Pumped-storage photovoltaic combined operation optimization method and system for data center load cooling

Abstract

The pumped storage photovoltaic joint operation optimization method and system for data center load cooling, the method constructs and solves the joint operation optimization model, and outputs the system joint operation plan; the joint operation optimization model not only considers the combination of air conditioning cooling, water side cooling, and ice storage, and fully utilizes the advantages of various cooling methods in different seasons and time periods, but also considers the power support of distributed pumped storage and distributed photovoltaics for data center loads, forming dynamic complementarity. The model objective function is to minimize the system comprehensive cost under the premise of meeting the data center load cooling demand. When designing the system comprehensive cost, carbon emission costs and water evaporation costs are also considered on the basis of various facility configuration costs and electricity purchase costs, and the design is more reasonable. This method can make full use of various cooling methods and form dynamic complementarity with distributed power sources, reducing the system comprehensive cost on the basis of meeting the data center cooling demand.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system dispatching, and in particular relates to a pumped-storage photovoltaic combined operation optimization method and system for data center load cooling. Background Art

[0002] With the advent of the digital age, the development of society and technology is increasing the demand for big data and cloud computing. In order to support users' demand for data and computing power, major Internet companies are configuring data centers of various sizes. However, the operation of data centers consumes a lot of electricity and generates huge heat.

[0003] In order to ensure the safe and stable operation of the data center, it needs to be cooled uninterruptedly. The mainstream data center cooling methods include air conditioning cooling, water side cooling and ice storage. Air conditioning cooling inputs cold air into the computer room through the air conditioning system, and then discharges the hot air to take away the heat; water side cooling uses external low-temperature water bodies such as rivers and lakes to cool the data center; ice storage uses staggered electricity to make ice for storage, and then melts the ice to release cold energy when the opportunity arises. Air conditioning cooling can provide stable cooling capacity, but because it requires uninterrupted consumption of a large amount of electricity, the electricity cost and carbon neutrality cost generated are high; water side cooling does not consume additional electricity and does not produce additional carbon emissions, but this method will cause excessive evaporation of water in natural water bodies, affecting the natural environment; ice storage has the characteristics of being able to effectively utilize electricity storage during off-peak periods, and has certain advantages in saving electricity costs, but it will cause cooling capacity loss during the cold storage process.

[0004] Reasonable matching of distributed pumped storage systems and distributed photovoltaic systems for data centers can help improve the flexible support capacity for data center loads. Distributed pumped storage systems pump water to the upper reservoir during low load periods and release water to generate electricity during peak hours. Compared with traditional large-scale pumped storage systems, their application is more decentralized and flexible. Similarly, distributed photovoltaic systems are a type of photovoltaic power generation mode that is decentralized and installed at or near the user end, and are usually small in scale.

[0005] In order to make full use of the advantages of various cooling methods in different seasons and time periods, and form dynamic complementarity with distributed pumped storage systems and photovoltaic systems, how to optimize the configuration and operation of air conditioning cooling, water side cooling, ice storage, distributed pumped storage systems and distributed photovoltaic systems for joint operation, while minimizing the overall system cost while meeting the cooling needs of data centers and ensuring their stable operation, is a problem that needs to be solved urgently. Summary of the invention

[0006] The purpose of the present invention is to address the above-mentioned problems existing in the prior art and to provide a method and system for optimizing the joint operation of pumped-storage photovoltaics and central load cooling that can fully utilize various cooling methods and form dynamic complementarity with distributed power sources, thereby minimizing the overall cost of the system.

[0007] To achieve the above objectives, the technical solution of the present invention is as follows:

[0008] In a first aspect, the present invention provides a pumped-storage photovoltaic combined operation optimization method for data center load cooling, the combined operation optimization method comprising:

[0009] S1. Construct a joint operation optimization model; the joint operation optimization model includes an objective function and constraints. The objective function is to minimize the system comprehensive cost under the premise of meeting the load cooling demand of the data center. The system comprehensive cost includes the total system configuration cost, electricity purchase cost, carbon neutrality cost, and water evaporation cost. The total system configuration cost includes the distributed power system configuration cost and the cooling system configuration cost. The distributed power system includes a distributed pumped storage system and a distributed photovoltaic system. The cooling system includes an air conditioning cooling system, a water side cooling system, and an ice storage system.

[0010] S2. Solve the joint operation optimization model and output the system joint operation plan.

[0011] The objective function is:

[0012] min C P [(1+α) γ -1] / αγ 2 +C E +C Z +C W ;

[0013]

[0014] In the above formula, C P , C E , C Z , C W are the total system configuration cost, electricity purchase cost, carbon neutrality cost, and water evaporation cost; α and γ are the discount rate and investment period, respectively; d S is the number of typical days in season S in a year; N S The number of units configured in the distributed pumped storage system; V US 、V DS are the upper and lower reservoir capacities of the distributed pumped storage system; N P 、N A 、N I are the number of photovoltaic cells, cooling air conditioners, and ice machines respectively; PR NS, PR VS , PR P , PR I , PR A They are the unit costs of pumped storage units, pumped storage reservoirs, photovoltaic cells, cooling air conditioners, and ice machines; PR is the power supplied by the upper power grid to the distribution network during period t in season s; E is the unit electricity purchase cost of the system; T U is the unit time; η W is the amount of water evaporation caused by unit cooling capacity when water side cooling is adopted; η S is the evaporation rate of water body, which represents the natural evaporation of unit water body in unit time; is the cooling capacity generated by water cooling in period t of season s; PR W is the unit water evaporation cost; μ C Carbon emissions per unit of electricity generated; PR C is the unit carbon neutrality cost.

[0015] The constraint conditions include cooling system configuration operation constraints, and the cooling system configuration operation constraints include cooling demand balance constraints, air conditioning cooling system cooling constraints, ice storage system power and cooling constraints, and ice storage system working state constraints;

[0016] The cooling demand balance constraint includes:

[0017]

[0018] In the above formula, is the total load of the data center in time period t of season s; CD The cooling demand factor generated by the total load of the data center; are the cooling capacity generated by air conditioning cooling, water side cooling, and ice storage and melting ice in time period t of season s;

[0019] The cooling capacity constraints of the air conditioning cooling system include:

[0020]

[0021] In the above formula, is the electric power consumed by air conditioning cooling in period t of season s; η A is the energy efficiency ratio of the air conditioning system; P UA is the maximum power of a single air conditioner;

[0022] The power and cooling capacity constraints of the ice storage system include:

[0023]

[0024] In the above formula, P is the electric power consumed by ice storage in period t of season s; UI is the maximum power of a single ice machine; is the cold storage capacity in the cold storage in the period t of season s; η L is the self-loss coefficient per unit time of the ice storage system; η I IS is the energy efficiency ratio of the ice machine; M The upper limit of the cold storage capacity of the ice storage system; is the cooling capacity generated by melting ice in empty ice storage in period t-1 of season s;

[0025] The working state constraints of the ice storage system include:

[0026]

[0027] In the above formula, δ M is the large M constant; is a binary variable indicating the working status of the ice storage system in period t of season s; tn is the last period of a typical day.

[0028] The constraints also include distributed power configuration and operation constraints, distribution network operation constraints, the distributed power configuration and operation constraints include distributed pumped storage system power constraints, distributed pumped storage system operating conditions constraints, distributed pumped storage system water delivery constraints, distributed pumped storage system upper and lower reservoir water storage constraints, distributed photovoltaic system operation constraints; the distribution network operation constraints include data center access node power balance constraints, distribution network active and reactive power balance constraints, distribution network line power upper and lower limit constraints, distribution network voltage constraints;

[0029] The power constraints of the distributed pumped storage system include:

[0030]

[0031] In the above formula, are the input and output power of the distributed pumped storage system in time period t of season s, respectively; is the actual output or input power of the distributed pumped storage system in period t of season s; is the amount of water pumped from the lower reservoir to the upper reservoir of the distributed pumped storage system during period t in season s; is the amount of water discharged from the upper reservoir to the lower reservoir of the distributed pumped storage system in time period t of season s; ρ is the density of water; g is the acceleration of gravity; h is the height difference between the upper and lower reservoirs of the distributed pumped storage system; γ S is the energy transmission efficiency of the reversible unit in the distributed pumped storage system; T U is the unit time;

[0032] The operating constraints of the distributed pumped storage system include:

[0033]

[0034] In the above formula, is the binary operating variable of the distributed pumped storage system in period t of season s;

[0035] The distributed pumped storage system water delivery constraints include:

[0036]

[0037] In the above formula, ΔV s，t is the amount of water transported between the upper and lower reservoirs of the distributed pumped storage system in time period t of season s; ΔVM is the upper limit of the amount of water transported per unit time of the distributed pumped storage system;

[0038] The water storage constraints of the upper and lower reservoirs of the distributed pumped storage system include:

[0039]

[0040] In the above formula, VH s，t , VH s，t-1 are the upper reservoir water volume of the distributed pumped storage system in period t and period t-1 of season s; VD s，t 、VD s，t-1 are the water volume of the lower reservoir of the distributed pumped storage system in time period t and time period t-1 of season s respectively; η S is the evaporation rate of water body, which represents the natural evaporation of unit water body in unit time;

[0041] The distributed photovoltaic system operation constraints include:

[0042]

[0043] In the above formula, is the maximum output power of the distributed photovoltaic system in period t of season s; It is the benchmark timing output characteristic of photovoltaic cells; is the light intensity coefficient of season s, which represents the light intensity in different seasons; is the actual output power of the distributed photovoltaic system in period t of season s;

[0044] The data center access node power balance constraint includes:

[0045]

[0046] The active and reactive power balance constraints of the distribution network include:

[0047]

[0048] In the above formula, are the basic active and reactive loads connected to the distribution network node e in time period t of season s; υ e is the binary association coefficient of the data center access point; are the active and reactive power on the distribution network line w in time period t of season s, respectively; κ w，e represents the binary correlation coefficient between line w and distribution network node e;

[0049] The power upper and lower limit constraints of the distribution network line include:

[0050]

[0051] In the above formula, LC w represents the configured capacity of line w;

[0052] The distribution network voltage constraints include:

[0053]

[0054] In the above formula, ΔU s，t，w is the voltage drop on line w during period t in season s; U s，t，a , U s，t，b , U s，t，e are the bus voltages of nodes a, b, and e in time period t of season s, respectively. Nodes a and b are the two endpoints of line w; are the resistance and reactance of line w respectively; U m , U M They are the upper and lower limits of the distribution network bus voltage respectively.

[0055] In a second aspect, the present invention provides a pumped-storage photovoltaic combined operation optimization system for data center load cooling, the combined operation optimization system comprising a model building module and a simulation calculation module;

[0056] The model building module is used to build a joint operation optimization model; the joint operation optimization model includes an objective function and constraints, the objective function is to minimize the system comprehensive cost under the premise of meeting the load cooling demand of the data center, the system comprehensive cost includes the system total configuration cost, electricity purchase cost, carbon neutrality cost, and water evaporation cost, the system total configuration cost includes the configuration cost of the distributed pumped storage system, the distributed photovoltaic system and the cooling system, and the cooling system includes the air conditioning cooling system, the water side cooling system, and the ice storage system;

[0057] The simulation calculation module is used to solve the joint operation optimization model and output the system joint operation plan.

[0058] The objective function is:

[0059] min C P [(1+α) γ -l] / αγ 2 +C E +C Z +C W ;

[0060]

[0061] In the above formula, C P , C E , C Z , C W are the total system configuration cost, electricity purchase cost, carbon neutrality cost, and water evaporation cost; α and γ are the discount rate and investment period, respectively; d S is the number of typical days in season S in a year; N S The number of units configured in the distributed pumped storage system; V US 、V DS are the upper and lower reservoir capacities of the distributed pumped storage system; N P 、N A 、N I are the number of photovoltaic cells, cooling air conditioners, and ice machines respectively; PR NS , PR VS , PR P , PR I , PR A They are the unit costs of pumped storage units, pumped storage reservoirs, photovoltaic cells, cooling air conditioners, and ice machines; PR is the power supplied by the upper power grid to the distribution network during period t in season s; E is the unit electricity purchase cost of the system; T U is the unit time; η W is the amount of water evaporation caused by unit cooling capacity when water side cooling is adopted; η S is the evaporation rate of water body, which represents the natural evaporation of unit water body in unit time; is the cooling capacity generated by water cooling in period t of season s; PR W is the unit water evaporation cost; μ C Carbon emissions per unit of electricity generated; PR C is the unit carbon neutrality cost.

[0062] The constraint conditions include cooling system configuration operation constraints, and the cooling system configuration operation constraints include cooling demand balance constraints, air conditioning cooling system cooling constraints, ice storage system power and cooling constraints, and ice storage system working state constraints;

[0063] The cooling demand balance constraint includes:

[0064]

[0065] In the above formula, is the total load of the data center in time period t of season s; CD The cooling demand factor generated by the total load of the data center; are the cooling capacity generated by air conditioning cooling, water side cooling, and ice storage and melting ice in time period t of season s;

[0066] The cooling capacity constraints of the air conditioning cooling system include:

[0067]

[0068] In the above formula, is the electric power consumed by air conditioning cooling in period t of season s; η A is the energy efficiency ratio of the air conditioning system; P UA is the maximum power of a single air conditioner;

[0069] The power and cooling capacity constraints of the ice storage system include:

[0070]

[0071] In the above formula, P is the electric power consumed by ice storage in period t of season s; UI is the maximum power of a single ice machine; is the cold storage capacity in the cold storage in the period t of season s; η L is the self-loss coefficient per unit time of the ice storage system; η I IS is the energy efficiency ratio of the ice machine; M The upper limit of the cold storage capacity of the ice storage system; is the cooling capacity generated by melting ice in empty ice storage in period t-1 of season s;

[0072] The working state constraints of the ice storage system include:

[0073]

[0074] In the above formula, δ M is the large M constant; is a binary variable indicating the working status of the ice storage system in period t of season s; tn is the last period of a typical day.

[0075] The constraints also include distributed power configuration and operation constraints, distribution network operation constraints, the distributed power configuration and operation constraints include distributed pumped storage system power constraints, distributed pumped storage system operating conditions constraints, distributed pumped storage system water delivery constraints, distributed pumped storage system upper and lower reservoir water storage constraints, distributed photovoltaic system operation constraints, the distribution network operation constraints include data center access node power balance constraints, distribution network active and reactive power balance constraints, distribution network line power upper and lower limit constraints, distribution network voltage constraints;

[0076] The power constraints of the distributed pumped storage system include:

[0077]

[0078] In the above formula, are the input and output power of the distributed pumped storage system in time period t of season s, respectively; is the actual output or input power of the distributed pumped storage system in period t of season s; is the amount of water pumped from the lower reservoir to the upper reservoir of the distributed pumped storage system during period t in season s; is the amount of water discharged from the upper reservoir to the lower reservoir of the distributed pumped storage system in time period t of season s; ρ is the density of water; g is the acceleration of gravity; h is the height difference between the upper and lower reservoirs of the distributed pumped storage system; γ S is the energy transmission efficiency of the reversible unit in the distributed pumped storage system; T U is the unit time;

[0079] The operating constraints of the distributed pumped storage system include:

[0080]

[0081] In the above formula, is the binary operating variable of the distributed pumped storage system in period t of season s;

[0082] The distributed pumped storage system water delivery constraints include:

[0083]

[0084] In the above formula, ΔV s，t is the amount of water transported between the upper and lower reservoirs of the distributed pumped storage system in time period t of season s; ΔVM is the upper limit of the amount of water transported per unit time of the distributed pumped storage system;

[0085] The water storage constraints of the upper and lower reservoirs of the distributed pumped storage system include:

[0086]

[0087] In the above formula, VH s，t , VH s，t-1 are the upper reservoir water volume of the distributed pumped storage system in period t and period t-1 of season s; VD s，t 、VD s，t-1 are the water volume of the lower reservoir of the distributed pumped storage system in time period t and time period t-1 of season s respectively; η S is the evaporation rate of water body, which represents the natural evaporation of unit water body in unit time;

[0088] The distributed photovoltaic system operation constraints include:

[0089]

[0090] In the above formula, is the maximum output power of the distributed photovoltaic system in period t of season s; It is the benchmark timing output characteristic of photovoltaic cells; is the light intensity coefficient of season s, which represents the light intensity in different seasons; is the actual output power of the distributed photovoltaic system in period t of season s;

[0091] The data center access node power balance constraint includes:

[0092]

[0093] The active and reactive power balance constraints of the distribution network include:

[0094]

[0095] In the above formula, are the basic active and reactive loads connected to the distribution network node e in time period t of season s; υ e is the binary association coefficient of the data center access point; are the active and reactive power on the distribution network line w in time period t of season s, respectively; K w，e represents the binary correlation coefficient between line w and distribution network node e;

[0096] The power upper and lower limit constraints of the distribution network line include:

[0097]

[0098] In the above formula, LC w represents the configured capacity of line w;

[0099] The distribution network voltage constraints include:

[0100]

[0101] In the above formula, ΔU s，t，w is the voltage drop on line w during period t in season s; U s，t，a , U s，t，b , U s，t，e are the bus voltages of nodes a, b, and e in time period t of season s, respectively. Nodes a and b are the two endpoints of line w; are the resistance and reactance of line w respectively; U m , U M They are the upper and lower limits of the distribution network bus voltage respectively.

[0102] In a third aspect, the present invention provides a pumped-storage photovoltaic combined operation optimization device for data center load cooling, wherein the control device comprises a memory and a processor; the memory is used to store computer program code and transmit the computer program code to the processor; the processor is used to execute the aforementioned method according to the instructions in the computer program code.

[0103] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the aforementioned method when executed by a processor.

[0104] Compared with the prior art, the present invention has the following beneficial effects:

[0105] The present invention provides a pumped storage photovoltaic joint operation optimization method for data center load cooling, constructs and solves a joint operation optimization model, and outputs a system joint operation plan; the joint operation optimization model is constructed based on a joint operation system, in which not only the joint use of multiple cooling methods of the data center is considered, such as air conditioning cooling, water side cooling and ice storage, which can fully utilize the advantages of various cooling methods in different seasons and time periods, but also the power support of multiple types of distributed power sources for the data center load is considered, such as distributed pumped storage and distributed photovoltaic, which realizes the dynamic complementarity of multiple types of distributed power sources and multiple cooling methods; the objective function of the joint operation optimization model is to minimize the system comprehensive cost under the premise of meeting the data center load cooling demand, in which the system comprehensive cost, on the basis of the traditional various types of facility configuration costs and electricity purchase costs, also considers the carbon emission costs and water evaporation costs generated by data center cooling, so that the design of the system comprehensive cost is more reasonable. Therefore, the present invention can make full use of various cooling methods and form dynamic complementarity with distributed power sources, and can reduce the system comprehensive cost on the basis of meeting the data center cooling demand. BRIEF DESCRIPTION OF THE DRAWINGS

[0106] Figure 1The present invention is a flowchart of the method.

[0107] Figure 2 It is a schematic diagram of the structure of the system of the present invention.

[0108] Figure 3 It is a structural schematic diagram of the device described in the present invention.

[0109] Figure 4 It is a schematic diagram of the structure of the system faced by the present invention.

[0110] Figure 5 This is a schematic diagram of the topological structure of the 12-node distribution network in Example 1.

[0111] Figure 6 This is the data center load characteristic curve used in Example 1.

[0112] Figure 7 This is the time-of-use electricity price adopted in Example 1.

[0113] Figure 8 This is the photovoltaic output characteristic curve used in Example 1. DETAILED DESCRIPTION

[0114] The present invention is further described in detail below in conjunction with specific implementations and drawings.

[0115] See also Figure 4 The system faced by the present invention includes a distribution network system, a data center operation and cooling load, an air conditioning cooling system, a water-side cooling system, an ice storage system, a distributed pumped storage system, and a distributed photovoltaic system, wherein the distribution network system, the distributed pumped storage system, and the distributed photovoltaic system provide power support for the data center load, and the air conditioning cooling system, the water-side cooling system, and the ice storage system provide the cooling capacity required for the data center. Since the ice storage system, the distributed pumped storage system, and the distributed photovoltaic system are sensitive to temperature and light, the different cold storage self-loss coefficients, water evaporation rates, and photovoltaic power generation efficiencies in different seasons need to be considered when configuring the operation plan. By optimizing the configuration and operation of the above-mentioned cooling systems and distributed power sources, the overall system cost can be minimized while meeting the cooling capacity demand of the data center and ensuring its stable operation.

[0116] Embodiment 1:

[0117] See also Figure 1 , a pumped-storage photovoltaic combined operation optimization method for data center load cooling is carried out according to the following steps:

[0118] S1. Construct a joint operation optimization model; the joint operation optimization model includes an objective function and constraints. The objective function is to minimize the system comprehensive cost under the premise of meeting the load cooling demand of the data center. The system comprehensive cost includes the total system configuration cost, electricity purchase cost, carbon neutrality cost, and water evaporation cost. The total system configuration cost includes the distributed power system configuration cost and the cooling system configuration cost. The distributed power system includes a distributed pumped storage system and a distributed photovoltaic system. The cooling system includes an air conditioning cooling system, a water side cooling system, and an ice storage system. The objective function is specifically:

[0119] min C P [(1+α) γ -1] / αγ 2 +C E +C Z +C W ;

[0120]

[0121] In the above formula, C P , C E , C Z , C W are the total system configuration cost, electricity purchase cost, carbon neutrality cost, and water evaporation cost; α and γ are the discount rate and investment period, respectively; d S is the number of typical days in season S in a year; N S The number of units configured in the distributed pumped storage system; V US 、V DS are the upper and lower reservoir capacities of the distributed pumped storage system; N P 、N A 、N I are the number of photovoltaic cells, cooling air conditioners, and ice machines respectively; PR NS , PR VS , PR P , PR I , PR A They are the unit costs of pumped storage units, pumped storage reservoirs, photovoltaic cells, cooling air conditioners, and ice machines; PR is the power supplied by the upper power grid to the distribution network during period t in season s; E is the unit electricity purchase cost of the system; T U is the unit time; η W is the amount of water evaporation caused by unit cooling capacity when water side cooling is adopted; η S is the evaporation rate of water body, which represents the natural evaporation of unit water body in unit time; is the cooling capacity generated by water cooling in period t of season s; PR Wis the unit water evaporation cost; μ C Carbon emissions per unit of electricity generated; PR C is the unit carbon neutrality cost.

[0122] The constraints include cooling system configuration and operation constraints, distributed power supply configuration and operation constraints, and distribution network operation constraints; the cooling system configuration and operation constraints include cooling demand balance constraints, air conditioning cooling system cooling constraints, ice storage system power and cooling constraints, and ice storage system working state constraints; wherein, the cooling demand balance constraints include:

[0123]

[0124] In the above formula, is the total load of the data center in time period t of season s; CD The cooling demand factor generated by the total load of the data center; are the cooling capacity generated by air conditioning cooling, water side cooling, and ice storage and melting ice in time period t of season s;

[0125] The cooling capacity constraints of the air conditioning cooling system include:

[0126]

[0127] In the above formula, is the electric power consumed by air conditioning cooling in period t of season s; η A is the energy efficiency ratio of the air conditioning system; P UA is the maximum power of a single air conditioner;

[0128] The power and cooling capacity constraints of the ice storage system include:

[0129]

[0130] In the above formula, P is the electric power consumed by ice storage in period t of season s; UI is the maximum power of a single ice machine; is the cold storage capacity in the cold storage in the period t of season s; η L is the self-loss coefficient per unit time of the ice storage system; η I IS is the energy efficiency ratio of the ice machine; M The upper limit of the cold storage capacity of the ice storage system; is the cooling capacity generated by melting ice in empty ice storage in period t-1 of season s;

[0131] The ice storage system used in this embodiment only works in one working condition in the same period, that is, melting ice to release cold energy or consuming electric energy to store cold energy. Therefore, the working state constraints of the ice storage system are set; the working state constraints of the ice storage system include:

[0132]

[0133] In the above formula, δ M is the large M constant; is a binary variable of the working state of the ice storage system in period t of season s. When When tn is the last period of a typical day, the ice storage system consumes electric energy to store cold.

[0134] The distributed power configuration and operation constraints include distributed pumped storage system power constraints, distributed pumped storage system operating conditions constraints, distributed pumped storage system water delivery constraints, distributed pumped storage system upper and lower reservoir water storage constraints, and distributed photovoltaic system operation constraints; wherein, the distributed pumped storage system power constraints include:

[0135]

[0136] In the above formula, are the input and output power of the distributed pumped storage system in time period t of season s, respectively; is the actual output or input power of the distributed pumped storage system in period t of season s. It indicates the output power when : indicates input power; is the amount of water pumped from the lower reservoir to the upper reservoir of the distributed pumped storage system during period t in season s; is the amount of water discharged from the upper reservoir to the lower reservoir of the distributed pumped storage system in time period t of season s; ρ is the density of water; g is the acceleration of gravity; h is the height difference between the upper and lower reservoirs of the distributed pumped storage system; γ S is the energy transmission efficiency of the reversible unit in the distributed pumped storage system; T U is the unit time;

[0137] The operating constraints of the distributed pumped storage system include:

[0138]

[0139] In the above formula, is the binary operating variable of the distributed pumped storage system in time period t of season s. Indicates that the distributed pumped storage system is in the working condition of output power. Indicates that the distributed pumped storage system is in the condition of storing electric energy;

[0140] The distributed pumped storage system water delivery constraints include:

[0141]

[0142] In the above formula, ΔV s，t is the amount of water transported between the upper and lower reservoirs of the distributed pumped storage system in time period t of season s. s，t >0 means water is released from the upper reservoir to the lower reservoir. s，t <0 means pumping water from the upper reservoir to the lower reservoir; ΔVM is the upper limit of the amount of water delivered per unit time by the distributed pumped storage system;

[0143] The water storage constraints of the upper and lower reservoirs of the distributed pumped storage system include:

[0144]

[0145] In the above formula, VH s，t , VH s，t-1 are the upper reservoir water volume of the distributed pumped storage system in period t and period t-1 of season s; VD s，t 、VD s，t-1 are the water volume of the lower reservoir of the distributed pumped storage system in time period t and time period t-1 of season s respectively; η S is the evaporation rate of water body, which represents the natural evaporation of unit water body in unit time;

[0146] The distributed photovoltaic system operation constraints include:

[0147]

[0148] In the above formula, is the maximum output power of the distributed photovoltaic system in period t of season s; It is the benchmark timing output characteristic of photovoltaic cells; is the light intensity coefficient of season s, which represents the light intensity in different seasons; is the actual output power of the distributed photovoltaic system in period t of season s;

[0149] Considering the impact of the data center and its supporting facilities on the power flow and voltage of the distribution network after being connected to the distribution network, distribution network operation constraints are established to ensure the safety performance of the distribution network; the distribution network operation constraints include power balance constraints of data center access nodes, active and reactive power balance constraints of the distribution network, upper and lower power limits of the distribution network lines, and voltage constraints of the distribution network; wherein, the power balance constraints of data center access nodes include:

[0150]

[0151] The active and reactive power balance constraints of the distribution network include:

[0152]

[0153] In the above formula, are the basic active and reactive loads connected to the distribution network node e in time period t of season s; υ e is the binary association coefficient of the data center access point, υ e =1, indicating that the distribution network node e is the access point of the data center; are the active and reactive power on the distribution network line w in time period t of season s, respectively; K w，e represents the binary correlation coefficient between line w and distribution network node e. When K w，e =l, line w is connected to the distribution network node e;

[0154] The power upper and lower limit constraints of the distribution network line include:

[0155]

[0156] In the above formula, LC w represents the configured capacity of line w;

[0157] The distribution network voltage constraints include:

[0158]

[0159] In the above formula, ΔU s，t，w is the voltage drop on line w during period t in season s; U s，t，a , U s，t，b , U s，t，e are the bus voltages of nodes a, b, and e in time period t of season s, respectively. Nodes a and b are the two endpoints of line w; are the resistance and reactance of line w respectively; U m , U M They are the upper and lower limits of the distribution network bus voltage respectively;

[0160] S2. Use the matlab / Cplex platform to solve the joint operation optimization model and output the system joint operation plan.

[0161] Performance Verification:

[0162] In order to verify the effectiveness of the pumped storage photovoltaic combined operation optimization method for data center load cooling described in the present invention (method 1), a distributed source-load independent configuration and operation method (method 2) is used to compare with method 1. Method 2 does not consider the combined configuration and operation of the data center load and its cooling facilities, photovoltaics, and pumped storage, but performs independent configuration and operation respectively. Figure 5The 12-node distribution network shown in the figure is simulated and calculated. The data center load characteristic curve, time-of-use electricity price, and photovoltaic output characteristic curve used in the simulation calculation are as follows: Figure 6-8 As shown; the system facility configuration scheme is shown in Table 1, and the economic index comparison is shown in Table 2:

[0163] Table 1 System facility configuration plan

[0164]

[0165] Table 2 Comparison of economic indicators

[0166]

[0167]

[0168] Comparing Method 1 with Method 2, it can be seen that the various costs of Method 1 are lower than those of Method 2; compared with Method 2, the annualized total configuration cost, annual electricity purchase cost, annual carbon neutrality cost, and annual water evaporation cost of Method 1 are reduced by 15.8%, 6.5%, 8%, and 16.6%, respectively; from the perspective of annual comprehensive cost, the annual comprehensive cost of Method 1 is reduced by 8.2% compared with that of Method 2. The above results show that the pumped-storage photovoltaic combined operation optimization method for data center load cooling proposed in the present invention achieves the effect of minimizing the comprehensive cost of the system.

[0169] Embodiment 2:

[0170] See also Figure 2 The present invention provides a pumped storage photovoltaic joint operation optimization system for data center load cooling, the joint operation optimization system includes a model building module and a simulation calculation module; the model building module is used to build a joint operation optimization model, the joint operation optimization model includes an objective function and constraints, the objective function is to minimize the system comprehensive cost under the premise of meeting the data center load cooling demand, the system comprehensive cost includes the system total configuration cost, electricity purchase cost, carbon neutrality cost, water evaporation cost, the system total configuration cost includes the configuration cost of distributed pumped storage system, distributed photovoltaic system and cooling system, the cooling system includes air conditioning cooling system, water side cooling system, ice storage system; the objective function is specifically:

[0171] min C P [(1+α) γ -1] / αγ 2 +C E +C Z +C W ;

[0172]

[0173] In the above formula, C P , C E , C Z , C W are the total system configuration cost, electricity purchase cost, carbon neutrality cost, and water evaporation cost; α and γ are the discount rate and investment period, respectively; d S is the number of typical days in season S in a year; N S The number of units configured in the distributed pumped storage system; V US 、V DS are the upper and lower reservoir capacities of the distributed pumped storage system; N P 、N A 、N I are the number of photovoltaic cells, cooling air conditioners, and ice machines respectively; PR NS , PR VS , PR P , PR I , PR A They are the unit costs of pumped storage units, pumped storage reservoirs, photovoltaic cells, cooling air conditioners, and ice machines; PR is the power supplied by the upper power grid to the distribution network during period t in season s; E is the unit electricity purchase cost of the system; T U is the unit time; η W is the amount of water evaporation caused by unit cooling capacity when water side cooling is adopted; η S is the evaporation rate of water body, which represents the natural evaporation of unit water body in unit time; is the cooling capacity generated by water cooling in period t of season s; PR W is the unit water evaporation cost; μ C Carbon emissions per unit of electricity generated; PR C is the unit carbon neutrality cost.

[0174] The constraints include cooling system configuration and operation constraints, distributed power configuration and operation constraints, and distribution network operation constraints; the cooling system configuration and operation constraints include cooling demand balance constraints, air conditioning cooling system cooling constraints, ice storage system power and cooling constraints, and ice storage system working state constraints; the distributed power configuration and operation constraints include distributed pumped storage system power constraints, distributed pumped storage system operating conditions constraints, distributed pumped storage system water delivery constraints, distributed pumped storage system upper and lower reservoir water storage constraints, and distributed photovoltaic system operation constraints; the distribution network operation constraints include data center access node power balance constraints, distribution network active and reactive power balance constraints, distribution network line power upper and lower limit constraints, and distribution network voltage constraints; the cooling demand balance constraints include:

[0175]

[0176] In the above formula, is the total load of the data center in time period t of season s; CD The cooling demand factor generated by the total load of the data center; are the cooling capacity generated by air conditioning cooling, water side cooling, and ice storage and melting ice in time period t of season s;

[0177] The cooling capacity constraints of the air conditioning cooling system include:

[0178]

[0179] In the above formula, is the electric power consumed by air conditioning cooling in period t of season s; η A is the energy efficiency ratio of the air conditioning system; P UA is the maximum power of a single air conditioner;

[0180] The power and cooling capacity constraints of the ice storage system include:

[0181]

[0182] In the above formula, P is the electric power consumed by ice storage in period t of season s; UI is the maximum power of a single ice machine; is the cold storage capacity in the cold storage in the period t of season s; η L is the self-loss coefficient per unit time of the ice storage system; η I IS is the energy efficiency ratio of the ice machine; M The upper limit of the cold storage capacity of the ice storage system; is the cooling capacity generated by melting ice in empty ice storage in period t-1 of season s;

[0183] The working state constraints of the ice storage system include:

[0184]

[0185] In the above formula, δ M is the large M constant; is a binary variable of the working state of the ice storage system in period t of season s. When When tn is the last period of a typical day, the ice storage system consumes electric energy to store cold.

[0186] The power constraints of the distributed pumped storage system include:

[0187]

[0188] In the above formula, are the input and output power of the distributed pumped storage system in time period t of season s, respectively; is the actual output or input power of the distributed pumped storage system in period t of season s. It indicates the output power when : indicates input power; is the amount of water pumped from the lower reservoir to the upper reservoir of the distributed pumped storage system during period t in season s; is the amount of water discharged from the upper reservoir to the lower reservoir of the distributed pumped storage system in time period t of season s; ρ is the density of water; g is the acceleration of gravity; h is the height difference between the upper and lower reservoirs of the distributed pumped storage system; γ S is the energy transmission efficiency of the reversible unit in the distributed pumped storage system; T U is the unit time;

[0189] The operating constraints of the distributed pumped storage system include:

[0190]

[0191] In the above formula, is the binary operating variable of the distributed pumped storage system in time period t of season s. Indicates that the distributed pumped storage system is in the working condition of output power. Indicates that the distributed pumped storage system is in the condition of storing electric energy;

[0192] The distributed pumped storage system water delivery constraints include:

[0193]

[0194] In the above formula, ΔV s，t is the amount of water transported between the upper and lower reservoirs of the distributed pumped storage system in time period t of season s. s，t >0 means water is released from the upper reservoir to the lower reservoir. s，t <0 means pumping water from the upper reservoir to the lower reservoir; ΔVM is the upper limit of the amount of water delivered per unit time by the distributed pumped storage system;

[0195] The water storage constraints of the upper and lower reservoirs of the distributed pumped storage system include:

[0196]

[0197] In the above formula, VH s，t , VH s，t-1 are the upper reservoir water volume of the distributed pumped storage system in period t and period t-1 of season s; VDs，t 、VD s，t-1 are the water volume of the lower reservoir of the distributed pumped storage system in time period t and time period t-1 of season s respectively; η S is the evaporation rate of water body, which represents the natural evaporation of unit water body in unit time;

[0198] The distributed photovoltaic system operation constraints include:

[0199]

[0200] In the above formula, is the maximum output power of the distributed photovoltaic system in period t of season s; It is the benchmark timing output characteristic of photovoltaic cells; is the light intensity coefficient of season s, which represents the light intensity in different seasons; is the actual output power of the distributed photovoltaic system in period t of season s;

[0201] The data center access node power balance constraint includes:

[0202]

[0203] The active and reactive power balance constraints of the distribution network include:

[0204]

[0205] In the above formula, are the basic active and reactive loads connected to the distribution network node e in time period t of season s; υ e is the binary association coefficient of the data center access point, υ e =1, indicating that the distribution network node e is the access point of the data center; are the active and reactive power on the distribution network line w in time period t of season s, respectively; κ w，e represents the binary correlation coefficient between line w and distribution network node e, when κ w，e =l, line w is connected to the distribution network node e;

[0206] The power upper and lower limit constraints of the distribution network line include:

[0207]

[0208] In the above formula, LC w represents the configured capacity of line w;

[0209] The distribution network voltage constraints include:

[0210]

[0211] In the above formula, ΔU s，t，w is the voltage drop on line w during period t in season s; U s，t，a , U s，t，b , U s，t，e are the bus voltages of nodes a, b, and e in time period t of season s, respectively. Nodes a and b are the two endpoints of line w; are the resistance and reactance of line w respectively; U m , U M They are the upper and lower limits of the distribution network bus voltage respectively.

[0212] The simulation calculation module is used to solve the joint operation optimization model and output the system joint operation plan.

[0213] Embodiment 3:

[0214] See also Figure 3 , a pumped-storage photovoltaic combined operation optimization device for data center load cooling, comprising a memory and a processor; the memory is used to store computer program code and transmit the computer program code to the processor; the processor is used to execute the method described in Example 1 according to the instructions in the computer program code.

[0215] Embodiment 4:

[0216] A computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the method described in Embodiment 1 when executed by a processor.

[0217] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0218] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0219] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0220] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0221] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A pumped-storage photovoltaic combined operation optimization method for data center load cooling, characterized by: The combined operation optimization method comprises: S1. Construct a joint operation optimization model; the joint operation optimization model includes an objective function and constraints. The objective function is to minimize the system comprehensive cost under the premise of meeting the load cooling demand of the data center. The system comprehensive cost includes the total system configuration cost, electricity purchase cost, carbon neutrality cost, and water evaporation cost. The total system configuration cost includes the distributed power system configuration cost and the cooling system configuration cost. The distributed power system includes a distributed pumped storage system and a distributed photovoltaic system. The cooling system includes an air conditioning cooling system, a water side cooling system, and an ice storage system. S2, solve the joint operation optimization model and output the system joint operation plan; The objective function is: min C P [(1+a) γ -1] / ag 2 +C E +C Z +C W ; In the above formula, C P , C E , C Z , C W are the total system configuration cost, electricity purchase cost, carbon neutrality cost, and water evaporation cost; α and γ are the discount rate and investment period, respectively; d S is the number of typical days in season S in a year; N S The number of units configured in the distributed pumped storage system; V US 、V DS are the upper and lower reservoir capacities of the distributed pumped storage system; N P 、N A 、N I are the number of photovoltaic cells, cooling air conditioners, and ice machines respectively; PR NS , PR VS , PR P , PR I , PR A They are the unit costs of pumped storage units, pumped storage reservoirs, photovoltaic cells, cooling air conditioners, and ice machines; PR is the power supplied by the upper power grid to the distribution network during the period t of season S; E is the unit electricity purchase cost of the system; T U is the unit time; η W is the amount of water evaporation caused by unit cooling capacity when water side cooling is adopted; η S is the evaporation rate of water body, which represents the natural evaporation of unit water body in unit time; is the cooling capacity generated by water cooling in period t of season S; PR W is the unit water evaporation cost; μ C Carbon emissions per unit of electricity generated; PR C is the unit carbon neutrality cost; The constraint conditions include cooling system configuration operation constraints; the cooling system configuration operation constraints include cooling demand balance constraints, air conditioning cooling system cooling constraints, ice storage system power and cooling constraints, and ice storage system working state constraints; The cooling demand balance constraint includes: In the above formula, is the total load of the data center in time period t of season s; CD The cooling demand factor generated by the total load of the data center; are the cooling capacity generated by air conditioning cooling, water side cooling, and ice storage and melting ice in time period t of season s; The cooling capacity constraints of the air conditioning cooling system include: In the above formula, is the electric power consumed by air conditioning cooling in period t of season s; η A is the energy efficiency ratio of the air conditioning system; P UA is the maximum power of a single air conditioner; The power and cooling capacity constraints of the ice storage system include: In the above formula, P is the electric power consumed by ice storage in period t of season s; UI is the maximum power of a single ice machine; is the cold storage capacity in the cold storage in the period t of season s; η L is the self-loss coefficient per unit time of the ice storage system; η I IS is the energy efficiency ratio of the ice machine; M The upper limit of the cold storage capacity of the ice storage system; is the cooling capacity generated by melting ice in empty ice storage in period t-1 of season s; The working state constraints of the ice storage system include: In the above formula, δ M is the large M constant; is a binary variable indicating the working status of the ice storage system in period t of season s; tn is the last period of a typical day.

2. The method for optimizing pumped storage and photovoltaic combined operation for data center load cooling according to claim 1, characterized in that: The constraints also include distributed power configuration and operation constraints, distribution network operation constraints, the distributed power configuration and operation constraints include distributed pumped storage system power constraints, distributed pumped storage system operating conditions constraints, distributed pumped storage system water delivery constraints, distributed pumped storage system upper and lower reservoir water storage constraints, distributed photovoltaic system operation constraints; the distribution network operation constraints include data center access node power balance constraints, distribution network active and reactive power balance constraints, distribution network line power upper and lower limit constraints, distribution network voltage constraints; The power constraints of the distributed pumped storage system include: In the above formula, are the input and output power of the distributed pumped storage system in time period t of season s, respectively; is the actual output or input power of the distributed pumped storage system in the period of season s; is the amount of water pumped from the lower reservoir to the upper reservoir of the distributed pumped storage system during the period of season s; is the amount of water discharged from the upper reservoir to the lower reservoir of the distributed pumped storage system in time period t of season s; ρ is the density of water; g is the acceleration of gravity; h is the height difference between the upper and lower reservoirs of the distributed pumped storage system; γ S is the energy transmission efficiency of the reversible unit in the distributed pumped storage system; T U is the unit time; The operating constraints of the distributed pumped storage system include: In the above formula, is the binary operating variable of the distributed pumped storage system in period t of season s; The distributed pumped storage system water delivery constraints include: In the above formula, ΔV s，t is the amount of water transported between the upper and lower reservoirs of the distributed pumped storage system in time period t of season s; ΔVM is the upper limit of the amount of water transported per unit time of the distributed pumped storage system; The water storage capacity constraints of the upper and lower reservoirs of the distributed pumped storage system include: In the above formula, VH s，t , VH s，t-1 are the upper reservoir water volume of the distributed pumped storage system in period t and period t-1 of season s; VD s，t 、VD s，t-1 are the water volume of the lower reservoir of the distributed pumped storage system in time period t and time period t-1 of season S respectively; η S is the evaporation rate of water body, which represents the natural evaporation of unit water body in unit time; The distributed photovoltaic system operation constraints include: In the above formula, is the maximum output power of the distributed photovoltaic system in period t of season s; It is the benchmark timing output characteristic of photovoltaic cells; is the light intensity coefficient of season S, which represents the light intensity in different seasons; is the actual output power of the distributed photovoltaic system in period t of season S; The data center access node power balance constraint includes: The active and reactive power balance constraints of the distribution network include: In the above formula, are the basic active and reactive loads connected to the distribution network node e in the period t of season S; v e is the binary association coefficient of the data center access point; are the active and reactive power on the distribution network line w in time period t of season S, respectively; κ w，e represents the binary correlation coefficient between line w and distribution network node e; The power upper and lower limit constraints of the distribution network line include: In the above formula, LC w represents the configured capacity of line w; The distribution network voltage constraints include: In the above formula, ΔU s，t，w is the voltage drop on line w during period t in season S; U s，t，a , U s，t，b , U s，t，e are the bus voltages of nodes a, b, and e in time period t of season s, respectively. Nodes a and b are the two endpoints of line w; are the resistance and reactance of line w respectively; U m , U M They are the upper and lower limits of the distribution network bus voltage respectively.

3. Pumped-storage photovoltaic combined operation optimization system for data center load cooling, characterized by: The joint operation optimization system includes a model building module and a simulation calculation module; The model building module is used to build a joint operation optimization model; the joint operation optimization model includes an objective function and constraints, the objective function is to minimize the system comprehensive cost under the premise of meeting the load cooling demand of the data center, the system comprehensive cost includes the system total configuration cost, electricity purchase cost, carbon neutrality cost, and water evaporation cost, the system total configuration cost includes the configuration cost of the distributed pumped storage system, the distributed photovoltaic system and the cooling system, and the cooling system includes the air conditioning cooling system, the water side cooling system, and the ice storage system; The simulation calculation module is used to solve the joint operation optimization model and output the system joint operation plan; The objective function is: min C P [(1+a) γ -1] / ag 2 +C E +C Z +C W ; In the above formula, C P , C E , C Z , C W are the total system configuration cost, electricity purchase cost, carbon neutrality cost, and water evaporation cost; α and γ are the discount rate and investment period, respectively; d s is the number of typical days in season S in a year; N S The number of units configured in the distributed pumped storage system; V US 、V DS are the upper and lower reservoir capacities of the distributed pumped storage system; N P 、N A 、N I are the number of photovoltaic cells, cooling air conditioners, and ice machines respectively; PR NS , PR VS , PR P , PR I , PR A They are the unit costs of pumped storage units, pumped storage reservoirs, photovoltaic cells, cooling air conditioners, and ice machines; is the power supplied from the upper power grid to the distribution network during period t in season s; PR E is the unit electricity purchase cost of the system; T U is the unit time; η W It is the amount of water evaporation caused by unit cooling capacity when water side cooling is adopted; η S is the evaporation rate of water body, which represents the natural evaporation of unit water body in unit time; is the cooling capacity generated by water cooling in period t of season s; PR W is the unit water evaporation cost; μ C Carbon emissions per unit of electricity generated; PR C is the unit carbon neutrality cost; The constraint conditions include cooling system configuration operation constraints, and the cooling system configuration operation constraints include cooling demand balance constraints, air conditioning cooling system cooling constraints, ice storage system power and cooling constraints, and ice storage system working state constraints; The cooling demand balance constraint includes: In the above formula, is the total load of the data center in time period t of season s; CD The cooling demand factor generated by the total load of the data center; are the cooling capacity generated by air conditioning cooling, water side cooling, and ice storage and melting ice in time period t of season s; The cooling capacity constraints of the air conditioning cooling system include: In the above formula, is the electric power consumed by air conditioning cooling in period t of season s; η A is the energy efficiency ratio of the air conditioning system; P UA is the maximum power of a single air conditioner; The power and cooling capacity constraints of the ice storage system include: In the above formula, P is the electric power consumed by ice storage in period t of season s; UI is the maximum power of a single ice machine; is the cold storage capacity in the cold storage in the period t of season s; η L is the self-loss coefficient per unit time of the ice storage system; η I IS is the energy efficiency ratio of the ice machine; M The upper limit of the cold storage capacity of the ice storage system; is the cooling capacity generated by melting ice in empty ice storage in period t-1 of season s; The working state constraints of the ice storage system include: In the above formula, δ M is the large M constant; is a binary variable indicating the working status of the ice storage system in period t of season s; tn is the last period of a typical day.

4. The pumped-storage photovoltaic combined operation optimization system for data center load cooling according to claim 3 is characterized by: The constraints also include distributed power configuration and operation constraints, distribution network operation constraints, the distributed power configuration and operation constraints include distributed pumped storage system power constraints, distributed pumped storage system operating conditions constraints, distributed pumped storage system water delivery constraints, distributed pumped storage system upper and lower reservoir water storage constraints, distributed photovoltaic system operation constraints; the distribution network operation constraints include data center access node power balance constraints, distribution network active and reactive power balance constraints, distribution network line power upper and lower limit constraints, distribution network voltage constraints; The power constraints of the distributed pumped storage system include: In the above formula, are the input and output power of the distributed pumped storage system in time period t of season s, respectively; is the actual output or input power of the distributed pumped storage system during the period of season S; is the amount of water pumped from the lower reservoir to the upper reservoir of the distributed pumped storage system during period t in season S; is the amount of water discharged from the upper reservoir to the lower reservoir of the distributed pumped storage system during period t in season S; ρ is the density of water; g is the acceleration of gravity; h is the height difference between the upper and lower reservoirs of the distributed pumped storage system; γ S is the energy transmission efficiency of the reversible unit in the distributed pumped storage system; T U is the unit time; The operating constraints of the distributed pumped storage system include: In the above formula, is the binary operating condition variable of the distributed pumped storage system in period t of season S; The distributed pumped storage system water delivery constraints include: In the above formula, ΔV s，t is the amount of water transported between the upper and lower reservoirs of the distributed pumped storage system in time period t of season S; ΔVM is the upper limit of the amount of water transported per unit time of the distributed pumped storage system; The water storage constraints of the upper and lower reservoirs of the distributed pumped storage system include: In the above formula, VH s，t , VH s，t-1 are the upper reservoir water volume of the distributed pumped storage system in period t and period t-1 of season S; VD s，t 、VD s，t-1 are the water volume of the lower reservoir of the distributed pumped storage system in time period t and time period t-1 of season S respectively; η S is the evaporation rate of water body, which represents the natural evaporation of unit water body in unit time; The distributed photovoltaic system operation constraints include: In the above formula, is the maximum output power of the distributed photovoltaic system in period t of season S; It is the benchmark timing output characteristic of photovoltaic cells; is the light intensity coefficient of season S, which represents the light intensity in different seasons; is the actual output power of the distributed photovoltaic system in period t of season S; The data center access node power balance constraint includes: The active and reactive power balance constraints of the distribution network include: In the above formula, are the basic active and reactive loads connected to the node e of the internal distribution network during the period of season s; v e is the binary association coefficient of the data center access point; are the active and reactive power on the distribution network line w in time period t of season s, respectively; κ w，e represents the binary correlation coefficient between line w and distribution network node e; The power upper and lower limit constraints of the distribution network line include: In the above formula, LC w represents the configured capacity of line w; The distribution network voltage constraints include: In the above formula, ΔU s，t，w is the voltage drop on line w during period t in season S; U s，t，a , U s，t，b , U s，t，e are the bus voltages of nodes a, b, and e in time period t of season s, respectively. Nodes a and b are the two endpoints of line w; are the resistance and reactance of line w respectively; U m , U M They are the upper and lower limits of the distribution network bus voltage respectively.

5. Pumped-storage photovoltaic combined operation optimization equipment for data center load cooling, characterized by: The optimization device includes a memory and a processor; The memory is used to store computer program code and transmit the computer program code to the processor; The processor is configured to execute the method according to claim 1 or 2 according to instructions in the computer program code.

6. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to claim 1 or 2 is implemented.

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