CN115081758A - Calculation transfer demand response system oriented to coordination data center and power grid - Google Patents
Calculation transfer demand response system oriented to coordination data center and power grid Download PDFInfo
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Abstract
The application discloses calculation transfer demand response system for coordination data center and power grid, comprising: the system comprises a data center A capable of participating in power market demand response, a data center cluster B incapable of participating in power market demand response, a declaration calculation module, a scheduling center, a power trading center, an intelligent pricing module and an execution calculation module. The system coordinates the operation of the power grid and the communication network, provides the constraints and characteristics of the communication network and the power grid of the data center with different nodes, transfers the load to the nodes with distributed power generation or energy storage by using a calculation task transfer method, and realizes the scheme that the data center participates in the power grid demand response. Furthermore, in consideration of the fact that the optimal transfer data volume needs to be obtained by adopting an optimization algorithm when the data center transfers the calculation task, the invention provides an optimization algorithm reconstruction model, which greatly shortens the calculation time while maintaining the accuracy of the optimal solution, thereby playing a role in reducing the energy consumption of the data center.
Description
Technical Field
The application relates to the technical field of electric power, in particular to a calculation transfer demand response system facing a coordination data center and a power grid.
Background
5G communication is an application in the latest generation mobile networks. It makes possible a new type of network designed to connect almost all people and everything together, including machines, living beings and devices. The 5G wireless technology type mass data processing is accelerated, meanwhile, the reliability and the usability are greatly improved, and the user experience quality is obviously improved. Meanwhile, the higher the 5G communication performance, the faster the data is processed, and the higher the power consumption required for the data. Studies have shown that in 5G communication networks, the energy consumption of one data center is about 3-4 times that of a data center in 4G communication networks. Therefore, as the data age develops, data becomes more and more, and 5G communication may cause the computing tasks required to be processed by the data center to increase exponentially, so that a low-voltage distribution network has a remarkable power load increase phenomenon.
In order to improve the energy efficiency of the 5G data center, researchers focus on energy consumption reduction and electric energy utilization. Research on reducing energy consumption is mainly focused on detailed operation and management inside data centers; in terms of power utilization, there is a major focus on how to utilize the backup battery in demand response without considering the internal operational details of the data center. In fact, the operational details inside the data center are with the potential to participate in demand response, in addition to the backup battery. For example, during a peak or power gap period, the data center under a certain node in the power grid needs to be under-loaded, and the computing task of the data center of the node can be partially transferred to the data center or the base station of other nodes for processing, so as to provide a new direction for the data center of the node to participate in the power market.
In summary, under the conditions of limited computing resources and short power consumption, a solution capable of effectively exploring potential resources is very important, so that the quality of user experience is ensured, and resource allocation and potential resources are optimized and utilized fully and reasonably.
Disclosure of Invention
The application provides a calculation transfer demand response system facing a coordination data center and a power grid, which is used for fully and reasonably optimizing resource allocation and utilizing potential resources.
In view of the above, a first aspect of the present application provides a compute-shift demand response system for coordinating a data center and a power grid, the system including: the system comprises a data center A which can participate in power market demand response, a data center cluster B which cannot participate in power market demand response, a declaration calculation module, a dispatching center, a power trading center, an intelligent pricing module and an execution calculation module;
the declaration calculation module is used for receiving the CPU calculation task and the required electric energy of the data center A, and receiving the electric energy, the distributed generation data and the energy storage battery data required by the CPU calculation task of the data center cluster B; establishing a first objective function and a mathematical model of declaration constraint by taking the lowest power consumption generated by processing a calculation task by the data center A in a declaration time period as a target, reconstructing the mathematical model, and solving and processing the reconstructed model by adopting an improved high-efficiency Lagrange reconstruction multiplier method to obtain a time sequence virtual output boundary of the data center A and the power consumption generated by processing the calculation task transferred by the data center A in the data center cluster B;
the intelligent pricing module is used for receiving the time sequence virtual output boundary, the historical market clearing result and the historical execution result, and meanwhile, determining the declared price of the data center A which is optimal for the data center A to participate in the current power market demand response according to the historical market clearing result, the historical execution result and the time sequence virtual output boundary by adopting a Q learning algorithm, so that the optimal time sequence reported price of the current participation in the power market demand response is output;
the scheduling center is used for providing energy demand information of the operation day, and the energy demand information comprises: time series demand capacity and time series demand price;
the electric power trading center is used for receiving the optimal time sequence reported electricity price, the time sequence demand capacity and the time sequence demand price, calculating and outputting a market clearing result, wherein the market clearing result comprises: winning capacity in time sequence of each virtual power plant;
the execution calculation module is used for receiving the bid winning capacity in the time sequence and receiving electric energy, distributed generation data and energy storage battery data required by the CPU calculation task of the data center cluster B; and establishing a second objective function and a mathematical model of execution constraint in execution time at the lowest cost of a middle scalar quantity which meets the virtual output of the data center A in the spot market, solving an optimal objective cost function value by adopting an improved high-efficiency Lagrange reconstruction multiplier method, and solving the execution resource combination and the time sequence output capacity of the data center cluster B in an execution stage.
Optionally, the declaration calculation module specifically includes:
the objective function module is used for receiving the CPU calculation task of the data center A and the required electric energy, establishing the first objective function by taking the lowest electricity consumption generated by the data center A for processing the calculation task in the declaration time period as a target, and inputting the first objective function into the optimization algorithm reconstruction module as a data model;
the declaration constraint module is used for receiving the CPU calculation task and the required electric energy of the data center A, and receiving the electric energy, the distributed generation data and the energy storage battery data required by the CPU calculation task of the data center cluster B; establishing power utilization constraint of the data center cluster B in a declaration time period, charging and discharging quantity upper and lower limit constraints of an energy storage battery of the ith data center in the data center cluster B in unit time, energy storage battery capacity constraint of the ith data center in the data center cluster B, calculation task processing time delay constraint and transmission time delay constraint among data, and inputting the constraints into an optimization algorithm reconstruction module as a data model;
an optimization algorithm reconstruction module, configured to receive the mathematical models of the objective function module and the declaration constraint module, perform mathematical model reconstruction at the same time, reconstruct the mathematical model containing inequality constraints and equality constraints into a mathematical model containing only equality constraints, obtain a reconstruction model, solve the optimal data amount of the data center a migrated to the ith data center in the data center cluster B and the independent variable of the first objective function by using an improved high-efficiency lagrangian reconstruction multiplier method, and solve a time sequence virtual output boundary of the data center a and an electricity consumption generated by a CPU of each data center in the data center cluster B due to processing of the calculation task transferred by the data center a;
and reconstructing inequality constraints in an index constraint function into the first objective function in a form of penalty terms by the improved efficient Lagrange reconstruction multiplier method.
Optionally, the power usage constraint comprises: reporting the electricity consumption generated by the CPU of the ith data center in the data center cluster B for processing the calculation task within a time period; declaring electric power generated by distributed power generation of the ith data center in the data center cluster B within a time period; and reporting the charge and discharge amount of the energy storage battery of the ith data center in the data center cluster B in the time period.
Optionally, the intelligent pricing module specifically includes:
the initialization module is used for setting an initialization Q table, wherein the initialization Q table is a two-dimensional table with M rows and 2+ N columns, M represents the number of times that the data center A participates in the historical demand response, N represents the action behavior selection of the data center, the first column of the initialization Q table is used for reporting the electricity price for the historical time sequence of the data center A, the second column of the initialization Q table is used for responding to the historical demand participation of the data center A, and the third column to 2+ N column of the initialization Q table are used for setting future reward expectation values obtained by the data center A for different actions made in time;
a behavior decision module for receiving the time sequence virtual output boundary, the historical market clearing result and the historical execution result, selecting an action Q for the intelligent agent, and acquiring a corresponding state according to the initialized Q table, so as to pass through
Determining an optimal time sequence reporting electricity price by a strategy, and outputting the optimal time sequence reporting electricity price, wherein a historical market clearing result is a scalar in a time sequence, and a historical execution result is a time sequence output capacity and execution resource combination;
and the updating module is used for receiving the electricity price reported by the optimal time sequence and updating the initialized Q table by using a Bellman equation.
Optionally, the executing calculation module specifically includes:
the execution constraint module is used for receiving the bid winning capacity in the time sequence and receiving electric energy, distributed generation data and energy storage battery data required by the CPU calculation task of the data center cluster B; establishing power utilization constraint of the ith data center of the data center cluster B in execution time, distributed generation power constraint of the ith data center of the data center cluster B, upper and lower limit constraint of charging and discharging power of an energy storage battery of the ith data center of the data center cluster B, capacity constraint of the energy storage battery of the ith data center of the data center cluster B and power consumption constraint of a CPU (central processing unit) of the ith data center of the data center cluster B in execution time period, wherein the power consumption constraint is generated by processing calculation tasks and is used as a data model to be input to an optimization algorithm module;
the target cost function module is used for receiving electric energy, distributed generation data and energy storage battery data required by the CPU calculation task of the data center cluster B; establishing the second objective function with the lowest cost of the medium scalar quantity which meets the virtual output of the data center A in the spot market, and inputting the second objective function serving as a data model into an optimization algorithm module;
the optimization algorithm module is used for receiving the mathematical models of the target cost function module and the execution constraint module, solving an optimal target cost function value by adopting an improved high-efficiency Lagrange reconstruction multiplier method, and solving an execution resource combination and a time sequence output capacity of the data center cluster B in an execution stage;
and reconstructing inequality constraints in the index constraint function into the second objective function in a form of penalty terms by using the improved efficient Lagrange reconstruction multiplier method.
Optionally, the first objective function of the reconstruction model is:
in the formula (I), the compound is shown in the specification,
the representative reconstruction model aims at minimizing the sum of the power consumption and each penalty term generated by the CPU of the data center A in the declaration time interval due to the processing of the calculation tasks;
an argument representing the first objective function is a total data volume of the reporting period migrated to the data center cluster B by the data center A;
the total data volume is the total data volume when the data center A does not carry out calculation task transfer;
calculating a CPU period required for a unit data volume for the data center A;
calculating the energy consumption of each CPU cycle for the data center A;
、
penalty coefficients with values larger than 1 are respectively;
an odd number greater than 1 for the amplification factor;
an upper boundary penalty term of a jth inequality constraint of an ith data center in the data center cluster B;
a lower boundary penalty term of a jth inequality constraint of an ith data center in the data center cluster B;
and
respectively an upper boundary penalty item and a lower boundary penalty item of the distributed power generation amount constraint of the ith data center in the data center cluster B in unit time;
and
respectively an upper boundary penalty item and a lower boundary penalty item of the charge and discharge amount constraint of the energy storage battery of the ith data center in the data center cluster B in unit time;
and
respectively limiting upper and lower boundary punishment items for the capacity of the energy storage battery of the ith data center in the data center cluster B;
an upper bound penalty term for declaring a data capacity constraint of the ith data center in the data center cluster B within the time period;
an upper boundary penalty item is constrained for the channel transmission delay between the data center A and the ith data center in the data center cluster B;
the distributed power generation amount of the ith data center in the data center cluster B in unit time is obtained;
the distributed power generation amount is the maximum value of the distributed power generation amount of the ith data center in the data center cluster B in unit time;
to declare the charging and discharging amount of the energy storage battery of the ith data center in the data center cluster B in the time period,
the charging and discharging amount of an energy storage battery of the ith data center in the data center cluster B in unit time is calculated;
the maximum charge capacity of an energy storage battery of the ith data center in the data center cluster B in unit time is obtained;
the maximum discharge capacity of an energy storage battery of the ith data center in the data center cluster B in unit time;
the minimum capacity of an energy storage battery of the ith data center in the data center cluster B is obtained;
the initial capacity of an energy storage battery of the ith data center in the data center cluster B before the declaration time period is obtained;
the maximum capacity of an energy storage battery of the ith data center in the data center cluster B is obtained;
reporting the data volume of the ith data center in the data center cluster B which is not subjected to calculation task transfer in the time period, namely the calculation task to be processed by the ith data center;
the data volume of the ith data center migrated to the data center cluster B for the data center A;
reporting the maximum data capacity of the ith data center in the data center cluster B in the time period;
the optical fiber transmission speed of the channel between each node;
computing the maximum transmission time delay of the ith data center in the cluster B of the data centers from the data center A to the computing task in the reporting time period;
the number of data centers included in data center cluster B.
Optionally, the equation for reporting electricity usage for data center cluster B over time is constrained by:
in the formula (I), the compound is shown in the specification,
reporting the total power consumption of the ith data center in the data center cluster B to the market in the day ahead for a reporting time period;
reporting the electricity consumption generated by the CPU of the ith data center in the data center cluster B for processing the calculation task in the time period;
generating power for distributed generation of an ith data center in the data center cluster B within the declaration period;
and reporting the charging and discharging amount of the energy storage battery of the ith data center in the data center cluster B in the time period.
Optionally, a variable
Is constrained by:
in the formula (I), the compound is shown in the specification,
calculating the CPU period required by unit data volume for the ith data center in the data center cluster B in the declaration time period;
calculating the energy consumption of each CPU cycle for the ith data center in the data center cluster B in the declaration time period;
calculating channel consumption power when an ith data center in a data center cluster B and a data center A perform task transmission in unit time;
calculating the time required for transferring the task from the data center A to the ith data center in the data center cluster B within the declaration time period;
the optical fiber transmission speed of the channel between each node;
reporting the channel capacity between the data center A and the ith data center in the data center cluster B in a time period, namely the maximum average information rate which can be transmitted by the channel between the data center A and the ith data center;
transferring the computing task from the data center A to the ith data center in the data center cluster B to obtain the maximum power consumption of the channel;
reporting the total data volume of the data center A migrated to the data center cluster B in the time period;
is a number ofThe number of data centers included in data center cluster B.
Optionally, a variable
The equation constraints are:
in the formula (I), the compound is shown in the specification,
to declare the power generated by distributed generation of the ith data center in data center cluster B over the period of time,
distributed power generation per unit time for the ith data center in the data center cluster B,
to declare a time period.
Optionally, a variable
Is constrained by:
in the formula (I), the compound is shown in the specification,
to declare the charging and discharging amount of the energy storage battery of the ith data center in the data center cluster B in the time period,
the charging and discharging amount of an energy storage battery of the ith data center in the data center cluster B in unit time is set;
and the charging and discharging efficiency of the energy storage battery of the ith data center in the data center cluster B is obtained.
Optionally, the solving of the time-sequence virtual output boundary of the data center a and the power consumption generated by the CPU of each data center in the data center cluster B due to processing the calculation task transferred by the data center a specifically includes:
calculating a time sequence virtual output boundary through a time sequence virtual output boundary calculation formula, and calculating power consumption through a power consumption calculation formula;
the time sequence virtual output boundary calculation formula is as follows:
the formula for calculating the electricity consumption is as follows:
in the formula (I), the compound is shown in the specification,
a timing virtual force boundary;
the electricity consumption is used;
data volume for migrating the computing tasks in the data center A to the data center cluster B;
calculating a CPU period required for a unit data volume for the data center A;
calculating the energy consumption of each CPU cycle for the data center A;
the data volume of the ith data center migrated to the data center cluster B for the data center A;
calculating the CPU period required by unit data volume for the ith data center in the data center cluster B in the declaration time period;
calculating the energy consumption of each CPU cycle for the ith data center in the data center cluster B in the declaration time period;
calculating channel consumption power when an ith data center in a data center cluster B and a data center A perform task transmission in unit time;
the time required for the transfer of the computing task from data center a to the ith data center in data center cluster B within the declaration period.
Optionally, the second objective function is:
in the formula (I), the compound is shown in the specification,
、
distributed generating capacity per unit time and charging and discharging capacity of an energy storage battery per unit time of the ith data center in the data center cluster B in the execution time are respectively also independent variables of the second objective function;
、
respectively the distributed power generation cost and the energy storage battery power generation cost of the ith data center in the data center cluster B;
、
distributed generation capital recovery coefficients and battery capital recovery coefficients for the ith data center in the data center cluster B, respectively;
、
respectively representing the distributed power generation and the sticking rate of the energy storage battery of the ith data center in the data center cluster B;
、
the service life of distributed power generation equipment and energy storage battery equipment of the ith data center in the data center cluster B respectively;
、
the installation costs of distributed power generation and energy storage batteries of the ith data center in the data center cluster B are respectively set;
、
respectively, of the ith data center in data center cluster BThe operation and maintenance costs of the distributed power generation and energy storage batteries;
is an execution time period;
charging and discharging efficiency of an energy storage battery of the ith data center in the data center cluster B;
the number of data centers included in data center cluster B;
the aim is that the sum of the distributed power generation cost of the data center cluster B and the power generation cost of the energy storage battery in the execution time period is the lowest.
Optionally, the power usage constraint is:
in the formula (I), the compound is shown in the specification,
total power consumption declared to the day-ahead market for the ith data center in the data center cluster B in the execution period;
the power consumption generated by the CPU of the ith data center in the data center cluster B for processing the calculation task in the execution period;
electric power generated for distributed power generation of the ith data center in the data center cluster B within the execution period;
for the ith data in the data center cluster B in the execution periodThe charge and discharge capacity of the core energy storage cell.
Optionally, the distributed generation power constraint is:
in the formula (I), the compound is shown in the specification,
the distributed power generation amount of the ith data center in the data center cluster B in the execution stage in unit time is calculated;
the maximum value of the distributed power generation amount of the ith data center in the data center cluster B in unit time.
Optionally, the charging and discharging power upper and lower limits of the energy storage battery are constrained as follows:
in the formula (I), the compound is shown in the specification,
the charging and discharging amount of an energy storage battery of the ith data center in the data center cluster B in the execution stage in unit time is calculated;
the maximum charging amount of an energy storage battery of the ith data center in the data center cluster B in unit time is obtained;
the maximum discharge amount of the energy storage battery of the ith data center in the data center cluster B in unit time.
Optionally, the energy storage battery capacity constraint is:
in the formula (I), the compound is shown in the specification,
、
respectively obtaining the minimum capacity and the maximum capacity of an energy storage battery of the ith data center in the data center cluster B;
the initial capacity of an energy storage battery of the ith data center in the data center cluster B before the execution stage is obtained;
the charging and discharging amount of the energy storage battery of the ith data center in the data center cluster B in the execution period is shown.
Optionally, the power usage constraint is:
in the formula (I), the compound is shown in the specification,
the data volume of the ith data center in the data center cluster B, which is not subjected to the calculation task transfer, is obtained;
the data volume of the ith data center in the data center cluster B migrated to the data center A in the execution stage;
calculating a CPU period required by unit data volume for the ith data center in the data center cluster B;
ith data in data center cluster BThe center calculates the energy consumption of each CPU period;
calculating the channel consumption power in unit time when the ith data center in the data center cluster B and the data center A in the execution stage perform calculation task transmission;
the time required for transferring the calculation task from the data center A to the ith data center in the data center cluster B in the execution stage is calculated;
the optical fiber transmission speed of the channel between each node;
the channel capacity between the data center A and the ith data center in the data center cluster B in the execution stage, namely the maximum average information rate which can be transmitted by the channel between the data center A and the ith data center in the data center cluster B;
transferring the computing task from the data center A to the ith data center in the data center cluster B to obtain the maximum power consumption of the channel;
to perform the total amount of data that data center a migrates to data center cluster B during the phase,
the number of data centers included in data center cluster B.
Optionally, the solving of the execution resource combination and the time-series capacity of the data center cluster B in the execution stage specifically includes:
substituting the execution resource combination into a time sequence output capacity calculation formula to calculate and obtain the sum time sequence output capacity of the data center cluster B in the execution stage;
wherein, the time sequence output capacity calculation formula is as follows:
in the formula (I), the compound is shown in the specification,
time-series capacity for the data center cluster B in the execution phase;
the number of data centers included in data center cluster B;
、
the distributed power generation amount per unit time and the charging and discharging amount per unit time of the energy storage battery of the ith data center in the data center cluster B in the execution time are also independent variables of the second objective function, namely the execution resource combination.
According to the technical scheme, the method has the following advantages:
the application provides a calculation transfer demand response system facing a coordination data center and a power grid, which not only considers communication networks among data centers of different nodes, but also combines processing calculation tasks of the data centers with operation of the power grid. The system coordinates the operation of the power grid and the communication network simultaneously, provides the constraints and characteristics of the communication network and the power grid of the data center with different nodes, transfers the load to the nodes with distributed power generation or energy storage by utilizing a calculation task transfer method, realizes the participation of the data center in a power grid demand response scheme, and plays a positive role in peak clipping and valley filling for the power system in the peak period of power utilization.
In consideration of the fact that an optimization algorithm is needed to obtain the optimal transfer data volume when a data center transfers a calculation task, although the traditional Lagrange multiplier method based on the KKT condition can solve the optimal transfer data volume, the traditional Lagrange multiplier method based on the KKT condition has more constraint conditions, so that the calculation time is longer, and the energy consumption is higher when a CPU of the data center performs the solution.
According to the invention, through the characteristics of a communication network and data transferability, the data center which can participate in market demand response transfers calculation tasks to the data center which has different nodes and a small power supply, thereby playing a guiding role in participating in the demand response market by a communication data type heavy load aggregator, playing a role in peak clipping and valley filling, reducing the high load operation cost of a power system, improving the social energy efficiency level, and having a substantial guiding role in promoting the communication data amount type load aggregator to participate in the demand response scheme of the power market. Under the condition of power supply shortage and power gap, or when the transformer, the line, the feeder line and the like exist in a local area and have heavy overload risks, the pressure of the power system is relieved, potential resources are explored, optimal configuration of response resources is realized, and a positive effect is provided for stable and reliable operation of the power system.
Drawings
Fig. 1 is a schematic structural diagram of an embodiment of a computation-based transfer demand response system for a coordination data center and a power grid provided in an embodiment of the present application;
FIG. 2 is a flow diagram of a declaration calculation module provided in an embodiment of the present application;
FIG. 3 is a flow diagram of an intelligent pricing module provided in an embodiment of the present application;
fig. 4 is a flowchart of a module for performing computations provided in an embodiment of the present application.
Detailed Description
In order to make those skilled in the art better understand the technical solutions of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Referring to fig. 1, in an embodiment of the present application, a computing transfer demand response system for coordinating a data center and a power grid includes: the system comprises a data center A capable of participating in power market demand response, a data center cluster B incapable of participating in power market demand response, a declaration calculation module, a scheduling center, a power trading center, an intelligent pricing module and an execution calculation module;
it should be noted that the data center a and the data center cluster B are located in different nodes of the power grid, but belong to the same data manager, and the data manager can schedule calculation tasks among different data centers according to needs, thereby indirectly transferring power consumption among the data centers.
The data center A is a load aggregator or a large electricity consumption user in an electricity market, consumed electric energy is from a power grid, an energy storage or small power supply is not arranged, and electric energy required by processing of a CPU (central processing unit) of the data center on a calculation task is mainly considered in electric load consumption. In an early declaration stage, the data center A has direct control capability and other conditions which are respectively aggregated to form a virtual power plant, the virtual power plant can be used as a demand side response of the virtual power plant participating in the power market, and a data manager can transfer a calculation task of the data center A to a data center cluster B with an energy storage or small power supply through a declaration calculation module. In the later execution calculation stage, the data center a is used for receiving a time sequence bid amount, which is a market clearing result issued by the power trading center, and transmitting the time sequence bid amount to the data center cluster B.
The data center cluster B is a load aggregation business cluster or a power consumption large user cluster of different nodes in the power grid, and the data center cluster B is connected with distributed power generation and energy storage, so that the power source of the data center cluster B comprises the power grid, self distributed power generation and self energy storage batteries. The data center cluster B is only used as a load in the power market, the power consumption purchased from the power grid is fixed as the power consumption declared in the market at the day before, and the power center cluster B does not serve as a demand response of the virtual power plant participating in the power market. In an early declaration stage, the data center cluster B is used as input data of a declaration calculation module, and the data volume of the data center cluster B which can maximally accommodate the data center A is obtained through an optimization algorithm. In the later execution calculation stage, the data center cluster B is used for receiving the time sequence bid-winning capacity obtained by the data center A from the power trading center and transmitting the time sequence bid-winning capacity to the execution calculation module, so that the optimal execution scheme of the data center cluster B is obtained.
The reporting and calculating module is used for receiving the CPU calculating task and the required electric energy of the data center A, and receiving the electric energy, the distributed generation data and the energy storage battery data required by the CPU calculating task of the data center cluster B; establishing a first objective function and a mathematical model of declaration constraint by taking the lowest power consumption generated by processing a calculation task by a data center A in a declaration time period as a target, reconstructing the mathematical model, and solving and processing the reconstructed model by adopting an improved high-efficiency Lagrange reconstruction multiplier method to obtain a time sequence virtual output boundary of the data center A and the power consumption generated by processing the calculation task transferred by the data center A in a data center cluster B;
as shown in fig. 2, in this embodiment, the declaration calculation module specifically includes an objective function module, a declaration constraint module, and an optimization algorithm reconstruction module.
Wherein:
the objective function module is used for receiving a CPU calculation task of the data center A and required electric energy, establishing an objective function by taking the lowest electricity consumption generated by the data center A for processing the calculation task in a reporting time interval (a time interval when the data center A participates in demand response) as a target, and inputting the objective function into the optimization algorithm reconstruction module as a data model;
it should be noted that the objective function is as follows:
The reporting constraint module is used for receiving the CPU calculation task and the required electric energy of the data center A, and receiving the electric energy, the distributed generation data and the energy storage battery data required by the CPU calculation task of the data center cluster B; establishing power utilization constraint of the data center cluster B in a declaration time period, charge and discharge quantity upper and lower limit constraint of an energy storage battery of the ith data center in the data center cluster B in unit time, energy storage battery capacity constraint of the ith data center in the data center cluster B, calculation task processing time delay constraint and transmission time delay constraint among data, and inputting the data serving as a data model into an optimization algorithm reconstruction module;
it should be noted that the declaration constraint module is configured to receive the CPU computation task and the required power from the data center a, the energy consumption, the distributed power generation data, the data related to the energy storage battery required by the CPU computation task from the data center cluster B, and the channel data between the data center a and each data center cluster B. For the data center cluster B, corresponding electric quantity is purchased to the power grid in the market at the day before in a contract form, and certain punishment is carried out if the quantity of the contract is violated. Therefore, the data manager reports the virtual output boundary to the electric power trading center in consideration of the data center AIn time, the power consumption purchased from each node of the data center cluster B to the power grid needs to be considered and fixed as the total power consumption declared in the market at present
. The power utilization constraint of the data center cluster B in the reporting time is shown as an equation (2):
Formula (4) represents the power generated by the distributed power generation of the ith data center in the data center cluster B within the declaration time period, and formula (5) is the upper and lower limit constraints of the distributed power generation of the ith data center in the data center cluster B within unit time. Wherein
To declare the power generated by distributed generation of the ith data center in data center cluster B over the period of time,
the distributed power generation amount of the ith data center in the data center cluster B in unit time,
in order to declare a time period,
the maximum value of the distributed power generation amount of the ith data center in the data center cluster B in unit time.
Formula (6) represents the charging and discharging amount of the energy storage battery of the ith data center in the data center cluster B in the declaration time period; formula (7) is the charge and discharge amount upper and lower limit constraints of the energy storage battery of the ith data center in the data center cluster B in unit time; equation (8) is the energy storage battery capacity constraint for the ith data center in data center cluster B.
Wherein
To declare the charging and discharging amount of the energy storage battery of the ith data center in the data center cluster B in the time period,
the charging and discharging amount of an energy storage battery of the ith data center in the data center cluster B in unit time is calculated;
for the charging and discharging efficiency of the energy storage battery of the ith data center in the data center cluster B, the energy storage battery has an electric energy conversion process, and the mutual conversion between the electric energy and the chemical energy can cause the electric energy loss, so that the coefficient is obtained;
to declare a time period;
the maximum charge capacity of an energy storage battery of the ith data center in the data center cluster B in unit time is obtained;
the maximum discharge capacity of an energy storage battery of the ith data center in the data center cluster B in unit time;
the minimum capacity of an energy storage battery of the ith data center in the data center cluster B is obtained;
the initial capacity of the energy storage battery of the ith data center in the data center cluster B before the reporting time period is reported;
and the maximum capacity of the energy storage battery of the ith data center in the data center cluster B.
The data transmission between the ith data center in the data center cluster B and the data center A needs to meet certain service quality, including the calculation task processing delay constraint and the transmission delay constraint between data. See formula (9) and formula (10):
equation (9) is a data capacity constraint of the ith data center in the data center cluster B in the declaration time period, and represents that the calculation task undertaken in the declaration time period does not exceed the upper limit of the calculation capacity, and once the upper limit of the capacity is exceeded, more time is needed for the calculationProcessing the computational tasks, resulting in a degradation of quality of service; equation (10) is the channel transmission delay constraint between data center a and the ith data center in data center cluster B, which is the maximum transmission delay that can be received by the user. Wherein the content of the first and second substances,
reporting the data volume (namely the calculation task to be processed) of the ith data center in the data center cluster B which is not subjected to calculation task transfer in the time period;
a data volume (bit) for migrating the data center A to the ith data center in the data center cluster B;
reporting the maximum data capacity of the ith data center in the data center cluster B in the time period;
the optical fiber transmission speed of the channel between each node;
and reporting the maximum transmission time delay of the calculation task transferred from the data center A to the ith data center in the data center cluster B in the time period.
The optimization algorithm reconstruction module is used for receiving the mathematical models of the target function module and the declaration constraint module, reconstructing the mathematical models containing inequality constraints and equality constraints into the mathematical models only containing equality constraints to obtain reconstructed models, solving the optimal data volume of the data center A transferred to the ith data center in the data center cluster B and the target function independent variable by adopting an improved high-efficiency Lagrange reconstruction multiplier method, and solving the time sequence virtual output boundary of the data center A and the power consumption generated by the CPU of each data center in the data center cluster B due to the processing of the calculation task transferred by the data center A;
and reconstructing inequality constraints in the index constraint function into the target function in a form of penalty terms by using the improved efficient Lagrange reconstruction multiplier method.
It should be noted that the optimization algorithm reconstruction module is used for receiving the mathematical models of the objective function module and the declaration constraint module, and solving the optimal mathematical model by adopting the improved high-efficiency Lagrange reconstruction multiplier method
And
thus, the time-series virtual output boundary is further calculated and the value is output. The improved high-efficiency Lagrange reconstruction multiplier method reconstructs inequality constraints in the index constraint function into the target function in a form of penalty terms, and the calculation speed is higher than that of a typical Lagrange multiplier method based on a KKT condition and has a similar global optimal value. The first objective function of its reconstructed mathematical model (reconstruction model) is as follows:
equation (11) is a first objective function of the reconstructed model; formula (12), formula (13)Equation (14) and equation (15) are the electricity consumption equation constraint and the variable of the data center cluster B in the declaration time respectively
Is constrained by an equality of (1), a variable
Equality constraint, variable
Is constrained by the equation of (a).
Wherein the content of the first and second substances,
for reporting in the data within the time periodThe distributed generation of electricity by the ith data center in the core cluster B,
distributed power generation per unit time for the ith data center in the data center cluster B,
in order to declare a time period,
the maximum value of the distributed power generation amount per unit time of the ith data center in the data center cluster B.
Wherein the content of the first and second substances,
to declare the charging and discharging amount of the energy storage battery of the ith data center in the data center cluster B in the time period,
the charging and discharging amount of an energy storage battery of the ith data center in the data center cluster B in unit time is calculated;
for the charging and discharging efficiency of the energy storage battery of the ith data center in the data center cluster B, the energy storage battery has an electric energy conversion process, and the mutual conversion between electric energy and chemical energy can cause electric energy loss, so that the coefficient is obtained;
the maximum charge capacity of an energy storage battery of the ith data center in the data center cluster B in unit time is obtained;
the maximum discharge capacity of an energy storage battery of the ith data center in the data center cluster B in unit time;
the minimum capacity of an energy storage battery of the ith data center in the data center cluster B is set;
the initial capacity of an energy storage battery of the ith data center in the data center cluster B before the declaration time period is obtained;
and the maximum capacity of the energy storage battery of the ith data center in the data center cluster B.
Reporting the maximum data capacity of the ith data center in the data center cluster B in the time period;
and reporting the maximum transmission time delay of the calculation task transferred from the data center A to the ith data center in the data center cluster B in the time period.
Therefore, the optimization algorithm reconstruction module reconstructs the mathematical model containing inequality constraint and equality constraint into the mathematical model only containing equality constraint, and the reconstruction model can be solved by adopting a Lagrange multiplier method to solve the optimal mathematical model
And
then, the time-series virtual output boundary of the data center a and the power consumption of the CPU of each data center in the data center cluster B due to processing the calculation task transferred by the data center a can be obtained by equations (16) and (17).
Wherein, the formula (16) is a time sequence virtual output boundary of the data center A
Represents; equation (17) is the power consumption of the CPU in the ith data center in the data center cluster B for processing the calculation task transferred by the data center A
And (4) showing.
The intelligent pricing module is used for receiving the time sequence virtual output boundary, the historical market clearing result and the historical execution result, and meanwhile, determining the declared price of the data center A which is the best in response to the current power market demand by adopting a Q learning algorithm according to the historical market clearing result, the historical execution result and the time sequence virtual output boundary, so that the time sequence reported price of the current power market demand response is output;
it should be noted that the machine learning method adopted by the intelligent pricing module is a Q learning algorithm, and an optimal action strategy is found by using a Q function, and the maximum reported price of electricity that can be profitable in the future is obtained. Q is the sum of Q (s,
) I.e. in s-state, take action
The expected value can be obtained, and the environment feeds back corresponding rewards according to the actions of the intelligent agent. The main idea of the Q learning algorithm is therefore to combine the state(s) and the action(s) ((s))
) Forming a Q table to store Q values that will represent the maximum future reward value to be achieved by taking the corresponding action given the best strategy
And the strategy selects the behavior action with the maximum Q value in the Q table.
As shown in fig. 3, the intelligent pricing module of this embodiment specifically includes an initialization module, a behavior decision module, and an update module.
Wherein:
the initialization module is used for setting an initialization Q table, the initialization Q table is a two-dimensional table with M rows and 2+ N columns, wherein M represents the number of times that the data center A historically participates in demand response, N represents action behavior selection of the data center, the first column of the initialization Q table is used for reporting the electricity price of the historical time sequence of the data center A, the second column of the initialization Q table is used for responding to the demand history of the data center A, and the third column to the 2+ N column of the initialization Q table are future reward expected values obtained by the data center A for different actions made within the time;
it should be noted that the initialization module is an initialization Q table, which is a two-dimensional table with M rows and 2+ N columns, where M represents the number of times that the data center a has historically participated in the demand response, N represents the selection of the action behavior of the data center, the first column of the Q table is the report price of electricity for the historical time sequence of the data center a, the second column of the Q table is the time that the data center a has historically participated in the demand response, and the third to 2+ N columns of the Q table are the expected future rewards obtained by the data center a for different actions performed within the time, and the value is 0 in the initial state (the Q table needs to be initialized only when the data center a first participates in the demand response). The corresponding relation of historical data corresponding to the time sequence reported electricity price and participation time of the data center A in the Q table determines N action states, and corresponding actions taken in each action state have corresponding reward values, namely reward values R fed back by the environment to the action. The calculation formula of the reward value R is shown as formula (18):
wherein the content of the first and second substances,
reporting the electricity price to the time sequence declared by the data center A to the power market;
a total data volume (bit) for migrating the data center A to the data center cluster B in the execution phase;
calculating a CPU period required by a unit data volume (1 bit) for the data center A;
calculating the energy consumption of each CPU cycle for the data center A;
the number of data centers included in data center cluster B;
、
the distributed power generation cost and the energy storage battery power generation cost of the ith data center in the data center cluster B are respectively.
A behavior decision module for receiving the time sequence virtual output boundary, the historical market clearing result and the historical execution result, selecting an action Q for the intelligent agent, and acquiring a corresponding state according to the initialized Q table, thereby passing through
Determining an optimal time sequence reporting electricity price by a strategy, and outputting the time sequence reporting electricity price, wherein a historical market clearing result is a scalar in the time sequence, and a historical execution result is a time sequence output capacity and execution resource combination;
it should be noted that the behavior decision module is configured to receive historical reported electricity prices, time sequence virtual output boundaries, and historical market clearing results (time sequence bid amount, time sequence output capacity, and execution resource combination) of the data center a; by passing
Policy determination of optimalityReporting the electricity price by the time sequence and outputting the reported electricity price by the time sequence. The behavior decision module will select an action for the agent
This action corresponds to the S state in the currently existing Q table. Decision module adoption
The strategy, ε, is the agent's exploration rate, and is typically set to a value of 0 to 1.
The strategy will cause the model to generate a random number and compare this random number to epsilon. If the random number is larger than the epsilon value, the action of the intelligent agent is random exploration; if the random number is less than or equal to epsilon, the action of the agent selects the action with the maximum Q value in the corresponding state in the existing Q table. The action value is the time sequence reported electricity price of the data center A. The Q value is calculated by the formula (19):
wherein the content of the first and second substances,
is at the same timesBehavior of actions in State
The new value of the Q value is obtained,
for the current value of Q, the value of Q,
is in the next state
The maximum Q value obtained for all of the next actions,
for the forward return to current value,
in order for the agent to feed back the learning rate,
the ratio of reward attenuation of the agent to the long term, and R is the reward value of the environment to the behavior feedback.
And the updating module is used for receiving the time sequence reported electricity price and updating the initialized Q table by using a Bellman equation.
It should be noted that the update module is configured to receive the time sequence reported electricity price output by the behavior decision module. For actions selected in a behavior decision module
Executing the action returns a new state
And corresponding feedback values. The new Q table is updated using the Bellman equation, see equation (18).
Wherein the content of the first and second substances,
selecting actions for the s state
The new value of Q is obtained in the following way,
is operated in s state
Current Q value of，
The reward value for the context to the behavioral feedback,
is the next new state
All possible actions
The maximum Q value of (2).
The body learning rate is fed back intelligently;
the larger the value, the more important the benefit of the agent to the distant period is, for the rate of decay of the agent to the distant period. The new Q value will be saved in the Q table to provide learning data for the next action of the behavior decision module.
The dispatching center is used for providing energy demand information of the operation days, and the energy demand information comprises: time series demand capacity and time series demand price;
the electric power trading center is used for receiving the time sequence reported electricity price, the time sequence required capacity and the time sequence required price, calculating and outputting a market clearing result, wherein the market clearing result comprises the following steps: winning capacity in time sequence of each virtual power plant;
the execution calculation module is used for receiving the bid winning capacity in the time sequence and receiving electric energy, distributed generation data and energy storage battery data required by the CPU calculation task of the data center cluster B; and establishing a second objective function and a mathematical model of execution constraint in execution time with the lowest cost of a middle scalar quantity which meets the virtual output of the data center A in the spot market, solving an optimal objective cost function value by adopting an improved high-efficiency Lagrange reconstruction multiplier method, and solving the execution resource combination and the time sequence output capacity of the data center cluster B in the execution stage.
As shown in fig. 4, in this embodiment, the execution calculation module specifically includes an execution constraint module, an objective cost function module, and an optimization algorithm module.
Wherein:
the target cost function module is used for receiving electric energy, distributed generation data and energy storage battery data required by the CPU calculation task of the data center cluster B; establishing a second objective function with the lowest cost of the medium scalar quantity which meets the virtual output of the data center A in the spot market, and inputting the second objective function serving as a data model into the optimization algorithm module;
it should be noted that the objective cost function module is configured to receive energy consumption, distributed power generation data, and energy storage battery related data required by the CPU computation task of the data center cluster B. Computing tasks by transfer to satisfy the medium scalar for data center A's virtual contribution in the spot market
And in the data cluster B, adjusting distributed power generation and battery energy storage of the data center cluster B to realize the optimal execution scheme with the lowest bid amount cost, wherein a second objective function is as follows:
in the formula (I), the compound is shown in the specification,
、
distributed generating capacity in unit time and charging and discharging capacity of an energy storage battery in unit time of the ith data center in the data center cluster B in the execution time are respectively also independent variables of the second objective function;
、
respectively the distributed power generation cost and the energy storage battery power generation cost of the ith data center in the data center cluster B;
、
distributed generation capital recovery coefficients and battery capital recovery coefficients for the ith data center in the data center cluster B, respectively;
、
respectively representing the distributed power generation and the sticking rate of the energy storage battery of the ith data center in the data center cluster B;
、
the service life of distributed power generation equipment and energy storage battery equipment of the ith data center in the data center cluster B respectively;
、
the installation costs of distributed power generation and energy storage batteries of the ith data center in the data center cluster B are respectively set;
、
the operation and maintenance costs of the distributed power generation and energy storage battery of the ith data center in the data center cluster B are respectively set;
is an execution time period;
charging and discharging efficiency of an energy storage battery of the ith data center in the data center cluster B;
the number of data centers included in data center cluster B.
The execution constraint module is used for receiving the bid winning capacity in the time sequence and receiving electric energy, distributed generation data and energy storage battery data required by the CPU calculation task of the data center cluster B; establishing power utilization constraint of the ith data center of the data center cluster B in execution time, distributed generation power constraint of the ith data center of the data center cluster B, energy storage battery charging and discharging power upper and lower limit constraint of the ith data center of the data center cluster B, energy storage battery capacity constraint of the ith data center of the data center cluster B and power consumption constraint of a CPU (central processing unit) in the ith data center of the data center cluster B in execution time period, wherein the power consumption constraint is generated by processing calculation tasks and is input to the optimization algorithm module as a data model;
it should be noted that the execution constraint module is configured to receive a scalar in a time sequence, that is, a medium bid amount of virtual output of the data center a in the spot market, energy consumption, distributed power generation data, data related to an energy storage battery required by a CPU computation task from the data center cluster B, and channel data between the data center a and each data center cluster B. For the data center cluster B, corresponding electric quantity is purchased to the power grid in the market at the day before in a contract form, and certain punishment is carried out if the quantity of the contract is violated. Therefore, during the execution period, the condition that the power consumption purchased from each node of the data center cluster B to the power grid is fixed as the total power consumption declared in the market at present
. Intra-execution-time data center groupThe electricity utilization constraint of the ith data center of the set B is shown in a formula (22); the distributed generation power constraint of the ith data center in the data center cluster B is shown as a formula (23); the upper and lower limits of the charging and discharging power of the energy storage battery of the ith data center in the data center cluster B are constrained as shown in a formula (24); the energy storage battery capacity constraint of the ith data center in the data center cluster B is shown in a formula (25); the constraint of the electricity consumption generated by the CPU in the ith data center in the data center cluster B for processing the calculation task in the execution period is shown as a formula (26);
in the formula (I), the compound is shown in the specification,
total power consumption declared to the day-ahead market for the ith data center in the data center cluster B in the execution period;
the power consumption generated by the CPU of the ith data center in the data center cluster B for processing the calculation task in the execution period;
electric power generated for distributed power generation of the ith data center in the data center cluster B within the execution period;
the charging and discharging amount of the energy storage battery of the ith data center in the data center cluster B in the execution period is shown.
The optimization algorithm module is used for receiving the mathematical models of the target cost function module and the execution constraint module, solving an optimal target cost function value by adopting an improved high-efficiency Lagrange reconstruction multiplier method, and solving execution resource combination and time sequence output capacity of the data center cluster B in an execution stage;
and reconstructing inequality constraints in the index constraint function into the target function in a form of penalty terms by using the improved efficient Lagrange reconstruction multiplier method.
It should be noted that the optimization algorithm module is configured to receive the mathematical models of the objective cost function module and the execution constraint module, and solve the optimal objective cost function value by using an improved and efficient lagrangian reconstruction multiplier method
And at the same time obtaining the independent variable
I.e. the combination of resources of the execution phase. The time-series output capacity can be obtained by the formula (27):
wherein the content of the first and second substances,
the time-ordered total capacity of data center cluster B within the execution phase is calculated.
In summary, the embodiment of the present invention provides a demand response system for computing transfer oriented to a coordination data center and a power grid, and a method for computing task transfer is adopted by a data center with different nodes, so as to participate in demand response transaction. In order to enable a data center manager participating in demand response to obtain the maximum benefit, the system also provides an intelligent pricing model based on machine learning, and the benefit maximization is realized. The calculation transfer demand response system for the coordination data center and the power grid has the functions of relieving the pressure of the power system, exploring potential resources, realizing the optimal configuration of response resources and providing a positive effect for the stable and reliable operation of the power system under the conditions of short power supply and power gaps or heavy overload risks of transformers, lines, feeders and the like in local areas.
The terms "first," "second," "third," "fourth," and the like in the description of the application and the above-described figures, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are, for example, capable of operation in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
It should be understood that in the present application, "at least one" means one or more, "a plurality" means two or more. "and/or" for describing an association relationship of associated objects, indicating that there may be three relationships, e.g., "a and/or B" may indicate: only A, only B and both A and B are present, wherein A and B may be singular or plural. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. "at least one of the following" or similar expressions refer to any combination of these items, including any combination of the singular or plural items. For example, at least one (one) of a, b, or c, may represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", wherein a, b, c may be single or plural.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, a division of a unit is merely a logical division, and an actual implementation may have another division, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
Units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit may be implemented in the form of hardware, or may also be implemented in the form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit 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 application may be substantially implemented or contributed to by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes 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 application. And the aforementioned storage medium includes: a U-disk, a portable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
The above embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions in the embodiments of the present application.
Claims (18)
1. A compute-shift demand response system oriented to coordination of a data center and a power grid, comprising: the system comprises a data center A which can participate in power market demand response, a data center cluster B which cannot participate in power market demand response, a declaration calculation module, a dispatching center, a power trading center, an intelligent pricing module and an execution calculation module;
the declaration calculation module is used for receiving the CPU calculation task and the required electric energy of the data center A, and receiving the electric energy, the distributed generation data and the energy storage battery data required by the CPU calculation task of the data center cluster B; establishing a first objective function and a mathematical model of declaration constraint by taking the lowest power consumption generated by processing a calculation task by the data center A in a declaration time period as a target, reconstructing the mathematical model, and solving and processing the reconstructed model by adopting an improved high-efficiency Lagrange reconstruction multiplier method to obtain a time sequence virtual output boundary of the data center A and the power consumption generated by processing the calculation task transferred by the data center A in the data center cluster B;
the intelligent pricing module is used for receiving the time sequence virtual output boundary, the historical market clearing result and the historical execution result, and meanwhile, determining the declared price of the data center A which is optimal for the data center A to participate in the current power market demand response according to the historical market clearing result, the historical execution result and the time sequence virtual output boundary by adopting a Q learning algorithm, so that the optimal time sequence reported price of the current participation in the power market demand response is output;
the dispatching center is used for providing energy demand information of the operation day, and the energy demand information comprises: time series demand capacity and time series demand price;
the electric power trading center is used for receiving the optimal time sequence reported electricity price, the time sequence demand capacity and the time sequence demand price, calculating and outputting a market clearing result, wherein the market clearing result comprises: winning capacity in time sequence of each virtual power plant;
the execution calculation module is used for receiving the bid winning capacity in the time sequence and receiving electric energy, distributed generation data and energy storage battery data required by the CPU calculation task of the data center cluster B; and establishing a second objective function and a mathematical model of execution constraint in execution time at the lowest cost of a middle scalar quantity which meets the virtual output of the data center A in the spot market, solving an optimal objective cost function value by adopting an improved high-efficiency Lagrange reconstruction multiplier method, and solving the execution resource combination and the time sequence output capacity of the data center cluster B in an execution stage.
2. The system for computing transfer demand response oriented to coordination of a data center and a power grid according to claim 1, wherein the declaration computing module specifically comprises:
the objective function module is used for receiving the CPU calculation task of the data center A and the required electric energy, establishing the first objective function by taking the lowest electricity consumption generated by the data center A for processing the calculation task in the declaration time period as a target, and inputting the first objective function into the optimization algorithm reconstruction module as a data model;
the declaration constraint module is used for receiving the CPU calculation task and the required electric energy of the data center A, and receiving the electric energy, the distributed generation data and the energy storage battery data required by the CPU calculation task of the data center cluster B; establishing power utilization constraint of the data center cluster B in a declaration time period, charge and discharge quantity upper and lower limit constraint of an energy storage battery of the ith data center in the data center cluster B in unit time, energy storage battery capacity constraint of the ith data center in the data center cluster B, calculation task processing time delay constraint and transmission time delay constraint among data, and inputting the constraints into an optimization algorithm reconstruction module as a data model;
an optimization algorithm reconstruction module, configured to receive the mathematical models of the objective function module and the declaration constraint module, perform mathematical model reconstruction at the same time, reconstruct the mathematical model including inequality constraint and equality constraint into a mathematical model including equality constraint only, obtain a reconstruction model, solve the optimal data amount of the data center a migrated to the ith data center in the data center cluster B and the argument of the first objective function by using an improved high-efficiency lagrangian reconstruction multiplier method, and solve a time sequence virtual output boundary of the data center a and power consumption generated by a CPU of each data center in the data center cluster B due to processing of the calculation task transferred by the data center a;
and reconstructing inequality constraints in an index constraint function into the first objective function in a form of penalty terms by the improved efficient Lagrange reconstruction multiplier method.
3. The coordinated data center and grid oriented computing transfer demand response system of claim 2, wherein the electricity usage constraint comprises: reporting the electricity consumption generated by the CPU of the ith data center in the data center cluster B for processing the calculation task within a time period; declaring electric power generated by distributed power generation of the ith data center in the data center cluster B within a time period; and reporting the charging and discharging amount of the energy storage battery of the ith data center in the data center cluster B in a time period.
4. The system for calculating and transferring demand response oriented to coordination of a data center and a power grid as claimed in claim 1, wherein the intelligent pricing module specifically comprises:
the initialization module is used for setting an initialization Q table, wherein the initialization Q table is a two-dimensional table with M rows and 2+ N columns, M represents the number of times that the data center A participates in the historical demand response, N represents the action behavior selection of the data center, the first column of the initialization Q table is used for reporting the electricity price for the historical time sequence of the data center A, the second column of the initialization Q table is used for responding to the historical demand participation of the data center A, and the third column to 2+ N column of the initialization Q table are used for setting future reward expectation values obtained by the data center A for different actions made in time;
a behavior decision module for receiving the time sequence virtual output boundary, the historical market clearing result and the historical execution result, selecting an action Q for the intelligent agent, and acquiring a corresponding state according to the initialized Q table, so as to pass through
Determining an optimal time sequence reporting electricity price by a strategy, and outputting the optimal time sequence reporting electricity price, wherein a historical market clearing result is a scalar in a time sequence, and a historical execution result is a time sequence output capacity and execution resource combination;
and the updating module is used for receiving the reporting electricity price of the optimal time sequence and updating the initialization Q table by using a Bellman equation.
5. The system for computing transfer demand response oriented to coordination of a data center and a power grid according to claim 1, wherein the executing the computing module specifically comprises:
the execution constraint module is used for receiving the bid winning capacity in the time sequence and receiving electric energy, distributed generation data and energy storage battery data required by the CPU calculation task of the data center cluster B; establishing power utilization constraint of the ith data center of the data center cluster B in execution time, distributed generation power constraint of the ith data center of the data center cluster B, energy storage battery charging and discharging power upper and lower limit constraint of the ith data center of the data center cluster B, energy storage battery capacity constraint of the ith data center of the data center cluster B and power consumption constraint generated by a CPU (central processing unit) processing a calculation task of the ith data center of the data center cluster B in execution time period, and inputting the power consumption constraint into an optimization algorithm module as a data model;
the target cost function module is used for receiving electric energy, distributed generation data and energy storage battery data required by the CPU calculation task of the data center cluster B; establishing a second objective function with the lowest cost of the intermediate scalar satisfying the virtual output of the data center A in the spot market, and inputting the second objective function serving as a data model into an optimization algorithm module;
the optimization algorithm module is used for receiving the mathematical models of the objective cost function module and the execution constraint module, solving an optimal objective cost function value by adopting an improved high-efficiency Lagrange reconstruction multiplier method, and solving execution resource combination and time sequence output capacity of a data center cluster B in an execution stage;
and reconstructing inequality constraints in the index constraint function into the second objective function in a form of penalty terms by using the improved efficient Lagrange reconstruction multiplier method.
6. The coordinated data center and power grid oriented compute transfer demand response system of claim 1, wherein the first objective function of the reconstructed model is:
in the formula (I), the compound is shown in the specification,
the representative reconstruction model aims at minimizing the sum of the power consumption and each penalty term generated by the CPU of the data center A in the declaration time interval due to the processing of the calculation tasks;
an argument representing the first objective function is a total data volume of the reporting period migrated to the data center cluster B by the data center A;
the total data volume is the total data volume when the data center A does not carry out calculation task transfer;
calculating a CPU period required for a unit data volume for the data center A;
calculating the energy consumption of each CPU cycle for the data center A;
、
penalty coefficients with values greater than 1, respectively;
is an amplification factor, with an odd number greater than 1;
is the j inequality of the ith data center in the data center cluster BAn upper bound penalty term for the bundle;
a lower boundary penalty term of a jth inequality constraint of an ith data center in the data center cluster B;
and
respectively an upper boundary penalty item and a lower boundary penalty item of the distributed power generation amount constraint of the ith data center in the data center cluster B in unit time;
and
respectively an upper boundary penalty item and a lower boundary penalty item of the charge and discharge amount constraint of the energy storage battery of the ith data center in the data center cluster B in unit time;
and
respectively limiting upper and lower boundary punishment items for the capacity of the energy storage battery of the ith data center in the data center cluster B;
an upper bound penalty term for declaring a data capacity constraint of the ith data center in the data center cluster B within the time period;
an upper boundary penalty item is constrained for the channel transmission delay between the data center A and the ith data center in the data center cluster B;
the distributed power generation amount of the ith data center in the data center cluster B in unit time is obtained;
the distributed power generation amount is the maximum value of the distributed power generation amount of the ith data center in the data center cluster B in unit time;
to declare the charging and discharging amount of the energy storage battery of the ith data center in the data center cluster B in the time period,
the charging and discharging amount of an energy storage battery of the ith data center in the data center cluster B in unit time is calculated;
the maximum charging amount of an energy storage battery of the ith data center in the data center cluster B in unit time is obtained;
the maximum discharge capacity of an energy storage battery of the ith data center in the data center cluster B in unit time;
the minimum capacity of an energy storage battery of the ith data center in the data center cluster B is obtained;
the initial capacity of the energy storage battery of the ith data center in the data center cluster B before the reporting time period is reported;
as a data centerThe maximum capacity of an energy storage battery of the ith data center in the cluster B;
reporting the data volume of the ith data center in the data center cluster B which is not subjected to calculation task transfer in the time period, namely the calculation task to be processed by the ith data center;
the data volume of the ith data center migrated to the data center cluster B for the data center A;
reporting the maximum data capacity of the ith data center in the data center cluster B in the time period;
the optical fiber transmission speed of the channel between each node;
calculating the maximum transmission time delay of the ith data center in the cluster B for transferring the task from the data center A to the data center in the declaration time period;
the number of data centers included in data center cluster B.
7. The coordinated data center and power grid oriented compute transfer demand response system of claim 6, wherein the equation constraint declaring the electricity usage of data center cluster B over time is:
in the formula (I), the compound is shown in the specification,
reporting the total power consumption of the ith data center in the data center cluster B to the market in the day ahead for a reporting time period;
reporting the electricity consumption generated by the CPU of the ith data center in the data center cluster B for processing the calculation task in the time period;
generating power for distributed generation of the ith data center in the data center cluster B within the declaration period;
and reporting the charging and discharging amount of the energy storage battery of the ith data center in the data center cluster B in the time period.
8. The coordinated data center and grid oriented compute transfer demand response system of claim 7, wherein variables are variables
Is constrained by:
in the formula (I), the compound is shown in the specification,
calculating the CPU period required by unit data volume for the ith data center in the data center cluster B in the declaration time period;
calculating the energy consumption of each CPU cycle for the ith data center in the data center cluster B in the declaration time period;
calculating channel consumption power when an ith data center in a data center cluster B and a data center A perform task transmission in unit time;
calculating the time required for transferring the task from the data center A to the ith data center in the data center cluster B within the declaration time period;
the optical fiber transmission speed of the channel between each node;
reporting the channel capacity between the data center A and the ith data center in the data center cluster B in a time period, namely the maximum average information rate which can be transmitted by the channel between the data center A and the ith data center;
transferring the computing task from the data center A to the channel maximum power consumption of the ith data center in the data center cluster B;
reporting the total data volume of the data center A migrated to the data center cluster B in the time period;
the number of data centers included in data center cluster B.
9. The coordinated data center and grid oriented compute transfer demand response system of claim 8, wherein variables are variables
The equation constraints are:
in the formula (I), the compound is shown in the specification,
to declare the power generated by distributed generation of the ith data center in data center cluster B over the period of time,
distributed power generation per unit time for the ith data center in the data center cluster B,
to declare a time period.
10. The coordinated data center and grid oriented compute transfer demand response system of claim 9, wherein variables are variables
Is constrained by:
in the formula (I), the compound is shown in the specification,
to declare the charging and discharging amount of the energy storage battery of the ith data center in the data center cluster B in the time period,
the charging and discharging amount of an energy storage battery of the ith data center in the data center cluster B in unit time is calculated;
and the charging and discharging efficiency of the energy storage battery of the ith data center in the data center cluster B is obtained.
11. The system according to claim 2, wherein the solving of the time-series virtual output boundary of the data center a and the power consumption of the CPUs of the data centers in the data center cluster B due to processing the calculation task transferred by the data center a specifically comprises:
calculating a time sequence virtual output boundary through a time sequence virtual output boundary calculation formula, and calculating power consumption through a power consumption calculation formula;
the time sequence virtual output boundary calculation formula is as follows:
the calculation formula of the power consumption is as follows:
in the formula (I), the compound is shown in the specification,
a timing virtual force boundary;
the electricity consumption is;
data volume for migrating the computing tasks in the data center A to the data center cluster B;
for data center ACalculating the CPU period required by unit data amount;
calculating the energy consumption of each CPU cycle for the data center A;
the data volume of the ith data center migrated to the data center cluster B for the data center A;
calculating the CPU period required by unit data volume for the ith data center in the data center cluster B in the declaration time period;
calculating the energy consumption of each CPU cycle for the ith data center in the data center cluster B in the declaration time period;
calculating channel power consumption when the ith data center in the data center cluster B and the data center A perform calculation task transmission in unit time;
the time required for the transfer of the computing task from data center a to the ith data center in data center cluster B within the declaration period.
12. The coordinated data center and grid oriented compute transfer demand response system of claim 5, wherein the second objective function is:
in the formula (I), the compound is shown in the specification,
、
distributed generating capacity in unit time and charging and discharging capacity of an energy storage battery in unit time of the ith data center in the data center cluster B in the execution time are respectively also independent variables of the second objective function;
、
respectively the distributed power generation cost and the energy storage battery power generation cost of the ith data center in the data center cluster B;
、
distributed generation capital recovery coefficients and battery capital recovery coefficients for the ith data center in the data center cluster B, respectively;
、
respectively representing the distributed power generation and the sticking rate of the energy storage battery of the ith data center in the data center cluster B;
、
the service life of distributed power generation equipment and energy storage battery equipment of the ith data center in the data center cluster B respectively;
、
the installation costs of distributed power generation and energy storage batteries of the ith data center in the data center cluster B are respectively set;
、
the operation and maintenance costs of the distributed power generation and energy storage battery of the ith data center in the data center cluster B are respectively set;
is an execution time period;
charging and discharging efficiency of an energy storage battery of the ith data center in the data center cluster B;
the number of data centers included in data center cluster B;
the lowest sum of the distributed power generation cost of the data center cluster B and the power generation cost of the energy storage battery in the execution period is taken as a target.
13. The coordinated data center and power grid oriented compute transfer demand response system of claim 5, wherein the power usage constraint is:
in the formula (I), the compound is shown in the specification,
total power consumption declared to the day-ahead market for the ith data center in the data center cluster B in the execution period;
the power consumption generated by the CPU of the ith data center in the data center cluster B for processing the calculation task in the execution period;
electric power generated for distributed power generation of the ith data center in the data center cluster B within the execution period;
the charging and discharging amount of the energy storage battery of the ith data center in the data center cluster B in the execution period is shown.
14. The coordinated data center and grid oriented compute transfer demand response system of claim 5, wherein the distributed generation power constraint is:
in the formula (I), the compound is shown in the specification,
the distributed power generation amount of the ith data center in the data center cluster B in the execution stage in unit time is calculated;
the maximum distributed power generation amount per unit time of the ith data center in the data center cluster B.
15. The system for calculating and transferring demand response oriented to the coordination data center and the power grid according to claim 5, wherein the upper and lower limits of the charge and discharge power of the energy storage battery are constrained as follows:
in the formula (I), the compound is shown in the specification,
the charging and discharging amount of an energy storage battery of the ith data center in the data center cluster B in the execution stage in unit time is calculated;
the maximum charge capacity of an energy storage battery of the ith data center in the data center cluster B in unit time is obtained;
the maximum discharge amount of the energy storage battery of the ith data center in the data center cluster B in unit time.
16. The coordinated data center and grid oriented compute transfer demand response system of claim 5, wherein the energy storage battery capacity constraints are:
in the formula (I), the compound is shown in the specification,
、
respectively, of the ith data center in data center cluster BEnergy storage battery minimum and maximum capacities;
the initial capacity of an energy storage battery of the ith data center in the data center cluster B before the execution stage is obtained;
the charging and discharging amount of the energy storage battery of the ith data center in the data center cluster B in the execution period is shown.
17. The coordination data center and grid oriented compute transfer demand response system of claim 5, wherein the power usage constraint is:
in the formula (I), the compound is shown in the specification,
the data volume of the ith data center in the data center cluster B, which is not subjected to the calculation task transfer, is obtained;
the data volume of the ith data center in the data center cluster B migrated to the data center A in the execution stage;
calculating a CPU period required by unit data volume for the ith data center in the data center cluster B;
calculating the energy consumption of each CPU cycle by the ith data center in the data center cluster B;
calculating the channel consumption power in unit time when the ith data center in the data center cluster B and the data center A in the execution stage perform calculation task transmission;
the time required for transferring the calculation task from the data center A to the ith data center in the data center cluster B in the execution stage is calculated;
the optical fiber transmission speed of the channel between each node;
the channel capacity between the data center A and the ith data center in the data center cluster B in the execution stage, namely the maximum average information rate which can be transmitted by the channel between the data center A and the ith data center in the data center cluster B;
transferring the computing task from the data center A to the channel maximum power consumption of the ith data center in the data center cluster B;
to perform the total amount of data that data center a migrates to data center cluster B during the phase,
the number of data centers included in data center cluster B.
18. The system of claim 5, wherein the solving of the execution resource combination and the time-series output capacity of the data center cluster B in the execution phase specifically comprises:
substituting the execution resource combination into a time sequence output capacity calculation formula to calculate and obtain the sum time sequence output capacity of the data center cluster B in the execution stage;
wherein, the time sequence output capacity calculation formula is as follows:
in the formula (I), the compound is shown in the specification,
time-series capacity for the data center cluster B in the execution phase;
the number of data centers included in data center cluster B;
、
the distributed power generation amount per unit time and the charging and discharging amount of the energy storage battery per unit time of the ith data center in the data center cluster B in the execution time are also independent variables of the second objective function, namely the execution resource combination.
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