generation model, solving the capacity configuration model to obtain the capacity configuration of each unit in the energy station; Among them, the comprehensive energy station model includes the conventional unit operation constraints of each energy station, the thermal load supply and demand balance constraints, the electric load supply and demand balance constraints and the electric power interaction constraints between energy stations; the multi-energy station resource generation model takes the maximum sum of the interaction cost between the energy station and the power grid, the interaction cost between energy stations and the amount of energy resources saved as the objective function; the capacity configuration model takes the minimum annual total resource consumption of the energy station as the objective function.

2. The method according to claim 1, characterized in that: The method of solving the capacity configuration model based on the integrated energy station model and the multi-energy station resource generation model to obtain the capacity configuration of each unit in the energy station includes: Determine the output status of each unit in the energy station on different typical days according to the power load supply and demand balance constraints in the multi-energy station resource generation model and the integrated energy station model; Solving the capacity configuration model based on the output status of each unit and the integrated energy station model to obtain the annual total resource consumption under different typical days; When the number of iterations is less than the maximum number of iterations, reconfigure the number and capacity of the energy supply units and energy storage units in the energy station according to the capacity configuration model, and execute the step of solving the capacity configuration model; Otherwise, the capacity configuration of each unit in the energy station is determined according to the capacity configuration model corresponding to the lowest annual total resource consumption among the annual total resource consumption on different typical days.

3. The method according to any one of claims 1-2, characterized in that: The objective function of the multi-energy station resource generation model for: in, The cost of interaction between energy station m and the grid is generated, The cost of interaction between energy stations m is generated, R ORC (t) is the energy cost of waste heat recovery; R PV (t) the cost of energy saved for photovoltaic power generation equipment; is the electric energy output from energy station m to the grid on the i-th typical day, is the electric energy obtained by energy station m from the grid on the i-th typical day; is the electric power output of energy station m participating in the inter-station interaction on the i-th typical day, is the electric power obtained by energy station m participating in the inter-station interaction on the i-th typical day; is the cost of energy station m obtaining electricity from the grid, The cost of energy station m exporting electricity to the grid; is the cost of obtaining electricity between energy stations m, is the cost of exporting electricity between energy stations m; is the waste heat power generation of the nth low-temperature waste heat power generation unit on the i-th typical day, is the photovoltaic power generation of the nth photovoltaic power generation equipment on the i-th typical day; C e is the unit cost of obtaining electric energy from the grid, and N is the total number of unit types in the energy station.

4. The method according to any one of claims 1-2, characterized in that: The objective function C of the capacity configuration model cost for: C cost =min(C inv +C oper ) Among them, C cost is the annual total resource consumption of the energy station, C inv is the investment cost of the energy station, C oper The operating cost of the energy station; is the unit input cost coefficient of the nth unit of the mth energy station, is the annual equal investment conversion coefficient of the nth unit of the mth energy station, is the fixed operation and maintenance coefficient of the nth unit of the mth energy station; S m,n is the configuration capacity of the nth unit of the mth energy station, L m,n is the life of the nth unit of the mth energy station, d is the resource loss rate, I is the total number of types on a typical day, λ i is the number of the i-th typical day, is the operating cost of the mth energy station during the tth period on the ith typical day; in, is the operating cost of the i-th typical daytime gas boiler at the m-th energy station, is the operating cost of the i-th typical daytime lithium bromide absorption chiller at the m-th energy station, is the operating cost of the low-temperature waste heat generator set in the i-th typical daytime at the m-th energy station, is the operating cost of the electric boiler in the mth energy station under the ith typical day, is the operating cost of the photovoltaic power generation equipment of the mth energy station under the i-th typical day; f GB is the operating cost coefficient of the gas boiler, f AR is the operating cost coefficient of the lithium bromide absorption chiller, f ORC is the operating cost coefficient of the low-temperature waste heat generator set, f EB is the operating cost coefficient of the electric boiler, f PV is the operating cost coefficient of photovoltaic power generation equipment; is the output of the ith typical daytime gas boiler at the mth energy station, is the output of the i-th typical daytime lithium bromide absorption chiller at the m-th energy station, is the output of the low-temperature waste heat generator set in the i-th typical daytime at the m-th energy station, is the output of the electric boiler in the mth energy station under the ith typical day, is the output of the photovoltaic power generation equipment of the mth energy station under the i-th typical day.

5. The method according to claim 1, characterized in that The conventional unit operation constraints at least include a gas boiler model, a lithium bromide absorption chiller model, a low-temperature waste heat power generation unit model based on an organic Rankine cycle (ORC), an electric boiler model, a photovoltaic power generation equipment model, an electric energy storage equipment model, and a thermal energy storage equipment model; Wherein, the gas boiler model is: in, is the output power of the nth gas boiler in the mth energy station during period t, ε gas is the calorific value of the natural gas input to the gas boiler, η GB is the heating efficiency of the gas boiler, is the gas consumption of the nth gas boiler in the mth energy station during period t, The upper limit of natural gas input for gas boilers, The lower limit of natural gas input for gas boilers, is the upper limit of the gas boiler’s climbing capacity. It is the lower limit of the climbing of the gas boiler; The lithium bromide absorption refrigerator model is: in, is the thermal power absorbed by the nth lithium bromide absorption refrigerator in the mth energy station, is the cooling power generated by the nth lithium bromide absorption chiller in the mth energy station, η LBR is the refrigeration efficiency of the lithium bromide absorption refrigerator, is the upper limit of the heat power absorbed by the lithium bromide absorption refrigerator, It is the lower limit of heat power absorbed by lithium bromide absorption refrigerator; The ORC low temperature waste heat power generation unit model is: in, is the waste heat power generation power of the nth ORC low-temperature waste heat power generation unit in the mth energy station during period t, is the waste heat power consumed by the nth ORC low-temperature waste heat power generation unit in the mth energy station during period t; η ORC The power generation efficiency of the ORC low-temperature waste heat power generation unit; is the maximum power of the ORC low-temperature waste heat generator set; is the maximum value of the climbing power of the ORC low-temperature waste heat generator set, It is the minimum value of the climbing power of the ORC low-temperature waste heat generator set; The electric boiler model is: in, is the upper limit of the input power of the electric boiler, is the input power of the nth electric boiler in the mth energy station, is the output thermal power of the nth electric boiler in the mth energy station, η EB is the conversion efficiency of the electric boiler; The photovoltaic power generation equipment model is: in, is the output power of the nth photovoltaic power generation equipment in the mth energy station during period t, η PV is the power generation efficiency of photovoltaic power generation equipment, S PV is the area of ​​photovoltaic power generation equipment, is the light intensity of the nth photovoltaic power generation equipment in the mth energy station during period t; The electric energy storage device model is: in, is the charging power of the nth energy storage device at the mth energy station during period t, is the discharge power of the nth energy storage device in the mth energy station during period t, is the maximum charging power of the electric energy storage device, is the maximum discharge power of the energy storage device, η ES,in is the charging efficiency of the electrical energy storage device, η ES,out is the discharge efficiency of the electrical energy storage device, is the charging state variable during period t, is the discharge state variable during period t, is the state of charge of the nth energy storage device in the mth energy station during time period t, S ES is the energy storage capacity of the electrical energy storage device, is the upper limit of the energy storage capacity of the electric energy storage device, It is the lower limit of energy storage capacity of electric energy storage equipment; The thermal energy storage device model is: in, is the thermal storage power of the nth thermal energy storage device in the mth energy station during period t, is the heat release power of the nth thermal energy storage device in the mth energy station during period t, is the maximum heat storage power of the thermal energy storage device, is the maximum heat release power of the thermal energy storage device, η HS is the heat dissipation rate of the thermal energy storage device, η HS,in is the heat storage efficiency of the thermal energy storage device, η HS,out is the heat release efficiency of the thermal energy storage device, is the heat storage state variable of the thermal energy storage device during period t, is the heat release state variable of the thermal energy storage device during period t, is the capacity of the nth thermal energy storage device at the mth energy station during period t, is the upper limit of the capacity of the thermal energy storage device, It is the lower limit of the capacity of thermal energy storage equipment.

6. The method according to claim 1, characterized in that The heat load supply and demand balance constraint is: in, is the electric load of the mth energy station in period t, is the output power of the nth power supply unit of the mth energy station in period t, is the input power of the nth power consumption unit in the mth energy station during period t, is the charging power of the nth energy storage device at the mth energy station during period t, is the discharge power of the nth energy storage device in the mth energy station during period t, is the electric power obtained by the mth energy station from the power grid during period t, is the electric power output from the mth energy station to the grid during period t, is the electric power obtained by the interaction of the mth energy station unit in time period t, is the electric power of the interactive output of the mth energy station unit in period t, N E is the number of power supply units running in energy station m, N ES is the number of power storage devices running in energy station m, N cone is the number of power-consuming units running in energy station m; The power load supply and demand balance constraint is: in, is the heat load of the mth energy station in period t, is the output power of the nth heating unit of the mth energy station in period t, is the input power of the nth heat-consuming unit in the mth energy station during period t, is the charging power of the nth heat storage device in the mth energy station during period t, is the energy release power of the nth heat storage device in the mth energy station during period t, N H is the number of heating units running in energy station m, N HS is the number of heat storage devices running in energy station m, N conh is the number of heat-consuming units operating in energy station m.

7. The method according to claim 1, characterized in that The electric power interaction constraint between energy stations is: in, is the state variable of the interactive input between energy stations m in period t, is the state variable of the interaction output between energy stations m during period t, is the upper limit of the interactive power between energy stations m, is the actual interaction power between energy stations m, η ex is the power transmission efficiency between energy station m.

8. A multi-integrated energy station configuration device considering the energy demand of the semiconductor industry chain, characterized in that: The device comprises: An acquisition module, used to acquire the typical daily load of each energy station in the semiconductor industry chain and the operating parameters of each unit in the energy station; A configuration module, used to randomly configure the number and capacity of energy supply units and energy storage units in the energy station; A first construction module is used to construct a comprehensive energy station model according to the operating parameters, the typical daily load, and the number and capacity of the energy supply units and the energy storage units; The second building module is used to build a multi-energy station resource generation model and a capacity configuration model; A solution module, used to solve the capacity configuration model based on the comprehensive energy station model and the multi-energy station resource generation model to obtain the capacity configuration of each unit in the energy station; Among them, the comprehensive energy station model includes the conventional unit operation constraints of each energy station, the thermal load supply and demand balance constraints, the electric load supply and demand balance constraints and the electric power interaction constraints between energy stations; the multi-energy station resource generation model takes the maximum sum of the interaction cost between the energy station and the power grid, the interaction cost between energy stations and the amount of energy resources saved as the objective function; the capacity configuration model takes the minimum annual total resource consumption of the energy station as the objective function.

9. An electronic device, characterized in that: The device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the multi-integrated energy station configuration method that takes into account the energy needs of the semiconductor industry chain as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the multi-integrated energy station configuration method that takes into account the energy needs of the semiconductor industry chain as described in any one of claims 1-7 when executed.

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