CN112966857A - Multi-energy collaborative optimization method and system for data center - Google Patents
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Abstract
The invention provides a method and a system for multi-energy collaborative optimization of a data center, and relates to the technical field of optimization of energy systems of the data center. Firstly, acquiring a data network load based on a network load prediction model; then classifying the data network load and determining the priority of the processing sequence; constructing a multi-energy collaborative optimization model of the data center based on a data center comprehensive energy system comprising a waste heat recovery unit; and finally, solving the multi-energy collaborative optimization model, and optimizing the data center based on the solution result. The invention realizes the collaborative optimization scheduling of data network load and various energy sources in the data center, realizes the cascade utilization of energy, simultaneously recycles the waste heat resources of the data center, reduces the energy consumption of the data center and improves the energy utilization efficiency.
Description
Technical Field
The invention relates to the technical field of optimization of energy systems of data centers, in particular to a method and a system for multi-energy collaborative optimization of a data center.
Background
The data center is an important part of 'new construction', the development is rapid in recent years, but the development of the data center is always restricted by the problems of high energy consumption and low energy efficiency, so that the energy system of the data center needs to be further optimized and managed. In the energy internet environment, the data center energy system gradually evolves into a comprehensive energy system including adjustable load, energy storage, distributed capacity and energy conversion equipment, which brings new opportunities and challenges for the optimal management of the energy system.
At present, for the optimal management of a data center energy system in an energy internet environment, a distributed energy storage architecture based on an energy internet idea and an optimal management method thereof are introduced into a data center to realize energy optimal distribution so as to solve the energy consumption problem of the data center; the data center is also considered to be accessed to the energy Internet and participate in a demand response project, and the data network load of the data center is subjected to space-time distribution so as to achieve the aim of optimizing energy consumption cost management; and a distributed comprehensive energy system is adopted to replace the traditional energy supply system of the data center, and an optimized scheduling model is designed for cold, heat and electric energy in the data center, so that the optimization of the data center is realized, and the like.
However, although these technologies can optimize and manage the energy system of the data center to a certain extent, these technologies either only perform optimized scheduling on the data network load, or only simply consider coupling complementation between different forms of energy such as cold, heat, electricity, gas, and the like, which often lack comprehensive consideration of data network load scheduling, energy system multi-energy coordination, and waste heat generated by recycling and reusing a machine room, and cannot further reduce energy consumption of the data center and improve energy efficiency thereof. Therefore, the problem that the prior art cannot realize the collaborative optimization scheduling of data network load and multiple energy sources exists.
Disclosure of Invention
Technical problem to be solved
Aiming at the defects of the prior art, the invention provides a data center multi-energy collaborative optimization method and a data center multi-energy collaborative optimization system, and solves the problem that the prior art cannot realize collaborative optimization scheduling of data network loads and multiple energy sources.
(II) technical scheme
In order to achieve the purpose, the invention is realized by the following technical scheme:
in a first aspect, the present invention first provides a method for collaborative optimization of multiple functions of a data center, where the method includes:
acquiring a data network load prediction result based on a network load prediction model;
classifying the data network load and determining the priority of the processing sequence of the data network load;
constructing a multi-energy collaborative optimization model of the data center based on a data center energy system comprising a waste heat recovery unit;
and solving the multi-energy collaborative optimization model based on the data network load prediction result, the priority of the processing sequence, the time-of-use electricity price and the gas price, and optimizing the data center based on the solving result.
Preferably, the network load prediction model comprises a differential integrated autoregressive moving average model.
Preferably, the objective function of the multi-energy collaborative optimization model is as follows:
wherein the content of the first and second substances,
the cost of the electricity purchase is represented,
for electricity purchase price of t period, Pt BSupplying power to a power grid in a time period t;
the cost of the gas purchase is shown,
natural gas consumption and purchase price, P, respectively, for a period of tt ICE、
The generated power and the generated efficiency, L, of the energy supply unit CCHP in the period of t respectivelyNGThe heat value of the fuel gas is adopted;
represents the cost of participating in the carbon market transaction,
is the carbon trade-price average, σGasA carbon emission factor for natural gas;
which represents the cost of maintenance of the equipment,
unit maintenance costs, P, for CCHP energy supply units, centrifugal chiller units, respectivelyt CCThe electrical load power of the centrifugal refrigerating unit;
representing depreciation cost of stored energy, Pt ESC、Pt ESDRespectively is the charging and discharging energy power, K, of the energy storage unit in the period of tESSThe cost is reduced for the unit of the energy storage unit.
Preferably, the constraint conditions of the objective function include:
data network load balancing constraints:
wherein the content of the first and second substances,
respectively the data network load and the batch processing load to be processed by the data center in the period t,
respectively a real-time interactive load total amount and a batch processing load total amount in a period t,
the total batch processing load accumulated to the delay processing of the t time period; cITReserving a load handling rate for the data center for delta, wherein the load handling capacity is the load handling capacity of the data center;
the load is the residual batch processing load after the end of a scheduling period T;
electrical load balancing constraints
Wherein, Pt DC、Pt BSC、Pt BSDRespectively the electric load power of the data center and the charging and discharging power of the energy storage battery in the time period of t, Pt IT、Pt CC、Pt OtherThe electric load power of IT equipment, the electric load power of a centrifugal refrigerating unit and the electric load power of other equipment in a t period are respectively, and rho is the power requirement of the IT equipment for processing unit data network load;
cold load balancing constraints
Wherein the content of the first and second substances,
respectively the cooling load of the data center at the time period t, the cooling capacity of the CCHP energy supply unit and the cooling capacity of the waste heat recovery unit, Pt ISTC、Pt ISTDThe cold accumulation power and the cold discharge power of the cold accumulation tank in the period of t are respectively etaCC、ηACEnergy efficiency ratio, LF, of a centrifugal refrigerator and a two-stage lithium bromide absorption refrigerator, respectively, in a period of tt、
The load coefficient and the waste heat recovery amount of the data center in the t period are respectively, and omega is a conversion coefficient.
Preferably, the classifying the data network load and determining the priority of the processing order thereof includes: according to the data network load characteristics, the data network load is divided into real-time interactive load and batch processing load, and then according to the service quality constraint, the priority of the load processing sequence is determined according to the sequence of the deadline time of each load.
In a second aspect, the present invention provides a data center multi-energy collaborative optimization system, including:
the data network load prediction module is used for acquiring a data network load prediction result based on the network load prediction model;
the data network load classification module is used for classifying the data network loads and determining the priority of the processing sequence of the data network loads;
the model construction module is used for constructing a multi-energy collaborative optimization model of the data center based on a data center energy system comprising a waste heat recovery unit;
and the model solving and executing module is used for solving the multi-energy collaborative optimization model based on the data network load prediction result, the priority of the processing sequence, the time-of-use electricity price and the gas price, and optimizing the data center based on the solving result.
Preferably, the network load prediction model in the data network load prediction module includes a differential integrated autoregressive moving average model.
Preferably, the objective function of the multi-energy collaborative optimization model is as follows:
wherein the content of the first and second substances,
the cost of the electricity purchase is represented,
for the electricity purchase price of the time period t,
supplying power to a power grid in a time period t;
the cost of the gas purchase is shown,
respectively the natural gas consumption and the gas purchase price in the time period t,
the generated power and the generated efficiency, L, of the energy supply unit CCHP in the period of t respectivelyNGThe heat value of the fuel gas is adopted;
represents the cost of participating in the carbon market transaction,
is the carbon trade-price average, σGasA carbon emission factor for natural gas;
indicating equipment maintenanceThe cost of the process is reduced, and the cost of the process,
unit maintenance costs, P, for CCHP energy supply units, centrifugal chiller units, respectivelyt CCThe electrical load power of the centrifugal refrigerating unit;
representing depreciation cost of stored energy, Pt ESC、Pt ESDRespectively is the charging and discharging energy power, K, of the energy storage unit in the period of tESSThe cost is reduced for the unit of the energy storage unit.
Preferably, the constraint conditions of the objective function include:
data network load balancing constraints:
wherein the content of the first and second substances,
respectively the data network load and the batch processing load to be processed by the data center in the period t,
respectively a real-time interactive load total amount and a batch processing load total amount in a period t,
the total batch processing load accumulated to the delay processing of the t time period; cITReserving a load handling rate for the data center for delta, wherein the load handling capacity is the load handling capacity of the data center;
the load is the residual batch processing load after the end of a scheduling period T;
electrical load balancing constraints
Wherein the content of the first and second substances,
respectively the electric load power of the data center and the charging and discharging power of the energy storage battery in the time period of t, Pt IT、Pt CC、Pt OtherThe electric load power of IT equipment in a t period, the electric load power of a centrifugal refrigerating unit and the electric load power of other equipment (including lighting equipment, power distribution equipment and the like) are respectively, and rho is the power requirement of the IT equipment for processing unit data network load;
cold load balancing constraints
Wherein the content of the first and second substances,
respectively the cooling load of the data center at the time period t, the cooling capacity of the CCHP energy supply unit and the cooling capacity of the waste heat recovery unit, Pt ISTC、Pt ISTDThe cold accumulation power and the cold discharge power of the cold accumulation tank in the period of t are respectively etaCC、ηACEnergy efficiency ratio, LF, of a centrifugal refrigerator and a two-stage lithium bromide absorption refrigerator, respectively, in a period of tt、Ht DCThe load coefficient and the waste heat recovery amount of the data center in the t period are respectively, and omega is a conversion coefficient.
Preferably, the data network load classification module classifying the data network load and determining the priority of the processing order thereof includes: according to the data network load characteristics, the data network load is divided into real-time interactive load and batch processing load, and then according to the service quality constraint, the priority of the load processing sequence is determined according to the sequence of the deadline time of each load.
(III) advantageous effects
The invention provides a data center multifunctional collaborative optimization method and a data center multifunctional collaborative optimization system, and compared with the prior art, the method has the following beneficial effects:
the invention discloses a method and a system for multi-energy collaborative optimization of a data center, which are characterized by comprising the following steps of firstly, acquiring a data network load based on a network load prediction model; then classifying the data network load and determining the priority of the processing sequence; constructing a multi-energy collaborative optimization model of the data center based on a data center comprehensive energy system comprising a waste heat recovery unit; and finally, solving the multi-energy collaborative optimization model, and optimizing the data center based on the solution result. The invention realizes the collaborative optimization scheduling of data network load and various energy sources in the data center, realizes the cascade utilization of energy, simultaneously recycles the waste heat resources of the data center, reduces the energy consumption of the data center and improves the energy utilization efficiency.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow chart of a method for multi-energy collaborative optimization of a data center according to an embodiment of the present invention;
fig. 2 is a structural diagram of a data center multi-energy collaborative optimization system in an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention are clearly and completely described, and it is obvious that the described embodiments are a part of the embodiments of the present invention, but not all of 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 invention.
The embodiment of the application provides a method and a system for multi-energy collaborative optimization of a data center, solves the problem that collaborative optimization of data network loads and various energy sources cannot be realized in the prior art, realizes cascade utilization of energy, improves the economy of the data center and the stability of a power grid, reduces the energy consumption of the data center, and improves the energy utilization efficiency.
In order to solve the technical problems, the general idea of the embodiment of the application is as follows:
in order to solve the problem that the prior art can not realize the collaborative optimization of the data network load and various energy sources, the method comprises the steps of firstly predicting the short-term data network load through a network load prediction model, then classifying the data network load and determining the priority of the processing sequence of the data network load, constructing a multi-energy collaborative optimization model of a data center based on a plurality of components including a waste heat recovery unit and the like in a data center comprehensive energy system, finally solving the multi-energy collaborative optimization model, and realizing the multi-energy collaborative optimization of the data center based on the solving result.
In order to better understand the technical solution, the technical solution will be described in detail with reference to the drawings and the specific embodiments.
Example 1:
in a first aspect, the present invention provides a data center multi-energy collaborative optimization method, including:
s1, acquiring a data network load prediction result based on the network load prediction model;
s2, classifying the data network load and determining the priority of the processing sequence;
s3, constructing a multi-energy collaborative optimization model of the data center based on the data center energy system comprising the waste heat recovery unit;
s4, solving the multi-energy collaborative optimization model based on the data network load prediction result, the priority of the processing sequence, the time-of-use electricity price and the gas price, and optimizing the data center based on the solution result.
It can be seen that, in the method for the data center multi-energy collaborative optimization according to the embodiment, firstly, a data network load is obtained based on a network load prediction model; then classifying the data network load and determining the priority of the processing sequence; constructing a multi-energy collaborative optimization model of the data center based on a data center comprehensive energy system comprising a waste heat recovery unit; and finally, solving the multi-energy collaborative optimization model, and optimizing the data center based on the solution result. The invention realizes the collaborative optimization scheduling of data network load and various energy sources in the data center, realizes the cascade utilization of energy, simultaneously recycles the waste heat resources of the data center, reduces the energy consumption of the data center and improves the energy utilization efficiency.
In the method of the embodiment of the present invention, in order to obtain a more accurate data network load prediction result, a preferred processing manner is to obtain the data network load prediction result by using a network load prediction model, where the network load prediction model includes a difference integration autoregressive moving average model.
In addition, in order to implement collaborative optimization of data network load and multiple energy sources, thereby further reducing the operation cost of the data center, a preferred processing mode is that the objective function of the constructed multi-energy collaborative optimization model is as follows:
wherein the content of the first and second substances,
the cost of the electricity purchase is represented,
for electricity purchase price of t period, Pt BSupplying power to a power grid in a time period t;
the cost of the gas purchase is shown,
natural gas consumption and purchase price, P, respectively, for a period of tt ICE、
The generated power and the generated efficiency, L, of the energy supply unit CCHP in the period of t respectivelyNGThe heat value of the fuel gas is adopted;
represents the cost of participating in the carbon market transaction,
is the carbon trade-price average, σGasA carbon emission factor for natural gas;
which represents the cost of maintenance of the equipment,
unit maintenance costs, P, for CCHP energy supply units, centrifugal chiller units, respectivelyt CCThe electrical load power of the centrifugal refrigerating unit;
representing depreciation cost of stored energy, Pt ESC、Pt ESDRespectively is the charging and discharging energy power, K, of the energy storage unit in the period of tESSThe cost is reduced for the unit of the energy storage unit.
Meanwhile, in order to ensure that the objective function solution result of the multi-energy collaborative optimization model is more accurate, in the method according to the embodiment of the present invention, the setting of the constraint condition of the objective function includes:
data network load balancing constraints:
wherein the content of the first and second substances,
respectively the data network load and the batch processing load to be processed by the data center in the period t,
respectively a real-time interactive load total amount and a batch processing load total amount in a period t,
the total batch processing load accumulated to the delay processing of the t time period; cITReserving a load handling rate for the data center for delta, wherein the load handling capacity is the load handling capacity of the data center;
is scheduled for oneThe remaining batch processing load after the period T is finished;
electrical load balancing constraints
Wherein, Pt DC、Pt BSC、Pt BSDRespectively the electric load power of the data center and the charging and discharging power of the energy storage battery in the time period of t, Pt IT、Pt CC、Pt OtherThe electric load power of IT equipment, the electric load power of a centrifugal refrigerating unit and the electric load power of other equipment in a t period are respectively, and rho is the power requirement of the IT equipment for processing unit data network load;
cold load balancing constraints
Wherein the content of the first and second substances,
respectively the cooling load of the data center at the time period t, the cooling capacity of the CCHP energy supply unit and the cooling capacity of the waste heat recovery unit, Pt ISTC、Pt ISTDThe cold accumulation power and the cold discharge power of the cold accumulation tank in the period of t are respectively etaCC、ηACEnergy efficiency ratio, LF, of a centrifugal refrigerator and a two-stage lithium bromide absorption refrigerator, respectively, in a period of tt、
The load coefficient and the waste heat recovery amount of the data center in the t period are respectively, and omega is a conversion coefficient.
In practice, when determining the priority of each load processing order of the data center, a preferred processing method is to classify the data network load and determine the priority of the processing order, and the method includes: according to the data network load characteristics, the data network load is divided into real-time interactive load and batch processing load, and then according to the service quality constraint, the priority of the load processing sequence is determined according to the sequence of the deadline time of each load.
The following takes a data center integrated energy system including a Combined Cooling, Heating and Power (CCHP) energy supply unit, a centrifugal chiller unit, an energy storage unit (an energy storage battery and an accumulator tank), and a waste heat recovery unit (a two-stage lithium bromide absorption chiller) as an example, and combines the specific explanation of steps S1-S4 to describe in detail the implementation process of an embodiment of the present invention. Referring to fig. 1, a method for multi-energy collaborative optimization of a data center includes:
and S1, acquiring a data network load prediction result based on the network load prediction model.
Predicting the data network load reaching the data center at intervals of preset time, determining the predicted data network load as the data network load input by the data center in the current time interval, specifically, predicting the short-term data network load by a differential integration moving average autoregressive model, and the specific process is as follows:
firstly, past data center operation data are obtained.
Then, the model is trained. And training a differential integrated moving average autoregressive model, wherein the model can be expressed as ARIMA (p, d, q), determining the values of parameters p, d and q when the optimal performance is obtained according to the average mean square error between the prediction result and the actual workload, and obtaining a data center data network load prediction model with the highest prediction precision, namely the differential integrated moving average autoregressive model with the highest prediction precision.
And finally, predicting the short-term data network load by using the trained difference integration moving average autoregressive model, and predicting the data network load reaching the data center every 1 hour every certain day by using the trained model.
S2, classifying the data network load and determining the priority of the processing sequence.
According to the data network load characteristics, the data network load is divided into a real-time interactive load (a load requiring real-time response) and a batch processing load (a load allowing delay processing), and then the priority of the load processing sequence is determined according to the order of the load deadline according to Quality of Service (QoS) constraints (ensuring that the load processing is completed before the load deadline).
S3, constructing a multi-energy collaborative optimization model of the data center based on the data center energy system comprising the waste heat recovery unit.
The method comprises the steps of considering energy requirements of cooling, electricity, gas and the like of a data center, simultaneously considering a plurality of components in a comprehensive energy system of the data center, including a CCHP energy supply unit, a centrifugal refrigerating unit, an energy storage unit, a waste heat recovery unit and a main power grid, then establishing a multi-energy collaborative optimization model of the data center, wherein an objective function of the multi-energy collaborative optimization model of the data center can be represented by the following formula:
wherein the content of the first and second substances,
The constraint conditions of the data center multi-energy collaborative optimization model comprise: data network load balancing constraints, electrical load balancing constraints, cold load balancing constraints, and other physical operational constraints. Specifically, the data network load balancing constraint is:
wherein the content of the first and second substances,
respectively the data network load and the batch processing load to be processed by the data center in the period t,
respectively a real-time interactive load total amount and a batch processing load total amount in a period t,
the total batch processing load accumulated to the delay processing of the t time period; cITProcessing capacity for data center load; delta is the reserved load handling rate of the data center;
the load is the residual batch processing load after the end of a scheduling period T;
the electrical load balancing constraints are:
wherein, Pt DC、Pt BSC、Pt BSDThe electric load power of the data center and the charging and discharging power of the energy storage battery are respectively in the t period; pt IT、Pt CC、Pt OtherThe electric load power of IT equipment in the t period, the electric load power of a centrifugal refrigerating unit and the electric load power of other equipment (including illumination, power distribution equipment and the like) are respectively; rho is the power requirement of the IT equipment for processing unit data network load;
the cold load balancing constraints are:
wherein the content of the first and second substances,
the refrigeration capacity of the cold load, the refrigeration capacity of the CCHP energy supply unit and the refrigeration capacity of the waste heat recovery unit of the data center at the time period t are respectively set; pt ISTC、Pt ISTDThe cold accumulation power and the cold discharge power of the cold accumulation tank are respectively in the period t; etaCC、ηACThe energy efficiency ratios of the centrifugal refrigerator and the two-stage lithium bromide absorption refrigerator in the t period are respectively; LF (Low frequency)t、
Respectively representing the load coefficient and the waste heat recovery of the data center at the t period; omega is a conversion coefficient;
other physical operating constraints include operating constraints of the CCHP power supply unit (i.e., the output electrical power needs to be between minimum and maximum output electrical power; the output cooling capacity needs to be between minimum and maximum cooling capacity); operational constraints of the centrifugal chiller unit (i.e., input electrical power needs to be between minimum and maximum input electrical power); energy storage constraint (namely the energy storage requirement is between the minimum energy storage requirement and the maximum energy storage requirement; the energy charging and discharging power requirement is between the minimum energy charging and discharging power and the maximum energy charging and discharging power; the energy charging and discharging state in the same time interval meets the mutual exclusion condition); the operation constraint (namely that the output refrigerating capacity is required to be between the minimum and the maximum refrigerating capacity) and the power grid power supply constraint (namely that the power grid power supply is required to be smaller than the upper limit of the power grid power supply) of the two-stage lithium bromide absorption refrigerating machine.
And S4, solving the multi-energy collaborative optimization model based on the data network load prediction result, the priority of the processing sequence, the time-of-use electricity price and the gas price, and optimizing the data center based on the solution result.
Determining parameter values of the multi-energy collaborative model of the data center (including unit maintenance cost of a CCHP energy supply unit and a centrifugal refrigerating unit, unit depreciation cost of an energy storage unit, power requirement of IT equipment for processing unit data network load, and maximum and minimum output electric power of the CCHP energy supply unit), calling CPLEX software to solve the model based on a data network load prediction result, a priority result of data network load classification and processing sequence, time-sharing electricity price and gas price, and determining output conditions of the equipment in the data center (including output electric power and refrigerating capacity of the CCHP energy supply unit, refrigerating capacity of the centrifugal refrigerating machine, refrigerating capacity of a waste heat recovery unit, output conditions of the energy storage equipment, such as charging and discharging electric power and gas discharge electric power of a storage battery, based on a solution result of the multi-energy collaborative optimization model of the data center, Cold accumulation and cold discharge power of the cold accumulation tank, and the like), an electricity purchasing condition (electricity purchasing from the data center to the power grid) and a gas purchasing condition (gas purchasing from the data center to a gas company), a multi-energy collaborative optimization scheme of the data center is formed, and finally the data center is optimized according to the multi-energy collaborative optimization scheme. The collaborative optimization scheme comprises the output situation of each device, the electricity and gas purchasing situation of the data center, and the processing situation of the data network load (the processing sequence of the data load).
Therefore, the whole process of the data center multifunctional collaborative optimization method is completed.
Example 2:
in a second aspect, the present invention further provides a data center multi-energy collaborative optimization system, referring to fig. 2, the system includes:
the data network load prediction module is used for acquiring a data network load prediction result based on the network load prediction model;
the data network load classification module is used for classifying the data network loads and determining the priority of the processing sequence of the data network loads;
the model construction module is used for constructing a multi-energy collaborative optimization model of the data center based on a data center energy system comprising a waste heat recovery unit;
and the model solving and executing module is used for solving the multi-energy collaborative optimization model based on the data network load prediction result, the priority of the processing sequence, the time-of-use electricity price and the gas price, and optimizing the data center based on the solving result.
Preferably, the network load prediction model in the data network load prediction module includes a differential integrated autoregressive moving average model.
Preferably, the objective function of the multi-energy collaborative optimization model is as follows:
wherein the content of the first and second substances,
the cost of the electricity purchase is represented,
for electricity purchase price of t period, Pt BSupplying power to a power grid in a time period t;
the cost of the gas purchase is shown,
natural gas consumption and purchase price, P, respectively, for a period of tt ICE、
The generated power and the generated efficiency, L, of the energy supply unit CCHP in the period of t respectivelyNGThe heat value of the fuel gas is adopted;
represents the cost of participating in the carbon market transaction,
is the carbon trade-price average, σGasA carbon emission factor for natural gas;
which represents the cost of maintenance of the equipment,
unit maintenance costs, P, for CCHP energy supply units, centrifugal chiller units, respectivelyt CCThe electrical load power of the centrifugal refrigerating unit;
representing depreciation cost of stored energy, Pt ESC、Pt ESDRespectively is the charging and discharging energy power, K, of the energy storage unit in the period of tESSThe cost is reduced for the unit of the energy storage unit.
Preferably, the constraint conditions of the objective function include:
data network load balancing constraints:
wherein the content of the first and second substances,
respectively the data network load and the batch processing load to be processed by the data center in the period t,
respectively a real-time interactive load total amount and a batch processing load total amount in a period t,
the total batch processing load accumulated to the delay processing of the t time period; cITReserving a load handling rate for the data center for delta, wherein the load handling capacity is the load handling capacity of the data center;
the load is the residual batch processing load after the end of a scheduling period T;
electrical load balancing constraints
Pt DC＝Pt B+Pt ICE+Pt BSD-Pt BSC
Wherein, Pt DC、Pt BSC、Pt BSDRespectively the electric load power of the data center and the charging and discharging power of the energy storage battery in the time period of t, Pt IT、Pt CC、Pt OtherThe electric load power of IT equipment in a t period, the electric load power of a centrifugal refrigerating unit and the electric load power of other equipment (including lighting equipment, power distribution equipment and the like) are respectively, and rho is the power requirement of the IT equipment for processing unit data network load;
cold load balancing constraints
Wherein the content of the first and second substances,
respectively the cooling load of the data center at the time period t, the cooling capacity of the CCHP energy supply unit and the cooling capacity of the waste heat recovery unit, Pt ISTC、Pt ISTDThe cold accumulation power and the cold discharge power of the cold accumulation tank in the period of t are respectively etaCC、ηACEnergy efficiency ratio, LF, of a centrifugal refrigerator and a two-stage lithium bromide absorption refrigerator, respectively, in a period of tt、
The load coefficient and the waste heat recovery amount of the data center in the t period are respectively, and omega is a conversion coefficient.
Preferably, the data network load classification module classifying the data network load and determining the priority of the processing order thereof includes: according to the data network load characteristics, the data network load is divided into real-time interactive load and batch processing load, and then according to the service quality constraint, the priority of the load processing sequence is determined according to the sequence of the deadline time of each load.
It can be understood that, the data center multifunctional collaborative optimization system provided by the embodiment of the present invention corresponds to the data center multifunctional collaborative optimization method, and the explanation, the example, the beneficial effects and the like of the relevant contents thereof may refer to the corresponding contents in the data center multifunctional collaborative optimization method, which is not described herein again.
In summary, compared with the prior art, the method has the following beneficial effects:
1. the invention discloses a method and a system for multi-energy collaborative optimization of a data center, which are characterized by comprising the following steps of firstly, acquiring a data network load based on a network load prediction model; then classifying the data network load and determining the priority of the processing sequence; constructing a multi-energy collaborative optimization model of the data center based on a data center comprehensive energy system comprising a waste heat recovery unit; and finally, solving the multi-energy collaborative optimization model, and optimizing the data center based on the solution result. The invention realizes the collaborative optimization scheduling of data network load and various energy sources in the data center, realizes the cascade utilization of energy, simultaneously recycles the waste heat resource of the data center, reduces the energy consumption of the data center and improves the energy utilization efficiency;
2. the method and the system fully schedule the data network load and various energy sources in the data center based on the data network load prediction result, the priority result of the processing sequence, the time-of-use electricity price and the gas price, realize the multi-energy collaborative optimization scheduling of the data network load and the energy system of the data center, realize the cascade utilization of the energy, improve the economy of the data center, realize peak clipping and valley filling at the same time and improve the stability of a power grid;
3. the invention considers the recovery and reuse of the waste heat resource of the data center, enriches the cooling source of the data center, reduces the energy waste, reduces the energy consumption of the data center and improves the energy utilization efficiency.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will 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 of the embodiments of the present invention.
Claims (10)
1. A data center multi-energy collaborative optimization method is characterized by comprising the following steps:
acquiring a data network load prediction result based on a network load prediction model;
classifying the data network load and determining the priority of the processing sequence of the data network load;
constructing a multi-energy collaborative optimization model of the data center based on a data center energy system comprising a waste heat recovery unit;
and solving the multi-energy collaborative optimization model based on the data network load prediction result, the priority of the processing sequence, the time-of-use electricity price and the gas price, and optimizing the data center based on the solving result.
2. The method of claim 1, wherein the network load prediction model comprises a differential integrated autoregressive moving average model.
3. The method of claim 1, wherein the objective function of the multi-energy co-optimization model is:
wherein the content of the first and second substances,
the cost of the electricity purchase is represented,
for electricity purchase price of t period, Pt BFor a period of tPower supply power of the power grid;
the cost of the gas purchase is shown,
natural gas consumption and purchase price, P, respectively, for a period of tt ICE、
The generated power and the generated efficiency, L, of the energy supply unit CCHP in the period of t respectivelyNGThe heat value of the fuel gas is adopted;
represents the cost of participating in the carbon market transaction,
is the carbon trade-price average, σGasA carbon emission factor for natural gas;
which represents the cost of maintenance of the equipment,
unit maintenance costs, P, for CCHP energy supply units, centrifugal chiller units, respectivelyt CCThe electrical load power of the centrifugal refrigerating unit;
representing depreciation cost of stored energy, Pt ESC、Pt ESDRespectively is the charging and discharging energy power, K, of the energy storage unit in the period of tESSThe cost is reduced for the unit of the energy storage unit.
4. The method of claim 3, wherein the constraints of the objective function include:
data network load balancing constraints:
wherein the content of the first and second substances,
respectively the data network load and the batch processing load to be processed by the data center in the period t,
respectively a real-time interactive load total amount and a batch processing load total amount in a period t,
the total batch processing load accumulated to the delay processing of the t time period; cITReserving a load handling rate for the data center for delta, wherein the load handling capacity is the load handling capacity of the data center;
the load is the residual batch processing load after the end of a scheduling period T;
electrical load balancing constraints
Pt DC＝Pt B+Pt ICE+Pt BSD-Pt BSC
Wherein, Pt DC、Pt BSC、Pt BSDRespectively the electric load power of the data center and the charging and discharging power of the energy storage battery in the time period of t, Pt IT、Pt CC、Pt OtherElectric load power and centrifugal refrigerating unit of IT equipment in t periodρ is the power demand of the IT device to process the unit data network load;
cold load balancing constraints
Wherein the content of the first and second substances,
respectively the cooling load of the data center at the time period t, the cooling capacity of the CCHP energy supply unit and the cooling capacity of the waste heat recovery unit, Pt ISTC、Pt ISTDThe cold accumulation power and the cold discharge power of the cold accumulation tank in the period of t are respectively etaCC、ηACEnergy efficiency ratio, LF, of a centrifugal refrigerator and a two-stage lithium bromide absorption refrigerator, respectively, in a period of tt、
The load coefficient and the waste heat recovery amount of the data center in the t period are respectively, and omega is a conversion coefficient.
5. The method of claim 1, wherein the classifying the data network load and prioritizing the order of processing thereof comprises: according to the data network load characteristics, the data network load is divided into real-time interactive load and batch processing load, and then according to the service quality constraint, the priority of the load processing sequence is determined according to the sequence of the deadline time of each load.
6. A data center multi-energy collaborative optimization system, the system comprising:
the data network load prediction module is used for acquiring a data network load prediction result based on the network load prediction model;
the data network load classification module is used for classifying the data network loads and determining the priority of the processing sequence of the data network loads;
the model construction module is used for constructing a multi-energy collaborative optimization model of the data center based on a data center energy system comprising a waste heat recovery unit;
and the model solving and executing module is used for solving the multi-energy collaborative optimization model based on the data network load prediction result, the priority of the processing sequence, the time-of-use electricity price and the gas price, and optimizing the data center based on the solving result.
7. The system of claim 6, wherein the network load prediction model in the data network load prediction module comprises a differential integrated autoregressive moving average model.
8. The system of claim 6, wherein the objective function of the multi-energy collaborative optimization model is:
wherein the content of the first and second substances,
the cost of the electricity purchase is represented,
for electricity purchase price of t period, Pt BSupplying power to a power grid in a time period t;
the cost of the gas purchase is shown,
natural gas consumption and purchase price, P, respectively, for a period of tt ICE、
The generated power and the generated efficiency, L, of the energy supply unit CCHP in the period of t respectivelyNGThe heat value of the fuel gas is adopted;
represents the cost of participating in the carbon market transaction,
is the carbon trade-price average, σGasA carbon emission factor for natural gas;
which represents the cost of maintenance of the equipment,
unit maintenance costs, P, for CCHP energy supply units, centrifugal chiller units, respectivelyt CCThe electrical load power of the centrifugal refrigerating unit;
representing depreciation cost of stored energy, Pt ESC、Pt ESDRespectively is the charging and discharging energy power, K, of the energy storage unit in the period of tESSThe cost is reduced for the unit of the energy storage unit.
9. The system of claim 8, wherein the constraints of the objective function include:
data network load balancing constraints:
wherein the content of the first and second substances,
respectively the data network load and the batch processing load to be processed by the data center in the period t,
respectively a real-time interactive load total amount and a batch processing load total amount in a period t,
the total batch processing load accumulated to the delay processing of the t time period; cITReserving a load handling rate for the data center for delta, wherein the load handling capacity is the load handling capacity of the data center;
the load is the residual batch processing load after the end of a scheduling period T;
electrical load balancing constraints
Pt DC＝Pt B+Pt ICE+Pt BSD-Pt BSC
Wherein, Pt DC、Pt BSC、Pt BSDRespectively the electric load power of the data center and the charging and discharging power of the energy storage battery in the time period of t, Pt IT、Pt CC、Pt OtherThe electric load power of IT equipment in a t period, the electric load power of a centrifugal refrigerating unit and the electric load power of other equipment (including lighting equipment, power distribution equipment and the like) are respectively, and rho is the power requirement of the IT equipment for processing unit data network load;
cold load balancing constraints
Wherein the content of the first and second substances,
respectively the cooling load of the data center at the time period t, the cooling capacity of the CCHP energy supply unit and the cooling capacity of the waste heat recovery unit, Pt ISTC、Pt ISTDThe cold accumulation power and the cold discharge power of the cold accumulation tank in the period of t are respectively etaCC、ηACEnergy efficiency ratio, LF, of a centrifugal refrigerator and a two-stage lithium bromide absorption refrigerator, respectively, in a period of tt、
Respectively is the load factor and the waste heat recovery of the data center in the t period, and omega isAnd (5) converting the coefficient.
10. The system of claim 6, wherein the data network load classification module classifying the data network load and prioritizing the processing order thereof comprises: according to the data network load characteristics, the data network load is divided into real-time interactive load and batch processing load, and then according to the service quality constraint, the priority of the load processing sequence is determined according to the sequence of the deadline time of each load.
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