CN109888817B - Method for carrying out position deployment and capacity planning on photovoltaic power station and data center - Google Patents
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Abstract
The invention provides a method for carrying out position deployment and capacity planning on a photovoltaic power station and a data center, which comprises the following steps: s101, establishing a simulation model for planning the access positions and the capacities of the photovoltaic power station and the data center in the power grid, wherein the simulation model is used for determining the optimal positions and the capacities of the data center and the photovoltaic power station; s102, determining optimization targets and constraint conditions for the position and capacity configuration of the data center and the photovoltaic power station; and S103, determining the optimal position and capacity plan with the minimum network loss target of the data center and the photovoltaic power station according to the simulation model based on a genetic algorithm. The optimal positions of the data center and the photovoltaic power station can be found through the simulation models of the access positions and the access capacities of the photovoltaic power station and the data center, so that the total loss of the power grid is effectively reduced, and the stability of the power grid is maintained.
Description
Technical Field
The invention relates to the technical field of capacity planning, in particular to a method for carrying out position deployment and capacity planning on a photovoltaic power station and a data center in an intelligent power grid.
Background
Driven by global challenges such as energy shortages, climate change and ever-increasing power demand, modern power systems are undergoing extensive revolution and re-establishment. The "smart grid" was proposed at the beginning of the 21 st century with the aim of building an intelligent, low-carbon, green grid. According to investigation, major cloud service providers such as microsoft, google and amazon are striving to build the largest data center in the world, and the energy consumption is huge, so that the load consumption of a building site is remarkably increased, and the power grid is greatly influenced. Currently, smart grid technology is being transformed into an interactive network with the aim of reducing the overall cost of electricity delivered to end users. With the continuous development of modern power grids, emerging technological trends have profound effects on the operation of power distribution systems. Taking a distributed renewable energy power generation facility as an example, it can be effectively integrated into a smart grid for balancing power and demand at different locations to perform appropriate frequency regulation.
When the photovoltaic power station and the data center are connected into the power grid, the tide state of the power grid can be changed, so that the total loss of the power grid is influenced, and the serious problems that the voltage deviation is overlarge or the branch power exceeds the limit and the stability of the power grid is influenced can be caused due to different connection positions.
Disclosure of Invention
In order to solve the technical problem, the present invention provides a method for performing location deployment and capacity planning on a photovoltaic power station and a data center, which includes:
s101, establishing a simulation model for planning the access positions and the capacities of the photovoltaic power station and the data center in the power grid, wherein the simulation model is used for determining the optimal positions and the capacities of the data center and the photovoltaic power station;
s102, determining optimization targets and constraint conditions for the position and capacity configuration of the data center and the photovoltaic power station;
and S103, determining the optimal position and capacity plan with the minimum network loss target of the data center and the photovoltaic power station according to the simulation model based on a genetic algorithm.
Further, the S101 specifically includes:
the method comprises the following steps that N represents a set of all bus nodes, i represents bus nodes to form a topological network structure, each bus node i belongs to N, the node i is connected with renewable energy sources/non-renewable energy sources and various loads, k photovoltaic power stations and m data centers are arranged in a power grid, and each photovoltaic power station and each data center are connected to one bus node of the power grid;
calculating active power, using PiRepresents the active power injected at bus node i:
Pi＝Pi Gen-Pi Load，
wherein, Pi GenAnd Pi LoadRespectively representing the power generation amount and the load consumption amount on a bus node i, wherein the total power generation amount and the load consumption amount are balanced;
dividing the generated energy on the bus node i into photovoltaic serving as renewable energy source power generation Pi RsAnd non-renewable energy power generation Pi Urs；
Pi Gen＝Pi Rs+Pi Urs，
For load division P on bus node ii dcFor total power consumption and P of data centeri otherTotal power consumption of other loads:
Pi Load＝Pi dc+Pi other。
further, the network loss target is set to select the minimum network loss as an optimization objective function, which is expressed as:
wherein i is 1,2, …, n, n represents the total number of nodes of the power grid, PlossIs the total network loss, RiIs the resistance, P, at the bus node iiAnd QiThe active and reactive power of the bus node i, respectively.
Further, the constraint conditions are set as:
after the intelligent power grid is added into the data center and the photovoltaic power station, the load flow calculation balance is satisfied:
in the formula, i is 1,2, …, n, n represents the total number of nodes of the power grid, and a bus node j is a node directly connected with the bus node i and represented by j epsilon i; delta PiAnd Δ QiError of active and reactive power, P, respectivelyiAnd QiThe active power and the reactive power of a bus node i are respectively; u shapeiAnd UjRespectively representing the voltage of a bus node i and the voltage of a bus node j; gij、BijAnd deltaijRespectively, the conductance, susceptance, and phase angle difference between busbar node i and busbar node j.
Further, the constraint condition includes an inequality constraint condition as follows:
(1) when influenced by external environment and operation condition factors, the output capacity of the photovoltaic power station is constrained as follows:
wherein,
for the input capacity of the photovoltaic plant at bus node i,
the maximum input capacity of the photovoltaic power station on a bus node i is obtained;
(2) the power constraints of a data center are:
the total capacity of the multiple data centers is consistent with the total power generation capacity of the photovoltaic power station, namely the following constraints are met:
wherein,
capacity for accessing a data center on a bus node i;
(3) the constraints on the voltage are:
the voltage needs to be controlled within a certain range:
UM(1-ε1)≤Ui≤UM(1+ε2)，
wherein, UMIs the system nominal voltage, ε1And ε2Is the internationally specified allowable deviation ratio.
(4) The branch power constraints are:
|Pij|≤Pij，max，
wherein, | Pij|、Pij,maxAre respectively a bus bar sectionThe branch power between point i and bus node j, and the maximum power that the branch can pass through.
Further, the S103 includes:
and (4) carrying out chromosome coding and population initialization, and setting individual coding and fitness function.
Further, the chromosome coding process is to adopt a binary coding form for the placement position of the data center, the data center is placed at a certain node, the node is denoted by 1, and the node where the data center is not placed is denoted by 0.
Further, the S103 further includes: selecting operation, specifically, a strategy for selecting a population takes out a certain number of individuals from the population each time, selects the best one of the individuals to enter a filial generation population, and repeats the operation until the new population size reaches the original population size.
Further, the S103 further includes: crossover operations, particularly for use in genetic algorithms, to pass superior genes in the chromosomal code to individuals of the next generation.
Further, the S103 further includes: and mutation operation, specifically, randomly selecting the chromosome coding individuals needing mutation operation according to the probability, and performing mutation operation.
Drawings
FIG. 1 is a flow chart of a method of the present invention for location deployment and capacity planning for photovoltaic power plants and data centers;
FIG. 2 is a schematic structural diagram of a chromosome coding form;
FIG. 3 is a schematic view of a crossover operation process;
FIG. 4 is a schematic diagram of a mutation operation;
FIG. 5 is a power grid system diagram of a case _ ieee30 node;
fig. 6 is a schematic diagram illustrating network loss comparison of data center placement and deployment in different scenarios;
fig. 7 is a schematic diagram of overall network loss distribution in different scenarios;
FIG. 8 is a voltage comparison diagram of data center position and capacity variation under different scenarios.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth such as particular equipment structures, interfaces, techniques, etc. in order to provide a thorough understanding of the present invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.
As shown in fig. 1, the present invention provides a method for performing location deployment and capacity planning on a photovoltaic power station and a data center, comprising:
step S301, establishing a simulation model of the access positions and capacities of the photovoltaic power station and the data center, and finding the optimal position and capacity configuration scheme for placing the data center and the photovoltaic power station;
step S302, determining an optimization target and constraint conditions, wherein the position and capacity configuration of the data center and the photovoltaic power station is in accordance with the optimization target and the constraint conditions;
step S303, determining the optimal position and capacity configuration of the data center and the photovoltaic power station based on a genetic algorithm.
Firstly, establishing a simulation model of the access position and capacity of the photovoltaic power station and the data center for finding the optimal position and capacity configuration scheme for placing the data center and the photovoltaic power station
For the power grid, a set of all bus nodes is represented by N, and a bus node is represented by i, so that a topological network structure is formed. Each bus node i belongs to N, and the node i can be connected with renewable energy sources/non-renewable energy sources and various loads. In the system model, the load includes a data center supporting cloud computing. In the power grid, k photovoltaic power stations and m data centers are arranged, and each photovoltaic power station and each data center are connected to a certain bus node of the power grid.
Calculating active power, using PiRepresents the active power injected on the busbar node i:
Pi＝Pi Gen-Pi Load (1)
Pi Genand Pi LoadRespectively representing the amount of power generation and the amount of load consumption at the bus node i. For the grid, the total power production and load consumption are balanced.
The generated energy on the bus node i can be divided into photovoltaic power generation serving as renewable energy source Pi RsAnd power generation by non-renewable energy source Pi Urs。
Pi Gen＝Pi Rs+Pi Urs (2)
For loads on bus node i can be divided into Pi dcFor total power consumption and P of data centeri otherOther loads total power consumption.
Pi Load＝Pi dc+Pi other (3)
Then, determining an optimization target and constraint conditions, wherein the position and capacity configuration for the data center and the photovoltaic power station meets the optimization target and the constraint conditions;
the network loss is an important economic index for measuring the operation of the intelligent power grid, and the reasonable position arrangement of the photovoltaic power station and the data center can effectively reduce the network loss of the system, so that the minimum network loss is selected as an optimization objective function and expressed as:
where i is 1,2, …, n, n represents the total number of nodes in the grid, PlossIs the total network loss, RiIs the resistance at bus node i. PiAnd QiThe active and reactive power of the bus node i, respectively.
The required constraints are as follows:
the tidal current calculation is an important analysis calculation of the power system, and the influence of the change of various network structures on the system safety can be predicted through the tidal current calculation. After the intelligent power grid is added into the data center and the photovoltaic power station, the load flow calculation balance is satisfied:
after the intelligent power grid is added into the data center and the photovoltaic power station, the load flow calculation balance is satisfied:
in the formula, i is 1,2, …, n, n represents the total number of nodes of the power grid, and a bus node j is a node directly connected with the bus node i and represented by j e i; delta PiAnd Δ QiError of active and reactive power, P, respectivelyiAnd QiThe active power and the reactive power of a bus node i are respectively; u shapeiAnd UjRespectively representing the voltage of a bus node i and the voltage of a bus node j; gij、BijAnd deltaijRespectively, the conductance, susceptance, and phase angle difference between busbar node i and busbar node j. In addition, the following inequality constraints should be considered:
(1) by factors such as external environment and operation conditions, the output capacity constraint of the photovoltaic power station is expressed as follows:
in the formula
For the input capacity of the photovoltaic plant on the busbar node i,
the maximum input capacity of the photovoltaic power station on the bus node i.
(2) Power constraints for data centers
In order to balance the influence of grid connection of the photovoltaic power stations and fully utilize renewable energy, when the scale of the data centers is planned, all the data centers are considered to consume all the generated energy of the photovoltaic power stations, namely the total capacity of the data centers is consistent with the total generated energy of the photovoltaic power stations, namely the following constraints are met:
in the formula,
the capacity of the data center is accessed on the bus node i.
(3) Confinement of voltage
Photovoltaic power stations and data centers are connected into an intelligent power grid, the original tidal current state is changed, voltage changes can be caused, and unreasonable voltage deviation can cause problems in operation of the power grid. Therefore, the voltage needs to be controlled within a certain range:
UM(1-ε1)≤Ui≤UM(1+ε2) (9)
in the formula of UMIs the system nominal voltage, ε1And ε2Is the internationally specified allowable deviation ratio.
(4) When the voltage stability is obtained, the limit of branch power constraint is usually ignored, and the power of some branches is out of limit when the voltage in the smart grid does not reach a critical point. Therefore, the loss judgment index of the power grid is lack of practical significance. The branch power constraint is treated as a constraint condition of an optimization problem. Branch power constraint of
|Pij|≤Pij，max (10)
In the formula | Pij|、Pij,maxThe branch power between bus node i and bus node j, and the maximum power allowed by the branch (i.e., the branch capacity limit), respectively.
In summary, the optimization problem to be solved is to find a reasonable location and capacity allocation scheme under the constraint conditions of (5) to (10), so that the optimization target represented by the expression (4) is minimum.
Then, optimal location and capacity configurations of the data center and the photovoltaic power plant are determined based on genetic algorithms.
The genetic algorithm is adept to solve the problem of global optimization, so that the genetic algorithm can be used for the planning of a power grid, the site selection of a photovoltaic power station, the constant volume and other complex power system optimization problems.
Fitness function
The fitness function is the fundamental basis for realizing the excellence and the disadvantage, and can be evaluated through the fitness degree of each individual in the population to the environment so as to obtain the optimal value. The goal is to minimize the grid loss of the entire grid, so the grid loss function is defined as the fitness function.
Selection operation
The selection operation is to make the probability of the excellent chromosome individuals being inherited to the next generation higher. The strategy of selecting the population by adopting the championship game takes a certain number of individuals out of the population each time, and then selects the best one of the individuals to enter the offspring population. This operation is repeated until the new population size reaches the original population size.
Interleaving
In the genetic algorithm, the cross operation is a main search operator, the cross operation can simulate the gene recombination process of sexual reproduction in nature, the original excellent gene can be transmitted to the next generation of individuals, and the new individuals generate the gene organization structure with more excellent existence. The specific operation process is shown in fig. 3.
The data centers of parent 1 are located on nodes 4 and 6, the data centers of parent 2 are located on nodes 3 and 5, and the intersection is performed at point 5, the data centers of generated child 1 are located on nodes 4 and 5, and the data centers of child 2 are located on nodes 3 and 6.
Mutation operation
The main function of mutation operation is to select the position of a node placed in a data center, and people need to randomly select individuals needing mutation operation according to a certain probability and finally perform mutation according to a certain specific rule. The operation process is shown in FIG. 4;
the original data centers were placed on nodes No. 5 and No. 7. And (4) carrying out mutation at nodes 5 and 6, wherein the positions of the data centers after mutation are on nodes 6 and 7.
In this embodiment, the method of the present invention is tested and verified by using a standard power grid system of IEEE30 node. As shown in fig. 5. Wherein, node 1 is a balance node, nodes 2, 5, 8, 9, and 13 are PV nodes, the rest are PQ nodes, and the reference power is 100 MW. This example uses that DEAP is a new evolved genetic algorithm framework for rapid prototyping and thought testing. It aims to make the algorithm explicit and the data structure transparent. Modeling and simulating the added data center and photovoltaic power generation, and improving the experiment. From the simulation, we can see the voltage variation and the network loss variation of each bus node.
In the following experiments, it is assumed that one photovoltaic power station and two data centers are built in the power grid, the number of nodes to be selected is 2-30 nodes, the target optimization is that the power generation of the photovoltaic power station is equal to the power consumption transmitted to the two data centers, and the capacity of a single photovoltaic power station and a single data center is an integral multiple of 0.001 MW. The parameters in the genetic algorithm are set as follows: the population size is 50, and the probability of crossover and mutation is 0.5 and 0.2 respectively.
Firstly, fixing the position of a photovoltaic, and finding the optimal position and capacity allocation of two data centers through an algorithm. A40 MW photovoltaic power station is placed at the No. 5 node, and two corresponding data centers are needed to consume the electricity generation amount of the photovoltaic power station. In order to verify the reliability of the algorithm, five scenarios are compared as follows:
case 1: two data centers are placed at No. 10 and No. 12 bus nodes;
case 2: the two data centers are arranged at No. 6 and No. 7 bus nodes and near the photovoltaic power station;
case 3: the two data centers are placed at No. 25 and No. 26 bus nodes and are far away from the photovoltaic power station;
case4, placing two data centers on the No. 5 bus bar node;
case 5: two data centers are placed on No. 5 and No. 30 nodes, one is at the position of the photovoltaic power station, and the other is far away from the photovoltaic power station.
The specific placement and capacity analysis is shown in table 1:
TABLE 1 analysis of the Access to 40MW photovoltaic plants
case1 is a solution for genetic algorithms to find data centers and capacity allocation optimality. The other four protocols were used for subsequent comparisons.
When the photovoltaic power station is connected with 40MW, five scenes of 6MW and 34MW are respectively placed in the two data centers, and corresponding network loss is shown in figure 6.
As can be seen from fig. 6 and 7, the network loss varies greatly depending on the placement of the data centers. The overall network loss of the case1 is the smallest, the network loss of the case3 is the largest, and the network loss is the worst under the condition that the photovoltaic power station is far away from the data center, but the network loss of the case1 is not small under the condition that the data center and the photovoltaic power station are built together.
Secondly, the genetic algorithm of the patent also restricts the problems of voltage out-of-limit and branch out-of-limit existing in the access position and capacity change of the data center. The voltage out-of-limit criteria are shown in table 2. The specific voltage and branch power variations are shown in fig. 8, table 3 and table 4:
TABLE 2 Voltage amplitude limits
As can be seen from table 2 and fig. 8, in the case of case1, case2, case4, and case5, the voltage is relatively stable, and in the case of case3, the voltage amplitude of the bus node No. 26 is 0.856p.u, which exceeds the range specified by the voltage deviation, there is a serious voltage violation, and the stability of the power grid will be affected.
The branch power is in a certain range, no loss is generated on the power grid, but if the branch power exceeds a certain range, the loss is generated on the power grid. There is a certain range based on the IEEE30 branch power, and part of the branch power ranges are shown in table 3:
TABLE 3 range of fractional branch powers
TABLE 4 data center branch power comparison at different locations
From table 3 and table 4, it can be seen that in the case of the case3 and the case5, the active power of the branch 22 → 24, 24 → 25, 25 → 27, 28 → 27 exceeds the specified range of the branch power, and there is a safety hazard. Experimental results show that the position arrangement of the photovoltaic power station and the data center has certain influence on the grid loss. Through the optimization algorithm provided by the patent, the found optimal scheme is as follows: and a data center with the capacity of 6MW is accessed at the No. 10 bus bar node, and a data center with the capacity of 34MW is accessed at the No. 12 bus bar node. Through a plurality of experiments, the result shows that the invention can find a better distribution scheme of data center placement and capacity planning.
When the photovoltaic power station and the data center are connected into the power grid, the tide state of the power grid can be changed, so that the total loss of the power grid is influenced, and the serious problems that the voltage deviation is overlarge or the branch power exceeds the limit and the stability of the power grid is influenced can be caused due to different connection positions. According to the method, the minimum network loss of the photovoltaic power station and the data center is used as a target function, and the constraint of voltage and branch power is used as a condition, so that the constraint optimization problem is designed; the corresponding solving algorithm is realized by combining the basic idea of the genetic algorithm, so that an effective method is provided for the problems of the access positions and the capacities of the photovoltaic power station and the data center. Depending on the particular network configuration, the optimal solution for multiple data center location placement and capacity configurations may be analyzed and solved.
The reader should understand that in the description of this specification, reference to the description of the terms "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.
Claims (5)
1. A method for location deployment and capacity planning for photovoltaic power plants and data centers, comprising:
s101, establishing a simulation model for planning the access positions and the capacities of the photovoltaic power station and the data center in the power grid, wherein the simulation model is used for determining the optimal positions and the capacity configurations of the data center and the photovoltaic power station, and specifically comprises the following steps:
the method comprises the following steps that N represents a set of all bus nodes, i represents bus nodes to form a topological network structure, each bus node i belongs to N, the node i is connected with renewable energy sources/non-renewable energy sources and various loads, k photovoltaic power stations and m data centers are arranged in a power grid, and each photovoltaic power station and each data center are connected to one bus node of the power grid;
calculating active power, using PiRepresents the active power injected at bus node i:
Pi＝Pi Gen-Pi Load，
wherein, Pi GenAnd Pi LoadRespectively representing the power generation amount and the load consumption amount on a bus node i, wherein the total power generation amount and the load consumption amount are balanced;
dividing the generated energy on the bus node i into photovoltaic serving as renewable energy source power generation Pi RsAnd non-renewable energy power generation Pi Urs；
Pi Gen＝Pi Rs+Pi Urs，
Divide by P for load consumption on bus node ii dcFor total power consumption and P of data centeri otherTotal power consumption of other loads:
Pi Load＝Pi dc+Pi other；
the network loss target is set to select the minimum network loss as an optimization objective function, which is expressed as:
where i is 1,2, …, n, n represents the total number of nodes in the grid, PlossIs the total network loss, RiIs the resistance, P, at the bus node iiAnd QiRespectively the active and reactive power of the bus node i
S102, determining optimization targets and constraint conditions for the position and capacity configuration of the data center and the photovoltaic power station, wherein the constraint conditions are set as follows:
after the intelligent power grid is added into the data center and the photovoltaic power station, the load flow calculation balance is satisfied:
in the formula, i is 1,2, …, n, n represents the total number of nodes of the power grid, and a bus node j is a node directly connected with the bus node i and represented by j e i; delta PiAnd Δ QiError of active and reactive power, P, respectivelyiAnd QiRespectively the active and reactive power of the bus node i; u shapeiAnd UjRespectively representing the voltage of a bus node i and the voltage of a bus node j; gij、BijAnd deltaijRespectively representing the conductance, susceptance and phase angle difference between a bus node i and a bus node j;
the constraint conditions comprise inequality constraint conditions as follows:
(1) when influenced by external environment and operation condition factors, the output capacity of the photovoltaic power station is constrained as follows:
wherein,
for the input capacity of the photovoltaic plant on the busbar node i,
the maximum input capacity of the photovoltaic power station on a bus node i is set;
(2) the power constraints of a data center are:
the total capacity of the multiple data centers is consistent with the total power generation capacity of the photovoltaic power station, namely the following constraints are met:
wherein,
the capacity of a data center is accessed to a bus node i, k is the number of photovoltaic power stations and m is of the data centerThe number of the cells;
(3) the constraints on the voltage are:
the voltage needs to be controlled within a certain range:
UM(1-ε1)≤Ui≤UM(1+ε2)，
wherein, UMIs the system nominal voltage, ε1And epsilon2Is an internationally specified allowable deviation ratio;
(4) the branch power constraints are:
|Pij|≤Pij，max，
wherein, | Pij|、Pij,maxThe branch power between bus node i and bus node j, and the maximum power allowed by the branch
S103, determining the optimal position and capacity plan with the minimum network loss target of the data center and the photovoltaic power station according to the simulation model based on a genetic algorithm, and specifically comprising the following steps:
and (5) initializing chromosome codes and population, and setting individual codes and fitness functions.
2. The method for location deployment and capacity planning for photovoltaic power plants and data centers as claimed in claim 1, wherein the chromosome coding is performed by binary coding the data center placement locations, the data center is placed at a node represented by 1, and the node where the data center is not placed is represented by 0.
3. The method for location deployment and capacity planning for photovoltaic power plants and data centers as claimed in claim 1, wherein said S103 further comprises: selecting operation, specifically, a strategy for selecting a population takes out a certain number of individuals from the population each time, selects the best one of the individuals to enter a filial generation population, and repeats the operation until the new population size reaches the original population size.
4. The method for location deployment and capacity planning for photovoltaic power plants and data centers as claimed in claim 1, wherein said S103 further comprises: crossover operations, particularly for use in genetic algorithms, to pass superior genes in the chromosomal code to individuals of the next generation.
5. The method for location deployment and capacity planning for photovoltaic power plants and data centers as claimed in claim 1, wherein said S103 further comprises: and mutation operation, specifically, randomly selecting the chromosome coding individuals needing mutation operation according to the probability, and performing mutation operation.
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| WO2015122911A1 (en) * | 2014-02-14 | 2015-08-20 | Schneider Electric It Corporpation | Automatic placement of data center |
| CN107423133A (en) * | 2017-06-29 | 2017-12-01 | 国网江苏省电力公司电力科学研究院 | Data network load allocation method between a kind of data center for reducing grid net loss |
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| CN107423133A (en) * | 2017-06-29 | 2017-12-01 | 国网江苏省电力公司电力科学研究院 | Data network load allocation method between a kind of data center for reducing grid net loss |
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