Power station and data center joint scheduling method based on grey wolf pack algorithm

Abstract

The invention discloses a power station and data center combined dispatching method based on a grey wolf pack algorithm, a power station is a pumped storage power station, and a data center is located under water of a lower reservoir. Comprising the following steps: step 1, calculating optimal target solutions of an energy storage parameter, a power generation parameter, a cooling water flow parameter, water flow stability and heat dissipation capability based on a grey wolf pack algorithm; 2, calculating a grey wolf pack multi-objective optimal solution; 3, under the condition that the optimal target solutions of the energy storage parameters, the power generation parameters, the cooling water flow parameters, the water flow stability and the heat dissipation capacity are calculated, the minimum value of the target function is calculated according to the constraint condition 1; and 4, guiding energy storage, power generation and water transfer of the pumped storage power station and heat dissipation adjustment of a data center according to the energy storage parameter, the power generation parameter, the cooling water flow parameter, the water flow stability and the heat dissipation capability of the minimum value of the target function obtained through calculation. The energy utilization efficiency and the system stability are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power station and data center linkage processing, and specifically, to a joint scheduling method for power station and data center based on grey wolf optimization algorithm. Background Art

[0002] A data center is a basic hardware facility for storing and running data. During the normal operation of a data center, a large amount of heat is generated. If the temperature of the data center cannot be well controlled, it will affect the normal use of the data center. Therefore, it is necessary to perform heat dissipation adjustment on the data center. Currently, an important technical solution for realizing heat dissipation adjustment of the data center is to use water flow for heat dissipation. With the relatively low operating cost and relatively high heat dissipation capacity of using water flow for heat dissipation, many data centers are built underwater, that is, the so-called underwater data centers. Underwater data centers can use natural water flow or artificially formed water flow for heat dissipation.

[0003] In order to use water flow to dissipate heat from the data center, most of the existing technical solutions are to build the data center underwater, and do not use the water flow generated by a power station (generally referring to a pumped-storage power station) for heat dissipation. That is, the prior art does not combine the pumped-storage power station and the data center to achieve heat dissipation adjustment of the data center, and when achieving the purpose of heat dissipation adjustment, it can also control the power generation process of the power station, that is, there is no method for jointly scheduling the existing power station and the data center.

[0004] The following deficiencies exist in the independent operation mode of the pumped-storage power station and the underwater data center:

[0005] 1. Due to the lack of coordinated control with heat dissipation requirements, it is difficult to further improve the utilization efficiency of the pumped-storage power station for energy (here referring to water and related power generation equipment).

[0006] 2. Only using natural flowing water or artificially formed fixed water flow, the heat dissipation method of the data center is single, and the requirement for the stability of the water flow speed is relatively high, and it is difficult to dynamically adjust the cooling demand for heat dissipation.

[0007] 3. The pumped-storage power station and the data center lack a joint scheduling mechanism, and it is impossible to effectively balance the energy storage efficiency, power generation power of the pumped-storage power station and the cooling stability and heat dissipation performance of the data center. Summary of the Invention

[0008] Aiming at the deficiencies of the prior art, the purpose of the present invention is to provide a joint scheduling method for power station and data center based on grey wolf optimization algorithm, which can solve the problems described in the background art.

[0009] The technical solution for achieving the purpose of the present invention is: a method for jointly dispatching a power station and a data center based on a gray wolf pack algorithm, wherein the power station is a pumped storage power station, which includes an upper reservoir, a lower reservoir, a generator set and a water delivery system, wherein the upper reservoir and the lower reservoir are located with a height difference, the lower reservoir is located below the upper reservoir, the water delivery system is used to release water from the upper reservoir into the lower reservoir, and to pump water from the lower reservoir to the upper reservoir, the generator set is used to send water from the upper reservoir into the lower reservoir, and the potential energy of the water flow caused by the height difference is converted into electrical energy to achieve power generation, and the data center is located underwater in the lower reservoir.

[0010] The joint scheduling method comprises the following steps:

[0011] Step 1: Calculate the energy storage parameters E based on the grey wolf pack algorithm stored , power generation parameters P out , Cooling water flow parameters V cooling , Water flow stability V flow And heat dissipation capacity Q thermal The optimal target solution, energy storage parameter E stored Characterizes the energy storage efficiency of the pumped storage power station in pumping mode, the power generation parameter P out Characterizes the output power of the pumped storage power station in the power generation mode, the cooling water flow parameter V cooling Characterizes the stability of water flow in the surrounding area of ​​the data center, water flow stability V flow Characterizes the stability of water flow velocity and heat dissipation capacity of pumped storage power stations during pumping and discharging. thermal Characterize the cooling capacity of the data center;

[0012] Step 2: Calculate the multi-objective optimization solution X of the gray wolf pack according to formula ⑥ (t+1),1 :

[0013]

[0014] Step 3: Calculate the energy storage parameter E stored , power generation parameters P out , Cooling water flow parameters V cooling , Water flow stability V flow And heat dissipation capacity Q thermal Under the optimal objective solution, the minimum value of the objective function F(X) is calculated according to constraint 1.

[0015] Constraint 1: Energy storage parameter E stored The optimal target solution E stored,1 Take the maximum value, power generation parameter P out The optimal target solution P out,1 Take the maximum value, cooling water flow parameter V cooling The optimal target solution V cooling,1Take the minimum value, the water flow stability V flow The optimal objective solution V flow,1 Take the minimum value, the heat dissipation capacity Q thermal The optimal objective solution Q thermal,1 Take the maximum value,

[0016] The minimum value of the objective function F(X) is calculated according to the following formula:

[0017]

[0018] In the formula, w1, w2, w3, w4, w5 represent the weight coefficients of the objective function F(X), which are preset constants, and their value ranges are 0-1, and satisfy w1 + w2 + w3 + w4 + w5 = 1;

[0019] Step 4: According to the calculated minimum value X of the objective function F(X) optimal , with X optimal The energy storage parameter E stored , the power generation parameter P out , the cooling water flow parameter V cooling , the water flow stability V flow , the heat dissipation capacity Q thermal Guide the energy storage, power generation, water regulation of the pumped-storage power station and the heat dissipation regulation of the data center.

[0020] Furthermore, the energy storage parameter E stored = ρghV, ρ represents the water density, g represents the acceleration due to gravity, h represents the pumping overshoot, the vertical height difference of the water body lifted from the lower reservoir to the upper reservoir, and V represents the volume of the water body pumped by the pumped-storage power station.

[0021] Furthermore, the power generation parameter P out = ηρgQH, η represents the efficiency of the generator set, Q represents the water flow rate, and H represents the vertical height difference between the upper reservoir and the lower reservoir.

[0022] Furthermore, the heat dissipation capacity Q thermal The heat balance constraint is used to optimize the heat dissipation process through the following formula:

[0023]

[0024] In the formula, η cooling represents the heat dissipation efficiency of the data center, C p represents the specific heat capacity of water, ΔT represents the temperature difference between the inlet and outlet of the water flowing through the data center, V c represents the water flow velocity, t represents the current time, t0 represents the current time, and dt is the time change amount.

[0025] Furthermore, the energy storage parameter E storedOptimal objective solution E based on Grey Wolf Optimizer algorithm stored,1 It is calculated according to formula ①:

[0026] E stored,1 = X α - A α ·|C α ·X α - X stored | ------ ①

[0027] In the formula, X α represents the optimal solution of the current energy storage efficiency, A α represents the first coefficient regarding the energy storage parameter E stored C α represents the second coefficient regarding the energy storage parameter E stored X stored represents the historical optimal position of the calculated energy storage efficiency in the Grey Wolf Optimizer algorithm up to the current time.

[0028] Furthermore, the power generation parameter P out The optimal objective solution P based on the Grey Wolf Optimizer algorithm out,1 is calculated according to formula ②:

[0029] P out,1 = X β - A β ·|C β ·X β - X output | ------ ②

[0030] In the formula, X β represents the optimal solution of the current power generation power, A β represents the first coefficient regarding the power generation parameter P out C β represents the second coefficient regarding the power generation parameter P out X output represents the historical optimal position of the calculated power generation power in the Grey Wolf Optimizer algorithm up to the current time.

[0031] Furthermore, the cooling water flow parameter V cooling The optimal objective solution V based on the Grey Wolf Optimizer algorithm cooling,1 is calculated according to formula ③:

[0032] V cooling,1 = X δ - A δ ·|C δ ·X δ - X cooling | ------ ③

[0033] In the formula, X δRepresents the optimal solution of the current cooling water flow, A δ Represents the first coefficient C with respect to the cooling water flow parameter V cooling δ Represents the second coefficient X with respect to the cooling water flow parameter V cooling cooling Represents the historical optimal position of the calculated cooling water flow stability in the Grey Wolf Optimizer algorithm up to the current time.

[0034] Furthermore, the water flow smoothness V flow Based on the optimal objective solution V of the Grey Wolf Optimizer algorithm flow,1 Is calculated according to Equation ④:

[0035] V flow,1 = X ε - A ε · |C ε · X ε - X flow | ------ ④

[0036] In the formula, X ε Represents the optimal solution of the current water flow smoothness, A ε Represents the first coefficient with respect to the water flow smoothness V flow C ε Represents the second coefficient with respect to the water flow smoothness V flow X flow Represents the historical optimal position of the calculated water flow smoothness in the Grey Wolf Optimizer algorithm up to the current time.

[0037] Furthermore, the heat dissipation capacity Q thermal Based on the optimal objective solution Q of the Grey Wolf Optimizer algorithm thermal,1 Is calculated according to Equation ⑤:

[0038] Q thermal,1 = X ξ - A ξ · |C ξ · X ξ - X themal | ------ ⑤

[0039] In the formula, X ξ Represents the optimal solution of the current heat dissipation capacity, A ξ Represents the first coefficient with respect to the heat dissipation capacity Q thermal C ξ Represents the second coefficient with respect to the heat dissipation capacity Q thermal X themal Represents the historical optimal position of the calculated heat dissipation capacity in the Grey Wolf Optimizer algorithm up to the current time.

[0040] Furthermore, based on X optimal ​​The energy storage parameter E in stored Guide the pumped - storage power station to store energy based on X optimal The power generation parameter P in out Guide the generator set to generate electricity based on X optimal The cooling water flow parameter V cooling and the water flow stability V flow Guide the water conveyance system to discharge and pump water based on X in optimal The heat dissipation capacity Q in thermal Guide the data center to adjust heat dissipation.

[0041] Advantages of the present invention: According to the optimization results, the present invention dynamically adjusts the operation mode of the pumped - storage power station and the heat dissipation method of the data center, improves the energy utilization efficiency and system stability. It can make good use of the pumping and discharging in the power generation process of the pumped - storage power station to effectively dissipate heat from the data center, and at the same time, can well maintain the power generation efficiency of the pumped - storage power station, improving the utilization rate of water resources. Brief Description of the Drawings

[0042] Figure 1 It is a flow schematic diagram of the present invention. Detailed Embodiment

[0043] The following further describes the present invention in combination with the drawings and specific implementation schemes:

[0044] As Figure 1 shown, a joint scheduling method for a power station and a data center based on the grey wolf optimization algorithm. The power station is a pumped - storage power station, which includes an upper reservoir, a lower reservoir, a generator set and a water conveyance system. There is a height difference between the locations of the upper reservoir and the lower reservoir, and the lower reservoir is located below the upper reservoir. The water conveyance system is used to discharge the water in the upper reservoir into the lower reservoir and pump the water in the lower reservoir to the upper reservoir. The generator set is used to send the water in the upper reservoir into the lower reservoir, and convert the potential energy brought by the height difference of the water flow into electric energy to achieve power generation. The data center is located underwater in the lower reservoir.

[0045] The joint scheduling method includes the following steps:

[0046] Step 1: Calculate the optimal objective solutions of the energy storage parameter E stored , the power generation parameter P out , the cooling water flow parameter V cooling , the water flow stability V flow and the heat dissipation capacity Q thermal respectively based on the grey wolf optimization algorithm. The energy storage parameter E stored characterizes the energy storage efficiency of the pumped - storage power station in the pumping mode. The higher the efficiency, the higher the efficiency of converting electric energy into potential energy by raising the water level of the upper reservoir. The power generation parameter P outCharacterize the output power of the pumped-storage power station in the power generation mode, that is, the power output of the generator set that converts the water potential energy into electrical energy. Cooling water flow parameter V cooling Characterize the stability of the water flow in the surrounding area where the data center is located, that is, the stability of the water flow used to cool the data center and within a preset range. The stability of the water flow can be quantified by a level to determine a specific value. The purpose of the stability of the water flow is to enable the data center to dissipate heat efficiently and stably. Water flow stability V flow Characterize the stability of the water flow velocity during the pumping and discharging processes of the pumped-storage power station, and its specific value can be obtained by level quantification. In the pumping mode, the more stable the water flow velocity, the more stable the load of the water pump in the water conveyance system, the smaller the energy loss, and the higher the energy storage efficiency, that is, the energy storage parameter E stored The larger. In the discharging mode, the fluctuation of the water flow velocity may cause the unstable operation of the water turbine in the generator set, thereby affecting the power generation parameter P out , that is, affecting the output of the power generation power. The water flow fluctuation will also be transmitted through the cooling water flow (reflected by the cooling water flow parameter V cooling ) system, and thus indirectly affect the heat dissipation performance of the data center. Heat dissipation capacity Q thermal Characterize the heat dissipation capacity of the data center, and a specific value can be obtained by quantification.

[0047] Exemplarily, the energy storage parameter E stored =ρghV, where ρ represents the water density, g represents the acceleration due to gravity, h represents the pumping overshoot, the vertical height difference of the water body lifted from the lower reservoir to the upper reservoir, and V represents the volume of the water body pumped by the pumped-storage power station.

[0048] Power generation parameter P out =ηρgQH, where η represents the efficiency of the generator set, Q represents the water flow rate, and H represents the vertical height difference between the upper reservoir and the lower reservoir.

[0049] Heat dissipation capacity Q thermal The heat balance constraint is used to optimize the heat dissipation process through the following formula:

[0050]

[0051] In the formula, η cooling represents the heat dissipation efficiency of the data center, C p represents the specific heat capacity of water, ΔT represents the temperature difference between the inlet and outlet of the water flowing through the data center, V c represents the water flow velocity, t represents the current time, t0 represents the current time, that is, the start and end time, and dt is the time change amount.

[0052] Exemplarily, the energy storage parameter E stored and the power generation parameter P are calculated respectively based on the grey wolf optimization algorithmout , the cooling water flow parameter V cooling , the water flow stability V flow and the heat dissipation capacity Q thermal of the optimal objective solution, and the optimal objective solutions corresponding to each parameter can be calculated according to the following formulas respectively.

[0053] Among them, the energy storage parameter E stored The optimal objective solution E based on the grey wolf optimization algorithm stored,1 is calculated according to formula ①:

[0054] E stored,1 = X α - A α · |C α · X α - X stored | ------ ①

[0055] In the formula, X α represents the optimal solution of the current energy storage efficiency, A α represents the first coefficient regarding the energy storage parameter E stored C α represents the second coefficient regarding the energy storage parameter E stored X stored represents the historical optimal position of the calculated energy storage efficiency in the grey wolf optimization algorithm up to the current.

[0056] The power generation parameter P out The optimal objective solution P based on the grey wolf optimization algorithm out,1 is calculated according to formula ②:

[0057] P out,1 = X β - A β · |C β · X β - X output | ------ ②

[0058] In the formula, X β represents the optimal solution of the current power generation power, A β represents the first coefficient regarding the power generation parameter P out C β represents the second coefficient regarding the power generation parameter P out X output represents the historical optimal position of the calculated power generation power in the grey wolf optimization algorithm up to the current.

[0059] The cooling water flow parameter V cooling The optimal objective solution V based on the grey wolf optimization algorithm cooling,1 is calculated according to formula ③:

[0060] Vcooling,1 = X δ - A δ ·|C δ ·X δ - X cooling |------③

[0061] In the formula, X δ represents the optimal solution of the current cooling water flow, and A δ represents the first coefficient with respect to the cooling water flow parameter V cooling and C δ represents the second coefficient with respect to the cooling water flow parameter V cooling and X cooling represents the historical optimal position of the calculated cooling water flow stability in the Grey Wolf Optimization algorithm up to the current time.

[0062] The water flow stability V flow The optimal objective solution V based on the Grey Wolf Optimization algorithm flow,1 is calculated according to Formula ④ as follows:

[0063] V flow,1 = X ε - A ε ·|C ε ·X ε - X flow |------④

[0064] In the formula, X ε represents the optimal solution of the current water flow stability, and A ε represents the first coefficient with respect to the water flow stability V flow and C ε represents the second coefficient with respect to the water flow stability V flow and X flow represents the historical optimal position of the calculated water flow stability in the Grey Wolf Optimization algorithm up to the current time.

[0065] The heat dissipation capacity Q thermal The optimal objective solution Q based on the Grey Wolf Optimization algorithm thermal,1 is calculated according to Formula ⑤ as follows:

[0066] Q thermal,1 = X ξ - A ξ ·|C ξ ·X ξ - X themal |------⑤

[0067] In the formula, X ξ represents the optimal solution of the current heat dissipation capacity, and A ξ represents the first coefficient with respect to the heat dissipation capacity Q thermal and Cξ Represents the second coefficient, X, regarding the heat dissipation capacity Q thermal themal Represents the historical optimal position of the calculated heat dissipation capacity in the Grey Wolf Optimizer so far, that is, the historical optimal position calculated in the Grey Wolf Optimizer based on the heat dissipation capacity of the data center.

[0068] The above parameters are all optimized based on the position update formula of the Grey Wolf Optimizer to obtain the corresponding optimal objective solutions. The position update formula of the Grey Wolf Optimizer is as follows:

[0069] X(t + 1) = X p (t) + A·|CX p (t) - X(t)|

[0070] In the formula, X(t) represents the position vector of the current (i.e., the t-th generation) grey wolf, X(t + 1) represents the position vector of the (t + 1)-th generation grey wolf, t ≥ 0. When t = 0, X(0) is the initial position of the grey wolf in the grey wolf group, and the initial position can be randomly generated. A and C represent the cooperation coefficient vectors, which are both sets, that is, the first coefficient and the second coefficient of the above parameters respectively. A = a(2r1 - 1), C = 2r2, A = {A α , A β , A δ , A ε , A ξ}}, C = {C α , C β , C δ , C ε , C ξ}. The cooperation coefficient vector A is used to adjust the search process, and the cooperation coefficient vector C is used to control the attraction intensity to increase the diversity of the search by adding randomness. a is a preset constant parameter. As the number of iterations increases, the value of a gradually decreases from 2 to 0. r1 and r2 are random vectors with values in the range of 0 - 1. X p (t) represents the position vector of the current dominant grey wolf.

[0071] Step 2: Calculate the multi-objective optimization solution X of the grey wolf group according to formula ⑥ (t+1),1 :

[0072]

[0073] According to formula ⑥, the multi-objective optimization solution of the overall grey wolf algorithm in multiple surfaces can be calculated from the parameters of each optimal objective solution, so as to optimize in the direction of the multi-objective optimization solution, that is, as the optimization direction.

[0074] Step 3: After calculating the energy storage parameter E stored and the power generation parameter P​out 、Cooling water flow parameter V cooling 、Water flow stability V flow and heat dissipation capacity Q thermal Under the optimal objective solution, calculate the minimum value of the objective function F(X) according to Constraint Condition 1.

[0075] Constraint Condition 1: Energy storage parameter E stored The optimal objective solution E stored,1 takes the maximum value, and the optimal objective solution P of the power generation parameter P out takes the maximum value, the optimal objective solution V of the cooling water flow parameter V out,1 takes the minimum value, the optimal objective solution V of the water flow stability V cooling takes the minimum value, the optimal objective solution Q of the heat dissipation capacity Q cooling,1 takes the maximum value, flow The optimal objective solution V of the water flow stability V flow,1 takes the minimum value, the optimal objective solution Q of the heat dissipation capacity Q thermal takes the maximum value, thermal,1 takes the maximum value.

[0076] The minimum value of the objective function F(X) is calculated according to the following formula:

[0077]

[0078] In the formula, w1, w2, w3, w4, w5 represent the weight coefficients of the objective function F(X), which are preset constants, and their value ranges are 0 - 1, and satisfy w1 + w2 + w3 + w4 + w5 = 1.

[0079] Step 4: According to the calculated minimum value X of the objective function F(X) optimal , with X optimal The energy storage parameter E in stored , the power generation parameter P out , the cooling water flow parameter V cooling , the water flow stability V flow , the heat dissipation capacity Q thermal Guide the energy storage, power generation, water transfer (including water release and pumping) of the pumped - storage power station and the heat dissipation regulation of the data center. Specifically, based on the energy storage parameter E in X optimal Guide the pumped - storage electronics to perform energy storage. Based on the power generation parameter P in X stored Guide the generator set to generate power. Based on the cooling water flow parameter V in X optimal , the water flow stability V out in X to guide the water conveyance system to release and pump water. Based on the heat dissipation capacity Q in X optimal Guide the data center to perform heat dissipation regulation. cooling The water flow stability V flow in X to guide the water conveyance system to release and pump water. Based on the heat dissipation capacity Q in X optimal to guide the data center to perform heat dissipation regulation. thermal Based on the heat dissipation capacity Q in X

[0080] The present invention dynamically adjusts the operation mode of the pumped-storage power station and the heat dissipation method of the data center according to the optimization results, improves the energy utilization efficiency and system stability, and can make good use of the pumping and discharging processes during the power generation of the pumped-storage power station to effectively dissipate the heat of the data center. At the same time, it can well maintain the power generation efficiency of the pumped-storage power station and improve the utilization rate of water resources.

[0081] The embodiments disclosed in this specification are only an illustration of the unilateral features of the present invention. The protection scope of the present invention is not limited to this embodiment, and any other functionally equivalent embodiments fall within the protection scope of the present invention. For those skilled in the art, various corresponding changes and deformations can be made according to the technical solutions and concepts described above, and all these changes and deformations should fall within the protection scope of the claims of the present invention.

Claims

1. A method for joint scheduling of power stations and data centers based on a grey wolf pack algorithm, characterized in that: The power station is a pumped storage power station, which includes an upper reservoir, a lower reservoir, a generator set and a water delivery system. The upper reservoir and the lower reservoir are located at a height difference. The lower reservoir is located below the upper reservoir. The water delivery system is used to release water from the upper reservoir into the lower reservoir, and to pump water from the lower reservoir to the upper reservoir. The generator set is used to send water from the upper reservoir to the lower reservoir, and convert the potential energy of the water flow due to the height difference into electrical energy to achieve power generation. The data center is located underwater in the lower reservoir. The joint scheduling method comprises the following steps: Step 1: Calculate the energy storage parameters E based on the grey wolf pack algorithm stored , power generation parameters P out , Cooling water flow parameters V cooling , Water flow stability V flow And heat dissipation capacity Q thermal The optimal target solution, energy storage parameter E stored Characterizes the energy storage efficiency of the pumped storage power station in pumping mode, the power generation parameter P out Characterizes the output power of the pumped storage power station in the power generation mode, the cooling water flow parameter V cooling Characterizes the stability of water flow in the surrounding area of ​​the data center, water flow stability V flow Characterizes the stability of water flow velocity and heat dissipation capacity of pumped storage power stations during pumping and discharging. thermal Characterize the cooling capacity of the data center; Step 2: Calculate the multi-objective optimization solution X of the gray wolf pack according to formula ⑥ (t+1),1 : Step 3: Calculate the energy storage parameter E stored , power generation parameters P out , Cooling water flow parameters V cooling , Water flow stability V flow And heat dissipation capacity Q thermal Under the optimal objective solution, the minimum value of the objective function F(X) is calculated according to constraint 1. Constraint 1: Energy storage parameter E stored The optimal target solution E stored,1 Take the maximum value, power generation parameter P out The optimal target solution P out,1 Take the maximum value, cooling water flow parameter V cooling The optimal target solution V cooling,1 Take the minimum value, water flow stability V flow The optimal target solution V flow,1 Take the minimum value, heat dissipation capacity Q thermal The optimal target solution Q thermal,1 Take the maximum value, The minimum value of the objective function F(X) is calculated as follows: Wherein, w1, w2, w3, w4, w5 represent weight coefficients of the objective function F(X), which are preset constants, whose value range is 0-1, and satisfy w1+w2+w3+w4+w5=1; Step 4: Based on the calculated minimum value X of the objective function F(X) optimal , with X optimal Energy storage parameter E stored , power generation parameters P out , Cooling water flow parameters V cooling , Water flow stability V flow , Heat dissipation capability Q thermal Guide the energy storage, power generation, water diversion of pumped storage power stations and heat dissipation regulation of data centers.

2. The method for joint scheduling of power stations and data centers based on the grey wolf group algorithm according to claim 1 is characterized in that: Energy storage parameter E stored =ρghV, ρ represents water density, g represents gravitational acceleration, h represents pumping overshoot, the vertical height difference of water from the lower reservoir to the upper reservoir, and V represents the volume of water pumped by the pumped-storage power station.

3. The method for joint scheduling of power stations and data centers based on the grey wolf group algorithm according to claim 2 is characterized in that: Power generation parameters P out =ηρgQH, η represents the efficiency of the generator set, Q represents the water flow, and H represents the vertical height difference between the upper reservoir and the lower reservoir.

4. The method for joint dispatching of power plants and data centers based on the grey wolf group algorithm according to claim 3 is characterized in that: Heat dissipation capability Q thermal The heat dissipation process is optimized by implementing the thermal balance constraint through the following formula: Where η cooling represents the cooling efficiency of the data center, C p represents the specific heat capacity of water, ΔT represents the temperature difference between the inlet and outlet of the data center, V c represents the flow rate of water, t represents the current time, t0 represents the current time, and dt is the time change.

5. The method for joint scheduling of power stations and data centers based on the grey wolf group algorithm according to claim 4 is characterized in that: Energy storage parameter E stored Optimal target solution based on grey wolf pack algorithm stored,1 According to formula ①, we can get: E stored,1 =X α -A α ·|C α ·X α -X stored |------① Where, X α represents the optimal solution for the current energy storage efficiency, A α Indicates the energy storage parameter E stored The first coefficient, C α Indicates the energy storage parameter E stored The second coefficient, X stored It indicates the historical optimal position of the calculated energy storage efficiency in the grey wolf pack algorithm up to now.

6. The method for joint dispatching of power stations and data centers based on the grey wolf group algorithm according to claim 5 is characterized in that: Power generation parameters P out Optimal target solution based on grey wolf pack algorithm out,1 According to formula ②, we can get: P out,1 =X β -A β ·|C β ·X β -X output |------② Where, X β represents the optimal solution for the current power generation, A β Represents the power generation parameter P out The first coefficient, C β Represents the power generation parameter P out The second coefficient, X output It indicates the historical optimal position of the calculated power generation in the grey wolf pack algorithm up to now.

7. The method for joint dispatching of power stations and data centers based on the grey wolf group algorithm according to claim 6 is characterized in that: Cooling water flow parameter V cooling Optimal target solution based on grey wolf pack algorithm V cooling,1 According to formula ③, we can get: V cooling,1 =X δ -A δ ·|C δ ·X δ -X cooling ·------③ Where, X δ represents the optimal solution for the current cooling water flow, A δ Indicates the cooling water flow parameter V cooling The first coefficient, C δ Indicates the cooling water flow parameter V cooling The second coefficient, X cooling It indicates that the stability of the cooling water flow calculated so far is the historical optimal position in the grey wolf pack algorithm.

8. The method for joint dispatching of power stations and data centers based on the grey wolf group algorithm according to claim 7 is characterized in that: Water flow stability V flow Optimal target solution based on grey wolf pack algorithm V flow,1 According to formula ④, we can get: V flow,1 =X ε -A ε ·|C ε ·X ε -X flow |------④ Where, X ε represents the optimal solution for the current water flow stability, A ε Indicates the stability of water flow V flow The first coefficient, C ε Indicates the stability of water flow V flow The second coefficient, X flow It indicates the historical optimal position of the calculated water flow stability in the grey wolf pack algorithm up to now.

9. The method for joint dispatching of power stations and data centers based on the grey wolf group algorithm according to claim 8, characterized in that: Heat dissipation capability Q thermal Optimal target solution Q based on grey wolf pack algorithm thermal,1 According to formula ⑤, we can get: Q thermal,1 =X ξ -A ξ ·|C ξ ·X ξ -X themal |------⑤ Where, X ξ represents the optimal solution for the current heat dissipation capability, A ξ Indicates the heat dissipation capability Q thermal The first coefficient, C ξ Indicates the heat dissipation capability Q thermal The second coefficient, X themal It indicates the historical optimal position of the heat dissipation capacity calculated so far in the grey wolf pack algorithm.

10. The method for joint dispatching of power stations and data centers based on the grey wolf group algorithm according to claim 1, characterized in that: X-based optimal Energy storage parameter E stored Guide to pumped storage electronics for energy storage, based on X optimal The power generation parameter P out Guide the generator set to generate electricity, based on X optimal Cooling water flow parameter V cooling , Water flow stability V flow The water delivery system in the guidance system is used to release and pump water, based on X optimal Medium heat dissipation capacity Q thermal Provide guidance for data center cooling adjustment.

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