CN112103997B - Active power distribution network operation flexibility improving method considering data center adjustment potential - Google Patents
Active power distribution network operation flexibility improving method considering data center adjustment potential Download PDFInfo
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Abstract
An active power distribution network operation flexibility improving method considering data center adjustment potential comprises the following steps: inputting basic parameter information of the active power distribution network according to the selected active power distribution network; establishing a flexible operation scheduling model of a data center according to the parameter information of the active power distribution network; establishing a power distribution network operation flexibility improvement model containing a data center, and taking the data center flexible operation scheduling model as one constraint in a base; solving a power distribution network operation flexibility improvement model containing a data center by adopting a second-order cone programming method, and outputting a solving result, wherein the method comprises the following steps: the voltage level, the line load rate and the operation loss of the active power distribution network, the time sequence output power of the intelligent soft switch and the data load flexible scheduling strategy of the data center. According to the invention, a data center load flexible scheduling strategy for improving the operation flexibility of the active power distribution network is obtained, the voltage fluctuation of the active power distribution network and the unbalanced degree of the feeder load are reduced, and the flexible operation of the system is realized.
Description
Technical Field
The invention relates to a method for improving the operation flexibility of an active power distribution network. In particular to an active power distribution network operation flexibility improving method considering data center regulation potential.
Background
With the rapid development of Internet technology, the scale and number of data centers (IDCs) are rapidly expanding, and gradually become important power consumers in an active power distribution network, thereby greatly increasing the power consumption load. According to statistics, the power consumption of the data center accounts for more than 1.3% of the total power supply amount of the whole world, and accounts for more than 2.35% in China. The wide access of data centers puts higher demands on the operational flexibility of active power distribution networks.
On a time scale, the data center can flexibly transfer data loads with long tolerance service delay, geographical distribution difference of the data center is further considered, and the data center loads can be adjusted on a spatial dimension so as to achieve the effect of balancing regional loads.
Researches on electricity utilization characteristics and flexible scheduling of data centers have been carried out at home and abroad, and the researches mainly focus on price-based data center demand response and guide time distribution of data center loads through electricity price signals so as to balance regional loads. There are still some challenges in regulating data centers to improve the flexibility of operation of power distribution networks. The power consumption characteristic of the data center needs to be considered, the load regulation potential of the data center is further expanded from the aspects of time transfer and space distribution, a flexible scheduling strategy of the data center is provided, the voltage level of a power distribution network and the load balance state of a feeder line are improved, and the operation flexibility of a system is improved. Therefore, an active power distribution network operation flexibility improving method considering data center adjustment potential is urgently needed, the power consumption level of the data center is reduced by flexibly scheduling data loads on a space-time scale, the voltage fluctuation of the active power distribution network and the unbalanced degree of feeder line loads are further reduced, and the flexible operation of the active power distribution network containing the data center is realized.
Disclosure of Invention
The invention aims to solve the technical problem of providing an active power distribution network operation flexibility improving method which can reduce the voltage fluctuation of an active power distribution network and the unbalanced degree of feeder load and realize the flexible operation of a system and considers the adjustment potential of a data center.
The technical scheme adopted by the invention is as follows: an active power distribution network operation flexibility improving method considering data center adjustment potential comprises the following steps:
1) Inputting basic parameter information of the active power distribution network according to the selected active power distribution network, wherein the basic parameter information comprises network topology and parameter information, an access position and data load change curve of a data center, a load access position and power change curve, a distributed power supply access position and output curve, an intelligent soft switch access position and capacity, and system reference voltage and reference power;
2) Establishing a flexible operation scheduling model of the data center according to the parameter information of the active power distribution network provided in the step 1), wherein the flexible operation scheduling model comprises the following steps: the method comprises the steps that a data center power consumption constraint, a delay sensitive data load processing delay constraint, a delay tolerant data load time transfer strategy, a data load space distribution strategy and a computing capacity constraint are based on piecewise linearization;
3) The method for establishing the power distribution network operation flexibility improvement model with the data center comprises the following steps: setting the minimum sum of the voltage deviation degree, the line load rate out-of-limit degree and the operation loss of the active power distribution network as a target function, and respectively considering the flexible operation constraint of the active power distribution network, the intelligent soft switch operation constraint and the flexible operation scheduling model constraint of the data center;
4) Solving the power distribution network operation flexibility improvement model containing the data center obtained in the step 3) by adopting a second-order cone planning method, and outputting a solving result, wherein the solving result comprises the following steps: the voltage level, the line load rate and the operation loss of the active power distribution network, the time sequence output power of the intelligent soft switch and the data load flexible scheduling strategy of the data center.
The method for improving the operation flexibility of the active power distribution network by considering the adjustment potential of the data center solves the problem of improving the operation flexibility of the active power distribution network comprising the data center on the basis of fully considering the load adjustment potential of the data center on a space-time scale, establishes an active power distribution network operation flexibility improvement model comprising the data center, obtains a data center load flexible scheduling strategy for improving the operation flexibility of the active power distribution network, reduces the voltage fluctuation and feeder load imbalance degree of the active power distribution network, and realizes the flexible operation of a system.
Drawings
FIG. 1 is a flow chart of an active power distribution network operation flexibility enhancement method of the present invention that considers data center regulation potential;
FIG. 2 is a diagram of an example of a modified IEEE33 node;
FIG. 3 is a photovoltaic, fan and load operating curve;
FIG. 4 is a data workload fluctuation curve;
FIG. 5 is a diagram of the number of active servers in a data center in scenario 2;
FIG. 6 is a data center workload storage in scenario 2;
FIG. 7 is a graph comparing data center power consumption for scenarios 1 and 2;
FIG. 8 is the active power of the intelligent soft switch transmission in scenario 2;
FIG. 9 is the reactive power delivered by the intelligent soft switch in scenario 2;
FIG. 10 is a graph comparing system losses for scenarios 1 and 2;
FIG. 11 is a graph of the maximum load rate of each line versus case 1 and 2;
fig. 12 is a comparison of the system voltage extremes for scenarios 1 and 2.
Detailed Description
The method for improving the operation flexibility of the active power distribution network considering the adjustment potential of the data center is described in detail below with reference to embodiments and drawings.
As shown in fig. 1, the method for improving the operation flexibility of the active power distribution network considering the adjustment potential of the data center includes the following steps:
1) Inputting basic parameter information of the active power distribution network according to the selected active power distribution network, wherein the basic parameter information comprises network topology and parameter information, an access position and data load change curve of a data center, a load access position and power change curve, a distributed power supply access position and output curve, an intelligent soft switch access position and capacity, and system reference voltage and reference power;
for this embodiment, the line parameters, load parameters and network topology connection relationship in the improved IEEE33 node system are first input, and the detailed parameters are shown in tables 1 to 4. The areas where the nodes 28-33 are located are supplied with direct current instead, and are interconnected with an alternating current system through intelligent soft switches SOP1 and SOP2, and the improved IEEE33 node arithmetic structure is shown in FIG. 2. Wherein SOP1 is used as balance node of DC system and adopts U dc Q control, SOP2 adopts PQ control, the voltage grade of two intelligent soft switches is 10.0kV, the capacity is 3000kVA, the upper limit of reactive power output is 500kvar, and the loss coefficient is 0.01; setting up two portalsThe server transmits data to the data center, the data center is respectively connected with nodes 29, 30 and 33, 2500 servers are installed in each sub-data center, the servers in each data center are homogeneous (the performance and the power consumption parameters are the same), and the detailed data center parameters are shown in table 5; then, respectively accessing photovoltaic systems at nodes 14 and 17 with access capacity of 800kVA, and accessing wind generation sets at nodes 15 and 32 with access capacities of 800kVA and 500kVA; and finally, setting the voltage of an alternating current system to be 12.66kV, the reference voltage of a direct current system to be 10.0kV, the reference power of the system to be 1MVA, and setting the photovoltaic, fan and load operation curves as shown in figure 3 and the data working load fluctuation curve as shown in figure 4.
Table 1 ac load access location and power in modified IEEE33 node algorithm
TABLE 2 DC load access location and power in the modified IEEE33 node algorithm
| Node numbering | Active power (kW) | Node numbering | Active power (kW) |
| 29 | 120 | 32 | 210 |
| 30 | 200 | 33 | 60 |
| 31 | 150 |
Table 3 ac line parameters in the modified IEEE33 node algorithm
Table 4 dc link parameters in the modified IEEE33 node algorithm
TABLE 5 data center operating parameters
| Electric power for single active server (kW) | 0.25/0.35/0.4 |
| Electric power for fixing data processing equipment (kW) | 50 |
| Efficiency of electric energy utilization | 1.2 |
| Efficiency of data load processing per server (request/s) | 30 |
| Data load maximum memory (request) | 2×10 9 |
2) Establishing a flexible operation scheduling model of the data center according to the parameter information of the active power distribution network provided in the step 1), wherein the flexible operation scheduling model comprises the following steps: the method comprises the steps that a data center power consumption constraint, a delay sensitive data load processing delay constraint, a delay tolerant data load time transfer strategy, a data load space distribution strategy and a computing capacity constraint are based on piecewise linearization; wherein,
(1) The data center power consumption constraint based on the piecewise linearization is expressed as follows:
in the formula,
representing the active power consumed by the data center at the node i in the period t;
representing active power consumed by a server in the data center at a node i in a period t;
the active power consumed by cooling equipment in the data center at the node i in the period t is represented;
representing the time period t in the data center at the node iActive power consumed by the auxiliary power supply device; m is i,t Representing the number of servers in an active state of the data center at the node i in the period t; k is a radical of 1 、k 2 And k 3 Respectively representing the slope of each segmented broken line; m is a unit of 1 、m 2 、m 3 And m 4 Respectively representing the abscissa values of 4 broken line points after the server equipment power consumption curve is subjected to piecewise linearization; alpha is alpha 1,i Representing a power consumption proportionality coefficient of data center equipment at a node i; beta is a 1,i And beta 2,i Respectively representing the fixed power consumption of data processing equipment and other equipment in the data center at the node i; PUE i Representing the electric energy utilization efficiency of the data center at the node i;
introduction of a continuous variable alpha i,t,k K =1,2,3,4 and binary variable γ i,t,k K =1,2,3, and the active power consumed by the server in the data center at the node i in the t period
The further linearization is expressed as:
in the formula, P k Longitudinal coordinate values of 4 broken line points after piecewise linearization on the power consumption curve of the server equipment are represented; binary variable gamma i,t,k The segmentation interval represents the number of the active servers in the t period; alpha (alpha) ("alpha") i,t,k Representing the functional relation between the server power consumption and the number of active servers in the subsection interval corresponding to the t period; m is a unit of k And the abscissa value of the kth broken line point after the server equipment power consumption curve is subjected to piecewise linearization is represented.
(2) The delay-sensitive data load processing delay constraint is expressed as:
in the formula, N s Representing the number of front-end servers; n is a radical of n Representing the total number of network nodes; d i,f,t Representing the f-type data load amount processed by the data center at the node i in the t period; lambda [ alpha ] i,δ,f,t Representing the f-type data load quantity transmitted from the front-end server delta to the data center at the node i in the t period; lambda i,j,f,t Representing the f-type data load quantity transmitted from the data center at the node j to the data center at the node i in the t period;
representing the total data load transferred from the data center at the node i to other data centers in the period t; m is i,t Representing the number of servers in an active state of the data center at the node i in the period t; mu.s i The data calculation efficiency of each server of the data center at the node i is represented; d f Indicating the delay tolerance time of the class f delay sensitive data load.
(3) The delay tolerant data load time transfer strategy and the data load space distribution strategy are respectively expressed as follows:
(a) Data load time transfer strategy:
in the formula, N s Representing the number of front-end servers; n is a radical of n Representing the total number of network nodes; n is a radical of ρ Representing a total number of data payload types; delta lambda i,f,t Representing the variable quantity of f type data load in the data center at the node i in the t period; lambda i,δ,f,t Representing the f-type data load quantity transmitted from the front-end server delta to the data center at the node i in the t period; lambda [ alpha ] i,j,f,t Representing the f-type data load quantity transmitted from the data center at the node j to the data center at the node i in the t period;
data representing the transfer of data center at node i to other data centers in the period tThe total amount of load; d i,f,t Representing the f-type data load amount processed by the data center at the node i in the t period; e i,f,t Representing the f-type data load total quantity stored in the data center at the node i in the period t; e i,f,T And E i,f,t0 Representing the total load of f-type data stored in the data center at the node i at the moment when the computing service is ended and started; Δ t represents the duration of each period; e i,max Representing the data center data load storage upper limit at the node i;
representing the total f-type data load amount which should be processed at the end time of the t' time period; l is δ,f,t Representing the f-type data load total quantity transmitted by the front-end server delta in the t period; t is t 0 Representing an initial period of operation of the data center; t is t f Representing the delay tolerant time of the f-type delay tolerant data load;
(b) Data load space allocation strategy:
in the formula, L δ,f,t Representing the f-type data load total quantity transmitted by the front-end server delta in the t period; l is a radical of an alcohol i,f,t Representing that the data center at the node i receives f-type data load total amount from other data centers in the period t; lambda [ alpha ] j,i,f,t Representing the f-type data load quantity transmitted from the data center at the node i to the data center at the node j in the period t;
and
respectively representing the data load state zone bits received and transmitted by the data center at the node i in the period of t when
When the time is 1, the time indicates that the data center at the node i receives data load transmitted from other data centers in the time period tWhen is coming into contact with
And when the time is 1, the data center at the node i transmits data load to other data centers in the time period t.
(4) The computing power constraint is expressed as:
in the formula, N ρ Representing a total number of data payload types; d i,f,t Representing the f type data load amount processed by the data center at the node i in the t period; CR i,t Representing the data calculation efficiency of the data center at the node i in the period t; mu.s i The data calculation efficiency of each server of the data center at the node i is represented; m is i,t Representing the number of servers in an active state of the data center at the node i in the period t; m i Representing the total number of data center servers at node i.
3) The method for establishing the power distribution network operation flexibility improvement model with the data center comprises the following steps: setting the minimum sum of the voltage deviation degree of the active power distribution network, the line load rate out-of-limit degree and the operation loss as a target function, and respectively considering the flexible operation constraint of the active power distribution network, the intelligent soft switch operation constraint and the flexible operation scheduling model constraint of the data center; wherein,
(1) The minimum sum of the voltage deviation degree of the active power distribution network, the line load rate out-of-limit degree and the running loss is expressed as a target function:
wherein C represents an objective function; c B Representing the line load rate out-of-limit degree; c V Indicating the degree of voltage deviation; c P RepresentData center power consumption; c I Representing network operating losses; omega 1 、ω 2 、ω 3 And ω 4 Respectively represent C B 、C V 、C P 、C I The weight parameter of (a); n is a radical of hydrogen T Represents the total number of time segments; n is a radical of L Representing the total number of network lines; n is a radical of n Representing the total number of network nodes;
represents the load margin of the line ij during the period t;
representing the voltage margin of node i during the period t; l ij,t Represents the square of the current amplitude of the line ij in the period t; v. of i,t Represents the square of the voltage amplitude of the node i in the period t;
represents a desired threshold of current for line ij;
and
respectively representing desired threshold values of the node voltages;
representing the active power consumed by the data center at the node i in the period t; r is ij Representing the resistance value of line ij.
(2) The active power distribution network flexible operation constraint is expressed as follows:
in the formula,
and
representing a set of alternating current and direct current lines; omega ac And Ω dc Representing a set of alternating current and direct current nodes; p ij,t And P jh,t The active power transmitted by the line ij and the line jh in the t period is represented; q ij,t And Q jh,t Representing the reactive power transmitted by the line ij and the line jh in the period t; p j,t And Q j,t Representing active power and reactive power injected by a node j in a period t; l ij,t Represents the square of the current amplitude of line ij during period t; v. of i,t Represents the square of the voltage amplitude of the node i in the period t; r is ij Represents the resistance value of the line ij; x is the number of ij Represents the reactance value of line ij;
and
representing active power and reactive power injected by the distributed power supply at the node j in the period t;
and
representing active and reactive loads at a node j in a period t;
representing the active power consumed by the data center at the node j in the period t;
and
the active power and the reactive power transmitted by a j-side port of the intelligent soft switch node in the t period are represented; v min And V max Indicating node electricityPressing an upper limit and a lower limit of the amplitude value; i is ij,max Representing the maximum value of the current on line ij.
(3) The intelligent soft switch operation constraint is expressed as:
in the formula, omega ac And Ω dc Representing a set of ac and dc nodes;
and
the active power transmitted by the side ports of the intelligent soft switch node i and the node j in the t period is represented;
the reactive power transmitted by the i-side port of the intelligent soft switch node in the t period is represented; a. The i Representing the loss coefficient of the i-side converter of the intelligent soft switching node;
representing the capacity of an i-side port of the intelligent soft switch node;
and
and the reactive compensation upper and lower limits of the i-side port of the intelligent soft switch node are represented.
4) Solving the power distribution network operation flexibility improvement model containing the data center obtained in the step 3) by adopting a second-order cone planning method, and outputting a solving result, wherein the solving result comprises the following steps: the voltage level, the line load rate and the operation loss of the active power distribution network, the time sequence output power of the intelligent soft switch and the data load flexible scheduling strategy of the data center.
In order to fully verify the advancement of the method of the present invention, in this example, the following two schemes are adopted for comparative analysis:
scheme 1: the data center load is not flexibly scheduled, and the initial running state of the active power distribution network is obtained;
scheme 2: the method of the invention is adopted to flexibly adjust the strategy of the data center to improve the operation flexibility of the active power distribution network;
the optimization results of the scheme 1 and the scheme 2 are compared in a table 6, the flexible scheduling strategy of the data center working load in the scheme 2 is shown in a figure 5 and a figure 6, the power consumption of the data center in the two schemes is shown in a figure 7, the active power and the reactive power generated by the intelligent soft switch are shown in a figure 8 and a figure 9, the system loss condition in the two schemes is shown in a figure 10, the maximum load rate condition of each line is shown in a figure 11, and the extreme voltage condition of the system at each moment is shown in a figure 12.
Table 6 comparison of operation results of active power distribution network including data center
The computer hardware environment for executing the optimized calculation is Intel (R) Xeon (R) CPU E5-1620, the main frequency is 3.70GHz, and the internal memory is 32GB; the software environment is a Windows 10 operating system.
Compared with the scheme 1 and the scheme 2, the method has the advantages that the load adjusting potential of the data center is fully developed from the angles of time transfer and space distribution, the working load of the data center is flexibly scheduled, the line load imbalance degree caused by the fact that the data center is connected into a power distribution network can be effectively reduced, meanwhile, a good loss reduction effect can be obtained, the problem of voltage fluctuation of the power distribution network is solved, the voltage deviation of a system is reduced, and the flexible operation of the system is guaranteed.
Therefore, by flexibly scheduling the data loads on a time-space scale, the power consumption of the data center is reduced while the voltage fluctuation of the active power distribution network and the unbalanced degree of the feeder loads are effectively reduced, and the coordinated and flexible operation of the active power distribution network and the data center is realized.
When the data center participates in operation scheduling of the power distribution network, the fluctuation of data load of the data center brings new challenges to flexible operation of the power distribution network. Further research on a distributed robust operation strategy of the data center is needed to deal with uncertainty of real-time data load and achieve flexible and efficient operation of an active power distribution network including the data center.
Claims (2)
1. An active power distribution network operation flexibility improving method considering data center adjustment potential is characterized by comprising the following steps:
1) Inputting basic parameter information of the active power distribution network according to the selected active power distribution network, wherein the basic parameter information comprises network topology and parameter information, an access position and data load change curve of a data center, a load access position and power change curve, a distributed power supply access position and output curve, an intelligent soft switch access position and capacity, and system reference voltage and reference power;
2) Establishing a flexible operation scheduling model of the data center according to the parameter information of the active power distribution network provided in the step 1), wherein the flexible operation scheduling model comprises the following steps: the method comprises the steps that a data center power consumption constraint, a delay sensitive data load processing delay constraint, a delay tolerant data load time transfer strategy, a data load space distribution strategy and a computing capacity constraint are based on piecewise linearization; wherein,
the data center power consumption constraint based on the piecewise linearization is expressed as follows:
in the formula (1), the reaction mixture is,
representing the active power consumed by the data center at the node i in the period t;
representing active power consumed by a server in the data center at a node i in a period t;
represents the t period sectionActive power consumed by cooling equipment in the data center at point i;
the active power consumed by auxiliary power supply equipment in the data center at a node i in a period t is represented; m is i,t Representing the number of servers in an active state of the data center at the node i in the period t; k is a radical of formula 1 、k 2 And k 3 Respectively representing the slope of each segmented broken line; m is 1 、m 2 、m 3 And m 4 Respectively representing the abscissa values of 4 broken line points after the server equipment power consumption curve is subjected to piecewise linearization; alpha (alpha) ("alpha") 1,i Representing a power consumption proportional coefficient of data center equipment at a node i; beta is a 1,i And beta 2,i Respectively representing the fixed power consumption of a server and other equipment in the data center at the node i; PUE i Representing the electric energy utilization efficiency of the data center at the node i;
introduction of a continuous variable alpha i,t,k K =1,2,3,4 and binary variable γ i,t,l L =1,2,3, and the active power consumed by the server in the data center at the node i in the period t
The further linearization is expressed as:
in the formula (2), P k A longitudinal coordinate value of a k-th broken line point after the piecewise linearization on the power consumption curve of the server equipment is represented; binary variable gamma i,t,l The segment interval l represents the number of the data center active servers at the node i in the period t; alpha is alpha i,t,k Representing active power consumed by data center server at node i in t period
Middle P k The weight parameter of (2); m is k Abscissa representing kth broken line point after server equipment power consumption curve piecewise linearizationA value;
the delay-sensitive data load processing delay constraint is expressed as:
in the formula (3), N s Representing the number of front-end servers; n is a radical of hydrogen n Representing the total number of network nodes; d i,f,t Representing the f-type delay sensitive data load amount processed by the data center at the node i in the t period; lambda [ alpha ] i,δ,f,t Representing the f-type delay sensitive data load quantity transmitted from the front-end server delta to the data center at the node i in the t period; lambda [ alpha ] i,j,f,t Representing the f-type delay sensitive data load quantity transmitted from the data center at the node j to the data center at the node i in the t period;
representing the f-type delay sensitive data load total amount transferred from the data center at the node i to other data centers in the t period; m is i,t Representing the number of servers in an active state of the data center at the node i in the period t; mu.s i The data calculation efficiency of each server of the data center at the node i is represented; d f Representing the delay tolerance time of the f-type delay sensitive data load;
the delay tolerant data load time transfer strategy and the data load space distribution strategy are respectively expressed as follows:
delay tolerant data load time transfer strategy:
the data load space allocation strategy is as follows:
in the formulae (4) and (5), N s Representing the number of front-end servers; n is a radical of hydrogen n Representing the total number of network nodes; n is a radical of hydrogen ρ Representing the total number of type f delay tolerant data payload types; delta lambda i,f,t Representing the variation of f-type delay tolerant data loads in the data center at a node i in a t period; lambda [ alpha ] i,δ,f,t Representing f-type delay tolerant data load quantity transmitted from the front-end server delta to the data center at the node i in the t period; lambda i,j,f,t Representing the f-type delay tolerant data load quantity transmitted from the data center at the node j to the data center at the node i in the t period;
representing the f-type delay tolerant data load total amount transferred from the data center at the node i to other data centers in the t period; d i,f,t Representing the f-type delay tolerant data load amount processed by the data center at the node i in the t period; e i,f,t Representing the f-type delay tolerant data load total amount stored in the data center at the node i in the t period; e i,f,T And
representing the f-type delay tolerant data load total quantity stored in the data center at the node i at the ending and starting time of the calculation service; Δ t represents the duration of each period; e i,max Representing the data center data load storage upper limit at the node i;
representing the f-type delay tolerant data load total amount which is processed at the end time of the t' time period; l is δ,f,t F-type delay tolerant data negative for representing delta transmission of front-end server in t periodTotal loading amount; t is t 0 Representing an initial period of operation of the data center; t is t f Representing the delay tolerance time of the f-type delay tolerant data load; l is a radical of an alcohol i,f,t Representing that the data center at the node i in the period t receives the f-type delay tolerant data load total amount from other data centers; lambda j,i,f,t Representing the f-type delay tolerant data load quantity transmitted from the data center at the node i to the data center at the node j in the t period;
and
respectively representing the data load state zone bits received and transmitted by the data center at the node i in the period of t when
When the time is 1, the data center at the node i receives data load transmitted from other data centers in the time period t
When the time is 1, the data load is transmitted from the data center at the node i to other data centers at the time interval t;
the computing power constraint is expressed as:
in the formula (6), the reaction mixture is,
representing the total number of f-type data load types, including f-type delay sensitive type and f-type delay tolerant type; d i,f,t Representing the f-type data load amount processed by the data center at the node i in the t period; CR i,t Representing the data calculation efficiency of the data center at the node i in the period t; mu.s i The data calculation efficiency of each server of the data center at the node i is represented; m is i,t Representing the number of servers in an active state of the data center at the node i in the period t; m i Representing the total number of data center servers at the node i;
3) The method for establishing the power distribution network operation flexibility improvement model with the data center comprises the following steps: setting the minimum sum of the voltage deviation degree, the line load rate out-of-limit degree and the operation loss of the active power distribution network as a target function, and respectively considering the flexible operation constraint of the active power distribution network, the intelligent soft switch operation constraint and the flexible operation scheduling model constraint of the data center;
4) Solving the power distribution network operation flexibility improvement model containing the data center obtained in the step 3) by adopting a second-order cone planning method, and outputting a solving result, wherein the solving result comprises the following steps: the voltage level, the line load rate and the operation loss of the active power distribution network, the time sequence output power of the intelligent soft switch and the data load flexible scheduling strategy of the data center.
2. The method for improving the operation flexibility of the active power distribution network considering the adjustment potential of the data center according to claim 1, wherein the step 3) of setting the minimum sum of the voltage deviation degree of the active power distribution network, the line load rate out-of-limit degree and the operation loss as an objective function is represented as follows:
in the formula (7), C represents an objective function; c B Representing the line load rate out-of-limit degree; c V Indicating the degree of voltage deviation; c P Representing data center power consumption; c I Representing network operating losses; omega 1 、ω 2 、ω 3 And ω 4 Respectively represent C B 、C V 、C P 、C I The weight parameter of (2); n is a radical of hydrogen T Represents the total number of time segments; n is a radical of hydrogen L Representing the total number of network lines; n is a radical of n Representing the total number of network nodes;
when represents tLoad margin of segment line ij;
representing the voltage margin of node i during the period t; l. the ij,t Represents the square of the current amplitude of the line ij in the period t; v. of i,t Represents the square of the voltage amplitude of the node i in the period t;
represents a desired threshold of current for line ij;
and
respectively representing desired threshold values of the node voltages;
representing the active power consumed by the data center at the node i in the period t; r is a radical of hydrogen ij Representing the resistance value of line ij.
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