A source and storage resource collaborative optimization regulation method and device

Abstract

The application provides a source storage and computing resource collaborative optimization regulation method and device. A multi-objective optimization model of a source storage and computing system is constructed. The multi-objective optimization model includes an energy efficiency model and a net income model of the source storage and computing system, the energy efficiency model is constructed according to the power consumption of the computing load, and the net income model is constructed according to the energy purchase cost of the computing load. The output of the obtained new energy is used to solve the multi-objective optimization model by using the epsilon constraint method, and the optimized energy efficiency and the optimized net income of the source storage and computing system are obtained. The source storage and computing system is optimized and regulated according to the optimized energy efficiency and the optimized net income. The multi-objective optimization model is constructed by the power consumption and the energy purchase cost of the computing load, that is, not only the energy efficiency of the computing load is considered, but also the economy of the computing load is considered, the stable operation of the computing load is ensured, and the energy consumption of the computing load is greatly reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of integrated energy, in particular to a source-storage-computing resource collaborative optimization regulation method and device. BACKGROUND

[0002] With the advent of digital transformation, the era of big data, and the promotion of new infrastructure strategy, data centers, as the core hub of modern digital technology and the platform for carrying computing power, are in a stage of rapid expansion and development. Data centers are generally facing challenges such as limited resources for optimization of computing power-electricity collaboration and difficulty in effectively utilizing waste heat resources.

[0003] In the case of peak operation of computing power load, the existing technology usually reduces the power consumption of the cooling system for heat dissipation of the computing power load by increasing the indoor temperature set point of the computing power load. When the electricity price is low, the cooling system maintains the indoor temperature at a lower level. When the electricity price is high, the cooling system reduces the output by using thermal inertia, and the output of the cooling system will maintain the temperature for a period of time. The cooling system adopts different heat dissipation modes at different electricity prices, although the power consumption of the cooling system is reduced, the computing power load cannot be stably operated, and the energy consumption of the computing power load is higher. SUMMARY

[0004] In order to solve the problem that the computing power load cannot be stably operated and the energy consumption of the computing power load is high in the prior art, the present application provides a source-storage-computing resource collaborative optimization regulation method, which can include:

[0005] A multi-objective optimization model of a source-storage-computing system is constructed. The source-storage-computing system includes new energy equipment, energy storage equipment and computing power load. The multi-objective optimization model includes an energy efficiency model of the source-storage-computing system and a net income model, the energy efficiency model is constructed according to the power consumption of the computing power load, and the net income model is constructed according to the energy purchase cost of the computing power load.

[0006] The multi-objective optimization model is solved by using an epsilon constraint method according to the output of the obtained new energy, and the optimized energy efficiency and optimized net income of the source-storage-computing system are obtained.

[0007] The source-storage-computing system is optimized and regulated according to the optimized energy efficiency and optimized net income.

[0008] In some possible implementation manners, the energy efficiency model includes a first objective function and a first constraint condition.

[0009] The first objective function is constructed with the maximum energy efficiency of the source-storage-computing system as the target.

[0010] The first constraint condition includes a power purchase quantity constraint, a heat purchase quantity constraint, a hydrogen purchase quantity constraint, an output constraint and a energy sale price constraint.

[0011] In some other possible implementations, the net income model includes a second objective function and a second constraint.

[0012] The second objective function is constructed with the goal of maximizing the net profit of the source storage computing system.

[0013] The second set of constraints includes electricity purchase price constraints, hydrogen purchase price constraints, heat purchase price constraints, and waste heat utilization price constraints.

[0014] For example, the first objective function satisfies:

[0015]

[0016] in, Indicates the energy efficiency of the source-storage computing system. This represents the cooling load of the computing power load. This represents the power consumption of the computing load. Indicates the first j The standard coal equivalent of the power output of the new energy source meets the requirements. , Indicates the first j The contribution of new energy sources This represents the conversion factor. n This indicates the number of types of new energy sources, which include wind power and photovoltaics.

[0017] Optional, the electricity purchase constraint must be met. :in, Indicates computing load at t Real-time electricity purchase volume Indicates computing load at t The maximum amount of electricity that can be purchased at any given time.

[0018] Purchase of heat constraints satisfied .in, Indicates computing load at t Purchase calories at any time Indicates computing load at t The maximum amount of calories that can be purchased at any given time.

[0019] Hydrogen purchase constraints met .in, Indicates computing load at t The amount of hydrogen purchased at any given time. Indicates computing load at t The upper limit for hydrogen purchases at any given time.

[0020] Output constraints satisfied ,in, Indicates new energy equipment or energy storage equipment in t Constant effort Indicates new energy equipment or energy storage equipment int The minimum effort required at any given moment. Indicates new energy equipment or energy storage equipment in t The maximum output at any given moment.

[0021] Energy sales price constraints are met ,in, Indicates the first k This type of energy in t The price of energy sold at any given time, including electricity, heat, or hydrogen. Indicates the first k This type of energy in t The lower limit of the selling price of energy at any given time. Indicates the first k This type of energy in t The upper limit of the energy sales price at any given time.

[0022] In some other possible implementations, the second objective function satisfies:

[0023]

[0024] in, This represents the net revenue of the source storage computing system. Indicates the first k This type of energy in t The price of energy sold at any given moment. This indicates the optimization time interval.

[0025] T The shiftable time of the computing load satisfies , N Indicates the amount of computing power load. Indicates the first n The state of computing load =0 indicates the first n The computing load did not shift. =1 indicates the first n The computing load has shifted.

[0026] Indicates computing load at t The translation compensation cost at any given time satisfies , This represents the unit compensation price for computing power load. Indicates the computing load before shifting t Load demand at any given time Indicates the shift of computing power load. t The load demand at any given moment.

[0027] Indicates computing load at t The cost of purchasing energy at any time, to meet .in, Indicates computing load at t Real-time heating costs, meet , Indicates computing load at t Purchase calories at any time Indicates computing load at t The price of hot water at any time. Indicates computing load at t The amount of waste heat utilized at any given time. Indicates computing load at t The price of utilizing residual heat at any given time. Indicates computing load at t The cost of purchasing electricity at any time, to meet , Indicates computing load at t Real-time electricity purchase volume Indicates computing load at t The electricity price at any given time. Indicates computing load at t The cost of purchasing hydrogen at any time, to meet , Indicates computing load at t The amount of hydrogen purchased at any given time. Indicates computing load at t The price of hydrogen at any given time.

[0028] Optionally, the electricity purchase price constraint is satisfied. ,in, Indicates computing load at t The minimum amount of electricity that can be purchased at any time. Computing load at t The maximum amount of electricity that can be purchased at any given time.

[0029] Hydrogen purchase price constraint satisfied .in, Indicates computing load at t The lower limit of the hydrogen purchase price at any given time. Indicates computing load at t The upper limit of the price for purchasing hydrogen at any given time.

[0030] Heat purchase price constraints satisfied .in, Indicates computing load at t The lower limit of the purchase price of heat at any time. Indicates computing load at t The upper limit of the purchase price of heat at any time.

[0031] Waste heat utilization price meets ,in, Indicates computing load att a lower limit of the waste heat utilization price at the time, indicates that the computing power load is in t an upper limit of the waste heat utilization price at the time.

[0032] Exemplarily, the multi-objective optimization model is solved by using an epsilon constraint method to obtain the optimized energy efficiency and the optimized net income of the source-storage-computing system, including:

[0033] The first objective function of the energy efficiency model and the second objective function of the net income model are normalized.

[0034] The boundary points of the Pareto front set are obtained by solving the normalized first objective function and the normalized second objective function, and the boundary line of the Pareto front set is determined according to the boundary points of the Pareto front set and the output of the new energy.

[0035] The constraint point epsilon is solved according to the boundary line of the Pareto front set.

[0036] The optimized energy efficiency of the source-storage-computing system is obtained by solving the normalized first objective function according to the constraint point epsilon, and the optimized net income of the source-storage-computing system is obtained by solving the normalized second objective function according to the constraint point epsilon.

[0037] Optionally, the source-storage-computing system is optimized and regulated according to the optimized energy efficiency and the optimized net income, including:

[0038] The output and the running state of the new energy equipment, the charge and discharge capacity and the running state of the energy storage equipment, and the translatable load amount of the computing power load are obtained. The running state includes a normal state and a fault state, and the translatable load amount of the computing power load is the difference between the load demand amount before the computing power load is translated and the load demand amount after the computing power load is translated.

[0039] The power generation of the new energy equipment is evaluated according to the output and the running state of the new energy equipment, taking the optimized energy efficiency and the optimized net income as the optimization and regulation targets.

[0040] The first regulation instruction and the second regulation instruction are generated, taking the optimized energy efficiency and the optimized net income as the optimization and regulation targets, the charge and discharge capacity and the running state of the energy storage equipment are optimized and regulated according to the first regulation instruction, and the translatable load amount of the computing power load is optimized and regulated according to the second regulation instruction.

[0041] On the other hand, the application provides a source-storage-computing resource cooperative optimization and regulation device, which can include:

[0042] The modeling module is used to construct a multi-objective optimization model for the source-storage-computing system. This system includes new energy equipment, energy storage equipment, and computing load. The multi-objective optimization model comprises an energy efficiency model and a net revenue model for the source-storage-computing system. The energy efficiency model is constructed based on the power consumption of the computing load, while the net revenue model is constructed based on the energy purchase cost of the computing load.

[0043] The solution module is used to solve a multi-objective optimization model based on the obtained power output of new energy sources and using the ε-constraint method to obtain the optimized energy efficiency and optimized net benefit of the source-storage-computing system.

[0044] The optimization and control module is used to optimize and control the source-storage computing system based on optimized energy efficiency and optimized net income.

[0045] Optionally, the energy efficiency model includes a first objective function and a first constraint condition.

[0046] The first objective function is constructed with the goal of maximizing the energy efficiency of the source-storage computing system.

[0047] The first set of constraints includes constraints on electricity purchase, heat purchase, hydrogen purchase, power output, and energy sales price.

[0048] For example, the net income model includes a second objective function and a second constraint.

[0049] The second objective function is constructed with the goal of maximizing the net profit of the source storage computing system.

[0050] The second set of constraints includes electricity purchase price constraints, hydrogen purchase price constraints, heat purchase price constraints, and waste heat utilization price constraints.

[0051] In some possible implementations, the first objective function satisfies:

[0052]

[0053] in, Indicates the energy efficiency of the source-storage computing system. This represents the cooling load of the computing power load. This represents the power consumption of the computing load. For the first j The standard coal equivalent of the power output of the new energy source meets the requirements. , Indicates the first j The contribution of new energy sources This represents the conversion factor. n This indicates the number of types of new energy sources, which include wind power and photovoltaics.

[0054] Optional, the electricity purchase constraint must be met. :in, Indicates computing load at tThe electricity purchase amount at the time, The computing power load at the time t The upper limit of the heat purchase amount at the time.

[0055] The heat purchase constraint satisfies . Wherein, The computing power load at the time t The heat purchase amount at the time, The computing power load at the time t The upper limit of the heat purchase amount at the time.

[0056] The hydrogen purchase constraint satisfies . Wherein, The computing power load at the time t The hydrogen purchase amount at the time, The computing power load at the time t The upper limit of the hydrogen purchase amount at the time.

[0057] The output constraint satisfies , wherein, The output of the new energy equipment or energy storage equipment at the time t The output of the new energy equipment or energy storage equipment at the time The lower limit of the output of the new energy equipment or energy storage equipment at the time t The upper limit of the output of the new energy equipment or energy storage equipment at the time t The energy selling price constraint satisfies , wherein,

[0058] The energy selling price of the energy of the The energy selling price of the energy of the The energy selling price of the energy of the k t The energy selling price of the energy of the The lower limit of the energy selling price of the energy of the k The upper limit of the energy selling price of the energy of the t In still some possible implementation manners, the second target function satisfies: k t

[0059]

[0060]

[0061] Wherein, The net income of the source storage calculation system. The energy selling price of the energy of the k The energy selling price of the energy of the t The optimization time interval.

[0062] T ​​​​​​The shiftable time of the computing load satisfies , N Indicates the amount of computing power load. Indicates the first n The state of computing load =0 indicates the first n The computing load did not shift. =1 indicates the first n The computing load has shifted.

[0063] Indicates computing load at t The translation compensation cost at any given time satisfies , This represents the unit compensation price for computing power load. Indicates the computing load before shifting t Load demand at any given time Indicates the shift of computing power load. t The load demand at any given moment.

[0064] Indicates computing load at t The cost of purchasing energy at any time, to meet .in, Indicates computing load at t Real-time heating costs, meet , Indicates computing load at t Purchase calories at any time Indicates computing load at t The price of hot water at any time. Indicates computing load at t The amount of waste heat utilized at any given time. Indicates computing load at t The price of utilizing residual heat at any given time. Indicates computing load at t The cost of purchasing electricity at any time, to meet , Indicates computing load at t Real-time electricity purchase volume Indicates computing load at t The electricity price at any given time. Indicates computing load at t The cost of purchasing hydrogen at any time, to meet , Indicates computing load at t The amount of hydrogen purchased at any given time. Indicates computing load at t The price of hydrogen at any given time.

[0065] Optionally, the electricity purchase price constraint is satisfied. wherein, represents a lower limit of the electricity purchase amount of the computing power load at the time point, t represents an upper limit of the electricity purchase amount of the computing power load at the time point. t represents a lower limit of the hydrogen purchase price of the computing power load at the time point,

[0066] represents an upper limit of the hydrogen purchase price of the computing power load at the time point. represents a lower limit of the heat purchase price of the computing power load at the time point, t represents an upper limit of the heat purchase price of the computing power load at the time point. t

[0067] represents a lower limit of the waste heat utilization price of the computing power load at the time point, represents an upper limit of the waste heat utilization price of the computing power load at the time point. t t

[0068] represents a lower limit of the waste heat utilization price of the computing power load at the time point, represents an upper limit of the waste heat utilization price of the computing power load at the time point. t t

[0069] For example, the solving module is specifically configured to:

[0070] normalize the first objective function of the energy efficiency model and the second objective function of the net income model.

[0071] obtain boundary points of a Pareto frontier set by solving the normalized first objective function and the normalized second objective function, and determine a boundary line of the Pareto frontier set according to the boundary points of the Pareto frontier set and the output of the new energy.

[0072] obtain the constraint point ε according to the boundary line of the Pareto frontier set.

[0073] obtain the optimized energy efficiency of the source-storage-computing system by solving the normalized first objective function according to the constraint point ε, and obtain the optimized net income of the source-storage-computing system by solving the normalized second objective function according to the constraint point ε.

[0074] Optionally, the optimization and control module is specifically configured to:

[0075] ​​​​​​​​​​​​The output and running state of the new energy equipment, the charge and discharge capacity and running state of the energy storage equipment, and the translatable load amount of the computing power load are obtained. The running state includes a normal state and a fault state, and the translatable load amount of the computing power load is the difference between the load demand amount before the computing power load is translated and the load demand amount after the computing power load is translated.

[0076] The power generation of the new energy equipment is evaluated according to the output and running state of the new energy equipment, taking optimization of energy efficiency and optimization of net income as the optimization control target.

[0077] The first control instruction and the second control instruction are generated taking optimization of energy efficiency and optimization of net income as the optimization control target, the charge and discharge capacity and running state of the energy storage equipment are optimized and controlled according to the first control instruction, and the translatable load amount of the computing power load is optimized and controlled according to the second control instruction.

[0078] In another aspect, the application further provides a computer device, comprising: one or more processors.

[0079] The processor is configured to execute one or more programs.

[0080] When the one or more programs are executed by the one or more processors, the optimization control method as described above is implemented.

[0081] In another aspect, the application further provides a computer readable storage medium having a computer program stored thereon. When the computer program is executed, the optimization control method as described above is implemented.

[0082] Compared with the prior art, the application has the following beneficial effects:

[0083] In the source-storage-computing resource collaborative optimization control method provided by the application, a multi-objective optimization model of the source-storage-computing system is constructed. The source-storage-computing system includes new energy equipment, energy storage equipment, and computing power load. The multi-objective optimization model includes an energy efficiency model and a net income model of the source-storage-computing system. The energy efficiency model is constructed according to the power consumption of the computing power load, and the net income model is constructed according to the energy purchase cost of the computing power load. The optimization energy efficiency and the optimization net income of the source-storage-computing system are obtained by solving the multi-objective optimization model according to the obtained output of the new energy and using the ε constraint method. The source-storage-computing system is optimized and controlled according to the optimization energy efficiency and the optimization net income. The multi-objective optimization model is constructed by the power consumption and the energy purchase cost of the computing power load, that is, not only the energy efficiency of the computing power load is considered, but also the economy of the computing power load is considered, so that the stable operation of the computing power load is ensured, and the energy consumption of the computing power load is greatly reduced.

[0084] The present application meets the demand of computing power load by configuring multiple new energy (photovoltaic and wind power) power generation and energy storage, improves the economic operation ability of the source storage computing system and the wind power consumption level, highly integrates new energy equipment, energy storage equipment and computing power load, improves energy utilization efficiency, reduces carbon emissions, and provides more reliable and stable power supply, and provides support for the sustainable development of computing power load. BRIEF DESCRIPTION OF DRAWINGS

[0085] In order to more clearly illustrate the technical solutions in the present application or the prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced below. Obviously, the drawings in the following description are some embodiments of the present application, and those skilled in the art can also obtain other drawings according to these drawings without creative labor.

[0086] Figure 1 A schematic flow chart of the source storage computing resource collaborative optimization and regulation method in the embodiments of the present application;

[0087] Figure 2 A schematic flow chart of solving the multi-objective optimization model in the embodiments of the present application;

[0088] Figure 3 A schematic structural diagram of the source storage computing resource collaborative optimization and regulation device in the embodiments of the present application. DETAILED DESCRIPTION

[0089] The technical solutions in the present application will be described below with reference to the drawings.

[0090] The terms "first", "second", etc. in the description of the embodiments of the present application and the claims and drawings are only used for the purpose of distinguishing description, and cannot be understood as indicating or implying relative importance, nor can it be understood as indicating or implying order. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, inclusion of a series of steps or units. The method, system, product or device is not necessarily limited to those steps or units clearly listed, but can include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0091] It should be understood that in the present application, "at least one" refers to one or more, and "multiple" refers to two or more. "And / or" is used to describe the association relationship of the associated objects, which means that there can be three relationships, for example, "A and / or B" can mean: only A, only B, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally represents an "or" relationship between the associated objects before and after it. "At least one of the following" or similar expressions means any combination of these items, including any combination of single or multiple items. For example, at least one of a, b or c, can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0092] Embodiment 1

[0093] The embodiment of the present application provides a source-storage-computing resource cooperative optimization and regulation method, as shown in the following figure. Figure 1 The optimization and regulation method 100 includes the following steps:

[0094] Step S1: Construct a multi-objective optimization model of the source-storage-computing system. The source-storage-computing system includes new energy equipment, energy storage equipment and computing load. The multi-objective optimization model includes an energy efficiency model and a net income model of the source-storage-computing system. The energy efficiency model is constructed according to the power consumption of the computing load, and the net income model is constructed according to the energy purchase cost of the computing load.

[0095] Step S2: According to the output of the obtained new energy and by using the ε constraint method, the multi-objective optimization model is solved to obtain the optimized energy efficiency and the optimized net income of the source-storage-computing system.

[0096] Step S3: According to the optimized energy efficiency and the optimized net income, the source-storage-computing system is optimized and regulated.

[0097] In some embodiments, the energy efficiency model in step S1 includes a first objective function and a first constraint condition.

[0098] The first objective function is constructed with the maximum energy efficiency of the source-storage-computing system as the target.

[0099] The first constraint condition includes a power purchase amount constraint, a heat purchase amount constraint, a hydrogen purchase amount constraint, an output constraint and a energy sale price constraint.

[0100] In other embodiments, the net income model includes a second objective function and a second constraint condition.

[0101] The second objective function is constructed with the maximum net income of the source-storage-computing system as the target.

[0102] The second set of constraints includes electricity purchase price constraints, hydrogen purchase price constraints, heat purchase price constraints, and waste heat utilization price constraints.

[0103] For example, the first objective function satisfies:

[0104]

[0105] in, Indicates the energy efficiency of the source-storage computing system. This represents the cooling load of the computing power load. This represents the power consumption of the computing load. For the first j The standard coal equivalent of the power output of the new energy source meets the requirements. , Indicates the first j The contribution of new energy sources This represents the conversion factor. n This indicates the number of types of new energy sources, which include wind power and photovoltaics.

[0106] Optional, the electricity purchase constraint must be met. :in, Indicates computing load at t Real-time electricity purchase volume Indicates computing load at t The maximum amount of electricity that can be purchased at any given time.

[0107] Purchase of heat constraints satisfied .in, Indicates computing load at t Purchase calories at any time Indicates computing load at t The maximum amount of calories that can be purchased at any given time.

[0108] Hydrogen purchase constraints met .in, Indicates computing load at t The amount of hydrogen purchased at any given time. Indicates computing load at t The upper limit for hydrogen purchases at any given time.

[0109] Output constraints satisfied ,in, Indicates new energy equipment or energy storage equipment in t Constant effort Indicates new energy equipment or energy storage equipment in t The minimum effort required at any given moment. Indicates new energy equipment or energy storage equipment in t The maximum output at any given moment.

[0110] Energy sales price constraints are met ,in, Indicates the first k This type of energy in t The price of energy sold at any given time, including electricity, heat, or hydrogen. Indicates the first k This type of energy in t The lower limit of the selling price of energy at any given time. Indicates the first k This type of energy in t The upper limit of the energy sales price at any given time.

[0111] In some possible implementations, the second objective function satisfies:

[0112]

[0113] in, This represents the net revenue of the source storage computing system. This indicates the optimization time interval. Indicates the first k This type of energy in t The price of energy sold at any given moment.

[0114] T The shiftable time of the computing load satisfies , N Indicates the amount of computing power load. Indicates the first n The state of computing load =0 indicates the first n The computing load did not shift. =1 indicates the first n The computing load has shifted.

[0115] Indicates computing load at t The translation compensation cost at any given time satisfies , This represents the unit compensation price for computing power load. Indicates the computing load before shifting t Load demand at any given time Indicates the shift of computing power load. t The load demand at any given moment.

[0116] Indicates computing load at t The cost of purchasing energy at any time, to meet .in, Indicates computing load at t Real-time heating costs, meet , Indicates computing load at t Purchase calories at any time represents the heat purchase price at the moment when the computing power load is t represents the waste heat utilization amount at the moment when the computing power load is t represents the waste heat utilization price at the moment when the computing power load is t represents the electricity purchase cost at the moment when the computing power load is t represents the electricity purchase amount at the moment when the computing power load is t represents the electricity purchase price at the moment when the computing power load is t represents the hydrogen purchase cost at the moment when the computing power load is t represents the hydrogen purchase amount at the moment when the computing power load is t represents the hydrogen purchase price at the moment when the computing power load is t

[0117] Optionally, the electricity purchase price constraint satisfies wherein, represents the lower limit of the electricity purchase amount at the moment when the computing power load is t represents the upper limit of the electricity purchase amount at the moment when the computing power load is t

[0118] The hydrogen purchase price constraint satisfies wherein, represents the lower limit of the hydrogen purchase price at the moment when the computing power load is t represents the upper limit of the hydrogen purchase price at the moment when the computing power load is t

[0119] The heat purchase price constraint satisfies wherein, represents the lower limit of the heat purchase price at the moment when the computing power load is t represents the upper limit of the heat purchase price at the moment when the computing power load is t

[0120] The waste heat utilization price satisfies wherein, represents the lower limit of the waste heat utilization price at the moment when the computing power load is t represents the upper limit of the waste heat utilization price at the moment when the computing power load is t

[0121] ​​​​​​​​​​​​​​​​​​​Exemplarily, the optimization energy efficiency and the optimization net income of the source-storage-computing system are obtained by solving the multi-objective optimization model according to the output of the new energy and by using the ε-constraint method in step S2, as shown in the following table. Figure 2 Specifically, the step S21 can include the following steps:

[0122] Step S21: The first objective function of the energy efficiency model and the second objective function of the net income model are normalized.

[0123] Step S22: The boundary points of the Pareto frontier set are obtained by solving the normalized first objective function and the normalized second objective function, and the boundary line of the Pareto frontier set is determined according to the boundary points of the Pareto frontier set and the output of the new energy.

[0124] Step S23: The constraint point ε is solved according to the boundary line of the Pareto frontier set.

[0125] Step S24: The optimization energy efficiency of the source-storage-computing system is obtained by solving the normalized first objective function according to the constraint point ε, and the optimization net income of the source-storage-computing system is obtained by solving the normalized second objective function according to the constraint point ε.

[0126] Optionally, the source-storage-computing system is optimized and controlled according to the optimization energy efficiency and the optimization net income in step S3, including:

[0127] The output and the running state of the new energy equipment, the charge and discharge capacity and the running state of the energy storage equipment, and the translatable load amount of the computing load are obtained. The running state includes a normal state and a fault state, and the translatable load amount of the computing load is the difference between the load demand amount before the computing load is translated and the load demand amount after the computing load is translated.

[0128] The power generation of the new energy equipment is evaluated according to the output and the running state of the new energy equipment, taking the optimization energy efficiency and the optimization net income as the optimization and control targets.

[0129] The first control instruction and the second control instruction are generated, taking the optimization energy efficiency and the optimization net income as the optimization and control targets, the charge and discharge capacity and the running state of the energy storage equipment are optimized and controlled according to the first control instruction, and the translatable load amount of the computing load is optimized and controlled according to the second control instruction.

[0130] Embodiment 2

[0131] Based on the same inventive concept, the embodiments of the present application also provide a source-storage-computing resource collaborative optimization and control device. As shown in the following table, Figure 3 The optimization and control device 200 can include:

[0132] Modeling module 201 is used to construct a multi-objective optimization model for the source-storage-computing system. The source-storage-computing system includes new energy equipment, energy storage equipment, and computing load. The multi-objective optimization model includes an energy efficiency model and a net revenue model for the source-storage-computing system. The energy efficiency model is constructed based on the power consumption of the computing load, and the net revenue model is constructed based on the energy purchase cost of the computing load.

[0133] The solution module 202 is used to solve the multi-objective optimization model based on the obtained output of new energy sources and using the ε-constraint method to obtain the optimized energy efficiency and optimized net benefit of the source-storage-computing system.

[0134] The optimization and control module 203 is used to optimize and control the source-storage computing system based on optimized energy efficiency and optimized net income.

[0135] Optionally, the energy efficiency model includes a first objective function and a first constraint condition.

[0136] The first objective function is constructed with the goal of maximizing the energy efficiency of the source-storage computing system.

[0137] The first set of constraints includes constraints on electricity purchase, heat purchase, hydrogen purchase, power output, and energy sales price.

[0138] For example, the net income model includes a second objective function and a second constraint.

[0139] The second objective function is constructed with the goal of maximizing the net profit of the source storage computing system.

[0140] The second set of constraints includes electricity purchase price constraints, hydrogen purchase price constraints, heat purchase price constraints, and waste heat utilization price constraints.

[0141] In some possible implementations, the first objective function satisfies:

[0142]

[0143] in, Indicates the energy efficiency of the source-storage computing system. This represents the cooling load of the computing power load. This represents the power consumption of the computing load. For the first j The standard coal equivalent of the power output of the new energy source meets the requirements. , Indicates the first j The contribution of new energy sources This represents the conversion factor. n This indicates the number of types of new energy sources, which include wind power and photovoltaics.

[0144] Optional, the electricity purchase constraint must be met. :in, Indicates computing load att Real-time electricity purchase volume Indicates computing load at t The maximum amount of electricity that can be purchased at any given time.

[0145] Purchase of heat constraints satisfied .in, Indicates computing load at t Purchase calories at any time Indicates computing load at t The maximum amount of calories that can be purchased at any given time.

[0146] Hydrogen purchase constraints met .in, Indicates computing load at t The amount of hydrogen purchased at any given time. Indicates computing load at t The upper limit for hydrogen purchases at any given time.

[0147] Output constraints satisfied ,in, Indicates new energy equipment or energy storage equipment in t Constant effort Indicates new energy equipment or energy storage equipment in t The minimum effort required at any given moment. Indicates new energy equipment or energy storage equipment in t The maximum output at any given moment.

[0148] Energy sales price constraints are met ,in, Indicates the first k This type of energy in t The price of energy sold at any given time, including electricity, heat, or hydrogen. Indicates the first k This type of energy in t The lower limit of the selling price of energy at any given time. Indicates the first k This type of energy in t The upper limit of the energy sales price at any given time.

[0149] In some other possible implementations, the second objective function satisfies:

[0150]

[0151] This represents the net revenue of the source storage computing system. This indicates the optimization time interval. T The shiftable time of the computing load satisfies , N Indicates the amount of computing power load. Indicates the first nThe state of the computing power load, =0 indicates that the first n computing power load does not occur translation, =1 indicates that the first n computing power load occurs translation.

[0152] represent the translation compensation cost of the computing power load at t time, satisfying , represent the unit compensation price of the computing power load, represent the load demand quantity of the computing power load before translation at t time, represent the load demand quantity of the computing power load after translation at t time.

[0153] represent the energy purchase cost of the computing power load at t time, satisfying . Wherein, represent the heat purchase cost of the computing power load at t time, satisfying , represent the heat purchase quantity of the computing power load at t time, represent the heat purchase price of the computing power load at t time, represent the waste heat utilization quantity of the computing power load at t time, represent the waste heat utilization price of the computing power load at t time. represent the electricity purchase cost of the computing power load at t time, satisfying , represent the electricity purchase quantity of the computing power load at t time, represent the electricity purchase price of the computing power load at t time. represent the hydrogen purchase cost of the computing power load at t time, satisfying , represent the hydrogen purchase quantity of the computing power load at t time, represent the hydrogen purchase price of the computing power load at t time.

[0154] Optionally, the electricity purchase price constraint satisfies , wherein, represent the lower limit of the electricity purchase quantity of the computing power load at t time, the computing power load att The maximum amount of electricity that can be purchased at any given time.

[0155] Hydrogen purchase price constraint satisfied .in, Indicates computing load at t The lower limit of the hydrogen purchase price at any given time. Indicates computing load at t The upper limit of the price for purchasing hydrogen at any given time.

[0156] Heat purchase price constraints satisfied .in, Indicates computing load at t The lower limit of the purchase price of heat at any time. Indicates computing load at t The upper limit of the purchase price of heat at any time.

[0157] Waste heat utilization price meets ,in, Indicates computing load at t The lower limit of the price for utilizing residual heat at any given time. Indicates computing load at t The upper limit of the price for utilizing residual heat at any given time.

[0158] For example, the solver module 202 is specifically used for:

[0159] The first objective function of the energy efficiency model and the second objective function of the net income model are normalized.

[0160] Solving the first and second objective functions after standardization yields the boundary points of the Pareto front set. The boundary line of the Pareto front set is then determined based on the boundary points of the Pareto front set and the output of the new energy source.

[0161] Solve for the constraint point ε based on the boundary line of the Pareto front set.

[0162] Solve the standardized first objective function based on the constraint point ε to obtain the optimized energy efficiency of the source-storage computing system, and solve the standardized second objective function based on the constraint point ε to obtain the optimized net benefit of the source-storage computing system.

[0163] Optionally, the optimization and control module 203 is specifically used for:

[0164] The system acquires the output and operating status of new energy equipment, the charging and discharging capacity and operating status of energy storage equipment, and the transferable load of computing power load. Operating status includes normal and fault states, and the transferable load of computing power load is the difference between the load demand before and after the load transfer.

[0165] The power generation of the new energy equipment is evaluated according to the output and the running state of the new energy equipment, taking optimization of energy efficiency and optimization of net income as optimization control targets.

[0166] The first control instruction and the second control instruction are generated taking optimization of energy efficiency and optimization of net income as optimization control targets, the charge and discharge capacity and the running state of the energy storage equipment are optimized and controlled according to the first control instruction, and the translatable load capacity of the computing power load is optimized and controlled according to the second control instruction.

[0167] Embodiment 3

[0168] Based on the same inventive concept, the embodiments of the present application further provide a computer device, which comprises a processor and a memory. The memory is used to store a computer program, and the computer program comprises program instructions. The processor is used to execute the program instructions stored in the computer storage medium. The processor can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The processor is the computing core and control core of the terminal, and is suitable for implementing one or more instructions. Specifically, the processor is suitable for loading and executing one or more instructions in the computer storage medium to implement a corresponding method flow or a corresponding function, so as to implement the steps of the optimization control method provided in the above embodiments.

[0169] Embodiment 4

[0170] Based on the same inventive concept, the embodiment of the present application further provides a storage medium, specifically a computer readable storage medium (Memory). The computer readable storage medium is a memory device in a computer device, and is used to store programs and data. It can be understood that the computer readable storage medium herein can include an internal storage medium of the computer device, and of course can also include an extended storage medium supported by the computer device. The computer readable storage medium provides a storage space, and the storage space stores an operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and the instructions can be one or more computer programs (including program codes). It should be noted that the computer readable storage medium herein can be a high-speed RAM memory, or a non-volatile memory such as at least one disk memory. One or more instructions stored in the computer readable storage medium can be loaded and executed by the processor to implement the steps of the optimization control method provided in the above embodiment.

[0171] Those skilled in the art should understand that the embodiments of the application can be provided as a method, a system, or a computer program product. Therefore, the application can adopt a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the application can adopt a computer program product implemented on one or more computer usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer usable program codes.

[0172] The application is described with reference to flowcharts and / or block diagrams of the method, device (system), and computer program product according to the embodiments of the application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of the flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to produce a machine, so that the instructions executed by the computer or other programmable data processing devices produce a device implemented in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 an apparatus that performs the functions specified in the flow(s) or block(s).

[0173] These computer program instructions can also be stored in a computer readable memory capable of directing the computer or other programmable data processing devices to work in a specific manner, so that the instructions stored in the computer readable memory produce a manufactured product including instruction apparatus, which implements the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1the function(s) specified in the block or blocks.

[0174] These computer program instructions can also be loaded into computer or other programmable data processing devices, so that a series of operation steps are performed on the computer or other programmable data processing devices to generate computer-implemented processes, thus the instructions executed on the computer or other programmable data processing devices provide processes for implementing the flows Figure 1 the flow or flows and / or blocks Figure 1 the function(s) specified in the block or blocks.

[0175] The above merely provides the embodiment of the application and is not intended to limit the application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the application shall be included in the scope of the claims of the application to be granted.

Claims

1. A source-storage-algorithm resource collaborative optimization regulation method, characterized in that, The method comprises the following steps: constructing a multi-objective optimization model of a source-storage-computing system, wherein the source-storage-computing system comprises a new energy device, a storage device and a computing load, the multi-objective optimization model comprises an energy efficiency model and a net income model of the source-storage-computing system, the energy efficiency model is constructed according to power consumption of the computing load, and the net income model is constructed according to a power purchase cost of the computing load; solving the multi-objective optimization model by using an epsilon constraint method to obtain optimized energy efficiency and optimized net income of the source-storage-computing system; optimizing and regulating the source-storage-computing system according to the optimized energy efficiency and the optimized net income; the energy efficiency model comprises a first target function and a first constraint condition; the first target function is constructed with a maximum energy efficiency of the source-storage-computing system as a target; the first constraint condition comprises a power purchase amount constraint, a heat purchase amount constraint, a hydrogen purchase amount constraint, a power output constraint and a power sale price constraint; the net income model comprises a second target function and a second constraint condition; the second target function is constructed with a maximum net income of the source-storage-computing system as a target; the second constraint condition comprises a power purchase price constraint, a hydrogen purchase price constraint, a heat purchase price constraint and a waste heat utilization price; the first target function satisfies: wherein, represents the energy efficiency of the source storage system, represents the cooling consumption of the computing power load, represents the power consumption of the computing power load, is the standard coal equivalent of the first j new energy output conversion, n represents the number of new energy types, and the new energy includes wind power and photovoltaic power. The electricity purchase quantity constraint satisfies : wherein, represents the electricity purchase quantity of the computing power load at the time t , and represents the electricity purchase quantity upper limit of the computing power load at the time t . The heat purchase constraint satisfies ; wherein, represents the heat purchase of the computing power load at the time t , and represents the upper limit of the heat purchase of the computing power load at the time t . The hydrogen purchase quantity constraint satisfies ; wherein, represents the hydrogen purchase quantity of the computing power load at the time t , and represents the upper limit of the hydrogen purchase quantity of the computing power load at the time t . The output constraint satisfies ,in, Indicates new energy equipment or energy storage equipment in t Constant effort This indicates that the new energy equipment or the energy storage equipment is in t The minimum effort required at any given moment. This indicates that the new energy equipment or the energy storage equipment is in t The maximum output at any given moment; The energy sales price constraint satisfies ,in, Indicates the first k This type of energy in t The price of energy sold at any given time, where the energy source includes electricity, heat, or hydrogen; the second target function satisfies: wherein, represents the net benefit of the source storage system; represents the optimization time interval; T This represents the shiftable time of the computing load, satisfying... , N This indicates the quantity of computing power load. Indicates the first n The state of computing load =0 indicates that the first n The computing load did not shift. =1 indicates that the first n The computing load has shifted; This indicates that the computing load is at t The translation compensation cost at any given time satisfies , This represents the unit compensation price for the computing power load. This indicates the computing power load before translation. t Load demand at any given time This indicates that the computing power load has been shifted. t The load demand at any given moment; This indicates that the computing load is at t The cost of purchasing energy at any time, to meet ;in, This indicates that the computing load is at t Real-time heating costs, meet , This indicates that the computing load is at t Purchase calories at any time This indicates that the computing load is at t The price of hot water at any time This indicates that the computing load is at t The amount of waste heat utilized at any given time. This indicates that the computing load is at t The price of utilizing residual heat at any given time; This indicates that the computing load is at t The cost of purchasing electricity at any time, to meet , This indicates that the computing load is at t Real-time electricity purchase volume This indicates that the computing load is at t The electricity price at any given time; This indicates that the computing load is at t The cost of purchasing hydrogen at any time, to meet , This indicates that the computing load is at t The amount of hydrogen purchased at any given time. This indicates that the computing load is at t The price of hydrogen at any given time; The electricity purchase price constraint satisfies ,in, This indicates that the computing load is at t The minimum amount of electricity that can be purchased at any time. The computing load is t The maximum amount of electricity that can be purchased at any given time; The hydrogen purchase price constraint satisfies ; wherein, represents a lower limit of the hydrogen purchase price at the time when the computing power load is t , and represents an upper limit of the hydrogen purchase price at the time when the computing power load is t . The heat purchase price constraint satisfies ; wherein, represents a lower limit of the heat purchase price at the time when the computing power load is t ; and represents an upper limit of the heat purchase price at the time when the computing power load is t . The waste heat utilization price satisfies ,in, This indicates that the computing load is at t The lower limit of the price for utilizing residual heat at any given time. This indicates that the computing load is at t The upper limit of the price for utilizing residual heat at any given time.

2. The method of claim 1, wherein, the solving of the multi-objective optimization model by using the epsilon constraint method to obtain the optimized energy efficiency and the optimized net income of the source-storage-computing system comprises: performing a dimensionless processing on the first target function of the energy efficiency model and the second target function of the net income model; solving the first target function after the dimensionless processing and the second target function after the dimensionless processing to obtain boundary points of a Pareto frontier set, and determining a boundary line of the Pareto frontier set according to the boundary points of the Pareto frontier set; solving a constraint point epsilon according to the boundary line of the Pareto frontier set; solving the first target function after the dimensionless processing according to the constraint point epsilon to obtain the optimized energy efficiency of the source-storage-computing system, and solving the second target function after the dimensionless processing according to the constraint point epsilon to obtain the optimized net income of the source-storage-computing system.

3. The method of claim 1, wherein the step of modulating comprises: the optimizing and regulating of the source-storage-computing system according to the optimized energy efficiency and the optimized net income comprises: obtaining power output and a running state of the new energy device, charge and discharge amounts and a running state of the storage device, and a translatable load amount of the computing load; wherein the running state comprises a normal state and a fault state, and the translatable load amount of the computing load is a difference between a load demand amount before the computing load is translated and a load demand amount after the computing load is translated; taking the optimized energy efficiency and the optimized net income as optimization and regulation targets, evaluating power generation of the new energy device according to the power output and the running state of the new energy device, optimizing and regulating the charge and discharge amounts and the running state of the storage device, and optimizing and regulating the translatable load amount of the computing load.

4. A source and storage resource collaborative optimization and regulation device, characterized in that, The method comprises the following steps: The modeling module is configured to construct a multi-objective optimization model of a source-storage-computing system, wherein the source-storage-computing system comprises a new energy device, a storage device, and a computing load; the multi-objective optimization model comprises an energy efficiency model and a net income model of the source-storage-computing system, the energy efficiency model is constructed according to power consumption of the computing load, and the net income model is constructed according to a power purchase cost of the computing load; The solving module is configured to solve the multi-objective optimization model by using an epsilon constraint method to obtain optimized energy efficiency and optimized net income of the source-storage-computing system. The optimization control module is configured to perform optimization control on the source-storage-computing system according to the optimized energy efficiency and the optimized net income. The energy efficiency model comprises a first objective function and a first constraint condition. The first objective function is constructed to maximize energy efficiency of the source-storage-computing system. The first constraint condition comprises a power purchase amount constraint, a heat purchase amount constraint, a hydrogen purchase amount constraint, a power output constraint, and a power sale price constraint. The net income model comprises a second objective function and a second constraint condition. The second objective function is constructed to maximize net income of the source-storage-computing system. The second constraint condition comprises a power purchase price constraint, a hydrogen purchase price constraint, a heat purchase price constraint, and a waste heat utilization price. The first objective function satisfies: wherein, represents the energy efficiency of the source storage system, represents the cooling consumption of the computing power load, represents the power consumption of the computing power load, is the standard coal equivalent of the new energy output conversion, j represents the number of new energy types, including wind power and photovoltaic power, n represents the number of new energy types, including wind power and photovoltaic power; The electricity purchase constraint is satisfied :in, This indicates that the computing load is at t Real-time electricity purchase volume This indicates that the computing load is at t The maximum amount of electricity that can be purchased at any given time; The heat purchase constraint satisfies ; wherein, represents the heat purchase of the computing power load at the time t , represents the upper limit of the heat purchase of the computing power load at the time t . The hydrogen purchase quantity constraint satisfies ; wherein, represents the hydrogen purchase quantity of the computing power load at t , and represents the upper limit of the hydrogen purchase quantity of the computing power load at t . The output constraint satisfies ,in, Indicates new energy equipment or energy storage equipment in t Constant effort This indicates that the new energy equipment or the energy storage equipment is in t The minimum effort required at any given moment. This indicates that the new energy equipment or the energy storage equipment is in t The maximum output at any given moment; The energy sales price constraint satisfies ,in, Indicates the first k This type of energy in t The price of energy sold at any given time, where the energy source includes electricity, heat, or hydrogen; The second objective function satisfies: wherein, represents the net benefit of the source storage system; represents the optimization time interval; T This represents the shiftable time of the computing load, satisfying... , N This indicates the quantity of computing power load. Indicates the first n The state of computing load =0 indicates that the first n The computing load did not shift. =1 indicates that the first n The computing load has shifted; This indicates that the computing load is at t The translation compensation cost at any given time satisfies , This represents the unit compensation price for the computing power load. This indicates the computing power load before translation. t Load demand at any given time This indicates that the computing power load has been shifted. t The load demand at any given time; This indicates that the computing load is at t The cost of purchasing energy at any time, to meet ;in, This indicates that the computing load is at t Real-time heating costs, meet , This indicates that the computing load is at t Purchase calories at any time This indicates that the computing load is at t The price of hot water at any time. This indicates that the computing load is at t The amount of waste heat utilized at any given time. This indicates that the computing load is at t The price of utilizing residual heat at any given time; This indicates that the computing load is at t The cost of purchasing electricity at any time, to meet , This indicates that the computing load is at t Real-time electricity purchase volume This indicates that the computing load is at t The electricity price at any given time; This indicates that the computing load is at t The cost of purchasing hydrogen at any time, to meet , This indicates that the computing load is at t The amount of hydrogen purchased at any given time. This indicates that the computing load is at t The price of hydrogen at any given time; The electricity purchase price constraint satisfies ,in, This indicates that the computing load is at t The minimum amount of electricity that can be purchased at any time. The computing load is t The maximum amount of electricity that can be purchased at any given time; The hydrogen purchase price constraint satisfies ; wherein, represents a lower limit of the hydrogen purchase price at the time when the computing power load is t , and represents an upper limit of the hydrogen purchase price at the time when the computing power load is t . The heat purchase price constraint satisfies ; wherein, represents a lower limit of the heat purchase price at the time when the computing power load is t , and represents an upper limit of the heat purchase price at the time when the computing power load is t . The waste heat utilization price satisfies ,in, This indicates that the computing load is at t The lower limit of the price for utilizing residual heat at any given time. This indicates that the computing load is at t The upper limit of the price for utilizing residual heat at any given time.

5. The conditioning device of claim 4, wherein, The solving module is specifically configured to: perform normalization processing on the first objective function of the energy efficiency model and the second objective function of the net income model; obtain boundary points of a Pareto frontier set according to the normalized first objective function and the normalized second objective function, and determine a boundary line of the Pareto frontier set according to the boundary points of the Pareto frontier set; obtain a constraint point epsilon according to the boundary line of the Pareto frontier set; obtain the optimized energy efficiency of the source-storage-computing system by solving the normalized first objective function according to the constraint point epsilon, and obtain the optimized net income of the source-storage-computing system by solving the normalized second objective function according to the constraint point epsilon.

6. The conditioning device of claim 4, wherein, The optimization control module is specifically configured to: obtain power output and a running state of the new energy device, charge and discharge amounts and a running state of the storage device, and a translatable load amount of the computing load; wherein the running state comprises a normal state and a fault state, and the translatable load amount of the computing load is a difference between a load demand amount before the computing load is translated and a load demand amount after the computing load is translated; take the optimized energy efficiency and the optimized net income as optimization control targets, evaluate power generation of the new energy device according to the power output and the running state of the new energy device, perform optimization control on the charge and discharge amounts and the running state of the storage device, and perform optimization control on the translatable load amount of the computing load.

7. A computer device, comprising: comprise: one or more processors; the processor is configured to store one or more programs; when the one or more programs are executed by the one or more processors, the optimization control method in any one of claims 1 to 3 is implemented.

8. A computer-readable storage medium, characterized in that, The computer program is stored thereon, and when the computer program is executed, the optimization control method in any one of claims 1 to 3 is implemented.

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