CN111461920A - Smart grid-oriented data center demand response method and system - Google Patents
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Abstract
The invention discloses a data center demand response method and system for a smart power grid, and relates to the smart power grid. The method comprises the following steps: the data center predicts the running state of the power grid based on the real-time electricity price information to obtain a demand response signal; the intelligent power grid sends out corresponding excitation information according to the demand response signal, the data center responds to the demand response signal, and load adjustment corresponding to the demand response signal is carried out; the smart power grid gives corresponding rewards according to the execution rate of the data center responding to the demand response signals, the energy consumption cost of the data center can be reduced, the power cost is saved, and the power grid load is adjusted through a demand response strategy to improve the reliability of the power grid.
Description
Technical Field
The invention relates to the field of smart power grids, in particular to a smart power grid-oriented data center demand response method and system.
Background
In order to improve the reliability, stability and sustainability of the power grid, power companies in some countries have set forth a series of demand response planning projects that encourage their users to participate in order to reduce the load on the power grid during peak periods and improve the reliability of the power grid. Generally, demand response items in smart grids include incentive based and price based items. Wherein, the incentive-based demand response items mainly comprise direct load control, interruptible load control and the like, and the price-based demand response items mainly comprise time-of-use electricity price, real-time electricity price, spike electricity price and the like. Some utility companies are also attempting to launch demand response programs to achieve the goal of load shifting, and demand response programs also provide incentives for consumers to encourage consumer participation in order to tailor power consumption to demand response goals.
With the increasing complexity of computing services and storage services provided by data centers, and with the development of technologies and the advancement of communication technologies, users have an increasing demand for data centers. These factors lead to a dramatic increase in power requirements for data centers and an increasing problem of high energy consumption.
Disclosure of Invention
The invention aims to solve the technical problems that aiming at the defects of the prior art, a data center is used as a power utilization consumer of a power grid, the load of the data center generally has the characteristics of high flexibility, adjustable characteristic and the like, is a potential object participating in a power grid demand response plan, participates in the demand response plan by adjusting the load of the data center, and provides a data center demand response method and system facing to an intelligent power grid.
The technical scheme for solving the technical problems is as follows:
a smart grid-oriented data center demand response method comprises the following steps:
s1, the data center predicts the operation state of the power grid based on the real-time electricity price information to obtain a demand response signal; the smart grid sends out corresponding excitation information according to the demand response signal, wherein the excitation information is used for promoting the data center to respond to the demand response signal;
s2, the data center responds to the demand response signal and carries out load adjustment corresponding to the demand response signal;
and S3, the smart grid gives corresponding rewards according to the execution rate of the data center responding to the demand response signals.
The invention has the beneficial effects that: the scheme predicts and judges the arrival time of the demand response signal by judging the real-time electricity price on the future power grid condition, and simultaneously the intelligent power grid sends the excitation information to the data center; the data center participates in demand response of the smart power grid, assists the power grid in adjusting the load, meanwhile, the energy consumption cost of the data center can be reduced, the power cost is saved, the power grid load is adjusted through a demand response strategy, the reliability of the power grid is improved, meanwhile, the execution rate of the demand response by the data center is correspondingly awarded, more power grid user parameters are attracted for adjustment, and the effect of income of both the power grid and the data center is achieved.
Further, the S2 specifically includes: the data center issues bidding information according to the demand response signal, and the user submits bidding information according to the bidding information;
the data center selects a winning-bid user according to the bidding information, delays the task of the winning-bid user and pays the delay cost of the winning-bid user;
and finishing the response of the demand response signal and carrying out load regulation.
The beneficial effect of adopting the further scheme is that: according to the scheme, through a data center bid inviting demand response task, a power grid user participates in demand response by submitting a bid to adjust the load of the power grid user; the data center pays the required cost to the winning-bid user, stimulates the user to actively participate in the required task, redistributes the task of the user according to the auction result, delays the task of the winning-bid user, and reduces the user load at the required moment so as to reduce the power consumption of the data center. The data center realizes parameter demand response by effectively calling tasks submitted by users of the data center.
Further, the data center specifically includes, according to the bid information, selecting a winning user: establishing a fitness function formula, and solving the fitness function formula by using a random search algorithm to obtain an optimal solution, wherein a user corresponding to the optimal solution is the target user, and the fitness function formula is as follows:
wherein the constraint conditions are as follows:
xu∈[0,1],0≤t＜τ，
t′＞t，
where u denotes the bidding user, #u(t, t') denotes the number of tasks, xuRepresenting a binary variable, m representing the number of users submitting tasks to the data center at time t, γ representing the price at which the data center processes a task, t representing the current time, t' representing a delay time, α (u) representing the cost of delaying execution of a portion of the task, and τ representing the total time the data center takes part in the demand response simulation.
The beneficial effect of adopting the further scheme is that: the data center selects the winning-target users according to the objective function, namely the fitness function and the constraint condition formula, adopts a random search algorithm to solve to obtain an optimal solution, and selects the winning-target users according to the optimal solution; and obtaining the target users through a random algorithm, so that the total amount of the delay tasks and the cost is optimal.
Further, the S3 specifically includes: the demand response signal requires that a preset power consumption value is reduced at the current moment, the smart grid calculates the execution rate of the data center according to the preset power consumption value and the actually reduced power consumption value of the data center, and the smart grid gives a reward amount corresponding to the data center according to the execution rate.
The beneficial effect of adopting the further scheme is that: according to the scheme, the data center is rewarded according to the execution rate of the data center, the enthusiasm of demand response is improved through an incentive mode, and meanwhile the double-income effect of a power grid and the data center is achieved.
Further, the S3 further includes: when the time for the data center to delay the task exceeds a preset time threshold, the data center obtains a penalty corresponding to the violation of the user service quality requirement.
The beneficial effect of adopting the further scheme is that: according to the scheme, when the time for the data center to delay the task exceeds the preset time threshold, punishment can be carried out on the data center, and through a punishment mechanism, the data center not only guarantees the requirement of the intelligent power grid for reducing the load, but also guarantees the service quality of users of delayed services and reduces the bidding risk of the users.
Another technical solution of the present invention for solving the above technical problems is as follows:
a smart grid-oriented data center demand response system, comprising: smart grid and data center devices;
the data center device is used for predicting the power grid operation state based on the real-time electricity price information to obtain a demand response signal; the load regulation module is also used for responding the demand response signal and carrying out load regulation corresponding to the demand response signal;
the intelligent power grid is used for sending excitation information corresponding to the demand response signal to a data center according to the electricity price information; and the incentive information is used for promoting the data center to respond to the demand response signal.
The invention has the beneficial effects that: the scheme determines the arrival time of the demand response signal by judging the real-time electricity price of the smart grid, and simultaneously sends the excitation information to the data center; the data center participates in demand response of the smart power grid, assists the power grid in adjusting the load, meanwhile, the energy consumption cost of the data center can be reduced, the power cost is saved, the power grid load is adjusted through a demand response strategy, the reliability of the power grid is improved, meanwhile, the execution rate of the demand response by the data center is correspondingly awarded, more power grid user parameters are attracted for adjustment, and the effect of income of both the power grid and the data center is achieved.
Further, the data center device is specifically configured to issue bidding information according to the demand response signal, so that the user submits the bidding information; the system is also used for selecting a winning-bid user according to the bidding information, postponing the task of the winning-bid user and paying the postponed cost of the winning-bid user;
and the controller is also used for finishing the response of the demand response signal and carrying out load regulation.
The beneficial effect of adopting the further scheme is that: according to the scheme, through a data center bid inviting demand response task, a power grid user participates in demand response by submitting a bid to adjust the load of the power grid user; the data center pays the required cost to the winning-bid user, stimulates the user to actively participate in the required task, redistributes the task of the user according to the auction result, delays the task of the winning-bid user, and reduces the user load at the required moment so as to reduce the power consumption of the data center. The data center realizes parameter demand response by effectively calling tasks submitted by users of the data center.
Further, the data center device is specifically configured to establish a fitness function formula, and solve the fitness function formula by using a random search algorithm to obtain an optimal solution, where a user corresponding to the optimal solution is the landmark user, and the fitness function formula is as follows:
wherein the constraint conditions are as follows:
xu∈[0,1],0≤t＜τ，
t′＞t，
where u denotes the bidding user, #u(t, t') denotes the number of tasks, xuRepresenting a binary variable, m representing the number of users submitting tasks to the data center at time t, γ representing the price at which the data center processes a task, t representing the current time, t' representing a delay time, α (u) representing the cost of delaying execution of a portion of the task, and τ representing the total time the data center takes part in the demand response simulation.
The beneficial effect of adopting the further scheme is that: the data center selects the winning-target users according to the objective function, namely the fitness function and the constraint condition formula, adopts a random search algorithm to solve to obtain an optimal solution, and selects the winning-target users according to the optimal solution; and obtaining the target users through a random algorithm, so that the total amount of the delay tasks and the cost is optimal.
Further, the smart grid is specifically configured to reduce a preset power consumption value at the current time by the demand response signal, calculate an execution rate of the data center according to the preset power consumption value and the actually reduced power consumption value of the data center, and give a reward amount corresponding to the data center according to the execution rate.
The beneficial effect of adopting the further scheme is that: according to the scheme, the data center is rewarded according to the execution rate of the data center, the enthusiasm of demand response is improved through an incentive mode, and meanwhile the double-income effect of a power grid and the data center is achieved.
Further, the smart grid is further configured to, when the time for the data center to postpone the task exceeds a preset time threshold, make the data center device obtain a penalty corresponding to a violation of a user quality of service requirement.
The beneficial effect of adopting the further scheme is that: according to the scheme, when the time for the data center to delay the task exceeds the preset time threshold, punishment can be carried out on the data center, and through a punishment mechanism, the data center not only guarantees the requirement of the intelligent power grid for reducing the load, but also guarantees the service quality of users of delayed services and reduces the bidding risk of the users.
Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
Fig. 1 is a schematic flowchart of a smart grid-oriented data center demand response method according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of interaction between a power grid, a data center and a user according to another embodiment of the present invention;
fig. 3 is a block diagram of a smart grid-oriented data center demand response system according to another embodiment of the present invention;
FIG. 4 is a schematic diagram of load tasks performed in a data center according to another embodiment of the present invention;
FIG. 5 is a schematic diagram of a real-time electricity price prediction result according to another embodiment of the present invention;
FIG. 6 is a diagram illustrating task scheduling according to another embodiment of the present invention;
FIG. 7 is a schematic diagram of a peak load condition provided by other embodiments of the present invention;
FIG. 8 is a schematic diagram of a data center history 5 balance average power consumption according to another embodiment of the present invention;
fig. 9 is a schematic diagram of detailed power consumption of a data center according to another embodiment of the present invention;
FIG. 10 is a diagram illustrating the total penalty and reward for DR time according to another embodiment of the present invention;
FIG. 11 is a schematic diagram of the electricity charges and total rewards for three strategies provided by other embodiments of the invention;
FIG. 12 is a graphical representation of the relative reduction in total cost of a data center provided by other embodiments of the present invention.
Detailed Description
The principles and features of this invention are described below in conjunction with the following drawings, which are set forth to illustrate, but are not to be construed to limit the scope of the invention.
As shown in fig. 1, a smart grid-oriented data center demand response method provided in an embodiment of the present invention includes:
s1, the data center predicts the operation state of the power grid based on the real-time electricity price information to obtain a demand response signal; the smart grid sends out corresponding excitation information according to the demand response signal, wherein the excitation information is used for promoting the data center to respond to the demand response signal;
the data center process predicts the peak electricity rate at a future time based on the historical electricity rate information, and performs demand response to the time in advance based on the time of the peak electricity rate. And the intelligent power grid sends out excitation information according to the predicted peak electricity price time of the data center.
In one embodiment, a Neural network long and Short time memory network L STM (L ong Short-term memory) model can be used for predicting real-time electricity price information, the model is a recurrent Neural network trained by using reverse propagation time and can overcome the problem of gradient disappearance of a traditional Neural network, L STM is a special recurrent Neural network RNN (recurrent Neural networks), L STM can solve the problem of long-term dependence which cannot be solved by RNN and can well support long-term dependence, and L STM uses four Neural network layers and interacts with each other in a special relation.
S2, the data center responds to the demand response signal and carries out load adjustment corresponding to the demand response signal;
it should be noted that, as shown in fig. 2, in the interaction process between the power grid and the data center, as a heavy-load user of the smart power grid, participates in the demand response of the smart power grid, and the purpose of participating in the demand response is mainly achieved by effectively scheduling tasks submitted by the user;
the load of the power grid is adjusted, the power consumption of the data center is adjusted, the calculated power consumption is the dominant part in the power consumption composition of the data center, and the power consumption of the server is mainly related to the change of the load and the frequency change. By the symbol Pn tTo represent the computational power consumption of host n during the t-th time period. The scheme calculates the power consumption by adopting the following linear calculation mode:
wherein
Represents the maximum power consumption of the server, c represents the proportion of the static power consumption, unIndicating the utilization of the server.
Thus, the total power consumption P of the data center is calculated at time ttComprises the following steps:
wherein N represents the number of the host computers of the data center, and N is more than or equal to 1.
And S3, the smart grid gives corresponding rewards according to the execution rate of the data center responding the demand response signals.
It should be noted that the demand response signal of the general power grid requires that a certain amount of power consumption value needs to be reduced at the current moment, then the execution rate of the data center is calculated according to the basic amount and the actually reduced power consumption of the data center, and then a certain reward amount is given to the data center according to the execution rate. In a certain embodiment, the average value of power consumption of the data center at each moment in the past 5 days is used as the reference value pbaseIf the actual reduced power consumption after the data center participates in the demand response is predThen, define the execution rate as:
the reward θ (t) at time t is defined as:
wherein, the baseline is a basic value of the reward and is a constant.
The scheme determines the arrival time of the demand response signal by judging the real-time electricity price of the smart grid, and simultaneously sends the excitation information to the data center; the data center participates in demand response of the smart power grid, assists the power grid in adjusting the load, meanwhile, the energy consumption cost of the data center can be reduced, the power cost is saved, the power grid load is adjusted through a demand response strategy, the reliability of the power grid is improved, meanwhile, the execution rate of the demand response by the data center is correspondingly awarded, more power grid user parameters are attracted for adjustment, and the effect of income of both the power grid and the data center is achieved.
Preferably, in any of the above embodiments, S2 specifically includes that the data center issues bid inviting information according to the demand response signal, and the user submits bid information according to the bid inviting information; wherein the bid information may include: the number of tasks that can be deferred and the cost that the data center needs to pay to defer performing the tasks.
The data center selects the winning users according to the bidding information, delays the tasks of the winning users and pays the delay cost of the winning users;
and finishing the response of the demand response signal and carrying out load regulation.
It should be noted that at each time, the user submits task information to the data center, including tasks that may be deferred and the number of tasks that require immediate response. The scheme adopts a load mode of transferring users in time to reduce peak load;
in one embodiment, the number of batch tasks executed at time t (t) includes both batch tasks submitted by the user at time t and executed by the partial users at the previous times, which are deferred until time t, so that (t) is:
t'＞t,t1＞t，
where m represents the number of users submitting tasks to the data center at time t and the number of tasks φ that a user u plans to defer executionu(t, t'). Using binary variables xuAnd recording whether the user u wins the mark, if so, recording as 1, otherwise, recording as 0, and Bu represents the number of tasks needing immediate response submitted to the data center by the user t.
And λ (t) represents the total task amount required to be executed by the data center at the current moment, and λ (t) is:
wherein Iu(t) represents the number of interactive tasks submitted by user u at time t.
According to the scheme, through a data center bid inviting demand response task, a user of the data center participates in demand response by submitting a bid to adjust the load of the user; the data center pays the required cost to the winning-bid user, stimulates the user to actively participate in the required task, redistributes the task of the user according to the auction result, delays the task of the winning-bid user, and reduces the user load at the required moment so as to reduce the power consumption of the data center. The data center realizes parameter demand response by effectively calling tasks submitted by users of the data center.
Preferably, in any of the above embodiments, the data center establishes a fitness function formula, and solves the fitness function formula by using a random search algorithm to obtain an optimal solution, where a user corresponding to the optimal solution is a landmark user, and the fitness function formula is:
wherein the constraint conditions are as follows:
xu∈[0,1],0≤t＜τ，
t′＞t，
where u denotes the bidding user, #u(t, t') denotes the number of tasks, xuThe method comprises the following steps that binary variables are represented, m represents the number of users submitting tasks to a data center at time t, gamma represents the price of processing one task by the data center, t represents the current time, t 'represents a delay time, the delay from the time t to the time t' is carried out, α (u) represents the cost caused by delaying execution of a part of tasks, the delayed tasks are divided into more sensitive tasks and less sensitive tasks according to the length of time intervals which can be delayed, tau represents the total time of participation of the data center in demand response simulation, and one day is divided into 24 control areas in the time range of one whole day, namely each control area is 60 minutes.
It should be noted that, according to the scheme of the present embodiment, the data center manages the tasks of the users in a reverse auction manner. When the data center receives a demand response signal sent by the power grid, the user voluntarily carries out bidding, and the bidding information comprises the number of tasks planned to be delayed to execute and the related cost. The data center then selects winning bidders, pays the cost required to winning users, and redistributes the users' tasks according to the auction results.
And (3) reverse bidding process of the user: the data center acts as a buyer and the user acts as a seller. The users submit the bidding information in a binary group mode, the data center selects the winning users according to the fitness function and the constraint condition, and the tasks of the winning users are redistributed. In the scheme of the embodiment, a random search Algorithm based on the idea of a Genetic Algorithm is adopted for solving, wherein the Genetic Algorithm (GA) is a random parallel search Algorithm based on natural selection and Genetic genetics principle, and from a randomly initialized population, individuals with higher fitness are continuously generated through multiple iterations. And (4) adopting a binary coding mode, if the user wins the bid in the bidding process, the corresponding code value is 1, otherwise, the code value is 0.
In the scheme of the embodiment, the data center participates in the demand response signal prediction in the first stage, and manages the tasks of the users in a reverse bidding mode in the second stage, so that the load transfer is realized. The users submit the bidding information in a mode of bidding to the data center, and the data center selects the winning-bid users through a random search algorithm. Finally, the data center accomplishes the goal of engaging in demand responses by deferring the execution of tasks submitted by winning users that may be deferred to execute.
The data center selects the winning-target users according to the objective function, namely the fitness function and the constraint condition formula, adopts a random search algorithm to solve to obtain an optimal solution, and selects the winning-target users according to the optimal solution; and obtaining the target users through a random algorithm, so that the total amount of the delay tasks and the cost is optimal.
Preferably, in any of the above embodiments, S3 specifically includes: the demand response signal requires that a preset power consumption value is reduced at the current moment, the smart grid calculates the execution rate of the data center according to the preset power consumption value and the actually reduced power consumption value of the data center, and the smart grid gives the reward amount corresponding to the data center according to the execution rate.
In an embodiment, the award amount corresponding to the data center according to the execution rate may be, for example, an execution rate of 100% is an award amount a, an execution rate of 90% is an award amount B, an execution rate of 80% is an award amount C, a power consumption value to be reduced set by the power grid is 100, if the data center executes 80, the execution rate is 80%, and the award amount obtained by the data center corresponding to the execution rate is C.
According to the scheme, the data center is rewarded according to the execution rate of the data center, the enthusiasm of demand response is improved through an incentive mode, and meanwhile the double-income effect of a power grid and the data center is achieved.
Preferably, in any of the above embodiments, S3 further includes: when the time for the data center to postpone the task exceeds a preset time threshold, the data center obtains a penalty corresponding to the violation of the user service quality requirement.
It should be noted that, when providing services for users, the data center needs to ensure the quality of service (qos) of the users; in one embodiment, through the penalty model of the tasks, if a part of tasks needing immediate response or tasks needing immediate response are delayed by the tasks submitted by the users because the data center participates in the demand response plan of the power grid, or the execution of some deferrable tasks is delayed beyond the deferrable time of the tasks, the data center bears certain penalty. By tsubAnd texecRespectively representing the submission time and the actual execution time of the task, the penalty model corresponding to the penalty of the deferred task i on the data center may be:
μ(i)＝(texec-tsub)·β，
where β is a constant used to reflect the penalty.
The beneficial effect of adopting the further scheme is that: according to the scheme, when the time for delaying the tasks of the data center exceeds the preset time threshold, punishment can be carried out on the data center, and through a punishment mechanism, the data center not only guarantees the requirement of reducing the load of the intelligent power grid, but also guarantees the service quality of users of delayed services and reduces the bidding risk of the users.
In one embodiment, if the number of tasks violating the maximum deferrable time of a task at time t is π (t), the reward given to the user by the data center is recorded as ruser，ruserBy data centre in order to respondThe power grid demand response plan delays penalty caused by the user task and cost paid by a part of users who actively bid, wherein the penalty caused by delaying the user task corresponds to the following formula:
the cost of payment corresponds to the formula:
where α (u) represents the cost incurred by deferring the execution of a portion of a task, γ represents the price at which the data center processes a task, so the cost calculation formula for the data center at time t is:
C(t)＝Et·σ(t)+ruser-λ(t)·γ-θ(t)，
wherein E istIs the data center energy consumption value at time t.
The cost of the data center is reduced, and the demand response plan of the smart grid actively participating in the first stage is needed. The main goal of the second phase optimization problem is to reduce the power consumption of the data center as much as possible at the moment of the response period, and it is desirable to defer as many tasks as possible during the response period.
In one embodiment, as shown in fig. 3, a smart grid-oriented data center demand response system includes: a smart grid 11 and a data center device 12;
the data center device 12 is used for predicting the power grid operation state based on the real-time electricity price information to obtain a demand response signal; the load adjusting device is also used for responding the demand response signal and carrying out load adjustment corresponding to the demand response signal;
the intelligent power grid 11 is used for sending excitation information corresponding to the demand response signal to the data center according to the electricity price information; and the incentive module is further configured to award a corresponding reward according to a rate of execution of the data center response demand response signal, wherein the incentive information is used to facilitate the data center response demand response signal.
According to the scheme, the arrival time of the demand response signal is judged by predicting the real-time electricity price information of the smart grid, and meanwhile, the smart grid sends excitation information to the data center; the data center participates in demand response of the smart power grid, assists the power grid in adjusting the load, meanwhile, the energy consumption cost of the data center can be reduced, the power cost is saved, the power grid load is adjusted through a demand response strategy, the reliability of the power grid is improved, meanwhile, the execution rate of the demand response by the data center is correspondingly awarded, more power grid user parameters are attracted for adjustment, and the effect of income of both the power grid and the data center is achieved.
Preferably, in any of the above embodiments, the data center device 12 is specifically configured to issue bidding information according to the demand response signal, so that the user submits the bidding information; the system is also used for selecting the winning users according to the bidding information, postponing tasks of the winning users and paying the postponed cost of the winning users;
and the controller is also used for finishing the response of the demand response signal and carrying out load regulation.
According to the scheme, through a data center bid inviting demand response task, a power grid user participates in demand response by submitting a bid to adjust the load of the power grid user; the data center pays the required cost to the winning-bid user, stimulates the user to actively participate in the required task, redistributes the task of the user according to the auction result, delays the task of the winning-bid user, and reduces the user load at the required moment so as to reduce the power consumption of the data center. The data center realizes parameter demand response by effectively calling tasks submitted by users of the data center.
Preferably, in any of the above embodiments, the data center device 12 is specifically configured to establish a fitness function formula, and solve the fitness function formula by using a random search algorithm to obtain an optimal solution, where a user corresponding to the optimal solution is a landmark user, and the fitness function formula is:
wherein the constraint conditions are as follows:
xu∈[0,1],0≤t＜τ，
t′＞t，
where u denotes the bidding user, #u(t, t') denotes the number of tasks, xuRepresenting a binary variable, m representing the number of users submitting tasks to the data center at time t, γ representing the price at which the data center processes a task, t representing the current time, t' representing a delay time, α (u) representing the cost of delaying execution of a portion of the task, and τ representing the total time the data center takes part in the demand response simulation.
The data center selects the winning-target users according to the objective function, namely the fitness function and the constraint condition formula, adopts a random search algorithm to solve to obtain an optimal solution, and selects the winning-target users according to the optimal solution; and obtaining the target users through a random algorithm, so that the total amount of the delay tasks and the cost is optimal.
Preferably, in any embodiment described above, the smart grid 11 is specifically configured to request the response signal to reduce the preset power consumption value at the current time, calculate the execution rate of the data center according to the preset power consumption value and the power consumption value actually reduced by the data center, and give the reward amount corresponding to the data center according to the execution rate.
According to the scheme, the data center is rewarded according to the execution rate of the data center, the enthusiasm of demand response is improved through an incentive mode, and meanwhile the double-income effect of a power grid and the data center is achieved.
Preferably, in any of the above embodiments, the smart grid 11 is further configured to, when the time for the data center to defer the task exceeds a preset time threshold, make the data center device obtain a penalty corresponding to a violation of the user service quality requirement.
According to the scheme, when the time for delaying the tasks of the data center exceeds the preset time threshold, punishment can be carried out on the data center, and through a punishment mechanism, the data center not only guarantees the requirement of reducing the load of the intelligent power grid, but also guarantees the service quality of users of delayed services and reduces the bidding risk of the users.
In certain embodiments, the interaction between the data center and its users is simulated using the Cloudsim-plus tool. Specific values of some parameter settings used in the present embodiment are given in table 1.
TABLE 1 parameter settings
In the experiment, real load data in Google-trace is adopted for simulation, tasks are distributed to 10 users, and the number of interactive tasks and batch processing tasks in a data center is 1: a ratio of 1.5. Fig. 4 shows the daily load of the Google-trace used in this example, and this divides the batch-type task into tasks that can be delayed by 4 time intervals and 8 time intervals, which are respectively marked as more time-sensitive and less sensitive. As in fig. 4, the blue portion represents interactive tasks that may not be postponed from execution, the green portion represents tasks that may be postponed for 4 time intervals, and the yellow portion represents tasks that may be postponed for 8 time intervals.
In the embodiment, real-time electricity price is used as a basis for judging the arrival of a Demand Response signal, and in order to enable users of a power grid to have more time to adjust self loads to better participate in a Demand Response plan, the real-time electricity price is predicted to participate in the Demand Response time, in the electricity price prediction process, actual electricity price information in a certain power company is used, here, historical data of 1 month in 2018 is used as a training set to predict the real-time electricity price situation of 1 day in 2 months, L STM time sequence prediction model is used for prediction, in the embodiment, an AdaDelta optimizer is used, the iteration times are 300 times, and a machine-learned sciit-lean library is used for training and normalizing a data set, the prediction result is shown in figure 5, the error rate is basically kept within 15%, two peak electricity price periods obviously exist, and the two periods are respectively between 6:00-7:00 and 17:00-20:00, in the subsequent experiment, the two periods can reach the Demand signal, wherein DR is expressed as Demand DR.
As shown in fig. 6, the task scheduling under three strategies is shown. Tasks under the ST strategy are not scheduled at all, and it is clear that the number of tasks performed in the data center for six demand response time periods of 6:00, 7:00, 17:00, 18:00, 19:00, and 20:00 is reduced under both strategies BS and OP, but is reduced more under the OP strategy. This is because the OP strategy uses the user's bid to participate in the demand response program, and can reduce the execution of tasks in the DR time period as much as possible. In the strategy, a data center participates in a demand response plan of a first-stage power grid, but user auction and bidding processes are not performed in a second stage, only partial tasks are selectively delayed according to the needs of the data center, and whether the tasks submitted by users can be delayed or not is not considered, so that the data center can bear certain punishment due to the fact that the tasks of the users are delayed randomly. The static Strategy (ST) indicates that in this strategy the data center does not participate in the demand response of the grid, nor does the data center adjust the task execution.
In the scheme, by referring to a calculation method of the peak average of the power grid, the DR participation condition of three strategies is measured by defining the peak average of the data center at the DR moment. The calculation of the peak average is defined in the present scheme as the average of the load at the moment of demand response divided by the average of the load at 24 moments. As fig. 7 shows the peak average of the load under the three strategies, compared with the BS strategy, the peak load under the OP strategy is the lowest, so that it can be demonstrated that the proposed OP strategy can achieve the goal of better participating in demand response, and reduce the load pressure at the peak time of the power grid.
The average power consumption of 5 days history data in the Google-trace is used to calculate the execution rate of the demand response, and a detailed average power consumption situation is shown in FIG. 8.
Fig. 9 shows the detailed power consumption of the data center at each time instant, the red dotted line representing the real-time electricity rate information, and there are two peak electricity rate periods. As is apparent from the figure, the power consumption under the BS and OP strategies is reduced at the times of peak electricity prices, such as 6:00, 7:00, 17:00, 18:00, 19:00, and 20:00, while the reduction of the OP strategy is greatest.
As shown in fig. 10(a), the green bar represents a situation where the data center rewards users who actively participate in the demand response program under the OP strategy. The blue column is a part of penalty caused by that the data center randomly postpones the tasks submitted by the user to participate in the demand response under the BS policy, and part of tasks which cannot be postponed to be executed, such as interactive tasks, are postponed in the postponing process, so that the requirement of the user on the service quality is violated. As fig. 10(b) shows the reward given to the data center by the grid at each demand response time, it is obvious that the reward under the OP policy is almost higher than that of the BS policy at each time, and thus it can be proved that the execution rate of the OP policy is relatively high.
As shown in fig. 11, fig. 11(a) represents the electricity rate of the data center under three strategies, and it is obvious that the electricity rate of the data center under the ST strategy is the highest, and the electricity rate under the OP strategy is the lowest, because the OP strategy actively participates in the demand response plan at the time when the electricity rate is high, reduces a part of task execution at the time of peak electricity rate, and postpones execution until the electricity rate is low, so that the total electricity rate is relatively low, which indicates that the OP strategy can reduce the electricity cost of the data center. Fig. 11(b) shows that the reward given to the data center due to the participation of the data center in the demand response plan of the power grid, it is obvious that the reward of the OP policy is the highest, because the execution rate of the policy is higher, and it also reflects that the method proposed by the present scheme can achieve a higher demand response participation degree to a great extent.
The scheme also analyzes the cost reduction of the BS strategy and the OP strategy relative to the ST strategy, and the reduction is shown in detail in FIG. 12, while the relative reduction of the OP strategy is the highest. Because the OP strategy participates in the demand response program, a part of tasks that can be postponed to be executed when the price of electricity is high are reduced, thereby reducing the cost of electricity charges. Compared with the BS strategy, the OP strategy participates in the demand response plan in a user bidding manner, while the BS strategy incurs higher penalty due to the fact that the data center randomly defers tasks of some users, and the reward obtained from the power grid side is higher due to the higher execution rate of the OP strategy. Therefore, the total cost under the OP strategy is relatively low.
It is understood that some or all of the alternative embodiments described above may be included in some embodiments.
It should be noted that the above embodiments are product embodiments corresponding to the previous method embodiments, and for the description of each optional implementation in the product embodiments, reference may be made to corresponding descriptions in the above method embodiments, and details are not described here again.
The reader should understand that in the description of this specification, reference to the description of the terms "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described method embodiments are merely illustrative, and for example, the division of steps into only one logical functional division may be implemented in practice in another way, for example, multiple steps may be combined or integrated into another step, or some features may be omitted, or not implemented.
The above method, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention essentially or partially contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
While the invention has been described with reference to specific embodiments, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A data center demand response method facing to a smart power grid is characterized in that,
s1, the data center predicts the operation state of the power grid based on the real-time electricity price information to obtain a demand response signal; the smart grid sends out corresponding excitation information according to the demand response signal, wherein the excitation information is used for promoting the data center to respond to the demand response signal;
s2, the data center responds to the demand response signal and carries out load adjustment corresponding to the demand response signal;
and S3, the smart grid gives corresponding rewards according to the execution rate of the data center responding to the demand response signals.
2. The smart grid-oriented data center demand response method according to claim 1, wherein the S2 specifically includes: the data center issues bidding information according to the demand response signal, and the user submits bidding information according to the bidding information;
the data center selects a winning-bid user according to the bidding information, delays the task of the winning-bid user and pays the delay cost of the winning-bid user;
and finishing the response of the demand response signal and carrying out load regulation.
3. The smart grid-oriented data center demand response method according to claim 2, wherein the data center specifically selecting a winning-bid user according to the bid information comprises: establishing a fitness function formula, and solving the fitness function formula by using a random search algorithm to obtain an optimal solution, wherein a user corresponding to the optimal solution is the target user, and the fitness function formula is as follows:
wherein the constraint conditions are as follows:
xu∈[0,1],0≤t＜τ，
t′＞t，
where u denotes the bidding user, #u(t, t') denotes the number of tasks, xuRepresenting a binary variable, m representing the number of users submitting tasks to the data center at time t, γ representing the price at which the data center processes a task, t representing the current time, t' representing a delay time, α (u) representing the cost of delaying execution of a portion of the task, and τ representing the total time the data center takes part in the demand response simulation.
4. The smart grid-oriented data center demand response method according to any one of claims 1 to 3, wherein the S3 specifically includes: the demand response signal requires that a preset power consumption value is reduced at the current moment, the smart grid calculates the execution rate of the data center according to the preset power consumption value and the actually reduced power consumption value of the data center, and the smart grid gives a reward amount corresponding to the data center according to the execution rate.
5. The smart grid-oriented data center demand response method according to claim 4, wherein the S3 further includes: when the time for the data center to delay the task exceeds a preset time threshold, the data center obtains a penalty corresponding to the violation of the user service quality requirement.
6. A smart grid-oriented data center demand response system, comprising: smart grid and data center devices;
the data center device is used for predicting the power grid operation state based on the real-time electricity price information to obtain a demand response signal; the load regulation module is also used for responding the demand response signal and carrying out load regulation corresponding to the demand response signal;
the intelligent power grid is used for sending excitation information corresponding to the demand response signal to a data center according to the electricity price information; and the incentive information is used for promoting the data center to respond to the demand response signal.
7. The smart grid-oriented data center demand response system according to claim 6, wherein the data center device is specifically configured to issue bid inviting information according to the demand response signal, so that a user submits bid information; the system is also used for selecting a winning-bid user according to the bidding information, postponing the task of the winning-bid user and paying the postponed cost of the winning-bid user;
and the controller is also used for finishing the response of the demand response signal and carrying out load regulation.
8. The smart grid-oriented data center demand response system as recited in claim 7, wherein the data center device is specifically configured to establish a fitness function formula, and solve the fitness function formula by using a random search algorithm to obtain an optimal solution, the user corresponding to the optimal solution is the landmark user, and the fitness function formula is:
wherein the constraint conditions are as follows:
xu∈[0,1],0≤t＜τ，
t′＞t，
where u denotes the bidding user, #u(t, t') denotes the number of tasks, xuRepresenting a binary variable, m representing the number of users submitting tasks to the data center at time t, γ representing the price at which the data center processes a task, t representing the current time, t' representing a delay time, α (u) representing the cost of delaying execution of a portion of the task, and τ representing the total time the data center takes part in the demand response simulation.
9. The smart grid-oriented data center demand response system according to any one of claims 6 to 8, wherein the smart grid is specifically configured to request that a preset power consumption value is reduced at a current time by the demand response signal, calculate an execution rate of the data center according to the preset power consumption value and an actually reduced power consumption value of the data center, and give a reward amount corresponding to the data center according to the execution rate.
10. The smart grid-oriented data center demand response system according to claim 9, wherein the smart grid is further configured to make the data center device obtain a penalty corresponding to a violation of the user qos requirement when the time for the data center to postpone the task exceeds a preset time threshold.
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