CN116454988A - Day-ahead optimal operation method and system for power distribution network, storage medium and computing equipment - Google Patents
Day-ahead optimal operation method and system for power distribution network, storage medium and computing equipment Download PDFInfo
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Abstract
The invention discloses a method, a system, a storage medium and a computing device for optimizing operation of a power distribution network in the future, wherein step 1 is used for acquiring data load quantity of each period of the next day and reference electricity price of a data center; step 2, determining a distribution plan of data loads of all time periods of the next day; step 3, carrying out safety verification on the distribution plan, if overload of the distribution network line is not caused, completing daily optimization according to the distribution plan, if overload of the distribution network line is caused, adding line overload constraint, then re-determining the distribution plan of data loads in each time period of the next day, and calculating the guide electricity price; and 4, returning to the step 2, and after replacing the reference electricity price of the data center by the guide electricity price, controlling the data center operator to re-optimize the distribution plan of the data load of each time period of the next day according to the guide electricity price. The advantages are that: the power price guiding mechanism enables the data center operator and the power grid company to realize the optimized operation of the power distribution network in the future after one-time interaction, and the complexity of the optimized operation of the power distribution network in the future is greatly reduced.
Description
Technical Field
The invention relates to a method, a system, a storage medium and computing equipment for day-ahead optimal operation of a power distribution network, and belongs to the technical field of day-ahead optimal operation of power distribution networks.
Background
Along with the continuous improvement of the permeability of the distributed power supply and the flexible load in the power distribution network, the power consumption plan made by different participants in the power market in the gradual process may cause the power flow space of the power distribution network to be unevenly distributed, and further cause the occurrence of the line overload problem. The data center is an increasingly important flexible resource, and has important practical significance in researching and guiding the operation mode of the data center to realize the daily optimized operation of the power distribution network.
The data center is used as a flexible resource, and the outstanding characteristic is the space transfer characteristic of the load, namely the effect of balancing the area load is realized by transferring the data load among different data centers. The energy consumption characteristics of the data center are partially researched and modeled, but the bottom object of the energy consumption model is an individual server, the influence of specific working conditions of the CPU such as working frequency and service rate parameters cannot be carefully considered, and the model is rough. There have also been studies that began with a CPU level to characterize the data center model, but where a constant set of active servers greatly constrained their tuning capabilities. In addition, some researchers encapsulate the data center energy consumption model and research the relation between the adjustment potential and the data load delay limit, but consider the working frequency of the server as a fixed value, and the widely-used CPU dynamic frequency modulation technology is not combined. In summary, research on energy consumption characteristics of the visible data center is yet to be explored.
Since the flexible resource owners are not the same benefit agents as the data center operators and the grid managers, it is not practical to apply the conventional centralized solution to the power system optimization problem including the data center. Considering that economy is often used as a unified pursuit target of a flexible resource owner, students put forward to influence the distribution of flexible resources through subsidy excitation or electricity price guiding modes, but the subsidy or electricity price can be determined only by multiple interactions between a power grid company and a data center operator, so that the complexity of optimizing operation management flow is increased. How to consider the bottom constraint of flexible resources when making the boot price, and simultaneously reduce the complexity of the optimized operation flow is needed to be solved.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a day-ahead optimal operation method, a day-ahead optimal operation system, a storage medium and a computing device for a power distribution network.
In order to solve the technical problems, the invention provides a day-ahead optimal operation method of a power distribution network, which comprises the following steps:
step 1, acquiring the data load quantity of each time period of the next day and the reference electricity price of a data center;
step 2, determining a distribution plan of the data load of each time period of the next day by combining the data load of each time period of the next day and the reference electricity price of the data center based on the data center energy consumption model and the optimal working mode of the server;
step 3, carrying out safety check on the distribution plan of the data load of each time period of the next day,
if the distribution plan does not cause overload of the distribution network lines, day-ahead optimization is completed according to the distribution plan,
if the distribution plan causes overload of the power distribution network line, after adding line overload constraint, re-determining a distribution plan of data loads of each time period of the next day, and calculating a guide electricity price according to the re-determined distribution plan of the data loads of each time period of the next day;
and 4, returning to the step 2, and after replacing the reference electricity price of the data center by the guide electricity price, controlling the data center operator to re-optimize the distribution plan of the data load of each time period of the next day according to the guide electricity price.
Further, the acquiring the data load amount of each time period of the next day and the reference electricity price of the data center node includes:
the data load amount of each time period of the next day provided by the data center operator and the reference electricity price of the data center node are obtained.
Further, the data center energy consumption model is expressed as:
wherein P is i data An active load for data center i; PUE (PUE) i An energy efficiency index for data center i; s is(s) ij The number of active servers of the j-type server group in the data center i;the power consumption of other constant-value components of the j-type server in the data center i; k (K) ij CPU energy consumption coefficient of j-type server in data center i with service rate as object; mu (mu) ij The service rate of the j-type server CPU in the data center i; lambda (lambda) ij The j-type server group in the data center i bears the total data load; n (N) i Number of internal server models for data center i.
Further, the optimal working mode of the server is an optimal working mode of the server considering CPU service rate, and the CPU service rate of the server is the optimal working modeExpressed as:
wherein,,the service rate lower limit and the service rate upper limit of the j-type server are set; d is the average response time of the server to process the data load; mu' j The ideal service rate for the j-type server is as follows:
wherein,,the power consumption of other constant-value components of the j-type server of the data center is calculated; k (K) j CPU energy consumption coefficient taking service rate as object for j-type server of data center;
the optimal operation modes include:
first caseAnd->The maximum number of server activities is set to +.>When->When the data load is increased, only the number of active servers is increased; when (when)When the number of active servers reaches the upper limit, the service rate is increased to meet the requirement of increasing the data load;
second caseAnd->Under, the server fixes the service rate +.>Invariably, only increasing the number of active servers until the upper limit is reached when the data load increases;
third caseAnd the maximum service rate of the server cannot meet the response time requirement, and the server is stopped.
Further, in the step 2, the optimization problem for determining the distribution plan of the data load of each period of the next day is expressed as:
wherein alpha is i,t The reference price of the data center i at the moment t; beta t Is a price sensitivity coefficient;the active load of the data center i at the moment t; n is the total number of data centers in the system;
the constraints are:
wherein lambda is i,t The data load accepted by the data center i at the moment t;for the data load accepted by all data centers at time t, lambda ij,t The data load accepted by the j-type server group in the data center i at the moment t is; /> Adjusting upper and lower limits, mu, for service rates of j-type servers in data center i ij,t For the service rate of j-type server in data center i at time t,/>An upper limit on the number of j-type servers in the data center i; s is(s) ij,t The number of j-type active servers in the data center i; d is the average response time of the server to handle the data load.
Further, the calculation of the guided electricity price is expressed as:
wherein,,a leading electricity price for data center i; r is the consumption micro-increment coefficient; l (L) i Is in the dataLoad factor of heart i, P i data Active load for data center inode; lambda (lambda) i A data load received by the data center i; lambda (lambda) ij Data load received by the j-type server group in the data center i; lambda (lambda) i ' is the data load accepted by the data center i calculated after adding the security constraint; lambda (lambda) ij ' is the data load accepted by the j-type server group in the data center i obtained by calculation after adding the security constraint; alpha i An original reference electricity price for the electricity price of the data center i; beta i Price sensitivity coefficient for data center i; i' is the number of the data center which is not fully loaded; l (L) i Load rate of the data center i; l (L) ij The load rate of the j-type server group in the data center i; the PUE is an energy efficiency index of a data center;active load, s, consumed by a j-server group in data center i ij The number of active servers of the j-type server group in the data center i; mu (mu) ij The service rate of the j-type server CPU in the data center i; d is the average response time of the server to handle the data load.
A day-ahead optimal operation system for a power distribution network, comprising:
the acquisition module is used for acquiring the data load quantity of each time period of the next day and the reference electricity price of the data center;
the first determining module is used for determining a distribution plan of the data load of each time period of the next day by combining the data load of each time period of the next day with the reference electricity price of the data center based on the data center energy consumption model and the optimal working mode of the server;
a verification module for carrying out safety verification on the distribution plan of the data load of each time period of the next day,
if the distribution plan does not cause overload of the distribution network lines, day-ahead optimization is completed according to the distribution plan,
if the distribution plan causes overload of the power distribution network line, after adding line overload constraint, re-determining a distribution plan of data loads of each time period of the next day, and calculating a guide electricity price according to the re-determined distribution plan of the data loads of each time period of the next day;
and the second determining module is used for controlling the data center operator to re-optimize the distribution plan of the data load of each time period of the next day according to the guide electricity price after replacing the reference electricity price of the data center by the guide electricity price.
Further, the acquisition module is used for
The data load amount of each time period of the next day provided by the data center operator and the reference electricity price of the data center node are obtained.
Further, the first determining module is configured to construct a data center energy consumption model, which is expressed as:
wherein P is i data An active load for data center i; PUE (PUE) i An energy efficiency index for data center i; s is(s) ij The number of active servers of the j-type server group in the data center i;the power consumption of other constant-value components of the j-type server in the data center i; k (K) ij CPU energy consumption coefficient of j-type server in data center i with service rate as object; mu (mu) ij The service rate of the j-type server CPU in the data center i; lambda (lambda) ij The j-type server group in the data center i bears the total data load; n (N) i Number of internal server models for data center i.
Further, the first determining module is configured to determine an optimal working mode of the server according to the CPU service rate, where the CPU service rate of the server is the optimal working modeExpressed as:
wherein,,the service rate lower limit and the service rate upper limit of the j-type server are set; d is the average response time of the server to process the data load; mu' j The ideal service rate for the j-type server is as follows:
wherein,,the power consumption of other constant-value components of the j-type server of the data center is calculated; k (K) j CPU energy consumption coefficient taking service rate as object for j-type server of data center;
the optimal operation modes include:
first caseAnd->The maximum number of server activities is set to +.>When->When the data load is increased, only the number of active servers is increased; when (when)When the number of active servers reaches the upper limit, the service rate is increased to meet the requirement of increasing the data load;
second caseAnd->Under, the server fixes the service rate +.>Invariably, only increasing the number of active servers until the upper limit is reached when the data load increases;
third caseAnd the maximum service rate of the server cannot meet the response time requirement, and the server is stopped.
Further, the first determining module is configured to determine an optimization problem of a distribution plan of the data load in each period of the next day, where the optimization problem is expressed as:
wherein alpha is i,t The reference price of the data center i at the moment t; beta t Is a price sensitivity coefficient;the active load of the data center i at the moment t; n is the total number of data centers in the system;
the constraints are:
wherein lambda is i,t The data load accepted by the data center i at the moment t;for the data load accepted by all data centers at time t, lambda ij,t The data load accepted by the j-type server group in the data center i at the moment t is; /> Adjusting upper and lower limits, mu, for service rates of j-type servers in data center i ij,t For the service rate of j-type server in data center i at time t,/>An upper limit on the number of j-type servers in the data center i; s is(s) ij,t The number of j-type active servers in the data center i; d is the average response time of the server to handle the data load.
Further, the second determining module is configured to calculate a pilot electricity price, where the calculation of the pilot electricity price is expressed as:
wherein,,a leading electricity price for data center i; r is the consumption micro-increment coefficient; l (L) i Load factor, P, of data center i i data Active negative of data center inodeA lotus; lambda (lambda) i A data load received by the data center i; lambda (lambda) ij Data load received by the j-type server group in the data center i; lambda (lambda) i ' is the data load accepted by the data center i calculated after adding the security constraint; lambda (lambda) ij ' is the data load accepted by the j-type server group in the data center i obtained by calculation after adding the security constraint; alpha i An original reference electricity price for the electricity price of the data center i; beta i Price sensitivity coefficient for data center i; i' is the number of the data center which is not fully loaded; l (L) i Load rate of the data center i; l (L) ij The load rate of the j-type server group in the data center i; the PUE is an energy efficiency index of a data center;active load, s, consumed by a j-server group in data center i ij The number of active servers of the j-type server group in the data center i; mu (mu) ij The service rate of the j-type server CPU in the data center i; d is the average response time of the server to handle the data load.
A computer readable storage medium storing one or more programs, wherein the one or more programs comprise instructions, which when executed by a computing device, cause the computing device to perform any of the methods.
A computing device, comprising,
one or more processors, memory, and one or more programs, wherein one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs comprising instructions for performing any of the methods.
The invention has the beneficial effects that:
the advantage of flexible resources in the power distribution network is fully utilized, the data center energy consumption model and the optimal working mode of the server in the data center are comprehensively considered, and the power price guiding mechanism enables the data center operator and the power grid company to realize the optimized operation of the power distribution network in the future after one-time interaction, so that the complexity of the optimized operation of the power distribution network in the future is greatly reduced.
Drawings
FIG. 1 is a schematic flow chart of the present invention;
fig. 2 is a flowchart of the optimized operation of the power distribution network provided by the invention before the day;
FIG. 3 is a system diagram of an example of a 33-node power distribution network provided by the invention;
FIG. 4 is a diagram of the next day data center data load distribution situation provided by the invention;
FIG. 5 is a graph of load distribution changes of the data center before and after the optimized operation provided by the invention;
fig. 6 is a diagram of the line flow change condition before and after the optimized operation provided by the invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The following examples are only for more clearly illustrating the technical aspects of the present invention, and are not intended to limit the scope of the present invention.
As shown in fig. 1, the present invention provides a day-ahead optimal operation method for a power distribution network, including:
step 1, acquiring the data load quantity of each time period of the next day and the reference electricity price of a data center;
step 2, determining a distribution plan of the data load of each time period of the next day by combining the data load of each time period of the next day and the reference electricity price of the data center based on the data center energy consumption model and the optimal working mode of the server;
step 3, carrying out safety check on the distribution plan of the data load of each time period of the next day,
if the distribution plan does not cause overload of the distribution network lines, day-ahead optimization is completed according to the distribution plan,
if the distribution plan causes overload of the power distribution network line, after adding line overload constraint, re-determining a distribution plan of data loads of each time period of the next day, and calculating a guide electricity price according to the re-determined distribution plan of the data loads of each time period of the next day;
and 4, returning to the step 2, and after replacing the reference electricity price of the data center by the guide electricity price, controlling the data center operator to re-optimize the distribution plan of the data load of each time period of the next day according to the guide electricity price.
The specific contents include:
1. establishing a data center energy consumption model considering CPU dynamic frequency modulation technology and variable active server quantity
The server is used as an energy consumption main body in the data center, and the individual power consumption P of the server server Can be regarded as CPU power consumption P CPU Power consumption P with other constant value components fixed The CPU power consumption of the server can be regarded as a function of the CPU load rate and the working frequency, meanwhile, the working frequency of the CPU is positively related to the service rate, and the energy consumption function relationship in the data center is as follows:
P server ＝P fixed +P CPU
P CPU ＝K·μ 2 ·λ
wherein, K is the CPU energy consumption coefficient taking the service rate as the object; lambda is the CPU data load; μ is the service rate of the CPU.
The total energy consumption of a data center i can be expressed as:
wherein the PUE i An energy efficiency index for data center i; n (N) i The number of internal server models for data center i; s is(s) ij The number of active servers of the j-type server group in the data center i;the power consumption of other constant-value components of the j-type server in the data center i; k (K) ij CPU energy consumption coefficient of j-type server in data center i with service rate as object; mu (mu) ij The service rate of the j-type server CPU in the data center i; lambda (lambda) ij The j-type server group in the data center i bears the total data load.
2. Establishing an optimal working mode of a server considering CPU service rate
For s j The total power of the server group formed by j-type serversConsumption amountThe method comprises the following steps:
wherein s is j The number of active servers in the j-type server group of the data center;the power consumption of other constant-value components of the j-type server of the data center is calculated; k (K) j CPU energy consumption coefficient taking service rate as object for j-type server of data center; mu (mu) j The service rate of the CPU of the j-type server in the data center is the service rate of the CPU of the j-type server in the data center; lambda (lambda) j And bearing the total data load for the j-type server group of the data center.
The upper limit of the average residence time of the server processing data load is D for the server considering the service quality constraint with response time of D, and the constraint can be expressed as follows according to an M/M/1 queuing theory model:
as can be seen from the increasing and decreasing analysis of the service rate of the total power consumption function of the server group, the optimal service rate of the j-type serverThe method comprises the following steps:
wherein,,the lower limit and the upper limit of the service rate of the j-type server are set. Mu' j For ideal service rate, the following is satisfied:
first case [ ]And->) The maximum number of server activities is set to +.>When (when)The server fixed service rate is unchanged when the data load increases, and only the number of active servers increases. When->When the number of active servers reaches the upper limit, the optimal service rate cannot be kept to work, and the service rate must be increased to meet the requirement of increasing the data load.
In the second caseAnd->) Under, the server fixes the service rate +.>The number of active servers is only increased as the data load increases, until the upper limit is reached.
Third caseAnd the maximum service rate of the server cannot meet the response time requirement, and the server is stopped.
3. Data center operator data load distribution scheme based on predicted electricity price
The data center operator needs to make a data load distribution plan of the next day according to the predicted electricity prices and data load amounts of each small period of the next day, and the optimization problem is described as follows:
wherein alpha is i,t The reference price of the data center node at the moment i is t; beta t Is a price sensitivity coefficient;the active load of the data center node at the moment i is t; n is the number of data centers in the system.
The constraints are:
wherein lambda is i,t The data load accepted by the data center i at the moment t;the data load accepted by all the data centers at the moment t; lambda (lambda) ij,t The data load accepted by the j-type server group in the data center i at the moment t is; /> Adjusting the upper limit and the lower limit of the service rate of the j-type server in the data center i; mu (mu) ij,t The service rate of the j-type server in the data center i at the moment t; />An upper limit on the number of j-type servers in the data center i; s is(s) ij,t the number of j-type active servers in the data center i at the moment t; d is the average response time of the server to handle the data load.
DSO safety verification and guided electricity price solving method
The power distribution system operator DSO (distribution system operators) distributes the scheme { lambda }, according to the conventional load forecast values of each node and each period of the next day and the data load transmitted by the data center operator i And (3) carrying out power distribution network safety check on the energy consumption value of each data center obtained through calculation:
D con P t con +D data P t data ≤f
wherein D is con For a conventional load factor matrix, P t con For a conventional load matrix, D data For the data center load factor matrix, P t data And f is a line capacity upper limit matrix.
If the safety check condition is met, the optimization operation is finished, if the safety check condition is not met, the DSO adds the line capacity constraint into the original constraint, the data load distribution plan is recalculated, and a group of data load distribution solutions { lambda } meeting the safety check is obtained through calculation i '}. By making a lead price, the data center operator actively follows the score { lambda }, in a per-interest process i ' data load is allocated, and specific flow isAs shown in fig. 2, wherein the guided electricity price solving equation is as follows:
wherein,,a leading electricity price for data center i; r is the consumption micro-increment coefficient; l (L) i Load factor, P, of data center i i data Active load for data center inode; lambda (lambda) i A data load received by the data center i; lambda (lambda) ij Data load received by the j-type server group in the data center i; lambda (lambda) i ' is the data load accepted by the data center i calculated after adding the security constraint; lambda (lambda) ij ' is the data load accepted by the j-type server group in the data center i obtained by calculation after adding the security constraint; alpha i An original reference electricity price for the electricity price of the data center i; beta i Price sensitivity coefficient for data center i; i' is the number of the data center which is not fully loaded; l (L) i Load rate of the data center i; l (L) ij The load rate of the j-type server group in the data center i; the PUE is an energy efficiency index of a data center;active load, s, consumed by a j-server group in data center i ij The number of active servers of the j-type server group in the data center i; mu (mu) ij The service rate of the j-type server CPU in the data center i; d is the average response time of the server to handle the data load.
In order to enable those skilled in the art to more clearly understand the technical solutions of the present application, the technical solutions of the present application will be described in detail below with reference to specific simulation examples and comparative examples.
Fig. 3 is a system diagram of an example of a 33-node power distribution network, in which 600 AMD Athlon processors and 600 Intel Pentium 4630 processors are adopted in the data center 1, 1200 Intel Pentium950 processors are adopted in the data center 2, and 1200 Intel Pentium 4630 processors are adopted in the data center 3.
The 24-hour data distribution scheme calculated by the data center operator according to the original electricity price information is shown in fig. 4. Under the distribution scheme, according to the safety verification calculation, overload phenomenon can occur when the line L1 exceeds the line L1 on the next day 11, and the safety of the power grid is threatened. The optimized operation strategy provided by the invention is adopted for the problems, the safety constraint is added to the original optimized problem, a new data distribution scheme which accords with the safety check is obtained by calculation, the new scheme and the old scheme are shown in the figure 5, it can be seen that the data centers 1 and 3 transfer part of data load to the data center 2 for processing, the guided electricity price is calculated according to the scheme, and the guided electricity price alpha of the data center 1 and the data center 3 is calculated at the next 11 days 1 、α 3 0.4639RMB/kW and 0.8092RMB/kW, and re-sending the data to the data center side to re-perform data load optimization, and performing security check again, wherein the overload condition of the lines L1 and L3 disappears the next day 11, the flow of the line L1 is limited to 1200kW, and the flow of the line L3 is limited to 900kW, as shown in FIG. 6. The load space transfer between data centers is realized through the guidance of electricity price, the problem of line overload on the next day of the system is eliminated, and the daily optimized operation of the power distribution network taking the load adjustment potential of the data center into account is realized.
The invention also provides a power distribution network day-ahead optimization operation system, which comprises:
the acquisition module is used for acquiring the data load quantity of each time period of the next day and the reference electricity price of the data center;
the first determining module is used for determining a distribution plan of the data load of each time period of the next day by combining the data load of each time period of the next day with the reference electricity price of the data center based on the data center energy consumption model and the optimal working mode of the server;
a verification module for carrying out safety verification on the distribution plan of the data load of each time period of the next day,
if the distribution plan does not cause overload of the distribution network lines, day-ahead optimization is completed according to the distribution plan,
if the distribution plan causes overload of the power distribution network line, after adding line overload constraint, re-determining a distribution plan of data loads of each time period of the next day, and calculating a guide electricity price according to the re-determined distribution plan of the data loads of each time period of the next day;
and the second determining module is used for controlling the data center operator to re-optimize the distribution plan of the data load of each time period of the next day according to the guide electricity price after replacing the reference electricity price of the data center by the guide electricity price. The acquisition module is used for
The data load amount of each time period of the next day provided by the data center operator and the reference electricity price of the data center node are obtained.
The first determining module is configured to construct a data center energy consumption model, and is expressed as:
wherein P is i data An active load for data center i; PUE (PUE) i An energy efficiency index for data center i; s is(s) ij The number of active servers of the j-type server group in the data center i;the power consumption of other constant-value components of the j-type server in the data center i; k (K) ij CPU energy consumption coefficient of j-type server in data center i with service rate as object; mu (mu) ij The service rate of the j-type server CPU in the data center i; lambda (lambda) ij The j-type server group in the data center i bears the total data load; n (N) i Number of internal server models for data center i.
The first determining module is configured to determine an optimal working mode of the server according to the CPU service rate, where the CPU service rate of the server is in the optimal working modeExpressed as:
wherein,,the service rate lower limit and the service rate upper limit of the j-type server are set; d is the average response time of the server to process the data load; mu' j The ideal service rate for the j-type server is as follows:
wherein,,the power consumption of other constant-value components of the j-type server of the data center is calculated; k (K) j CPU energy consumption coefficient taking service rate as object for j-type server of data center;
the optimal operation modes include:
first caseAnd->The maximum number of server activities is set to +.>When (when)When the data load is increased, only the number of active servers is increased; when->When the number of active servers reaches the upper limit, the service rate is increased to meet the requirement of increasing the data load;
second caseAnd->Under, the server fixes the service rate +.>Invariably, only increasing the number of active servers until the upper limit is reached when the data load increases;
third caseAnd the maximum service rate of the server cannot meet the response time requirement, and the server is stopped.
The first determining module is configured to determine an optimization problem of a distribution plan of data loads in each period of the next day, where the optimization problem is expressed as:
wherein alpha is i,t The reference price of the data center i at the moment t; beta t Is a price sensitivity coefficient;the active load of the data center i at the moment t; n is the total number of data centers in the system;
the constraints are:
wherein lambda is i,t The data load accepted by the data center i at the moment t;for the data load accepted by all data centers at time t, lambda ij,t The data load accepted by the j-type server group in the data center i at the moment t is; /> Adjusting upper and lower limits, mu, for service rates of j-type servers in data center i ij,t For the service rate of j-type server in data center i at time t,/>An upper limit on the number of j-type servers in the data center i; s is(s) ij,t The number of j-type active servers in the data center i; d is the average response time of the server to handle the data load.
The second determining module is configured to calculate a pilot electricity price, where the calculation of the pilot electricity price is expressed as:
wherein,,a leading electricity price for data center i; r is the consumption micro-increment coefficient; l (L) i Load factor of data center i, +.>Active load for data center inode; lambda (lambda) i A data load received by the data center i; lambda (lambda) ij Data load received by the j-type server group in the data center i; lambda (lambda) i ' is the data load accepted by the data center i calculated after adding the security constraint; lambda (lambda) ij ' is the data load accepted by the j-type server group in the data center i obtained by calculation after adding the security constraint; alpha i An original reference electricity price for the electricity price of the data center i; beta i Price sensitivity coefficient for data center i; i' is the number of the data center which is not fully loaded; l (L) i Load rate of the data center i; l (L) ij The load rate of the j-type server group in the data center i; the PUE is an energy efficiency index of a data center;active load, s, consumed by a j-server group in data center i ij The number of active servers of the j-type server group in the data center i; mu (mu) ij The service rate of the j-type server CPU in the data center i; d is the average response time of the server to handle the data load.
The corresponding invention also provides a computer readable storage medium storing one or more programs, characterized in that said one or more programs comprise instructions, which when executed by a computing device, cause said computing device to perform any of the methods described.
The corresponding invention also provides a computing device comprising,
one or more processors, memory, and one or more programs, wherein one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs comprising instructions for performing any of the methods.
The foregoing is merely a preferred embodiment of the present invention, and it should be noted that modifications and variations could be made by those skilled in the art without departing from the technical principles of the present invention, and such modifications and variations should also be regarded as being within the scope of the invention.
Claims (14)
1. The day-ahead optimal operation method of the power distribution network is characterized by comprising the following steps of:
step 1, acquiring the data load quantity of each time period of the next day and the reference electricity price of a data center;
step 2, determining a distribution plan of the data load of each time period of the next day by combining the data load of each time period of the next day and the reference electricity price of the data center based on the data center energy consumption model and the optimal working mode of the server;
step 3, carrying out safety check on the distribution plan of the data load of each time period of the next day,
if the distribution plan does not cause overload of the distribution network lines, day-ahead optimization is completed according to the distribution plan,
if the distribution plan causes overload of the power distribution network line, after adding line overload constraint, re-determining a distribution plan of data loads of each time period of the next day, and calculating a guide electricity price according to the re-determined distribution plan of the data loads of each time period of the next day;
and 4, returning to the step 2, and after replacing the reference electricity price of the data center by the guide electricity price, controlling the data center operator to re-optimize the distribution plan of the data load of each time period of the next day according to the guide electricity price.
2. The method for optimizing operation of a power distribution network according to claim 1, wherein the step of obtaining the data load amount of each period of the next day and the reference electricity price of the data center node comprises the steps of:
the data load amount of each time period of the next day provided by the data center operator and the reference electricity price of the data center node are obtained.
3. The method for optimized operation of power distribution network according to claim 1, wherein the data center energy consumption model is expressed as:
wherein P is i data An active load for data center i; PUE (PUE) i An energy efficiency index for data center i; s is(s) ij The number of active servers of the j-type server group in the data center i;the power consumption of other constant-value components of the j-type server in the data center i; k (K) ij CPU energy consumption coefficient of j-type server in data center i with service rate as object; mu (mu) ij The service rate of the j-type server CPU in the data center i; lambda (lambda) ij The j-type server group in the data center i bears the total data load; n (N) i Number of internal server models for data center i.
4. The day-ahead optimal operation method for a power distribution network according to claim 3, wherein the optimal operation mode of the server is an optimal operation mode of the server considering a CPU service rate, and the CPU service rate of the server is in the optimal operation modeExpressed as:
wherein,,the service rate lower limit and the service rate upper limit of the j-type server are set; d is the average response time of the server to process the data load; mu' j The ideal service rate for the j-type server is as follows:
wherein,,the power consumption of other constant-value components of the j-type server of the data center is calculated; k (K) j CPU energy consumption coefficient taking service rate as object for j-type server of data center;
the optimal operation modes include:
first caseAnd->The maximum number of server activities is set to +.>When->When the data load is increased, only the number of active servers is increased; when (when)When the number of active servers reaches the upper limit, the service rate is increased to meet the requirement of increasing the data load;
second caseAnd->Under, the server fixes the service rate +.>Invariably, only increasing the number of active servers until the upper limit is reached when the data load increases;
third kindCase(s)And the maximum service rate of the server cannot meet the response time requirement, and the server is stopped.
5. The method for optimizing operation of a power distribution network according to claim 1, wherein in step 2, the optimization problem for determining the distribution plan of the data load for each period of the next day is expressed as:
wherein alpha is i,t The reference price of the data center i at the moment t; beta t Is a price sensitivity coefficient;the active load of the data center i at the moment t; n is the total number of data centers in the system;
the constraints are:
wherein lambda is i,t The data load accepted by the data center i at the moment t;for the data load accepted by all data centers at time t, lambda ij,t The data load accepted by the j-type server group in the data center i at the moment t is; /> Adjusting upper and lower limits, mu, for service rates of j-type servers in data center i ij,t For the service rate of j-type server in data center i at time t,/>An upper limit on the number of j-type servers in the data center i; s is(s) ij,t The number of j-type active servers in the data center i; d is the average response time of the server to handle the data load.
6. The method for optimized operation of power distribution network according to claim 4, wherein the calculation of the guided electricity price is expressed as:
wherein,,a leading electricity price for data center i; r is the consumption micro-increment coefficient; l (L) i Load factor, P, of data center i i data Active load for data center inode; lambda (lambda) i Data load accepted by data center i；λ ij Data load received by the j-type server group in the data center i; lambda (lambda) i ' is the data load accepted by the data center i calculated after adding the security constraint; lambda (lambda) ij ' is the data load accepted by the j-type server group in the data center i obtained by calculation after adding the security constraint; alpha i An original reference electricity price for the electricity price of the data center i; beta i Price sensitivity coefficient for data center i; i' is the number of the data center which is not fully loaded; l (L) i Load rate of the data center i; l (L) ij The load rate of the j-type server group in the data center i; the PUE is an energy efficiency index of a data center; />Active load, s, consumed by a j-server group in data center i ij The number of active servers of the j-type server group in the data center i; mu (mu) ij The service rate of the j-type server CPU in the data center i; d is the average response time of the server to handle the data load.
7. A day-ahead optimal operation system for a power distribution network, comprising:
the acquisition module is used for acquiring the data load quantity of each time period of the next day and the reference electricity price of the data center;
the first determining module is used for determining a distribution plan of the data load of each time period of the next day by combining the data load of each time period of the next day with the reference electricity price of the data center based on the data center energy consumption model and the optimal working mode of the server;
a verification module for carrying out safety verification on the distribution plan of the data load of each time period of the next day,
if the distribution plan does not cause overload of the distribution network lines, day-ahead optimization is completed according to the distribution plan,
if the distribution plan causes overload of the power distribution network line, after adding line overload constraint, re-determining a distribution plan of data loads of each time period of the next day, and calculating a guide electricity price according to the re-determined distribution plan of the data loads of each time period of the next day;
and the second determining module is used for controlling the data center operator to re-optimize the distribution plan of the data load of each time period of the next day according to the guide electricity price after replacing the reference electricity price of the data center by the guide electricity price.
8. The day-ahead optimal operation system for a power distribution network according to claim 7, wherein the acquisition module is configured to
The data load amount of each time period of the next day provided by the data center operator and the reference electricity price of the data center node are obtained.
9. The day-ahead optimal operation system for a power distribution network according to claim 7, wherein the first determining module is configured to construct a data center energy consumption model, expressed as:
wherein P is i data An active load for data center i; PUE (PUE) i An energy efficiency index for data center i; s is(s) ij The number of active servers of the j-type server group in the data center i;the power consumption of other constant-value components of the j-type server in the data center i; k (K) ij CPU energy consumption coefficient of j-type server in data center i with service rate as object; mu (mu) ij The service rate of the j-type server CPU in the data center i; lambda (lambda) ij The j-type server group in the data center i bears the total data load; n (N) i Number of internal server models for data center i.
10. The day-ahead optimal operation system for a power distribution network according to claim 9, wherein the first determining module is configured to determine an optimal operation mode of the server taking into account a CPU service rate, and wherein the CPU service rate of the server is in the optimal operation modeExpressed as:
wherein,,the service rate lower limit and the service rate upper limit of the j-type server are set; d is the average response time of the server to process the data load; mu' j The ideal service rate for the j-type server is as follows:
wherein,,the power consumption of other constant-value components of the j-type server of the data center is calculated; k (K) j CPU energy consumption coefficient for data center j-type server with service rate as object. The method comprises the steps of carrying out a first treatment on the surface of the
The optimal operation modes include:
first caseAnd->The maximum number of server activities is set to +.>When->The fixed service rate of the server is unchanged when the data load is increased, and only the active server is increased when the data load is increasedNumber of pieces; when (when)When the number of active servers reaches the upper limit, the service rate is increased to meet the requirement of increasing the data load;
second caseAnd->Under, the server fixes the service rate +.>Invariably, only increasing the number of active servers until the upper limit is reached when the data load increases;
third caseAnd the maximum service rate of the server cannot meet the response time requirement, and the server is stopped.
11. The system for optimized operation of power distribution network as set forth in claim 7, wherein said first determining module is configured to determine an optimization problem of a distribution plan of data load for each period of the next day, expressed as:
wherein alpha is i,t The reference price of the data center i at the moment t; beta t Is a price sensitivity coefficient;the active load of the data center i at the moment t; n is the total number of data centers in the system;
the constraints are:
wherein lambda is i,t The data load accepted by the data center i at the moment t;for the data load accepted by all data centers at time t, lambda ij,t The data load accepted by the j-type server group in the data center i at the moment t is; /> Adjusting upper and lower limits, mu, for service rates of j-type servers in data center i ij,t For the service rate of j-type server in data center i at time t,/>An upper limit on the number of j-type servers in the data center i; s is(s) ij,t The number of j-type active servers in the data center i; d is the average response time of the server to handle the data load.
12. The day-ahead optimal operation system for a power distribution network according to claim 11, wherein the second determining module is configured to calculate a guided electricity price, and the calculation of the guided electricity price is expressed as:
wherein,,a leading electricity price for data center i; r is the consumption micro-increment coefficient; l (L) i Load factor, P, of data center i i data Active load for data center inode; lambda (lambda) i A data load received by the data center i; lambda (lambda) ij Data load received by the j-type server group in the data center i; lambda (lambda) i ' is the data load accepted by the data center i calculated after adding the security constraint; lambda (lambda) ij ' is the data load accepted by the j-type server group in the data center i obtained by calculation after adding the security constraint; alpha i An original reference electricity price for the electricity price of the data center i; beta i Price sensitivity coefficient for data center i; i' is the number of the data center which is not fully loaded; l (L) i Load rate of the data center i; l (L) ij The load rate of the j-type server group in the data center i; the PUE is an energy efficiency index of a data center; />Active load, s, consumed by a j-server group in data center i ij The number of active servers of the j-type server group in the data center i; mu (mu) ij The service rate of the j-type server CPU in the data center i; d is the average response time of the server to handle the data load.
13. A computer readable storage medium storing one or more programs, wherein the one or more programs comprise instructions, which when executed by a computing device, cause the computing device to perform any of the methods of claims 1-6.
14. A method for the manufacture of a computer program product, it is characterized by comprising the following steps of,
one or more processors, memory, and one or more programs, wherein one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs comprising instructions for performing any of the methods of claims 1-6.
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