CN112103997A - 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 PDF

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CN112103997A
CN112103997A CN202010921222.2A CN202010921222A CN112103997A CN 112103997 A CN112103997 A CN 112103997A CN 202010921222 A CN202010921222 A CN 202010921222A CN 112103997 A CN112103997 A CN 112103997A
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CN112103997B (en
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冀浩然
陈思睿
王成山
李鹏
赵金利
宋关羽
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Tianjin University
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381—Dispersed generators
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
    • H02J3/14—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by switching loads on to, or off from, network, e.g. progressively balanced loading
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/22—The renewable source being solar energy
    • H02J2300/24—The renewable source being solar energy of photovoltaic origin
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/28—The renewable source being wind energy
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2310/00—The network for supplying or distributing electric power characterised by its spatial reach or by the load
    • H02J2310/50—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B70/00—Technologies for an efficient end-user side electric power management and consumption
    • Y02B70/30—Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
    • Y02B70/3225—Demand response systems, e.g. load shedding, peak shaving
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/50—Photovoltaic [PV] energy
    • Y02E10/56—Power conversion systems, e.g. maximum power point trackers
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
    • Y04S20/20—End-user application control systems
    • Y04S20/222—Demand response systems, e.g. load shedding, peak shaving

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

Active power distribution network operation flexibility improving method considering data center adjustment potential
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 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 in the 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 mainly focus on price-based data center demand response, and time distribution of data center loads is guided 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 improved IEEE33 node algorithm;
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 of the invention 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 an improved IEEE33 node arithmetic structure is shown in FIG. 2. The SOP1 is used as a balance node of a direct current system, and U is adopteddcQ 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 factor is 0.01; two portal servers are set to transmit data to a data center, the data center is connected to nodes 29, 30 and 33 respectively, 2500 servers are installed in each sub-data center, the servers in each data center are identical (the performance and the power consumption parameters are identical), and the detailed data center parameters are shown in a 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 500 kVA; 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
Figure BDA0002666792720000031
Table 2 dc load access position and power in modified IEEE33 node calculation
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
Figure BDA0002666792720000032
Figure BDA0002666792720000041
Table 4 dc link parameters in the modified IEEE33 node algorithm
Figure BDA0002666792720000042
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) 2×109
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 content of the first and second substances,
(1) the data center power consumption constraint based on the piecewise linearization is expressed as follows:
Figure BDA0002666792720000043
in the formula (I), the compound is shown in the specification,
Figure BDA0002666792720000044
representing the active power consumed by the data center at the node i in the period t;
Figure BDA0002666792720000045
representing active power consumed by a server in the data center at a node i in a period t;
Figure BDA0002666792720000046
the active power consumed by cooling equipment in the data center at the node i in the period t is represented;
Figure BDA0002666792720000047
the active power consumed by auxiliary power supply equipment in the data center at a node i in a period t is represented; m isi,tRepresenting the number of servers in an active state of the data center at the node i in the period t; k is a radical of1、k2And k3Respectively representing the slope of each segmented broken line; m is1、m2、m3And m4Respectively representing the abscissa values of 4 broken line points after the server equipment power consumption curve is subjected to piecewise linearization; alpha is alpha1,iRepresenting a power consumption proportionality coefficient of data center equipment at a node i; beta is a1,iAnd beta2,iRespectively representing the fixed power consumption of data processing equipment and other equipment in the data center at the node i; PUEiRepresenting the electric energy utilization efficiency of the data center at the node i;
introduction of a continuous variable alphai,t,kK is 1,2,3,4 and a binary variable γi,t,kAnd k is 1,2 and 3, and the active power consumed by the server in the data center at the node i in the period t
Figure BDA0002666792720000051
The further linearization is expressed as:
Figure BDA0002666792720000052
in the formula, PkThe longitudinal coordinate values of 4 broken line points after the piecewise linearization on the power consumption curve of the server equipment are represented; binary variable gammai,t,kThe segmentation interval represents the number of the active servers in the t period; alpha is alphai,t,kRepresenting the functional relation between the server power consumption and the number of active servers in the subsection interval corresponding to the t period; m iskAnd 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:
Figure BDA0002666792720000053
in the formula, NsRepresenting the number of front-end servers; n is a radical ofnRepresenting the total number of network nodes; di,f,tRepresenting the f-type data load amount processed by the data center at the node i in the t period; lambda [ alpha ]i,,f,tRepresenting f-type data load quantity transmitted to a data center at a node i by a front-end server in a period t; lambda [ alpha ]i,j,f,tRepresenting 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;
Figure BDA0002666792720000054
representing the total data load transferred from the data center at the node i to other data centers in the period t; m isi,tRepresenting the number of servers in an active state of the data center at the node i in the period t; mu.siThe data calculation efficiency of each server of the data center at the node i is represented; dfIndicating 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:
Figure BDA0002666792720000055
Figure BDA0002666792720000061
in the formula, NsRepresenting the number of front-end servers; n is a radical ofnRepresenting the total number of network nodes; n is a radical ofρRepresenting a total number of data payload types; delta lambdai,f,tRepresenting the variation of f type data load in the data center at a node i in the period t; lambda [ alpha ]i,,f,tRepresenting f-type data load quantity transmitted to a data center at a node i by a front-end server in a period t; lambda [ alpha ]i,j,f,tRepresenting 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;
Figure BDA0002666792720000062
representing the total data load transferred from the data center at the node i to other data centers in the period t; di,f,tRepresenting the f-type data load amount processed by the data center at the node i in the t period; ei,f,tRepresenting the total load of f-type data stored in the data center at the node i in the period t; ei,f,TAnd Ei,f,t0Representing 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; ei,maxRepresenting the data center data load storage upper limit at the node i;
Figure BDA0002666792720000063
representing the total f-type data load amount which should be processed at the end time of the t' time period; l is,f,tRepresenting the total load of f-type data transmitted by the front-end server in the t period; t is t0Representing an initial period of operation of the data center; t is tfRepresenting the delay tolerant time of the f-type delay tolerant data load;
(b) data load space allocation strategy:
Figure BDA0002666792720000064
in the formula, L,f,tFront-end garment capable of indicating t time periodThe total load of f-type data transmitted by the server; l isi,f,tRepresenting 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,tRepresenting 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;
Figure BDA0002666792720000065
and
Figure BDA0002666792720000066
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
Figure BDA0002666792720000067
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
Figure BDA0002666792720000068
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:
Figure BDA0002666792720000069
Figure BDA0002666792720000071
in the formula, NρRepresenting a total number of data payload types; di,f,tRepresenting the f-type data load amount processed by the data center at the node i in the t period; CRi,tRepresenting the data calculation efficiency of the data center at the node i in the period t; mu.siThe data calculation efficiency of each server of the data center at the node i is represented; m isi,tRepresenting the number of servers in an active state of the data center at the node i in the period t; miRepresenting 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, 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; wherein the content of the first and second substances,
(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:
Figure BDA0002666792720000072
wherein C represents an objective function; cBRepresenting the line load rate out-of-limit degree; cVIndicating the degree of voltage deviation; cPRepresenting data center power consumption; cIRepresenting network operating losses; omega1、ω2、ω3And ω4Respectively represent CB、CV、CP、CIThe weight parameter of (2); n is a radical ofTRepresents the total number of time segments; n is a radical ofLRepresenting the total number of network lines; n is a radical ofnRepresenting the total number of network nodes;
Figure BDA0002666792720000073
represents the load margin of the line ij during the period t;
Figure BDA0002666792720000074
representing the voltage margin of node i during the period t; lij,tRepresents the square of the current amplitude of the line ij in the period t; v. ofi,tRepresents the square of the voltage amplitude of the node i in the period t;
Figure BDA0002666792720000075
represents a desired threshold of current for line ij;
Figure BDA0002666792720000076
and
Figure BDA0002666792720000077
respectively representing desired threshold values of the node voltages;
Figure BDA0002666792720000078
representing the active power consumed by the data center at the node i in the period t; r isijRepresenting the resistance value of line ij.
(2) The flexible operation constraint of the active power distribution network is expressed as follows:
Figure BDA0002666792720000079
Figure BDA0002666792720000081
in the formula (I), the compound is shown in the specification,
Figure BDA0002666792720000082
and
Figure BDA0002666792720000083
representing a set of ac and dc lines; omegaacAnd ΩdcRepresenting a set of ac and dc nodes; pij,tAnd Pjh,tThe active power transmitted by the line ij and the line jh in the t period is represented; qij,tAnd Qjh,tRepresenting the reactive power transmitted by the line ij and the line jh in the period t; pj,tAnd Qj,tRepresenting the active power and reactive power injected by the node j in the period t; lij,tRepresents the square of the current amplitude of line ij during period t; v. ofi,tRepresents the square of the voltage amplitude of the node i in the period t; r isijRepresents the resistance value of the line ij; x is the number ofijRepresents the reactance value of line ij;
Figure BDA0002666792720000084
and
Figure BDA0002666792720000085
representing active power and reactive power injected by the distributed power supply at the node j in the period t;
Figure BDA0002666792720000086
and
Figure BDA0002666792720000087
representing active and reactive loads at a node j in a period t;
Figure BDA0002666792720000088
representing the active power consumed by the data center at the node j in the period t;
Figure BDA0002666792720000089
and
Figure BDA00026667927200000810
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; vminAnd VmaxRepresenting the upper and lower limits of the node voltage amplitude; i isij,maxRepresenting the maximum value of the current on line ij.
(3) The intelligent soft switch operation constraint is expressed as:
Figure BDA00026667927200000811
in the formula, omegaacAnd ΩdcRepresenting a set of ac and dc nodes;
Figure BDA00026667927200000812
and
Figure BDA00026667927200000813
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;
Figure BDA00026667927200000814
the reactive power transmitted by the i-side port of the intelligent soft switch node in the t period is represented; a. theiRepresenting the loss coefficient of the i-side converter of the intelligent soft switching node;
Figure BDA00026667927200000815
representing the capacity of an i-side port of the intelligent soft switch node;
Figure BDA00026667927200000816
and
Figure BDA00026667927200000817
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 shown in a table 6 in a comparison manner, the flexible scheduling strategy of the data center working load in the scheme 2 is shown in a figure 5 and a figure 6 in a comparison manner, the power consumption of the data center in the two schemes is shown in a figure 7 in a comparison manner, the active power and the reactive power generated by the intelligent soft switch are shown in a figure 8 and a figure 9 in a comparison manner, the system loss condition in the two schemes is shown in a figure 10 in a comparison manner, the maximum load rate condition of each line is shown in a figure 11 in a.
Table 6 comparison of operation results of active power distribution network including data center
Figure BDA0002666792720000091
The computer hardware environment for executing the optimization calculation is Intel (R) Xeon (R) CPU E5-1620, the main frequency is 3.70GHz, and the memory is 32 GB; the software environment is a Windows 10 operating system.
Compared with the scheme 1 and the scheme 2, the load regulation potential of the data center is fully developed from the angles of time transfer and space distribution, the flexible scheduling of the working load of the data center is carried out, the unbalanced line load 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 the 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 realize flexible and efficient operation of the active power distribution network including the data center.

Claims (6)

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;
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 2) of the piecewise linearization-based data center power consumption constraint is expressed as:
Figure FDA0002666792710000011
in the formula (I), the compound is shown in the specification,
Figure FDA0002666792710000012
representing the active power consumed by the data center at the node i in the period t;
Figure FDA0002666792710000013
representing active power consumed by a server in the data center at a node i in a period t;
Figure FDA0002666792710000014
the active power consumed by cooling equipment in the data center at the node i in the period t is represented;
Figure FDA0002666792710000015
the active power consumed by auxiliary power supply equipment in the data center at a node i in a period t is represented; m isi，tRepresenting the number of servers in an active state of the data center at the node i in the period t；k1、k2And k3Respectively representing the slope of each segmented broken line; m is1、m2、m3And m4Respectively representing the abscissa values of 4 broken line points after the server equipment power consumption curve is subjected to piecewise linearization; alpha is alpha1，iRepresenting a power consumption proportionality coefficient of data center equipment at a node i; beta is a1，iAnd beta2，iRespectively representing the fixed power consumption of data processing equipment and other equipment in the data center at the node i; PUEiRepresenting the electric energy utilization efficiency of the data center at the node i;
introduction of a continuous variable alphai，t，kK is 1,2,3,4 and a binary variable γi，t，kAnd k is 1,2 and 3, and the active power consumed by the server in the data center at the node i in the period t
Figure FDA0002666792710000016
The further linearization is expressed as:
Figure FDA0002666792710000021
in the formula, PkThe longitudinal coordinate values of 4 broken line points after the piecewise linearization on the power consumption curve of the server equipment are represented; binary variable gammai，t，kThe segmentation interval represents the number of the active servers in the t period; alpha is alphai，t，kRepresenting the functional relation between the server power consumption and the number of active servers in the subsection interval corresponding to the t period; m iskAnd the abscissa value of the kth broken line point after the server equipment power consumption curve is subjected to piecewise linearization is represented.
3. 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 delay-sensitive data load processing delay constraint in the step 2) is expressed as:
Figure FDA0002666792710000022
in the formula, NsRepresenting the number of front-end servers; n is a radical ofnRepresenting the total number of network nodes; di，f，tRepresenting the f-type data load amount processed by the data center at the node i in the t period; lambda [ alpha ]i，，f，tRepresenting f-type data load quantity transmitted to a data center at a node i by a front-end server in a period t; lambda [ alpha ]i，j，f，tRepresenting 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;
Figure FDA0002666792710000023
representing the total data load transferred from the data center at the node i to other data centers in the period t; m isi，tRepresenting the number of servers in an active state of the data center at the node i in the period t; mu.siThe data calculation efficiency of each server of the data center at the node i is represented; dfIndicating the delay tolerance time of the class f delay sensitive data load.
4. 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 delay tolerant data load time transfer strategy and the data load space distribution strategy in step 2) are respectively expressed as:
data load time transfer strategy:
Figure FDA0002666792710000024
Figure FDA0002666792710000031
in the formula, NsRepresenting the number of front-end servers; n is a radical ofnRepresenting the total number of network nodes; n is a radical ofρRepresenting a total number of data payload types; delta lambdai，f，tRepresenting the variation of f type data load in the data center at a node i in the period t; lambda [ alpha ]i，，f，tFront-end server bar for representing t time periodF-type data load transmitted by the data center at the point i; lambda [ alpha ]i，j，f，tRepresenting 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;
Figure FDA0002666792710000032
representing the total data load transferred from the data center at the node i to other data centers in the period t; di，f，tRepresenting the f-type data load amount processed by the data center at the node i in the t period; ei，f，tRepresenting the total load of f-type data stored in the data center at the node i in the period t; ei，f，TAnd
Figure FDA00026667927100000310
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; ei，maxRepresenting the data center data load storage upper limit at the node i;
Figure FDA0002666792710000033
representing the total f-type data load amount which should be processed at the end time of the t' time period; l is，f，tRepresenting the total load of f-type data transmitted by the front-end server in the t period; t is t0Representing an initial period of operation of the data center; t is tfRepresenting the delay tolerant time of the f-type delay tolerant data load;
data load space allocation strategy:
Figure FDA0002666792710000034
in the formula, L，f，tRepresenting the total load of f-type data transmitted by the front-end server in the t period; l isi，f，tRepresenting 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，tRepresenting 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;
Figure FDA0002666792710000035
and
Figure FDA0002666792710000036
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
Figure FDA0002666792710000037
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
Figure FDA0002666792710000038
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.
5. 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 computing capacity constraint in the step 2) is expressed as:
Figure FDA0002666792710000039
mi，t≤Mi
in the formula, NρRepresenting a total number of data payload types; di，f，tRepresenting the f-type data load amount processed by the data center at the node i in the t period; CRi，tRepresenting the data calculation efficiency of the data center at the node i in the period t; mu i represents the data calculation efficiency of each server of the data center at the node i; m isi，tRepresenting the number of servers in an active state of the data center at the node i in the period t; miRepresenting the total number of data center servers at node i.
6. 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:
Figure FDA0002666792710000041
wherein C represents an objective function; cBRepresenting the line load rate out-of-limit degree; cVIndicating the degree of voltage deviation; cPRepresenting data center power consumption; cIRepresenting network operating losses; omega1、ω2、ω3And ω4Respectively represent CB、CV、CP、CIThe weight parameter of (2); n is a radical ofTRepresents the total number of time segments; n is a radical ofLRepresenting the total number of network lines; n is a radical ofnRepresenting the total number of network nodes;
Figure FDA0002666792710000042
represents the load margin of the line ij during the period t;
Figure FDA0002666792710000043
representing the voltage margin of node i during the period t; lij，tRepresents the square of the current amplitude of the line ij in the period t; v. ofi，tRepresents the square of the voltage amplitude of the node i in the period t;
Figure FDA0002666792710000044
represents a desired threshold of current for line ij;
Figure FDA0002666792710000045
and
Figure FDA0002666792710000046
respectively representing desired threshold values of the node voltages;
Figure FDA0002666792710000047
active power consumed by data center at node i in t periodPower; r isijRepresenting the resistance value of line ij.
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113394786A (en) * 2021-07-20 2021-09-14 天津大学 Data center energy supply system economic operation method considering data load uncertainty

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20090235097A1 (en) * 2008-03-14 2009-09-17 Microsoft Corporation Data Center Power Management
CN107482766A (en) * 2017-07-05 2017-12-15 国网江苏省电力公司经济技术研究院 Electric power system dispatching method based on data network and the interactive operation of electric power networks
CN107644118A (en) * 2017-08-04 2018-01-30 天津大学 A kind of intelligent power distribution Sofe Switch timing optimization method of integrated energy storage
CN110084444A (en) * 2019-05-27 2019-08-02 华北电力大学 A kind of cloud data center power load dispatching method considering natural resources randomness

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20090235097A1 (en) * 2008-03-14 2009-09-17 Microsoft Corporation Data Center Power Management
CN107482766A (en) * 2017-07-05 2017-12-15 国网江苏省电力公司经济技术研究院 Electric power system dispatching method based on data network and the interactive operation of electric power networks
CN107644118A (en) * 2017-08-04 2018-01-30 天津大学 A kind of intelligent power distribution Sofe Switch timing optimization method of integrated energy storage
CN110084444A (en) * 2019-05-27 2019-08-02 华北电力大学 A kind of cloud data center power load dispatching method considering natural resources randomness

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
孙充勃等: "基于SOP的多电压等级混联配电网运行二阶锥规划方法", 《电网技术》 *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113394786A (en) * 2021-07-20 2021-09-14 天津大学 Data center energy supply system economic operation method considering data load uncertainty
CN113394786B (en) * 2021-07-20 2022-09-23 天津大学 Data center energy supply system economic operation method considering data load uncertainty

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