Method and device for planning of a coordinated energy system, electronic device and storage medium

Abstract

The present disclosure provides a planning method and device of a coordinated energy system, an electronic device and a storage medium. The coordinated energy system comprises a data center for processing data load and an integrated energy system for meeting power supply demand and cooling demand of the data center. The planning method comprises: constructing an upper-layer planning model, decision variables of the upper-layer planning model comprising a server planning quantity of the data center, a storage energy equipment planning quantity of the integrated energy system, an electric refrigeration equipment planning quantity and an absorption refrigeration equipment planning quantity, the absorption refrigeration equipment being used for absorbing waste heat generated due to operation of the data center to perform refrigeration; constructing a lower-layer operation model, decision variables of the lower-layer operation model comprising an operation strategy of the coordinated energy system; and obtaining the server planning quantity, the storage energy equipment planning quantity, the electric refrigeration equipment planning quantity and the absorption refrigeration equipment planning quantity meeting the conditions by solving a double-layer optimization model comprising the upper-layer planning model and the lower-layer operation model.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of power supply systems in general, and more particularly, to a planning method and device for a coordinated energy system, an electronic device, and a storage medium. BACKGROUND

[0002] With the rapid development of the global digital economy, the construction scale of Internet Data Center (IDC) gradually expands as an important infrastructure supporting its development, and its energy consumption gradually rises. In 2022, the power consumption of data centers accounted for 3% of the total power consumption of society, and this proportion is still growing. The coordinated optimization operation of Integrated Energy Systems (IES) and data centers is an important measure to promote the healthy development of IDC.

[0003] The data load of IDC has obvious spatiotemporal migration characteristics. Compared with the mode of demand response participated by high-energy-consuming industries such as steel, cement, and glass, the power consumption of IDC is generated by electronic components driven by computing power. It can be said that computing power has natural immediate adjustment characteristics and can be used as a high-quality demand-side flexible resource to participate in the regulation of the power system, thereby reducing the planning and operation cost of the system.

[0004] In recent years, research on power and computing power coordination optimization strategies is still in its infancy. Data centers consume a large amount of energy, but existing scheduling models still have deficiencies in energy efficiency optimization. SUMMARY

[0005] An exemplary embodiment of the present disclosure provides a planning method and device for a coordinated energy system, an electronic device, and a storage medium to solve at least one of the problems in the related art described above.

[0006] According to a first aspect of an embodiment of the present disclosure, a planning method for a coordinated energy system is provided, the coordinated energy system comprising a data center for processing data load and an integrated energy system for meeting power supply demand and cooling demand of the data center, the planning method comprising: constructing an upper-layer planning model, decision variables of the upper-layer planning model comprising: a server planning quantity of the data center, a storage energy equipment planning quantity of the integrated energy system, an electric refrigeration equipment planning quantity, and an absorption refrigeration equipment planning quantity, wherein the absorption refrigeration equipment is used to absorb waste heat generated by the operation of the data center for refrigeration; constructing a lower-layer operation model, decision variables of the lower-layer operation model comprising: an operation strategy of the coordinated energy system; and solving a double-layer optimization model comprising the upper-layer planning model and the lower-layer operation model to obtain a server planning quantity, a storage energy equipment planning quantity, an electric refrigeration equipment planning quantity, and an absorption refrigeration equipment planning quantity that meet the conditions.

[0007] Optionally, the optimization objective of the upper planning model is to minimize the annual total cost of the collaborative energy system; and the optimization objective of the lower running model is to minimize the intraday comprehensive cost of the collaborative energy system; wherein the intraday comprehensive cost at least includes operation cost of the data center, operation cost of the comprehensive energy system, and abandoned energy cost of the new energy generation unit of the comprehensive energy system; and the operation cost of the data center at least includes operation cost of the server of the data center and delay processing cost of the delay-tolerant data load; and the operation cost of the comprehensive energy system at least includes power purchase cost from the external power grid, operation cost of the energy storage device, operation cost of the electric refrigeration device, and operation cost of the absorption refrigeration device.

[0008] Optionally, the delay processing cost of the delay-tolerant data load at time t is calculated based on the delay processing compensation cost at time t and the load amount that is scheduled to be processed at time t but fails to be completed on time.

[0009] Optionally, the constraint conditions of the upper planning model include the number constraint of the server, the number constraint of the energy storage device, the number constraint of the electric refrigeration device, and the number constraint of the absorption refrigeration device.

[0010] Optionally, the constraint conditions of the lower running model include the operation constraint of the data center, the operation constraint of the electric refrigeration device, the operation constraint of the absorption refrigeration device, the operation constraint of the energy storage device, and the power balance constraint of the collaborative energy system.

[0011] Optionally, the operation constraint of the data center includes the upper limit constraint of the load amount of the delay-tolerant data load received at time i and processed at time t, the number constraint of the server started at time t, the computing power utilization rate constraint of the server started at time t, and the temperature constraint of the data center; wherein the upper limit constraint is constructed based on the number of servers started at time t, the rated data processing amount of a single server, and the correspondence relationship between the access time and the processing time of the delay-tolerant data load constructed based on graph theory; wherein the number constraint of the server started at time t is constructed based on the data processing average delay of the server, the total amount of data load to be processed at time t, and the rated data processing amount of a single server by using the M / M / 1 queuing method; and the temperature of the data center is determined based on the waste heat power generated by the operation of the data center and the cold power provided by the comprehensive energy system to the data center.

[0012] Optionally, the power balance constraint of the collaborative energy system comprises: for any time, the sum of the power purchased from the external power grid, the power generated by the new energy power generation unit of the integrated energy system, and the power discharged by the energy storage device is balanced with the sum of the power consumption of the data center, the charging power of the energy storage device, the electric power of the electric refrigeration device, and other electric load amounts; for any time, the waste heat power of the data center is balanced with the heat power of the absorption refrigeration device; for any time, the sum of the cold power of the absorption refrigeration device and the cold power of the electric refrigeration device is balanced with the sum of the cooling load demand of the data center and other cooling load amounts.

[0013] Optionally, the obtaining of the qualified server planning quantity, the energy storage device planning quantity, the electric refrigeration device planning quantity, and the absorption refrigeration device planning quantity by solving the bi-level optimization model comprising the upper-level planning model and the lower-level operation model comprises: converting the lower-level operation model into an additional constraint condition of the upper-level planning model, and converting the bi-level optimization model into a mixed integer linear programming model by using a big M method; obtaining the qualified server planning quantity, the energy storage device planning quantity, the electric refrigeration device planning quantity, the absorption refrigeration device planning quantity, and the operation strategy of the integrated energy system by solving the mixed integer linear programming model.

[0014] According to a second aspect of the embodiments of the present disclosure, a planning device of a collaborative energy system is provided, the collaborative energy system comprising: a data center for processing data load, and an integrated energy system for meeting power supply demand and cooling demand of the data center, the planning device comprising: an upper-level construction unit configured to construct an upper-level planning model, decision variables of the upper-level planning model comprising: a server planning quantity of the data center, an energy storage device planning quantity of the integrated energy system, an electric refrigeration device planning quantity, and an absorption refrigeration device planning quantity, wherein the absorption refrigeration device is used to absorb waste heat generated by operation of the data center to perform refrigeration; a lower-level construction unit configured to construct a lower-level operation model, decision variables of the lower-level operation model comprising: an operation strategy of the collaborative energy system; and a solving unit configured to obtain the qualified server planning quantity, the energy storage device planning quantity, the electric refrigeration device planning quantity, and the absorption refrigeration device planning quantity by solving a bi-level optimization model comprising the upper-level planning model and the lower-level operation model.

[0015] According to a third aspect of the embodiments of the present disclosure, a computer readable storage medium storing a computer program is provided, when the computer program is executed by a processor, the processor is caused to execute the planning method of the collaborative energy system as described above.

[0016] According to a fourth aspect of the embodiments of the present disclosure, an electronic device is provided, which comprises a processor, and a memory storing a computer program, which, when executed by the processor, causes the processor to perform the planning method of the collaborative energy system as described above.

[0017] According to a fifth aspect of the embodiments of the present disclosure, a computer program product is provided, which comprises a computer program, which, when executed by a processor, implements the planning method of the collaborative energy system as described above.

[0018] The planning method, device, electronic device and storage medium of the collaborative energy system according to the exemplary embodiments of the present disclosure propose an energy system planning method of power-electricity-heat collaborative optimization on the basis of considering the utilization of low-grade heat energy such as data center waste heat.

[0019] In the following description, some aspects and / or advantages of the general concept of the present disclosure will be set forth and / or will become apparent. BRIEF DESCRIPTION OF DRAWINGS

[0020] These and / or other aspects and advantages of the application will become more apparent and more fully understood from the following detailed description, taken in conjunction with the accompanying drawings, in which:

[0021] Figure 1 A flow chart of the planning method of the collaborative energy system according to the exemplary embodiments of the present disclosure is shown;

[0022] Figure 2 A structural block diagram of the collaborative energy system according to the exemplary embodiments of the present disclosure is shown;

[0023] Figure 3 An example of the double-layer optimization model according to the exemplary embodiments of the present disclosure is shown;

[0024] Figure 4 Examples of the data load scheduling of scenario 1 and scenario 2 according to the exemplary embodiments of the present disclosure are shown;

[0025] Figure 5 Examples of the number of server starts of scenario 1 and scenario 2 according to the exemplary embodiments of the present disclosure are shown;

[0026] Figure 6 Examples of the comprehensive energy system scheduling results of scenario 1 and scenario 2 according to the exemplary embodiments of the present disclosure are shown;

[0027] Figure 7 A structural block diagram of the planning device of the collaborative energy system according to the exemplary embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0028] Reference will now be made in detail embodiments of the present disclosure, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to like elements throughout. The embodiments will be described below by referring to the drawings, in order to explain the present disclosure.

[0029] It should be noted that the terms "first", "second", and the like in the description and claims of the present disclosure and the above drawings are used to distinguish similar objects, and do not necessarily indicate a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present disclosure described herein can be implemented in an order other than that illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Rather, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0030] It should be noted herein that "at least one of a plurality of items" appearing in the present disclosure means that the three types of alternatives, "any one of the plurality of items", "a combination of any two or more of the plurality of items", and "all of the plurality of items", are included. For example, "including at least one of A and B" includes the following three alternatives: (1) including A; (2) including B; and (3) including A and B. For another example, "performing at least one of step one and step two" means the following three alternatives: (1) performing step one; (2) performing step two; and (3) performing step one and step two.

[0031] Data centers have huge energy consumption, but existing scheduling models still have deficiencies in energy efficiency optimization: on the one hand, there is a lack of comprehensive consideration of cooling systems, renewable energy and other factors, and there is a lack of linkage optimization of computing power load and thermodynamic model; on the other hand, there is a problem of low utilization rate (usually only 30%-50%) and long-term idling of server resources in data centers, resulting in serious energy waste.

[0032] The present disclosure considers that the current data center flexible adjustment model has poor scalability, and the change strategy will cause waste of resources. A data center flexible adjustment model is constructed using graph theory, the adjustment strategy can be flexibly modified, the problem of low server utilization rate is solved; and a computing power-electricity-heat collaborative optimization model is established, considering the utilization of low-grade heat energy such as server waste heat, through multi-energy coupling interaction, the energy utilization efficiency is improved. The exemplary embodiments of the present disclosure will be described below in conjunction with Figures 1 to 7

[0033] Figure 1 A flowchart of a planning method of a collaborative energy system according to an exemplary embodiment of the present disclosure is shown.

[0034] ​As an example, the planning method of the collaborative energy system according to the example embodiment of the present disclosure can be executed by an electronic device with data processing capability, for example, can be a terminal (such as a personal notebook, a desktop computer, etc.), or a server (such as a stand-alone server, a server cluster, a cloud platform, etc.). The present disclosure does not limit this.

[0035] The collaborative energy system includes a data center (for example, an Internet data center IDC) for processing data load, and an integrated energy system IES for meeting the power supply demand and cooling demand of the data center.

[0036] As an example, the integrated energy system can include at least one new energy power generation unit, at least one energy storage device, at least one electric refrigeration device, and at least one absorption refrigeration device.

[0037] The absorption refrigeration device is used to absorb the waste heat generated by the operation of the data center (for example, the server of the data center) to refrigerate. The electric refrigeration device is used to absorb electric energy to refrigerate.

[0038] As shown in Figure 2 The integrated energy system can include two types of new energy power generation units: wind turbine (WT) and photovoltaic (PV), and the energy storage device of the integrated energy system can include an electricity storage (ES). In addition, the integrated energy system can also include two types of energy conversion facilities: electric chillers (EC) and absorption chillers (AC). The data center consumes electric energy to process data load, and the integrated energy system meets the cooling demand of the server and the cooling load demand through EC and AC refrigeration, while recycling the waste heat of the server to improve energy utilization efficiency. Figure 2 In the figure, the green line represents the electric energy transmission line, the red line represents the heat energy transmission line, and the blue line represents the cold energy transmission line.

[0039] Data load can be divided into two categories according to its tolerance to delay: delayed sensitive (DS) type data load and delayed tolerance (DT) type data load. DS type data load The tolerance to delay is low, and usually requires to complete the response within a few milliseconds to a few seconds, which is real-time or near real-time data processing demand, usually involves user interaction or instant feedback application, real-time processing is critical, and the tolerance to delay of this load is low, usually needs fast response to meet user demand. DT type data load It refers to the need to collect and process a large amount of data within a certain period of time, has a high tolerance for delay, allows the task to be completed within a long time, is usually executed without the need for real-time feedback, usually involves a large amount of data, but the update frequency is low, the data volume is large, but the processing is relatively concentrated, and is usually performed in an offline state.

[0040] With reference to Figure 1 In step S101, an upper-layer planning model is constructed.

[0041] The decision variables of the upper-layer planning model include: the number of server planning of the data center, the number of energy storage device planning of the integrated energy system, the number of electric refrigeration device planning, and the number of absorption refrigeration device planning.

[0042] As an example, the optimization objective of the upper-layer planning model can be to minimize the annualized total cost of the collaborative energy system. For example, the annualized total cost can include annualized investment cost and annualized operation cost . For example, the annualized investment cost can be calculated by formula (2), and the annualized operation cost can be calculated by formula (3), wherein the superscript x∈{Ser, EC, AC, ES} represents a server, EC represents an electric refrigeration device, AC represents an absorption refrigeration device, and ES represents an energy storage device; represents the single installation cost of device x; , respectively represent the benchmark discount rate and the planning period of device x; represents the planning number of device x; represents the intra-day operation cost of the operation layer on typical day i; represents the number of days corresponding to typical day i; represents the total number of typical days.

[0043] (1)

[0044] (2)

[0045] (3)

[0046] As an example, the constraint conditions of the upper-layer planning model can include but are not limited to: the number of servers, the number of energy storage devices, the number of electric refrigeration devices, and the number of absorption refrigeration devices. For example, as shown in formula (4), wherein, represents the upper limit of the construction number of device x.

[0047] (4)

[0048] In step S102, a lower-layer operation model is constructed.

[0049] The decision variable of the lower-layer operation model includes an operation strategy of the coordinated energy system.

[0050] As an example, the optimization objective of the lower-layer operation model can be to minimize the daily comprehensive cost of the coordinated energy system.

[0051] As an example, the daily comprehensive cost can at least include an operation cost of the data center, an operation cost of the comprehensive energy system, and an abandoned energy cost of the new energy power generation unit.

[0052] As an example, the operation cost of the data center can at least include an operation cost of a server of the data center and a delay processing cost of a delay-tolerant data load. As an example, the delay processing cost of the delay-tolerant data load at time t is calculated based on a delay processing compensation cost at time t and a load amount that is scheduled to be processed at time t but fails to be completed on time.

[0053] As an example, the operation cost of the comprehensive energy system can at least include a power purchase cost from an external power grid, an operation cost of an energy storage device, an operation cost of an electric refrigeration device, and an operation cost of an absorption refrigeration device.

[0054] For example, as shown in equations (5) to (13), the daily comprehensive cost F can include an IDC operation cost (composed of a server operation cost and a delay-tolerant data load delay processing cost , an IES operation cost (composed of a power purchase cost , an operation cost of an energy storage device, an operation cost of an electric refrigeration device, and an operation cost of an absorption refrigeration device , and an abandoned wind and abandoned light penalty term . Wherein c Ser , c DT respectively represent a server unit power operation cost and a data load delay processing compensation cost; represents a power purchase price at time t; , c ES , c s,cut , c w,cut respectively represent an operation cost of refrigeration device x, an operation cost of an energy storage device, an abandoned light cost, and an abandoned wind cost; , , , respectively represent a power purchase power at time t, a source end power of refrigeration device x, a charging power of the energy storage device, and a discharging power of the energy storage device; , , respectively represent a server power consumption at time t, a photovoltaic power, and a wind power; and variables represents the amount of data load of type DT received at time i allocated to the load processed at time t.

[0055] The server power consumption is linearly related to the number of servers that are turned on. Assuming that the server power consumption is a linear model, the idle power of a single server when not processing tasks and in a low power state is ; the peak power of a single server when fully loaded is ; represents the number of servers turned on at time t; represents the average utilization rate of the server CPU at time t; represents the power utilization efficiency of the server; represents the rated data processing capacity of a single server; represents the total amount of data load processed by the server at time t; 、 respectively represent the load shedding proportion of the photovoltaic generator set and the load shedding proportion of the wind power generator set.

[0056] (5)

[0057] (6)

[0058] (7)

[0059] (8)

[0060] (9)

[0061] (10)

[0062] (11)

[0063] (12)

[0064] (13)

[0065] As an example, the constraint conditions of the lower layer operation model can include: operation constraints of the data center, operation constraints of the electric refrigeration device, operation constraints of the absorption refrigeration device, operation constraints of the energy storage device, and power balance constraints of the collaborative energy system.

[0066] As an example, the operation constraints of the data center can include but are not limited to: an upper limit constraint of the amount of load of delay-tolerant data load received at time i allocated to the load processed at time t, a constraint of the number of servers turned on at time t, a constraint of the computing power utilization rate of the server turned on at time t, and a temperature constraint of the data center.

[0067] As an example, the utilization rate constraint of the on-server at time t can be shown as equation (14), where, represents the server capacity margin.

[0068] (14)

[0069] As an example, the upper limit constraint of the load amount of the delay-tolerant data load received at time i allocated to the processing at time t can be constructed based on the following: the number of on-servers at time t, the rated data processing amount of a single server, the correspondence relationship between the access time and the processing time of the delay-tolerant data load constructed based on graph theory.

[0070] As an example, the DT-type data load model can be constructed based on graph theory, for example, as shown in equations (15) to (17), using matrix A Ser The correspondence relationship between the data accessed at a certain time and the processing time is described by variable represents the load amount of the data load received at time i allocated to the processing at time t, represents the correspondence relationship between the delay-tolerant data load flowing into the data center at time i and the server processing time t; represents the rated data processing amount of a single server; represents the server capacity margin; represents the number of on-servers at time t; represents the delay-tolerant data load amount flowing into the data center at time i; represents the delay-tolerant data load amount processed at time t.

[0071] (15)

[0072] (16)

[0073] (17)

[0074] As an example, the upper limit constraint of the number of on-servers at time t can be shown as equation (18), where, represents the total number of servers in the data center.

[0075] (18)

[0076] As an example, the lower limit constraint of the number of on-servers at time t can be constructed based on the following using the M / M / 1 queuing method: the average data processing delay of the server, the total amount of data load to be processed at time t, and the rated data processing amount of a single server. For example, according to the M / M / 1 queuing theory, the data processing delay can be described as equation (19), where, represents the total amount of data load handled by the server at time t; represents the average delay of data processing.

[0077] (19)

[0078] As an example, the temperature of the data center can be determined based on the waste heat power generated by the operation of the data center and the cold power provided to the data center by the integrated energy system. For example, the present disclosure establishes an equivalent thermal parameter model of the data center machine room based on the first law of thermodynamics. In the planning and operation strategy of the IDC, the thermal parameter model of the IDC refers to the recommended temperature of the server and memory rack of the American Society of Heating, Refrigerating and Air-Conditioning Engineers, and the IDC room temperature should be maintained between 17.78-27.22℃. For example, the present disclosure uses an improved first-order equivalent thermal parameter (ETP) model to describe the interaction between the waste heat generated by the operation of the server and the refrigeration capacity of the integrated energy system, as shown in formulas (20) and (21), wherein, , respectively represent the indoor / outdoor temperature of the IDC at time t; , respectively represent the equivalent thermal resistance and equivalent heat capacity of the heat transfer medium in the IDC; represents the cold power delivered to the IDC by the refrigeration equipment at time t; represents the waste heat power of the data center at time t; represents the heat recovery coefficient.

[0079] (20)

[0080] (21)

[0081] As an example, the operation constraints of the electric refrigeration equipment EC and the operation constraints of the absorption refrigeration equipment AC can be as shown in formulas (22) to (24), wherein, , respectively represent the cold power and electric power of EC; , respectively represent the heat power and cold power of AC; , respectively represent the energy conversion efficiency of EC and AC; , respectively represent the upper / lower limit of the source output of the refrigeration equipment x; , respectively represent the upper and lower ramping power of the source of x; represents the rated power of a single refrigeration equipment y.

[0082] (22)

[0083] (23)

[0084] (24)

[0085] As an example, the operation constraints of the energy storage device can be shown as equations (25) to (28), where, represents the amount of electricity stored by the energy storage device at time t; , represent the maximum charging / discharging power of the energy storage device, respectively; represents the self-discharge rate of the energy storage device; , represent the charging / discharging efficiency of the energy storage device; represents the rated capacity of a single energy storage device.

[0086] (25)

[0087] (26)

[0088] (27)

[0089] (28)

[0090] As an example, the power balance constraints of the coordinated energy system can include: for any time, the sum of the power purchased from the external grid, the power generated by the new energy power generation unit, and the power discharged by the energy storage device is balanced with the sum of the power consumption of the data center, the charging power of the energy storage device, the electric power of the electric refrigeration device, and other electrical loads (e.g., as shown in equation (29)); for any time, the waste heat power of the data center is balanced with the heat power of the absorption refrigeration device (e.g., as shown in equation (30)); for any time, the sum of the cold power of the absorption refrigeration device and the cold power of the electric refrigeration device is balanced with the sum of the cooling load demand of the data center and other cooling loads (e.g., as shown in equation (31)). Wherein, , represent the electrical load and the cooling load at time t, respectively; represents the cooling load demand of the data center at time t; represents the waste heat power of the data center at time t; represents the heat power of the absorption refrigeration device at time t.

[0091] (29)

[0092] (30)

[0093] (31)

[0094] In step S103, by solving the bi-level optimization model including the upper planning model (i.e., the planning layer) and the lower running model (i.e., the running layer), the qualified server planning quantity, the energy storage device planning quantity, the electric refrigeration device planning quantity, and the absorption refrigeration device planning quantity are obtained.

[0095] As an example, step S103 can include: converting the lower running model into additional constraint conditions of the upper planning model, and using the big M method to convert the bi-level optimization model into a mixed integer linear programming model; then, by solving the mixed integer linear programming model, the qualified server planning quantity, the energy storage device planning quantity, the electric refrigeration device planning quantity, the absorption refrigeration device planning quantity, and the operation strategy of the comprehensive energy system are obtained.

[0096] As an example, the running layer can be converted into additional constraints of the planning layer according to the Lagrange function of the running layer and the KKT complementary relaxation condition of the running layer, and then the entire optimization model is converted into a mixed integer linear programming problem by using the big M method, and then a commercial solver is directly called for solving.

[0097] According to the example embodiments of the present disclosure, an energy system planning method considering IDC collaboration is proposed, and specifically, an IDC flexible scheduling control model based on graph theory and M / M / 1 queuing theory is proposed, and a computing power-electricity-heat collaborative optimization model considering the utilization of low-grade heat energy such as server waste heat is proposed.

[0098] According to the example embodiments of the present disclosure, considering that the time scales of system facilities and system operation are different, a hierarchical optimization architecture as shown in Figure 3 is designed, which is divided into a system planning layer and a system running layer. The system planning layer decides the planning scheme of each device of the system with a time scale of years, takes the optimal economy of the system in a year as the objective function, and takes the maximum number of a single device type that can be configured as the constraint condition; the system running layer decides the internal energy flow distribution strategy and the output of each unit in a typical day, takes the minimum of the daily operation cost and the penalty of abandoned wind and light of the system as the objective function, and takes the operation characteristics and energy supply balance of each part of the system as the constraint condition.

[0099] As an example, the Internet data center park as shown in Figure 2 is used to verify the proposed planning method. For example, the solver Gurobi 12.01 can be called to solve in the python 3.1 environment.

[0100] As an example, the scenario settings are shown in Table 1. √ indicates that the item is considered; × indicates that the item is not considered. Table 2 shows the planning results for different scenarios. Comparing Scenario 1 and Scenario 2, after the DT-type data load participates in scheduling, the number of planned devices of various types decreases significantly, the planning cost decreases by 44.94%, the operating cost decreases by 8.23%, and the annualized total cost decreases by 30.45%. This indicates that the participation of the DT-type data load in scheduling can significantly reduce the various costs of the entire system.

[0101] Table 1 Comparison of Scene Settings

[0102]

[0103] Table 2 Planning results for different scenarios

[0104]

[0105] The data load scheduling situation for Spring Scenarios 1 and Scenario 2 is as follows: Figure 4 As shown, the number of servers started is as follows Figure 5 As shown, introducing DT-type load into the scheduling process makes the data load curve smoother, reduces the pressure of data load on the entire system, reduces the overall number of planned servers, and improves server utilization. During the peak data load period of 11:00-15:00, both types of data load are at their daily peak load, while the DT load in scenario 2 is at a relatively low level on a typical day. Because the introduction of data processing latency constraints to limit the lower limit of the number of servers started results in all servers being started on a typical spring day in scenario 2.

[0106] The IES scheduling results for typical summer days in scenarios 1 and 2 are as follows: Figure 6 As shown, the peak power and cooling loads in Scenario 2 are 1404.92 kW and 1029.21 kW lower than those in Scenario 1, respectively. Introducing data center DT load into the scheduling process significantly reduces the control pressure on the IES (Environmental Engineering System). Because the heat load demand is relatively low and the cooling load demand is relatively high on typical summer days, absorption chillers operate at high power to perform secondary energy conversion to meet the high cooling load demand in summer.

[0107] Figure 7 A structural block diagram of a planning apparatus for a collaborative energy system according to an exemplary embodiment of the present disclosure is shown.

[0108] Collaborative energy systems include: data centers for handling data loads and integrated energy systems for meeting the power and cooling needs of data centers.

[0109] Reference Figure 7The planning device 700 of the cooperative energy system according to the example embodiment of the present disclosure comprises: an upper layer construction unit 701, a lower layer construction unit 702, and a solving unit 703.

[0110] Specifically, the upper layer construction unit 701 is configured to construct an upper layer planning model, and decision variables of the upper layer planning model comprise: a server planning quantity of the data center, a storage energy equipment planning quantity of the comprehensive energy system, an electric refrigeration equipment planning quantity, and an absorption refrigeration equipment planning quantity, wherein the absorption refrigeration equipment is used to absorb waste heat generated by the data center due to operation to perform refrigeration.

[0111] The lower layer construction unit 702 is configured to construct a lower layer operation model, and decision variables of the lower layer operation model comprise: an operation strategy of the cooperative energy system.

[0112] The solving unit 703 is configured to obtain the server planning quantity, the storage energy equipment planning quantity, the electric refrigeration equipment planning quantity, and the absorption refrigeration equipment planning quantity that meet the conditions by solving a double-layer optimization model comprising the upper layer planning model and the lower layer operation model.

[0113] As an example, an optimization target of the upper layer planning model can be to minimize the annualized total cost of the cooperative energy system.

[0114] As an example, an optimization target of the lower layer operation model can be to minimize the intraday comprehensive cost of the cooperative energy system.

[0115] As an example, the intraday comprehensive cost can at least comprise: an operation cost of the data center, an operation cost of the comprehensive energy system, and an abandoned energy cost of a new energy generation unit of the comprehensive energy system; wherein the operation cost of the data center at least comprises: an operation cost of a server of the data center, and a delay processing cost of a delay-tolerant data load; and the operation cost of the comprehensive energy system at least comprises: a purchase power cost from an external power grid, an operation cost of the storage energy equipment, an operation cost of the electric refrigeration equipment, and an operation cost of the absorption refrigeration equipment.

[0116] As an example, the delay processing cost of the delay-tolerant data load at time t is calculated based on a delay processing compensation cost at time t and a load quantity that is planned to be processed at time t but fails to be completed on time.

[0117] As an example, the constraint conditions of the upper layer planning model can comprise: a quantity constraint of the server, a quantity constraint of the storage energy equipment, a quantity constraint of the electric refrigeration equipment, and a quantity constraint of the absorption refrigeration equipment.

[0118] As an example, the constraint conditions of the lower layer operation model can comprise: an operation constraint of the data center, an operation constraint of the electric refrigeration equipment, an operation constraint of the absorption refrigeration equipment, an operation constraint of the storage energy equipment, and a power balance constraint of the cooperative energy system.

[0119] As an example, the operation constraints of the data center can include: an upper limit constraint of a load amount of the delay-tolerant data load received at time i to be processed at time t, a number of servers to be started at time t, a utilization rate of computing power of the server to be started at time t, and a temperature constraint of the data center.

[0120] As an example, the upper limit constraint can be constructed based on: the number of servers to be started at time t, the rated data processing amount of a single server, and the correspondence relationship between the access time and the processing time of the delay-tolerant data load constructed based on graph theory.

[0121] As an example, the number of servers to be started at time t can be constructed based on: the average data processing delay of the server, the total amount of data load to be processed at time t, and the rated data processing amount of a single server by using the M / M / 1 queuing method.

[0122] As an example, the temperature of the data center can be determined based on the waste heat power generated by the operation of the data center and the cold power provided by the integrated energy system to the data center.

[0123] As an example, the power balance constraint of the collaborative energy system can include: for any time, the sum of the power purchased from the external power grid, the power generated by the new energy power generation unit of the integrated energy system, and the discharging power of the energy storage device is balanced with the sum of: the power consumption of the data center, the charging power of the energy storage device, the electric power of the electric refrigeration device, and other electric load amounts; for any time, the waste heat power of the data center is balanced with the heat power of the absorption refrigeration device; for any time, the sum of the cold power of the absorption refrigeration device and the cold power of the electric refrigeration device is balanced with the sum of the cold load demand of the data center and other cold load amounts.

[0124] As an example, the solving unit 703 can be configured to: convert the lower layer operation model into additional constraint conditions of the upper layer planning model, and convert the double-layer optimization model into a mixed integer linear programming model by using the big M method; obtain the server planning quantity, the energy storage device planning quantity, the electric refrigeration device planning quantity, the absorption refrigeration device planning quantity, and the operation strategy of the integrated energy system that meet the conditions by solving the mixed integer linear programming model.

[0125] It should be understood that the specific processes performed by the planning device of the collaborative energy system according to the exemplary embodiments of the present disclosure have been described in detail with reference to Figures 1 to 6 herein will not be repeated.

[0126] It should be understood that each unit in the planning apparatus of the collaborative energy system according to the exemplary embodiments of the present disclosure can be implemented by hardware components and / or software components. A person skilled in the art can implement each unit, for example, using a field programmable gate array (FPGA) or an application specific integrated circuit (ASIC), according to the processing performed by each unit as defined.

[0127] The electronic device according to the exemplary embodiments of the present disclosure includes at least one processor (not shown) and at least one memory (not shown), wherein the at least one memory stores a computer program, and when the computer program is executed by the at least one processor, the at least one processor is caused to perform the planning method of the collaborative energy system as described in the above exemplary embodiments.

[0128] As an example, the electronic device can be an electronic device with data processing capability, for example, the electronic device can be a terminal (such as a personal notebook, a desktop computer, etc.), or a server (such as a standalone server, a server cluster, a cloud platform, etc.). The embodiments of the present disclosure do not limit this.

[0129] According to exemplary embodiments of the present disclosure, there can also be provided a computer-readable storage medium storing instructions, wherein the instructions, when executed by at least one processor, cause the at least one processor to perform the method of planning a collaborative energy system as described in the above exemplary embodiments. Examples of the computer-readable storage medium herein include read-only memory (ROM), random-access programmable read-only memory (PROM), electrically erasable programmable read-only memory (EEPROM), random-access memory (RAM), dynamic random-access memory (DRAM), static random-access memory (SRAM), flash memory, non-volatile memory, CD-ROM, CD-R, CD+R, CD-RW, CD+RW, DVD-ROM, DVD-R, DVD+R, DVD-RW, DVD+RW, DVD-RAM, BD-ROM, BD-R, BD-R LTH, BD-RE, Blu-ray or optical disk memory, a hard disk drive (HDD), a solid state drive (SSD), a card-type memory (such as a multimedia card, a secure digital (SD) card, or an extreme digital (XD) card), a magnetic tape, a floppy disk, a magneto-optical data storage device, an optical data storage device, a hard disk, a solid state disk, and any other device configured to store a computer program and any associated data, data files, and data structures in a non-transitory manner and provide the computer program and any associated data, data files, and data structures to a processor or computer so that the processor or computer can execute the computer program. The computer program in the above computer-readable storage medium can be executed in an environment deployed in a computer device such as a client, a host, an agent device, a server, etc., and, in addition, in one example, the computer program and any associated data, data files, and data structures are distributed over a networked computer system so that the computer program and any associated data, data files, and data structures are stored, accessed, and executed in a distributed manner by one or more processors or computers.

[0130] According to exemplary embodiments of the present disclosure, there can also be provided a computer program product in which instructions executable by at least one processor to accomplish the method of planning a collaborative energy system as described in the above exemplary embodiments.

[0131] Other embodiments of the present disclosure will be readily apparent to those skilled in the art upon considering the specification and practice of the disclosed application. The present application is intended to cover any variations, uses, or adaptations of the present disclosure following the general principles thereof and including such departures from the present disclosure that come within known, accepted, or customary practice in the art to which the present disclosure pertains. The specification and examples are to be regarded as exemplary only, with the true scope and spirit of the present disclosure being indicated by the following claims.

[0132] It should be understood that the present disclosure is not limited to the precise construction that has been described above and shown in the accompanying drawings, and that various modifications and changes can be made by those skilled in the art without departing from the scope of the present disclosure. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A method for planning a coordinated energy system, characterized by, The collaborative energy system comprises a data center for processing data load, and a comprehensive energy system for meeting power supply demand and cooling demand of the data center, and the planning method comprises: building an upper-layer planning model, decision variables of the upper-layer planning model comprising: planned number of servers of the data center, planned number of energy storage devices of the comprehensive energy system, planned number of electric refrigeration devices, and planned number of absorption refrigeration devices, wherein the absorption refrigeration devices are used for absorbing waste heat generated by operation of the data center to perform refrigeration; building a lower-layer operation model, decision variables of the lower-layer operation model comprising: operation strategy of the collaborative energy system; solving the double-layer optimization model comprising the upper-layer planning model and the lower-layer operation model to obtain the planned number of servers, the planned number of energy storage devices, the planned number of electric refrigeration devices, and the planned number of absorption refrigeration devices that meet the conditions.

2. The planning method of claim 1, wherein, The optimization target of the upper-layer planning model is to minimize annualized total cost of the collaborative energy system; The optimization target of the lower-layer operation model is to minimize intraday comprehensive cost of the collaborative energy system; The intraday comprehensive cost at least comprises: operation cost of the data center, operation cost of the comprehensive energy system, and abandoned energy cost of a new energy generation unit of the comprehensive energy system; The operation cost of the data center at least comprises: operation cost of the servers of the data center, and delay processing cost of the delay-tolerant data load; and the operation cost of the comprehensive energy system at least comprises: power purchase cost from an external power grid, operation cost of the energy storage devices, operation cost of the electric refrigeration devices, and operation cost of the absorption refrigeration devices.

3. The planning method of claim 2, wherein, The delay processing cost of the delay-tolerant data load at time t is calculated based on delay processing compensation cost at time t and load amount that is planned to be processed at time t but fails to be completed on time.

4. The planning method of claim 1, wherein, The constraint conditions of the upper-layer planning model comprise: quantity constraint of the servers, quantity constraint of the energy storage devices, quantity constraint of the electric refrigeration devices, and quantity constraint of the absorption refrigeration devices.

5. The planning method of claim 1, wherein, The constraint conditions of the lower-layer operation model comprise: operation constraint of the data center, operation constraint of the electric refrigeration devices, operation constraint of the absorption refrigeration devices, operation constraint of the energy storage devices, and power balance constraint of the collaborative energy system.

6. The planning method of claim 5, wherein, The operation constraint of the data center comprises: upper limit constraint of load amount of the delay-tolerant data load received at time i and allocated to processing at time t, quantity constraint of servers started at time t, computing power utilization rate constraint of the servers started at time t, and temperature constraint of the data center; The upper limit constraint is built based on: number of servers started at time t, rated data processing amount of a single server, and correspondence relationship between access time and processing time of the delay-tolerant data load built based on graph theory; The quantity constraint of the servers started at time t is built based on: data processing average delay of the servers, total data load amount to be processed at time t, and rated data processing amount of a single server by using M / M / 1 queuing method; The temperature of the data center is determined based on waste heat power generated by operation of the data center and cold power provided by the comprehensive energy system to the data center.

7. The planning method of claim 5, wherein, The power balance constraint of the collaborative energy system comprises: For any moment, the sum of the power purchased from the external power grid, the power generated by the new energy power generation unit of the integrated energy system, and the discharging power of the energy storage device is balanced with the sum of the power consumption of the data center, the charging power of the energy storage device, the electric power of the electric refrigeration device, and other electric load amounts; For any moment, the waste heat power of the data center is balanced with the heat power of the absorption refrigeration device; For any moment, the sum of the cold power of the absorption refrigeration device and the cold power of the electric refrigeration device is balanced with the sum of the cooling load demand of the data center and other cooling load amounts.

8. The planning method of claim 1, wherein, The solving of the bi-level optimization model comprising the upper-level planning model and the lower-level operation model to obtain the qualified server planning quantity, energy storage device planning quantity, electric refrigeration device planning quantity, and absorption refrigeration device planning quantity comprises: Converting the lower-level operation model into additional constraint conditions of the upper-level planning model, and converting the bi-level optimization model into a mixed integer linear programming model by using a large M method; Solving the mixed integer linear programming model to obtain the qualified server planning quantity, energy storage device planning quantity, electric refrigeration device planning quantity, absorption refrigeration device planning quantity, and operation strategy of the integrated energy system.

9. A planning device of a coordinated energy system, characterized by, The collaborative energy system comprises a data center for processing data load and an integrated energy system for meeting the power supply demand and cooling demand of the data center, and the planning device comprises: An upper-level construction unit configured to construct an upper-level planning model, decision variables of the upper-level planning model comprising a server planning quantity of the data center, an energy storage device planning quantity of the integrated energy system, an electric refrigeration device planning quantity, and an absorption refrigeration device planning quantity, wherein the absorption refrigeration device is used to absorb waste heat generated by the operation of the data center for refrigeration; A lower-level construction unit configured to construct a lower-level operation model, decision variables of the lower-level operation model comprising an operation strategy of the collaborative energy system; A solving unit configured to solve a bi-level optimization model comprising the upper-level planning model and the lower-level operation model to obtain the qualified server planning quantity, energy storage device planning quantity, electric refrigeration device planning quantity, and absorption refrigeration device planning quantity.

10. A computer readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, the processor is caused to perform the planning method of the collaborative energy system according to any one of claims 1 to 8.

11. An electronic device, comprising: The electronic device comprises: A processor; A memory storing a computer program, when the computer program is executed by the processor, the processor is caused to perform the planning method of the collaborative energy system according to any one of claims 1 to 8.

12. A computer program product comprising a computer program, characterized in that, The computer program is executed by the processor to implement the planning method of the collaborative energy system according to any one of claims 1 to 8.

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