Data center microgrid planning method considering water resource constraints and risk measurement

Abstract

The present invention discloses a data center microgrid planning method that takes into account water resource constraints and risk metrics, relating to the field of computer technology applications. Taking water resource constraints and multiple uncertainties into account, the present invention uses data-driven modeling and the DRO‑CVaR algorithm to not only improve the accuracy and reliability of data center planning, but also achieve a balance between economy and robustness, thus having significant practical value and theoretical significance.

Description

Technical Field

[0001] The present invention relates to the field of computer technology applications, and in particular to a data center microgrid planning method taking into account water resource constraints and risk metrics. Background Art

[0002] With the rapid development of cloud computing services and the exponential growth in the number of data centers, the economic and environmental efficiency of data center energy consumption has become one of the research hotspots of the power system. The huge energy consumption of data centers has led operators to equip photovoltaic units to reduce the high cost of purchasing electricity and at the same time reduce the power usage efficiency (PUE) parameters of data centers. On the one hand, they will form a data center microgrid and reduce their own energy costs. On the other hand, in the large-scale data center construction projects coordinated by the "East Data West Computing" policy, 90% of the new ones are large and super-large data centers. More and more data center operators have begun to choose water-cooled equipment units with higher efficiency and lower power consumption to replace the original air-cooled equipment units. However, the resulting water use problem has also attracted the attention of data center operators and the government. Taking a single data center with a power capacity of 20MW and using open cooling towers for heat dissipation as an example, the theoretical annual evaporation water consumption is about 200,000 m 3 , which is equivalent to the water consumption required by 3,000 urban residents. This poses a major challenge to data center planning that takes into account both economic and environmental factors.

[0003] Defects and shortcomings of existing technology:

[0004] 1. Inadequate water consumption modeling: Currently, scholars at home and abroad mostly build a mapping relationship between data center water consumption and electricity power based on traditional data center planning models, thereby achieving the construction and solution of data center planning models that take into account the water footprint. However, existing research, on the one hand, uses the water usage effectiveness (WUE) approach to map data center water consumption as a linear function of electricity consumption. However, there is little research on the refined modeling of water consumption that takes outdoor temperature into account. This makes it difficult to quantitatively assess the impact of temperature on data centers, which in turn leads to insufficient or excessive planned capacity for renewable energy units such as photovoltaics. Furthermore, few studies have considered the water resource constraints of the system or region where water-cooled data centers are located. This makes data center planning solutions calculated by traditional methods potentially infeasible due to constraint violations.

[0005] 2. Inadequate Uncertainty Handling: Compared to traditional microgrid loads, data center microgrids not only have output uncertainty from their photovoltaic units, but also experience significant randomness in the data and computing service requests processed by data center servers. This leads to high uncertainty in data center server loads. This uncertainty, combined with outdoor temperature, also results in uncertainty in the power and water consumption of data center cooling systems. In existing research, although DRO (Decentralized Regression) (DRO) has been more widely used than RO and stochastic optimization (SO) algorithms due to its economic and robust performance, DRO models still consider all elements and risk scenarios in the dataset and cannot solve system planning optimization problems without ignoring some extreme scenarios. Therefore, for data center operators with high risk tolerance, developing a DRO model that is compatible with risk management models is particularly necessary. Furthermore, existing DRO models often use methods such as CCG and Benders decomposition to transform and decompose the min-max-min model used in microgrid planning and operation. However, when the objective function takes the expected or integral form, rather than the linear form, as used in risk control models such as CVaR, these existing methods struggle to calculate the optimal solution.

[0006] 3. Insufficient model validity: Existing studies have the following three problems: (1) Only the impact of load or photovoltaic output uncertainty on system operation planning is considered, and few studies consider the impact of temperature on data center operation planning, which makes the model validity insufficient; (2) For the few studies that consider multiple uncertainties in planning problems, existing methods mostly describe the joint distribution of multiple uncertainties through ideal probability distribution functions rather than data-driven methods, resulting in insufficient reliability of planning schemes; (3) The existing DRO algorithm only optimizes decisions for the worst probability distribution in the fuzzy set, which makes the formulation of planning strategies take into account the most extreme cases. Although the robustness of the model is guaranteed, when the decision maker has the willingness and ability to avoid risks, the resulting model has the problem of insufficient economic efficiency. Summary of the Invention

[0007] To solve the above problems, the present invention provides a data center microgrid planning method that takes into account water resource constraints and risk metrics, including the following steps:

[0008] Based on the workload and heat transfer methods of water-cooled data centers, the water footprint of water-cooled data centers during operation is quantified. Taking the total cost of the water-cooled data center planning period as the target, a data center planning model that considers water resource constraints is constructed.

[0009] Based on the data center planning model, the uncertainty of data center load and photovoltaic unit power is taken into account, and the uncertainty of the data center's outdoor temperature is taken into account. The three uncertainties are converted into conditional risk values, and the DRO algorithm is used to actively manage risks in a multi-uncertainty environment.

[0010] Preferably, in the process of constructing a data center planning model that considers water resource constraints, based on the workload of the water-cooled data center, the power consumption of the data center is obtained according to the total power of the data center in time period t, the equivalent power consumption of processing batch loads, interactive loads, and the power consumption of the cooling system, and the water footprint is quantified based on the power consumption.

[0011] Preferably, in the process of quantifying the water footprint, the water footprint is quantified based on the electricity consumption, according to the heat transfer method of water circulation-evaporation, by setting the server temperature constraints of the data center and the temperature change and evaporation rate constraints of the cooling water.

[0012] Preferably, in the process of obtaining the total cost of the data center during the planning period, the total cost of the data center during the planning period is obtained based on minimizing the annual equivalent planning cost, the annual equivalent operating cost and the water cost of the data center.

[0013] Preferably, in the process of obtaining the total cost of the data center during the planning period, the total cost of the data center during the planning period is expressed as:

[0014] C=C inv +365·C ope

[0015]

[0016] Where C is the total cost of the data center microgrid during the planning period; C inv is the investment cost of data center equipment; r is the discount rate; y s is the equipment life; S is the equipment set, including photovoltaic units, chillers and energy storage units; c s is the installation cost per unit capacity of the equipment; Q s Install capacity for equipment; C ope is the total operating cost of the data center; C buy is the electricity purchase cost; C carbon is the carbon emission cost, C water is the water purchase cost; C w Penalty for excessive water use in data centers; C s The operating cost of energy storage; is the time-of-use electricity price during period t; p W is the penalty price when the water purchase amount of the data center exceeds the total water consumption limit; maxis the city's limit on the total water consumption of data centers, and WUE is the city's policy limit on the WUE of data centers; is the charging and discharging loss cost coefficient of the energy storage equipment.

[0017] Preferably, in the process of converting the three uncertainties into conditional risk value, the three uncertainties include the equivalent electric load P of the data center t DC , the output of photovoltaic units installed in data centers P t PV and the temperature T at the data center's location t O .

[0018] Preferably, in the process of converting the three uncertainties into conditional risk values, the historical data of the three uncertainties are processed and merged by normalizing and adding multipliers, the original three uncertainties are converted into a one-dimensional uncertainty problem, and the multiplier combination that can make the planning economically optimal is calculated.

[0019] The present invention also discloses a data center microgrid planning system that takes into account water resource constraints and risk metrics for implementing the method, including:

[0020] A model building module is used to quantify the water footprint of a water-cooled data center during operation based on its workload and heat transfer method. It also constructs a data center planning model that considers water resource constraints, taking the total cost of the water-cooled data center during the planning period as the target.

[0021] The active risk management module is used to convert the three uncertainties into conditional risk values ​​based on the data center planning model, according to the uncertainty of data center load and photovoltaic unit power, and taking into account the uncertainty of the data center's outdoor temperature. It also uses the DRO algorithm to actively manage risks in a multi-uncertainty environment.

[0022] The present invention discloses the following technical effects:

[0023] Taking into account water resource constraints and multiple uncertainties, this invention not only improves the accuracy and reliability of data center planning, but also achieves a balance between economy and robustness through data-driven modeling and the DRO-CVaR algorithm, which has significant practical value and theoretical significance. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0025] Figure 1 This is the data center system structure of the present invention;

[0026] Figure 2 This is the data center evaporative cooling chiller system structure of the present invention;

[0027] Figure 3 It is a flow chart of the DRO-CVaR algorithm described in the present invention. DETAILED DESCRIPTION

[0028] In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work are within the scope of protection of this application.

[0029] like Figure 1-3 As shown, the present invention provides a data center microgrid planning method taking into account water resource constraints and risk metrics, which specifically includes the following contents:

[0030] 1. Data Center Microgrid Model:

[0031] 1.1 Water-cooled data center operation model:

[0032] A typical data center system consists of servers, energy storage units, photovoltaic units, cooling systems, etc. The power supply of the data center is first supplied by the photovoltaic units, and the insufficient part is supplemented by the energy storage units and the power grid. The energy storage units can reduce the operating costs of the data center by arbitrage of peak and valley time-of-use electricity prices. On the other hand, they can absorb the excessive output of the photovoltaic units to avoid the phenomenon of abandoned light. The data center system structure studied in this invention is as follows Figure 1 shown.

[0033] Considering the business characteristics of data centers, the workload of data centers consists of batch workloads that can be processed later and interactive workloads that cannot be processed later

[15] . The power consumption of data centers can be expressed as:

[0034] P t DC =P t DC.bat +P t DC.int +P t DCW (1)

[0035] Where: P t DC 、P t DC.bat 、P t DC.int 、P t DCW Where, the total power of the data center in period t, the equivalent power consumption of processing batch workloads, the equivalent power consumption of interactive loads, and the power consumption of the cooling system are respectively. Among them, the batch workload can be delayed to another period before the processing deadline. The operation mode of the batch workload: Equation (2a) shows that the total load of the data center remains unchanged before and after the batch workload is delayed. Equation (2b) shows the transfer of the batch workload within the allowed delay:

[0036]

[0037]

[0038] Where: P t DC,ad is the power of the data center after the transfer during period t; T max is the upper limit of the number of delay periods. The power balance of the data center is shown in Equation (3a). Equation (3b) shows that the maximum charge and discharge power of the energy storage unit is limited by its planned capacity. Equation (3c) shows the energy storage unit's energy balance at the beginning and end of each regulation cycle. Equation (3d) shows the energy storage unit's energy constraint, which prevents the energy storage device from exceeding the power limit at any time.

[0039]

[0040] Where: P t buy 、P t PV 、P t s They are the data center's electricity purchase volume, photovoltaic power generation, and energy storage unit power during period t; is the planned capacity of the energy storage unit; Δt is the scheduling time interval; E0 is the initial value of the energy storage in time period 0; Emax 、E min are the upper and lower limits of the energy storage device's power; t' is any period from 1 to 24.

[0041] The equivalent carbon emissions of a data center come from the carbon emissions of power plants during the electricity purchase process, which can be described by formula (4):

[0042] F t C =η C [P t DC -P t PV ] + (4)

[0043] Where: F t C is the carbon footprint of the data center in period t; η C is the ratio coefficient of grid carbon emissions to power generation.

[0044] 1.2 Data Center Water Footprint Model:

[0045] Water-cooled data centers reduce the cooling power consumption of data centers by transferring the heat generated by data center servers through water circulation and evaporation. This reduces the power consumption outside the servers, that is, the power of the cooling system, thereby reducing the PUE of the data center. Figure 2 It is a typical evaporative cooling chiller system structure.

[0046] Figure 2 In the data center, the hot air generated by server heat dissipation is transported to the evaporator. This hot air is cooled by chilled water and returned to the data center as cold air. The heated chilled water returns to the condenser, where it absorbs the heat released by the liquefied refrigerant and returns to cooler chilled water for a new round of cooling in the data center. Cooling the condenser is accomplished by a cooling tower, which pumps low-temperature cooling water into the condenser and recovers the high-temperature cooling water output from the condenser. This recovered water is then cooled through evaporation and other methods. This evaporation represents the data center's water consumption.

[0047] During operation, the water used in the data center comes from the evaporation of cooling water, and the evaporation rate increases with the increase of cooling water temperature. The server temperature constraint of the data center is: Formula (5a) updates the server temperature of the data center according to the outdoor temperature and the heat generated by the data center. Formula (5b) represents the upper and lower limit constraints of the data center server temperature. Formula (5c) represents the heat generated during the operation of the data center.

[0048]

[0049] Where: Tt DC is the temperature of the data center server in period t; U wt 、A wt 、C wt They are the heat loss coefficient, heat loss effective area, and heat capacity of the server respectively; are the power generation and heat production and expected heat demand of the data center during the t period respectively; T is the heat generated by the data center during period t; t O is the outdoor temperature during period t; and T DC are the upper and lower limits of the data center server temperature; κ DC is the ratio of heat generation to power in the data center.

[0050] Cooling water temperature change and evaporation rate constraints: Equation (6a) updates the data center cooling water temperature based on the outdoor air temperature and the heat generated by the data center. Equation (6b) represents the upper and lower limit constraints of the data center cooling water temperature. Equation (6c) represents the data center heat dissipation water consumption. Equation (6d) represents the total power consumption of the chiller in the data center cooling system. Equation (6e) indicates that the power of the data center chiller will not exceed its planned capacity.

[0051]

[0052] Where: T t DCW is the temperature of the cooling water in the data center during period t; R o is the equivalent thermal resistance of the data center cooling unit room; C r The heat capacity of the data center cooling unit room; U w 、A w are the heat loss coefficient and heat dissipation area of ​​the cooling system room respectively; is the cooling capacity of the data center cooling unit during period t; and T DCW W is the upper and lower limits of the cooling water temperature in the data center; t DCW is the cooling water consumption of the data center during period t; η DC is the proportional coefficient of water evaporation rate and water temperature; η DCW The ratio coefficient between the power of the data center cooling system and the cooling capacity of the cooling unit; Plan capacity for data center chillers.

[0053] Based on the above content, formula (7) can be further used to quantify the water footprint of the data center during operation. The water footprint of the data center includes the water used for cooling the server itself and the water used for power generation and cooling caused by purchased electricity.

[0054] W t =W t DCW +η E [P t DC -P t PV ] + (7)

[0055] Where: W t is the water footprint of the data center during period t; η E is the ratio coefficient of grid cooling water consumption to power generation, where [·] + =max{·,0}.

[0056] 2. Data center microgrid planning considering water resource constraints:

[0057] Based on the data center energy consumption characteristic model constructed above, we construct a data center planning model that considers water resource constraints, taking the total cost of the data center planning period as the objective. The data center planning cost includes minimizing the annual equivalent planning cost, the annual equivalent operating cost, and the data center's water cost. The corresponding data center planning objective function can be expressed as follows: Equation (8a) is the total cost of the data center microgrid during the planning period, Equation (8b) is the equipment investment cost of the data center, and Equation (8c) is the operating cost of the data center.

[0058] C=C inv +365·C ope (8a)

[0059]

[0060] Where: C is the total cost of the data center microgrid during the planning period; C inv is the investment cost of data center equipment; r is the discount rate; y s is the equipment life; S is the equipment set, including photovoltaic units, chillers and energy storage units; c s is the installation cost per unit capacity of the equipment; Q s Install capacity for equipment; C ope is the total operating cost of the data center; C buy is the electricity purchase cost; C carbon is the carbon emission cost, C water is the water purchase cost; C w Penalty for excessive water use in data centers; C s The operating cost of energy storage; is the time-of-use electricity price during period t; p W is the penalty price when the water purchase amount of the data center exceeds the total water consumption limit; max is the city's limit on the total water consumption of data centers, and WUE is the city's policy limit on the WUE of data centers; is the charging and discharging loss cost coefficient of the energy storage equipment.

[0061] Since equation (8c) contains nonlinear terms, we introduce the auxiliary variable P t s,+ =max{0,P t s} and P t s,- =max{0,-P t s Convert the energy storage operating cost in formula (8c) into:

[0062]

[0063] The carbon emission price and water purchase price of the data center adopt the step-by-step price form of formula (10) and formula (11).

[0064]

[0065]

[0066] Where: μ c,0 is the benchmark carbon emission right price; υ is the electricity carbon emission factor of the coal-fired power unit; C c,p is the carbon quota; η is the carbon quota increment; σ is the carbon price step increment; m is the number of carbon price steps; μ w,0 is the benchmark water purchase price; C c,w is the water quota; ψ is the water quota increment; ζ is the water price step increment; and n is the number of water price steps.

[0067] In summary, the planning model of the data center can be expressed in a compact form as described in Equation (12).

[0068]

[0069] 3. Data Center DRO-CVaR Algorithm Framework Based on Uncertainty:

[0070] 3.1 Data-driven fuzzy sets of uncertain quantities:

[0071] During data center operations, data center operators face the economic risk of excessively high "operation and planning" costs. Existing research shows that data center server load and photovoltaic systems are two of the most common uncertainties, due to the randomness of data user service demand and sunlight intensity. We have added temperature uncertainty as a research topic. Therefore, the factors influencing data center economic risk can be expressed using the following three uncertainties:

[0072] ① Equivalent electrical load P of the data center t DC ;

[0073] ② The output of the photovoltaic unit installed in the data center P t PV ;

[0074] ③ Temperature T of the data center's location t O .

[0075] In traditional risk measurement models, decision makers need to know the probability distribution function of the risk quantity. However, in reality, due to the limited size of the data set, it is impossible to obtain an accurate probability distribution. Therefore, we use a data-driven approach to process the data set of the model. Considering that the units and numerical values ​​of the three variables of data center load, photovoltaic output and external temperature are somewhat different, and the CVaR fuzzy set that relies on the mean and variance of the uncertainty is difficult to handle physical quantities with different dimensions and large numerical differences, the present invention uses normalization and adding multipliers to process and merge the historical data of the three uncertainties, converting the original three uncertainties into a one-dimensional uncertainty problem, and calculating the multiplier combination that can optimize the planning economy. The conversion method is shown in Equations (13) to (15).

[0076]

[0077] Where: DC ,λ PV ,λ TP is the per-unit value of the equivalent electrical load, photovoltaic output and outdoor temperature of the data center; DC,0 ,λ PV,0 ,λ TP,0 is the historical data of three sets of variables; DC,base ,λ PV,base ,λ TP,base is the baseline value of the three groups of variables; k DC 、k PV 、k TP are the multipliers corresponding to the three groups of variables.

[0078] Let T be the number of time periods in a typical day, and the three sets of 1×T dimensional data obtained by equations (12) to (14) are transformed by λ=[λ DC λ PV λ TP ] in the form of a combination, a set of 1×3T-dimensional uncertainty λ is used to represent the original multidimensional uncertainty. Based on this, the ellipsoidal fuzzy set of the data center uncertainty λ can be constructed.

[0079]

[0080] Where: is a closed convex set containing P, express The set of all probability distributions on . To simplify the expression, the present invention will use D M1 replace γ1≥0 and γ2≥0 are characteristic parameters of the fuzzy set; M is the number of samples; and are the mean and variance of the random vector λ, respectively, and their expressions are:

[0081]

[0082] 3.2 Data Center Uncertainty Characterization Based on DRO-CVaR

[0083] The fuzzy set D constructed in Section 3.1 is driven by the uncertainty data of load, photovoltaic and temperature. M1 , we need to construct the DRO-CVaR form of the model corresponding to formula (12) so that data center planning decision makers can make decisions based on D M1 decision.

[0084] First, we construct the CVaR form of the objective function through equations (19) and (20) to quantify the risk cost in extreme scenarios. Equation (19) represents the minimum planning cost that does not exceed the expected threshold α, and equation (20) represents the objective function (8a) in extreme scenarios that exceeds α. β (x) when the expected value of the objective function φ β (x).

[0085]

[0086] Where Ψ(x,α) is the probability that the planning cost does not exceed the desired threshold α, and β is a confidence parameter with a value between 0 and 1. Assuming h(x,α,λ)=max{α,C(x,λ) / (1-β)-βα / (1-β)}, we can obtain:

[0087]

[0088] Combining the objective functions of RO in formula (22) and SO in formula (23), based on the fuzzy set D M1 The data center planning cost under DRO-CVaR can be expressed as shown in formula (24):

[0089]

[0090] By using the duality theory and Schur complement model semi-definite programming transformation method, the data center planning cost calculation problem of formula (24) can be equivalently transformed into the semi-definite programming problem shown in formula (25):

[0091]

[0092] In summary, the optimization problem expressed in formula (24) is transformed into a semi-definite programming form that can be directly solved by the solver. The process of the algorithm proposed in this invention is as follows: Figure 3 shown.

[0093] To more accurately describe the water usage characteristics of data centers, this paper establishes a water consumption model for data centers that takes outdoor temperature into account, by considering the data center's outdoor temperature, indoor and outdoor heat transfer processes, and water resource constraints. Based on this, a data center microgrid planning model that takes data center water usage characteristics into account is proposed.

[0094] Based on the traditional uncertainty of data center load and photovoltaic unit power, this paper takes into account the uncertainty of the data center's outdoor temperature and converts the three uncertainties into a combination of uncertainties that can be directly used by the Condition Value at Risk (CVaR). This method uses a data-driven approach to characterize the multiple uncertainties in data center planning and calculates the parameter combination that maximizes system economics.

[0095] Based on the DRO algorithm, this paper integrates the CVaR model to construct the DRO-CVaR algorithm, and proposes a data center planning model for data center operators that can actively manage risks under multiple uncertainties. The effectiveness of the method proposed in this paper is verified through examples.

[0096] The present invention has the following advantages:

[0097] 1. More accurate water consumption modeling:

[0098] Considering the influence of outdoor temperature: The data center water footprint model developed in this paper fully considers the impact of outdoor temperature on data center water consumption. Using the heat conduction equation, a model is established for the cooling water consumption rate of data centers. This model also takes into account outdoor temperature, indoor and outdoor heat conduction processes, and water resource constraints, enabling it to more accurately reflect actual water usage.

[0099] Penalty price constraint: By setting an excess penalty price, the water resource utilization of the data center is restricted, effectively reducing excess water consumption and improving the reliability of data center planning results. For example, taking a single data center with a power capacity of 20MW as an example, the theoretical annual evaporation water consumption is about 200,000m 3 By using penalty price constraints, water consumption can be controlled within a reasonable range, avoiding resource waste and cost increase caused by excessive water use.

[0100] 2. Multiple uncertainty handling:

[0101] Data-driven uncertainty modeling: This paper uses a data-driven approach to transform the three uncertainties of data center load, PV output, and outdoor temperature into a one-dimensional uncertainty problem and calculates the multiplier combination that optimizes planning economics. This approach not only considers the joint distribution of multiple uncertainties but also improves the reliability and scientific nature of the model through a data-driven approach.

[0102] DRO-CVaR Algorithm: Based on the DRO algorithm, the DRO-CVaR algorithm was constructed by integrating the CVaR model. This algorithm effectively measures the potential risk of planning by adjusting confidence parameters while balancing robustness and economy. Compared to the traditional DRO algorithm, the DRO-CVaR algorithm can ignore some extreme scenarios, avoid overly conservative planning strategies, and improve the model's economy. For example, under different risk parameters, the DRO-CVaR algorithm can provide more economical planning solutions while maintaining a certain degree of robustness.

[0103] 3. Improve model effectiveness and economy:

[0104] Comprehensive consideration of multiple uncertainties: This paper not only considers the uncertainties of data center load and photovoltaic output, but also adds temperature uncertainty as a research object. By comprehensively considering these three uncertainties, the model can more comprehensively reflect the uncertainties in data center operations, improving its effectiveness.

[0105] Balancing Economy and Robustness: The DRO-CVaR algorithm can provide optimal planning solutions based on varying risk preferences by adjusting parameters. Decision makers can choose different confidence levels for planning results based on their risk tolerance to balance robustness and economy. For example, by adjusting the characteristic parameters and confidence parameters of fuzzy sets, planning costs can be reduced and economy improved while maintaining a certain level of robustness.

[0106] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes 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 processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. 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.

[0107] In the description of the present invention, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of the technical features indicated. Therefore, a feature specified as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, "plurality" means two or more, unless otherwise specifically defined.

[0108] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A data center microgrid planning method considering water resource constraints and risk measurement, characterized by: The following steps are involved: Based on the workload of the water-cooled data center and the heat transfer method of the water-cooled data center, the water footprint of the water-cooled data center during operation is quantified. Taking the total cost of the water-cooled data center during the planning period as the target, a data center planning model that considers water resource constraints is constructed; Based on the data center planning model, the uncertainty of data center load and photovoltaic unit power is taken into account, and the uncertainty of the data center's outdoor temperature is taken into account. The three uncertainties are converted into conditional value at risk. The DRO algorithm is then used to actively manage risks under multiple uncertainties. In the process of obtaining the total cost of the data center during the planning period, the total cost of the data center during the planning period is expressed as: C=C inv +365.C ope W t =W t DCW +η E [P t DC -P t PV ] + Where C is the total cost of the data center microgrid during the planning period; C inv is the investment cost of data center equipment; r is the discount rate; y s is the equipment life; S is the equipment set, including photovoltaic units, chillers and energy storage units; c s is the installation cost per unit capacity of the equipment; Q s Install capacity for equipment; C ope is the total operating cost of the data center; C buy is the electricity purchase cost; C carbon is the carbon emission cost, C water is the water purchase cost; C w Penalty for excessive water use in data centers; C s The operating cost of energy storage; is the time-of-use electricity price during period t; p W is the penalty price when the water purchase amount of the data center exceeds the total water consumption limit; max is the city's limit on the total water consumption of data centers, and WUE is the city's policy limit on the WUE of data centers; is the charge and discharge loss cost coefficient of the energy storage equipment; W t is the water footprint of the data center during period t; η E P is the ratio coefficient of grid cooling water and power generation; t DC is the total power of the data center during period t; P t PV is the photovoltaic power generation of the data center during period t; W t DCW P is the cooling water consumption of the data center during period t; t s is the power of the data center energy storage unit during period t.

2. The data center microgrid planning method considering water resource constraints and risk metrics according to claim 1 is characterized by: In the process of constructing a data center planning model that considers water resource constraints, based on the workload of the water-cooled data center, the data center's power consumption is obtained according to the data center's total power in time period t, the equivalent power consumption of processing batch loads and interactive loads, and the cooling system's power consumption. The water footprint is quantified based on the power consumption.

3. The data center microgrid planning method considering water resource constraints and risk metrics according to claim 2 is characterized by: In the process of quantifying the water footprint, based on the electricity consumption and according to the heat transfer method through water circulation-evaporation, the water footprint is quantified by setting the server temperature constraints of the data center and the temperature change and evaporation rate constraints of the cooling water.

4. The data center microgrid planning method considering water resource constraints and risk metrics according to claim 3 is characterized by: In the process of obtaining the total cost of the data center during the planning period, the total cost of the data center during the planning period is obtained by minimizing the annual equivalent planning cost, the annual equivalent operating cost and the water cost of the data center.

5. The data center microgrid planning method considering water resource constraints and risk metrics according to claim 1 is characterized by: In the process of converting the three uncertainties into conditional risk value, the three uncertainties include the equivalent electric load P of the data center. t DC , the output of photovoltaic units installed in data centers P t PV and the temperature T at the data center's location t O .

6. The data center microgrid planning method considering water resource constraints and risk metrics according to claim 5 is characterized by: In the process of converting the three uncertainties into conditional risk values, the historical data of the three uncertainties are processed and merged by normalization and adding multipliers, the original three uncertainties are converted into a one-dimensional uncertainty problem, and the multiplier combination that can make the planning economically optimal is calculated.

7. A data center microgrid planning system taking into account water resource constraints and risk metrics, for implementing the data center microgrid planning method taking into account water resource constraints and risk metrics as claimed in claim 1, characterized in that: include: a model building module for quantifying the water footprint of a water-cooled data center during operation based on the workload of the water-cooled data center and the heat transfer method of the water-cooled data center, and building a data center planning model that considers water resource constraints with the total cost during the planning period of the water-cooled data center as the target; The active risk management module is used to convert the three uncertainties into conditional risk values ​​based on the data center planning model, according to the uncertainty of the data center load and photovoltaic unit power, and taking into account the uncertainty of the outdoor temperature of the data center. It also uses the DRO algorithm to actively manage risks in a multiple uncertainty environment.

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